Introduction

IMPORTANT: Watson Data API documentation is deprecated and might be out of date. The relevant functionality is now described in the following documents:

You can use a collection of Watson Data REST APIs associated with Watson Studio and Watson Knowledge Catalog to manage data-related assets and the people who need to use these assets.

Refine data Use the sampling APIs to create representative subsets of the data on which to test and refine your data cleansing and shaping operations. To better understand the contents of your data, you can create profiles of your data assets that include a classification of the data and additional distribution information which assists in determining the data quality.

Catalog data Use the catalog APIs to create catalogs to administer your assets, associate properties with those assets, and organize the users who use the assets. Assets can be notebooks or connections to files, database sources, or data assets from a connection.

Data policies Use the data policy APIs to implement data policies and a business glossary that fits to your organization to control user access rights to assets and to make it easier to find data.

Ingest streaming data Use the streams flow APIs to hook up continuous, unidirectional flows of massive volumes of moving data that you can analyze in real time.

API Endpoint

https://api.dataplatform.cloud.ibm.com

Creating an IAM bearer token

Before you can call a Watson Data API you must first create an IAM bearer token. Each token is valid only for one hour, and after a token expires you must create a new one if you want to continue using the API. The recommended method to retrieve a token programmatically is to create an API key for your IBM Cloud identity and then use the IAM token API to exchange that key for a token.

You can create a token in IBM Cloud or by using the IBM Cloud command line interface (CLI).

To create a token in the IBM Cloud:

Log in to IBM Cloud and select Manage > Access (IAM) > API keys.
Create an API key for your own personal identity, copy the key value, and save it in a secure place. After you leave the page, you will no longer be able to access this value.
With your API key, set up Postman or another REST API tool and run the following command to the right

You can read more about managing API keys at Understanding API keys documentation page.

Curl command with API key to retrieve token

curl -X POST 'https://iam.cloud.ibm.com/identity/token' \
  -H 'Content-Type: application/x-www-form-urlencoded' \
  -d 'grant_type=urn:ibm:params:oauth:grant-type:apikey&apikey=MY_APIKEY'

Response

{
"access_token": "eyJhbGciOiJIUz......sgrKIi8hdFs",
"refresh_token": "SPrXw5tBE3......KBQ+luWQVY=",
"token_type": "Bearer",
"expires_in": 3600,
"expiration": 1473188353
}

Use the value of the access_token property for your Watson Data API calls. Set the access_token value as the authorization header parameter for requests to the Watson Data APIs. The format is Authorization: Bearer <access_token_value_here>. For example: Authorization: Bearer eyJhbGciOiJIUz......sgrKIi8hdFs

To create a token by using the IBM Cloud CLI:

Follow the steps to install the CLI, log in to IBM Cloud, and get the token described here.

Remove Bearer from the returned IAM token value in your API calls.

Versioning

Watson Data API has a major, minor, and patch version, following industry conventions on semantic versioning: Using the version number format MAJOR.MINOR.PATCH, the MAJOR version is incremented when incompatible API changes are made, the MINOR version is incremented when functionality is added in a backwards-compatible manner, and the PATCH version is incremented when backwards-compatible bug fixes are made. The service major version is represented in the URL path.

Sorting

Some of the Watson Data API collections provide custom sorting support. Custom sorting is implemented using the sort query parameter. Service collections can also support single-field or multi-field sorting. The sort parameter in collections that support single-field sorting can contain any one of the valid sort fields.

For example, the following expression would sort accounts on company name (ascending):GET /v2/accounts?sort=company_name.

You can also add a + or - character, indicating “ascending” or “descending,” respectively.

For example, the expression below would sort on the last name of the account owner, in descending order:GET /v2/accounts?sort=-owner.last_name.

The sort parameter in collections that support sorting on multiple fields can contain a comma-separated sequence of fields (each, optionally, with a + or -) in the same format as the single-field sorting. Sorts are applied to the data set in the order that they are provided. For example, the expression below would sort accounts first on company name (ascending) and second on owner last name (descending): GET /v2/accounts?sort=company_name,-owner.last_name

Filtering

Some of the Watson Data API collections provide filtering support. You can specify one or more filters where each supported field is required to match a specific value for basic filtering. The query parameter names for a basic filter must exactly match the name of a primitive field on a resource in the collection or a nested primitive field where the '.' character is the hierarchical separator. The only exception to this rule is for primitive arrays. In primitive arrays, such as tags, a singular form of the field is supported as a filter that matches the resource if the array contains the supplied value. Some of the Watson Data API collections can also support extended filtering comparisons for the following field types: Integer and float, date and date/time, identifier and enumeration, and string.

Rate Limiting

The following rate limiting headers are supported by some of the Watson Data service APIs: 1. X-RateLimit-Limit: If rate limiting is active, this header indicates the number of requests permitted per hour; 2. X-RateLimit-Remaining: If rate limiting is active, this header indicates the number of requests remaining in the current rate limit window; 3. X-RateLimit-Reset: If rate limiting is active, this header indicates the time at which the current rate limit window resets, as a UNIX timestamp.

Error Handling

Responses with 400-series or 500-series status codes are returned when a request cannot be completed. The body of these responses follows the error model, which contains a code field to identify the problem and a message field to explain how to solve the problem. Each individual endpoint has specific error messages. All responses with 500 or 503 status codes are logged and treated as a critical failure requiring an emergency fix.

Connections

A connection is the information necessary to create a connection to a data source or a repository. You create a connection asset by providing the connection information.

List data source types

Data sources are where data can be written or read and might include relational database systems, file systems, object storage systems and others.

To list supported data source types, call the following GET method:

GET /v2/datasource_types

The response to the GET method includes information about each of the sources and targets that are currently supported. The response includes a unique ID property value metadata.asset_id, name, and a label. The metadata.asset_id property value should be used for the data source in other APIs that reference a data source type. Additional useful information such as whether that data source can be used as a source or target (or both) is also included.

You can also view a table of the individual data source properties obtained by this GET method at https://dataplatform.cloud.ibm.com/connections/docs.

Use the connection_properties=true query parameter to return a set of properties for each data source type that is used to define a connection to it. Use the interaction_properties=true query parameter to return a set of properties for each data source type that is used to interact with a created connection. Interaction properties for a relational database might include the table name and schema from which to retrieve data.

Use the _sort query parameter to order the list of data source type returned in the response.

A default maximum of 100 data source type entries are returned per page of results. Use the _limit query parameter with an integer value to specify a lower limit.

More data source types than those on the first page of results might be available. Additional properties generated from the page size initially specified with _limit are returned in the response. Call a GET method using the value of the next.href property to retrieve the next page of results. Call a GET method using the value in the prev.href property to retrieve the previous page of results. Call a GET method using the value in the last.href property to retrieve the last page of results.

These URIs use the _offset and _limit query parameters to retrieve a specific block of data source types from the full list. Alternatively, you can use a combination of the _offset and _limit query parameters to retrieve a custom block of results.

Create a connection

Connections to any of the supported data source types returned by the previous method can be created and persisted in a catalog or project.

To create a connection, call the following POST method:

POST /v2/connections

A new connection can be created in a catalog or project. Use the catalog_id or project_id query parameter to specify where to create the connection asset. Either catalog_id or project_id is required.

The request body for the method is a UTF-8 encoded JSON document and includes the data source type ID (obtained in the List data source types section), its unique name in the catalog or project space, and a set of connection properties specific to the data source. Some connection properties are required.

The following example shows the request body used for creating a connection to IBM dashDB:

{
     "datasource_type": "cfdcb449-1204-44ba-baa6-9a8a878e6aa7",
	 "name":"My-DashDB-Connection",
     "properties": {
	   "host":"dashDBhost.com",
	   "port":"50001",
	   "database":"MYDASHDB",
       "password": "mypassword",
       "username": "myusername"
     }
}

By default, the physical connection to the data source is tested when the connection is created. Use the test=false query parameter to disable the connection test.

A response payload containing a connection ID and other metadata is returned when a connection is successfully created. Use the connection ID as path parameter in other REST APIs when a connection resource must be referenced.

Discover connection assets

Data sources contain data and metadata describing the data they contain.

To discover or browse the data or metadata in a data source, call the following GET method:

GET /v2/connections/{connection_id}/assets?path=

Use the catalog_id or project_id query parameter to specify where the connection asset was created. Either catalog_id or project_id is required.

connection_id is the ID of the connection asset returned from the POST https://{service_URL}/v2/connections method, which created the connection asset.

The path query parameter is required and is used to specify the hierarchical path of the asset within the data source to be browsed. In a relational database, for example, the path might represent a schema and table. For a file object, the path might represent a folder hierarchy.

Each asset in the assets array returned by this method includes a property containing its path in the hierarchy to facilitate the next call to drill down deeper in the hierarchy.

For example, starting at the root path in an RDBMS will return a list of schemas:

{
    "path": "/",
    "asset_types": [
        {
            "type": "schema",
            "dataset": false,
            "dataset_container": true
        }
    ],
    "assets": [
        {
            "id": "GOSALES",
            "type": "schema",
            "name": "GOSALES",
            "path": "/GOSALES"
        },
    ],
    "fields": [],
    "first": {
        "href": "https://wdp-dataconnect-ys1dev.stage1.mybluemix.net/v2/connections/4b28b5c1-d818-4ad2-bcf9-7de08e776fde/assets?catalog_id=75a3062b-e40f-4bc4-9519-308ee1b5b251&_offset=0&_limit=100"
    },
    "prev": {
        "href": "https://wdp-dataconnect-ys1dev.stage1.mybluemix.net/v2/connections/4b28b5c1-d818-4ad2-bcf9-7de08e776fde/assets?catalog_id=75a3062b-e40f-4bc4-9519-308ee1b5b251&_offset=0&_limit=100"
    },
    "next": {
        "href": "https://wdp-dataconnect-ys1dev.stage1.mybluemix.net/v2/connections/4b28b5c1-d818-4ad2-bcf9-7de08e776fde/assets?catalog_id=75a3062b-e40f-4bc4-9519-308ee1b5b251&_offset=100&_limit=100"
    }
}

Drill down into the GOSALES schema using the path property for the GOSALES schema asset to discover the list of table assets in the schema.

GET /v2/connections/{connection_id}/assets?catalog_id={catalog_id}&path=/GOSALES

The list of table type assets is returned in the response.

{
    "path": "/GOSALES",
    "asset_types": [
        {
            "type": "table",
            "dataset": true,
            "dataset_container": false
        }
    ],
    "assets": [
        {
            "id": "BRANCH",
            "type": "table",
            "name": "BRANCH",
            "description": "BRANCH contains address information for corporate offices and distribution centers.",
            "path": "/GOSALES/BRANCH"
        },
        {
            "id": "CONVERSION_RATE",
            "type": "table",
            "name": "CONVERSION_RATE",
            "description": "CONVERSION_RATE contains currency exchange values.",
            "path": "/GOSALES/CONVERSION_RATE"
        }
    ],
    "fields": [],
    "first": {
        "href": "https://wdp-dataconnect-ys1dev.stage1.mybluemix.net/v2/connections/4b28b5c1-d818-4ad2-bcf9-7de08e776fde/assets?catalog_id=75a3062b-e40f-4bc4-9519-308ee1b5b251&_offset=0&_limit=100"
    },
    "prev": {
        "href": "https://wdp-dataconnect-ys1dev.stage1.mybluemix.net/v2/connections/4b28b5c1-d818-4ad2-bcf9-7de08e776fde/assets?catalog_id=75a3062b-e40f-4bc4-9519-308ee1b5b251&_offset=0&_limit=100"
    },
    "next": {
        "href": "https://wdp-dataconnect-ys1dev.stage1.mybluemix.net/v2/connections/4b28b5c1-d818-4ad2-bcf9-7de08e776fde/assets?catalog_id=75a3062b-e40f-4bc4-9519-308ee1b5b251&_offset=100&_limit=100"
    }
}

Use the fetch query parameter with a value of either data, metadata, or both. Data can only be fetched for data set assets. In the response above, note the asset_type has the property type value of table. Its dataset property value is true. This means that data can be fetched from table type assets. However, if you fetched assets from the connection root, the response would contain schema asset types, which are not data sets and thus fetching this data is not relevant.

A default maximum of 100 metadata assets are returned per page of results. Use the _limit query parameter with an integer value to specify a lower limit. More assets than those on the first page of results might be available.

Additional properties generated from the page size initially specified with _limit are returned in the response. Call a GET method using the value of the next.href property to retrieve the next page of results. Call a GET method using the value in the prev.href property to retrieve the previous page of results. Call a GET method using the value in the last.href property to retrieve the last page of results.

These URIs use the _offset and _limit query parameters to retrieve a specific block of assets from the full list. Alternatively, use a combination of the _offset and _limit query parameters to retrieve a custom block of results.

Specify properties for reading delimited files

When reading a delimited file using this method, specify property values to correctly parse the file based on its format. These properties are passed to the method as a JSON object using the properties query parameter. The default file format (property file_format) is a CSV file. If the file is a CSV, the following property values are set by default:

Property Name | Property Description | Default Value | Value Description --- | --- | --- | --- quote_character | quote character | double_quote | double quotation mark field_delimiter | field delimiter | comma | comma row_delimiter | row delimiter | carriage_return_linefeed | carriage return followed by line feed escape_character | escape character | double_quote | double quotation mark

For CSV file formats, these property values can not be overwritten. If it is necessary to modify these properties to properly read a delimited file, set the file_format property to delimited. For generic delimited files, these properties have the following values:

Property Name | Property Description | Default Value | Value Description --- | --- | --- | --- quote_character | quote character | none | no character is used for a quote field_delimiter | field delimiter | null | no field delimiter value is set by default row_delimiter | row delimiter | new_line | Any new line representation escape_character | escape character | none | no character is used for an escape

This example sets file format properties for a generic delimited file:

GET https://{service_URL}/v2/connections/{connection_id}/assets?catalog_id={catalog_id}&path=/myFolder/myFile.txt&fetch=data&properties={"file_format":"delimited", "quote_character":"single_quote","field_delimiter":"colon","escape_character":"backslash"}

For more information about this method see the REST API Reference.

Discover assets using a transient connection

A data source's assets can be discovered without creating a persistent connection.

To browse assets without first creating a persistent connection, call the following POST method:

POST https://{service_URL}/v2/connections/assets?path=

This method is identical in behavior to the GET method in the Discover connection assetssection except for two differences:

You define the connection properties in the request body of the REST API. You do not reference the connection ID of a persistent connection with a query parameter. The same JSON object used to create a persistent connection is used in the request body.
You do not specify a catalog or project ID with a query parameter.

See the previous section to learn how to set properties used to read delimited files.

For more information about this method see the REST API Reference.

Update a connection

To modify the properties of a connection, call the following PATCH method:

PATCH /v2/connections/{connection_id}

connection_id is the ID of the connection asset returned from the POST https://{service_URL}/v2/connections method, which created the connection asset.

Use the catalog_id or project_id query parameter to specify where the connection asset was created. Either catalog_id or project_id is required.

Set the Content-Type header to application/json-patch+json. The request body contains the connection properties to update using a JSON object in JSON Patch format.

Change the port number of the connection and add a description using this JSON Patch:

[
	{
		"op": "add",
		"path": "/description",
		"value": "My new PATCHed description"
	},
	{
		"op":"replace",
		"path":"/properties/port",
		"value":"40001"
	}
]

By default, the physical connection to the data source is tested when the connection is modified. Use the test=false query parameter to disable the connection test.

For more information about this method see the REST API Reference.

Delete a connection

To delete a persistent connection, call the following DELETE method:

DELETE /v2/connections/{connection_id}

connection_id is the ID of the connection asset returned from the POST https://{service_URL}/v2/connections method, which created the connection asset.

Use the catalog_id or project_id query parameter to specify where the connection asset was created. Either catalog_id or project_id is required.

Schedules

Introduction

Schedules allow you to run a data flow, a notebook, a data profile, or any other given source more than once. It supports various repeat types namely hour, day, week, month, and year with 2 repeat end options namely, end date and the maximum number of runs.

Create a schedule

To create a schedule in a specified catalog or project, call the following POST method:

     HTTP Method : POST
     URI : /v2/schedules

Before you create a schedule, you must consider the following points:

You must have a valid IAM token to make REST API calls and a project or catalog ID.
You must be authorized (be assigned the correct role) to create schedules in the catalog or project.
The start and end dates must be in the following format: YYYY-MM-DDTHH:mm:ssZ or YYYY-MM-DDTHH:mm:ss.sssZ (specified in RFC 3339).
The supported repeat types are hour, day, week, month, and year.
There are 2 repeat end options, namely max_invocations and end_date.
The supported repeat interval is 1.
There are 3 statuses for schedules, namely enabled, disabled, and finished. To create a schedule, the status must be enabled. The scheduling service updates the status to finished once it has finished running. You can stop or pause the scheduling service by updating the status to disabled.
You can update the endpoint URI in the target HREF. Supported target methods are POST, PUT, PATCH, DELETE, and GET.
Set generate_iam_token=true. When this option is set to true, the scheduling service generates an IAM token and passes it to the target URL at runtime. This IAM token is required to run schedules automatically at the scheduled intervals. This token is not to be confused with the IAM token required to make Watson Data API REST calls.

This POST method creates a schedule in a catalog with a defined start and a given end date:

    {
    "catalog_id": "aeiou",
    "description": "aeiou",
    "name": "aeiou",
    "tags": ["aeiou"],
    "start_date": "2017-08-22T01:02:14.859Z",
    "status": "enabled",
    "repeat": {
        "repeat_interval": 1,
        "repeat_type": "hour"
    },
    "repeat_end": {
        "end_date": "2017-08-24T01:02:14.859Z"
    },
    "target": {
        "href": "https://api.dataplatform.cloud.ibm.com/v2/data_profiles?start=false",
        "generate_iam_token": true,
        "method": "POST",
        "payload": "aeiou",
        "headers": [
         {
            "name": "content-type",
            "value": "application/json",
            "sensitive": false
           }
        ]
      }
    }

Get multiple schedules in a catalog or project

To get all schedules in the specified catalog or project, call the following GET method:

 HTTP Method: GET
 URI :/v2/schedules

You need the following information to get multiple schedules:

A valid IAM token, schedule ID, and the catalog or project ID.
You must be authorized to get schedules in the catalog or project.

You can filter the returned results by using the options entity.schedule.name and entity.schedule.status and can filter matching types by using StartsWith(starts:) and Equals(e:).

You can sort the returned results either in ascending or descending order by using one or more of the following options: entity.schedule.name, metadata.create_time, and entity.schedule.status.

Get a schedule

To get a schedule in the specified catalog or project, call the following GET method:

     HTTP Method: GET
     URI :/v2/schedules/{schedule_id}

You need the following information to get a schedule:

A valid IAM token, schedule ID, and the catalog or project ID.
You must be authorized to get a schedule in the catalog or project.

Update a schedule

To update a schedule in the specified catalog or project, call the following PATCH method:

HTTP Method: PATCH
URI :/v2/schedules/{schedule_id}

You need the following information to update a schedule:

A valid IAM token, schedule ID, and the catalog or project ID.
You must be authorized to update a schedule in the catalog or project.

You can update all the attributes under entity but can't update the attributes under meta-data.

Patch supports the replace, add, and remove operations. The replace operation can be used with all the attributes under entity. The add and remove operations can only be used with the repeat end options, namely max_invocations and end_date.

The start and end dates must be in the following format: YYYY-MM-DDTHH:mm:ssZ or YYYY-MM-DDTHH:mm:ss.sssZ (specified in RFC 3339).

This PATCH method replaces the repeat type, removes the max invocations and adds an end date:

    [
     {
     "op": "remove",
     "path": "/entity/schedule/repeat_end/max_invocations",
     "value":  20
     },
     {
     "op": "add",
     "path": "/entity/schedule/repeat_end/end_date",
     "value": "date"
     },
     {
     "op": "replace",
     "path": "/entity/schedule/repeat/repeat_type",
     "value":  "week"
     }
    ]

Delete a schedule

To delete a schedule in the specified catalog or project, call the following DELETE method:

    HTTP Method : DELETE
    URI :{GATEWAY_URL}/v2/schedules/{schedule_id}

":guid" represents the schedule_id of the deleted schedule.

You need the following information to delete a schedule:

A valid IAM token, schedule ID, and the catalog or project ID.
You must be authorized to delete a schedule in the catalog or project.

Delete multiple schedules

To delete multiple schedules in the specified catalog or project, call the following DELETE method:

HTTP Method: DELETE
URI :{GATEWAY_URL}/v2/schedules

":guid" represents the schedule_id of the deleted schedule.

You need the following information to delete multiple schedules:

A valid IAM token, schedule ID, and the catalog or project ID.
You must be authorized to delete schedules in the catalog or project.
A comma-separated list of the schedule IDs. If schedule IDs are not listed in the parameter schedule_ids, the scheduling service will delete all the schedules in the catalog or project.

Catalogs

Catalog Overview

Catalog APIs

Get a Catalog
Get Catalogs

IBM Knowledge Catalog helps you easily organize, find and share data assets, analytical assets, etc. for many data science projects and for the users who need to use those assets.

You can use the Catalog API to create catalogs which are rich metadata repositories for organizing and exploring metadata.

There are two phrases that will be used repeatedly throughout this (and the "Assets" and "Asset Types") documentation:

asset resource: The primary content of the asset. Many assets have a resource that is stored in an external repository: a data file, connected data set, notebook file, dashboard definition, or model definition.
asset metadata: The information about the asset resource. Each asset has a primary metadata document in a project or catalog and might have additional metadata documents.

See the Asset Terminology section for more information about those two phrases.

There is one special user-provided storage that must be specified by the creator of a catalog at the time the catalog is created: a Cloud Object Storage bucket for public cloud deployment and a file system for hybrid cloud deployment. We'll informally call that the "catalog's bucket". The creator of the catalog owns that bucket, but by providing that bucket's identification info during catalog creation, the catalog creator is allowing the IBM Knowledge Catalog graphical User Interface to store asset resources in that bucket and is allowing other IBM Knowledge Catalog APIs to stored (extended) asset metadata in that bucket.

If a user wants to store and retrieve asset resources (like spreadsheets, images, etc.) in the catalog's bucket, then that user can use the Assets API API to assist in that process.

In some cases, one of the other IBM Knowledge Catalog APIs (for example, the "Profiling" API) will store (extended) asset metadata documents in the catalog's bucket.

This section describes some of the individual Catalog APIs.

Get a Catalog

You can get metadata about a catalog using the get Catalog API. (Note: you aren't retrieving the actual data catalog with the GET Catalog API - you're just retrieving metadata that describes the catalog.)

Get Catalog - Request URL:

GET {service_URL}/v2/catalogs/{catalog_id}

Get Catalog - Response Body:

{
	"metadata": {
		"guid": "c6f3cbd8-2b7f-42fb-aa60-___",
		"url": "https://api.dataplatform.cloud.ibm.com/v2/catalogs/c6f3cbd8-2b7f-42fb-aa60-___",
		"creator_id": "IBMid-___",
		"create_time": "2018-11-06T17:40:32Z"
	},
	"entity": {
		"name": "CatalogForGettingStartedDoc",
		"description": "Catalog created for Getting Started doc",
		"generator": "Your catalog generator",
		"bss_account_id": "12345___",
		"capacity_limit": 0,
		"is_governed": false,
		"saml_instance_name": "IBM w3id"
	},
	"href": "https://api.dataplatform.cloud.ibm.com/v2/catalogs/c6f3cbd8-2b7f-42fb-aa60-___"
}

In this case, the response for the Get Catalog request is identical to the response for the Create Catalog request. If more activity had occurred with the catalog between the Create Catalog and the Get Catalog requests then there might have been some differences between the two responses.

Get Catalogs

To obtain the metadata for all the catalogs that you have access to (ie, are a collaborator of), you can call the GET Catalogs API.

Get Catalogs - Request URL:

GET {service_URL}/v2/catalogs

Note: the above URL is the simplest URL for getting catalogs because it doesn't contain any parameters. There are a number of optional parameters (limit, bookmark, skip, include, bss_account_id) to the above URL that you can make use of to limit the number of catalogs for which metadata is returned.

Get Catalogs - Response Body:

{
  "catalogs": [
    {
      "metadata": {
        "guid": "c6f3cbd8-2b7f-42fb-aa60-___",
        "url": "https://api.dataplatform.cloud.ibm.com/v2/catalogs/c6f3cbd8-2b7f-42fb-aa60-___",
        "creator_id": "IBMid-___",
        "create_time": "2018-11-06T17:40:32Z"
      },
      "entity": {
        "name": "CatalogForGettingStartedDoc",
        "description": "Catalog created for Getting Started doc",
        "generator": "Your catalog generator",
        "bss_account_id": "12345___",
        "capacity_limit": 0,
        "is_governed": false,
        "saml_instance_name": "IBM w3id"
      },
      "href": "https://api.dataplatform.cloud.ibm.com/v2/catalogs/c6f3cbd8-2b7f-42fb-aa60-___"
    }
  ],
  "nextBookmark": "g1AAAAFCeJzLYWBgYMlgTmHQSklKzi9KdUhJMjT___",
  "nextSkip": 0
}

In the above example, metadata for only one catalog is returned - the catalog created above. An advantage of calling the GET Catalogs API is you don't have to remember the ID of any particular catalog in order to get the metadata for that catalog.

Assets

Asset Terminology
Assets API Overview
- Asset's Primary Metadata Document (or Card)
- Other Assets API objects
Assets API Examples
Processing Duplicate Asset

From a high level, an asset is an item of data or data analysis in a project or catalog. Most of these assets consist of two parts:

Asset resource: The primary content of the asset. Many assets have a resource that is stored in an external repository: a data file (eg. text file, image, video, etc.), connected data set (eg. database table), notebook file, dashboard definition, or model definition. The Assets API does not affect this part of the asset. Think of this as the object that's being described by asset metadata (ie, an asset resource is a "decribee").
Asset metadata: The information about the asset resource. Each asset has a primary metadata document in a project or catalog and might have additional metadata documents. This is the part of the asset that you can get, create, or operate on with the Assets API. Think of this as the object that's doing the describing of an asset resource (ie, asset metadata is a "describer").

A library is a useful analogy for understanding the scope of the Assets API. A library contains a set of books and an index. The index, or card catalog, contains a card about each book. A card has information about the book, including the location of the book. A Watson project or catalog contains only the card catalog part of the library. The books, or asset resources, are elsewhere. Consequently, the Assets API can return the location of an asset resource, but not affect the asset resource in any way.

The term asset encapsulates the following:

[1] asset resource: the primary / initial resource that a user wants described by a primary metadata document.
[2] primary metadata document: a document added to a catalog to describe an asset resource.
[3] attributes: chunks of data inside a primary metadata document that describe either the asset resource or a secondary / extended metadata document.
[4] secondary / extended metadata documents: additional documents containing information related to the asset resource. Attached to the primary metadata document. Can be generated by catalog processes, such as profiling.
[5] a combination of all of the above: the IBM Knowledge Catalog UI presents information from each of the above on a single page and calls all that information an "asset".

For example, when you call the Get Assets API, you receive asset metadata (in a primary metadata document). The asset metadata might point to the location of the asset resource, but the Get Assets API does not return the asset resource. Similarly, when you run the Create Assets API, you create a primary metadata document that can, eventually, include the location of an existing asset resource.

This overview section provides a picture of the parts of a "primary metadata document" and then explains the parts of that picture. The picture provides a kind of "map" of a primary metadata document, so it's recommended to spend a few minutes studying it. Readers who prefer API examples can skip over the explanation of that picture that follows, and go straight to the Assets API Examples section. However, the Assets API Examples section will often refer back to the terms and explanations discussed in this Assets API Overview section.

Note: when calling any of the endpoints in the Assets API you must specify either a catalog ID or a project ID to indicate whether the metadata for an asset is (to be) in a catalog or a project. Because the Assets API endpoints can be applied to either a catalog or a project, rather than repeating the phrase "either a catalog or a project" over and over throughout the rest of this documentation, only the term "catalog" will be used. The possibility of instead using a "project" will be implied.

Asset Primary Metadata Document (or Card)

A primary metadata document is a document that contains the primary metadata for an asset resource. Once a primary metadata document has been created and stored in the catalog, it's often informally said that that asset resource has been "cataloged", or "added to the catalog". Note: being cataloged, or added to the catalog, does not mean the asset resource has been moved or copied and is now physically stored inside the catalog - it just means a primary metadata document has been created for that asset resource, and that primary metadata document is now stored in the catalog.

Almost every Assets API endpoint revolves around creating, reading, modifying or deleting a primary metadata document. JSON is natively used to store primary metadata documents in a catalog, and to transfer those documents in Assets API REST calls. So, JSON examples of primary metadata documents will be used throughout this documentation.

In this documentation, the term card (as in, an index card in a library's catalog) will often be used as a short nickname for the phrase "primary metadata document". In this documentation, "card" and "primary metadata document" mean exactly the same thing. The term "card" just saves us from reading and writing the lengthier phrase "primary metadata document" over and over.

A primary metadata document (ie, card) is a JSON object that's composed of up to three top-level fields, named as follows:

metadata: a JSON object containing metadata common to all asset types
entity: a JSON object containing attributes, each containing metadata specific to one asset type
attachments: an optional JSON array, each item of which is a JSON object containing metadata for an attached (ie, externally stored) asset resource or extended metadata document

For a pictorial representation of a primary metadata document (ie, card) and its associated asset resource and extended metadata documents, see the Parts of a Primary Metadata Document figure below:

In particular, note that:

red rectangles are used in the figure to highlight the three top-level fields of a card.
the green rectangles illustrate how important the name of the primary asset type is in relating various parts of the card, and the attached asset resource, to each other. In the example figure, the value of "metadata.asset_type" is "data_asset". The value you'll see in your card depends on the "asset_type" you've specified for your asset.

"metadata" field of a Primary Metadata Document

The "metadata" field of a primary metadata document (ie, of a card) is a JSON object that contains metadata fields that are common across all types of assets. (See the top red rectangle in the parts figure.) The Assets API specifies the names of the fields that go into the "metadata" part of the card. The user must supply values for some of the fields in "metadata"; the values of other fields in "metadata" will be filled in by the Assets API during the life of the card. Here's a list of some of the fields inside "metadata" (see example cards in the Get Asset section for more extensive lists):

"asset_id":
- The ID of the card (ie, primary metadata document) rather than of the asset resource described by the card.
- Created internally by the Assets API at the time the card is created. That is, you do not supply this value.
"asset_type":
- You must supply this value.
- Declares the primary asset type of this card.
- Describes the type of the asset resource attached (if any) to this card.
- Specifies the name of the primary attribute in this card.
- See Asset Types for more details on asset types.
"asset_attributes":
- You must not supply any value for this field when creating a primary metadata document. The Assets APIs maintain the contents of this field.
- An array of attribute names (only the names, not the actual attributes).
- Each attribute / asset type name listed in this array will have a correspondingly named attribute in the "entity" field of the card.
- The name of each attribute must match the name of an existing asset type, so this is also an array of the names of the primary and secondary / extended asset types used by this card.
"name": the name of the asset resource this card describes
"description": a description of the asset resource
"origin_country": the originating country for the asset resource
"tags": an array of terms that users want to associate with the asset resource
"rov": Rules Of Visibility.
- "mode": -1 - this is the default, which corresponds to "mode" : 0, public (see below)
- "mode": 0 - indicates public visibility, in which everybody can view and search the values of the asset's primary metadata document (card), and preview the asset's data. Note: access can still be denied based on actionable governance policy rules.

	"rov": {
		"mode": 0,
		"collaborator_ids": []
	}

- "mode": 8 - indicates private visibility, which allows users listed as members of the asset (as denoted by collaborator_ids list) to view and search all fields (including metadata, entity, and attachments) of the asset's primary metadata document (card), and preview the asset's data. Non-members are allowed to only view and search the metadata field, and cannot preview the asset's data. This mode is only available through the API, and is not exposed in the IKC UI. Note: access can still be denied based on actionable governance policy rules.

	"rov": {
	"mode": 8,
	"collaborator_ids": [
		{
			"IBMid-06___": {
				"user_iam_id": "IBMid-06___"
			}
		},
		{
			"IBMid-27___": {
				"user_iam_id": "IBMid-27___"
			}
		}
	]
}

- "mode": 16 - indicates hidden visibility, in which only users listed as members of the asset (as denoted by collaborator_ids list) have any access to fields in the asset, and non-members have no access to the asset. Note: access can still be denied based on actionable governance policy rules.

	"rov": {
	"mode": 16,
	"collaborator_ids": [
		{
			"IBMid-06___": {
				"user_iam_id": "IBMid-06___"
			}
		},
		{
			"IBMid-27___": {
				"user_iam_id": "IBMid-27___"
			}
		}
	]
}

"entity" field of a Primary Metadata Document

The "entity" field of a card (ie, primary metadata document) is a JSON object that contains additional JSON objects called attributes, each of which contains metadata fields that are specific to one asset type. (See the middle red rectangle in the parts figure.) The only contents of the "entity" field are attributes, which are discussed in the next section.

Note: the fact that the "entity" section contains attributes for more than one asset type does not mean that a single card contains metadata for more than one asset resource. A card always contains metadata for exactly one asset resource, and that asset resource will have exactly one attribute associated with it (see primary attribute below). All the other attributes in the "entity" field contain extended metadata describing the single asset resource that the card was created for. Really, asset types ought to be thought of as attribute types because asset types literally define (some of) the fields that will appear in attributes.

Attributes

In the Assets API, an attribute is a collection of metadata that:

is contained directly inside the "entity" field of the primary metadata document.
is identically named with, and has fields that are partially defined by, an Asset Type
describes an asset resource or something related to that asset resource, such as an extended metadata document

There is one attribute in the "entity" field for each attribute name that appears in the "metadata.asset_attributes" array. So, for example, if the "metadata.asset_attributes" array contains these two attribute names:

   "metadata": {
      ...
      "asset_attributes": [
         "data_asset",
         "data_profile"
      ],
   }

then the "entity" field will contain these two correspondingly named attributes:

   "entity": {
      "data_asset": {  // attribute name matches "data_asset" in "metadata.asset_attributes"
         ...attribute contents...
      },
      "data_profile": {  // attribute name matches "data_profile" in "metadata.asset_attributes"
         ...attribute contents...
      }
   }

The name of each attribute in "entity" must also match the name of an existing asset type. That is, an attribute named "X" will contain metadata related to an asset type also named "X". So, an attribute's name can be thought of as simultaneously telling us that attribute's "type". For example, in this asset metadata document example, both the attribute names "data_asset" and "data_profile" refer to asset types with those same names.

There is one special attribute that will be referred to as the primary attribute. The primary attribute is the main attribute used to describe an asset resource. Every primary metadata document will have exactly one primary attribute. The name of the primary attribute is the same as the name that appears in the "metadata.asset_type" field.

Any attribute other than the primary attribute is a "secondary" / "extended" attribute whose name must match the name of a secondary / extended asset type. A common example of an attribute for extended metadata is named "data_profile", which is created by the Profiling API. For example, see the underlined names in the Parts of a Primary Metadata Document figure, or the "entity.data_profile" field in this asset metadata document.

Although the Assets API restricts the names of attribute objects to match the names of asset types, the Assets API does not (in general) specify what the contents of those attributes should be. So, in some sense, the fields within an attribute are the opposite of the fields within the "metadata" field:

the Assets API "owns" (or, specifies) which fields go inside "metadata"
the user "owns" (or, specifies) which fields go inside the attributes (except for some fields of already available asset types)

The following example shows two attributes, whose names must match asset types, but whose contents are (for the most part) up to the user:

   "entity": {
      "data_asset": { // attribute name must match some asset type's name
         ...
         data_asset *type creator* and
         data_asset *attribute creator*
         decide what fields go here
         ...
      },
      "data_profile": { // attribute name must match some asset type's name
         ...
         data_profile *type creator* and
         data_profile *attribute creator*
         decide what fields go here
         ...
      }
   }

Because the Asset Types API is itself the creator of some already available asset types, the Asset Types API specifies some of the fields for any attribute whose name corresponds to one of those already available asset types. For example, see the discussion of the already available asset type called "data_asset".

Note: there is a GET attribute API that can be used to retrieve just the attributes in the "entity" section of the primary metadata document, instead of the entire primary metadata document as returned by the GET asset API.

"attachments" (optional) field of a Primary Metadata Document

The "attachments" field of a card (ie, primary metadata document) is a JSON array, each item of which contains metadata for one attachment. (See the bottom red rectangle in the parts figure.)

The word attachment (or attachments) can be interpreted in multiple ways:

the "attachments" array in the primary metadata document
an attachment item in the "attachments" array
a metadata document that will be returned from a call to the GET Attachment API. That metadata document will contain information that points to, and can be used to retrieve, either...
the asset resource being described by the primary metadata document
an extended metadata document stored in the catalog's bucket and containing extended metadata for the asset resource

Each attribute in the "entity" field can have a corresponding attachment item in the "attachments" array. An attribute and its corresponding attachment item are related to each other by using the name of the attribute as the value for the attachment item's "asset_type" field. For example, notice in the following card snippet how the attribute name "data_asset" is used to link that "data_asset" attribute to its attachment item in the "attachments" array:

   "entity": {
      ...other attributes

      "data_asset": { // <-- attribute's name matches its...
         ...
      },

      ...other attributes
   },

   "attachments": [
      ...other attachment items

      {
         ...
         "asset_type": "data_asset",  // <-- ...attachment's asset_type
         ...
         "connection_id": "...",   // connection_ fields are one way
         "connection_path": "...", // that item points to attached object
         ...
      },

      ...other attachment items
   ]

Notice also in the above card snippet that, in this case, the attachment item contains two "connection_..." fields that point to the attachment object located in external storage. So, an attribute has an attachment item which points to an attachment object.

Like the fields of "metadata", the fields of an attachment item are specified by the Assets API. Some of the most important fields in an attachment item are:

"asset_type":
- describes the type of the attachment
- figuratively connects the attachment item to the attribute with the same name
"connection_id" and "connection_path" (optional):
- this pair of fields specify the ID of a WDP Connection and a path in the associated data repository that points to the attached object
- always used for an attached asset like a database table
- can also be used for an attached asset resource (eg, spreadsheet) that can be stored in the catalog's bucket
- the presence of these two fields means the attachment will be known as a remote attachment
"object_key" and "handle" (optional):
- this pair of fields contain information identifying and pointing to the location of an attached object (either an asset or an extended metadata document) in the catalog's bucket
- the presence of these two fields means the attachment will be known as a referenced attachment

For any attachment, only one of the following two pairs of fields will be used:

"connection_id" and "connection_path" (ie, remote attachment), or
"object_key" and "handle" (ie, referenced attachment).

Interestingly, being remote does not tell you whether or not an attachment is in the catalog. Remote only tells you how the attached object can be retrieved: by using a connection.

An attachment item (in the card) points to one of two kinds of attached object (in external storage):

an asset, or
and extended metadata document.

Those are briefly discussed in the next 2 sections.

Asset Resource Attachment

The most typical attachment object is the asset resource being described by the card.

Follow the green arrows in the Parts of a Primary Metadata Document figure to see how:

the asset's type name leads to
an attribute name, which leads to
a primary attribute, which leads to
an attachment metadata item for that attribute, which finally leads to
the attached asset resource.

For a full example that shows an attachment metadata item for an attached csv file, see the (only) item in the "attachments" array in Get Asset - CSV File - Response Body - Before Profiling.

Extended Metadata Document Attachment(s)

The other kind of attachment objects are extended metadata documents. A card can have 0, 1, or many attached extended metadata documents. These documents each contain a related set of (additional) metadata describing the asset resource. Extended metadata documents are stored externally in the catalog's bucket.

See the underlined "data_profile" type name in the Parts of a Primary Metadata Document figure for a visualization of how, for one extended metadata document, the three parts ("metadata", "entity", "attachments") of a card are related to each other.

See the second item in the "attachments" array in Get Asset - CSV File - Response Body - After Profiling for an example showing an attachment item for a "data_profile" extended metadata document.

Uses of `"asset_type"` value

From the previous sections, you can see that the "asset_type" value shows up in:

the "metadata.asset_type" field
the "metadata.asset_attributes" array
a field (ie, object) in the "entity" field. This object is the primary attribute.
the asset_type field of the primary attribute's attachment (if such an attachment exists, which it typically does). This (primary) attachment will be the asset resource (eg, database table, spreadsheet, csv file, etc.).

For example, see the Parts of a Primary Metadata Document figure above, where the name of the primary attribute is, in this case, "data_asset" and is highlighted with green rectangles in all the places it's used. The path shown by the green arrows in the figure starts at the "metadata.asset_type" field and ends at the asset resource, in this case a file called Sample.csv.

Other Assets API Objects

Finally, here is a brief list of some of the remaining objects that can be manipulated with the Assets APIs:

owner
- the owner of the asset
collaborators
- users who are allowed to see and possibly edit (some parts of) the asset
perms
- permissions for viewing / editing an asset
ratings
- indications of how popular or useful the asset is
stats
- statistics on how often and when the asset was viewed or edited, and who did that viewing or editing.

Getting an Asset

It's important to understand that the GET Asset API does not return an asset resource like a database table, a spreadsheet, a csv file, etc. Instead, it returns a primary metadata document (ie, card) that describes an asset resource.

Obviously, a primary metadata document (ie, card) must have been created before it can be retrieved. Still, it's instructive to see actual examples of a card and its parts before attempting to create those things. After all, many users will retrieve cards that were previously created by someone else.

This and the following sections show how to retrieve asset metadata and attachments (eg, an asset resource and extended metadata documents).

Getting an Asset - for a Connection

We'll start by retrieving a common primary metadata document (ie, card): one for a "connection" asset type. This is a simple card because it has no attachments. That makes it an easy example to start with, even though many of the other cards you'll encounter do have attachments.

Use the following GET Asset API to retrieve the primary metadata document for a connection. Note that this requires that you know and supply the IDs of both the primary metadata document (ie, card) and of the catalog that contains the card. Either someone has given you both of those IDs or you can browse to the asset's page using the IBM Knowledge Catalog UI and then extract both the catalog ID and the primary metadata document ID from within the URL in the browser's address bar.

Getting an Asset - Request URL:

GET {service_URL}/v2/assets/{asset_id}?catalog_id={catalog_id}

The following is the primary metadata document (ie, card) that's returned.

Note: you may find it helpful to look at the Parts of a Primary Metadata Document Figure before looking at the following Response Body.

Getting an Asset - Connection - Response Body:

{
  "metadata": {
    "rov": {
      "mode": 0,
      "collaborator_ids": {}
    },
    "usage": {
      "last_updated_at": "2018-11-06T17:40:37Z",
      "last_updater_id": "IBMid-___",
      "last_update_time": 1541526037227,
      "last_accessed_at": "2018-11-06T17:40:37Z",
      "last_access_time": 1541526037227,
      "last_accessor_id": "IBMid-___",
      "access_count": 0
    },
    "name": "ConnectionForCSVFile",
    "description": "Connection for CSV file",
    "tags": [],
    "asset_type": "connection",
    "origin_country": "us",
    "rating": 0,
    "total_ratings": 0,
    "catalog_id": "c6f3cbd8-2b7f-42fb-aa60-___",
    "created": 1541526037227,
    "created_at": "2018-11-06T17:40:37Z",
    "owner_id": "IBMid-___",
    "size": 0,
    "version": 2,
    "asset_state": "available",
    "asset_attributes": [
      "connection"
    ],
    "asset_id": "070e9be2-40a8-4e0e-___",
    "asset_category": "SYSTEM"
  },
  "entity": {
    "connection": {
      "datasource_type": "193a97c1-4475-4a19-b90c-295c4fdc6517",
      "context": "source,target",
      "properties": {
        "bucket": "catalogforgettingsta___",
        "secret_key": "{wdpaes}12345___=",
        "api_key": "{wdpaes}eo/12345_=",
        "resource_instance_id": "crn:v1:bluemix:public:cloud-object-storage:global:a/12345c___:7240b198-b0f6-___::",
        "access_key": "12345___",
        "region": "us-geo",
        "url": "https://s3.us-south.objectstorage.softlayer.net"
      },
      "flags": []
    }
  },
  "href": "https://api.dataplatform.cloud.ibm.com/v2/assets/070e9be2-40a8-4e0e-___?catalog_id=c6f3cbd8-2b7f-42fb-aa60-___"
}

The above response has two of the three primary groups of metadata that were described in the Primary Metadata Document section: "metadata" and "entity".

As discussed in Assets API Overview section, the contents of the "metadata" field are common to all primary metadata documents (ie, cards). The set of fields in "metadata" is completely defined by the Assets API. The values for some of those fields must be provided by the creator of the card, while other fields' values will be populated by various Assets APIs during the life of the card. Note the following fields' values in particular:

"metadata" fields whose values are provided by the creator of the card:
- "name": "ConnectionForCSVFile"
- "description": "Connection for CSV file"
- "asset_type": "connection"
- "asset_attributes": [ "connection"
  
  ]
"metadata" fields whose values are set by various Assets APIs during the life of the card:
- "usage": contains various statistics describing usage of the card/asset
- "catalog_id": the ID of the catalog that contains the card
- "created_at": the time and date at which the card was created
- "asset_id": the ID of the card (not the asset resource)

For more info about the "metadata" fields, see the discussion on "metadata" in the Assets API Overview section above.

The contents of the "entity" field are only partially defined by the Assets API. In particular, the "entity" field shown in the above card contains a field whose name must match the value in "metadata.asset_type", in this case, "connection". That field is the primary attribute.

On the other hand, both the names and the values of all the fields inside the primary attribute "entity.connection" are completely determined by the creator of the "connection" asset type and the creator of the "connection" attribute. The Assets API does not, in general, decide what fields go inside the primary attribute (or any other attribute). In the example "connection" attribute above, some of the more interesting fields are:

"datasource_type" - specifies the ID of the type of the data source to which a connection will be formed.
"properties" - specifies connection metadata specific to the type of the datasource. The exact contents of this field will change according to the type of the datasource.

For more info on the contents of "entity" in general, see the discussion on "entity" in the Assets API Overview section.

Notice the above card contains no "attachments" array. That means there is no attached asset resource associated with this card. A natural question is: how can "connection" asset metadata exist for, or describe, a non-existent "connection" asset resource? Actually, a "connection" asset resource does exist, but only when the metadata in the connection's primary metadata document is used to create a client-server connection at runtime.

Get Asset - for a CSV File

This section shows a far more typical example in which the primary metadata document (ie, card) does have an attached asset resource - in this case, a csv file named Sample.csv. Here's the very simple contents of the Sample.csv file:

Sample.csv file contents

Name,Number
abc,123
def,456

Use the GET Asset API to retrieve the asset metadata for the Sample.csv asset resource. Note: the GET Asset API only returns a primary metadata document (ie, card) that describes the Sample.csv file - it does not return the actual Sample.csv file.

Get Asset - Request URL:

GET {service_URL}/v2/assets/{asset_id}?catalog_id={catalog_id}

It's instructive to show two different versions of the primary metadata document for the Sample.csv asset:

Before profiling (which returns a small metadata document - without extended metadata)
After profiling (which returns a much larger metadata document - with extended metadata)

Note: you may find it helpful to look at the Parts of a Primary Metadata Document Figure before looking at either of the following two Get Asset Response Bodies.

Here is the smaller primary metadata document that exists before the Profile API is invoked on the Sample.csv file.

Get Asset - CSV File - Response Body - Before Profiling:

{
  "metadata": {
    "name": "Sample.csv",
    "description": "A simple csv file.",
    "asset_type": "data_asset",
    "rov": {
      "mode": 0,
      "collaborator_ids": {}
    },
    "usage": {
      "last_updated_at": "2018-11-06T17:45:23Z",
      "last_updater_id": "IBMid-___",
      "last_update_time": 1541526323713,
      "last_accessed_at": "2018-11-06T17:45:23Z",
      "last_access_time": 1541526323713,
      "last_accessor_id": "IBMid-___",
      "access_count": 0
    },
    "origin_country": "united states",
    "rating": 0,
    "total_ratings": 0,
    "catalog_id": "c6f3cbd8-2b7f-42fb-aa60-___",
    "created": 1541526321437,
    "created_at": "2018-11-06T17:45:21Z",
    "owner_id": "IBMid-___",
    "size": 0,
    "version": 2,
    "asset_state": "available",
    "asset_attributes": [
      "data_asset"
    ],
    "asset_id": "45f4ab8c-37d5-45a1-8adf-___",
    "asset_category": "USER"
  },
  "entity": {
    "data_asset": {
      "mime_type": "text/csv",
      "dataset": false
    }
  },
  "attachments": [
    {
      "id": "b8c7a390-e857-4c34-add8-___",
      "version": 2,
      "asset_type": "data_asset",
      "name": "remote",
      "description": "remote",
      "connection_id": "070e9be2-40a8-4e0e-___",
      "connection_path": "catalogforgettingsta-datacatalog-r1s___/data_asset/Sample_SyjEQUy6m.csv",
      "create_time": 1541526323713,
      "size": 0,
      "is_remote": true,
      "is_managed": false,
      "is_referenced": false,
      "is_object_key_read_only": false,
      "is_user_provided_path_key": true,
      "transfer_complete": true,
      "is_partitioned": false,
      "complete_time_ticks": 1541526323713,
      "user_data": {},
      "test_doc": 0,
      "usage": {
        "access_count": 0,
        "last_accessor_id": "IBMid-___",
        "last_access_time": 1541526323713
      }
    }
  ],
  "href": "https://api.dataplatform.cloud.ibm.com/v2/assets/45f4ab8c-37d5-45a1-8adf-___?catalog_id=c6f3cbd8-2b7f-42fb-aa60-___"
}

The above primary metadata document has all three primary groups of metadata ("metadata", "entity", and "attachments") that were described in the Assets API Overview section.

The contents of the "metadata" field are very similar to those shown above for the Connection card example. The most important difference is the value that the user specified as the "asset type" for the Sample.csv asset, namely "data_asset". That asset type name shows up in two places inside the "metadata" section of the primary metadata document:

"metadata":
- "asset_type": "data_asset"
- "asset_attributes": [ "data_asset" ]

As discussed in the Attributes section, the fact that "metadata.asset_type" has the value "data_asset" means the "entity" field of the card must contain a primary attribute called "data_asset". The Asset Types API provides the predefined asset type "data_asset". That "data_asset" type definition declares that there are two mandatory fields in a "data_asset" attribute: "mime_type" and "dataset", as can be seen in the card above and repeated here:

"entity":
- "data_asset":
  - "mime_type": "text/csv"
    - specifies the mime type of the asset resource. Here, the mime type indicates that the asset resource is a text csv file.
  - "dataset": false
    - false because there is no "columns" field in this primary attribute.
    - Note: false does not mean there are no columns in the asset resource. Clearly, our Sample.csv file does have columns. The problem here is that no one has (yet) told the card that the asset resource has columns. Compare this "data_set" attribute to the one shown in the next example Get Asset - CSV File - Response Body - After Profiling, where the value of "dataset" has been changed to true, and the primary attribute does have a "columns" field.

Unlike in the Connection card example above, the card for the Sample.csv file does have an "attachments" field. In this case, the "attachments" array has one item in it. That item contains metadata that points to the attached asset resource (ie, the Sample.csv file). Some of the more interesting fields in that attachment item are:

"id": "b8c7a390-e857-4c34-add8-___"
- identifies the metadata document that points to the attached asset resource
"asset_type": "data_asset"
- matches the name of the primary attribute in "entity", so linking the primary attribute to this attachment item and designating this item as the item that points to the asset resource.
"connection_id": "070e9be2-40a8-4e0e-___"
- identifies a connection primary metadata document (ie, card) which contains credentials and other info that can be use to connect to the external repository that contains the attached asset resource (ie, the "Sample.csv" file)
- not coincidentally, the particular connection card referred to by "070e9be2-40a8-4e0e-___" is the exact same connection card shown above in Get Asset - Connection Primary Metadata Document
"connection_path": "catalogforgettingsta-datacatalog-r1s___/data_asset/Sample_SyjEQUy6m.csv",
- identifies the path in the external repository that contains the attached asset (ie, the "Sample.csv" file)
"is_remote": true
- as discussed in the "attachments" overview section, is_remote is true because "connection_id" and "connection_path" are being used to describe how to get the Sample.csv asset resource.
"is_referenced": false (at most one of "is_referenced" and "is_remote" will be true)

Get Asset - CSV File - Response Body - After Profiling:

Now, let's compare what GET {service_URL}/v2/assets/{asset_id}?catalog_id={catalog_id} returns for the same asset after the Profile API has been invoked on the Sample.csv file:

{
  "metadata": {
    "rov": {
      "mode": 0,
      "collaborator_ids": {}
    },
    "usage": {
      "last_updated_at": "2018-11-12T15:33:34Z",
      "last_updater_id": "iam-ServiceId-12345___",
      "last_update_time": 1542036814782,
      "last_accessed_at": "2018-11-12T15:33:34Z",
      "last_access_time": 1542036814782,
      "last_accessor_id": "iam-ServiceId-12345___",
      "access_count": 0
    },
    "name": "Sample.csv",
    "description": "Simple csv file for experiment for getting started document.",
    "tags": [],
    "asset_type": "data_asset",
    "origin_country": "united states",
    "rating": 0,
    "total_ratings": 0,
    "catalog_id": "c6f3cbd8-2b7f-42fb-aa60-___",
    "created": 1541526321437,
    "created_at": "2018-11-06T17:45:21Z",
    "owner_id": "IBMid-___",
    "size": 9238,
    "version": 2,
    "asset_state": "available",
    "asset_attributes": [
      "data_asset",
      "data_profile"
    ],
    "asset_id": "45f4ab8c-37d5-45a1-8adf-___",
    "asset_category": "USER"
  },
  "entity": {
    "data_asset": {
      "mime_type": "text/csv",
      "dataset": true,
      "columns": [
        {
          "name": "Name",
          "type": {
            "type": "varchar",
            "length": 1024,
            "scale": 0,
            "nullable": true,
            "signed": false
          }
        },
        {
          "name": "Number",
          "type": {
            "type": "varchar",
            "length": 1024,
            "scale": 0,
            "nullable": true,
            "signed": false
          }
        }
      ]
    },
    "data_profile": {
      "971e9c66-be4c-44b4-91f3-___": {
        "metadata": {
          "guid": "971e9c66-be4c-44b4-91f3-___",
          "asset_id": "971e9c66-be4c-44b4-91f3-___",
          "dataset_id": "45f4ab8c-37d5-45a1-8adf-___",
          "url": "https://api.dataplatform.cloud.ibm.com/v2/data_profiles/971e9c66-be4c-44b4-91f3-___?catalog_id=c6f3cbd8-2b7f-42fb-aa60-___&dataset_id=45f4ab8c-37d5-45a1-8adf-___",
          "catalog_id": "c6f3cbd8-2b7f-42fb-aa60-___",
          "created_at": "2018-11-12T15:32:53.902Z",
          "accessed_at": "2018-11-12T15:32:53.902Z",
          "owner_id": "IBMid-___",
          "last_updater_id": "IBMid-___"
        },
        "entity": {
          "data_profile": {
            "options": {
              "disable_profiling": false,
              "max_row_count": 5000,
              "max_distribution_size": 100,
              "max_numeric_stats_bins": 200,
              "classification_options": {
                "disabled": false,
                "use_all_ibm_classes": true,
                "ibm_class_codes": [],
                "custom_class_codes": []
              }
            },
            "execution": {
              "status": "finished",
              "is_supported": true,
              "dataflow_id": "3f1ace02-4d40-451d-9bc7-___",
              "dataflow_run_id": "f774f92f-5a61-49ca-8a68-___"
            },
            "columns": [],
            "attachment_id": "8d614be0-6900-403b-ab50-___"
          }
        },
        "href": "https://api.dataplatform.cloud.ibm.com/v2/data_profiles/971e9c66-be4c-44b4-91f3-___?catalog_id=c6f3cbd8-2b7f-42fb-aa60-___&dataset_id=45f4ab8c-37d5-45a1-8adf-___"
      },
      "attribute_classes": [
        "NoClassDetected",
        "Organization Name"
      ]
    }
  },
  "attachments": [
    {
      "id": "b8c7a390-e857-4c34-add8-___",
      "version": 2,
      "asset_type": "data_asset",
      "name": "remote",
      "description": "remote",
      "connection_id": "070e9be2-40a8-4e0e-___",
      "connection_path": "catalogforgettingsta-datacatalog-r1s___/data_asset/Sample_SyjEQUy6m.csv",
      "create_time": 1541526323713,
      "size": 0,
      "is_remote": true,
      "is_managed": false,
      "is_referenced": false,
      "is_object_key_read_only": false,
      "is_user_provided_path_key": true,
      "transfer_complete": true,
      "is_partitioned": false,
      "complete_time_ticks": 1541526323713,
      "user_data": {},
      "test_doc": 0,
      "usage": {
        "access_count": 0,
        "last_accessor_id": "IBMid-___",
        "last_access_time": 1541526323713
      }
    },
    {
      "id": "8d614be0-6900-403b-ab50-___",
      "version": 2,
      "asset_type": "data_profile",
      "name": "data_profile_971e9c66-be4c-44b4-91f3-___",
      "object_key": "data_profile_971e9c66-be4c-44b4-91f3-___",
      "create_time": 1542036813627,
      "size": 9238,
      "is_remote": false,
      "is_managed": false,
      "is_referenced": true,
      "is_object_key_read_only": false,
      "is_user_provided_path_key": true,
      "transfer_complete": true,
      "is_partitioned": false,
      "complete_time_ticks": 1542036813627,
      "user_data": {},
      "test_doc": 0,
      "handle": {
        "bucket": "catalogforgettingsta-datacatalog-r1s___",
        "location": "us-geo",
        "key": "data_profile_971e9c66-be4c-44b4-91f3-___",
        "upload_id": "done",
        "max_part_num": 1
      },
      "usage": {
        "access_count": 0,
        "last_accessor_id": "iam-ServiceId-12345___",
        "last_access_time": 1542036813627
      }
    }
  ],
  "href": "https://api.dataplatform.cloud.ibm.com/v2/assets/45f4ab8c-37d5-45a1-8adf-___?catalog_id=c6f3cbd8-2b7f-42fb-aa60-___"
}

Let's look at a few of the most important differences between the primary metadata document for the Sample.csv file before and after profiling:

"metadata":
- "asset_attributes": [ "data_asset", "data_profile" ]
  - Note the "data_profile" attribute name has been added
"entity":
- "data_asset":
  - "columns": the Profile API has added the "columns" field to the data_asset attribute,
  - "dataset": the Profile API caused this to change from false to true because of the newly added "columns" field
- "data_profile":
  - this "data_profile" attribute is entirely new, and was added by the Profile API.
  - the name of this secondary attribute matches the name of the secondary asset type "data_profile", which was (previously) created by the Profile API.
  - the contents of this "data_profile" attribute was entirely decided by the Profile API, not by the Assets API.
  - this attribute contains a lot of extended metadata about the "data_profile" run that produced a "data_profile" extended metadata document.
"attachments":
- a new item has been added to the "attachments" array
- that new item contains the following metadata about an extended metadata document:
  - "id": "8d614be0-6900-403b-ab50-___"
  - "asset_type": "data_profile"
    - note that the value "data_profile" matches the name of the "data_profile" attribute that this attachment item belongs to, so linking the attachment item and the attribute.
  - "handle": contains various fields pointing to the actual attached extended metadata document which is located in some external repository. That extended metadata document will contain a great deal more metadata about the asset resource, that is, about the "Sample.csv" file.

The next section shows how to retrieve the Extended Metadata Document that's referred to by the new "data_profile" "attachments" item just described above.

Get Attachment - Extended Metadata Document:

The following example builds on the GET Asset example from the previous section and shows how to retrieve an attachment that is an extended metadata document.

An attachment can be retrieved in 4 steps.

Step 1: Decide the asset_type of the attachment you want.

The only choices you have for asset_type in a given primary metadata document are listed in that document's "metadata.asset_attributes" field. In the example above those values are:

"data_asset"
"data_profile"

The asset_type of the extended metadata document we want is "data_profile".

Step 2: Get the "id" of the "attachments" item whose "asset_type" field has the value you chose in Step 1.

In the primary metadata document, look for the only "attachments" item whose "asset_type" field has the value you chose in Step 1, namely "data_profile". In our example primary metadata document above, that "attachments" item has the "id" value "8d614be0-6900-403b-ab50-___".

Step 3: Invoke the Get Attachment API to get attachment metadata for the attached extended metadata document.

Get Asset Attachment - Request URL

GET /v2/assets/{asset_id}/attachments/{attachment_id}

The values for the above URL parameters are obtained as follows:

{asset_id}: is the same as what appears in the "metadata.asset_id" field of the above primary metadata document, namely "45f4ab8c-37d5-45a1-8adf-___"
{attachment_id} is the of "id" that was obtained in Step 2, namely "8d614be0-6900-403b-ab50-___".

Invoke the above GET Attachment API with the above values, which will return an attachment metadata document as shown in the following response body:

Get Asset Attachment - Response Body:

{
  "attachment_id": "8d614be0-6900-403b-ab50-___",
  "asset_type": "data_profile",
  "is_partitioned": false,
  "name": "data_profile_971e9c66-be4c-44b4-91f3-___",
  "created_at": "2018-11-12T15:33:33Z",
  "object_key": "data_profile_971e9c66-be4c-44b4-91f3-___",
  "object_key_is_read_only": false,
  "bucket": {
    "bucket_name": "catalogforgettingsta-datacatalog-r1s___",
    "bluemix_cos_connection": {
      "viewer": {
        "bucket_connection_id": "5b6bc03d-577d-4609-b3a4-___"
      },
      "editor": {
        "bucket_connection_id": "070e9be2-40a8-4e0e-a468-___"
      }
    }
  },
  "url": "https://s3.us-south.objectstorage.softlayer.net/catalogforgettingsta-datacatalog-r1s___/data_profile_971e9c66-be4c-44b4-91f3-___?response-content-disposition=attachment%3B%20filename%3D%22data_profile_971e9c66-be4c-44b4-91f3-___%22&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20190423T162446Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=d2d518b66ac64de___%2F2019___%2Fus-geo%2Fs3%2Faws4_request&X-Amz-Signature=ce7322d7291396c511a6df38635df4e85b7c78c173___",
  "transfer_complete": true,
  "size": 9238,
  "user_data": {},
  "creator_id": "iam-ServiceId-12345___",
  "usage": {
    "access_count": 1,
    "last_accessor_id": "IBMid-___",
    "last_access_time": 1556036686480
  },
  "href": "https://api.dataplatform.cloud.ibm.com/v2/assets/45f4ab8c-37d5-45a1-8adf-726c65b68008/attachments/8d614be0-6900-403b-ab50-___?catalog_id=c6f3cbd8-2b7f-42fb-aa60-___"
}

It's important to understand that the GET Attachment API only returns a metadata document that describes where, or how, an attached asset resource or extended metadata document can be accessed or retrieved.

The most important field in the above response is "url" which contains a signed URL that can be used to retrieve the actual extended metadata document. Note that the "url" points to a completely different server than the server that responds to "Assets API" calls! Extended metadata documents are not stored in the catalog.

Step 4: Use the "url" in the response from Step 3 to call the relevant server to get the extended metadata document.

The simplest way to use that "url" value is to paste it into the address bar of a browser, and let the browser retrieve the extended metadata document. Here's a peek at some of the contents of the large extended metadata document that can be retrieved using that "url" value. That large extended metadata document was created by the Profile API and contains a great deal of extended metadata about our small Sample.csv file:

{
	"summary": {
		"version": "1.9.3",
		"row_count": 2,
		"score": 1,
		"score_stats": {
			"n": 2,
			"mean": 1.0,
			"variance": 0.0,
			"stddev": 0.0,
			"min": 1.0,
			"max": 1.0,
			"sum": 2.0
		},
...
	},
	"columns": [{
			"name": "Name",
			"value_analysis": {
				"distinct_count": 2,
				"null_count": 0,
				"empty_count": 0,
				"unique_count": 2,
				"max_value_frequency": 1,
				"min_string": "abc",
				"max_string": "def",
				"inferred_type": {
					"type": {
						"length": 3,
						"precision": 0,
						"scale": 0,
						"type": "STRING"
					}
				},
...
		}, {
			"name": "Number",
			"value_analysis": {
				"distinct_count": 2,
				"null_count": 0,
				"empty_count": 0,
				"unique_count": 2,
				"max_value_frequency": 1,
				"min_string": "123",
				"max_string": "456",
				"min_number": 123.0,
				"max_number": 456.0,
				"inferred_type": {
					"type": {
						"length": 3,
						"precision": 3,
						"scale": 0,
						"type": "INT16"
					}
				},
...
	]
}

Get Attachment - Asset Resource:

The 4 steps given above to retrieve an extended metadata document can also be used to retrieve an asset resource like the Sample.csv file example.

The main difference is that in Step 1 you would choose the asset_type "data_asset" because that is the primary asset type of the primary metadata document, ie. the asset_type that identifies both the primary attribute and the primary attachment, ie, the asset resource.

Create Asset: book

Before you can create a primary metadata document (ie, card) the asset type that you want to use for that card must already exist. You can use one of the already available asset types, or you can use an asset type that you have created.

The Create Asset Type: book section shows how to create an asset type named book. In this section, that asset type will be used to create a primary metadata document for a book asset resource. That primary metadata document will have:

a "metadata.asset_type" field with the value "book"
a primary attribute called "book".

Use the following endpoint to create a primary metadata document for a book asset resource:

Create Asset: book - Request URL:

POST {service_URL}/v2/assets?catalog_id={catalog_id}

Create Asset: book - Request Body:

{
  "metadata": {
    "name": "Getting Started with Assets",
    "description": "Describes how to create and use metadata for assets",
    "tags": ["getting", "started", "documentation"],
    "asset_type": "book",
    "origin_country": "us",
    "rov": {
      "mode": 0
    }
  },
  "entity": {
    "book": {
	  "author": {
		  "first_name": "Tracy",
		  "last_name": "Smith"
	  },
	  "price": 29.95
    }
  }
}

The above request body specifies the preliminary contents for the primary metadata document about to be created. Most of the fields have been described previously in the Asset's Primary Metadata Document section. However, there are a few things to note in particular about the above request:

"metadata": you supply the values of only some of the fields that will end up appearing inside the "metadata" field of the primary metadata document about to be created, including:
- "asset_type": the value "book" matches the name of the asset type for this document
- "name": the name to use for the asset being described by this document
- "description": a description for the asset

Notice that you do not supply a "metadata.asset_attributes" field in the request body. If you include a "metadata.asset_attributes" field in your Create Asset request body then the request will be rejected because it tried to supply a reserved value. The Assets API reserves control of the contents of the "metadata.asset_attributes" field.

"entity": you supply the entire contents of the "entity" field
- "book":
  - this is the primary attribute of the primary metadata document
  - the name of this attribute matches the name of the corresponding primary asset type "book"
  - contains metadata describing a book (does not contain the actual book asset resource)

Notice the above "book" attribute doesn't contain a field called "title" - a field which might be expected in an attribute for a book. In this case, we've chosen to put the title of the book in the "metadata.name" field of the card. However, the creator of the "book" attribute is free to include whatever fields they want in that attribute, including a field called "title" if desired.

Create Asset: book - Response Body:

{
  "metadata": {
    "rov": {
      "mode": 0,
      "collaborator_ids": {}
    },
    "usage": {
      "last_updated_at": "2019-04-30T14:37:57Z",
      "last_updater_id": "IBMid-___",
      "last_update_time": 1556635077746,
      "last_accessed_at": "2019-04-30T14:37:57Z",
      "last_access_time": 1556635077746,
      "last_accessor_id": "IBMid-___",
      "access_count": 0
    },
    "name": "Getting Started with Assets",
    "description": "Describes how to create and use metadata for assets",
    "tags": [
      "getting",
      "started",
      "documentation"
    ],
    "asset_type": "book",
    "origin_country": "us",
    "rating": 0,
    "total_ratings": 0,
    "catalog_id": "c6f3cbd8-___",
    "created": 1556635077746,
    "created_at": "2019-04-30T14:37:57Z",
    "owner_id": "IBMid-___",
    "size": 0,
    "version": 2,
    "asset_state": "available",
    "asset_attributes": [
      "book"
    ],
    "asset_id": "3da5389d-d4a4-43da-be1f-___",
    "asset_category": "USER"
  },
  "entity": {
    "book": {
      "author": {
        "first_name": "Tracy",
        "last_name": "Smith"
      },
      "price": 29.95
    }
  },
  "href": "https://api.dataplatform.cloud.ibm.com/v2/assets/3da5389d-d4a4-43da-be1f-___?catalog_id=c6f3cbd8-___",
  "asset_id": "3da5389d-d4a4-43da-be1f-___"
}

Notice that the card returned in the Create Asset Response Body has many more fields than were present in the Request Body. The Create Asset API has added a lot of information to the "metadata" part of the primary metadata document:

"asset_id": most importantly, the Create Asset API has given your primary metadata document an id
"owner_id": the API has made the caller of the API be the owner of the asset
"created_at": the API has recorded the time at which the metadata document was created. In general, this is not the same as the time at which an attached asset resource was created (although in this case there is no attached asset resource).
"total_ratings": contains the number of ratings this asset has recieved. 0 for now because the primary metadata document is brand new.
"usage": usage statistics. Since this is a brand new card these statistics don't yet contain much interesting data.
"asset_attributes": notice that the Create Asset API has added the name of the primary attribute to this array.

On other hand, notice that the Create Asset API did not modify the contents of the "entity" field in any way. In particular, the Create Asset API did not modify the contents of the primary attribute "book".

Your catalog now contains a primary metadata document for a "book" asset resource.

Associate a Term with an Asset

You can associate one or more terms with an asset. Within the asset's primary metadata document a pre-defined attribute called "asset_terms" (one of the global asset_types) is used to contain the array of terms associated with the asset.

The first time any term is associated with an asset, the Create Asset Attribute API must be used to create and populate an instance of the "asset_terms" attribute inside the asset's primary metadata document. In the following example, we're only associating a single term with an asset, so the "list" value in the request body is an array with a single element containing info related to that single term.

POST {service_URL}/v2/assets/{asset_id}/attributes?catalog_id={catalog_id}

Body:

{
  "name": "asset_terms",
  "entity": {
    "list": [
        {
            "term_id": "--insert your term's id here--",
            "term_display_name": "first_term"
        }
    ]
  }
}

Note: the field named "term_display_name" is not strictly mandatory, but is highly recommended because its value is what will be used when searching for assets by term. Its value is also the term name that users will see in the Catalog UI.

The response should look like:

{
  "asset_id": "--your asset id--",
  "asset_terms": {
    "list": [
      {
        "term_id": "--your term id--",
        "term_display_name": "first_term"
      }
    ]
  },
  "href": ".../v2/{asset_id}/attributes/asset_terms?catalog_id={catalog_id}"
}

The next time you GET the asset you should see the "asset_terms" attribute you just created:

{
  "metadata": {
     ...
  },

  "entity": {
    ...
    "asset_terms": {
      "list": [
        {
          "term_id": "--your term id--",
          "term_display_name": "first_term"
        }
      ]
    }
  },

  "attachments": [
     ...
  ]
}

If you want to associate additional terms with an asset, or remove existing terms, then use the Update Asset Attribute API. For example, the following adds an additional term to the already existing "asset_terms" attribute of the asset used in the above example:

PATCH {service_URL}/v2/assets/{asset_id}/attributes/asset_terms?catalog_id={catalog_id}

Body:

[
	{
		"op": "replace",
		"path": "/list",
		"value": [
			{
				"term_id": "--insert your term's id here--",
				"term_display_name": "first_term"
			},
			{
				"term_id": "--insert your second term's id here--",
				"term_display_name": "second_term"
			}
		]
	}
]

You can use GET the asset to see the new collection of terms in the asset_terms attribute of the asset.

Associate a Classification with an Asset

You can associate one or more classifications with an asset. The classifications associated with the asset reside within the "data_profile" attribute of the asset. When user runs asset profiling, then the profiling job will add the suitable classifications. However, users can manually add classifications to an asset also.

If profile has not run on the asset, then the "data_profile" attribute will not exist. In such a scenario, the Create Asset Attribute API must be used to create and populate an instance of the "data_profile" attribute inside the asset's primary metadata document. Since the classification is added manually, the property added will be "data_classification_manual". In the example below, we are adding a single classification to an asset in a catalog. If you don't know the id and global id of the classification, you can run the API GET artifact of a given type. If you know the id of the classification, you can obtain the global_id of the classification by running the GET classification API.

POST {service_URL}/v2/assets/{asset_id}/attributes?catalog_id={catalog_id}

Body:

{
  "name": "data_profile",
  "entity": {
                 "data_classification_manual": [
                {
                    "id": "--insert your classification's id here--",
                    "name": "your_classification_name",
                    "global_id": "--insert your classification's global id here--"
                }
            ]
        }
}

The response should look like:

{
  "asset_id": "{asset_id}",
  "data_profile": {
    "data_classification_manual": [
      {
        "id": "{classification id}",
        "name": "your_classification_name",
        "global_id": "{classification global id}"
      }
    ]
  },
  "href": "/v2/assets/{asset_id}/attributes/data_profile?catalog_id={catalog_id}"
}

You can use GET the asset to see the new collection of classifications in the data_profile attribute of the asset.

{
  "metadata": {
     ...
  },

  "entity": {
    ...
        "data_profile": {
            "data_classification_manual": [
                {
                    "id": "{classification id}",
                    "name": "your_classification_name",
                    "global_id": "{classification global id}"
                }
            ]
        }
  },

  "attachments": [
     ...
  ]
}

If you want to associate additional classifications with an asset, or remove existing classifications, then use the Update Asset Attribute API. For example, the following adds an additional classification, to the existing "data_profile" attribute of the asset, without replacing the classification added in the previous example:

PATCH /v2/assets/{asset_id}/attributes/data_profile?catalog_id={catalog_id}

Body:

[
      {
        "op": "add",
        "path": "/data_classification_manual/1",
        "value":
            {
               "id":"--insert your second classification's id here--",
               "name":"your_second_classification_name",
               "global_id":"--insert your second classification's global id here--"
            }
      }
]

The response should look like:

{
  "data_classification_manual": [
    {
      "id": "{classification id}",
      "name": "your_classification_name",
      "global_id": "{classification global id}"
    },
    {
      "id": "{second classification id}",
      "name": "your_second_classification_name",
      "global_id": "{second classification global id}"
    }
  ]
}

The following example replaces all the classifications associated with the asset and replaces with a new list

PATCH /v2/assets/{asset_id}/attributes/data_profile?catalog_id={catalog_id}

Body:

[
      {
        "op": "replace",
        "path": "/data_classification_manual",
        "value":
            [
                {
                   "id":"--insert your classification's id here--",
                   "name":"your_classification_name",
                   "global_id":"--insert your classification's global id here--"
                }
            ]
      }
]

The response should look like:

{
  "data_classification_manual": [
    {
      "id": "{classification id}",
      "name": "your_classification_name",
      "global_id": "{classification global id}"
    }
  ]
}

Duplicate Asset

Duplicate Asset Overview

When a CAMS call tries to create an asset (e.g. create a new asset, promote/publish/clone an asset, etc.), CAMS can optionally detect pre-existing duplicate assets and take appropriate actions based on configurations and query parameters, e.g., ignoring the duplicates and create a new asset, or failing the call and returning an error saying duplicates were found, or updating the existing duplicate.

Similarly, when a CAMS call tries to update an asset, CAMS can also optionally detect if the asset with the incoming change would have any duplicates if the change were persisted and take appropriate actions based on configurations and query parameters, e.g., ignoring the duplicates and update the asset, or failing the call and returning an error saying the change would result in duplicates.

This process is called duplicate asset processing. This section describes how the duplicate asset processing works in CAMS and how you can make it work in the ways you desire.

What is a duplicate

An asset is considered a duplicate if it fits any of the following scenarios:

Original asset - the asset that the incoming asset was originally cloned/published from.

For instance, if you cloned/published an asset A to a project/catalog and resulted in asset B, and then try to publish/clone the asset B back to the original catalog/project, the asset A will be seen as the original asset and considered as a duplicate.

Copies of the same asset - an asset is cloned/published/promoted from the same asset as the incoming asset

For example, if you cloned/published/promoted an asset A to a project/catalog/space and resulted in asset B, and then try to clone/publish/promote the asset A again to the same target project/catalog/space, the asset B will be seen as the copy of the same asset and considered as a duplicate.

Asset with the same values - an asset has the same values as the incoming asset based on the effective duplicate detection strategy of the asset type. See Duplicate Detection Strategy section for more details about duplicate detection strategy.

Let's say that the effective duplicate detection strategy of the asset type data_asset in a project is DUPLICATE_DETECTION_BY_NAME (i.e., the duplicate detection will base on the metadata.name field). If you try to create an asset of type data_asset with the name KPIReport2021 in this project, and there is an existing asset A of type data_asset with the same name, then the asset A will be considered as a duplicate.

What to do with a duplicate

Users can set the configuration duplicate_action of the asset containers and/or specify the query parameter duplicate_action while calling endpoints to control how the service handles duplicate assets. The valid values of duplicate_action for calls creating a new asset are:

IGNORE - ignore the duplicates and create a new asset
REJECT - fail the call and return an error response similar to the one below (no asset will be created):

{
  "trace": "290c281c-4adc-4e40-aa49-aaf7cd2dbf6a",
  "errors": [
    {
      "code": "already_exists",
      "message": "ASTSV3040E: Duplicate assets exist. '[cc5f7412-5c96-4d66-9c14-40b3c944ad79, 244a3612-63a8-4140-9423-f40841be33ee]'"
    }
  ]
}

UPDATE - update the duplicate with the incoming changes. See Multiple duplicates for how to choose the duplicate for updating if more than one duplicate is found. See here on how the duplicate asset is updated.
REPLACE - overwrite the duplicate with the input values. See Multiple duplicates for how to choose the duplicate for overwriting if more than one duplicate is found. See here on how the duplicate asset is overwritten.

The valid values of duplicate_action for calls making changes to an existing asset (including restoring a deleted asset) are:

IGNORE - ignore the duplicates and update the asset
REJECT - fail the call and return an error response similar to the one below. The asset will not be updated or restored.

{
  "trace": "8ea27315-c958-435a-b415-e3632a664dbc",
  "errors": [
    {
      "code": "already_exists",
      "message": "ASTSV3128E: The asset will have duplicate assets with IDs '[a0a54f65-8a3d-4f22-95b7-5456d8e43a71]' after saving the change."
    }
  ]
}

Or the below for restoring an asset. Note that the reason that the id of the asset that will have duplicates is also in the message is that restoring an asset may potentially restore multiple related assets and it is possible that some of these related assets will have duplicates.

{
  "trace": "7b2668a4-c794-4720-83dd-f4f16e9d0a04",
  "errors": [
    {
      "code": "already_exists",
      "message": "ASTSV3128E: The asset '8c740814-5fc0-4294-a2be-f042f15098f1' will have duplicate assets with IDs '[a0a54f65-8a3d-4f22-95b7-5456d8e43a71]' after being restored."
    }
  ]
}

UPDATE and REPLACE are not allowed to be used for the query parameter duplicate_action for calls updating assets. However, if the query parameter duplicate_action is not supplied and the configuration duplicate_action is set to one of these values in the asset container level, they will be the effective value of the duplicate_action. In which case, they will be treated the same as REJECT.

The configuration duplicate_action can be set in the asset container level during the creation of a container and can be modified later by using the endpoint PUT /v2/asset_containers/configurations. If the configuration duplicate_action is not specified in an asset container, it will be equivalent to IGNORE.

The following example shows how to supply the configuration duplicate_action (along with other duplicate asset processing related configurations) while creating a catalog:

{
  "name": "my catalog",
  ...
  "configurations": {
    "duplicate_action": "REJECT",
    ...
  }
}

The following example shows how to update the configuration by using the endpoint PUT /v2/asset_containers/configurations:

{
  "duplicate_action": "REPLACE",
  ...
}

The configuration duplicate_action can be overwritten by a query parameter duplicate_action for individual calls to control how CAMS handles duplicates for these particular calls. The endpoints that support the query parameter are listed below. Note that the allowed options may differ from endpoint to endpoint depending on if the endpoint supports all available options.

POST /v2/assets
POST /v2/data_assets
POST /v2/assets/{asset_id}/publish
POST /v2/assets/{asset_id}/clone
POST /v2/assets/{asset_id}/promote
POST /v2/assets/{asset_id}/deepcopy
POST /v2/assets/bulk_create
POST /v2/assets/bulk_patch
PATCH /v2/assets/{asset_id}
POST /v2/assets/{asset_id}/attributes
PATCH /v2/assets/{asset_id}/attributes/{attribute_key}
DELETE /v2/assets/{asset_id}/attributes/{attribute_key}
POST /v2/assets/{asset_id}/attachments
DELETE /v2/assets/{asset_id}/attachments/{attachment_id}
POST /v2/trashed_assets/{asset_id}/restore

Duplicate Detection Strategy

The duplicate detection strategy defines what fields to be used for determining if assets of a particular asset type are duplicates. The available duplicate detection strategies are:

DUPLICATE_DETECTION_BY_NAME - the metadata.name field
DUPLICATE_DETECTION_BY_RESOURCE_KEY - the metadata.resource_key field
DUPLICATE_DETECTION_BY_NAME_AND_RESOURCE_KEY - the metadata.name and the metadata.resource_key fields
DUPLICATE_DETECTION_NOT_APPLICABLE - no duplicate will be determined. If this strategy is used, no duplicate will be determined by the strategy. At the same time, it also disables the duplicate detection for original asset and copies of the same asset. In other words, it disables duplicate detection for assets of the asset type completely.

The duplicate detection strategy can be set in several levels as shown below (from the highest priority to the lowest priority). Setting with a higher priority will take precedence over the setting with a lower priority.

Strategy of the asset type defined in the asset container configuration, e.g.,

{
  "duplicate_strategies": [
    {
      "asset_type": "data_asset",
      "strategy": "DUPLICATE_DETECTION_BY_NAME_AND_RESOURCE_KEY"
    }
  ]
}

Strategy specified in the asset type definition, e.g.,

{
  "description": "Job Run",
  "fields": [],
  "identity": {
  	"strategy": "DUPLICATE_DETECTION_NOT_APPLICABLE"
  }
}

System default strategy of the asset type in the residing asset container. i.e.,
- Project/Space:
  - connection: DUPLICATE_DETECTION_BY_NAME_AND_RESOURCE_KEY
- Catalog:
  - connection: DUPLICATE_DETECTION_BY_NAME_AND_RESOURCE_KEY
  - data_asset: DUPLICATE_DETECTION_BY_NAME_AND_RESOURCE_KEY
The default_duplicate_strategy specified in the asset container configuration (which applies to all asset types), i.e.,

{
  "default_duplicate_strategy": "DUPLICATE_DETECTION_BY_NAME"
}

System default strategy DUPLICATE_DETECTION_BY_NAME (which applies to all asset types)

Multiple duplicates

It is possible that a call may find more than one duplicate for a given asset. It could be that the duplicates were created before CAMS started processing duplicate assets, or the duplicates were created because multiple calls were creating the same asset at the same time.

If the effective value for duplicate_action is REJECT, CAMS will fail the call and return the asset ids of all the duplicates in the error response. If the effective value for duplicate_action is UPDATE or REPLACE, CAMS will rank the duplicates based on the following order and choose the duplicate that has the highest score and the caller has permission to update as the target for updating or overwriting. When all things are equal, the duplicates will be ranked by the created time (i.e., the metadata.created field) from the earliest to the most recent.

Original asset
Copies of the same asset
Asset with the same values

How the duplicate asset is updated or overwritten

If the effective value of duplicate_action is UPDATE or REPLACE, the following information of the duplicate asset that has the highest score and the caller has permission to update will be updated or overwritten with the values from the incoming asset based on the rules. Any other aspects of the duplicate asset will not be updated or overwritten, e.g., privacy settings, collaborators, owners, source asset, ratings, revisions, non-inline relationships, related assets, etc.

metadata
- metadata.name
- metadata.description
- metadata.resource_key
- metadata.origin_country
- metadata.tags
attributes (e.g., entity.data_asset, entity.data_profile, entity.column_info, etc.)
attachments (i.e., attachments[*])

If duplicate_action is UPDATE, the chosen duplicate asset will be updated as described below. The result will look the same as the duplicate asset but with the incoming changes.

The listed metadata fields will be replaced with the values from the incoming asset if the value from the incoming asset is not null
All attributes in the incoming asset will be copied over and any existing attribute will be replaced. Some exceptions may apply, e.g.,
- If the existing or incoming attribute connection represents a reference connection, the existing attribute connection will not be replaced.
- Reference copy terms are preserved
All attachments in the incoming asset will be copied over. Any existing attachments with the same asset_type as the attachments in the incoming asset will be removed.

If duplicate_action is REPLACE, the chosen duplicate asset will be updated as described below. The result will look the same as the incoming asset with some exceptions.

All listed metadata fields will be replaced with the values from the incoming asset (even if the value from the incoming asset is null)
All existing attributes will be removed and all attributes in the incoming asset will be copied over with the exception of the attribute connection. If the existing or incoming attribute connection represents a reference connection, the existing attribute connection will remain unchanged.
All existing attachments will be removed and all attachments in the incoming asset will be copied over

Let's say you have an existing asset that looks like below

{
  "metadata": {
    "name":"B",
    "description":"C",
    "tags":[
      "confidential"
    ]
  },
  "entity": {
    "data_asset": {
      "mime_type": "binary"
    },
    "something": {}
  },
  "attachments":[
    {
      "asset_type": "data_asset",
      "name": "attachment 1"
    },
    {
      "asset_type": "something",
      "name": "attachment 2"
    }
  ]
}

and you try to add the following asset

{
  "metadata": {
    "name":"A",
  },
  "entity": {
    "data_asset": {
      "mime_type": "text/csv"
    }
  },
  "attachments":[
    {
      "asset_type": "data_asset",
      "name": "attachment 3"
    }
  ]
}

If the effective duplicate_action is UPDATE, the existing asset will be modified to be the following

{
  "metadata": {
    "name":"A",
    "description":"C",
    "tags":[
      "confidential"
    ]
  },
  "entity": {
    "data_asset": {
      "mime_type": "text/csv"
    },
    "something": {}
  },
  "attachments":[
    {
      "asset_type": "something",
      "name": "attachment 2"
    },
    {
      "asset_type": "data_asset",
      "name": "attachment 3"
    }
  ]
}

If the effective duplicate_action is REPLACE, the existing asset will be modified to be the following

{
  "metadata": {
    "name":"A"
  },
  "entity": {
    "data_asset": {
      "mime_type": "text/csv"
    }
  },
  "attachments":[
    {
      "asset_type": "data_asset",
      "name": "attachment 3"
    }
  ]
}

Backup revision

When the best duplicate is updated as a result of asset duplicate processing, a revision is created in case we want to go back or review what was changed. The revision will contain commit information similar to below:

{
  "committed_at": "2020-11-17T08:13:39.103Z",
  "commit_message": "Backup prior to update the best duplicate",
  "reason": "update_duplicate",
  "duplicate_source": {
    "operation": "clone",
    "asset_id": "ca0007d9-051d-478f-b87f-82f38fc6997c",
    "catalog_id": "c548b021-e026-49a6-aa60-35fe478afdb5"
  }
}

The asset in the response will contain the previous_revision field in such a case. Which can be used to determine if the call indeed created a new asset or updated an existing asset.

{
    "metadata": {
        ...,
        "commit_info": {
            "previous_revision": 1
        }
    },
    "entity": {
        ...
    },
    "asset_id": "c1bf6686-836c-4f93-b173-c2ed52da8e76"
}

Check duplicates before creating an asset

The duplicate asset processing automatically kicks in when a CAMS call tries to create an asset. However, in some cases, you may want to check possible duplicates before creating an asset or before publish/clone/promote/deepcopy an asset. CAMS provides an endpoint POST /v2/assets/duplicates/search to help you do this. You can either supply an existing asset or an asset payload to check the duplicates. The endpoint will list all the potential duplicates and why they were considered duplicates.

Lineage and Activity event messages change

If the query parameter duplicate_action is UPDATE or REPLACE and duplicate assets are found, the calls will change from creating a new asset to updating an existing asset. As a result, the corresponding Lineage and Activity event messages will also change from creating an asset to updating an asset.

Known issues/limitations

Duplicate assets may be created due to race condition

When a call is made to create an asset, CAMS searches for potential duplicates and creates the asset only if no duplicate is found. If multiple calls are made at the exact same time to create the same asset, it is possible to result in a duplicate. For example, two calls try to create the same asset at the same time, and both calls search for potential duplicates and do not find any duplicates; both calls think there is no duplicate and create the asset. As the result, the same asset will be created twice and each asset is a duplicate of the other.

There is currently no way to prevent such cases from happening. If this happened, the user would have to choose one of these assets and delete others. Internally, CAMS always favours the asset that has the earliest value in the metadata.created field and chooses it for future duplicate updating/overwriting operations.

Troubleshooting

Duplicate assets are not detected

Sometimes you may see assets that look like duplicates but are not detected as duplicates. You can call the GET /v2/assets/{asset_id} API to get the JSON representation of these assets and compare the fields that are used for identifying duplicates. If the fields are not the same, then the assets are not duplicates. Otherwise, it may fall into one of the situations where real duplicates are not detected. Please see the Known issues/limitations section for more details.

For instance, if the effective duplicate detection strategy is DUPLICATE_DETECTION_BY_NAME_AND_RESOURCE_KEY, you can compare the metadata.name and the metadata.resource_key fields of these assets and see if they are indeed the same; if the effective duplicate detection strategy is DUPLICATE_DETECTION_BY_NAME, you can compare the metadata.name field of these assets. For details about what fields are used for a strategy, please see Duplicate detection strategy section.

Asset Types

Asset Types Overview

Asset Type Fields
- Search Path Examples
"data_asset" Type
Using Secondary / Extended / Decorator Asset Types to Simulate Type Inheritance

Asset Types API Examples

Get Asset Types
Get Asset Type: data_asset
Create Asset Type: book
Search Asset Type: attribute - book
Search Asset Type: metadata - name

Asset Types serve multiple purposes in the Assets API. Asset types fall into two categories:

Primary asset type:
- describes the primary type of an asset
- every primary metadata document (ie, card) will have exactly one primary asset type, whose name will be stored in the card's "metadata.asset_type" field
- every card will have exactly one primary attribute whose name matches the name of the primary asset type
- a very common example of a primary asset type is the "data_asset" type, examples of which are shown throughout this documentation

Secondary / Extended asset type:
- a secondary / extended asset type describes an inter-related group of additional metadata for an asset resource
- a primary metadata document can have 0, 1, or many secondary / extended asset types
- information for a secondary / extended asset type is stored in a secondary / extended attribute in a primary metadata document
- a very common example of a secondary / extended asset type is "data_profile". See Get Asset - CSV File - Response Body - After Profiling for an example "data_profile" attribute.
- secondary / extended / decorator asset types can be used to simulate type inheritance

The names of various asset types are used in the following ways, all at once, within a single primary metadata document:

describe the type of an asset resource, via the "metadata.asset_type" field
describe the type of an object that contains extended information for an asset resource. For example, the type of an extended metadata document via an "attachments[_].asset_type" field.
assign names and types to attributes in the "entity" field of a primary metadata document
implicitly tie various related parts of a primary metadata document to each other. For example, see the green rectangles and arrows in the Parts of a Primary Metadata Document Figure.

The content, or definition, of an asset type serves the following purposes:

tell the catalog what fields of an attribute should be indexed for searching
specify search paths and cross attribute searching
specify additional features like relationships and external asset previews (both of which are beyond the scope of this document)

An asset type must exist in the catalog before it can be used for any of the above purposes.

As of this writing there are several asset types available, including the following:

data_asset
folder_asset
policy_transform
asset_terms
column_info
connection
ai_training_definition
data_flow
activity
notebook
machine-learning-stream
dashboard
data_profile_nlu

You are free to use any of the above asset types. You do not have to, nor are you allowed to, create or over-write any of the above asset types.

Use the Create Asset Type API to create your own asset type. See Asset Type Fields for an overview of the specification of an asset type. See Create Asset Type: book for an example of creating an asset type.

Asset Type Fields

Here is a description for each of the fields in the definition of an asset type. You supply values for these fields when creating an asset type. You will see those same values returned when you get a list of asset types or get a specific asset type.

"name":
- the name and identifier for the asset type
- should contain only lowercase letters
- will be used in various places in primary metadata documents, including:
- can be used in catalog searches of attribute contents
"description": a description for this asset type
"fields":
- an array that contains information for the fields in the corresponding attribute that should be indexed for subsequent searches.
- does not (necessarily) describe all the fields in attributes of this asset type.
- there must be at least one item in this array. In other words, there must be at least one index for an asset type.
- see the following Fields Table for a description of the contents of an item in the "fields" array
- see "fields" and "properties" Note below
"properties":
- an object that contains "non-index" information for the fields in the corresponding attribute. This information is typically used by UIs that display/edit assets.
- does not (necessarily) describe all the fields in attributes of this asset type.
- see the following Properties Table for a description of the contents of an item in the "properties" object
- see "fields" and "properties" Note below
"external_asset_preview": beyond the scope of this document
"relationships": beyond the scope of this document
"localized_metadata_attributes": a description for this asset type
"decorates": list of pointers to types this type can decorate
"identity": identity definition of the assets of the type
"global_search_searchable": list of fields made searchable through Global Search functionality
- The fields correspond the the "key" defined in the field
"attribute_behavior": define the behavior only if this asset type is used as an attribute
- it contains a field "remove_on_copy". This field can take boolean values true or false.
- when "remove_on_copy" is true, then during cloning, publishing or promoting the asset, the attribute is removed from the asset
"attribute_only": takes boolean values true or false. When true, then this attribute will be used as custom attributes
"data_protection": asset type properties and attachments that should be removed when user who does not have access is trying to view the data
"is_column_custom_attribute": takes boolean values true or false. When true, then this attribute can be applied as a column level attribute. See Columns and Column Level Attributes
"can_have_image": takes boolean values true or false. Set to true if asset type is expected to support uploading of images
"icon_id": name or id of the svg image to be used as the icon for the asset type
"allow_decorators": takes boolean value true or false. When true, custom attributes can decorate this asset type

Note: "fields" and "properties" can, optionally, both be used to describe the exact same field in an attribute. Whether you use "fields" and/or "properties" depends on what you want to specify for a field. For example, if you're creating an asset type named "person" and a person has a field called "birthdate" (resulting in "entity.person.birthdate" being present in the primary metadata document) then:

if you want birthdate to be indexed (for searching) then you would include an entry in the "fields" array for birthdate
if you want a UI to understand/display the birthdate properly then you would include an entry in the "properties" object for that same birthdate field

See this example which shows both an example "fields" array and an example "properties" object.

Fields Table: Asset Type "fields" array - item contents

Key	Description	Example	Required
key	the name of both the field that will appear in an attribute for this asset type, and the name of the corresponding index for that attribute field	data_asset.mime_type	Yes
type	the data type of the field being indexed	boolean, or number, or string	Yes
facets	beyond the scope of this document	true or false	No. Defaults to false.
search_path	a json path that locates a field in the attribute	See Search Path Examples below.	Yes
is_searchable_across_types	specifies whether this field can be used in a query without specifying the asset type	true or false	No. Defaults to false.

Properties Table: Asset Type "properties" object - item contents

Name	Type	Description
type	String	Specifies the data type for the property. This value is required. Possible types are: string, number
description	String	A displayable string to describe the property.
is_array	boolean	true if the property value is multi-valued (json array).
required	boolean	true if the property requires a value to be set.
hidden	boolean	true if the application UI should not display the property or value.
readonly	boolean	true if the property should not be changed once set.
default_value	matches the "type"	A value that should be set if no value is provided when the asset attribute is created.
placeholder	string	A string an application UI can use as a prompt before a value is entered.
values	array, elements matching "type"	An array of allowed values for the property. Used to describe a limited enumeration or "choice list".
minimum	integer/number	For an integer or number property, the minimum allowed value.
maximum	integer/number	For an integer or number property, the maximum allowed value. If both minimum and maximum are specified, minimum must be less than or equal to maximum.
min_length	integer	For a string property, the minimum allowed length. If specified, must be greater than or equal to zero.
max_length	integer	For a string property, the maximum allowed length. If specified, must be greater than or equal to zero. If both min_length and max_length are specified, min_length must be less than or equal to max_length.
properties	object	For a property of type 'object', the recursive definition of the properties, described as in this table. This allows describing nested object-valued properties.

Search Path Examples

See the request body in Create Asset Type: book for an example of where a search path is used in the definition of an asset type.
- Note: when you specify a search path in the definition of an asset type's "field", you only specify the path within the correspondingly named attribute. You needn't specify the attribute name. For example if you have an attribute called "book" that has a field called "author.last_name" within it, you only need to specify "author.last_name" as the search path - not "book.author.last_name".
See Search Asset Type: attribute - book for an example of where a search path is used in the body of a search.
- Note: when you specify a search path in the body of search you must specify the name of the attribute being searched. For example if you have an attribute called "book" that has a field called "author.last_name" within it, you would include the name of the attribute in the search path: "book.author.last_name".
"price" : a simple path contains just the name of the field to be searched. In this case the attribute being searched should have a simple field called "price".
"tags[]" : traverse a json array called "tags". Because tags[] is not followed by any further names it must be a basic type (e.g. string, boolean, or number), and so its elements will be indexed directly.
"asset_terms[].name" : this search path indicates a path starting with a json object named "term_assignments" at the top, traversing through a json array named asset_terms (you use the [] at the end of the field name to indicate it's an array), landing on another json object that has a field called "name". The "name" field will be indexed.
"asset_terms[0].name" : same as above but only the first element in the "asset_terms" array will be traversed.
"columns.*.tags[]" : traverse an object called "columns" followed by any column name (the '*' indicates a wildcard), followed by a json array called "tags". Because tags[] is not followed by any further names it must be a basic type (e.g. string, boolean, or number), and so its elements will indexed directly.
"column_tags.*[]" : the json object "column_tags" contains a series of arrays indicated by *[]. The name of the array object doesn't matter - we want to index it.

Global Search searchable custom attributes

Fields that have been identified as Global Search searchable by being included in global_search_searchable array by their key, will be synchronized as custom attributes to Global Search microservice, and will become searchable via Global Search. Note that field will only be synchronized to Global Search if field definition contains a valid search_path. Values found at that search_path will be synchronized to Global Search, otherwise field will be ignored. Any values provided in global_search_searchable array which do not correspond to any existing fields of the type will be ignored.

An example of valid configuration of global_search_searchable attributes would be:

{
    "name": "book",
    "description": "Book asset type",
    "fields": [
        {
            "key": "last_name",
            "type": "string",
            "facet": false,
            "is_array": false,
            "search_path": "author.last_name",
            "is_searchable_across_types": true
        }
    ],
    "global_search_searchable": [
      "last_name"
    ],
    "properties": {
        "price" : {
            "type": "number",
            "description": "Suggested retail price",
        }
    }
}

Following are key characteristics of a valid definition:

Key of a field last_name is included in global_search_searchable array.
Field last_name has a properly defined search_path which contains a valid json path.

Common pitfalls:

Not including search_path into the field definition will cause the field to be ignored and prevent it from indexing. Similarly, an invalid json path definition within search_path which either is not formatted correctly, or points to a wrong location will prevent field value from being indexed as global_search_searchable.
Make sure that only the key value of the field is used inside global_search_searchable. In the above example last_name is the name of the field, so the global_search_searchable array contains an last_name.

data_asset Type

"data_asset" is by far the most commonly used already available asset type. It can be seen in:

the Parts of a Primary Metadata Document Figure
many of the examples in the Assets API Examples and Asset Types API Examples sections
the default asset type used when you drag an asset resource file onto the Create Asset page.

The reason "data_asset" is so popular is that it is a generic asset type that allows you to declare a specific type for a given asset resource without explicitly creating an asset type named after that specific type. For example, say you want to create a primary metadata document for a csv file. You could first create a specific asset type named, say, "csv_file", and then create a primary metadata document (for that csv file) and specify "csv_file" as the value for "metadata.asset_type". However, you can avoid creating a specific "csv_file" asset type by instead using the generic "data_asset" asset type and then use the "mime_type" field of the "data_asset" attribute to declare that the specific type of your asset resource is a csv file. To do so, the primary metadata document for the csv file would have:

a "metatada.asset_type" value of the generic type "data_asset"
a "entity.data_asset.mime_type" value of the specific type "text/csv".

The fields "asset_type" and "mime_type" both describe the "type" of the asset resource. However:

the type specified by the "metatada.asset_type" field (ie, "data_asset") is generic
the type specified by the "entity.data_asset.mime_type" field (ie, "text/csv") is specific

It is the "mime_type" field of the data_asset type that allows you to declare a specific type for an asset without creating that specific type(!).

So, in its most basic use, the "data_asset" asset type is a very "lite" asset type. It's used to avoid creating many other "heavier" asset types. However, if you need to create more complex attributes with indexes for specific fields in your attribute then you will have to create your own asset type (see Create Asset Type: book for an example).

The other two fields of the type "data_asset" are "dataset" and "columns".

"dataset" value of false means that the "columns" field is absent in a "data_asset" attribute
"dataset" value of true means that the "columns" field is present in a "data_asset" attribute

The "columns" field of a "data_asset" attribute is optionally used to specify metadata for columns of assets that have columns, like csv files, spreadsheets, database tables, etc.

The full definition of the "data_asset" type is shown in Get Asset Type: data_asset - Response Body.

See Get Asset - CSV File - Response Body - Before Profiling and Get Asset - CSV File - Response Body - After Profiling for examples where a "data_asset" is used for a csv asset resource.

Using Secondary / Extended / Decorator Asset Types to Simulate Type Inheritance

Asset type inheritance/subclassing is not supported by the Asset Type API. Asset types can be combined through the use of Secondary / Extended / Decorator Asset Types, to simulate inheritance through composition of multiple types. A "decorator" can combine a common set of properties that under a traditional inheritance model would be part of the super class and "decorate" those types that would traditionally be the child class. The asset type decorates field specifies the child asset types that are decorated. Note that type decoration is not enforced by the API, i.e. there is no checking that the contents of an asset has fields which match what is declared in any asset type. However, the IKC UI honors the type decorator setting by presenting the fields in decorator types as custom attributes in the Asset Overview screen UI under "Details" section. For more details on using the asset type decorator feature, see Tailor your Data Catalog with custom asset attributes.

Get Asset Types

You can get a list of the asset types in a catalog using the following Asset Types API:

Get Asset Types - Request URL:

GET {service_URL}/v2/asset_types?catalog_id={catalog_id}

Get Asset Types - Response Body:

{
  "resources": [
    {
      "description": "Data Asset Type",
      "fields": [
        {
          "key": "dataset",
          "type": "boolean",
          "facet": true,
          "is_array": false,
          "is_searchable_across_types": false
        },
        {
          "key": "mime_type",
          "type": "string",
          "facet": true,
          "is_array": false,
          "is_searchable_across_types": false
        },
        {
          "key": "columns",
          "type": "string",
          "facet": true,
          "is_array": true,
          "search_path": "columns[].name",
          "is_searchable_across_types": true
        }
      ],
      "external_asset_preview": {},
      "relationships": [],
      "name": "data_asset",
      "version": 3
    },
    "global_search_searchable": [
      "mime_type"
    ],
    {
      "description": "An asset type you can use to describe the columns of a data asset.  Normally attached as a property to an existing data asset.",
      "fields": [
        {
          "key": "column_info_term_display_name",
          "type": "string",
          "facet": true,
          "is_array": false,
          "search_path": "*.column_terms[].term_display_name",
          "is_searchable_across_types": true
        },
        {
          "key": "column_info_term_id",
          "type": "string",
          "facet": true,
          "is_array": false,
          "search_path": "*.column_terms[].term_id",
          "is_searchable_across_types": false
        },
        {
          "key": "column_info_tag",
          "type": "string",
          "facet": true,
          "is_array": false,
          "search_path": "*.column_tags[]",
          "is_searchable_across_types": true
        },
        {
          "key": "column_info_description",
          "type": "string",
          "facet": false,
          "is_array": false,
          "search_path": "*.column_description",
          "is_searchable_across_types": true
        },
        {
          "key": "column_info_omrs_guid",
          "type": "string",
          "facet": true,
          "is_array": false,
          "search_path": "*.omrs_guid",
          "is_searchable_across_types": true
        }
      ],
      "external_asset_preview": {},
      "relationships": [],
      "name": "column_info",
      "version": 4
    },
    {
      "description": "An asset type that you can use to assign terms from a business glossary to any asset.  Attach items of this type as attributes to other assets.",
      "fields": [
        {
          "key": "asset_term_display_name",
          "type": "string",
          "facet": true,
          "is_array": false,
          "search_path": "list[].term_display_name",
          "is_searchable_across_types": true
        },
        {
          "key": "asset_term_id",
          "type": "string",
          "facet": true,
          "is_array": false,
          "search_path": "list[].term_id",
          "is_searchable_across_types": false
        }
      ],
      "external_asset_preview": {},
      "relationships": [],
      "name": "asset_terms",
      "version": 1
    },
...
  ]
}

See Asset Type Fields for descriptions of the fields in each of the above asset types.

In a scenario in which the user has not yet created any of their own asset types, the result will contain only the pre-existing, global, asset types. For brevity, the actual sample result shown above includes only a subset of those asset types. Try the GET Asset Types API on your catalog to see the complete set of pre-existing, global, asset types.

Get Asset Type: data_asset

You can get an individual asset type in a catalog using the following Asset Types API:

Get Asset Type: data_asset - Request URL:

GET {service_URL}/v2/asset_types/{type_name}?catalog_id={catalog_id}

Supplying "data_asset" as the value for the {type_name} parameter in the above url will produce a response like the following:

Get Asset Type: data_asset - Response Body:

{
  "description": "Data Asset Type",
  "fields": [
    {
      "key": "mime_type",
      "type": "string",
      "facet": true,
      "is_array": false,
      "is_searchable_across_types": false
    },
    {
      "key": "dataset",
      "type": "boolean",
      "facet": true,
      "is_array": false,
      "is_searchable_across_types": false
    },
    {
      "key": "columns",
      "type": "string",
      "facet": true,
      "is_array": true,
      "search_path": "columns[].name",
      "is_searchable_across_types": true
    }
  ],
  "global_search_searchable": [
      "mime_type"
  ],
  "external_asset_preview": {},
  "relationships": [],
  "name": "data_asset",
  "version": 3
}

See Asset Type Fields for descriptions of the fields in the above asset type definition.

Since an asset type called "data_asset" exists, you can create a primary metadata document (ie, card) with a "metadata.asset_type" value of "data_asset". That card must then also have a primary attribute called "data_asset".

The most interesting item in the "fields" array in the above "data_asset" asset type definition is the item with "key" value "mime_type". That item means that a primary attribute named "data_asset" will have a field called "mime_type". The value of that "mime_type" attribute field will declare the specific type of the asset resource represented by the primary metadata document. For example, see the field "entity.data_asset.mime_type" in Get Asset - CSV File - Response Body - Before Profiling where the "mime_type" value is "text/csv".

Notice the "data_asset" attribute in Get Asset - CSV File - Response Body - Before Profiling only contains two fields - "mime_type" and dataset. The columns field specified in the definition of the "data_asset" asset type is not present in the "data_asset" attribute.

Now compare all the items in the "fields" array in the above "data_asset" asset type definition with the "entity.data_asset" attribute fields as shown, for example, in Get Asset - CSV File - Response Body - After Profiling. Notice that now all the fields described in the "fields" array of the "data_asset" type are present as fields in the "entity.data_asset" attribute. In particular, profiling has added the "columns" field to the "data_asset" attribute.

The Before Profiling and After Profiling examples illustrate that not all the fields defined in an asset type need be present in a corresponding attribute.

Lastly, note that asset type definition includes a global_search_searchable list of field keys, including the value mime_type. That indicates that mime_type value of every instance of this asset type will be seachable via Global Search microservice.

Create Asset Type: book

Say you have a book asset resource and you want to create a primary metadata document to describe that book. You will first need to create an asset type called "book" (as shown below) so you can then:

use the name of that asset type as the value for the "metadata.asset_type" field in the primary metadata document
create a primary attribute named "book" that will contain data about your book.

Say you want that primary attribute to look like the following:

    "book": {
        "author": {
            "first_name": "Tracy",
            "last_name": "Smith"
          },
          "price": 29.95
        }
    }

The above "book" attribute has:

one complex field called "author" (complex fields are allowed in attributes)
one simple field called "price".

For this example, assume you'll want to be able to search inside the "author.last_name" field of "book" attributes.

In addition to that, lets assume that you would like to use value of "author.last_name" field to search for "books" via Global Search microservice.

To create an asset type named "book" that will allow you to do all of the above, use a request like the following:

Create Asset Type: book - Request URL:

POST {service_URL}/v2/asset_types?catalog_id={catalog_id}

Create Asset Type: book - Request Body:

{
    "name": "book",
    "description": "Book asset type",
    "fields": [
        {
            "key": "author.last_name",
            "type": "string",
            "facet": false,
            "is_array": false,
            "search_path": "author.last_name",
            "is_searchable_across_types": true
        }
    ],
    "global_search_searchable": [
      "author.last_name"
    ],
    "properties": {
        "price" : {
            "type": "number",
            "description": "Suggested retail price",
        }
    }
}

The purpose of most of the fields used in the above request was described in the Asset Type Fields section. Here are some things to note specifically in the above request:

"name": uses only lowercase letters, ie, "book"
"fields": even though our goal attribute has multiple fields in it, there is only one item in the asset type's "fields" array. That is because the "fields" array should only contain items for the fields of an attribute that we want the catalog to create an index for. In this case, we only want an index for the "author.last_name" field of "book" attributes.
- "key": the name of the attribute field that we want indexed, and the name for that index. In this case, "author.last_name".
- "type": the type of the "author.last_name" field is "string"
- "facet": an explanation of this field is beyond the scope of this document
- "is_array": false because "author.last_name" is not an array
- "search_path": this is the path inside the attribute to the value that we want indexed
- "is_searchable_across_types": an explanation of this field is beyond the scope of this document
"global_search_searchable" since we would like to be able to search for author.last_name value using Global Search - we include corresponding field.key value.

Create Asset Type: book - Response Body:

{
  "description": "Book asset type",
  "fields": [
     {
        "key": "author.last_name",
        "type": "string",
        "facet": false,
        "is_array": false,
        "search_path": "author.last_name",
        "is_searchable_across_types": true
     }
  ],
  "global_search_searchable": [
    "author.last_name"
  ],
  "relationships": [],
  "name": "book",
  "version": 1
}

The response to the POST /v2/asset_types API echoes the input, with two additional fields:

relationships: an explanation of the contents of this field is beyond the scope of this document
version: the version of the newly created asset type

You now have an asset type called "book" that specifies one indexed, search-able, field called "author.last_name". See Create Asset: book for an example of the ways in which that "book" asset type can be used when creating a primary metadata document.

Search Asset Type: attribute - book

The Search Asset Type API can be used to search inside a catalog for all the primary metadata documents that satisfy both of the following conditions:

have a "metadata.asset_type" value that matches the asset type name specified in the {type_name} URL parameter
have an attribute whose fields' values match those specified in the request body.

Recall that one of the primary reasons for creating an asset type is to specify fields in attributes (named after that asset type) that will be indexed for searching. The Create Asset Type: book section showed how to create an asset type named "book". The Create Asset: book section showed how to create a primary metadata document whose "metadata.asset_type" value and primary attribute name are both "book". So, if you use the value "book" for the {type_name} parameter in the URL below, and if you supply the following request body, then you'll get back matching metadata for books.

Search Asset Type: attribute - book - Request URL

POST {service_URL}/v2/asset_types/{type_name}/search?catalog_id={catalog_id}

Search Asset Type: attribute - book - Request Body:

{
     "query":"book.author.last_name:Smith"
}

Notice how the query specifies both the attribute (book) to be searched and the search path (author.last_name) within that attribute. The value to match is specified after the colon (:). In this case, the value is Smith.

The following is the result of the above search:

Search Asset Type: attribute - book - Response Body:

{
  "total_rows": 1,
  "results": [
    {
      "metadata": {
        "rov": {
          "mode": 0,
          "collaborator_ids": {}
        },
        "usage": {
          "last_updated_at": "2019-05-01T18:58:51Z",
          "last_updater_id": "IBMid-___",
          "last_update_time": 1556737131140,
          "last_accessed_at": "2019-05-01T18:58:51Z",
          "last_access_time": 1556737131140,
          "last_accessor_id": "IBMid-___",
          "access_count": 0
        },
        "name": "Getting Started with Assets",
        "description": "Describes how to create and use metadata for assets",
        "tags": [
          "getting",
          "started",
          "documentation"
        ],
        "asset_type": "book",
        "origin_country": "us",
        "rating": 0,
        "total_ratings": 0,
        "catalog_id": "c6f3cbd8-___",
        "created": 1556635077746,
        "created_at": "2019-04-30T14:37:57Z",
        "owner_id": "IBMid-___",
        "size": 0,
        "version": 0,
        "asset_state": "available",
        "asset_attributes": [
          "book"
        ],
        "asset_id": "3da5389d-d4a4-43da-be1f-___",
        "asset_category": "USER"
      },
      "href": "https://api.dataplatform.cloud.ibm.com/v2/assets/3da5389d-d4a4-43da-be1f-___?catalog_id=c6f3cbd8-___"
    }
  ]
}

In this case, there is only one primary metadata document returned in the "results" array (namely, the primary metadata document that was created in the Create Asset: book section). In general, there can be many matching documents in the "results" array.

Notice the results of an Asset Type Search, as shown above, only contain the "metadata" section of a primary metadata document. In particular, the "entity" section that contains the attributes is not returned. That is done to reduce the size of the response because, in general, the "entity" section of a primary metadata document can be much larger than the "metadata" section. Use the value of the "metadata.asset_id" in one of the items in "results" to retrieve either:

the entire primary metadata document (using the GET Asset API), or
just the attributes of the primary metadata document (using the GET Attributes API).

Notes:

searching is not limited to just primary attributes (like book above). Searches may also be performed on:
- Secondary, or extended, attributes
- the "metadata" field of a primary metadata document, as shown in the next section.
other parameters available for searches are:
- limit (number): limit number of search results
- sort (string): sort columns for search results
- counts: beyond the scope of this document
- drilldown: beyond the scope of this document

Search Asset Type: metadata - name

You're not limited to searching within attributes (like the attribute search shown in the previous section). You can also search within the "metadata" section of a primary metadata document.

Search Asset Type: metadata - name - Request URL:

POST {service_URL}/v2/asset_types/{type_name}/search?catalog_id={catalog_id}

Search Asset Type: metadata - name - Request Body:

{
    "query":"asset.name:Getting Started with Assets"
}

Notice the query signifies that the search should take place in the "metadata" section of the primary metadata document by using the term asset at the beginning of the search path. Then the field to be searched within "metadata" is specified - name in the example above. The value to match is specified after the colon (:), in this case the value is Getting Started with Assets.

The following is the result of the above search:

Search Asset Type: metadata - name - Response Body:

{
  "total_rows": 1,
  "results": [
    {
      "metadata": {
        "rov": {
          "mode": 0,
          "collaborator_ids": {}
        },
        "usage": {
          "last_updated_at": "2019-04-30T17:27:56Z",
          "last_updater_id": "IBMid___",
          "last_update_time": 1556645276827,
          "last_accessed_at": "2019-04-30T17:27:56Z",
          "last_access_time": 1556645276827,
          "last_accessor_id": "IBMid___",
          "access_count": 0
        },
        "name": "Getting Started with Assets",
        "description": "Describes how to create and use metadata for assets",
        "tags": [
          "getting",
          "started",
          "documentation"
        ],
        "asset_type": "book",
        "origin_country": "us",
        "rating": 0,
        "total_ratings": 0,
        "catalog_id": "c6f3cbd8-___",
        "created": 1556635077746,
        "created_at": "2019-04-30T14:37:57Z",
        "owner_id": "IBMid-___",
        "size": 0,
        "version": 0,
        "asset_state": "available",
        "asset_attributes": [
          "book"
        ],
        "asset_id": "3da5389d-d4a4-43da-be1f-___",
        "asset_category": "USER"
      },
      "href": "https://api.dataplatform.cloud.ibm.com/v2/assets/3da5389d-d4a4-43da-be1f-___?catalog_id=c6f3cbd8-___"
    }
  ]
}

In this case, the result is the same as was described in Search Asset Type: attribute - book - Response Body. See that section for more details.

Data assets represent tables or files from a connection to a data source (ex. tables) or files uploaded into cloud object storage associated with Projects or Catalog. Data assets extend the generic asset service and attachments functionality. This means that like generic assets, data assets also have name, description, visibility, members, tags, classifications and asset_type. In addition, data assets have mime type, column related attributes and properties. The asset type for data asset is data_asset and it is one of the asset types already available.

Like other assets, we can add or update attributes on data assets also. In addition, data assets can have attributes which are associated with individuals columns. Given below is a simple format of a data asset, which has attributes associated with columns.

{
    "metadata": {
        ...
        ...
        "name": "Name of the data asset",
        "asset_type": "data_asset",
        "asset_attributes": [
            "data_asset",
            "column_info",
            "column_level_custom_attribute",
            ... other attributes
        ],
        "asset_id": "id of the data asset"
    },
    "entity": {
        "data_asset": {
            "mime_type": "text/csv",
            "dataset": false,
            "columns": [
                {
                    "name": "NameOfColumn", // Name of the column
                    "type": {
                        "type": "varchar",
                        "length": 1024,
                        "scale": 0,
                        "nullable": true,
                        "signed": false
                    }
                }
            ]
        },
        "column_info": {
            "NameOfColumn": {            // we are listing the column_info attributes associated with each column
                "column_description": "description of the column",
                "column_terms": [
                    {
                        "term_display_name": "test_term",
                        "term_id": "d3d667e1-____"
                    }
                ]
            }
        },
        "column_level_custom_attribute": { // Column level custom attribute
            "columns": [
                {
                    "name": "NameOfColumn",
                    "OtherColumnProperty": "dummy value"
                }
            ]
        },
        ... other attributes
    },
    "attachments": [
      ...
      ...
   ]
}

All asset APIs listed in Assets API Overview apply to data assets as well. In additions, data assets have special APIs.

Creating a Data Asset

Creating a data asset is similar to creating any other asset. In here,

a "metadata.asset_type" field will hold value "data_asset"
a primary attribute called "data_asset".

In the first example, we will create a connected data asset. It represents a table or a file from an exisitng connection. The sample request body and the corresponding response is given below.

Create Data Asset: Request URL:

POST {service_URL}/v2/data_assets

Create Data Asset: -Sample Request Body:

{
    "metadata": {
        "name": "CUSTOMER", // Name of table
        "description": "customer table", // description
        "tags": [
            "public"
        ],
        "asset_type": "data_asset",
        "origin_country": "us",
        "rov": {
            "mode": 0
        }
    },
    "entity": {
        "data_asset": {
            "mime_type": "application/x-ibm-rel-table",
            "dataset": true,
            "properties": [
                {
                    "name": "schema_name",
                    "value": "DB2INST1"
                },
                {
                    "name": "table_name",
                    "value": "CUSTOMER"
                }
            ],
            "columns": [ // list of columns in the data asset
                {
                    "name": "CID",
                    "type": {
                        "type": "bigint",
                        "length": 19,
                        "scale": 0,
                        "nullable": false,
                        "signed": true,
                        "native_type": "BIGINT"
                    }
                },
                {
                    "name": "INFO",
                    "type": {
                        "type": "sqlxml",
                        "length": 0,
                        "scale": 0,
                        "nullable": true,
                        "signed": false,
                        "native_type": "XML"
                    }
                },
                {
                    "name": "HISTORY",
                    "type": {
                        "type": "sqlxml",
                        "length": 0,
                        "scale": 0,
                        "nullable": true,
                        "signed": false,
                        "native_type": "XML"
                    }
                }
            ]
        }
    },
    "attachments": [ // details of the connection
        {
            "asset_type": "data_asset",
            "name": "CUSTOMER",
            "description": "customer table",
            "mime": "application/x-ibm-rel-table",
            "connection_id": "3a______",
            "connection_path": "/DB2_path_/CUSTOMER_table_path",
            "is_partitioned": false
        }
    ]
}

Create Data Asset: - Response Body:

{
  "metadata": {
    "usage": {
      "last_updated_at": "2023-04-20T20:13:39Z",
      "last_updater_id": "-- creator id--",
      "last_update_time": 1682021619570,
      "last_accessed_at": "2023-04-20T20:13:39Z",
      "last_access_time": 1682021619570,
      "last_accessor_id": "-- creator id--",
      "access_count": 0
    },
    "rov": {
      "mode": 0,
      "collaborator_ids": {},
      "member_roles": {
        "-- creator id--": {
          "user_iam_id": "-- creator id--",
          "roles": [
            "OWNER"
          ]
        }
      }
    },
    "name": "CUSTOMER",
    "description": "customer table",
    "tags": [
      "public"
    ],
    "asset_type": "data_asset",
    "origin_country": "us",
    "resource_key": "0000:0000:0000:0000:0000:FFFF:0914:9C3E|50000|sample:/DB2INST1/CUSTOMER",
    "rating": 0,
    "total_ratings": 0,
    "catalog_id": "e1____",
    "created": 1682021619570,
    "created_at": "2023-04-20T20:13:39Z",
    "owner_id": "-- creator id--",
    "size": 0,
    "version": 2,
    "asset_state": "available",
    "asset_attributes": [
      "data_asset"
    ],
    "asset_id": "12____",
    "asset_category": "USER",
    "creator_id": "-- creator id--"
  },
  "entity": {
    "data_asset": {
      "mime_type": "application/x-ibm-rel-table",
      "dataset": true,
      "properties": [
        {
          "name": "schema_name",
          "value": "DB2INST1"
        },
        {
          "name": "table_name",
          "value": "CUSTOMER"
        }
      ],
      "columns": [
        {
          "name": "CID",
          "type": {
            "type": "bigint",
            "length": 19,
            "scale": 0,
            "nullable": false,
            "signed": true,
            "native_type": "BIGINT"
          }
        },
        {
          "name": "INFO",
          "type": {
            "type": "sqlxml",
            "length": 0,
            "scale": 0,
            "nullable": true,
            "signed": false,
            "native_type": "XML"
          }
        },
        {
          "name": "HISTORY",
          "type": {
            "type": "sqlxml",
            "length": 0,
            "scale": 0,
            "nullable": true,
            "signed": false,
            "native_type": "XML"
          }
        }
      ]
    }
  },
  "href": "/v2/assets/12____?catalog_id=e1____",
  "asset_id": "12____",
  "attachments": [
    {
      "attachment_id": "2a____",
      "asset_type": "data_asset",
      "attachment_type": "remote",
      "datasource_type": "8c____",
      "is_partitioned": false,
      "name": "CUSTOMER",
      "description": "customer table",
      "mime": "application/x-ibm-rel-table",
      "connection_id": "3a____",
      "connection_path": "/DB2INST1/CUSTOMER",
      "user_data": {},
      "href": "/v2/assets/12____/attachments/2a____?catalog_id=e1____",
      "asset_category": "USER"
    }
  ]
}

In the next example, we will create a data asset which represents a local file uploaded to catalog. The URL for the API remains same. The sample request body and the corresponding response are given below.

Create Data Asset for Local File: -Sample Request Body:

{
    "metadata": {
        "name": "New data Asset",
        "description": "data table",
        "tags": [
            "public"
        ],
        "asset_type": "data_asset",
        "origin_country": "us",
        "rov": {
            "mode": 0
        }
    },
    "entity": {
        "data_asset": {
            "mime_type": "application/octet-stream",
            "dataset": true,
            "columns": [
                {
                    "name": "COLUMN1",
                    "type": {
                        "length": 1024,
                        "nullable": true,
                        "type": "varchar",
                        "scale": 0,
                        "signed": false
                    }
                },
                {
                    "name": "COLUMN2",
                    "type": {
                        "length": 1024,
                        "nullable": true,
                        "type": "varchar",
                        "scale": 0,
                        "signed": false
                    }
                },
                {
                    "name": "COLUMN3",
                    "type": {
                        "length": 1024,
                        "nullable": true,
                        "type": "varchar",
                        "scale": 0,
                        "signed": false
                    }
                },
                {
                    "name": "COLUMN4",
                    "type": {
                        "length": 1024,
                        "nullable": true,
                        "type": "varchar",
                        "scale": 0,
                        "signed": false
                    }
                },
                {
                    "name": "COLUMN5",
                    "type": {
                        "length": 1024,
                        "nullable": true,
                        "type": "varchar",
                        "scale": 0,
                        "signed": false
                    }
                },
                {
                    "name": "COLUMN6",
                    "type": {
                        "length": 1024,
                        "nullable": true,
                        "type": "varchar",
                        "scale": 0,
                        "signed": false
                    }
                },
                {
                    "name": "COLUMN7",
                    "type": {
                        "length": 1024,
                        "nullable": true,
                        "type": "varchar",
                        "scale": 0,
                        "signed": false
                    }
                },
                {
                    "name": "COLUMN8",
                    "type": {
                        "length": 1024,
                        "nullable": true,
                        "type": "varchar",
                        "scale": 0,
                        "signed": false
                    }
                }
            ],
            "properties": [
                {
                    "name": "bucket",
                    "value": "--bucket name--"
                },
                {
                    "name": "file_name",
                    "value": "file_path/file_name-csv"
                },
                {
                    "name": "first_line_header",
                    "value": "false"
                },
                {
                    "name": "encoding",
                    "value": "UTF-8"
                },
                {
                    "name": "invalid_data_handling",
                    "value": "fail"
                },
                {
                    "name": "file_format",
                    "value": "csv"
                }
            ]
        }
    },
    "attachments": [
        {
            "asset_type": "data_asset",
            "name": "remote",
            "description": "remote",
            "mime": "text/csv",
            "connection_id": "26____",
            "connection_path": "Cloud_object_storage_path/file_name-csv.csv",
            "is_partitioned": false
        }
    ]
}

Create Data Asset For Local File: - Response Body:

For the above request, the expected response is given below.

{
  "metadata": {
    "usage": {
      "last_updated_at": "2023-04-20T20:23:21Z",
      "last_updater_id": "-- creator id--",
      "last_update_time": 1682022201699,
      "last_accessed_at": "2023-04-20T20:23:21Z",
      "last_access_time": 1682022201699,
      "last_accessor_id": "-- creator id--",
      "access_count": 0
    },
    "rov": {
      "mode": 0,
      "collaborator_ids": {},
      "member_roles": {
        "-- creator id--": {
          "user_iam_id": "-- creator id--",
          "roles": [
            "OWNER"
          ]
        }
      }
    },
    "name": "New data Asset",
    "description": "data table",
    "tags": [
      "public"
    ],
    "asset_type": "data_asset",
    "origin_country": "us",
    "resource_key": "New data Asset",
    "rating": 0,
    "total_ratings": 0,
    "catalog_id": "e1______",
    "created": 1682022201699,
    "created_at": "2023-04-20T20:23:21Z",
    "owner_id": "-- creator id--",
    "size": 0,
    "version": 2,
    "asset_state": "available",
    "asset_attributes": [
      "data_asset"
    ],
    "asset_id": "80___",
    "asset_category": "USER",
    "creator_id": "-- creator id--"
  },
  "entity": {
    "data_asset": {
      "mime_type": "application/octet-stream",
      "dataset": true,
      "columns": [
        {
          "name": "COLUMN1",
          "type": {
            "length": 1024,
            "nullable": true,
            "type": "varchar",
            "scale": 0,
            "signed": false
          }
        },
        {
          "name": "COLUMN2",
          "type": {
            "length": 1024,
            "nullable": true,
            "type": "varchar",
            "scale": 0,
            "signed": false
          }
        },
        {
          "name": "COLUMN3",
          "type": {
            "length": 1024,
            "nullable": true,
            "type": "varchar",
            "scale": 0,
            "signed": false
          }
        },
        {
          "name": "COLUMN4",
          "type": {
            "length": 1024,
            "nullable": true,
            "type": "varchar",
            "scale": 0,
            "signed": false
          }
        },
        {
          "name": "COLUMN5",
          "type": {
            "length": 1024,
            "nullable": true,
            "type": "varchar",
            "scale": 0,
            "signed": false
          }
        },
        {
          "name": "COLUMN6",
          "type": {
            "length": 1024,
            "nullable": true,
            "type": "varchar",
            "scale": 0,
            "signed": false
          }
        },
        {
          "name": "COLUMN7",
          "type": {
            "length": 1024,
            "nullable": true,
            "type": "varchar",
            "scale": 0,
            "signed": false
          }
        },
        {
          "name": "COLUMN8",
          "type": {
            "length": 1024,
            "nullable": true,
            "type": "varchar",
            "scale": 0,
            "signed": false
          }
        }
      ],
      "properties": [
        {
          "name": "bucket",
          "value": "--bucket name--"
        },
        {
          "name": "file_name",
          "value": "file_path/file_name-csv"
        },
        {
          "name": "first_line_header",
          "value": "false"
        },
        {
          "name": "encoding",
          "value": "UTF-8"
        },
        {
          "name": "invalid_data_handling",
          "value": "fail"
        },
        {
          "name": "file_format",
          "value": "csv"
        }
      ]
    }
  },
  "href": "/v2/assets/80__?catalog_id=e1____",
  "asset_id": "805e24e6-2473-41d4-9e2a-679df9a40e7b",
  "attachments": [
    {
      "attachment_id": "2b____",
      "asset_type": "data_asset",
      "attachment_type": "remote",
      "is_partitioned": false,
      "name": "remote",
      "description": "remote",
      "mime": "text/csv",
      "connection_id": "26____",
      "connection_path": "Cloud_object_storage_path/file_name-csv.csv",
      "user_data": {},
      "href": "/v2/assets/80____/attachments/2b____?catalog_id=e1____",
      "asset_category": "USER"
    }
  ]
}

Retrieving a Data Asset

Retrieving a data asset can be achieved by calling APIs for retrieving any other asset. Since data assets are also uploaded locally we can retrieve csv files also. However, we have special APIs designed specifically to retrieve data assets.

If we retrieve the data asset created in the previous section

Retrieve a Data Asset: Request URL:

GET {service_URL}/v2/data_assets/{asset_id}?catalog_id={catalog_id}

Retrieve Data Asset: - Response Body:

{
  "metadata": {
    "usage": {
      "last_updated_at": "2023-04-17T20:20:56Z",
      "last_updater_id": "--id--",
      "last_update_time": 1681762856921,
      "last_accessed_at": "2023-04-17T20:20:56Z",
      "last_access_time": 1681762856921,
      "last_accessor_id": "--id--",
      "access_count": 0
    },
    "rov": {
      "mode": 0,
      "collaborator_ids": {},
      "member_roles": {
        "--id--": {
          "user_iam_id": "--id--",
          "roles": [
            "OWNER"
          ]
        }
      }
    },
    "name": "CUSTOMER",
    "description": "customer table",
    "tags": [
      "public"
    ],
    "asset_type": "data_asset",
    "origin_country": "us",
    "resource_key": "string",
    "rating": 0,
    "total_ratings": 0,
    "catalog_id": "e12____",
    "created": 1681762856921,
    "created_at": "2023-04-17T20:20:56Z",
    "owner_id": "--id--",
    "size": 0,
    "version": 2,
    "asset_state": "available",
    "asset_attributes": [
      "data_asset"
    ],
    "asset_id": "75____",
    "asset_category": "USER",
    "creator_id": "--id--"
  },
  "entity": {
    "data_asset": {
      "mime_type": "application/x-ibm-rel-table",
      "dataset": true,
      "columns": [
        {
          "name": "CID",
          "type": {
            "length": 19,
            "nullable": false,
            "type": "bigint",
            "scale": 0,
            "signed": true
          }
        },
        {
          "name": "INFO",
          "type": {
            "length": 0,
            "nullable": true,
            "type": "sqlxml",
            "scale": 0,
            "signed": false
          }
        },
        {
          "name": "HISTORY",
          "type": {
            "length": 0,
            "nullable": true,
            "type": "sqlxml",
            "scale": 0,
            "signed": false
          }
        }
      ],
      "properties": [
        {
          "name": "schema_name",
          "value": "DB2INST1"
        },
        {
          "name": "table_name",
          "value": "CUSTOMER"
        }
      ]
    }
  },
  "attachments": [
    {
      "id": "51d____",
      "version": 2,
      "asset_type": "data_asset",
      "name": "CUSTOMER",
      "description": "customer table",
      "mime": "application/x-ibm-rel-table",
      "connection_id": "3a____",
      "connection_path": "/DB2_path_/CUSTOMER_table_path",
      "datasource_type": "8c____",
      "creator_id": "--id--",
      "create_time": 1681762856936,
      "size": 0,
      "is_remote": true,
      "is_managed": false,
      "is_referenced": false,
      "is_object_key_read_only": false,
      "is_user_provided_path_key": true,
      "transfer_complete": true,
      "is_partitioned": false,
      "complete_time_ticks": 1681762856936,
      "user_data": {},
      "test_doc": 0,
      "usage": {
        "access_count": 0,
        "last_accessor_id": "--id--",
        "last_access_time": 1681762856936
      }
    }
  ],
  "href": "/v2/data_assets/75__?catalog_id=e1____"
}

Retrieving Columns from a Data Asset

Data assets often contain information regarding the columns in the table or file. Use the GET columns API to retrieve the column related information, such as the terms, column description or any other custom attribute assigned to a column etc.

Retrieve Columns from a Data Asset: Request URL:

GET {service_URL}/v2/data_assets/{asset_id}/columns?catalog_id={catalog_id}

Retrieve Columns from a Data Asset: - Response Body:

{
  "total_rows": 1, // Total number of columns
  "resources": [
    {
      "data_asset": {
        "columns": [
          {
            "name": "NameOfColumn",
            "type": {
              "type": "varchar",
              "length": 1024,
              "scale": 0,
              "nullable": true,
              "signed": false
            },
            "position": 1
          }
        ]
      },
      "column_info": {
        "NameOfColumn": {
          "column_description": "column description",
          "column_terms": [
            {
              "term_display_name": "test_term",
              "term_id": "d3d______"
            }
          ],
          "name": "NameOfColumn"
        }
      },
      "column_level_custom_attribute": {
        "columns": [
          {
            "name": "NameOfColumn",
            "OtherColumnProperty": "dummy value"
          }
        ]
      }
    }
  ]
}

Data assets can hold information about the columns of the data. It is important to note that they do not hold the actual column and the column values. The information it can stores can be the terms and data class assigned after profiling or manually, data quality score of the column, the type of the column, tags associated with the column and the description of the column. We can create custom attributes and assign them to columns also. The Create Asset Type: book section shows how to create an asset type named book.

Among the already available asset types, asset types "data_asset" and "column_info" contain attributes for columns.

Column Related Information Present in data_asset Asset Type

The basic structure of data_asset asset type is described in section data_asset Type. Here we will elaborate on the "columns" field of the data_asset.

The "columns" field consists of a list of elements. Each element represents a column. It is populated after profiling. Example of a single column element is given below

{
          "name": "MARITAL_STATUS", // Name of the column
          "type": {
            "type": "varchar",
            "length": 1024,
            "scale": 0,
            "nullable": true,
            "signed": false
          }
}

Column Related Information Present in column_info Asset Type

The column_info attribute in a data asset represents information about each column. The informations is added manually, or after running profiling or metadata enrichment. The "column_info" field consists of a map. Each key-value pair represents an individual column. Each key is the name of the column. The value of the key represents attributes assigned to the column. Example of a single column element's column_info is given below

"MARITAL_STATUS": { // name of the column
                    "data_class": {
                        "selected_data_class": {
                            "id": "7a8e___",
                            "name": "status",
                            "setByUser": false
                        }
                    },
                    "inferred_type": {
                        "length": 10,
                        "precision": 0,
                        "scale": 0,
                        "type": "STRING",
                        "display_name": "varchar(10)"
                    },
                    "quality": {
                        "score": 98
                    },
                    "column_terms": [
                        {
                            "term_id": "7a8e____",
                            "term_display_name": "TestTerm",
                            "confidence": 0.984,
                            "specification": "Data class based assignment"
                        }
                    ],
                    "suggested_terms": [],
                    "rejected_terms": [],
                    "name": "MARITAL_STATUS"
                }
            }

Assigning Terms and Data Class to a Column

We can assign terms and data class to a column manually also. The terms associated with individual columns reside as an array inside the column_info attribute.

In the first scenario we will add a single term and data class to a file which does not have the "column_info" attribute. This can happen if a csv file has been uploaded locally. In this case, we need to use the Create Asset API to create and populate an instance of "column_info" attribute inside the asset's primary metadata document.

POST {service_URL}/v2/assets/{asset_id}/attributes?catalog_id={catalog_id}

Body:

{
    "name": "column_info",
    "entity": {
        "BUSINESS_NAME": {
            "column_terms": [
                {
                    "term_id": "--your term id--",
                    "term_display_name": "term_name"
                }
            ],
            "data_class": {
                "selected_data_class": {
                    "id": "--your data class id--",
                    "name": "data_class_name"
                }
            }
        }
    }
}

The response should look like:

{
  "asset_id": "--your asset id--",
  "column_info": {
    "BUSINESS_NAME": { // Name of your column
      "column_terms": [
        {
          "term_id": "--your term id--",
          "term_display_name": "term_name"
        }
      ],
      "data_class": {
        "selected_data_class": {
          "id": "--your data class id--",
          "name": "data_class_name"
        }
      }
    }
  },
  "href": "/v2/assets/b8____/attributes/column_info?catalog_id=e1____"
}

If you want to associate additional terms or data class to a column, or add terms or data class to a column which does not have terms or data class, or remove existing terms or data class from a column, then use the Update Asset Attribute API. In the following example, we will replace terms assigned to the column BUSINESS_NAME and add terms to a column which does not have any terms yet.

PATCH {service_URL}/v2/assets/{asset_id}/attributes/column_info?catalog_id={catalog_id}

Body:

[
    {
        "op": "replace",
        "path": "/BUSINESS_NAME/column_terms", // column to which we already have terms and data class
        "value": [
            {
                "term_id": "--your term id--",
                "term_display_name": "new_term_name"
            }
        ]
    },
    {
        "op": "add",
        "path": "/ADDRESS", // column to which no terms are data classes are assigned
        "value": {
            "column_terms": [
                {
                    "term_id": "--your term id--",
                    "term_display_name": "term_name_2"
                }
            ]
        }
    }
]

The response should look like:

{
  "BUSINESS_NAME": {
    "column_terms": [
      {
        "term_id": "--your term id--",
        "term_display_name": "new_term_name"
      }
    ],
    "data_class": {
      "selected_data_class": {
        "id": "--your data class id--",
        "name": "data_class_name"
      }
    }
  },
  "ADDRESS": {
    "column_terms": [
      {
        "term_id": "--your term id--",
        "term_display_name": "term_name_2"
      }
    ]
  }
}

Custom Attributes for Columns

Custom attributes can be added to individual columns.

Creating an Asset Type for Column Level

The first step is to create an asset type which can be assigned to a column. To search based on column level custom attributes, use global search. Users are not allowed to create assets of asset type which have properties associated with columns. This asset type can only be used to create attributes on a data_asset or a cobol_copybook. All properties are on column level.

Ensure is_column_custom_attribute=true. This field can be edited from false to true, but cannot be edited from true to false value
Ensure allow_decorators=false
Ensure all searchable fields are listed in the fields list. All the elements of the fields list must have a valid key and a valid search_path.
Populate the global_search_searchable with the keys mentioned in the elements of the fields

Given below is an example

POST {service_URL}/v2/asset_types

{
  "description": "Asset type for custom column attribute",
  "name": "column_attribute",
  "is_column_custom_attribute" : true, // is_column_custom_attribute should be true
  "allow_decorators": false, // allow_decorators shuld be false
  "attribute_only":true,
  "decorates": [
      {
        "asset_type_name": "data_asset" // only two values allowed; data_asset and/or cobol_copybook
      }
  ],
  "fields": [
  {
      "key": "name",
      "type": "string",
      "facet": false,
      "search_path": "columns.*.name"
    },
    {
      "key": "field1",
      "type": "string",
      "facet": false,
      "search_path": "columns.*.field1"
    },
    {
      "key": "field2",
      "type": "string",
      "facet": false,
      "search_path": "columns.*.field2"
    }
  ],
  "global_search_searchable": ["name", "field1", "field2"],
  "properties" : {
      "name" : {
          "type": "string"
      },
      "host" : {
          "type": "string"
      },
      "new_prop" : {
          "type": "string"
      }
  }

}

Adding the Column Level Attribute to a Column

Users can add columns with a map. The top level field name will always be columns. The value of columns fields will be a map.

Example of attribute being listed as a map is

"columns": {
        "city": {
          "host": "host1",
          "field1": "column1field1",
          "field2": "column1field2"
        }
      }

The details are listed in section Adding the Attribute as a Map.

Adding the Attribute as a Map

When column level attribute is added to the asset, then the attribute in the asset's entity will have the following structure. Please note the top level columns field. e.g.

   "column_attribute": {
      "columns": {
        "city": { // column name
          "host": "host1",
          "field1": "column1field1",
          "field2": "column1field2"
        }
      }
    }
  }

In the above example, for column name "city", the associated property values are host=host1, field1=column1field1, field2=column1field2

To add this attribute to an asset:

POST /v2/assets/{asset_id}/attributes API. Sample body will look like

{
    "name": "column_attribute",
    "entity": {
        "columns": {
            "city": {
              "host": "host1",
              "field1": "column1field1",
              "field2": "column1field2"
            }
        }
    }
}

To update this attribute use the PATCH /v2/assets/{asset_id}/attributes/{attribute_key} or the POST /v2/assets/bulk_patch API

Data Profiles

Introduction

Data profiles contains classification and information about the distribution of your data, which helps you to understand your data better and make the appropriate data shaping decisions.

Data profiles are automatically created when a data set is added to a catalog with data policy enforcement. The profile summary helps you in analyzing your data more closely and in deciding which cleansing operations on your data will provide the best results for your use-case. You can also perform CRUD operations on data profiles for data sets in catalogs or projects without data policy enforcement.

Create a data profile

You can use this API to:

Create a data profile
Create and execute a data profile

To create a data profile for a data set in a specified catalog or project and not execute it, call the following POST method:

POST /v2/data_profiles?start=false

OR

POST /v2/data_profiles

To create a data profile for a data set in a specified catalog or project and execute it, call the following POST method:

POST /v2/data_profiles?start=true

The minimal request payload required to create a data profile is as follows:

{
    "metadata": {
        "dataset_id": "{DATASET_ID}",
        "catalog_id": "{CATALOG_ID}"
    }
}

OR

{
    "metadata": {
        "dataset_id": "{DATASET_ID}",
        "project_id": "{PROJECT_ID}"
    }
}

The request payload can have an entity part which is optional:

    {
        "metadata": {
            "dataset_id": "{DATASET_ID}",
            "catalog_id": "{CATALOG_ID}"
        },
        "entity": {
            "data_profile": {
                "options": {
                    "max_row_count": {MAX_ROW_COUNT_VALUE},
                    "max_distribution_size": {MAX_SIZE_OF_DISTRIBUTIONS},
                    "max_numeric_stats_bins": {MAX_NUMBER_OF_STATIC_BINS},
                    "classification_options": {
                        "disabled": {BOOLEAN_TO_ENABLE_OR_DISABLE_CLASSIFICATION_OPTIONS},
                        "class_codes": {DATA_CLASS_CODE},
                        "items": {ITEMS}
                }
            }
        }
    }

The following parameters are required in the URI and the payload:

start: Specifies whether to start the profiling service immediately after the data profile is created. The default is false.
max_row_count: Specifies the maximum number of rows to perform profiling on. If no value is provided or if the value is invalid (negative), the default is to 5000 rows.
row_percentage: Specifies the percentage of rows to perform profiling on. If no value is provided or if the value is invalid (<0 or >100).
max_distribution_size: Specifies the maximum size of various distributions produced by the profiling process. If no value is provided, the default is 100.
max_numeric_stats_bins: Specifies the maximum number of bins to use in the numerical statistics. If no bin size is provided, the default is 100 bins.
classification_options: Specifies the various options available for classification.

(i). disabled: If true, the classification options are disabled and default values are used.

(ii). class_codes: Specifies the data class code to consider during profiling.

(iii). items: Specifies the items.

Note: You can get various data class codes through the data class service.

To create a data profile for a data set, the following steps must be completed:

You must have a valid IAM token to make REST API calls and a project or catalog ID.
You must have an IBM Cloud Object Storage bucket, which must be associated with your catalog in the project.
The data set must be added to your catalog in the project.
Construct a request payload to create a data profile with the values required in the payload.
Send a POST request to create a data profile.

When you call the method, the payload is validated. If a required value is not specified or a value is invalid, you get a response message with an HTTP status code of 400 and information about the invalid or missing values.

The response of the method includes a location header with a value that indicates the location of the profile that was created. The response body also includes a field href which contains the location of the created profile.

The execution.status of the profile is none if the start parameter is not set or is set to false. Otherwise, it is in submitted state or any other state depending on the profiling execution status.

The following are possible response codes for this API call:

| Response HTTP status | Cause | Possible Scenarios | | -------------------- | ------------ | ----------------------- | | 201 | Created | A data profile was created. | | 400 | Bad Request | The request payload either had some invalid values or invalid/unwanted parameters. | | 401 | Unauthorized | Invalid IAM token was provided in the request header. | | 403 | Forbidden | User is not allowed to create a data profile. | | 500 | Internal Server Error | Some runtime error occurred. |

Get a data profile

To get a data profile for a data set in a specified catalog or project, call the following GET method:

GET /v2/data_profiles/{PROFILE_ID}?catalog_id={CATALOG_ID}&dataset_id={DATASET_ID}

OR

GET /v2/data_profiles/{PROFILE_ID}?project_id={PROJECT_ID}&dataset_id={DATASET_ID}

The value of PROFILE_ID is the value of metadata.guid from the successful response payload of the create data profile call.

For other runtime errors, you might get an HTTP status code of 500 indicating that profiling didn't finished as expected.

The following are possible response codes for this API call:

| Response HTTP status | Cause | Possible Scenarios | | -------------------- | --------------- | -------------------- | | 200 | Success | Data profile is created and executed. | | 202 | Accepted | Data profile is created and under execution. | | 401 | Bad Request | Invalid IAM token was provided in the request header. | | 403 | Forbidden | User is not allowed to get the data profile. | | 404 | Not Found | The data profile specified was not found. | | 500 | Internal Server Error | Some runtime error occurred. |

Update a data profile

To update a data profile for a data set in a specified catalog or project, call the following PATCH method:

PATCH /v2/data_profiles/{PROFILE_ID}?catalog_id={CATALOG_ID}&dataset_id={DATASET_ID}

OR

PATCH /v2/data_profiles/{PROFILE_ID}?project_id={PROJECT_ID}&dataset_id={DATASET_ID}

The value of PROFILE_ID is the value of metadata.guid from the successful response payload of the create data profile call.

The JSON request payload must be as follows:


    [
      {
        "op": "add",
        "path": "string",
        "from": "string",
        "value": {}
      }
    ]

During update, the entire data profile is replaced, apart from any read-only or response-only attributes.

If profiling processes are running and the start parameter is set to true, then a data profile is only updated if the stop_in_progress_runs parameter is set to true.

The updates must be specified by using the JSON patch format, described in RFC 6902.

Modify asset level classification

This API is used for CRUD operations on asset level classification.

To modify the asset level classification details in the data_profile parameter for a data set in a specified catalog or project, call the following PATCH method:

PATCH /v2/data_profiles/classification?catalog_id={CATALOG_ID}&dataset_id={DATASET_ID}

OR

PATCH /v2/data_profiles/classification?project_id={PROJECT_ID}&dataset_id={DATASET_ID}

The JSON request payload must be structured in the following way:

    [
      {
        "op": "add",
        "path": "/data_classification",
        "value": [
            {
               "id":"{ASSET_LEVEL_CLASSIFICATION_ID}",
               "name":"{ASSET_LEVEL_CLASSIFICATION_NAME}"
            }
         ]
      }
    ]

The path attribute must be set to what is written in the previous JSON request payload, otherwise you will get a validation error with an HTTP status code of 400.

The values of ASSET_LEVEL_CLASSIFICATION_ID and ASSET_LEVEL_CLASSIFICATION_NAME can be: PII and PII details respectively.

The data updates must be specified by using the JSON patch format, described in RFC 6902 [https://tools.ietf.org/html/rfc6902]. For more details about JSON patch, see [http://jsonpatch.com].

A successful response has an HTTP status code of 200 and lists the asset level classifications.

The following are possible response codes for this API call:

| Response HTTP status | Cause | Possible Scenarios | | -------------------- | ----- | ----------------- | | 200 | Success| Asset Level Classification is added to the asset. | | 400 | Bad Request | The request payload either had some invalid values or invalid/unwanted parameters. | | 401 | Unauthorized | Invalid IAM token was provided in the request header. | | 403 | Forbidden | User is not allowed to add asset level classification to the asset. | | 500 | Internal Server Error | A runtime error occurred. |

Delete a data profile

To delete a data profile for a data set in a specified catalog or project, call the following DELETE method:

DELETE /v2/data_profiles/{PROFILE_ID}?catalog_id={CATALOG_ID}&dataset_id={DATASET_ID}&stop_in_progress_profiling_runs=false

OR

DELETE /v2/data_profiles/{PROFILE_ID}?project_id={PROJECT_ID}&dataset_id={DATASET_ID}&stop_in_progress_profiling_runs=true

The value of PROFILE_ID is the value of metadata.guid from the successful response payload of the create data profile call.

You can't delete a profile if the profiling execution status is in running state and the query parameter stop_in_progress_profiling_runs is set to false.

A successful response has an HTTP status code of 204.

Stream Flows

Introduction

The streams flow service provides APIs to create, update, delete, list, start, and stop stream flows.

A streams flow is a continuous flow of massive volumes of moving data that real-time analytics can be applied to. A streams flow can read data from a variety of sources, process that data by using analytic operations or your custom code, and then write it to one or more targets. You can access and analyze massive amounts of changing data as it is created. Regardless of whether the data is structured or unstructured, you can leverage data at scale to drive real-time analytics for up-to-the-minute business decisions.

The sources that are supported include Kafka, Message Hub, MQTT, and Watson IoT. Targets that are supported include Db2 Warehouse on Cloud, Cloud Object Storage, and Redis. Analytic operators that are supported include Aggregation, Python Machine Learning, Code, and Geofence.

Authorization

Authorization is done via Identity Access Management (IAM) bearer token. All API calls will require this Bearer token in the header.

Create a Streams Flow

1. Streaming Analytics instance ID

The streams flow is submitted to a Streaming Analytics service for compilation and running. When creating a flow, the Streaming Analytics instance ID must be provided. The instance ID can be found in the service credentials, which can be accessed from the service dashboard.

2. The pipeline graph

The streams flow represents it's source, targets, and operations in a pipeline graph. The pipeline graph can be generated by choosing the relevant operators in the Streams Designer canvas. To retrieve a pipeline graphcreated by the Streams Designer, use:

GET /v2/streams_flows/85be3e09-1c71-45d3-8d5d-220d6a6ea850?project_id=ff1ab70b-0553-409a-93f9-ccc31471c218

This will return a streams flow containing a pipeline field in the entity. This pipeline object can be copied and submitted into another flow via:

POST /v2/streams_flows/?project_id=ff1ab70b-0553-409a-93f9-ccc31471c218

Request Payload:

{
"name": "My Streams Flow",
"description": "A Sample Streams Flow.",
"engines": {
   "streams": {
     "instance_id": "8ff81caa-1076-41ce-8de1-f4fe8d79e30e"
   }
},
"pipeline": {
     "doc_type": "pipeline",
     "version": "1.0",
     "json_schema": "http://www.ibm.com/ibm/wdp/flow-v1.0/pipeline-flow-v1-schema.json",
     "id": "",
     "app_data": {
         "ui_data": {
             "name": "mqtt 2"
          }
      },
      "primary_pipeline": "primary-pipeline",
      "pipelines": [
       {
          "id": "primary-pipeline",
          "runtime": "streams",
          "nodes": [
          {
              "id": "messagehubsample_29xse4zvabe",
              "type": "binding",
              "op": "ibm.streams.sources.messagehubsample",
              "outputs": [
                  {
                      "id": "target",
                      "schema_ref": "schema0",
                      "links": [
                      {
                        "node_id_ref": "mqtt_o6are9c4f",
                        "port_id_ref": "source"
                      }
                    ]
                  }
                ],
                "parameters": {
                  "schema_mapping": [
                    {
                      "name": "time_stamp",
                      "type": "timestamp",
                      "path": "/time_stamp"
                    },
                    {
                      "name": "customerId",
                      "type": "double",
                      "path": "/customerId"
                    },
                    {
                      "name": "latitude",
                      "type": "double",
                      "path": "/latitude"
                    },
                    {
                      "name": "longitude",
                      "type": "double",
                      "path": "/longitude"
                    }
                  ]
                },
                "connection": {
                  "ref": "EXAMPLE_MESSAGE_HUB_CONNECTION",
                  "project_ref": "EXAMPLE",
                  "properties": {
                    "asset": {
                      "path": "/geofenceSampleData",
                      "type": "topic",
                      "name": "Geospatial data",
                      "id": "geofenceSampleData"
                    }
                  }
                },
                "app_data": {
                  "ui_data": {
                    "label": "Sample Data",
                    "x_pos": 60,
                    "y_pos": 90
                  }
                }
              },
              {
                "id": "mqtt_o6are9c4f",
                "type": "binding",
                "op": "ibm.streams.targets.mqtt",
                "parameters": {},
                "connection": {
                  "ref": "cd5388c3-b203-4c77-803b-bc902d864a30",
                  "project_ref": "a912d673-54d3-4e5c-800f-5088554d3aa8",
                  "properties": {
                    "asset": "t"
                 }
               },
               "app_data": {
                 "ui_data": {
                   "label": "MQTT",
                   "x_pos": 420,
                   "y_pos": 90
                 }
               }
             },
             {
               "id": "mqtt_y84zc3vfche",
               "type": "binding",
               "op": "ibm.streams.sources.mqtt",
               "outputs": [
                 {
                   "id": "target",
                   "schema_ref": "schema1",
                   "links": [
                     {
                        "node_id_ref": "debug_9avg3zdig25",
                        "port_id_ref": "source"
                      }
                    ]
                  }
                ],
                "parameters": {
                "schema_mapping": [
                   {
                     "name": "time_stamp",
                     "type": "timestamp",
                     "path": "/time_stamp"
                   },
                   {
                     "name": "customerId",
                     "type": "double",
                     "path": "/customerId"
                   },
                   {
                     "name": "latitude",
                     "type": "double",
                     "path": "/latitude"
                   },
                   {
                     "name": "longitude",
                     "type": "double",
                     "path": "/longitude"
                   }
                 ]
               },
               "connection": {
                 "ref": "cd5388c3-b203-4c77-803b-bc902d864a30",
                 "project_ref": "a912d673-54d3-4e5c-800f-5088554d3aa8",
                 "properties": {
                   "asset": "t"
                 }
               },
               "app_data": {
                 "ui_data": {
                   "label": "MQTT",
                   "x_pos": -120,
                   "y_pos": -210
                 }
               }
             },
             {
               "id": "debug_9avg3zdig25",
               "type": "binding",
               "op": "ibm.streams.targets.debug",
               "parameters": {},
               "app_data": {
                 "ui_data": {
                   "label": "Debug",
                   "x_pos": 240,
                   "y_pos": -270
                 }
               }
             }
           ]
         }
      ],
      "schemas": [
         {
           "id": "schema0",
           "fields": [
              {
                   "name": "time_stamp",
                   "type": "timestamp"
              },
              {
                   "name": "customerId",
                   "type": "double"
              },
              {
                   "name": "latitude",
                   "type": "double"
              },
              {
                  "name": "longitude",
                  "type": "double"
              }
          ]
        },
        {
          "id": "schema1",
          "fields": [
            {
              "name": "time_stamp",
              "type": "timestamp"
            },
            {
              "name": "customerId",
              "type": "double"
            },
            {
              "name": "latitude",
              "type": "double"
            },
            {
              "name": "longitude",
              "type": "double"
            }
          ]
        }
     ]
  }
}

Streams Flow Lifecycle

After a Streams Flow is created it will be in the STOPPED state unless it's been submitted as a job to be started. When starting a job, a Cloudant asset is created to track the status of the streams flow run. The start job operation can take up to minute to complete, during which time the streams flow will be in the STARTING state. Once the submission and compilation has completed, the streams flow will be in the RUNNING state.

To change the run state use the POST api:

POST /v2/streams_flows/85be3e09-1c71-45d3-8d5d-220d6a6ea850/runs?project_id=ff1ab70b-0553-409a-93f9-ccc31471c218

Request Payload:

{
   "state": "started",
   "allow_streams_start": true
}

For starting the streams flow run, use { state: started }. To stop the flows run, use { state: stopped }.
Specify "allow_streams_start" to start the Streaming Analytics service in the event that it is stopped.

The start job operation triggers a long running process on the Streaming Analytics service instance. During this time the progress/status of this job can be viewed:

GET https://api.dataplatform.cloud.ibm.com/v2/streams_flows/85be3e09-1c71-45d3-8d5d-220d6a6ea850/runs?project_id=ff1ab70b-0553-409a-93f9-ccc31471c218

A version of the pipeline that has been deployed is saved to represent the Runtime Pipeline. The streams flow can still be edited in the Streams Designer, and it will not have an impact on the Runtime Pipeline that has been deployed, until the user stops the running flow, and starts it again..

Metadata Discovery

Introduction
Create a Metadata Discovery run
Monitor the status of a Metadata Discovery run
Retrieve Discovered asset Metadata
Retrieve the ID of a Metadata Discovery run
Abort a Metadata Discovery run

Metadata Discovery can be used to automatically discover assets from a connection. The connection used for a discovery run can be associated with a catalog or project, but new data assets will be created in a project. Each asset that is discovered from a connection is added as a data asset to the project.

For a list of the supported types of connections against which the Metadata Discovery service can be invoked, see Discover data assets from a connection.

In general, the discovery process takes a significant amount of time. Therefore, the API to create a discovery run actually only queues a discovery run and then returns immediately (typically before the discovery run is even started). Subsequent calls to different APIs can then be made to monitor the progress of the discovery run (see Monitoring a metadata discovery run and Retrieving discovered assets).

The following example shows a request to create a metadata discovery run. It assumes that a project, a connection, and a catalog have already been created, and that their IDs are known by the caller. If a catalog is provided (as in the following example), the connection is associated with the catalog. If no catalog is provided, the connection is associated with the project.

Note: In the following examples, the discovered assets are found in a connection to a DB2 database, but the details of the database are hidden within the connection. So, the caller of the data_discoveries API specifies the database to discover indirectly via the connection.

API request - Create discovery run:

POST /v2/data_discoveries

Request payload:

{
    "entity": {
        "catalog_id": "816882fa-dcda-46e1-8c6b-fa23c3cbad14",
        "connection_id": "f638398f-fcc7-4856-b78d-5c8efa5b9282",
        "project_id": "960f6aff-295f-4de1-a9d7-f3b6805b3590"
    }
}

In the example request payload, you can see the ID of the connection whose assets will be discovered, and the ID of the project into which the newly created assets will be added.

Response payload:

{
    "metadata": {
        "id": "dcb8a234ad5e438d904a4cdbe0ba70e2",
        "invoked_by": "IBMid-50S...",
        "bss_account_id": "e348e...",
        "created_at": "2018-06-22T15:42:02.843Z"
    },
    "entity": {
        "status": "CREATED",
        "connection_id": "f638398f-fcc7-4856-b78d-5c8efa5b9282",
        "catalog_id": "816882fa-dcda-46e1-8c6b-fa23c3cbad14",
        "project_id": "960f6aff-295f-4de1-a9d7-f3b6805b3590"
    }
}

In the response, you can see that the discovery run was created with the ID dcb8a234ad5e438d904a4cdbe0ba70e2, which you'll need to use if you want to get the status of the discovery run that you just created. Also shown in the response is:

invoked_by: the IAM ID of the account that kicked off the discovery process
bss_account_id: the BSS account ID of the catalog
created_at: the creation date and time of the discovery job

To get the status of a discovery run use the GET data_discoveries API. You can request the status of a discovery run as often as desired. In the following sections, you will be shown a few such calls to illustrate the progression of a discovery run.

API Request - Get status of discovery run:

GET /v2/data_discoveries/dcb8a234ad5e438d904a4cdbe0ba70e2

There is no request payload for the previous GET data_discoveries request. Instead, the ID of the discovery run whose status is being requested is supplied as a path parameter. In the previous URL, use the discovery run ID that was returned by the earlier call to POST data_discoveries. If you no longer have access to the ID of the discovery run for which you want to see status information, see the section Call Discovery API to get the ID of a metadata discovery run.

The following examples show various responses to the same GET data_discoveries monitor request previously shown, made at various points during the discovery run.

Response to status request immediately after creation of a discovery run:

{
    "metadata": {
        "id": "dcb8a234ad5e438d904a4cdbe0ba70e2",
        "invoked_by": "IBMid-50S...",
        "bss_account_id": "e348e...",
        "created_at": "2018-06-22T15:42:02.843Z"
    },
    "entity": {
        "status": "CREATED",
        "connection_id": "f638398f-fcc7-4856-b78d-5c8efa5b9282",
        "catalog_id": "816882fa-dcda-46e1-8c6b-fa23c3cbad14",
        "project_id": "960f6aff-295f-4de1-a9d7-f3b6805b3590"
    }
}

In the previous response, you can see that the status of the discovery run has not yet changed - it is still CREATED. This is because the request to discover assets is put into a queue and will be initiated in the order in which it was received.

Response to status request immediately after a discovery run has actually started:

{
    "metadata": {
        "id": "dcb8a234ad5e438d904a4cdbe0ba70e2",
        "invoked_by": "IBMid-50S...",
        "bss_account_id": "e348e...",
        "created_at": "2018-06-22T15:42:02.843Z",
        "started_at": "2018-06-22T15:42:06.167Z",
        "ref_project_connection_id": "2526ed95-dedd-4904-bb31-c06d9cb1e105"
    },
    "entity": {
        "statistics": {

        },
        "status": "RUNNING",
        "connection_id": "f638398f-fcc7-4856-b78d-5c8efa5b9282",
        "catalog_id": "816882fa-dcda-46e1-8c6b-fa23c3cbad14",
        "project_id": "960f6aff-295f-4de1-a9d7-f3b6805b3590"
    }
}

Now notice that the status has changed to RUNNING which indicates that the discovery process has actually started. Also, the metadata field has some additional fields added to it:

started_at: the date and time at which the discovery run started
ref_project_connection_id: a reference to a cloned project connection ID, internally set when a discovery is created for a connection in a catalog

In addition, notice that a new statistics object was introduced into the response body. In the response, that object is empty because the discovery run, which has just started hasn't yet discovered any assets.

Response to status request after some assets were discovered:

{
    "metadata": {
        "id": "dcb8a234ad5e438d904a4cdbe0ba70e2",
        "invoked_by": "IBMid-50S...",
        "bss_account_id": "e348e...",
        "created_at": "2018-06-22T15:42:02.843Z",
        "started_at": "2018-06-22T15:42:06.167Z",
        "discovered_at": "2018-06-22T15:42:27.970Z",
        "ref_project_connection_id": "2526ed95-dedd-4904-bb31-c06d9cb1e105"
    },
    "entity": {
        "statistics": {
            "discovered": 128,
            "submit_succ": 128
        },
        "status": "RUNNING",
        "connection_id": "f638398f-fcc7-4856-b78d-5c8efa5b9282",
        "catalog_id": "816882fa-dcda-46e1-8c6b-fa23c3cbad14",
        "project_id": "960f6aff-295f-4de1-a9d7-f3b6805b3590"
    }
}

Notice the statistics object now contains two fields:

discovered: the number of assets discovered so far during the discovery run
submit_succ: the number of assets successfully submitted for creation so far during the discovery run. A discovered asset goes through an internal pipeline with various stages from being discovered at the connection to being created in the project. Here, submitted means the asset was submitted to the internal pipeline.

Refer to Watson Data API schema for the complete list of the possible fields that might show up in the statistics object.

Because the discovery run isn't yet finished, the status in the previous response is still RUNNING.

Response to status request after the discovery run was completed:

{
    "metadata": {
        "id": "dcb8a234ad5e438d904a4cdbe0ba70e2",
        "invoked_by": "IBMid-50S...",
        "bss_account_id": "e348e...",
        "created_at": "2018-06-22T15:42:02.843Z",
        "started_at": "2018-06-22T15:42:06.167Z",
        "discovered_at": "2018-06-22T15:42:27.970Z",
        "processed_at": "2018-06-22T15:42:45.877Z",
        "finished_at": "2018-06-22T15:43:14.969Z",
        "ref_project_connection_id": "2526ed95-dedd-4904-bb31-c06d9cb1e105"
    },
    "entity": {
        "statistics": {
            "discovered": 179,
            "submit_succ": 179,
            "create_succ": 179
        },
        "status": "COMPLETED",
        "connection_id": "f638398f-fcc7-4856-b78d-5c8efa5b9282",
        "catalog_id": "816882fa-dcda-46e1-8c6b-fa23c3cbad14",
        "project_id": "960f6aff-295f-4de1-a9d7-f3b6805b3590"
    }
}

Notice the status field has changed to COMPLETED to indicate that the discovery run is finished. Other response fields to note:

finished_at: the date and time at which the discovery run finished
discovered: indicates that 179 assets were discovered at the connection
submit_succ: indicates that 179 of the discovered assets were successfully submitted to the discovery run's internal asset processing pipeline.
create_succ: indicates that 179 assets were successfully created in the project

At any time during or after a discovery run, you call Asset APIs to get the list of metadata for the currently discovered assets in the project. To retrieve metadata for any list of assets you can make the following call:

POST /v2/asset_types/{type_name}/search?project_id={project_id}

More specifically, to find the metadata for discovered assets the value to use for the {type_name} path parameter is discovered_asset. So, for the discovery run we created, the call to retrieve metadata for the discovered assets would look like this:

API Request - Get metadata for discovered assets:

POST /v2/asset_types/discovered_asset/search?project_id=960f6aff-295f-4de1-a9d7-f3b6805b3590

where the project_id query parameter value 960f6aff-295f-4de1-a9d7-f3b6805b3590 is the same value that was specified in the body of the POST request that was used to create the discovery run.

In addition, the ID of the connection that the discovery was run against has to be specified in the body of the POST, like this:

{
    "query": "discovered_asset.connection_id:\"f638398f-fcc7-4856-b78d-5c8efa5b9282\""
}

Here is part of the response body for the previous query:

{
    "total_rows": 179,
    "results": [
        {
            "metadata": {
                "name": "EMP_SURVEY_TOPIC_DIM",
                "description": "Warehouse table EMP_SURVEY_TOPIC_DIM describes employee survey questions for employees of the Great Outdoors Company, in supported languages.",
                "tags": [
                    "discovered",
                    "GOSALESDW"
                ],
                "asset_type": "data_asset",
                "origin_country": "ca",
                "rating": 0.0,
                "total_ratings": 0,
                "sandbox_id": "960f6aff-295f-4de1-a9d7-f3b6805b3590",
                "catalog_id": "a682c698-6019-437d-a0b9-224aa0a4dbc9",
                "created": 0,
                "created_at": "2018-06-22T15:41:47Z",
                "owner": "abc123@us.ibm.com",
                "owner_id": "IBMid-50S...",
                "size": 0,
                "version": 0.0,
                "usage": {
                    "last_update_time": 1.52968210955E12,
                    "last_updater_id": "iam-ServiceId-87f49...",
                    "access_count": 0.0,
                    "last_accessor_id": "iam-ServiceId-87f49...",
                    "last_access_time": 1.52968210955E12,
                    "last_updater": "ServiceId-87f49...",
                    "last_accessor": "ServiceId-87f49..."
                },
                "asset_state": "available",
                "asset_attributes": [
                    "data_asset",
                    "discovered_asset"
                ],
                "rov": {
                    "mode": 0
                },
                "asset_category": "USER",
                "asset_id": "e35cfd4d-590f-40a5-b75c-ec07c0a4bcbc"
            }
        },
        ...
    ]
}

Notice that the total_rows value 179 matches the create_succ value that was returned in the result of the API call to get the final status of the completed discovery run.

The results array in the previous response body has an entry containing metadata for each asset that was discovered by the discovery run. In the previous code snippet, for brevity, only 2 of the 179 entries are shown. The metadata created by the discovery run includes:

name: in this case, the name of the DB2 table that was discovered
description: a description of the table as provided by DB2
tags: these are useful for searching. The discovered tag is one of the tags set for a discovered asset.
asset_type: the type of the asset that was created in the project

Each entry in the results array also contains an href field that points to the actual asset that was created by the discovery run.

There might be times in which you no longer have the ID of the metadata discovery run whose status you're interested in, and so might not be able to call the following API for the specific discovery run you're interested in (which requires that ID):

GET /v2/data_discoveries/dcb8a234ad5e438d904a4cdbe0ba70e2

The following example illustrates how to get the IDs of metadata discovery runs for the connection and catalog that were used in the previous call to create a discovery run:

API Request - Get information for discovery runs:

GET /v2/data_discoveries?offset=0&limit=1000&connection_id=f638398f-fcc7-4856-b78d-5c8efa5b9282&catalog_id=816882fa-dcda-46e1-8c6b-fa23c3cbad14

Note that the values of the query parameters connection_id and catalog_id correspond to the values for the identically named fields in the payload for the previous request to create a discovery run.

Notice also that you can use the offset and limit query parameters to focus on a particular subset of the full list of related discoveries.

The response payload will look like this:

{
    "resources": [
        {
            "metadata": {
                "id": "dcb8a234ad5e438d904a4cdbe0ba70e2",
                "invoked_by": "IBMid-50S...",
                "bss_account_id": "e348e...",
                "created_at": "2018-06-22T15:42:02.843Z",
                "started_at": "2018-06-22T15:42:06.167Z",
                "discovered_at": "2018-06-22T15:42:27.970Z",
                "processed_at": "2018-06-22T15:42:45.877Z",
                "finished_at": "2018-06-22T15:43:14.969Z",
                "ref_project_connection_id": "2526ed95-dedd-4904-bb31-c06d9cb1e105"
            },
            "entity": {
                "statistics": {
                    "discovered": 179,
                    "submit_succ": 179,
                    "create_succ": 179
                },
                "status": "COMPLETED",
                "connection_id": "f638398f-fcc7-4856-b78d-5c8efa5b9282",
                "catalog_id": "816882fa-dcda-46e1-8c6b-fa23c3cbad14",
                "project_id": "960f6aff-295f-4de1-a9d7-f3b6805b3590"
            }
        }
    ],
    "first": {
        "href": "http://localhost:9080/v2/data_discoveries?offset=0&limit=1000&connection_id=f638398f-fcc7-4856-b78d-5c8efa5b9282&catalog_id=816882fa-dcda-46e1-8c6b-fa23c3cbad14"
    },
    "next": {
        "href": "http://localhost:9080/v2/data_discoveries?offset=1000&limit=1000&connection_id=f638398f-fcc7-4856-b78d-5c8efa5b9282&catalog_id=816882fa-dcda-46e1-8c6b-fa23c3cbad14"
    },
    "limit": 1000,
    "offset": 0
}

Anything that is found because it matches the query criteria is returned in the resources array. In the previous response, there is only one entry and it corresponds to the discovery run which was created in the previous Create a metadata discovery run section.

There might be times when you want to stop a discovery run before it's completed. To do so, use the PATCH data_discoveries API. The following illustrates how to abort a discovery run (a different discovery run than the one used in the previous examples):

API Request - Abort a discovery run:

PATCH /v2/data_discoveries/09cbff0981f84c51be4b4d93becc17b0

The previous PATCH request requires the following request body to set the status of the discovery run to "ABORTED":

{
    "op": "replace",
    "path": "/entity/status",
    "value": "ABORTED"
}

The response payload will look like this:

{
    "metadata": {
        "id": "09cbff0981f84c51be4b4d93becc17b0",
        "invoked_by": "IBMid-50S...",
        "bss_account_id": "e348e...",
        "created_at": "2018-06-22T15:45:54.638Z",
        "started_at": "2018-06-22T15:45:56.202Z",
        "finished_at": "2018-06-22T15:46:02.274Z",
        "ref_project_connection_id": "2526ed95-dedd-4904-bb31-c06d9cb1e105"
    },
    "entity": {
        "statistics": {

        },
        "status": "ABORTED",
        "connection_id": "f638398f-fcc7-4856-b78d-5c8efa5b9282",
        "catalog_id": "816882fa-dcda-46e1-8c6b-fa23c3cbad14",
        "project_id": "960f6aff-295f-4de1-a9d7-f3b6805b3590"
    }
}

Notice in the previous response payload that the status has now been set to ABORTED.

Any assets discovered before the run was aborted will remain discovered. In the example, the abort occurred so quickly after the creation of the discovery run that no assets had been discovered, hence the statistics object is empty.

Lineage

Introduction

The lineage of an asset includes information about all events, and other assets, that have led to its current state and its further usage. Asset and Event are the two main entities that are part of the lineage data model. An asset can either be generated from or used in subsequent events. An event can be any of:

asset-generation-events
asset-modification-events
asset-usage-events.

Use the Lineage API to publish events on an asset or to query the lineage of an asset.

Publish a lineage event

The following example shows a sample lineage event that can be posted when a data set is published from a project to a catalog:

Request URL

POST /v2/lineage_events

Request Body

{
  "message_version": "v1",

  "user_id": "IAM-Id_of_User",
  "account_id": "e86f2b06b0b267d559e7c387ceefb089",

  "event_details": {
    "event_id": "sample-event1",
    "event_type": "DATASET_PUBLISHED",
    "event_category": [
      "additions"
    ],
    "event_time": "2018-04-03T14:01:08.603Z",
    "event_source_service": "Watson Knowledge Catalog"
  },

  "generates_assets": [
    {
      "id": "9f9c961a-78d1-4c06-a601-4b5890fdataset03",
      "asset_type": "DataSet",
      "relation": {
        "name": "Created"
      },

      "properties": {
        "dataset": {
          "type": "dataset",
          "value": {
            "id": "9f9c961a-78d1-4c06-a601-4b5890fdataset03",
            "name": "Asset Name in Catalog XX",
            "catalog_id": "9f9c961a-78d1-4c06-a601-4b589catalog"
          }
        },
        "catalog": {
          "type": "catalog",
          "value": {
            "id": "9f9c961a-78d1-4c06-a601-4b589catalog"
          }
        }
      }
    }
  ],
  "uses_assets": [
    {
      "id": "9f9c961a-78d1-4c06-a601-4b5890fdataset02",
      "asset_type": "DataSet",
      "relation": {
        "name": "Used"
      },

      "properties": {
        "dataset": {
          "type": "dataset",
          "value": {
            "id": "9f9c961a-78d1-4c06-a601-4b5890fdataset02",
            "name": "2017_sales_data",
            "project_id": "9f9c961a-78d1-4c06-a601-4b589project"
          }
        },
        "project": {
          "type": "project",
          "value": {
            "id": "9f9c961a-78d1-4c06-a601-4b589project"
          }
        }
      }
    }
  ]
}

Response Body

{
  "metadata": {
    "id": "01014d1f-31cf-4956-bd41-7a77ba14004c",
    "source_event_id": "sample-event1"
  }
}

The id generated in the response can be used to query the details of the published event with the following request:

Request URL

GET v2/lineage_events/01014d1f-31cf-4956-bd41-7a77ba14004c

For more details on each field in the lineage event JSON payload, refer to the Lineage Events section of API documentation.

Query lineage of an asset

The lineage of an asset involved in the sample event can be queried using the following request:

Request URL

GET v2/asset_lineages/9f9c961a-78d1-4c06-a601-4b5890fdataset03

Response Body

{
  "resources": [
    {
      "metadata": {
        "id": "01014d1f-31cf-4956-bd41-7a77ba14004c",
        "source_event_id": "sample-event1",
        "created_at": "2018-04-03T14:01:08.603Z",
        "created_by": "IAM-Id_of_User"
      },
      "entity": {
        "type": "DATASET_PUBLISHED",
        "generates_assets": [
          {
            "id": "9f9c961a-78d1-4c06-a601-4b5890fdataset03",
            "type": "DataSet",
            "relation": {
              "name": "Created"
            },
            "properties": {
              "catalog": {
                "type": "catalog",
                "value": {
                  "id": "9f9c961a-78d1-4c06-a601-4b589catalog"
                }
              },
              "dataset": {
                "type": "dataset",
                "value": {
                  "id": "9f9c961a-78d1-4c06-a601-4b5890fdataset03",
                  "name": "Asset Name in Catalog XX",
                  "catalog_id": "9f9c961a-78d1-4c06-a601-4b589catalog"
                }
              }
            }
          }
        ],
        "uses_assets": [
          {
            "id": "9f9c961a-78d1-4c06-a601-4b5890fdataset02",
            "type": "DataSet",
            "relation": {
              "name": "Used"
            },
            "properties": {
              "dataset": {
                "type": "dataset",
                "value": {
                  "id": "9f9c961a-78d1-4c06-a601-4b5890fdataset02",
                  "name": "2017_sales_data",
                  "project_id": "9f9c961a-78d1-4c06-a601-4b589project"
                }
              },
              "project": {
                "type": "project",
                "value": {
                  "id": "9f9c961a-78d1-4c06-a601-4b589project"
                }
              }
            }
          }
        ],
        "properties": {
          "event_time": "2018-04-03T14:01:08.603Z",
          "event_category": [
            "additions"
          ],
          "event_source_service": "Watson Knowledge Catalog"
        }
      }
    }
  ],
  "limit": 50,
  "offset": 0,
  "first": {
    "href": "https://api.dataplatform.cloud.ibm.com/v2/asset_lineages/9f9c961a-78d1-4c06-a601-4b5890fdataset03?offset=0&_=1528182675331"
  }
}

Searching

Searching in Global Search

The Global Search API comes in two flavours: Simple and Advanced.

Simple Query

The simple query can be invoked like this:

GET /v3/search?query='fred flintstone'&limit=100

With the simple query you can peform simple textual searches using the Lucene syntax. The above query will return items containing fred, flintstone, or both.

Advanced Query

You can use the Global Search api to issue queries using the full capabilities of the Elasticsearch Query Language to search for Catalog assets and Governance artifacts. For details on the structure of an item indexed in global search see below. The advanced query can look something like this:

POST /v3/search -d '
{
  "_source":["provider_type_id", "artifact_id", "metadata.name"],
  "query": {
    "query_string" : { "query" : "flintstone" }
  }
}'

The above query returns any items containg the string "flintstone".

Searching with authorization cache

Global search searches across the Cloud Pak for Data platform and restricts search results to content that a user is authorized to view. For faster search results, you can use cached authorization information by setting the auth_cache parameter to true. The auth_cache parameter is set to false by default to use the most current authorization information.

A simple query using cached authorization information:

GET /v3/search?query='fred flintstone'&limit=100&auth_cache=true

An advanced query using cached authorization information:

POST /v3/search?auth_cache=true -d '
{
  "_source":["provider_type_id", "artifact_id", "metadata.name"],
  "query": {
    "query_string" : { "query" : "flintstone" }
  }
}'

Searching with limited authorization scope

Global Search searches across the Cloud Pak for Data platform. For a faster search, you can limit your search scope to certain platform components using the auth_scope parameter. For example, to limit the scope of your search to assets within catalogs, you can use auth_scope=catalog, or to limit your search to assets within projects, you can use auth_scope=project. Valid values for the auth_scope parameter are catalog, project, space, category, ibm_watsonx_governance_catalog, ibm_data_product_catalog and all (default). Note that only a single value can be provided.

A simple query limiting the scope of the search to catalog only:

GET /v3/search?query='fred flintstone'&limit=100&auth_scope=catalog

An advanced query limiting the scope of the search to catalogs only:

POST /v3/search?auth_scope=catalog -d '
{
  "_source":["provider_type_id", "artifact_id", "metadata.name"],
  "query": {
    "query_string" : { "query" : "flintstone" }
  }
}'

### Searching for terms in specific fields
The above query searched for the word `flintstone` anywhere in an indexed artifact.  You can specify which fields to search in, instead of searching throughout the document using the following example:

```json
{
  "_source":["provider_type_id", "artifact_id", "metadata.name"],
  "query": {
    "match" : { "metadata.name" : "flintstone"  }
  }
}

In the above example, the query is searching for the term flintstone but only in the metadata.name field.

Key-Value Search

Use key-value pairs to restrict search within specific properties such as the name, description, tags, column names, terms, custom properties and more

key:value : This matches value in the key property.
key:"value here" : This matches value here in the key property. Quoted value is treated as a whole phrase.
key1:value1 key2:value2 key3:value3 : Multiple key value pairs implies an AND between all pairs. The above matches (value1 in the key1 property) AND (value2 in the key2 property) AND (value3 in the key3 property).
text before key1:value1 in-between key2:value2 key3:value3 after: Key-value pairs mixed with regular strings. The above matches (text OR before OR in-between OR after in any property) AND (value1 in the key1 property) AND (value2 in the key2 property) AND (value3 in the key3 property)

The following properties can be specified as the key.

name:           Search within the name of an asset or artifact
desc:           Search within the description of an asset or artifact
type:           Search by the type of an asset (asset_type) or artifact (artifact_type)
owner:          Search by the user ID of the owner of an asset
term:           Search in assets and artifacts with the specified business term assigned
tag:            Search in assets and artifacts with the specified tag
category:       Search for artifacts with the specified primary category
category2:      Search for artifacts with the specified secondary category
abbr:           Search by the abbreviation of a business term
syn:            Search by the synonym of a business term
classification: Search by the classification of an asset or artifact
column:         Search with the name of a column in a data asset
columnDesc:     Search within the description of a column in a data asset
columnTerm:     Search with a business term assigned to a column in a data asset
columnTag:      Search with a tag on a column in a data asset
columnDataclass:Search with a data class of a column in a data asset
columnClassification: Search with a classification on a column in a data asset
connection:     Search with a connection path of an asset
schema:         Search for data assets with the specified schema name
table:          Search for data assets with the specified table name
resourceKey:    Search with a resource key of an asset
steward:        Search by the user ID of the steward of an artifact

Global Search searchable custom attribute names can also be used as a key to restrict search to the specified custom attribute.

Restrictions for keys:

For key-value search by type:, the value must be an asset_type such as data_asset (as referenced by GET /v2/asset_types) or artifact_type such as data_class (as referenced by GET /v3/governance_artifact_types). The value is case-sensitive.

For key-value search by owner: or steward:, the value must be the user ID and not the user name or display name. The value is case-sensitive.

Sample query to restrict search to few properties

{
    "query":{
        "bool":{
            "must":[
                {
                    "gs_user_query":{
                        "search_string":" name:job tag:Toronto",
                        "nlq_analyzer_enabled": true}
                }
            ]
        }
    }
}

In this sample query, we want to query on any asset or artifact having job in the name property AND Toronto in the tags property.

Sample query to restrict search to a custom attribute

{
    "query":{
        "bool":{
	    “must”:[
               "nested": {
                  "path": "custom_attributes",
                  "query": {
                      "gs_user_query":{
                        “search_string” : "book.author.last_name:Smith”,
                        "nlq_analyzer_enabled": true,
                        "nested": true
                       }
                   }
              }
           ]
      }
}

In this sample query, we want to query on assets of type book, having a Global Search searchable ("global_search_searchable") field called author.last_name with a value of Smith.

Sorting your queries

By default, Global Search will sort the search results based on their search score. The more relevant an item is to your search terms, the higher its score. You can override that by sorting your search results based on fields in the indexed item.

Sample Query with Sort

{
  "_source":["provider_type_id", "artifact_id", "metadata.name"],
  "query": {
    "query_string" : {
      "query" : "flintstone"
    }
  },
  "sort": [
      {"metadata.modified_on": {"order": "desc","unmapped_type": "date"}}
   ]

}

The above query will sort the search results based on the date the item was modified.

Getting Counts of Things

In addition to querying, you can use the Query Language to get counts of terms that occur in indexed items. For example you might want the top ten words that people add to the tags field of their assets or artifacts.

Sample Query with Aggregation

Here is a sample query of a search for the word flintstone with an aggregation (a count) of the words that people put in their tags fields and their terms fields. See the below for the fields that exist documents indexed in Global Search.

{
  "query": {
    "query_string" : {
      "query" : "flintstone"
    }
  },
  "aggregations" : {
    "num_tags" : {"terms" : { "field" : "metadata.tags.keyword" }},
    "num_terms" : {"terms" : { "field" : "metadata.terms.keyword" }}
  }
}

Nested Queries and Custom Attributes

You can add any number of custom attributes to an item you index with Global Search, and each custom attribute consists of combinations of a name field, and a value field.

      "custom_attributes": [
        {
          "last_updated_at": 0,
          "attribute_name": "string",
          "attribute_value": "string"
        }
      ]

Because custom attributes normally consist of two fields acting as one, they are nested objects and you must use nested queries to query on those nested objects.

The custom_attributes fields will be included in the response for any result which has custom_attributes. Nested queries are only required to query properties of the custom_attributes, such as custom_attributes.attribute_name or custom_attributes.attribute_value.

Sample Nested Query

In this sample query, we want to query on any asset having a custom attribute named city having a value of ottawa, and a second custom attribute named colour having a value red. In this example, the city attribute is treated as a text field, while the colour attribute will simulate an enumerated list of colours having exact values (i.e. red, blue, green, etc).

{
  "_source":["metadata.name", "custom_attributes"],
  "query": {
    "bool": {
      "must": [
        {
          "nested": {
            "path": "custom_attributes",
            "query": {
              "bool": {
                "must": [
                  {"term": {"custom_attributes.attribute_name": "city"}},
                  {"match": {"custom_attributes.attribute_value": "Ottawa"}}
                ]
              }
            }
          }
        },
        {
          "nested": {
            "path": "custom_attributes",
            "query": {
              "bool": {
                "must": [
                  {"term": {"custom_attributes.attribute_name": "colour"}},
                  {"term": {"custom_attributes.attribute_value.keyword": "red"}}
                ]
              }
            }
          }
        }
      ]
    }
  },
  "aggs": {
    "custom_attr_count": {
      "nested": {
        "path": "custom_attributes"
      },
      "aggs": {
        "city_count": {
          "filter": {
            "term": {"custom_attributes.attribute_name": "city"}
          },
          "aggs": {
            "city_count": {
              "terms": {
                "field": "custom_attributes.attribute_value.keyword",
                "size": 20
              }
            }
          }
        }
      }
    }
  }
}

In the query body illustrated above, there's a query portion, and an aggregations (aggs) portion. There can be any number of custom attributes. Because we only want counts of city we must include a filter in the aggregation so that only attributes whose name is city are counted. Notice that the count is returned for custom_attribute.attribute_value.keyword, not custom_attribute.attribute_value. This is important to note. You cannot sort or aggregate on text fields. You can only do so on keyword fields. Every text field in global search has a corresponding keyword field with a .keyword extension. Use the .keyword field for things you want to count or sort on. Finally, the size parameter restricts the number of counts to return to the top 20.

Caution The key-value search is not supported inside a nested clause.

General Purpose Search Function

Global Search provides a general purpose search function that is tailored to the requirements of CloudPak For Data users. You can invoke it using Global Search's Advanced API (see the Methods section below). It is this function that CloudPak for Data uses when a user enters a search term at the top search bar of the CloudPak for Data user interface. You can invoke it anywhere you would normally invoke a normal ElasticSearch search function. For example it can be the main function of your query:

{
    "query":{
        "gs_user_query":{
            "search_string":"The quick red fox jumped over the lazy brown dog"
         }
     }
}

The search_string field is required and specifies the search query string.

The following optional parameters can be specified as part of the gs_user_query:

search_fields - List of fields that the search will be restricted to. If not specified, the search will run across all fields in the configuration.
nlq_analyzer_enabled - Specify true to enable the natural language analyzer. The default value is false.
semantic_expansion_enabled - Specify true to enable semantic query expansion. The default value is false.
nested - Specify true to optimize nested queries. The default value is false.

{
    "query":{
        "gs_user_query":{
            "search_string": "The quick red fox jumped over the lazy brown dog",
            "search_fields": ["metadata.name", "metadata.description"],
            "nlq_analyzer_enabled": true,
            "semantic_expansion_enabled": true,
            "nested": false
         }
     }
}

This search function will find:

a single phrase
multiple individual words
partial words (within words or at the beginning of words)
the first letter of a word

If no search fields are specified, the function will search the entire document, including the name fields, the description field, tags, synonyms, custom attribute values, column names, and column descriptions, etc. It will give the highest priority to the name field of the document.

You can embed gs_user_query within a compound query:

{
    "query":{
        "bool":{
            "must":[
                {"gs_user_query":{"search_string": "the quick red fox jumped over the lazy brown dog"}}
            ],
            "filter":[
                {"term":{"provider_type_id":"cams"}}
            ]
        }
    },
    "sort": [
          {"metadata.modified_on": {"order": "desc","unmapped_type": "date"}}
    ]
}

You can include gs_user_query with complex queries that include aggregations along with sorts:

{
  "query": {
    "gs_user_query" : {
      "search_string": "fred flintstone"
    }
  },
   "sort" : [
      {"metadata.modified_on": {"order": "desc", "unmapped_type": "date"}}
   ],
  "aggregations": {
    "first_letter": {
      "terms": {
        "script": "doc['metadata.name.keyword'].getValue().substring(0,1)",
        "order": {
          "_key": "asc"
        }
      },
      "aggs": {
        "first_letter_group": {
          "terms": {
            "field": "metadata.name.keyword",
            "order": {
              "_key": "asc"
            }
          }
        }
      }
    }
  }
}

You can use gs_user_query in a nested query to search for custom attributes:

{
  "query": {
    "bool": {
      "should": [
        {
          "gs_user_query": {
            "search_string": "quick red fox",
            "nlq_analyzer_enabled": true
          }
        },
        {
          "nested": {
            "path": "custom_attributes",
            "query": {
              "gs_user_query": {
                "search_string": "lazy brown dog",
                "nlq_analyzer_enabled": true,
                "nested": true
              }
            }
          }
        }
      ]
   }
}

Searching for a quoted phrase

Wrap the phrase in quotes within your query as follows:

{
    "query":{
        "gs_user_query":{
            "search_string":"\"The quick red fox jumped over the lazy brown dog\""
         }
     }
}

The above query will search for exactly the phrase "The quick red fox jumped over the lazy brown dog".

A quoted phrase can also be included in a longer string:

{
    "query":{
        "gs_user_query":{
            "search_string":"The \"quick red fox\" jumped over the \"lazy brown dog\"",
            "nlq_analyzer_enabled": true
         }
     }
}

The above query will search for the phrases quick red fox and lazy brown dog, and will not return results containing only quick, red, fox, lazy, brown, or dog. The query will, however, return results matching the individual words jumped or over.

Searching for words starting with ...

To search for words starting with a letter or letters, enter only the first 1 to 3 letters of the word.

{
    "query":{
        "gs_user_query":{
            "search_string":"in"
         }
     }
}

The above query will return documents with words like infinite and invitation, but not words like definitive.

Searching for parts of words

If your search terms include more than three letters, then Global Search will search for any partial word matches. For example

{
    "query":{
        "gs_user_query":{
            "search_string":"init"
         }
     }
}

The above query will find documents with words like initialize (i.e. at the beginning of the word) and trinitoluene (i.e. within the word). Partial matches will not be found on the metadata.description or entity.assets.column_description fields.

Searching with natural language

Natural language analysis can be applied to English search strings to optimize search results in the following ways:

Words that are not important to the search intent are removed from the search query.
Phrases in the search string that are common in English are automatically ranked higher than results for individual words.

{
    "query": {
        "gs_user_query": {
            "search_string": "credit card interest in United States",
            "nlq_analyzer_enabled": true
        }
    }
}

The above query will find documents with:

Matches for the phrases credit card interest and United States ranked highest
Matches for individual words credit, card, interest, United, and States ranked lower

The above query will not return documents containing only the word in.

Result scoring

Results are prioritized using a combination of field priority and type of match as follows:

Type of match:

Matches for entire phrases will score highest.
Exact matches of complete words will score next highest.
If the search term is 3 characters or less results that contain words STARTING with that search term will score next highest.
Partial matches of complete words will score next highest.
Fuzzy matches (i.e. adidas vs adadas) will match, but will score lowest.

Field:

Name
Synonyms, Abbreviation, Terms, Tags or Classifications
Description, Primary or Secondary Category
Column Descriptions, Column Terms, Column Tags or Column Data Class Names

Documents in Global Search

You can query on any of the fields within the document by including the field name in a flattened json structure. For example the field:

{
    "entity":{
    	"artifacts":{
	    "artifact_id":"<id>"
	}
    }
}

is queried for by using the following

entity.artifacts.artifact_id

Documents indexed in global search have the following structure:

{
  "provider_type_id": "string",
  "tenant_id": "string",
  "artifact_id": "string",
  "last_updated_at": 0,
  "metadata": {
    "name": "string",
    "description": "string",
    "artifact_type": "string",
    "tags": [
      "string"
    ],
    "modified_on": "2021-02-11T11:25:59.384Z",
    "modified_by": "string",
    "terms": [
      "string"
    ],
    "term_global_ids": [
      "string"
    ],
    "steward_ids": [
      "string"
    ],
    "steward_group_ids": [
      "string"
    ]
    "state": "string",
    "classifications": [
      "string"
    ],
    "classification_global_ids": [
      "string"
    ]
  },
  "entity": {
    "artifacts": {
      "global_id": "string",
      "version_id": "string",
      "artifact_id": "string",
      "rule_type": "string",
      "effective_start_date": "2021-02-11T11:25:59.384Z",
      "effective_end_date": "2021-02-11T11:25:59.384Z",
      "abbreviation": [
        "string"
      ],
      "synonyms": [
        "string"
      ],
      "synonym_global_ids": [
        "string"
      ],
      "enabled": true
    },
    "assets": {
      "asset_id": "string",
      "asset_name": "string",
      "attribute_quality_score": 0,
      "catalog_id": "string",
      "project_id": "string",
      "space_id": "string",
      "column_names": [
        "string"
      ],
      "column_terms": [
        "string"
      ],
      "column_term_global_ids": [
        "string"
      ],
      "column_descriptions": [
        "string"
      ],
      "connection_paths": [
        "string"
      ],
      "column_tags": [
        "string"
      ],
      "connection_ids": [
        "string"
      ],
      "column_data_class_names": [
        "string"
      ],
      "data_quality_score": 0,
      "data_quality_score_dbl": 0,
      "data_quality_delta": 0,
      "data_quality_delta_dbl": 0,
      "metadata_enrichment_area_id": "string",
      "metadata_enrichment_review_date": 0,
      "resource_key": "string",
      "metadata_enrichment_area_id": "string",
      "asset_id": "string",
      "suggested_column_term_global_ids": [
          "string"
      ],
      "suggested_column_terms": [
          "string"
      ],
      "schema_name": "string",
      "table_name": "string",
      "structured_profiling_completed_date": "2021-02-11T11:25:59.384Z",
      "structured_profiling_status": "string",
      "term_assignment_completed_date": "2021-02-11T11:25:59.384Z",
      "metadata_import_id": "string",
      "first_imported_timestamp": "2021-02-11T11:25:59.384Z",
      "last_imported_timestamp": "2021-02-11T11:25:59.384Z",
      "outdated_timestamp": "2021-02-11T11:25:59.384Z",
      "last_discovered_timestamp": "2021-02-11T11:25:59.384Z",
      "outdated_reason": "string"
    },
    "data_asset_column": {
      "data_class_name": "string",
      "data_class_id": "string",
      "data_class_set_by_user": true,
      "data_quality_score": 0,
      "data_quality_score_dbl": 0,
      "metadata_enrichment_review_date": "2021-02-11T11:25:59.384Z",
      "data_quality_delta": 0,
      "data_quality_delta_dbl": 0,
      "column_order": 0,
      "suggested_class_ids": ["string", "string", "string"],
      "suggested_class_names": ["string", "string", "string"]
    },
    "ref_data_value": {
      "value": "string",
      "value_long": 0,
      "value_dbl": 0,
      "ref_data_set_artifact_id": "string",
      "ref_data_set_version_id": "string",
      "parent_ref_data_value_artifact_id": "string"
    }
  },
  "custom_attributes": [
    {
      "last_updated_at": 0,
      "attribute_name": "string",
      "attribute_value": "string"
    }
  ],
  "categories": [
    {
      "last_updated_at": 0,
      "primary_category_id": "string",
      "primary_category_global_id": "string",
      "primary_category_name": "string",
      "secondary_category_ids": [
        "string"
      ],
      "secondary_category_global_ids": [
        "string"
      ],
      "secondary_category_names": [
        "string"
      ]
    }
  ]
}

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