Spark Jobs - oleander

`getSparkCluster({ name })`

Look up Spark cluster details before submitting a job. Use oleander for the built-in managed cluster or the name of a registered external cluster.

const cluster = await client.getSparkCluster({ name: "emr-prod" });
console.log(cluster.type, cluster.properties);

Return shape

Field	Type	Description
`name`	`string`	Cluster name
`type`	`string`	`oleander`, `emr-serverless`, or `glue`
`properties`	`unknown`	Cluster-specific configuration

`listSparkJobs(options?)`

List your uploaded Spark job artifacts with pagination.

const { artifacts, hasMore } = await client.listSparkJobs();
for (const artifact of artifacts) {
  console.log(artifact.name, artifact.entrypoint, artifact.status);
}

let offset = 0;
const all = [];
while (true) {
  const page = await client.listSparkJobs({ limit: 50, offset });
  all.push(...page.artifacts);
  if (!page.hasMore) break;
  offset += 50;
}

Parameters

number

default:"20"

Number of artifacts to return per page.

number

default:"0"

Number of artifacts to skip for pagination.

Return type: `ListSparkJobsResult`

Field	Type	Description
`artifacts`	`SparkArtifactSummary[]`	Artifacts for the current page
`hasMore`	`boolean`	Whether more artifacts are available

Each SparkArtifactSummary has:

Field	Type	Description
`id`	`string`	Artifact ID
`entrypoint`	`string`	Entrypoint script or class reference
`name`	`string`	Artifact name
`version`	`number`	Artifact version
`language`	`string`	Artifact language
`mainClass`	nullable string	Main class for JVM jobs
`pyFiles`	nullable string	Python dependencies archive
`virtualenv`	nullable string	Virtualenv archive
`status`	`string`	Artifact status
`createdAt`	nullable string	ISO timestamp when created
`updatedAt`	nullable string	ISO timestamp when last updated

`submitSparkJob(options)`

Submit a Spark job for execution. cluster defaults to oleander. For oleander-managed Spark, entrypoint is the uploaded script name. For external clusters, entrypoint is cluster-specific, such as an S3 URI for EMR Serverless or a Glue job name for Glue.

const { runId } = await client.submitSparkJob({
  namespace: "my-namespace",
  name: "daily-etl",
  entrypoint: "etl_pipeline.py",
  args: ["--date", "2026-03-11"],
  executorNumbers: 4,
});

const run = await client.getRun(runId);

Common options

Option	Type	Default	Description
`cluster`	`string`	`"oleander"`	Managed cluster or registered cluster name
`namespace`	`string`		Job namespace
`name`	`string`		Job name
`entrypoint`	`string`		Script name, S3 URI, or Glue job name depending on cluster
`args`	`string[]`	`[]`	Entrypoint arguments
`sparkConf`	`string[]`	`[]`	Spark configuration values
`packages`	`string[]`	`[]`	Extra package coordinates
`jobTags`	`string[]`	`[]`	Tags applied to the job
`runTags`	`string[]`	`[]`	Tags applied to this run

Oleander-managed options

Option	Type	Default	Description
`driverMachineType`	`SparkMachineType`	`spark.1.b`	Driver machine type
`executorMachineType`	`SparkMachineType`	`spark.1.b`	Executor machine type
`executorNumbers`	`number`	`2`	Number of executors, from 1 to 20

EMR Serverless options

Option	Type	Description
`pyFiles`	`string`	Zip archive of Python dependencies
`virtualenv`	`string`	Virtualenv archive for Python jobs
`mainClass`	`string`	Main class for JVM jobs
`executionIamPolicy`	`string`	IAM policy applied to execution

Glue options

Option	Type	Default	Description
`workerType`	`string`		Glue worker type
`numberOfWorkers`	`number`	`1`	Number of Glue workers
`enableAutoScaling`	`boolean`		Enable Glue auto scaling
`timeoutMinutes`	`number`		Timeout in minutes
`executionClass`	`string`		Glue execution class such as `STANDARD` or `FLEX`
`executionIamPolicy`	`string`		IAM policy applied to execution

For Glue jobs, args are converted into key-value pairs. Pass them as alternating entries such as ["--source", "s3://bucket/input", "--target", "s3://bucket/output"].

External cluster example

const { runId } = await client.submitSparkJob({
  cluster: "emr-prod",
  namespace: "finance",
  name: "daily-etl",
  entrypoint: "s3://my-bucket/jobs/etl_pipeline.py",
  args: ["--date", "2026-03-11"],
  pyFiles: "s3://my-bucket/jobs/deps.zip",
  packages: ["org.example:my-lib:1.0.0"],
});

Machine types

The SparkMachineType enum covers compute-optimized (c), balanced (b), and memory-optimized (m) options:

Type	vCPUs	Category
`spark.1.c` / `spark.1.b` / `spark.1.m`	1	Compute / Balanced / Memory
`spark.2.c` / `spark.2.b` / `spark.2.m`	2	Compute / Balanced / Memory
`spark.4.c` / `spark.4.b` / `spark.4.m`	4	Compute / Balanced / Memory
`spark.8.c` / `spark.8.b` / `spark.8.m`	8	Compute / Balanced / Memory
`spark.16.c` / `spark.16.b` / `spark.16.m`	16	Compute / Balanced / Memory

`submitSparkJobAndWait(options)`

Submit a Spark job and poll until it reaches a terminal state (COMPLETE, FAIL, or ABORT). Throws an error if the timeout is exceeded.

const { runId, state, run } = await client.submitSparkJobAndWait({
  namespace: "my-namespace",
  name: "daily-etl",
  entrypoint: "etl_pipeline.py",
  pollIntervalMs: 5000,
  timeoutMs: 300000,
});

if (state === "COMPLETE") {
  const elapsed = run.duration;
  // proceed with downstream work ...
} else {
  throw new Error(`Run ${runId} ended with state: ${state}`);
}

Accepts all submitSparkJob options plus:

number

default:"10000"

Milliseconds between status polls.

number

default:"600000"

Maximum time to wait in milliseconds before throwing a timeout error.

`getRun(runId)`

Get the current status of a run. Use this to poll a job submitted with submitSparkJob().

const run = await client.getRun(runId);

if (run.state === "COMPLETE") {
  const duration = run.duration;
  const jobName = run.job.name;
  // handle completion ...
} else if (run.state === "FAIL") {
  const error = run.error;
  // handle failure ...
}

Return type: `RunResponse`

Field	Type	Description
`id`	`string`	Run ID
`state`	nullable string	Current state
`started_at`	nullable string	ISO timestamp when the run started
`queued_at`	nullable string	ISO timestamp when the run was queued
`scheduled_at`	nullable string	ISO timestamp when the run was scheduled
`ended_at`	nullable string	ISO timestamp when the run ended
`duration`	nullable number	Run duration in seconds
`error`	unknown	Error details if the run failed
`tags`	array	Array of `{ key, value, source }` objects
`job`	object	Job info with `id`, `name`, `namespace`
`pipeline`	object	Pipeline info with `id`, `name`, `namespace`

​getSparkCluster({ name })

​Return shape

​listSparkJobs(options?)

​Parameters

​Return type: ListSparkJobsResult

​submitSparkJob(options)

​Common options

​Oleander-managed options

​EMR Serverless options

​Glue options

​External cluster example

​Machine types

​submitSparkJobAndWait(options)

​getRun(runId)

​Return type: RunResponse

`getSparkCluster({ name })`

Return shape

`listSparkJobs(options?)`

Parameters

Return type: `ListSparkJobsResult`

`submitSparkJob(options)`

Common options

Oleander-managed options

EMR Serverless options

Glue options

External cluster example

Machine types

`submitSparkJobAndWait(options)`

`getRun(runId)`

Return type: `RunResponse`