gcp-spark

name	gcp-spark
description	Develops and executes Spark code on Dataproc Clusters and Serverless. Reads and writes data using BigLake Iceberg catalogs, BigQuery and Spanner. Debugs execution failures. Use when: - Writing Spark ETL pipelines on GCP. - Training or running inference with ML models with spark on GCP. - Managing Spark clusters, jobs, batches, and interactive sessions. Don't use when: - Writing generic Python scripts that don't use Spark. - Performing simple SQL queries that can be done directly in BigQuery.
license	Apache-2.0
metadata	{"version":"v7","publisher":"google"}

Spark on Dataproc

[!IMPORTANT]

You MUST ALWAYS follow the Task Execution Workflow when writing spark code.

Task Execution Workflow

1. Understand schemas: ALWAYS use @skill:discovering-gcp-data-assets skill or references/schema_direct_inspection.md to understand input and output schemas. Include the schema in your thought process BEFORE generating any code. Do NOT guess column names. Unless explicitly specified, assume that the assets are located in the same project. Avoid scanning for assets across other projects as it can take a long time. If an expected dataset or table does not exist, use @skill:discovering-gcp-data-assets to discover all similar tables in the namespace or project.

   MINOR TYPO RULE: If there is a minor typo (e.g. employees vs employee), you can fix the error and proceed.

   STRICT HALT RULE: If the discovered table names differ from the requested table by more than a minor typo (e.g. completely different words, prefixes, or suffixes), you must IMMEDIATELY report the missing table and a neutral list of all available alternatives in the same namespace to the user without making any recommendations. You MUST ask the user which alternative to use and then STOP EXECUTING your turn. Do NOT write any Spark code or notebooks. Do NOT proceed with code generation, do NOT add fallback logic to code, and do NOT automatically substitute any alternative table (even if its schema seems to match) without explicit user permission.

2. Verify source accessibility: verify access/existence using gcloud storage ls gs://<path-to-dataset>. If accessing or reading a GCS path fails with a storage error e.g., permission errors like 403 Forbidden/Forbidden/PermissionDenied, or location errors like 404 Not Found/NotFound/FileNotFoundException you should report the error immediately. Either (1) ask the user what to do next, or (2) if asked to execute a notebook, save the notebook with the error output and recommend next steps to resolve the issue. Do NOT scan all buckets for alternative fallback datasets when encountering GCS errors.

3. Generate spark code:

   • Output Format: ALWAYS generate code in Python Notebooks (.ipynb) format. Generate scripts (.py) only if explicitly requested.
   • Read and Write data: ALWAYS Refer to references/read_write_data.md when reading or writing data.
   • ML Tasks: Refer to @skill:ml-best-practices skill and references/ml_tasks.md when generating ML code.
   • Spark Optimizations: ALWAYS refer to references/spark_optimizations.md when generating spark code and apply optimization whenever applicable.

4. Verify schema before write: ALWAYS verify that the dataframe and destination schema match, use df.printSchema() for dataframe schema and refer to @skill:discovering-gcp-data-assets skill or references/schema_direct_inspection.md to verify destination schema.

5. Compile code before executing: For notebooks convert them to python script using jupyter nbconvert --to script your-notebook.ipynb first. Then compile the resulting python script using python3 -m py_compile your-script.py. The same can be done for pyspark source code.

6. Execute script: When requested to run a job, script, session refer to references/gcloud_dataproc.md on how to execute generated code on Managed Spark. This DOES NOT apply when generating notebooks.

---

Common Mistakes Checklist

[!CAUTION]

Ensure you verify this checklist to avoid mistakes

Before submitting a job, verify:

• All imports present (col, when, lit, etc. from pyspark.sql.functions)
• vector_to_array from correct module use from pyspark.ml.functions import vector_to_array (NOT pyspark.sql.functions)
• DataFrame schema matches target Iceberg table verify with df.printSchema() before writing
• CSV files read with header and inferSchema without these, the header row becomes data and all columns are strings
• Driver memory safety (toPandas() / collect()) NEVER call .toPandas() or .collect() on raw or un-aggregated DataFrames. ALWAYS perform transformations, aggregations (groupBy().agg()), or data reduction (limit(), sample()) in Spark before converting small summaries to Pandas for plotting or display.
• No inline pip install in Spark jobs: NEVER run pip install or subprocess package installations inside PySpark scripts. Pass dependencies using --properties=spark.jars.packages=..., --archives=gs://.../env.tar.gz#environment, --py-files, or a custom --container-image.

---

IAM Requirements

The Dataproc service account needs:

• roles/dataproc.worker: Job execution
• roles/biglake.admin: Iceberg table management
• roles/bigquery.jobUser: Query materialization
• roles/storage.objectUser: Read/write GCS
• roles/spanner.databaseUser: Spanner writes

---

Spark resource management

Refer to references/gcloud_dataproc.md for detailed guidelines on managing Spark clusters, jobs, batches, and interactive sessions.