Persisting and caching in Apache Spark
In Apache Spark, optimizing the performance of your data processing operations is essential, especially when working with large datasets and complex workflows. Caching and persistence are techniques that allow you to store intermediate or frequently used data in memory or on disk, reducing the need for recomputation and enhancing overall performance. This section explores the concepts of persisting and caching in Spark.
Understanding data persistence
Data persistence is the process of storing the intermediate or final results of Spark transformations in memory or on disk. By persisting data, you reduce the need to recompute it from the source data, thereby improving query performance.
The following key concepts are related to data persistence:
- Storage levels: Spark offers multiple storage levels for data, ranging from memory-only to disk, depending on your needs. Each storage level comes with its trade-offs in terms of speed and...
