Parquet stores data in columnar format, and is highly optimized in Spark. The best format for performance is parquet with snappy compression, which is the default in Spark 2.x. Spark can be extended to support many more formats with external data sources - for more information, see Apache Spark packages. Spark supports many formats, such as csv, json, xml, parquet, orc, and avro. No query optimization through Catalyst.You don't need to use RDDs, unless you need to build a new custom RDD.Adds serialization/deserialization overhead.Developer-friendly by providing domain object programming and compile-time checks.Not good in aggregations where the performance impact can be considerable.Good in complex ETL pipelines where the performance impact is acceptable.Not as developer-friendly as DataSets, as there are no compile-time checks or domain object programming.Provides query optimization through Catalyst.Choose the data abstractionĮarlier Spark versions use RDDs to abstract data, Spark 1.3, and 1.6 introduced DataFrames and DataSets, respectively. The following sections describe common Spark job optimizations and recommendations. For the best performance, monitor and review long-running and resource-consuming Spark job executions. ![]() ![]() You can speed up jobs with appropriate caching, and by allowing for data skew. The most common challenge is memory pressure, because of improper configurations (particularly wrong-sized executors), long-running operations, and tasks that result in Cartesian operations. Learn how to optimize an Apache Spark cluster configuration for your particular workload.
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