CORE-1562: ci: build the Spark test image from the official apache/spark image

[EladBarkay]

Fixes CORE-1562.

Summary

test-local (latest_official, spark) keeps timing out. The Spark Dockerfile downloads the ~300MB Spark 3.3.2 tarball from archive.apache.org on every run, and that mirror is heavily throttled: "Start Spark" takes 43–73 min. The Spark test step alone takes 16–43 min, so the job regularly exceeds timeout-minutes: 90 and is cancelled mid-test:

The job has exceeded the maximum execution time of 1h30m0s

Run	Start Spark	Job total	Result
37545978126	140s	47 min	success
37495061054	43 min	67 min	success
37485880475	53 min	91 min	timed out
37615558627	59 min	91 min	timed out
37595212528 (#1080)	73 min	90.5 min	timed out

This is the same fix as elementary#2384 for the CLI's e2e.

Changes

• Build on the official apache/spark:3.3.3 image and ADD only the jars Spark needs from Maven Central. The image includes none of them:

  • Delta (delta-core, delta-storage), loaded by spark-defaults.conf
  • the Postgres JDBC driver, for the Hive metastore
  • hadoop-aws and aws-java-sdk-bundle, for reading the seed CSVs from RustFS over s3a://. Without them, plain apache/spark:3.3.3 fails with ClassNotFoundException: Class org.apache.hadoop.fs.s3a.S3AFileSystem not found.

  RustFS and the S3 seeding are unchanged.

  Why hadoop-aws 3.3.2 and not 3.3.3: the version follows Hadoop, not Spark. Spark 3.3.3 is built against Hadoop 3.3.2: the image bundles hadoop-client-api-3.3.2.jar and hadoop-client-runtime-3.3.2.jar, but not the S3 connector. hadoop-aws is part of Hadoop and has to match those core jars, so 3.3.2 is the version master already used. aws-java-sdk-bundle 1.11.1026 is the SDK hadoop-aws 3.3.2 is built with. Moving these versions means moving Spark: the Spark 3.5.9 image bundles Hadoop 3.3.4, and the 4.2.0 image bundles Hadoop 3.5.0 (CORE-1561).

• Not apache/spark:v3.3.2: its JDK 11.0.16 crashes on the GitHub runners with JDK-8287073 (the same failure as actions/runner-images#13684). The 3.3.3 image ships JDK 11.0.20.

• Run as the image's spark user. The image creates Spark's default warehouse dir under its work-dir, so the volume mounted there inherits the spark ownership.

• Health-check the thrift server with a beeline select 1; nc isn't in the official image.

• Give the metastore a pg_isready healthcheck, and start Spark on service_healthy. That makes entrypoint.sh's wait loop redundant, so spark-submit is now the ENTRYPOINT.

Local results

Run against the new image with dbt-core 1.12.5 and dbt-spark 1.11.0 (PyHive):

• The thrift server is healthy 17–18s after docker compose up, as the spark user, and dbt debug -t spark connects.
• py.test -n4 --target spark on test_volume_anomalies, test_schema_changes, test_column_anomalies, test_dbt_artifacts/test_artifacts and test_cleanup_stale_test_tables, which seed through RustFS and write Delta tables: 69 passed, 8 skipped in 4 min.

The full Spark suite runs in this PR's CI.

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Summary by CodeRabbit

• Chores
  • Improved the Spark integration test environment’s startup checks, so services verify that required dependencies are ready before proceeding.
  • Updated the Spark test environment’s runtime and dependency setup. Removed the previous custom startup wait behavior; readiness is now checked through service health checks.

[linear]

CORE-1562

[github-actions]

👋 @EladBarkay
Thank you for raising your pull request.
Please make sure to add tests and document all user-facing changes.
You can do this by editing the docs files in the elementary repository.

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📝 Walkthrough

Walkthrough

The Spark integration test image now uses Apache Spark with configured dependency JARs and runs spark-submit directly. Compose checks metastore health and Thrift query readiness, and mounts the warehouse and configuration at /opt/spark paths.

Changes

Spark integration test setup

Layer / File(s)	Summary
Spark image runtime and dependencies integration_tests/docker/spark/Dockerfile, integration_tests/docker/spark/entrypoint.sh	The Dockerfile now uses apache/spark:3.3.3, downloads configurable dependencies into Spark’s JAR directory, creates spark-warehouse, and uses spark-submit as the entrypoint. The custom script that waited on configured hosts and ports was removed.
Compose service health checks integration_tests/docker-compose-spark.yml	spark-thrift waits for a healthy metastore and checks readiness by running select 1 through Beeline. The metastore uses pg_isready. Warehouse and configuration mounts now target /opt/spark paths, and WAIT_FOR was removed.

Priority: ⬆️ High

Estimated code review effort: 2 (Simple) | ~10 minutes

Change: Bug fix

Merge Risk: ⚪ Minimal · up to d2a59

No actionable regression from this change remains identified. The Spark image and readiness changes are mergeable after normal checks.

🚥 Pre-merge checks | ✅ 5

✅ Passed checks (5 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title clearly and concisely describes the main change: building the Spark test image from the official Apache Spark image. It matches the pull request objectives and changes.
Docstring Coverage	✅ Passed	No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check. Docstring coverage is scoped to functions touched by this diff. Analyzed 0 functions across 0…
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Inline comments:
Review comments at @integration_tests/docker-compose-spark.yml:
- Line 51: Update the PostgreSQL health check in the Compose test command to use
TCP at 127.0.0.1 and check the metastore database as dbt, so readiness waits for
the configured database.

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