How do you break a lineage in Apache Spark ? Why we need to break a lineage in Apache Spark ?

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In Apache Spark, a lineage refers to the series of RDD (Resilient Distributed Dataset) operations that are performed on a specific dataset. Breaking a lineage means to prevent the system from keeping track of the lineage of an RDD, which can be useful for reducing the amount of memory used by the system and for improving performance.

There are a few ways to break a lineage in Apache Spark:

  1. Persistence: By persist() or cache() an RDD, you can make it memory resident, which allows Spark to reuse it in later stages without having to recompute it.
  2. Checkpointing: By checkpoint() an RDD, you can save it to a reliable storage system, such as HDFS, which allows Spark to recover the RDD in case of failure. This also breaks the lineage of the RDD, as it is no longer stored in memory.
  3. Materializing: By calling the action() method on an RDD, such as count() or collect(), you can materialize the RDD, which means to force it to be computed and stored in memory.
  4. Using the “unpersist()” method: This method is used to remove an RDD from memory and breaking lineage of the RDD.

It’s important to note that breaking the lineage of an RDD can have a positive impact on performance, but it can also increase the memory usage of the system, so it should be used judiciously.

Real example on breaking the lineage
One scenario where breaking a lineage in Apache Spark may be necessary is when working with large datasets that are transformed multiple times before being used for analysis or storage. In this case, the lineage of the RDD can become quite complex, and the system may need to keep track of all the intermediate transformations, which can consume a significant amount of memory.

For example, you may have a large dataset that you need to filter, group, and aggregate multiple times before it is ready for analysis. Each of these operations would create a new lineage, and the system would need to keep track of all the previous transformations in order to recompute the final result if necessary. Breaking the lineage by caching the RDD after one or more of the transformations could help to reduce the memory usage, while also improving performance.

Another scenario can be when working with iterative algorithms like MLlib algorithms. These algorithms need to iterate over the same dataset multiple times and each iteration creates a new lineage, which can take a lot of memory. By breaking the lineage after each iteration, you can reduce the memory usage of the system and improve performance.

You can also break lineage to improve performance when RDD is not used again in the pipeline and it is not required to keep it in memory.

Author: user

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