gcp-spark

Managed Spark on Google Cloud

[!IMPORTANT]

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

Notebook Generation Environment Rules

[!CAUTION]

When the task is to generate a Spark notebook (as opposed to executing one), you MUST NOT modify your own local execution environment (i.e. do not run these commands with your shell or other tools). Specifically:

NEVER create a virtual environment (python -m venv, virtualenv, conda create, uv venv, poetry init/install, etc.).

NEVER install packages (pip install, %pip install, uv pip install, conda install, poetry add).

NEVER run environment discovery loops such as activating a venv, pip list, or probing for ipykernel / google-cloud-spark-connect as a precondition for writing the notebook.

Assume the user will select the appropriate kernel and dependencies after the notebook is generated.

Task Execution Workflow

Understand user request:

When asked to generate a new spark notebook you MUST clarify with the user which kernel type will be used: "Local Python" (Spark Connect) or "Remote Spark". If the user selects "Local Python", you MUST additionally clarify which dataprocSessionConfig should be used (scan for existing serverless session templates), and MUST initialize the session with ManagedSparkSession by adding the following cells to the notebook:

from google.cloud.dataproc_v1 import Session
# requires google-cloud-spark-connect pypi package for execution
from google.cloud.managed_spark_connect import ManagedSparkSession

session_config = Session()
session_config.session_template = (
    "projects/<PROJECT_ID>/locations/<REGION>/sessionTemplates/<TEMPLATE_ID>"
)
spark = (
    ManagedSparkSession.builder.projectId("<PROJECT_ID>")
    .location("<REGION>")
    .dataprocSessionConfig(session_config)
    .getOrCreate()
)

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. Cap GCP data asset discovery attempts at 3 retries max. 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.
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.
Generate spark code:
- Output Format: ALWAYS generate code in Python Notebooks (.ipynb) format. Generate scripts (.py) only if explicitly requested.
- Spark Session Initialization:
  
  [!IMPORTANT] Initializing a Spark session on Google Cloud can take 2-3 minutes. You MUST inform the user about the potential delay.
  
  [!CAUTION] NEVER create a local Spark session. The following are BANNED: - SparkSession.builder.master("local") - pyspark.sql.SparkSession.builder.getOrCreate() when used without ManagedSparkSession - Any try/except fallbacks that revert to a local SparkSession.
  
  You MUST ALWAYS use ManagedSparkSession from google-cloud-spark-connect to connect to Managed Spark Serverless. No exceptions.
  
  Refer to references/gcloud_dataproc.md for detailed configuration. Minimal initialization:
```
from google.cloud.managed_spark_connect import ManagedSparkSession

spark = ManagedSparkSession.builder.projectId(<PROJECT_ID>)
    .location(<REGION>)
    .getOrCreate()
```
- Production Logging: In all production PySpark jobs and scripts, you MUST use the standard Python logging module instead of print() statements for job lifecycle, progress, and record counts. Configure it with timestamps:
```
import logging

logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s [%(levelname)s] %(message)s",
)
```
- Security & Dynamic Variables: You MUST NOT hardcode plaintext credentials, passwords, or tokens (use Secret Manager). You MUST NOT hardcode environment-specific variables; parameterize project IDs, dataset IDs, table names, and bucket paths via os.environ.get(...) or arguments.
- Read and Write data: ALWAYS Refer to references/read_write_data.md when reading or writing data.
- Machine Learning Tasks: Refer to @skill:ml-best-practices skill and references/ml_tasks.md when generating Machine Learning code.
- Spark Optimizations & Broadcast Joins: ALWAYS refer to references/spark_optimizations.md. When joining a large DataFrame with a small lookup or dimension table, you MUST use broadcast() (from pyspark.sql.functions import broadcast) on the small table. Treat about 10 MB (Spark's default spark.sql.autoBroadcastJoinThreshold) as a guide to what counts as small, not a hard limit.
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.
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.
Execute script or notebook: When requested to run a job, script, session, or execute notebook cells against Managed Spark, refer to references/gcloud_dataproc.md on how to execute code on Dataproc Serverless using Spark Connect or Dataproc jobs.
Follow up with the user: If a brand new notebook was generated, instruct the user to select the appropriate kernel in the dropdown for cell execution.

Common Mistakes Checklist

[!CAUTION]

Ensure you verify this checklist to avoid mistakes

Before submitting a job, verify:

All imports present (col, when, lit, broadcast, 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 batch/script jobs: NEVER run pip install or subprocess package installations inside PySpark batch scripts (the %pip install setup cell in generated notebooks is exempt). Pass dependencies using --properties=spark.jars.packages=..., --archives=gs://.../env.tar.gz#environment, --py-files, or a custom --container-image.
Optimization & Pre-Submission Refactoring Verify the job against references/spark_optimizations.md and follow the confirmation protocol in references/spark_refactoring_guide.md.
No full file reads of executed notebooks: You MUST NOT use read_file or generic full file reading tools on executed .ipynb notebooks; they often contain massive base64-encoded image outputs that cause excessive token consumption. You MUST inspect execution outputs with cell-scoped reading tools (such as notebook__read_cell, jupyter__read_cell, or specific line slices).

IAM Requirements

The Managed Spark (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, interactive sessions, and Spark Connect sessions.

Code Optimization & Pre-Submission Verification

Before submitting any Spark job or Dataproc batch, enforce the pre-submission verification protocol in references/spark_refactoring_guide.md: inspect the code for the anti-patterns catalogued in references/spark_optimizations.md and obtain explicit user confirmation before applying refactoring.

Troubleshooting & Root Cause Analysis

For troubleshooting failed Spark jobs or Dataproc batches, inspecting/tailing GCS driver output logs, analyzing Spark event logs, or performing Root Cause Analysis (RCA), use the @skill:gcp-spark-troubleshooting skill.