Skip to main content

What is Shuffling in Spark

Shuffling in Spark is a mechanism that Re-Distributes the data across different executors or workers in the clusters. 

Why do we need to Re-Distribute the data? 

 A) Re-Distribution is needed when there is a need of increasing or decreasing the data partitions in the situations below:

  • When the partitions are not sufficient enough to process the data load in the cluster
  • When the partitions are too high in numbers that it creates task scheduling overhead and it becomes the bottleneck in the processing time.
Re-Distribution can also be achieved by executing the shuffling on existing distributed data collection like RDD, DataFrames, etc by using the "Repartition" and "Coalesce" APIs in Spark.

B) During Aggregation and Joins on data collection in Spark, all the data records belonging to aggregation or join should reside in the single partition and when the existing partitioning scheme doesn't satisfy this condition there is a need to re-distributing the data in input collections before performing the aggregations or joins.

Spark Shuffling is an expensive process as it is moving around data among different executors or workers in the cluster. Imagine, if you have 1000s of workers and have a huge volume of data, in this case, the Spark Shuffling can lead to expensive overheads.

Generally, Spark Shuffling involves
  • Disk I/O
  • Network I/O
  • Data Serialization & Deserialization
Some of the operations that might lead to the Shuffling are 

  • join
  • cogroup
  • groupWith
  • join
  • leftOuterJoin
  • rightOuterJoin
  • groupByKey
  • reduceByKey
  • repartition
  • coalesce
Data Engineers should always consider the possibility where they can avoid Shuffling in their process. For Example, they can choose to use reduceByKey instead of groupByKey due to the following reasons.

  • groupByKey shuffles all the data and therefore it is too slow.
  • reduceByKey shuffles only the sub-aggregation results in the partitions.

To summarize, Shuffling is one of the important operations of Spark that re-distributes the data to make the pipelines more efficient. But we are also aware of how expensive Shuffling could be especially when the number of workers in a cluster is too high or the volume of data is huge. So one should avoid the Shuffling in their pipelines. Also at the same time, one should also understand the concept clearly that could help them to design Fault-tolerant, Robust, and Reliable Data Pipelines.

Comments

Post a Comment

Popular posts from this blog

Building Scalable and Efficient Data Lakes with Apache Hudi

If you're looking to build a scalable and efficient data lake that can support both batch and real-time processing, Apache Hudi is a great tool to consider. In this blog post, we'll discuss what Apache Hudi is, how it works, and why it's a powerful tool for building data lakes. Apache Hudi is an open-source data management framework that provides several features to manage big data. It provides the ability to perform read and write operations on large datasets in real-time, while also supporting batch processing. With Hudi, you can also ensure data quality by performing data validation, data cleansing, and data profiling. One of the key advantages of Apache Hudi is its support for schema evolution. This means that as your data changes over time, Hudi can automatically update the schema of your data to accommodate these changes, without requiring any downtime or manual intervention. Another advantage of Hudi is its support for scalable and fault-tolerant data storage. Hudi p...
Post a Comment
Read more

Top 25 Data Engineer Interview Questions

In my last post  How to prepare for Data Engineer Interviews ,  I wrote about how one can prepare for the Data Engineer Interviews, and in this blog post, I am going to provide the  Top 25 Basic   data engineer interview questions  asked frequently and their brief answers. This is typically the first round of the Interview where the interviewer just wants to access whether you are aware of basic concepts or not and therefore you don't need to explain it in detail. Just a single statement would be sufficient. Let's get started Checkout the 5 Key Skills Data Engineers need in 2023 A. Programming  1. What is the Static method in Python? Static methods are the methods that are bound to the  Class  rather than the Class's Object. Thus, it can be called without creating objects of the class. We can just call it using the reference of the class. Also, all the objects of the class share only one copy of the static method. 2. What is a Decorator in Python?...
Post a Comment
Read more

How to prepare for the Data Engineering Interviews?

In recent years, due to the humongous growth of Data, almost all IT companies want to leverage the Data for their Businesses, and that's why the Data Engineering & Data Science opportunities in IT companies are increasing at a rapid rate, we can easily say that Data Engineers are currently at the top of the list of "most hired profiles" in the year 2021-22.  And due to huge demand companies wants to hire Data Engineers who are skilled in programming, SQL, are able to design and create scalable Data Pipelines, and are able to do Data Modelling. In a way, Data engineers should possess all the skills that Software engineers have and as well as skills Data Analysts to possess. And, in interviews also the companies look for all the skills mentioned above in Data Engineers. Checkout the 5 Key skills Data Engineer need in 2023 So in this blog post, I am going to cover all the topics and domains one can expect in Data Engineer Interviews A. Programming Round Most of the Produ...
Post a Comment
Read more