Spark Partitioning Overview

Overview

This lecture explains the concept of partitioning in Apache Spark, its importance for performance and resource utilization, and demonstrates how to implement and optimize partitioning strategies using practical code examples.

Introduction to Partitioning

• Partitioning involves splitting a large dataset into smaller, more manageable chunks.
• Similar to organizing books into sections on a bookshelf for faster access.

Partitioning in Spark

• Spark partitions data to optimize processing and searching.
• Uses the partitionBy function to specify the column(s) used for partitioning.
• Example: Partitioning a listening activity dataset by the listen date column groups all records for a given date together.

Implementing Partitioning

• Data can be partitioned on disk into folders representing partition keys (e.g., listen date).
• Multiple columns can be used for partitioning (multi-level partitioning) by passing them to partitionBy.
• The order of columns in partitionBy affects folder structure.

Resource Utilization and Parallelism

• Proper partitioning maximizes CPU and memory utilization by distributing tasks evenly across Spark cores.
• Too few partitions leaves resources idle; too many cause the small files problem and high IO overhead.
• Aim for an optimal number of partitions for best performance.

Choosing a Partition Column

• Columns with low to medium cardinality (moderate number of unique values) are ideal.
• Avoid high-cardinality columns (e.g., customer ID) and super-low cardinality (only one value).
• Frequently filtered columns should be prioritized for partitioning.

Controlling Number of Files per Partition

• Use repartition(n) before partitionBy to control the number of files in each partition.
• Using coalesce(n) after partitionBy may not always change the number of files as expected due to how it avoids a full shuffle.

Spark Partition Size Configuration

• The property spark.sql.files.maxPartitionBytes sets the max size (in bytes) of a partition when reading data.
• Example: Setting to 1KB partitions a 448KB file into approximately 448 partitions at read time.

Key Terms & Definitions

• Partitioning — Dividing a dataset into smaller, separate chunks for processing and storage.
• Cardinality — The number of unique values in a column.
• partitionBy — Spark function to specify column(s) used for partitioning data.
• Repartition — Spark method to increase or decrease the number of partitions, often causing a full shuffle.
• Coalesce — Spark method to reduce the number of partitions without a full shuffle.
• Max Partition Bytes — Configuration determining the maximum data size each partition can hold during reads.

Action Items / Next Steps

• Practice partitioning a dataset using different columns and observe the resulting file/folder structure.
• Experiment with repartition and coalesce to control partition file counts.
• Review Spark documentation on partitioning best practices and configuration options.