save-gcp-local 0.1.0

Run Airflow DAGs locally and execute Dataproc/Spark jobs in local Docker instead of creating GCP clusters. Generic, zero DAG edits, pluggable test-data providers.

save-gcp-local

Stop paying for Dataproc clusters just to test your Spark jobs. Run them locally in Docker or Podman instead — same code, zero cloud cost, no DAG changes.

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Why this exists

Testing Spark jobs on GCP Dataproc is slow and expensive. Every small code change means:

1. Trigger the DAG
2. Wait for a cluster to spin up (1–3 min)
3. Run the job on full data (often 30–40 min)
4. Tear the cluster down
5. Find a bug -> repeat — and pay for all of it

The cluster minutes add up fast, especially across a whole team iterating all day.

save-gcp-local removes the cluster entirely. It intercepts the Dataproc steps in your local Airflow and runs the same Spark job in a local container. You iterate in seconds for free, then do one real Dataproc run at the end to confirm scale.

Can you run Dataproc itself locally? No — Dataproc is GCP infrastructure. But your job is plain Apache Spark, which has a built-in local mode. This tool no-ops the cluster steps and runs your job locally. That is the whole trick, and it is enough to save the money.

What you save

Step	On Dataproc	Locally
Cluster create	1–3 min + $	skipped, $0
Job run	30–40 min + $	seconds–min, $0
Cluster delete	~1 min + $	skipped, $0
Per iteration	~40 min + cluster cost	~minutes, free

Key features

• Zero DAG edits — works by patching Dataproc operators at runtime
• Generic — any Dataproc operator, PySpark or Scala/Java JARs, any project layout
• Docker or Podman (or a local spark-submit) — auto-detected
• Jobs anywhere — in the Airflow repo, a subfolder, a JAR, or a separate repo
• Test data your way — none / real-data sample / synthetic / your own provider
• Two entry points — a CLI and an auto-loading Airflow plugin
• One switch to go back to GCP — DPL_ENABLED=false

Install

pip install "save-gcp-local[all]"        # from PyPI (when published)
# or from source:
git clone https://github.com/EshwarCVS/save-gcp-local
cd save-gcp-local && pip install -e ".[all]"

60-second start

# 1. Point at your test data (jobs inside the Airflow repo are auto-found)
export DPL_DATA_DIR=./data

# 2. (optional) make test data — pick ONE
save-gcp-local gen-data --provider sample    --input prod.csv --output ./data/events.csv --pct 1
save-gcp-local gen-data --provider synthetic --input prod.csv --output ./data/events.csv --rows 200000

# 3. run your DAG locally — Dataproc steps run in a container
save-gcp-local run --dags ./dags --dag my_pipeline --execution-date 2024-06-01

Prefer the UI? Drop a one-liner into $AIRFLOW_HOME/plugins/ and boot Airflow normally — see QUICKSTART.md.

Documentation

• QUICKSTART.md — 5-minute setup
• SETUP.md — full guide: install options, config, both entry points, test-data strategies, troubleshooting
• CONTRIBUTING.md — dev setup, tests, how to add a data provider

How it works

            +--------------- your local Airflow ---------------+
            |                                                   |
  DAG --->  CreateCluster -> SubmitJob -> DeleteCluster         |
            |   (no-op)         |            (no-op)            |
            |                   +-- runs in Docker/Podman --+   |
            +-------------------+--------------------------+----+
                                v
                       spark-submit --master local[*]
                       with /data, /jobs, /output mounted in

Cluster lifecycle operators become no-ops. Job-submit operators run your Spark code in a local container with your job files and test data mounted in.

Supported operators

Cluster lifecycle (no-op): DataprocCreateClusterOperator, DataprocDeleteClusterOperator, DataprocUpdate/Start/StopClusterOperator, workflow-template operators.

Job submission (runs locally): DataprocSubmitJobOperator, DataprocCreateBatchOperator, and legacy DataprocSubmitPySparkJobOperator / SparkJobOperator / SparkSqlJobOperator / HadoopJobOperator.

Limitations (be honest with your team)

• Local Spark is a single machine — validate logic locally, scale on GCP once.
• Absolute row counts / huge-shuffle behavior will not match production.
• If a job hardcodes gs:///BigQuery paths inside the code (not as an argument), parameterize the input so it can point at /data.

License

MIT — see LICENSE.