Designing Scalable Spark Pipelines

When dealing with terabytes of data daily, default Spark configurations won't cut it. In this post, we explore techniques to ensure your pipelines are robust and performant.

1. Partitioning Strategies

Effective partitioning is key. Avoid the small file problem by coalescing, but ensure you don't have partitions that are too large to fit in memory.

// Example of repartitioning based on a column
val df = rawData.repartition(col("date"))

2. Broadcast Variables

For joining large tables with small reference tables, always use broadcast joins to minimize shuffling.

3. Resource Tuning

Understanding spark.executor.memory, spark.executor.cores, and spark.driver.memory is crucial.