Hey Afaque Great tutorials. You should consider doing a full end to end spark project with a Big volume of data so we can understand the challenges faced and how to tackle them. Would be really helpful!
If we do not explicitly unpersist, what would happen to the data? Would it be cleaned by the next GC cycle ? Also what is the best practice , explicitly unpersist or leave it to GC.
Hey @anirbansom6682, Data would be kept in memory until the Spark application ends, or the context is stopped or is evicted because Spark needs to free up memory to make room for other data. It may also be evicted during next GC cycle. But this process is a little uncertain as it depends completely on Spark's own memory management policies and JVM's garbage collection process. Leaving it to GC would be a passive approach over which you've lesser control and is much more like a black box unless you're well aware of its policies. The best practice, however, is to explicitly unpersist when they're no longer needed. This will give you more control over your application's memory usage and can help prevent memory issues in long running Spark applications where different datasets are cached over time.
Thanks for sharing, small query Do we need to cache based on number of transformations being done on that dataframe or if we are doing more actions on that dataframe/using that dataframe
Thanks @gananjikumar5715, transformations are accumulated until an action is called. So, it would be based on the number of actions; If you're performing several actions, better to cache the Dataframe first, otherwise Spark will re-create the DAG when executing a new action.
Want to understand better, Read this: Spark CacheLLM Response: 1. **Why Use Caching?** - Caching can significantly improve performance by reusing persistent data instead of recomputing it - It helps avoid redundant computations on the same dataset across multiple actions 2. **Lazy Evaluation and Caching** - Apache Spark uses lazy evaluation, where transformations are not executed until an action is triggered - Caching can help by materializing the result of a long sequence of transformations, avoiding recomputation 3. **Spark's Lineage Graph** - Spark tracks the lineage of transformations using a Lineage Graph - Caching breaks the lineage, reducing the size of the graph and improving performance 4. **Caching vs. No Caching** - The demo shows a significant performance improvement when caching is used, as seen in the Spark UI 5. **Persist and Storage Levels** - The `persist()` method is used for caching, with different storage levels available - Storage levels like `MEMORY_ONLY`, `DISK_ONLY`, and combinations control memory/disk usage and replication - Choose the appropriate storage level based on your requirements and cluster resources 6. **When to Cache?** - Cache datasets that are reused multiple times, especially after a long sequence of transformations - Cache intermediate datasets that are expensive to recompute - Be mindful of cluster resources and cache judiciously 7. **Unpersist** - Use `unpersist()` to remove cached data and free up resources when no longer needed - Spark may automatically unpersist cached data if memory is needed If you liked it, Upvote it. NarutoLLM Response
