Submit Spark jobs on Snowflake (via Code Bundles)¶

Run your Spark applications as batch jobs on Snowflake. You submit a job, Snowflake runs it on Snowflake compute, and you get back an ID that you use to track, monitor, and cancel the run.

Overview¶

A Spark job runs your packaged Spark application (a .jar or .py on a stage) as a single batch run on warehouse compute and returns a job ID you use to monitor and manage the run.

This guide focuses on submitting a complete job in a single call: you pass the application, entrypoint, arguments, and specification (language, runtime version, and dependencies) inline with the request, so nothing has to be created in advance. You can make that call two ways:

• Submit a job with the REST API: synchronous, or asynchronous with asyncExec=true. Recommended for orchestrating from CI/CD systems and data pipelines such as Apache Airflow.
• Submit a job with SQL: a synchronous EXECUTE CODE BUNDLE statement that integrates natively with Snowflake tasks for scheduled Spark pipelines.

You can also persist a job definition as a named Code Bundle once and then rerun it by name from either the REST API or SQL.

Supported languages and runtimes¶

For accepted runtime version and language runtime values, see Job specification reference.

Prerequisites¶

Before you submit a Spark job, make sure you have:

• A warehouse to run the job on.
• A stage containing your packaged application (a fat JAR or .py file) and any dependencies. To develop and package an application, see Run Spark workloads on Snowflake and Spark application examples.

The role that submits the job (the session role for SQL, or the role in the X-Snowflake-Role header for REST) needs the following privileges:

• USAGE on the warehouse that runs the job.
• Read access to the stage that holds your application and dependencies.
• USAGE or OWNERSHIP on any external access integrations and artifact repositories referenced in the specification.
• READ or OWNERSHIP on any secrets referenced in the specification. (USAGE on a secret is not sufficient.)

To reuse a persisted job definition, also grant CREATE CODE BUNDLE on the schema and USAGE or OWNERSHIP on the stored Code Bundle:

GRANT CREATE CODE BUNDLE ON SCHEMA my_db.my_schema TO ROLE developer_role;
GRANT USAGE ON CODE BUNDLE my_db.my_schema.my_spark_job TO ROLE data_engineer_role;

Quickstart¶

This is the shortest path to running a packaged Spark application and checking its result. It uses SQL (EXECUTE CODE BUNDLE); to submit from an external orchestrator instead, see Submit a job with the REST API.

1. Package your application as a fat JAR (Scala/Java) or a .py file, and upload it to a stage. For a Scala or Java application, note the fully-qualified main class.

   Copy code

   -- Scala/Java
   PUT file://target/scala-2.12/my-app_2.12-1.0.0.jar @my_db.my_schema.my_stage/spark_jobs/ AUTO_COMPRESS=FALSE OVERWRITE=TRUE;
   
   -- Python
   PUT file://job.py @my_db.my_schema.my_stage/python_jobs/ AUTO_COMPRESS=FALSE OVERWRITE=TRUE;
   

2. Set your session warehouse and submit the job. The statement runs synchronously and returns when the job finishes; its query ID is the job ID.

   Copy code

   USE WAREHOUSE my_warehouse;
   
   EXECUTE CODE BUNDLE FROM '@my_db.my_schema.my_stage/spark_jobs/my-app_2.12-1.0.0.jar'
     ENTRYPOINT = 'com.example.MySparkApp'
     WITH SPECIFICATION $$
   bundle:
     type: spark
     compute_type: warehouse
     language: scala
     compute_options:
       language_version: "2.12"
   $$;
   

3. Check the run’s status and logs, by its job ID, in CODE_BUNDLE_HISTORY or the event table. See Monitor and manage jobs.

For all specification fields, see the Spark job specification reference.

Submit a job with the REST API¶

Submit a Spark job by posting the job definition to the Code Bundle executions endpoint. This is the recommended option for orchestrating Spark job pipelines from external orchestrators such as Apache Airflow and other clients such as CI/CD and custom UIs.

Endpoint¶

POST /api/v2/code-bundle-executions

By default the call is synchronous and returns when the run finishes. To submit asynchronously, set the asyncExec query parameter to true; the call returns immediately with a job ID that you poll for status.

POST /api/v2/code-bundle-executions?asyncExec=true

Request headers¶

Authenticate with any authentication method supported by the Snowflake REST API. Set the Authorization header, and for token-based methods the X-Snowflake-Authorization-Token-Type header:

Each request runs in a new session, so set the run’s context with the X-Snowflake-Warehouse, X-Snowflake-Database, X-Snowflake-Schema, and X-Snowflake-Role headers. If you omit a header, the value falls back to the authenticating user’s default (DEFAULT_WAREHOUSE for the warehouse, DEFAULT_NAMESPACE for the database and schema). A Spark job needs a warehouse, database, and schema to start, so make sure each is supplied either by a header or by a user default.

The following example authenticates with a key-pair JWT and supplies the session context in headers:

curl -X POST \
  "https://<account_identifier>.snowflakecomputing.com/api/v2/code-bundle-executions?asyncExec=true&requestId=5f3f8b16-1d2c-4b9a-9c2a-7c0d2a3b4c5d" \
  -H "Authorization: Bearer ${SNOWFLAKE_TOKEN}" \
  -H "X-Snowflake-Authorization-Token-Type: KEYPAIR_JWT" \
  -H "Content-Type: application/json" \
  -H "Accept: application/json" \
  -H "User-Agent: myApplicationName/1.0" \
  -H "X-Snowflake-Role: MY_ROLE" \
  -H "X-Snowflake-Warehouse: MY_SUBMIT_WAREHOUSE" \
  -H "X-Snowflake-Database: MY_DB" \
  -H "X-Snowflake-Schema: MY_SCHEMA" \
  -d @request-body.json

Idempotent submission¶

requestId is an optional query parameter (a client-generated UUID) that makes submission idempotent. If a submit call times out or you retry with the same requestId, Snowflake doesn’t start a duplicate run, which prevents orchestrator retries (Airflow, CI/CD) from starting the same batch twice. Idempotency is keyed only on requestId: the request body isn’t compared, so a retry that reuses a requestId with a different body is still treated as a duplicate and starts no new run.

Request body¶

The body contains the stage location of your application (from_location), the entrypoint, an optional arguments array, an optional execution_name, and the job specification: a JSON bundle object with the same structure and fields as the YAML you use in the SQL WITH SPECIFICATION clause. In the REST API, pass the specification as a JSON object, not a string. See Job specification reference for the fields.

The optional execution_name assigns a name to the run, the same as the SQL EXECUTION_NAME parameter: Snowflake stores it in CODE_BUNDLE_HISTORY and renders it into the server-side EXECUTE CODE BUNDLE statement. If you omit it, the run’s EXECUTION_NAME is empty.

Scala/Java:

{
  "from_location": "@my_db.my_schema.my_stage/spark_jobs/job.jar",
  "entrypoint": "com.example.MySparkApp",
  "arguments": ["--input", "@my_db.my_schema.my_stage/input.csv", "--partitions", "10"],
  "execution_name": "my_spark_application",
  "specification": {
    "bundle": {
      "type": "spark",
      "compute_type": "warehouse",
      "language": "scala",
      "compute_options": { "language_version": "2.12" },
      "properties": {
        "java_dependencies": {
          "jars": ["@my_db.my_schema.my_stage/source/jars/library1.jar"]
        }
      }
    }
  }
}

Python:

{
  "from_location": "@my_db.my_schema.my_stage/python_jobs/job.py",
  "entrypoint": "job.py",
  "arguments": ["--input", "@my_db.my_schema.my_stage/input.csv", "--partitions", "10"],
  "execution_name": "my_spark_application",
  "specification": {
    "bundle": {
      "type": "spark",
      "compute_type": "warehouse",
      "language": "python",
      "compute_options": {},
      "properties": {
        "python_dependencies": {
          "requirements_files": ["@my_db.my_schema.my_stage/python_jobs/requirements.txt"]
        }
      }
    }
  }
}

Response¶

An asynchronous submission (asyncExec=true) returns 202 Accepted with a job ID in the body. Use the job ID to monitor and manage the run.

HTTP/1.1 202 Accepted
Content-Type: application/json
Location: /api/v2/results/01b1f2e0-0000-df4f-0000-00100006589e0

{
  "code": "392604",
  "message": "Request execution in progress. Use provided Location header or result handler id to perform query monitoring and management.",
  "result_handler": "01b1f2e0-0000-df4f-0000-00100006589e0",
  "job_id": "01b1f2e0-0000-df4f-0000-00100006589e"
}

Use job_id as the run identifier for status and cancel calls. The Location header points to a results path keyed by result_handler, which is the job_id with a trailing 0 appended, so it is not the same value as job_id.

Submit a job with SQL¶

Submit a Spark job with the EXECUTE CODE BUNDLE command. The command takes the entrypoint, arguments, and an optional execution name as SQL parameters; the rest of the job definition is passed inline as YAML in the WITH SPECIFICATION clause. The job inherits the role that runs the statement and runs on the warehouse set in the session. The statement is synchronous: it returns when the run finishes.

Set the session context¶

Set the warehouse, database, and schema before submitting. Your job runs on the session warehouse.

USE WAREHOUSE my_warehouse;
USE DATABASE my_db;
USE SCHEMA my_schema;

Submit a job¶

Define and submit the job in a single statement, with the application, entrypoint, and specification all passed inline.

Scala/Java:

EXECUTE CODE BUNDLE FROM '@my_db.my_schema.my_stage/spark_jobs/job.jar'
  ENTRYPOINT = 'com.example.MySparkApp'
  ARGUMENTS = ('--input', '@my_db.my_schema.my_stage/input.csv', '--partitions', '10')
  EXECUTION_NAME = 'my_spark_application'
  WITH SPECIFICATION $$
bundle:
  type: spark
  compute_type: warehouse
  language: scala
  compute_options:
    language_version: "2.12"
  properties:
    java_dependencies:
      jars:
        - '@my_db.my_schema.my_stage/source/jars/library1.jar'
        - '@my_db.my_schema.my_stage/source/jars/library2.jar'
$$;

Python:

EXECUTE CODE BUNDLE FROM '@my_db.my_schema.my_stage/python_jobs/job.py'
  ENTRYPOINT = 'job.py'
  ARGUMENTS = ('--input', '@my_db.my_schema.my_stage/input.csv', '--partitions', '10')
  EXECUTION_NAME = 'my_spark_application'
  WITH SPECIFICATION $$
bundle:
  type: spark
  compute_type: warehouse
  language: python
  compute_options: {}
  properties:
    python_dependencies:
      requirements_files:
        - '@my_db.my_schema.my_stage/python_jobs/requirements.txt'
$$;

Command parameters¶

The statement runs synchronously and returns when the run finishes. Each run is identified by its query ID. This is the same value the REST API returns as job_id and that you pass as the executionId to the status and cancel endpoints, so you can use it to monitor and manage the run.

Persist a job definition¶

If you would like to reuse a stored job definition, you can register it once and then submit it by name (for example, EXECUTE CODE BUNDLE my_spark_job ENTRYPOINT = 'com.example.MySparkApp'). The submission parameters are the same as for inline submission. For details, see Snowflake Code Bundles.

For a Scala or Java job, running a persisted bundle by name does not automatically add the JARs inside the bundle to the classpath. Declare the JAR that contains your main class (and any dependency JARs) under properties.java_dependencies.jars in the specification; otherwise the run fails to find the class. Python entrypoints run by name with no extra configuration.

Schedule with a task¶

Because you submit the job with a SQL statement, you can wrap it in a Snowflake task to run it on a schedule. Tasks let you build and orchestrate Spark data pipelines natively in Snowflake: you can chain jobs into dependency graphs and run them on a schedule, similar to how teams orchestrate Spark workloads with Airflow, but without operating a separate scheduler.

Examples¶

These examples show common Spark-specific fields in the job specification. Each snippet is the bundle object you pass inline: as YAML in the SQL WITH SPECIFICATION clause, or as the bundle object in the REST request body. For the full field list, see Spark job specification reference.

Pin the Snowpark Connect client version¶

By default (when you omit runtime_version), a job runs with the latest available Snowpark Connect for Spark client version (snowpark-connect). Because the latest version changes over time, pin a specific version when you need reproducible runs. Set compute_options.runtime_version; Snowflake installs snowpark-connect==<version> at job startup. The version you choose also determines the snowflake-snowpark-python version and the range of Python interpreter versions you can request with language_version.

bundle:
  type: spark
  compute_type: warehouse
  language: python
  compute_options:
    runtime_version: "1.42.0"

To always run on the latest client version, omit runtime_version (the compute_options block is still required):

bundle:
  type: spark
  compute_type: warehouse
  language: python
  compute_options: {}

Add dependencies¶

For Scala or Java, list dependency JARs on a stage; they’re added to the classpath automatically:

bundle:
  type: spark
  compute_type: warehouse
  language: scala
  compute_options:
    language_version: "2.12"
  properties:
    java_dependencies:
      jars:
        - '@my_db.my_schema.my_stage/jars/library1.jar'
        - '@my_db.my_schema.my_stage/jars/library2.jar'

For Python, install packages from PyPI or a requirements file:

bundle:
  type: spark
  compute_type: warehouse
  language: python
  compute_options: {}
  properties:
    python_dependencies:
      packages:
        - numpy==1.26.4
      requirements_files:
        - '@my_db.my_schema.my_stage/python_jobs/requirements.txt'

Use a customer-managed artifact repository¶

By default, Python packages are resolved from snowflake.snowpark.pypi_shared_repository. To resolve them from your own package index instead (for example, a self-hosted Sonatype Nexus), first create a PYPI artifact repository that points at your index. This ties together a secret for credentials, an API integration for network access, and the repository’s index URL:

CREATE OR REPLACE SECRET my_repo_secret
  TYPE = PASSWORD
  USERNAME = 'your_username'
  PASSWORD = 'your_password_or_token';

CREATE OR REPLACE API INTEGRATION my_repo_integration
  API_PROVIDER = ARTIFACT_REPOSITORY_API
  API_ALLOWED_PREFIXES = ('https://nexus.example.com')
  ALLOWED_AUTHENTICATION_SECRETS = (my_repo_secret)
  ENABLED = TRUE;

CREATE OR REPLACE ARTIFACT REPOSITORY my_db.my_schema.my_nexus_repo
  TYPE = PYPI
  API_INTEGRATION = my_repo_integration
  INDEX_URL = 'https://nexus.example.com/repository/pypi-proxy/simple/'
  AUTHENTICATION_SECRET = my_repo_secret;

Then reference the repository by its fully-qualified name in the specification:

bundle:
  type: spark
  compute_type: warehouse
  language: python
  compute_options: {}
  artifact_repositories:
    - my_db.my_schema.my_nexus_repo
  properties:
    python_dependencies:
      packages:
        - my-internal-package==1.2.3

Attach secrets and external access integrations¶

Attach Snowflake secrets and external access integrations your job needs (for example, to reach an external API). The submitting role needs USAGE or OWNERSHIP on the external access integration and READ or OWNERSHIP on the secret:

bundle:
  type: spark
  compute_type: warehouse
  language: python
  compute_options: {}
  secrets:
    - my_db.my_schema.my_secret
  external_access_integrations:
    - my_db.my_schema.my_eai

Set Spark configuration¶

Pass Spark configuration properties with spark_conf:

bundle:
  type: spark
  compute_type: warehouse
  language: scala
  compute_options:
    language_version: "2.12"
  properties:
    spark_conf:
      spark.sql.shuffle.partitions: "200"
      spark.sql.session.timeZone: "UTC"

Job specification reference¶

The job specification defines how your Spark job runs. In SQL it’s the YAML in the WITH SPECIFICATION clause; in the REST API it’s the bundle object in the request body. For the full list of fields, see Spark job specification reference.

Monitor and manage jobs¶

Every Spark job has one identifier, and it’s the same value everywhere you refer to the run. The query ID returned by EXECUTE CODE BUNDLE, the job_id returned by a REST submission, and the executionId you pass to the status and cancel endpoints (returned as query_id in the status response) are all the same ID (for example, 01c51743-c819-4261-0000-5349586311aa). Use it to check status, cancel the run, and find logs. You can monitor and manage a run with SQL or the REST API, regardless of how you submitted it.

Check status¶

Check a run’s status by its job ID, using SQL or the REST API.

Using SQL:

Look up the run in the CODE_BUNDLE_HISTORY table function. Filter on QUERY_ID (the job ID returned at submission):

SELECT
  CODE_BUNDLE_NAME, DATABASE_NAME, SCHEMA_NAME, ENTRYPOINT, STATUS,
  RUNTIME_STATUS_DETAILS, SQL_ERROR_CODE, ERROR_MESSAGE, BUNDLE_TYPE,
  COMPUTE_TYPE, LANGUAGE_TYPE, RUNTIME_NAME, START_TIME, END_TIME,
  QUERY_ID, EXECUTION_NAME
FROM TABLE(SNOWFLAKE.INFORMATION_SCHEMA.CODE_BUNDLE_HISTORY(BUNDLE_NAME => NULL))
WHERE QUERY_ID = '<job_id>';

The query returns a single row for the run. For example:

{
  "CODE_BUNDLE_NAME": null,
  "DATABASE_NAME": null,
  "SCHEMA_NAME": null,
  "ENTRYPOINT": "com.example.MySparkApp",
  "STATUS": "DONE",
  "RUNTIME_STATUS_DETAILS": "",
  "SQL_ERROR_CODE": "",
  "ERROR_MESSAGE": "",
  "BUNDLE_TYPE": "SPARK",
  "COMPUTE_TYPE": "WAREHOUSE",
  "LANGUAGE_TYPE": "SCALA",
  "RUNTIME_NAME": "MY_WAREHOUSE",
  "START_TIME": "2026-07-14 10:50:00+00:00",
  "END_TIME": "2026-07-14 10:50:39+00:00",
  "QUERY_ID": "01c51743-c819-4261-0000-5349586311aa",
  "EXECUTION_NAME": "my_spark_application"
}

For a job submitted inline from a stage, CODE_BUNDLE_NAME, DATABASE_NAME, and SCHEMA_NAME are null because there’s no stored job definition. STATUS shows the run’s state, such as DONE or FAILED; when a run fails, ERROR_MESSAGE carries the failure details.

If you assigned an execution_name when submitting (with SQL or REST), you can look the run up by that name instead of the job ID: pass EXECUTION_NAME => '<execution_name>' to CODE_BUNDLE_HISTORY, or filter on the EXECUTION_NAME column.

Using the REST API:

Get the status of a run by ID. The {executionId} is the job ID returned at submission. Pass the database and schema as headers:

GET /api/v2/code-bundle-executions/{executionId}
X-Snowflake-Database: MY_DB
X-Snowflake-Schema: MY_SCHEMA

The response is an array with a single execution record. status comes from the account query history. A run is still in progress while status is RUNNING, QUEUED, RESUMING_WAREHOUSE, or BLOCKED, and it has finished when status is DONE, FAILED, or CANCELLED:

[
  {
    "query_id": "01b1f2e0-0000-df4f-0000-00100006589e",
    "status": "DONE",
    "start_time": "2026-07-14T10:50:00Z",
    "end_time": "2026-07-14T10:50:39Z",
    "database_name": "MY_DB",
    "schema_name": "MY_SCHEMA",
    "runtime_name": "MY_WAREHOUSE"
  }
]

View logs in the event table¶

Application logs, metrics, and traces are written to the event table configured for the run’s session database (the one you set with USE DATABASE in SQL, or the X-Snowflake-Database header for REST submissions). This can be a different event table from the one configured at the account level. Each record is tagged with the job’s query ID in RESOURCE_ATTRIBUTES['snow.query.id']. Resolve the event table for your session database, then query it by the job ID (replace <job_id> with the ID returned at submission):

SHOW PARAMETERS LIKE 'EVENT_TABLE' IN DATABASE <session_db>;

SELECT TIMESTAMP, RECORD_TYPE, VALUE
FROM my_event_table
WHERE RESOURCE_ATTRIBUTES['snow.query.id'] = '<job_id>'
  AND RECORD_TYPE = 'LOG'
ORDER BY TIMESTAMP;

Drop the RECORD_TYPE filter to also see the METRIC and SPAN records emitted for the run.

View a failed job’s stack trace¶

To find why a job failed, query the same event table and filter to ERROR and FATAL severity. The results include the full stack trace, with source line numbers:

SELECT
  TIMESTAMP,
  RECORD['severity_text']::string AS severity,
  SCOPE['name']::string AS scope,
  VALUE::string AS body
FROM my_event_table
WHERE RESOURCE_ATTRIBUTES['snow.query.id'] = '<job_id>'
  AND RECORD_TYPE = 'LOG'
  AND RECORD['severity_text']::string IN ('ERROR', 'FATAL')
ORDER BY TIMESTAMP;

Snowflake also records structured exception attributes on these records, which you can select individually: RECORD_ATTRIBUTES['exception.type'], RECORD_ATTRIBUTES['exception.message'], and RECORD_ATTRIBUTES['exception.stacktrace'].

Spark Monitoring UI¶

You can browse your Spark runs in the Spark Monitoring UI in Snowsight. Sign in to Snowsight, then open the Spark run history at https://app.snowflake.com/<organization>/<account>/#/compute/history/spark (replace <organization> and <account> with your own). The page lists Spark runs over a selectable time range, such as the last 7 days. Each run appears as a single entry identified by the same job ID returned at submission, so you can set the time range and locate a run by its ID.

Cancel a job¶

Cancel a running job by its job ID, using SQL or the REST API.

Using SQL:

Cancel the run’s query by ID with SYSTEM$CANCEL_QUERY:

SELECT SYSTEM$CANCEL_QUERY('<job_id>');

Using the REST API:

POST /api/v2/code-bundle-executions/{executionId}:cancel

SQL reference¶

Spark jobs are submitted and managed with the CODE BUNDLE SQL commands. This section covers the commands as they apply to Spark jobs. For the complete command reference, including custom (non-Spark) bundles, see Snowflake Code Bundles.

EXECUTE CODE BUNDLE¶

Submits a Spark job and waits for it to finish. Submit inline from a stage, or run a persisted job definition by name. The statement’s query ID is the job ID you use to monitor and manage the run.

EXECUTE CODE BUNDLE { <name> | FROM '<stage_path>' }
    ENTRYPOINT = '<entrypoint>'
    [ ARGUMENTS = ( '<arg>' [ , '<arg>' ... ] ) ]
    [ EXECUTION_NAME = '<name>' ]
    [ WITH SPECIFICATION $$ <yaml_spec> $$ ];

Parameters:

Example:

EXECUTE CODE BUNDLE FROM '@my_db.my_schema.my_stage/spark_jobs/my-app_2.12-1.0.0.jar'
  ENTRYPOINT = 'com.example.MySparkApp'
  WITH SPECIFICATION $$
bundle:
  type: spark
  compute_type: warehouse
  language: scala
  compute_options:
    language_version: "2.12"
$$;

For more examples, see Submit a job with SQL.

Access control requirements¶

The submitting role needs the privileges described in Access control: USAGE on the warehouse, read access to the stage, READ or OWNERSHIP on any referenced secrets, and USAGE or OWNERSHIP on any referenced external access integrations and artifact repositories.

CREATE CODE BUNDLE¶

Persists a Spark job definition so you can run it by name with EXECUTE CODE BUNDLE. See Persist a job definition.

CREATE [ OR REPLACE ] CODE BUNDLE [ IF NOT EXISTS ] <name>
    FROM '<stage_path>'
    [ COMMENT = '<string>' ];

FROM must reference a stage directory (the bundle is created from the files under that path), not an individual file. This differs from EXECUTE CODE BUNDLE FROM '<stage_path>', which accepts a single .jar or .py file.

ALTER CODE BUNDLE¶

Adds a new version to a persisted Spark job definition.

ALTER CODE BUNDLE <name> ADD VERSION FROM '<stage_path>';

DESCRIBE CODE BUNDLE¶

Returns metadata about a persisted Code Bundle.

DESCRIBE CODE BUNDLE <name>;

SHOW CODE BUNDLES¶

Lists the Code Bundles in the current schema.

SHOW CODE BUNDLES;

DROP CODE BUNDLE¶

Removes a persisted Code Bundle.

DROP CODE BUNDLE [ IF EXISTS ] <name>;

You must have OWNERSHIP of the Code Bundle to drop it. A role without ownership (even ACCOUNTADMIN) can’t drop a bundle owned by another role; drop it as the owning role, or transfer ownership first.

CODE_BUNDLE_HISTORY (table function)¶

Returns run history. Look up a single Spark run by its job ID with QUERY_ID, or filter to Spark runs with BUNDLE_TYPES => 'spark'. See Check status for the columns returned.

SELECT * FROM TABLE(INFORMATION_SCHEMA.CODE_BUNDLE_HISTORY(
    QUERY_ID       => '<job_id>',
    BUNDLE_TYPES   => 'spark',
    LANGUAGE_TYPES => 'scala, java, python',
    STATUS         => '<status>',
    RESULT_LIMIT   => 100
));

For the full parameter list, see CODE_BUNDLE_HISTORY.

SYSTEM$CANCEL_QUERY¶

Cancels a running Spark job by its job ID. See Cancel a job.

SELECT SYSTEM$CANCEL_QUERY('<job_id>');