Memory constrains running Flink on Kubernetes
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Hi, We're running a relatively simply Flink application that uses a bunch of state in RocksDB on Kubernetes. During the course of development and going to production, we found that we were often running into memory issues made apparent by Kubernetes OOMKilled and Java OOM log events. In order to tackle these, we're trying to account for all the memory used in the container, to allow proper tuning. Metric-wise we have: - container_memory_working_set_bytes = 6,5GB - flink_taskmanager_Status_JVM_Memory_Heap_Max = 4,7GB - flink_taskmanager_Status_JVM_Memory_NonHeap_Used = 325MB - flink_taskmanager_Status_JVM_Memory_Direct_MemoryUsed = 500MB This is my understanding based on all the documentation and observations: container_memory_working_set_bytes will be the total amount of memory in use, disregarding OS page & block cache. Heap will be heap. NonHeap is mostly the metaspace. Direct_Memory is mostly network buffers. Running the numbers I have 1 GB unaccounted for. I'm also uncertain as to RocksDB. According to the docs RocksDB has a "Column Family Write Buffer" where "You need to budget for 2 x your worst case memory use". We have 17 ValueStateDescriptors (ignoring state for windows) which I'm assuming corresponds to a "Column Family" in RockDB. Meaning our budget should be around 2GB. Is this accounted for in one of the flink_taskmanager metrics above? We've also enabled various rocksdb metrics, but it's unclear where this Write Buffer memory would be represented. Finally, we've seen that when our job has issues and is restarted rapidly, NonHeap_Used grows from an initial 50Mb to 700MB, before our containers are killed. We're assuming this is due to no form of cleanup in the metaspace as classes get (re)loaded. These are our taskmanager JVM settings: -XX:+UseG1GC -XX:MaxDirectMemorySize=1G -XX:+UnlockExperimentalVMOptions -XX:+UseCGroupMemoryLimitForHeap -XX:MaxRAMFraction=2 With flink config: taskmanager.heap.size: 5000m state.backend: rocksdb state.backend.incremental: true state.backend.rocksdb.timer-service.factory: ROCKSDB Based on what we've observed we're thinking about setting -XX:MaxMetaspaceSize to a reasonable value, so that we at least get an error message which can easily be traced back to the behavior we're seeing. Okay, all that said let's sum up what we're asking here: - Is there any more insight into how memory is accounted for than our current metrics? - Which metric, if any accounts for RocksDB memory usage? - What's going on with the Metaspace growth we're seeing during job restarts, is there something we can do about this such as setting -XX:MaxMetaspaceSize? - Any other tips to improve reliability running in resource constrained environments such as Kubernetes? Thanks, William |
Re: Memory constrains running Flink on Kubernetes
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Hi, Flink acquires these 'Status_JVM_Memory' metrics through the MXBean library. According to MXBean document, non-heap is "the Java virtual machine manages memory other than the heap (referred as non-heap memory)". Not sure whether that is equivalent to the metaspace. If the '-XX:MaxMetaspaceSize', it should trigger metaspcae clean up when the limit is reached. As for RocksDB, it mainly uses non-java memory. Heap, non-heap and direct memory could be considered as java memory (or at least allocated through the java process). That means, RocksDB is actually using the memory that is accounted in the total K8s container memory but not accounted in neither of java heap / non-heap / direct memory, which in your case the 1GB unaccounted. To leave more memory for RocksDB, you need to either configure more memory for the K8s containers, or configure less java memory through the config option 'taskmanager.heap.size'. The config option 'taskmanager.heap.size', despite the 'heap' in its key, also accounts for network memory (which uses direct buffers). Currently, memory configurations in Flink is quite complicated and confusing. The community is aware of this, and is planing for an overall improvement. To my understanding, once you set '-XX:MaxMetaspaceSize', there should be limits on heap, non-heap and direct memory in JVM. You should be able to find which part that requires memory more than the limit from the java OOM error message. If there is no java OOM but a K8s container OOM, then it should be non-java memory used by RocksDB. Thank you~ Xintong Song On Tue, Jul 23, 2019 at 8:42 PM wvl <[hidden email]> wrote:
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Hi William
Have you ever set the memory limit of your taskmanager pod when launching it in k8s? If not, I'm afraid your node might come across node out-of-memory [1]. You could increase the limit by analyzing your memory usage
When talking about the memory usage of RocksDB, a rough calculation formula could be: block-cache-memory + column-family-number * write-buffer-memory * write-buffer-number + index&filter memory. The block cache, write buffer memory&number could be mainly configured.
And the column-family number is decided by the state number within your operator. The last part of index&filter memory cannot be measured well only if you also cache them in block cache [2] (but this would impact the performance).
If you want to the memory stats of rocksDB, turn on the native metrics of RocksDB [3] is a good choice.
[3]
https://ci.apache.org/projects/flink/flink-docs-release-1.8/ops/config.html#rocksdb-native-metrics
Best
Yun Tang
From: Xintong Song <[hidden email]>
Sent: Wednesday, July 24, 2019 11:59 To: wvl <[hidden email]> Cc: user <[hidden email]> Subject: Re: Memory constrains running Flink on Kubernetes Hi,
Flink acquires these 'Status_JVM_Memory' metrics through the MXBean library. According to MXBean document, non-heap is "the Java virtual machine manages memory other than the heap (referred as non-heap memory)". Not sure whether that is equivalent to the
metaspace. If the '-XX:MaxMetaspaceSize', it should trigger metaspcae clean up when the limit is reached.
As for RocksDB, it mainly uses non-java memory. Heap, non-heap and direct memory could be considered as java memory (or at least allocated through the java process). That means, RocksDB is actually using the memory that is accounted in the total K8s container
memory but not accounted in neither of java heap / non-heap / direct memory, which in your case the 1GB unaccounted. To leave more memory for RocksDB, you need to either configure more memory for the K8s containers, or configure less java memory through the
config option 'taskmanager.heap.size'.
The config option 'taskmanager.heap.size', despite the 'heap' in its key, also accounts for network memory (which uses direct buffers). Currently, memory configurations in Flink is quite complicated and confusing. The community is aware of this, and is
planing for an overall improvement.
To my understanding, once you set '-XX:MaxMetaspaceSize', there should be limits on heap, non-heap and direct memory in JVM. You should be able to find which part that requires memory more than the limit from the java OOM error message. If there is no
java OOM but a K8s container OOM, then it should be non-java memory used by RocksDB.
Thank you~ Xintong Song On Tue, Jul 23, 2019 at 8:42 PM wvl <[hidden email]> wrote:
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Hi, The heap in a flink TaskManager k8s pod include the following parts:
So if your k8s pod is terminated by OOMKilled, the cause may be the non-heap memory or native memory. I suggest you add an environment FLINK_ENV_JAVA_OPTS_TM="-XX:MaxMetaspaceSize=512m" in your taskmanager.yaml. And then only the native memory could cause OOM. Leave enough memory for rocksdb, and then hope your job could run smoothly. Yun Tang <[hidden email]> 于2019年7月24日周三 下午3:01写道：
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Thanks for all the answers so far. Especially clarifying was that RocksDB memory usage isn't accounted for in the flink memory metrics. It's clear that we need to experiment to understand it's memory usage and knowing that we should be looking at the container memory usage minus all the jvm managed memory, helps. In mean while, we've set MaxMetaspaceSize to 200M based on our metrics. Sadly the resulting OOM does not result a better behaved job, because it would seem that the (taskmanager) JVM itself is not restarted - which makes sense in a multijob environment. So we're looking into ways to simply prevent this metaspace growth (job library jars in /lib on TM). Going back to RocksDB, the given formula "block-cache-memory + column-family-number * write-buffer-memory * write-buffer-number + index&filter memory." isn't completely clear to me. Block Cache: "Out of box, RocksDB will use LRU-based block cache implementation with 8MB capacity" Index & Filter Cache: "By default index and filter blocks are cached outside of block cache, and users won't be able to control how much memory should be use to cache these blocks, other than setting max_open_files.". The default settings doesn't set max_open_files and the rocksdb default seems to be 1000 (https://github.com/facebook/rocksdb/blob/master/include/rocksdb/utilities/leveldb_options.h#L89) .. not completely sure about this. Write Buffer Memory: "The default is 64 MB. You need to budget for 2 x your worst case memory use." May I presume a unique ValueStateDescriptor equals a Column Family? If so, say I have 10 of those. 8MB + (10 * 64 * 2) + $Index&FilterBlocks So is that correct and how would one calculate $Index&FilterBlocks? The docs suggest a relationship between max_open_files (1000) and the amount index/filter of blocks that can be cached, but is this a 1 to 1 relationship? Anyway, this concept of blocks is very unclear. > Have you ever set the memory limit of your taskmanager pod when launching it in k8s? Definitely. We settled on 8GB pods with taskmanager.heap.size: 5000m and 1 slot and were looking into downsizing a bit to improve our pod to VM ratio. On Wed, Jul 24, 2019 at 11:07 AM Yang Wang <[hidden email]> wrote:
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Hi
It's definitely not easy to calculate the accurate memory usage of RocksDB, but formula of "block-cache-memory + column-family-number * write-buffer-memory * write-buffer-number + index&filter memory" should give enough sophisticated hints.
When talking about the column-family-number, they are equals to the number of your states which are the declared state descriptors in one operator and potential one window state (if you're using window).
The default writer-buffer-number is 2 at most for each column family, and the default write-buffer-memory size is 4MB. Pay attention that if you ever configure the options for RocksDB, these memory usage would differ from default values.
The last part of index&filter memory is not easy to estimate, but from my experience this part of memory would not occupy too much only if you have many open files.
Last but not least, Flink would enable slot sharing by default, and even if you only one slot per taskmanager, there might exists many RocksDB within that TM due to many operator with keyed state running.
Apart from the theoretical analysis, you'd better to open RocksDB native metrics or track the memory usage of pods through Prometheus with k8s.
Best
Yun Tang
From: wvl <[hidden email]>
Sent: Thursday, July 25, 2019 17:50 To: Yang Wang <[hidden email]> Cc: Yun Tang <[hidden email]>; Xintong Song <[hidden email]>; user <[hidden email]> Subject: Re: Memory constrains running Flink on Kubernetes Thanks for all the answers so far.
Especially clarifying was that RocksDB memory usage isn't accounted for in the flink memory metrics. It's clear that we need to experiment to understand it's memory usage and knowing that we should be looking at the container memory usage minus all the jvm managed memory, helps. In mean while, we've set MaxMetaspaceSize to 200M based on our metrics. Sadly the resulting OOM does not result a better behaved job, because it would seem that the (taskmanager) JVM itself is not restarted - which makes sense in a multijob environment. So we're looking into ways to simply prevent this metaspace growth (job library jars in /lib on TM). Going back to RocksDB, the given formula "block-cache-memory + column-family-number * write-buffer-memory * write-buffer-number + index&filter memory." isn't completely clear to me. Block Cache: "Out of box, RocksDB will use LRU-based block cache implementation with 8MB capacity" Index & Filter Cache: "By default index and filter blocks are cached outside of block cache, and users won't be able to control how much memory should be use to cache these blocks, other than setting max_open_files.". The default settings doesn't set max_open_files and the rocksdb default seems to be 1000 (https://github.com/facebook/rocksdb/blob/master/include/rocksdb/utilities/leveldb_options.h#L89) .. not completely sure about this. Write Buffer Memory: "The default is 64 MB. You need to budget for 2 x your worst case memory use." May I presume a unique ValueStateDescriptor equals a Column Family? If so, say I have 10 of those. 8MB + (10 * 64 * 2) + $Index&FilterBlocks So is that correct and how would one calculate $Index&FilterBlocks? The docs suggest a relationship between max_open_files (1000) and the amount index/filter of blocks that can be cached, but is this a 1 to 1 relationship? Anyway, this concept of blocks is very unclear. > Have you ever set the memory limit of your taskmanager pod when launching it in k8s? Definitely. We settled on 8GB pods with taskmanager.heap.size: 5000m and 1 slot and were looking into downsizing a bit to improve our pod to VM ratio. On Wed, Jul 24, 2019 at 11:07 AM Yang Wang <[hidden email]> wrote:
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For the memory usage of RocksDB, there's already some discussion in FLINK-7289 and a good suggestion from Mike to use the WriteBufferManager to limit the total memory usage, FYI. We will drive to make the memory management of state backends more "hands free" in latter release (probably in release 1.10) and please watch the release plan and/or the weekly community update [1] threads. On Thu, 25 Jul 2019 at 15:12, Yun Tang <[hidden email]> wrote:
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Excellent. Thanks for all the answers so far. So there was another issue I mentioned which we made some progress gaining insight into, namely our metaspace growth when faced with job restarts. We can easily hit 1Gb metaspace usage within 15 minutes if we restart often. Then we stumbled upon this ticket. Now here are our results running the SocketWindowWordCount jar in a flink 1.8.0 cluster with one taskmanager. We achieve a class count by doing a jcmd 3052 GC.class_stats | grep -i org.apache.flink.streaming.examples.windowing.SessionWindowing | wc -l First run: After 800~ runs:
I've added this information to https://issues.apache.org/jira/browse/FLINK-11205. That said, I'd really like to confirm the following: On Mon, Jul 29, 2019 at 9:52 AM Yu Li <[hidden email]> wrote:
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Btw, with regard to: > The default writer-buffer-number is 2 at most for each column family, and the default write-buffer-memory size is 4MB. This isn't what I see when looking at the OPTIONS-XXXXXX file in the rocksdb directories in state: [CFOptions "xxxxxx"] ttl=0 report_bg_io_stats=false compaction_options_universal={allow_trivial_move=false;size_ratio=1;min_merge_width=2;max_size_amplification_percent=200;max_merge_width=4294967295;compression_size_percent=-1;stop_style=kCompactionStopStyleTotalSize;} table_factory=BlockBasedTable paranoid_file_checks=false compression_per_level= inplace_update_support=false soft_pending_compaction_bytes_limit=68719476736 max_successive_merges=0 max_write_buffer_number=2 level_compaction_dynamic_level_bytes=false max_bytes_for_level_base=268435456 optimize_filters_for_hits=false force_consistency_checks=false disable_auto_compactions=false max_compaction_bytes=1677721600 hard_pending_compaction_bytes_limit=274877906944 compaction_options_fifo={allow_compaction=false;max_table_files_size=1073741824;ttl=0;} max_bytes_for_level_multiplier=10.000000 level0_file_num_compaction_trigger=4 level0_slowdown_writes_trigger=20 compaction_pri=kByCompensatedSize compaction_filter=nullptr level0_stop_writes_trigger=36 write_buffer_size=67108864 min_write_buffer_number_to_merge=1 num_levels=7 target_file_size_multiplier=1 arena_block_size=8388608 memtable_huge_page_size=0 bloom_locality=0 inplace_update_num_locks=10000 memtable_prefix_bloom_size_ratio=0.000000 max_sequential_skip_in_iterations=8 max_bytes_for_level_multiplier_additional=1:1:1:1:1:1:1 compression=kSnappyCompression max_write_buffer_number_to_maintain=0 bottommost_compression=kDisableCompressionOption comparator=leveldb.BytewiseComparator prefix_extractor=nullptr target_file_size_base=67108864 merge_operator=StringAppendTESTOperator memtable_insert_with_hint_prefix_extractor=nullptr memtable_factory=SkipListFactory compaction_filter_factory=nullptr compaction_style=kCompactionStyleLevel Are these options somehow not applied or overridden? On Mon, Jul 29, 2019 at 4:42 PM wvl <[hidden email]> wrote:
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You are correct, the default value of write buffer size is 64 MB [1]. However, the java doc for this value is not correct [2]. Already created a PR to fix this.
Best
Yun Tang
From: wvl <[hidden email]>
Sent: Monday, August 5, 2019 17:55 To: Yu Li <[hidden email]> Cc: Yun Tang <[hidden email]>; Yang Wang <[hidden email]>; Xintong Song <[hidden email]>; user <[hidden email]> Subject: Re: Memory constrains running Flink on Kubernetes Btw, with regard to:
> The default writer-buffer-number is 2 at most for each column family, and the default write-buffer-memory size is 4MB.
This isn't what I see when looking at the OPTIONS-XXXXXX file in the rocksdb directories in state:
[CFOptions "xxxxxx"]
ttl=0 report_bg_io_stats=false compaction_options_universal={allow_trivial_move=false;size_ratio=1;min_merge_width=2;max_size_amplification_percent=200;max_merge_width=4294967295;compression_size_percent=-1;stop_style=kCompactionStopStyleTotalSize;} table_factory=BlockBasedTable paranoid_file_checks=false compression_per_level= inplace_update_support=false soft_pending_compaction_bytes_limit=68719476736 max_successive_merges=0 max_write_buffer_number=2 level_compaction_dynamic_level_bytes=false max_bytes_for_level_base=268435456 optimize_filters_for_hits=false force_consistency_checks=false disable_auto_compactions=false max_compaction_bytes=1677721600 hard_pending_compaction_bytes_limit=274877906944 compaction_options_fifo={allow_compaction=false;max_table_files_size=1073741824;ttl=0;} max_bytes_for_level_multiplier=10.000000 level0_file_num_compaction_trigger=4 level0_slowdown_writes_trigger=20 compaction_pri=kByCompensatedSize compaction_filter=nullptr level0_stop_writes_trigger=36 write_buffer_size=67108864 min_write_buffer_number_to_merge=1 num_levels=7 target_file_size_multiplier=1 arena_block_size=8388608 memtable_huge_page_size=0 bloom_locality=0 inplace_update_num_locks=10000 memtable_prefix_bloom_size_ratio=0.000000 max_sequential_skip_in_iterations=8 max_bytes_for_level_multiplier_additional=1:1:1:1:1:1:1 compression=kSnappyCompression max_write_buffer_number_to_maintain=0 bottommost_compression=kDisableCompressionOption comparator=leveldb.BytewiseComparator prefix_extractor=nullptr target_file_size_base=67108864 merge_operator=StringAppendTESTOperator memtable_insert_with_hint_prefix_extractor=nullptr memtable_factory=SkipListFactory compaction_filter_factory=nullptr compaction_style=kCompactionStyleLevel Are these options somehow not applied or overridden?
On Mon, Jul 29, 2019 at 4:42 PM wvl <[hidden email]> wrote:
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In reply to this post by wvl
+1 I also encountered a similar problem, but I run flink application that uses state in RocksDB on yarn. Yarn container was killed because OOM. I also saw rockdb tuning guide[1], tune some parameters,but it is useless , such as: class MyOptions1 implements OptionsFactory { @Override public DBOptions createDBOptions(DBOptions currentOptions) { return currentOptions.setDbWriteBufferSize(64 * 1024 * 1024) .setIncreaseParallelism(2) .setMaxBackgroundFlushes(2) .setInfoLogLevel(InfoLogLevel.DEBUG_LEVEL) .setMaxOpenFiles(4) .setUseFsync(false); } @Override public ColumnFamilyOptions createColumnOptions(ColumnFamilyOptions currentOptions) { return currentOptions.setTableFormatConfig( new BlockBasedTableConfig() .setBlockCacheSize(16 * 1024 * 1024) //increases read amplification but decreases memory useage and space amplification .setBlockSize(16 * 1024 * 1024)) .setWriteBufferSize(16 * 1024 * 1024) .setMaxWriteBufferNumber(1); } } Additional, this is FLINK-7289, it is similar to us. But I don’t find a good way to fix it. Best, Shengjk1 On 07/24/2019 03:48，[hidden email] wrote：
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Hi Shengjk1
setBlockCacheSize,
setWriteBufferSize and
setMaxWriteBufferNumber could help you to control memory usage. However, Flink would store state per column family which would increase the number of column family and each family has its own write
buffer. FRocksDB [1] already plan to fix this by introducing RocksDB's feature of write buffer manager. We would try to fix
FLINK-7289 before Flink-1.10 release.
If you are really urgent to fix this problem, I have a non-official built frocksDB based on rocksDB-5.18.3 which had been verified work well from Gyula Fora's experience. You could contact me in private to get this jar package and rebuild your Flink runtime
to enable write buffer manager future.
Best
Yun Tang
From: shengjk1 <[hidden email]>
Sent: Thursday, October 10, 2019 20:37 To: wvl <[hidden email]> Cc: [hidden email] <[hidden email]> Subject: Re:Memory constrains running Flink on Kubernetes +1
I also encountered a similar problem, but I run flink application that uses state in RocksDB on yarn. Yarn container was killed because OOM.
I also saw rockdb tuning guide[1], tune some parameters,but it is useless , such as:
class MyOptions1 implements OptionsFactory {
@Override
public DBOptions createDBOptions(DBOptions currentOptions) {
return currentOptions.setDbWriteBufferSize(64 * 1024 * 1024)
.setIncreaseParallelism(2)
.setMaxBackgroundFlushes(2)
.setInfoLogLevel(InfoLogLevel.DEBUG_LEVEL)
.setMaxOpenFiles(4)
.setUseFsync(false);
}
@Override
public ColumnFamilyOptions createColumnOptions(ColumnFamilyOptions currentOptions) {
return currentOptions.setTableFormatConfig(
new BlockBasedTableConfig()
.setBlockCacheSize(16 * 1024 * 1024)
//increases read amplification but decreases memory useage and space amplification
.setBlockSize(16 * 1024 * 1024))
.setWriteBufferSize(16 * 1024 * 1024)
.setMaxWriteBufferNumber(1);
}
}
Additional, this is FLINK-7289, it is similar to us. But I don’t find a good way to fix it.
Best,
Shengjk1
On 07/24/2019 03:48，[hidden email] wrote：
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