• PySpark is the Python API for Spark.

• The purpose of PySpark tutorial is to provide basic distributed algorithms using PySpark.

• PySpark supports two types of Data Abstractions:

  • RDDs
  • DataFrames

• PySpark Interactive Mode: has an interactive shell ($SPARK_HOME/bin/pyspark) for basic testing and debugging and is not supposed to be used for production environment.

• PySpark Batch Mode: you may use $SPARK_HOME/bin/spark-submit command for running PySpark programs (may be used for testing and production environemtns)

• PySpark Examples: RDDs
• PySpark Examples: DataFramess
• DNA Base Counting
• Classic Word Count
• Find Frequency of Bigrams
• Join of Two Relations R(K, V₁), S(K, V₂)
• Basic Mapping of RDD Elements
• How to add all RDD elements together
• How to multiply all RDD elements together
• Find Top-N and Bottom-N
• Find average by using combineByKey()
• How to filter RDD elements
• How to find average
• Cartesian Product: rdd1.cartesian(rdd2)
• Sort By Key: sortByKey() ascending/descending
• How to Add Indices
• Map Partitions: mapPartitions() by Examples
• Monoid: Design Principle

• Getting started with PySpark - Part 1
• Getting started with PySpark - Part 2
• A really really fast introduction to PySpark
• PySpark
• Basic Big Data Manipulation with PySpark
• Working in Pyspark: Basics of Working with Data and RDDs

• View Mahmoud Parsian's profile on LinkedIn
• Please send me an email: mahmoud.parsian@yahoo.com
• Twitter: @mahmoudparsian

Thank you!

best regards,
Mahmoud Parsian

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