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Spark: Serialization

Serialization in Spark involves converting data objects into a format suitable for storage or transmission. It’s essential for moving data across the network, especially during shuffling or when persisting data to disk. The choice of serialization framework (Java serialization vs. Kryo serialization) and how efficiently data is serialized have a substantial impact on the performance of Spark applications.

Consequences of Inefficient Serialization:

  • Increased Data Size: Poor serialization can significantly increase the size of the data being transferred or stored, leading to higher network and storage overhead.
  • Performance Overhead: Inefficient serialization can slow down the process of data transfer and increase the overall runtime of Spark jobs.

Handling Serialization

  • Use Kryo Serialization: Kryo is generally faster and more compact than Java serialization. Configuring Spark to use Kryo can optimize performance.
  • Optimize Serializable Classes: Ensure that classes are designed for efficient serialization, avoiding the serialization of unnecessary data.