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Posted to github@beam.apache.org by GitBox <gi...@apache.org> on 2021/08/09 20:47:00 UTC
[GitHub] [beam] anthonyqzhu opened a new pull request #15300: [WIP] [BEAM-10212] State cache memory limit
anthonyqzhu opened a new pull request #15300:
URL: https://github.com/apache/beam/pull/15300
Imposing a memory limit on the cross bundle state cache based on a percentage of memory allocated to the harness
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[GitHub] [beam] scwhittle commented on pull request #15300: [BEAM-10212] State cache memory limit
Posted by GitBox <gi...@apache.org>.
scwhittle commented on pull request #15300:
URL: https://github.com/apache/beam/pull/15300#issuecomment-907203685
One benefit of the static limit versus percentage is that if OOMs are caused by caching too much (or cache memory tracking not working as well as expected), the static limit allows for the machine size to be increased to avoid the OOMs as the cache will use the same amount of memory. If the max cache size is automatically increased proportional to the worker shape, the OOMs will just continue possibly negating VM right-sizing or manual size tuning.
Perhaps instead of a static value or a static percentage we could do a combination:
static_value + % of machine size
Then we don't scale entirely with the machine size but also will use a larger cache by default for larger machines.
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[GitHub] [beam] anthonyqzhu commented on a change in pull request #15300: [WIP] [BEAM-10212] State cache memory limit
Posted by GitBox <gi...@apache.org>.
anthonyqzhu commented on a change in pull request #15300:
URL: https://github.com/apache/beam/pull/15300#discussion_r686967100
##########
File path: sdks/java/harness/src/main/java/org/apache/beam/fn/harness/state/CachingBeamFnStateClient.java
##########
@@ -175,4 +179,71 @@ static StateCacheKey create(ByteString cacheToken, ByteString continuationToken)
return new AutoValue_CachingBeamFnStateClient_StateCacheKey(cacheToken, continuationToken);
}
}
+
+ /**
+ * Entry for a key in the state cache that adds a weight counter onto the map of cache keys to
+ * state responses.
+ */
+ public static class StateCacheEntry {
+ // Initial size of hash tables per entry.
+ private static final int INITIAL_HASH_MAP_CAPACITY = 4;
+ // Overhead of each hash map entry.
+ private static final int HASH_MAP_ENTRY_OVERHEAD = 16;
+ // Overhead of each StateCacheEntry. One long, plus a concurrent hash map
+ private static final int PER_CACHE_ENTRY_OVERHEAD =
+ 8 + HASH_MAP_ENTRY_OVERHEAD * INITIAL_HASH_MAP_CAPACITY;
Review comment:
These are based off of WindmillStateCache (https://github.com/apache/beam/blob/master/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/WindmillStateCache.java) - can someone let me know if these numbers are reasonable?
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[GitHub] [beam] anthonyqzhu commented on pull request #15300: [WIP] [BEAM-10212] State cache memory limit
Posted by GitBox <gi...@apache.org>.
anthonyqzhu commented on pull request #15300:
URL: https://github.com/apache/beam/pull/15300#issuecomment-895563924
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[GitHub] [beam] anthonyqzhu commented on pull request #15300: [BEAM-10212] State cache memory limit
Posted by GitBox <gi...@apache.org>.
anthonyqzhu commented on pull request #15300:
URL: https://github.com/apache/beam/pull/15300#issuecomment-896950737
Sorry for any annoyance I accidentally pushed testing changes to this branch ... it is now ready for review @amaliujia @lukecwik
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[GitHub] [beam] scwhittle commented on pull request #15300: [BEAM-10212] State cache memory limit
Posted by GitBox <gi...@apache.org>.
scwhittle commented on pull request #15300:
URL: https://github.com/apache/beam/pull/15300#issuecomment-908132864
Actually I see now you are saying that the amount of memory available for processing on a machine with a fixed amount of cpu but increased ram will be greater. That is true, however if the cache itself is the cause of OOMs (not user processing) it doesn't help.
With the windmill cache, the objects were cached using the serialized size as the weight but the cached objects were deserialized (as that added a large benefit to the cache). However this meant that the memory used by the cache could be much more than the configured limit depending on the objects cached. It seems here we might be caching the protos so perhaps the size estimate is reliable. However if we change to caching user objects in the future we might need to be more careful.
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[GitHub] [beam] lukecwik commented on pull request #15300: [WIP] [BEAM-10212] State cache memory limit
Posted by GitBox <gi...@apache.org>.
lukecwik commented on pull request #15300:
URL: https://github.com/apache/beam/pull/15300#issuecomment-895588554
> This is an initial implementation that reads max memory from a cgroup file (note: this does affect current tests such as RemoteExecution tests that are not containerized)
>
> Rui suggested using a hard coded cap vs a percentage like the legacy worker which defaulted to 100mb - wondering what your thoughts are @lukecwik
Using a hard-coded cap was a mistake as it didn't scale with machine size and in many cases we needed to tell users to specify a flag. Also a percent will likely work better with future VM right sizing work.
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[GitHub] [beam] anthonyqzhu commented on a change in pull request #15300: [WIP] [BEAM-10212] State cache memory limit
Posted by GitBox <gi...@apache.org>.
anthonyqzhu commented on a change in pull request #15300:
URL: https://github.com/apache/beam/pull/15300#discussion_r686968345
##########
File path: sdks/java/harness/src/main/java/org/apache/beam/fn/harness/state/CachingBeamFnStateClient.java
##########
@@ -175,4 +179,71 @@ static StateCacheKey create(ByteString cacheToken, ByteString continuationToken)
return new AutoValue_CachingBeamFnStateClient_StateCacheKey(cacheToken, continuationToken);
}
}
+
+ /**
+ * Entry for a key in the state cache that adds a weight counter onto the map of cache keys to
+ * state responses.
+ */
+ public static class StateCacheEntry {
+ // Initial size of hash tables per entry.
+ private static final int INITIAL_HASH_MAP_CAPACITY = 4;
+ // Overhead of each hash map entry.
+ private static final int HASH_MAP_ENTRY_OVERHEAD = 16;
+ // Overhead of each StateCacheEntry. One long, plus a concurrent hash map
+ private static final int PER_CACHE_ENTRY_OVERHEAD =
+ 8 + HASH_MAP_ENTRY_OVERHEAD * INITIAL_HASH_MAP_CAPACITY;
Review comment:
These are based off of WindmillStateCache (https://github.com/apache/beam/blob/master/runners/google-cloud-dataflow-java/worker/src/main/java/org/apache/beam/runners/dataflow/worker/WindmillStateCache.java) - can someone let me know if these numbers are reasonable?
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[GitHub] [beam] anthonyqzhu commented on pull request #15300: [WIP] [BEAM-10212] State cache memory limit
Posted by GitBox <gi...@apache.org>.
anthonyqzhu commented on pull request #15300:
URL: https://github.com/apache/beam/pull/15300#issuecomment-895566696
This is an initial implementation that reads max memory from a cgroup file (note: this does affect current tests such as RemoteExecution tests that are not containerized)
Rui suggested using a hard coded cap vs a percentage like the legacy worker which defaulted to 100mb - wondering what your thoughts are @lukecwik
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[GitHub] [beam] amaliujia closed pull request #15300: [BEAM-10212] State cache memory limit
Posted by GitBox <gi...@apache.org>.
amaliujia closed pull request #15300:
URL: https://github.com/apache/beam/pull/15300
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[GitHub] [beam] anthonyqzhu commented on pull request #15300: [WIP] [BEAM-10212] State cache memory limit
Posted by GitBox <gi...@apache.org>.
anthonyqzhu commented on pull request #15300:
URL: https://github.com/apache/beam/pull/15300#issuecomment-896948354
Run Java PreCommit
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[GitHub] [beam] lukecwik commented on pull request #15300: [BEAM-10212] State cache memory limit
Posted by GitBox <gi...@apache.org>.
lukecwik commented on pull request #15300:
URL: https://github.com/apache/beam/pull/15300#issuecomment-907350175
> One benefit of the static limit versus percentage is that if OOMs are caused by caching too much (or cache memory tracking not working as well as expected), the static limit allows for the machine size to be increased to avoid the OOMs as the cache will use the same amount of memory. If the max cache size is automatically increased proportional to the worker shape, the OOMs will just continue possibly negating VM right-sizing or manual size tuning.
>
> Perhaps instead of a static value or a static percentage we could do a combination:
> static_value + % of machine size
> Then we don't scale entirely with the machine size but also will use a larger cache by default for larger machines.
VMR and/or users should specify a machine with _more memory_ and not one with _more cores and memory_ since we want to increase the amount of memory per core. This would still allow a pure percentage based model to work since a machine with _more memory_ will still have a greater amount of memory that is not dedicated to the cache.
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[GitHub] [beam] scwhittle commented on pull request #15300: [BEAM-10212] State cache memory limit
Posted by GitBox <gi...@apache.org>.
scwhittle commented on pull request #15300:
URL: https://github.com/apache/beam/pull/15300#issuecomment-908127637
I was assuming you were talking about percentage of total memory for the process would be given to cache (and this is the current implementation). Are you instead proposing an option of bytes-per-core for cache scaling? That seems like it would scale with larger machines though it is a little unclear what a reasonable default would be given that there the ram/cpu ratio might vary between vendors.
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