This is a repo documenting the best practices in PySpark.

Spark-Syntax

This is a public repo documenting all of the “best practices” of writing PySpark code from what I have learnt from working with PySpark for 3 years. This will mainly focus on the Spark DataFrames and SQL library.

you can also visit ericxiao251.github.io/spark-syntax/ for a online book version.

Contributing/Topic Requests

If you notice an improvements in terms of typos, spellings, grammar, etc. feel free to create a PR and I’ll review it 😁, you’ll most likely be right.

If you have any topics that I could potentially go over, please create an issue and describe the topic. I’ll try my best to address it 😁.

Acknowledgement

Huge thanks to Levon for turning everything into a gitbook. You can follow his github at https://github.com/tumregels.

Table of Contexts:

Chapter 1 - Getting Started with Spark:

• 1.1 - Useful Material

• 1.2 - Creating your First DataFrame

• 1.3 - Reading your First Dataset

• 1.4 - More Comfortable with SQL?

Chapter 2 - Exploring the Spark APIs:

• 2.1 - Non-Trivial Data Structures in Spark

  • 2.1.1 - Struct Types (StructType)

  • 2.1.2 - Arrays and Lists (ArrayType)

  • 2.1.3 - Maps and Dictionaries (MapType)

  • 2.1.4 - Decimals and Why did my Decimals overflow :( (DecimalType)

• 2.2 - Performing your First Transformations

  • 2.2.1 - Looking at Your Data (collect/head/take/first/toPandas/show)

  • 2.2.2 - Selecting a Subset of Columns (drop/select)

  • 2.2.3 - Creating New Columns and Transforming Data (withColumn/withColumnRenamed)

  • 2.2.4 - Constant Values and Column Expressions (lit/col)

  • 2.2.5 - Casting Columns to a Different Type (cast)

  • 2.2.6 - Filtering Data (where/filter/isin)

  • 2.2.7 - Equality Statements in Spark and Comparisons with Nulls (isNotNull()/isNull())

  • 2.2.8 - Case Statements (when/otherwise)

  • 2.2.9 - Filling in Null Values (fillna/coalesce)

  • 2.2.10 - Spark Functions aren’t Enough, I Need my Own! (udf/pandas_udf)

  • 2.2.11 - Unionizing Multiple Dataframes (union)

  • 2.2.12 - Performing Joins (clean one) (join)

• 2.3 More Complex Transformations

  • 2.3.1 - One to Many Rows (explode)

  • 2.3.2 - Range Join Conditions (WIP) (join)

• 2.4 Potential Performance Boosting Functions

  • 2.4.1 - (repartition)

  • 2.4.2 - (coalesce)

  • 2.4.2 - (cache)

  • 2.4.2 - (broadcast)

Chapter 3 - Aggregates:

• 3.1 - Clean Aggregations

• 3.2 - Non Deterministic Behaviours

Chapter 4 - Window Objects:

• 4.1 - Default Ordering on a Window Object

• 4.2 - Ordering High Frequency Data with a Window Object

Chapter 5 - Error Logs:

Chapter 6 - Understanding Spark Performance:

• 6.1 - Primer to Understanding Your Spark Application

  • 6.1.1 - Understanding how Spark Works

  • 6.1.2 - Understanding the SparkUI

  • 6.1.3 - Understanding how the DAG is Created

  • 6.1.4 - Understanding how Memory is Allocated

• 6.2 - Analyzing your Spark Application

  • 6.1 - Looking for Skew in a Stage

  • 6.2 - Looking for Skew in the DAG

  • 6.3 - How to Determine the Number of Partitions to Use

• 6.3 - How to Analyze the Skew of Your Data

Chapter 7 - High Performance Code:

• 7.0 - The Types of Join Strategies in Spark

  • 7.0.1 - You got a Small Table? (Broadcast Join)

  • 7.0.2 - The Ideal Strategy (BroadcastHashJoin)

  • 7.0.3 - The Default Strategy (SortMergeJoin)

• 7.1 - Improving Joins

  • 7.1.1 - Filter Pushdown

  • 7.1.2 - Joining on Skewed Data (Null Keys)

  • 7.1.3 - Joining on Skewed Data (High Frequency Keys I)

  • 7.1.4 - Joining on Skewed Data (High Frequency Keys II)

  • 7.1.5 - Join Ordering

• 7.2 - Repeated Work on a Single Dataset (caching)

  • 7.2.1 - caching layers

• 7.3 - Spark Parameters

  • 7.3.1 - Running Multiple Spark Applications at Scale (dynamic allocation)

  • 7.3.2 - The magical number 2001 (partitions)

  • 7.3.3 - Using a lot of UDFs? (python memory)

• 7. - Bloom Filters :o?