How can I improve my Spark application performance?

Spark Performance Tuning – Best Guidelines & Practices

1. Use DataFrame/Dataset over RDD.
2. Use coalesce() over repartition()
3. Use mapPartitions() over map()
4. Use Serialized data format’s.
5. Avoid UDF’s (User Defined Functions)
6. Caching data in memory.
7. Reduce expensive Shuffle operations.
8. Disable DEBUG & INFO Logging.

What is the performance optimization features for Spark?

Spark supports many formats, such as CSV, JSON, XML, PARQUET, ORC, AVRO, etc. Spark jobs can be optimized by choosing the parquet file with snappy compression which gives the high performance and best analysis. Parquet file is native to Spark which carries the metadata along with its footer.

What are the Spark optimal coding practices?

In this section, we will show some techniques for tuning Apache Spark for optimal efficiency:

1. 1.3.1.
2. Do not use count() when you do not need to return the exact number of rows.
3. Avoid groupbykey on large datasets.
4. Avoid the flatmap-join-groupby pattern.
5. Use coalesce to repartition in decrease number of partition.

How do you optimize Spark data pipeline performance?

Tidy Up Pipeline Output read. parquet(“fs://path/file.parquet”).select(…) to limit reading to only useful columns. Reading fewer data into memory will speed up your application. It should be equally obvious that writing less output into your destination directory also improves performance easily.

Which component is used by Apache Spark for improved memory management?

An executor is the Spark application’s JVM process launched on a worker node. It runs tasks in threads and is responsible for keeping relevant partitions of data. Each process has an allocated heap with available memory (executor/driver).

What is catalyst optimiser in Spark?

Back to glossary At the core of Spark SQL is the Catalyst optimizer, which leverages advanced programming language features (e.g. Scala’s pattern matching and quasi quotes) in a novel way to build an extensible query optimizer. Easily add new optimization techniques and features to Spark SQL. …