CODEWITHSUNDEEP
apache-sparkrddmemory-layout
ng.newbie
ng.newbie

Reputation: 3227

What is the memory layout of a non-HDFS RDD?

I am new to Spark and I am trying to get an intuition for how RDDs are represented in memory.

HDFS RDDs are easy as the partitions are handled by the filesystem itself. That is, HDFS itself divides a large file into blocks to ensure fault tolerance, hence an RDD on top of that is trivial.

But what about an RDD that is NOT pointing to data on HDFS, maybe something that points to a RDBMS or MongoDB.

A couple of questions that immediately come to mind:

  1. How is the partitioning handled for something that is not natively partitioned?
    • Does Spark handle the partitioning by itself? Or does it rely on the RDD implementation to do that ?
  2. How does the execution work ?
    • Take for example, a RDD that points to a RDBMS, does Spark load all its data into memory for each executor process, and then run all transformations?
    • Or does understand the table structure of RDBMS and do some sort of partitioning when doing the transformations ?

In short, I am unable to understand how Spark can make RDD a generalized abstraction for all sorts of data sources.

Upvotes: -1

Views: 33

Answers (1)

Net Worth
Net Worth

Reputation: 1

In Apache Spark, when dealing with Resilient Distributed Datasets (RDDs) that are not stored in the Hadoop Distributed File System (HDFS), the memory layout depends on the storage level chosen for the RDD. Spark allows various storage levels, such as MEMORY_ONLY, MEMORY_AND_DISK, MEMORY_ONLY_SER, and MEMORY_AND_DISK_SER.

Here's a brief overview of each:

  1. MEMORY_ONLY: RDD partitions are stored as deserialized Java objects in the JVM heap memory of the Spark worker nodes. This storage level provides fast access to the data but consumes more memory.

  2. MEMORY_AND_DISK: RDD partitions are stored in memory, but if the memory is insufficient, some partitions are stored on disk. This storage level trades off between memory usage and potential disk I/O.

  3. MEMORY_ONLY_SER: RDD partitions are stored in a serialized format in the JVM heap memory. This reduces memory usage compared to MEMORY_ONLY as objects are stored in a more compact form, but it requires deserialization before processing.

  4. MEMORY_AND_DISK_SER: Similar to MEMORY_AND_DISK, but RDD partitions are stored in a serialized format. This reduces memory usage and can spill to disk if memory is insufficient, but again, it requires deserialization before processing.

In all cases, Spark uses a distributed memory model where data is partitioned across multiple nodes in the cluster. The exact memory layout within each node's memory depends on factors such as the partitioning scheme, serialization method, and the storage level chosen. Spark manages this layout transparently to the user, ensuring fault tolerance and efficient data processing.

Upvotes: 0

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