9 Spark SQL Shuffle Partitions Best Practices
Spark SQL shuffle partitions best practices help you optimize your Spark SQL jobs by ensuring that data is properly distributed across partitions.
Apache Spark SQL is a powerful tool for data processing and analysis. One of the key features of Spark SQL is its ability to perform data shuffling, which is a process of redistributing data across partitions.
Shuffling is a necessary step in many Spark SQL operations, such as joins, aggregations, and window functions. However, it can also be a performance bottleneck. In this article, we will discuss 9 best practices for configuring Spark SQL shuffle partitions. By following these best practices, you can optimize the performance of your Spark SQL applications.
1. The number of partitions is directly proportional to the size of data
When data is shuffled between nodes in a Spark SQL cluster, it is first divided into partitions. The number of partitions is determined by the spark.sql.shuffle.partitions parameter. If this parameter is not set, the default number of partitions is 200.
If the number of partitions is too low, then the data will not be evenly distributed among the nodes and some nodes will end up with more data than others. This can lead to performance issues because the nodes with more data will take longer to process the data.
On the other hand, if the number of partitions is too high, then there will be too much overhead involved in shuffling the data and the performance will suffer.
The best practice is to set the number of partitions based on the size of the data. For example, if the data is 1GB, then the number of partitions should be at least 100.
2. If you have a large amount of data and small number of partitions, it will take longer time for tasks to complete
Suppose you have 1TB of data and 100 partitions. If each task takes up 10MB, then there will be a lot of tasks (100,000 in total) and each task will take a long time to complete because it has to process a large amount of data.
On the other hand, if you have 1TB of data and 1,000 partitions, each task will only take up 1MB and there will be fewer tasks (1,000 in total). Even though each task is processing less data, the overall time taken will be shorter because there are more tasks running in parallel.
3. If you have a small amount of data and large number of partitions, it will create too many tasks which will slow down your job
When you have a small amount of data, it is better to have fewer partitions so that each task has more data to work with. This will help reduce the number of tasks and speed up your job.
On the other hand, if you have a large amount of data, it is better to have more partitions so that each task has less data to work with. This will help increase the number of tasks and speed up your job.
4. Always try to keep the number of partitions in between 100-200 per executor core
The main reason is that when you have too many partitions, each task will be processing a very small amount of data, which can lead to inefficiencies. On the other hand, if you have too few partitions, then each task will be processing a large amount of data, which can lead to OutOfMemoryErrors.
So by keeping the number of partitions somewhere in between 100-200 per executor core, you’ll be able to strike a good balance between efficiency and avoiding OutOfMemoryErrors.
5. You can use spark.sql.shuffle.partitions configuration property to set the default number of partitions
When data is shuffled between nodes in a Spark SQL cluster, it is first divided into partitions. By default, the number of partitions is set to 200, but this value can be too low for some workloads, resulting in poor performance.
If you know your workload will require more than 200 partitions, it’s best to set the spark.sql.shuffle.partitions property to a higher value. This will ensure that your data is shuffled efficiently and that your query performance is not impacted.
6. You can also specify the number of partitions while writing DataFrame or Dataset using repartition() method
When you’re reading data from a file, the default number of partitions is equal to the number of blocks in the file (which is usually the same as the number of files when you’re reading from HDFS). However, when you’re writing DataFrame or Dataset back to a file, the default number of partitions is only one.
This can cause two problems. First, if your input data is large, then the output file will also be large, which can lead to OutOfMemoryError. Second, if your input data is small, then the output file will be unnecessarily large, which wastes disk space and makes it more difficult to process the data later.
The solution to both of these problems is to specify the number of partitions when you’re writing DataFrame or Dataset using repartition() method. For example, if your input data has 100 partitions, then you can write the output data with 200 partitions by using repartition(200). This will make the output file smaller and easier to process.
7. In case of joins, if one side of join has more partitions than other side then the smaller side will be shuffled to match the larger one
This is because when Spark SQL joins two DataFrames, it needs to make sure that both sides have the same number of partitions so that each partition can be joined with its corresponding one on the other side.
If you have a join between two DataFrames where one side has more partitions than the other, then Spark will shuffle the smaller side to match the larger side. This can cause performance issues because shuffling data is an expensive operation. Therefore, it’s important to make sure that both sides of a join have the same number of partitions.
8. When you are joining two tables on primary key column(s), make sure that both sides have same number of partitions and same partitioning columns
If the two sides have different number of partitions, or even if they have same number of partitions but different partitioning columns, then Spark will perform a full shuffle on the join columns. This means that all data from both sides will be exchanged between all executors in the cluster, which can lead to significant performance degradation.
To avoid this, make sure that both sides have the same number of partitions and same partitioning columns when you are joining two tables on primary key column(s).
9. Avoid shuffle when possible by bucketing data based on join keys
Shuffle is an expensive operation because it requires moving data around the cluster. When you can avoid shuffle, you can improve performance and save on resources.
Bucketing data based on join keys is one way to avoid shuffle. When data is bucketed, Spark can perform a map-side join instead of a shuffle join. This means that the data is joined without having to move it around the cluster, which improves performance and saves on resources.
To bucket data in Spark SQL, you use the bucketBy() function. You specify the number of buckets and the columns to bucket by. For example, to bucket data by the user_id column, you would use the following code:
df.bucketBy(100, “user_id”)
This will create 100 buckets and will bucket data by the user_id column.