name	spark-best-practices
description	General Apache Spark best practices for scalable, maintainable, and performant DataFrame jobs.

Spark Best Practices

Use this skill for general Apache Spark guidance when optimizing performance, reliability, and maintainability.

1) Keep execution distributed

Avoid collect(), toPandas(), and large take() in core data paths.
Materialize to driver memory only for very small control outputs (metrics, IDs, summaries).
Keep heavy transformation and write paths in Spark DataFrame execution.

2) Prefer DataFrame APIs to Python loops

Use Spark SQL/DataFrame functions so Catalyst can optimize execution plans.
Avoid row-by-row Python logic when equivalent DataFrame expressions exist.
Keep transformations declarative and composable.

3) Reduce shuffle cost

Project and filter early to reduce data volume before joins/aggregations.
Repartition intentionally before heavy joins/writes.
Use coalesce when reducing output partitions.
Watch for skewed keys and apply skew mitigation.

4) Use efficient joins

Broadcast small dimension tables when appropriate.
Align join key types and null handling before joins.
Validate expected join cardinality to avoid explosive outputs.

5) Cache only reused intermediates

Cache/persist DataFrames only when reused across multiple downstream actions.
Unpersist promptly when no longer needed.
Consider checkpointing for very long lineage plans.

6) Write in table-friendly layouts

Prefer columnar formats (Parquet/Delta/Iceberg) when possible.
Partition by bounded-cardinality business keys.
Avoid small file explosion; compact files when needed.

7) Be explicit with schema and quality

Define schemas explicitly where practical.
Normalize data types across sources before joins/unions.
Handle null semantics intentionally in filters, joins, and aggregations.

8) Observe and verify

Use explain() and execution metrics/logs to inspect physical plans and shuffle boundaries.
Track row counts and key metrics at major steps.
Compare runtime and output quality after each optimization pass.

Spark Best Practices

Use this skill for general Apache Spark guidance when optimizing performance, reliability, and maintainability.

1) Keep execution distributed

Avoid collect(), toPandas(), and large take() in core data paths.

Materialize to driver memory only for very small control outputs (metrics, IDs, summaries).

Keep heavy transformation and write paths in Spark DataFrame execution.

2) Prefer DataFrame APIs to Python loops

Use Spark SQL/DataFrame functions so Catalyst can optimize execution plans.

Avoid row-by-row Python logic when equivalent DataFrame expressions exist.

Keep transformations declarative and composable.

3) Reduce shuffle cost

Project and filter early to reduce data volume before joins/aggregations.

Repartition intentionally before heavy joins/writes.

Use coalesce when reducing output partitions.

Watch for skewed keys and apply skew mitigation.

4) Use efficient joins

Broadcast small dimension tables when appropriate.

Align join key types and null handling before joins.

Validate expected join cardinality to avoid explosive outputs.

5) Cache only reused intermediates

Cache/persist DataFrames only when reused across multiple downstream actions.

Unpersist promptly when no longer needed.

Consider checkpointing for very long lineage plans.

6) Write in table-friendly layouts

Prefer columnar formats (Parquet/Delta/Iceberg) when possible.

Partition by bounded-cardinality business keys.

Avoid small file explosion; compact files when needed.

7) Be explicit with schema and quality

Define schemas explicitly where practical.

Normalize data types across sources before joins/unions.

Handle null semantics intentionally in filters, joins, and aggregations.

8) Observe and verify

Use explain() and execution metrics/logs to inspect physical plans and shuffle boundaries.

Track row counts and key metrics at major steps.

Compare runtime and output quality after each optimization pass.

spark-best-practices