oleander-spark-submit

name	oleander-spark-submit
description	How to submit, monitor, and configure Spark jobs on oleander using the CLI & TypeScript SDK.

oleander Spark Submit

Use this skill when submitting Spark jobs to oleander, monitoring run state, or building automated workflows that execute Spark jobs.

CLI submission

Upload a script and submit it from the terminal:

oleander spark jobs submit my_job.py \
  --namespace my_namespace \
  --name my-job-name \
  --wait

With arguments passed to the script:

oleander spark jobs submit my_job.py \
  --namespace my_namespace \
  --name my-job-name \
  --wait \
  --args "--input-table oleander.default.sf_311 --output-catalog oleander.results"

--wait blocks until the run reaches a terminal state (COMPLETE, FAIL, or ABORT).

TypeScript SDK submission

Use @oleanderhq/sdk to submit jobs programmatically:

import { Oleander, RunNotFoundError } from "@oleanderhq/sdk";

const oleander = new Oleander(); // reads OLEANDER_API_KEY from env

const { runId } = await oleander.submitSparkJob({
  cluster: "oleander",
  namespace: "my_namespace",
  name: "my-job-name",
  entrypoint: "my_job.py",
});

Submit and wait for completion with built-in polling:

const result = await oleander.submitSparkJobAndWait({
  cluster: "oleander",
  namespace: "my_namespace",
  name: "my-job-name",
  entrypoint: "my_job.py",
});

Polling for run state

When not using submitSparkJobAndWait, poll manually with getRun:

import { Oleander, RunNotFoundError } from "@oleanderhq/sdk";

const oleander = new Oleander();
const { runId } = await oleander.submitSparkJob({ ... });

const started = Date.now();
const timeoutMs = 900_000; // 15 minutes
let notFoundRetries = 10;

while (Date.now() - started < timeoutMs) {
  await wait.for({ seconds: 60 });
  try {
    const run = await oleander.getRun(runId);
    const state = run.state ?? "";
    if (state === "COMPLETE" || state === "FAIL" || state === "ABORT") {
      return { runId, state, run };
    }
  } catch (error) {
    if (error instanceof RunNotFoundError) {
      notFoundRetries--;
      if (notFoundRetries <= 0) throw error;
      continue; // run may not be visible yet; retry
    }
    throw error;
  }
}

throw  ();

Always handle RunNotFoundError — a freshly submitted run may not be immediately visible.

Cluster options

Cluster	Use case
"oleander"	Default managed Spark cluster
"emr-serverless"	AWS EMR Serverless
"glue"	AWS Glue

Use "oleander" unless you have a specific reason to use a different backend.

Machine types

Machine types follow the pattern spark.<size>.<class>:

• Classes: b (balanced), c (compute), m (memory)
• Sizes: 1 through 16
• Default: spark.1.b for both driver and executors

Choose memory-optimized (m) when joins or aggregations spill. Choose compute-optimized (c) for CPU-bound transformations.

Executor count

• Default: 2 executors
• Range: 1–20
• Scale up for large data volumes; keep low for small or exploratory jobs

Script structure conventions

• Accept table names and output destinations as argparse arguments, not hardcoded.
• Validate required env vars at startup and fail fast with a clear error message.
• Return a non-zero exit code on failure (sys.exit(2)), not just a log message.
• Print a structured JSON summary at the end for observability.