Demystifying Partitioning in Spark

Last updated on Apr 22,2020 54.2K Views

Prithviraj Bose Prithviraj has spent close to two decades in the software development industry... Prithviraj has spent close to two decades in the software development industry designing and developing applications ranging from Level 5 process control software at...

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Demystifying Partitioning in Spark

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Contributed by Prithviraj Bose

Spark’s Resilient Distributed Datasets (the programming abstraction) are evaluated lazily and the transformations are stored as directed acyclic graphs (DAG). So every action on the RDD will make Spark recompute the DAG. This is how the resiliency is attained in Spark because if any worker node fails then the DAG just needs to be recomputed.

It is also mandatory to cache (persist with appropriate storage level) the RDD such that frequent actions on the RDD do not force Spark to recompute the DAG. Topics covered in this blog are essentially required for Apache Spark and Scala Certification. Topics covered in this blog are essentially required for Apache Spark and Scala Certification.

Why Use a Partitioner?

In cluster computing, the central challenge is to minimize network traffic. When the data is key-value oriented, partitioning becomes imperative because for subsequent transformations on the RDD, there’s a fair amount of shuffling of data across the network. If similar keys or range of keys are stored in the same partition then the shuffling is minimized and the processing becomes substantially fast.

Transformations that require shuffling of data across worker nodes greatly benefit from partitioning. Such transformations are cogroup, groupWith, join, leftOuterJoin, rightOuterJoin, groupByKey, reduceByKey, combineByKey andlookup.

Partitions are configurable provided the RDD is key-value based.

Properties of Partition

Tuples in the same partition are guaranteed to be in the same machine.
Each node in a cluster can contain more than one partition.
The total number of partitions are configurable, by default it is set to the total number of cores on all the executor nodes.

Types of Partitioning in Spark

Spark supports two types of partitioning,

Hash Partitioning: Uses Java’s Object.hashCodemethod to determine the partition as partition = key.hashCode() % numPartitions.

Range Partitioning: Uses a range to distribute to the respective partitions the keys that fall within a range. This method is suitable where there’s a natural ordering in the keys and the keys are non negative. The below code snippet shows the usage of range partitioner.

Code Example

Let’s see an example on how to partition data across worker nodes. The full Scala code is available here.

Here’s some test data of 12 coordinates (as tuples),

Create an org.apache.spark.HashPartitioner of size 2, where the keys will be partitioned across these two partitions based on the hash code of the keys.