Spark: Storage

Storage optimization in Spark revolves around how data is stored, both in-memory and on disk. This includes the choice of data formats (like Parquet, ORC) and the efficient usage of caching and persistence mechanisms.

Consequences of Suboptimal Storage:

• Slower Data Access: Inefficient data formats or storage strategies can lead to slower read/write operations.
• Increased Resource Consumption: Poorly optimized storage can consume more memory or disk space, affecting the overall efficiency of Spark applications.

Handling Storage

• Choose Efficient Data Formats: Formats like Parquet and ORC are optimized for performance, offering advantages like columnar storage and data compression.
• Effective Use of Caching: Caching data in memory can speed up access, but it needs to be done judiciously to avoid excessive memory consumption.