What is the difference between cache and persist in Spark?

In Apache Spark, caching and persisting are optimization techniques used to improve the performance of Spark applications by storing intermediate results in memory or on disk. This can significantly reduce the time required to recompute these results, especially when used in multiple Spark job stages.

Caching: A temporary hold

The cache() operation in Spark is a mechanism to persist an RDD (Resilient distributed dataset) or DataFrame in memory for faster access. It is designed for transient use cases where the data can be recomputed if lost. When we cache an RDD or DataFrame, Spark retains the data in memory across multiple Spark operations, reducing the need to recompute it from the source.

However, it’s important to note that the cache operation is best suited for scenarios where recomputation is relatively inexpensive, and the data fits comfortably into the available memory. If the memory is exhausted, Spark might evict some cached data, leading to recomputation if needed.

Persisting: A durable commitment

On the other hand, persist() provides more flexibility than caching by allowing you to specify the storage level for the intermediate results. Storage levels determine where and how the data is stored, such as in memory, on disk, or replicated across multiple nodes. This allows us to choose the most appropriate storage location based on the size and access patterns of the data.

Storage levels

Spark supports five storage levels:

1. MEMORY_ONLY: This stores data in memory only. This is the fastest storage level but also the most volatile, as data can be evicted from memory without space.

2. MEMORY_AND_DISK: This method stores data in memory first and spills to disk if necessary, balancing speed and durability.

3. MEMORY_ONLY_SER: This stores data in memory in serialized form, which can save space.

4. MEMORY_AND_DISK_SER: This stores data in memory in serialized form first and spills to disk if necessary.

5. DISK_ONLY: This stores data on disk only. This is the most durable storage level but also the slowest.

Choosing between caching and persisting

We should use caching when we know that the intermediate results are small enough to fit in memory and will be accessed frequently. We should use persisting when we need more control over the storage level of the intermediate results.

Here is a table summarizing the key differences between caching and persisting:

The choice between cache() and persist() depends on the nature of the computation, the characteristics of the data, and the available resources. Use cache() when the data can be recomputed easily, fits comfortably in memory, and the default storage level suffices. Reserve persist() for scenarios where durability, fine-grained control over storage, or long-term storage is crucial.

Conclusion

In conclusion, cache() and persist() may seem synonymous at first glance, but they serve distinct purposes in the Spark ecosystem. The former is ideal for transient in-memory storage with minimal developer intervention, while the latter provides a more comprehensive and durable approach, allowing developers to customize storage levels and persist data across multiple nodes.

Quiz: Understanding cache and persist in Spark