[jira] [Created] (SPARK-44379) Broadcast Joins taking up too much memory

["Shardul Mahadik (Jira)" <ji...@apache.org>]

Shardul Mahadik created SPARK-44379:
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             Summary: Broadcast Joins taking up too much memory
                 Key: SPARK-44379
                 URL: https://issues.apache.org/jira/browse/SPARK-44379
             Project: Spark
          Issue Type: Improvement
          Components: SQL
    Affects Versions: 3.4.1
            Reporter: Shardul Mahadik


Context: After migrating to Spark 3 with AQE, we saw a significant increase in driver and executor memory usage in our jobs which contains star joins. By analyzing heapdump, we saw that majority of the memory was being taken up by {{UnsafeHashedRelation}} used for broadcast joins; in this case there were 92 broadcast joins in the query.

!image-2023-07-11-10-41-02-251.png|width=851,height=70!

This took up over 6GB of total memory, even though every table being broadcasted was around ~1MB and hence should only have been ~100MB total. I found that this is because {{BytesToBytesMap}} used within {{UnsafeHashedRelation}} allocates memory in ["pageSize" increments|https://github.com/apache/spark/blob/37aa62f629e652ed70505620473530cd9611018e/core/src/main/java/org/apache/spark/memory/MemoryConsumer.java#L117] which in our case was 64MB. Based on the [default page size calculation|https://github.com/apache/spark/blob/37aa62f629e652ed70505620473530cd9611018e/core/src/main/scala/org/apache/spark/memory/MemoryManager.scala#L251], this should be the case for any container with > 1 GB of memory (assuming executor.cores = 1) which is far too common. Thus in our case, most of the memory requested by {{BytesToBytesMap}} was un-utilized with just trailing 0s.

!image-2023-07-11-10-52-59-553.png|width=389,height=101!

I think this is a major inefficiency for broadcast joins (especially star joins). I think there are a few ways to tackle the problem.
1) Reduce {{spark.buffer.pageSize}} globally to a lower value. This does reduce the memory consumption of broadcast joins, but I am not sure what it implies for the rest of Spark machinery
2) Add a "finalize" operation to {{BytesToBytesMap}} which is called after all values are added to the map and allocates a new page only for the required bytes. 
3) Enhance the serialization of {{BytesToBytesMap}} to record the number of keys and values, and use those during deserialization to only request the required memory.
4) Use a lower page size for certain {{BytesToBytesMap}}s based on the estimated data size of broadcast joins.

I believe Option 3 would be simple enough to implement and I have a POC PR which I will post soon, but I am interested in knowing other people's thoughts here. 



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