10 Tips to Improve PySpark Performance — Science and Data

2023-08-16 19:36:00

PySpark can be a big resource hog, especially when you’re working with large datasets. Tweaking the settings can help optimize memory usage and improve performance.

Here are some parameters and tips you can consider:

1- Executor Memory Settings: The executor memory is used to store data from RDDs, broadcasts, accumulators, etc. Adjust this setting according to your needs through the parameter below (which can be used in Spark Submit or set in Spark settings file):

conf.set(“spark.executor.memory”, “4g”)

2- Driver Memory Settings: Adjust the amount of memory used by the driver process, which coordinates the executors. Use the parameter below:

conf.set(“spark.driver.memory”, “2g”)

3- Storage Memory and Shuffle Memory Configuration: You can divide the executor memory into two regions: the storage region that stores persistent RDD data, and the shuffle region that stores intermediate computation data with the parameter below:

conf.set(“spark.memory.storageFraction”, “0.5”)

4- Off-Heap Memory: This allows the use of memory outside the heap of the JVM.

conf.set(“spark.memory.offHeap.enabled”, True)
conf.set(“spark.memory.offHeap.size”, “2g”)

5- Garbage Collection (GC) Tuning: Extensive use of GC can affect performance. You can tune the JVM to use G1GC, which is generally more efficient.

conf.set(“spark.executor.extraJavaOptions”, “-XX:+UseG1GC”)

6- Repartition the Data: If you know you are working with a smaller number of partitions than the available executors, you can repartition the data accordingly. Fewer partitions can save memory.

7- Persistence with Storage Levels: Use persist() or cache() with an appropriate storage level such as MEMORY_AND_DISK to store the RDDs you are performing various operations on to avoid recomputing.

8- Broadcasting: If you are performing a join operation on a large table and a small table, broadcasting the small table can make the join more efficient.

9- Disable Broadcast Caching: If you are experiencing OOM (Out of Memory) issues, consider disabling broadcast caching with the parameter below:

conf.set(“spark.sql.autoBroadcastJoinThreshold”, “-1”)

10- Use Proper Data Types: Make sure you use the proper data types for your columns, which can save a significant amount of memory.

Bonus: Disable Verbose Logging: Logs are important, but verbose logging can consume more memory, so set the logging level to something like WARN or ERROR.

More possibilities can be found here: Spark Configuration.

David Matos

References:

Machine Learning and AI in Distributed Environments

Related