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Adaptive Query Execution (AQE) is an optimization technique in Spark SQL that makes use of the runtime statistics to choose the most efficient query execution plan, which is enabled by default since Apache Spark 3.2.0.
Apache Spark is an analytics engine that can handle very large data sets. This guide reveals strategies to optimize its performance using PySpark.
Serialization plays an important role in the performance of any distributed application. Formats that are slow to serialize objects into, or consume a large number of bytes, will greatly slow down the computation. Often, this will be the first thing you should tune to optimize a Spark application.
Jan 10, 2023 · Two key general approaches which can be used to increase Spark performance under any circumstances are: Reducing the amount of data ingested. Reducing the time Spark spends reading data (e.g. using Predicate Pushdown with Disk Partitioning/Z Order Clustering).
Sep 12, 2023 · Optimize Your Apache Spark Workloads: Master the Art of Peak Performance Tuning. Learn how to harness the full potential of Apache Spark with examples.
Mar 27, 2024 · Using cache() and persist() methods, Spark provides an optimization mechanism to store the intermediate computation of a Spark DataFrame so they can be reused in subsequent actions. When you persist a dataset, each node stores it’s partitioned data in memory and reuses them in other actions on that dataset.
Oct 31, 2022 · In this article, I shall give you a brief understanding of some widely used optimization techniques which are supported by Spark and how you can use them.
