---
name: watsonxdata-spk_cpp
title: Provisioning Apache Gluten accelerated Spark engine
description: ''
last-updated: 2026-02-11
---

> ## Documentation Index
> The table of contents for this documentation set is at https://cloud.ibm.com/docs/watsonxdata?format=markdown
> The index for all IBM Cloud docs is at: https://cloud.ibm.com/docs/llms.txt
> Use these files to discover more information as needed.

{:javascript: #javascript .ph data-hd-programlang='javascript'}
{:java: #java .ph data-hd-programlang='java'}
{:ruby: #ruby .ph data-hd-programlang='ruby'}
{:php: #php .ph data-hd-programlang='php'}
{:python: #python .ph data-hd-programlang='python'}
{:external: target="_blank" .external}
{:shortdesc: .shortdesc}
{:codeblock: .codeblock}
{:screen: .screen}
{:tip: .tip}
{:important: .important}
{:note: .note}
{:deprecated: .deprecated}
{:pre: .pre}
{:video: .video}

# Provisioning Apache Gluten accelerated Spark engine
{: #prov_cpp}

Apache Gluten accelerated Spark engine is an optimized, high-performance engine in watsonx.data. The Spark engine uses Apache Gluten for offloading SQL execution to Velox, which is an open source execution engine(implemented in C++) thereby accelerating the computation of SparkSQL to reduce the cost for running the workloads.


## Provisioning Apache Gluten accelerated Spark engine
{: #prov_cpp_1}

**Applies to** :  [Apache Gluten accelerated Spark engine]{: tag-green}

IBM watsonx.data allows you to provision Apache Gluten accelerated Spark engine to run complex large-scale workloads. Apache Gluten delivers exceptional performance when run on large hardware.

You can use the following methods to provision Apache Gluten Accelerated Spark engine:

* Provisioning through Console
* Provisioning through API

### Prerequisites
{: #prov_cpp_preq}


- You must have a subscription of watsonx.data on Cloud.
- `<engine-home-bucket>` : You must create a storage in watsonx.data, that will be associated with your Apache Gluten Accelerated Spark engine to store the logs.


### Provisioning through Console
{: #prov_cpp_1}

To add Apache Gluten accelerated Spark engine, complete the following steps.

1. Log in to watsonx.data console.

2. From the navigation menu, select **Infrastructure manager**.

3. To add Apache Gluten accelerated Spark engine, click **Add component**, Click **IBM Spark** and click **Next**.

5. In the **Add component-IBM Spark** page, from the **Type** section, select **ApacheGluten accelerated Spark engine**.

6. In the **Add component - IBM Spark** page, configure the following details:

      a. In the **Add component - IBM Spark** window, enter the **Display name** for your Apache Gluten accelerated Spark engine.
      c. Configure the following details:

      | Field | Description |
      | --- | --- |
      | Default Spark version | Select the Spark runtime version that must be considered for processing the applications. |
      | Engine home bucket | Select the registered Cloud Object Storage bucket from the list to store the Spark events and logs that are generated while running spark applications. \n [Note]{: tag-purple} Make sure you do not select the IBM-managed bucket as Spark engine home. If you select an IBM-managed bucket, you cannot access it to view the logs. \n For more information, see [Before you begin](https://cloud.ibm.com/docs/watsonxdata?topic=watsonxdata-prov_nspark&format=markdown#prereq_nspark_prov).|
      |Reserve capacity (Conditional)| Specify this field if you are using a version earlier than 2.3 of watsonx.data. 1. Select the **Node Type**. \n 2. Enter the number of nodes in the **No of nodes** field.     |
      |Associated catalogs (optional)| Select the catalogs that must be associated with the engine.   |
      {: caption="Provisioning Apache Gluten accelerated Spark engine" caption-side="bottom"}

6. Click **Create**. The engine is provisioned and is displayed in the **Infrastructure Manager** page.


### Provisioning through API
{: #prov_cpp_2}

1. Use the following CURL command to create Apache Gluten Accelerated Spark engine.


#### V2 API
{: #prov_v2_2}


   ```bash
   curl -X POST https://`<region>`.lakehouse.cloud.ibm.com/lakehouse/api/v2/spark_engines   -H "content-type: application/json" -H "accept: application/json" -H "AuthInstanceId: `<CRN>`" -d {

       "description": "",

       "engine_details": {

           "default_version": "3.4",

           "scale_config": {

               "node_type": "small",

               "number_of_nodes": 1

           },

           "engine_home_bucket_name": "`<engine-home-bucket>`"

       },

       "engine_display_name": "`<Apache Gluten_engine_name>`",

       "associated_catalogs": [

           "<catalog_name>"

       ],

       "origin": "native",
       "type": "Gluten"

   }

      }
   ```
   {: codeblock}

   Parameter values:

   * `<region>`: The region where the watsonx.data instance is available.
   * `<CRN>`: The watsonx.data instance CRN. You can retrieve the CRN from the watsonx.data information page.
   * `<engine-home-bucket>` : The storage that enables you to monitor and debug the Spark application.
   * `<Apache Gluten_engine_name>`: Specify a name for the Apache Gluten accelerated Spark engine.
   * `<catalog_name>`: Specify a name for the catalog you use. Apache Gluten accelerated Spark supports Iceberg, Hudi, Delta, and Hive catalogs.



#### V3 API
{: #prov_v3}

   ```bash

   curl -X POST -H "content-type: application/json" -H "accept: application/json" -H "AuthInstanceId: {instance_id}" -d '{ "description": "spark engine description", "configuration": { "api_key": "apikey", "connection_string": "1.2.3.4", "instance_id": "spark-id", "managed_by": "fully/self" }, "display_name": "sampleEngine", "origin": "discover/external", "tags": [ "tag1", "tag2" ], "type": "spark" }' "https://{region}.lakehouse.cloud.ibm.com/lakehouse/api/v3/spark_engines"

   ```
   {: codeblock}





To submit Spark applications using Apache Gluten accelerated Spark engine, see [Submitting a Spark application](https://cloud.ibm.com/docs/watsonxdata?topic=group=submitting-spark-application-by-using-native-spark-engine&format=markdown).