Details
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Improvement
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Status: Closed
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Major
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Resolution: Fixed
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None
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None
Description
I've created a Hive table with 2000 partitions, each backed by two files, with one row in each file. When I execute some number of concurrent queries against this table, e.g. as follows
for i in `seq 1 50`; do beeline -u jdbc:hive2://localhost:10000 -n admin -p admin -e "select count(i_f_1) from misha_table;" & done
it results in a big memory spike. With 20 queries I caused an OOM in a HS2 server with -Xmx200m and with 50 queries - in the one with -Xmx500m.
I am attaching the results of jxray (www.jxray.com) analysis of a heap dump that was generated in the 50queries/500m heap scenario. It suggests that there are several opportunities to reduce memory pressure with not very invasive changes to the code. One (duplicate strings) has been addressed in https://issues.apache.org/jira/browse/HIVE-15882 In this ticket, I am going to address the fact that almost 20% of memory is used by instances of java.util.Properties. These objects are highly duplicate, since for each partition each concurrently running query creates its own copy of Partion, PartitionDesc and Properties. Thus we have nearly 100,000 (50 queries * 2,000 partitions) Properties in memory. By interning/deduplicating these objects we may be able to save perhaps 15% of memory.
Note, however, that if there are queries that mutate partitions, the corresponding Properties would be mutated as well. Thus we cannot simply use a single "canonicalized" Properties object at all times for all Partition objects representing the same DB partition. Instead, I am going to introduce a special CopyOnFirstWriteProperties class. Such an object initially internally references a canonicalized Properties object, and keeps doing so while only read methods are called. However, once any mutating method is called, the given CopyOnFirstWriteProperties copies the data into its own table from the canonicalized table, and uses it ever after.
Attachments
Attachments
Issue Links
- relates to
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HIVE-15882 HS2 generating high memory pressure with many partitions and concurrent queries
- Resolved