Debugging "Container is running beyond physical memory limits" on YARN for a long running streaming job

[Shubham Kumar]

Hey everyone, 

We had deployed a streaming job using Flink 1.10.1 one month back and now we are encountering a Yarn container killed due to memory issues very frequently. I am trying to figure out the root cause of this issue in order to fix it. 

 

We have a streaming job whose basic structure looks like this:

- Read 6 kafka streams and combine stats from them (union) to form a single stream

- stream.keyBy(MyKey)

             .window(TumblingEventTimeWindows.of(Time.minutes(1)))

             .reduce(MyReduceFunction)

             .addSink(new FlinkKafkaProducer011<>...);

We are using RocksDB as state backend. In flink-conf.yaml, we used taskmanager.memory.process.size = 10GB with a parallelism of 12 and only one slot per task manager.

So, a taskmanager process gets started with the following memory components as indicated in logs:

TaskExecutor container... will be started on ... with TaskExecutorProcessSpec {cpuCores=1.0, frameworkHeapSize=128.000mb (134217728 bytes), frameworkOffHeapSize=128.000mb (134217728 bytes), taskHeapSize=4.125gb (4429184954 bytes), taskOffHeapSize=0 bytes, networkMemSize=896.000mb (939524110 bytes), managedMemorySize=3.500gb (3758096440 bytes), jvmMetaspaceSize=256.000mb (268435456 bytes), jvmOverheadSize=1024.000mb (1073741824 bytes)}.

 

  

 which are as per defaults.

Now, after 25 days we started encountering the following yarn container kill error:

Association with remote system [akka.tcp://flink@...] has failed, address is now gated for [50] ms. Reason: [Association failed with [akka.tcp://flink@...]] Caused by: [java.net.ConnectException: Connection refused: .../...:37679]

2020-09-09 00:53:24 INFO Closing TaskExecutor connection container_e193_1592804717489_149347_01_000011 because: [2020-09-09 00:53:21.417]Container [pid=44371,containerID=container_e193_1592804717489_149347_01_000011] is running beyond physical memory limits. Current usage: 12.0 GB of 12 GB physical memory used; 14.4 GB of 25.2 GB virtual memory used. Killing container.

Yarn container size is 12GB as it is only allowed as a multiple of 3 GB (as per our settings).

Now, when the YARN reallocates a new container, the program starts again (without any issues) and after a few hours another container is killed with the same error and the cycle repeats.

At this point, I want to debug it as a running process without changing or playing around with various config options for memory as I don't think just to reproduce the error, I want to wait for ~1 month.

I have tried to figure out something from Graphite metrics (see attachments):

[1]: JVM Heap Memory (First 25 days) -> The memory goes up and after reaching a point goes does and again starts going up. (No container kills were encountered until 09/09/2020, program started on 14/08/2020)

[2]: JVM Heap Memory (Recent) -> The memory is still going up but it seems it doesn't even reaches its peak, but instead container is killed before that itself (within a few hours)

From [1] and [2], JVM heap memory should not rise up I think, but that doesn't explain container kill in [2] case if JVM heap memory was the issue causing container kill.

[3]: Direct Memory and Off heap Memory -> I don't think this is causing the issue as most of the network buffers are free and off heap memory is well below threshold.

At this point I thought RocksDB might be the culprit. I am aware that it uses the managed memory limits (I haven't changed any default config) which is completely off heap. But when I see the rocksDB size maintained at location:

/data_4/yarn-nm-local-dir/usercache/root/appcache/application_.../flink-io-a48d1127-58a1-41c5-a5f0-32c5180fe74d/job_0bff1881431b5774c3b496a98febed1a_op_WindowOperator_4061fbe16fb95459a1a8d207644e2e63__4_12__uuid_9fe0b2ff-24bc-4301-8044-3fe8e1b3a3a0/db/ 

 

It is only 17MB which doesn't seem much. I also took a heap dump of org.apache.flink.yarn.YarnTaskExecutorRunner process but it shows only 30MB of data is being used (not sure what I am missing here as it doesn't match with metrics shown by flink).

Although top -p 'pid' (for task manager process) does show RES = 10-12 GB for every container constantly going up and eventually dies.

Has someone encountered a similar situation or have guidelines that I can continue with to figure out and debug the issue? Let me know if there is anything else that you might wanna know.

--

[Xintong Song]

Hi Shubham,

Java heap memory cannot cause a container memory exceeding. Heap memory is strictly limited by the JVM `-Xmx` parameter. If the program does need more memory than the limit, it will run into a heap space OOM, rather than implicitly using more memory than the limit.

Several reasons that might lead to container memory exceeding.

- RocksDB, whose memory controlling is based on estimation rather than hard limit. This is one of the most common reasons for such memory exceedings. However, usually the extra memory usage introduced by RocksDB, if there's any, should not be too large. Given that your container size is 12GB and Flink only plans to use 10GB, I'm not sure whether RocksDB is the cause in your case. I've CC'ed @Yun Tang, who is the expert of Flink's RocksDB state backend.

- Does your job use mmap memory? MMap memory, if used, is controlled by the operating system, not Flink. Depending on your Yarn cgroup configurations, some clusters would also count that as part of the container memory consumption.

- Native memory leaks in user code dependencies and libraries could also lead to container memory exceeding.

Another suggestion is, do not trust Flink's "Non-Heap" metrics. It is practically helpless and misleading. The "Non-Heap" accounts for SOME of the non-heap memory usage, but NOT ALL of them. The community is working on a new set of metrics and Web UI for the task manager memory tuning.

On Sun, Sep 20, 2020 at 12:10 AM Shubham Kumar <[hidden email]> wrote:

Hey everyone, 

We had deployed a streaming job using Flink 1.10.1 one month back and now we are encountering a Yarn container killed due to memory issues very frequently. I am trying to figure out the root cause of this issue in order to fix it. 

 

We have a streaming job whose basic structure looks like this:

- Read 6 kafka streams and combine stats from them (union) to form a single stream

- stream.keyBy(MyKey)

             .window(TumblingEventTimeWindows.of(Time.minutes(1)))

             .reduce(MyReduceFunction)

             .addSink(new FlinkKafkaProducer011<>...);

We are using RocksDB as state backend. In flink-conf.yaml, we used taskmanager.memory.process.size = 10GB with a parallelism of 12 and only one slot per task manager.

So, a taskmanager process gets started with the following memory components as indicated in logs:

TaskExecutor container... will be started on ... with TaskExecutorProcessSpec {cpuCores=1.0, frameworkHeapSize=128.000mb (134217728 bytes), frameworkOffHeapSize=128.000mb (134217728 bytes), taskHeapSize=4.125gb (4429184954 bytes), taskOffHeapSize=0 bytes, networkMemSize=896.000mb (939524110 bytes), managedMemorySize=3.500gb (3758096440 bytes), jvmMetaspaceSize=256.000mb (268435456 bytes), jvmOverheadSize=1024.000mb (1073741824 bytes)}.

 

  

 which are as per defaults.

Now, after 25 days we started encountering the following yarn container kill error:

Association with remote system [akka.tcp://flink@...] has failed, address is now gated for [50] ms. Reason: [Association failed with [akka.tcp://flink@...]] Caused by: [java.net.ConnectException: Connection refused: .../...:37679]

2020-09-09 00:53:24 INFO Closing TaskExecutor connection container_e193_1592804717489_149347_01_000011 because: [2020-09-09 00:53:21.417]Container [pid=44371,containerID=container_e193_1592804717489_149347_01_000011] is running beyond physical memory limits. Current usage: 12.0 GB of 12 GB physical memory used; 14.4 GB of 25.2 GB virtual memory used. Killing container.

Yarn container size is 12GB as it is only allowed as a multiple of 3 GB (as per our settings).

Now, when the YARN reallocates a new container, the program starts again (without any issues) and after a few hours another container is killed with the same error and the cycle repeats.

At this point, I want to debug it as a running process without changing or playing around with various config options for memory as I don't think just to reproduce the error, I want to wait for ~1 month.

I have tried to figure out something from Graphite metrics (see attachments):

[1]: JVM Heap Memory (First 25 days) -> The memory goes up and after reaching a point goes does and again starts going up. (No container kills were encountered until 09/09/2020, program started on 14/08/2020)

[2]: JVM Heap Memory (Recent) -> The memory is still going up but it seems it doesn't even reaches its peak, but instead container is killed before that itself (within a few hours)

From [1] and [2], JVM heap memory should not rise up I think, but that doesn't explain container kill in [2] case if JVM heap memory was the issue causing container kill.

[3]: Direct Memory and Off heap Memory -> I don't think this is causing the issue as most of the network buffers are free and off heap memory is well below threshold.

At this point I thought RocksDB might be the culprit. I am aware that it uses the managed memory limits (I haven't changed any default config) which is completely off heap. But when I see the rocksDB size maintained at location:

/data_4/yarn-nm-local-dir/usercache/root/appcache/application_.../flink-io-a48d1127-58a1-41c5-a5f0-32c5180fe74d/job_0bff1881431b5774c3b496a98febed1a_op_WindowOperator_4061fbe16fb95459a1a8d207644e2e63__4_12__uuid_9fe0b2ff-24bc-4301-8044-3fe8e1b3a3a0/db/ 

 

It is only 17MB which doesn't seem much. I also took a heap dump of org.apache.flink.yarn.YarnTaskExecutorRunner process but it shows only 30MB of data is being used (not sure what I am missing here as it doesn't match with metrics shown by flink).

Although top -p 'pid' (for task manager process) does show RES = 10-12 GB for every container constantly going up and eventually dies.

Has someone encountered a similar situation or have guidelines that I can continue with to figure out and debug the issue? Let me know if there is anything else that you might wanna know.

--

Thanks & Regards

Shubham Kumar

[Shubham Kumar]

Hi Xintong,

Thanks for your insights, they are really helpful.

I understand now that it most certainly is a native memory issue rather than a heap memory issue and about not trusting Flink's Non-Heap metrics.

I do believe that our structure of job is so simple that I couldn't find any use of mmap memory or any other straight forward native memory leak issue. That leads me to believing that it can be a rocksDB issue, although you do make a valid point about that there is extra 2GB in the yarn container which should account for RocksDB extra usage. I also saw this JIRA ticket for RocksDB memory leak issue on K8 kubernetes and was wondering if the same could happen on yarn containers and is related to my issue [1]. Let me know what you guys think about this.

Also, I tried running the same job using FileSystemBackend (as a separate job) and it went fine with no container kills and native memory not rising over time, which hints further towards RocksDB being the culprit. My state size in the checkpoint is around 1GB (can probably even think of switching to FileSystemBackend for this job but still want to figure out the case for RocksDB). I am using incremental checkpoints in my main job which has RocksDB state backend, if that's relevant.

I read about native memory tracking and probably go ahead and use Native Memory Tracking (NMT) or jemalloc to confirm about the RocksDB issue and update here.

[1]: https://issues.apache.org/jira/browse/FLINK-18712

Thanks

Shubham

On Mon, Sep 21, 2020 at 8:23 AM Xintong Song <[hidden email]> wrote:

Hi Shubham,

Java heap memory cannot cause a container memory exceeding. Heap memory is strictly limited by the JVM `-Xmx` parameter. If the program does need more memory than the limit, it will run into a heap space OOM, rather than implicitly using more memory than the limit.

Several reasons that might lead to container memory exceeding.

- RocksDB, whose memory controlling is based on estimation rather than hard limit. This is one of the most common reasons for such memory exceedings. However, usually the extra memory usage introduced by RocksDB, if there's any, should not be too large. Given that your container size is 12GB and Flink only plans to use 10GB, I'm not sure whether RocksDB is the cause in your case. I've CC'ed @Yun Tang, who is the expert of Flink's RocksDB state backend.

- Does your job use mmap memory? MMap memory, if used, is controlled by the operating system, not Flink. Depending on your Yarn cgroup configurations, some clusters would also count that as part of the container memory consumption.

- Native memory leaks in user code dependencies and libraries could also lead to container memory exceeding.

Another suggestion is, do not trust Flink's "Non-Heap" metrics. It is practically helpless and misleading. The "Non-Heap" accounts for SOME of the non-heap memory usage, but NOT ALL of them. The community is working on a new set of metrics and Web UI for the task manager memory tuning.

Thank you~

Xintong Song

On Sun, Sep 20, 2020 at 12:10 AM Shubham Kumar <[hidden email]> wrote:

Hey everyone, 

We had deployed a streaming job using Flink 1.10.1 one month back and now we are encountering a Yarn container killed due to memory issues very frequently. I am trying to figure out the root cause of this issue in order to fix it. 

 

We have a streaming job whose basic structure looks like this:

- Read 6 kafka streams and combine stats from them (union) to form a single stream

- stream.keyBy(MyKey)

             .window(TumblingEventTimeWindows.of(Time.minutes(1)))

             .reduce(MyReduceFunction)

             .addSink(new FlinkKafkaProducer011<>...);

We are using RocksDB as state backend. In flink-conf.yaml, we used taskmanager.memory.process.size = 10GB with a parallelism of 12 and only one slot per task manager.

So, a taskmanager process gets started with the following memory components as indicated in logs:

TaskExecutor container... will be started on ... with TaskExecutorProcessSpec {cpuCores=1.0, frameworkHeapSize=128.000mb (134217728 bytes), frameworkOffHeapSize=128.000mb (134217728 bytes), taskHeapSize=4.125gb (4429184954 bytes), taskOffHeapSize=0 bytes, networkMemSize=896.000mb (939524110 bytes), managedMemorySize=3.500gb (3758096440 bytes), jvmMetaspaceSize=256.000mb (268435456 bytes), jvmOverheadSize=1024.000mb (1073741824 bytes)}.

 

  

 which are as per defaults.

Now, after 25 days we started encountering the following yarn container kill error:

Association with remote system [akka.tcp://flink@...] has failed, address is now gated for [50] ms. Reason: [Association failed with [akka.tcp://flink@...]] Caused by: [java.net.ConnectException: Connection refused: .../...:37679]

2020-09-09 00:53:24 INFO Closing TaskExecutor connection container_e193_1592804717489_149347_01_000011 because: [2020-09-09 00:53:21.417]Container [pid=44371,containerID=container_e193_1592804717489_149347_01_000011] is running beyond physical memory limits. Current usage: 12.0 GB of 12 GB physical memory used; 14.4 GB of 25.2 GB virtual memory used. Killing container.

Yarn container size is 12GB as it is only allowed as a multiple of 3 GB (as per our settings).

Now, when the YARN reallocates a new container, the program starts again (without any issues) and after a few hours another container is killed with the same error and the cycle repeats.

At this point, I want to debug it as a running process without changing or playing around with various config options for memory as I don't think just to reproduce the error, I want to wait for ~1 month.

I have tried to figure out something from Graphite metrics (see attachments):

[1]: JVM Heap Memory (First 25 days) -> The memory goes up and after reaching a point goes does and again starts going up. (No container kills were encountered until 09/09/2020, program started on 14/08/2020)

[2]: JVM Heap Memory (Recent) -> The memory is still going up but it seems it doesn't even reaches its peak, but instead container is killed before that itself (within a few hours)

From [1] and [2], JVM heap memory should not rise up I think, but that doesn't explain container kill in [2] case if JVM heap memory was the issue causing container kill.

[3]: Direct Memory and Off heap Memory -> I don't think this is causing the issue as most of the network buffers are free and off heap memory is well below threshold.

At this point I thought RocksDB might be the culprit. I am aware that it uses the managed memory limits (I haven't changed any default config) which is completely off heap. But when I see the rocksDB size maintained at location:

/data_4/yarn-nm-local-dir/usercache/root/appcache/application_.../flink-io-a48d1127-58a1-41c5-a5f0-32c5180fe74d/job_0bff1881431b5774c3b496a98febed1a_op_WindowOperator_4061fbe16fb95459a1a8d207644e2e63__4_12__uuid_9fe0b2ff-24bc-4301-8044-3fe8e1b3a3a0/db/ 

 

It is only 17MB which doesn't seem much. I also took a heap dump of org.apache.flink.yarn.YarnTaskExecutorRunner process but it shows only 30MB of data is being used (not sure what I am missing here as it doesn't match with metrics shown by flink).

Although top -p 'pid' (for task manager process) does show RES = 10-12 GB for every container constantly going up and eventually dies.

Has someone encountered a similar situation or have guidelines that I can continue with to figure out and debug the issue? Let me know if there is anything else that you might wanna know.

--

Thanks & Regards

Shubham Kumar

--

[Xintong Song]

Hi Shubham,

Concerning FLINK-18712, thanks for the pointer. I was not aware of this issue before. Running on Kubernetes or Yarn should not affect this issue. I cannot tell whether this issue is the cause of your problem. The simplest way to confirm this is probably just try the solution to see if that fixes your problem.

Given that it could take weeks to reproduce your problem, I would suggest to keep track of the native memory usage with jemalloc and jeprof. This should provide direct information about which component is using extra memory.

On Tue, Sep 22, 2020 at 10:42 PM Shubham Kumar <[hidden email]> wrote:

Hi Xintong,

Thanks for your insights, they are really helpful.

I understand now that it most certainly is a native memory issue rather than a heap memory issue and about not trusting Flink's Non-Heap metrics.

I do believe that our structure of job is so simple that I couldn't find any use of mmap memory or any other straight forward native memory leak issue. That leads me to believing that it can be a rocksDB issue, although you do make a valid point about that there is extra 2GB in the yarn container which should account for RocksDB extra usage. I also saw this JIRA ticket for RocksDB memory leak issue on K8 kubernetes and was wondering if the same could happen on yarn containers and is related to my issue [1]. Let me know what you guys think about this.

Also, I tried running the same job using FileSystemBackend (as a separate job) and it went fine with no container kills and native memory not rising over time, which hints further towards RocksDB being the culprit. My state size in the checkpoint is around 1GB (can probably even think of switching to FileSystemBackend for this job but still want to figure out the case for RocksDB). I am using incremental checkpoints in my main job which has RocksDB state backend, if that's relevant.

I read about native memory tracking and probably go ahead and use Native Memory Tracking (NMT) or jemalloc to confirm about the RocksDB issue and update here.

[1]: https://issues.apache.org/jira/browse/FLINK-18712

Thanks

Shubham

On Mon, Sep 21, 2020 at 8:23 AM Xintong Song <[hidden email]> wrote:

Hi Shubham,

Java heap memory cannot cause a container memory exceeding. Heap memory is strictly limited by the JVM `-Xmx` parameter. If the program does need more memory than the limit, it will run into a heap space OOM, rather than implicitly using more memory than the limit.

Several reasons that might lead to container memory exceeding.

- RocksDB, whose memory controlling is based on estimation rather than hard limit. This is one of the most common reasons for such memory exceedings. However, usually the extra memory usage introduced by RocksDB, if there's any, should not be too large. Given that your container size is 12GB and Flink only plans to use 10GB, I'm not sure whether RocksDB is the cause in your case. I've CC'ed @Yun Tang, who is the expert of Flink's RocksDB state backend.

- Does your job use mmap memory? MMap memory, if used, is controlled by the operating system, not Flink. Depending on your Yarn cgroup configurations, some clusters would also count that as part of the container memory consumption.

- Native memory leaks in user code dependencies and libraries could also lead to container memory exceeding.

Another suggestion is, do not trust Flink's "Non-Heap" metrics. It is practically helpless and misleading. The "Non-Heap" accounts for SOME of the non-heap memory usage, but NOT ALL of them. The community is working on a new set of metrics and Web UI for the task manager memory tuning.

Thank you~

Xintong Song

On Sun, Sep 20, 2020 at 12:10 AM Shubham Kumar <[hidden email]> wrote:

Hey everyone, 

We had deployed a streaming job using Flink 1.10.1 one month back and now we are encountering a Yarn container killed due to memory issues very frequently. I am trying to figure out the root cause of this issue in order to fix it. 

 

We have a streaming job whose basic structure looks like this:

- Read 6 kafka streams and combine stats from them (union) to form a single stream

- stream.keyBy(MyKey)

             .window(TumblingEventTimeWindows.of(Time.minutes(1)))

             .reduce(MyReduceFunction)

             .addSink(new FlinkKafkaProducer011<>...);

We are using RocksDB as state backend. In flink-conf.yaml, we used taskmanager.memory.process.size = 10GB with a parallelism of 12 and only one slot per task manager.

So, a taskmanager process gets started with the following memory components as indicated in logs:

TaskExecutor container... will be started on ... with TaskExecutorProcessSpec {cpuCores=1.0, frameworkHeapSize=128.000mb (134217728 bytes), frameworkOffHeapSize=128.000mb (134217728 bytes), taskHeapSize=4.125gb (4429184954 bytes), taskOffHeapSize=0 bytes, networkMemSize=896.000mb (939524110 bytes), managedMemorySize=3.500gb (3758096440 bytes), jvmMetaspaceSize=256.000mb (268435456 bytes), jvmOverheadSize=1024.000mb (1073741824 bytes)}.

 

  

 which are as per defaults.

Now, after 25 days we started encountering the following yarn container kill error:

Association with remote system [akka.tcp://flink@...] has failed, address is now gated for [50] ms. Reason: [Association failed with [akka.tcp://flink@...]] Caused by: [java.net.ConnectException: Connection refused: .../...:37679]

2020-09-09 00:53:24 INFO Closing TaskExecutor connection container_e193_1592804717489_149347_01_000011 because: [2020-09-09 00:53:21.417]Container [pid=44371,containerID=container_e193_1592804717489_149347_01_000011] is running beyond physical memory limits. Current usage: 12.0 GB of 12 GB physical memory used; 14.4 GB of 25.2 GB virtual memory used. Killing container.

Yarn container size is 12GB as it is only allowed as a multiple of 3 GB (as per our settings).

Now, when the YARN reallocates a new container, the program starts again (without any issues) and after a few hours another container is killed with the same error and the cycle repeats.

At this point, I want to debug it as a running process without changing or playing around with various config options for memory as I don't think just to reproduce the error, I want to wait for ~1 month.

I have tried to figure out something from Graphite metrics (see attachments):

[1]: JVM Heap Memory (First 25 days) -> The memory goes up and after reaching a point goes does and again starts going up. (No container kills were encountered until 09/09/2020, program started on 14/08/2020)

[2]: JVM Heap Memory (Recent) -> The memory is still going up but it seems it doesn't even reaches its peak, but instead container is killed before that itself (within a few hours)

From [1] and [2], JVM heap memory should not rise up I think, but that doesn't explain container kill in [2] case if JVM heap memory was the issue causing container kill.

[3]: Direct Memory and Off heap Memory -> I don't think this is causing the issue as most of the network buffers are free and off heap memory is well below threshold.

At this point I thought RocksDB might be the culprit. I am aware that it uses the managed memory limits (I haven't changed any default config) which is completely off heap. But when I see the rocksDB size maintained at location:

/data_4/yarn-nm-local-dir/usercache/root/appcache/application_.../flink-io-a48d1127-58a1-41c5-a5f0-32c5180fe74d/job_0bff1881431b5774c3b496a98febed1a_op_WindowOperator_4061fbe16fb95459a1a8d207644e2e63__4_12__uuid_9fe0b2ff-24bc-4301-8044-3fe8e1b3a3a0/db/ 

 

It is only 17MB which doesn't seem much. I also took a heap dump of org.apache.flink.yarn.YarnTaskExecutorRunner process but it shows only 30MB of data is being used (not sure what I am missing here as it doesn't match with metrics shown by flink).

Although top -p 'pid' (for task manager process) does show RES = 10-12 GB for every container constantly going up and eventually dies.

Has someone encountered a similar situation or have guidelines that I can continue with to figure out and debug the issue? Let me know if there is anything else that you might wanna know.

--

Thanks & Regards

Shubham Kumar

--

Thanks & Regards

Shubham Kumar

[Kye Bae]

Not sure about Flink 1.10.x. Can share a few things up to Flink 1.9.x:

1. If your Flink cluster runs only one job, avoid using dynamic classloader for your job: start it from one of the Flink class paths. As of Flink 1.9.x, using the dynamic classloader results in the same classes getting loaded every time the job restarts (self-recovery or otherwise), and it could eat up all the JVM "off-heap" memory. Yarn seems to immediately kill the container when that happens.

2. Be sure to leave enough for the JVM "off-heap" area: GC + code cache + thread stacks + other Java internal resources end up there.

-K

On Sat, Sep 19, 2020 at 12:09 PM Shubham Kumar <[hidden email]> wrote:

Hey everyone, 

We had deployed a streaming job using Flink 1.10.1 one month back and now we are encountering a Yarn container killed due to memory issues very frequently. I am trying to figure out the root cause of this issue in order to fix it. 

 

We have a streaming job whose basic structure looks like this:

- Read 6 kafka streams and combine stats from them (union) to form a single stream

- stream.keyBy(MyKey)

             .window(TumblingEventTimeWindows.of(Time.minutes(1)))

             .reduce(MyReduceFunction)

             .addSink(new FlinkKafkaProducer011<>...);

We are using RocksDB as state backend. In flink-conf.yaml, we used taskmanager.memory.process.size = 10GB with a parallelism of 12 and only one slot per task manager.

So, a taskmanager process gets started with the following memory components as indicated in logs:

TaskExecutor container... will be started on ... with TaskExecutorProcessSpec {cpuCores=1.0, frameworkHeapSize=128.000mb (134217728 bytes), frameworkOffHeapSize=128.000mb (134217728 bytes), taskHeapSize=4.125gb (4429184954 bytes), taskOffHeapSize=0 bytes, networkMemSize=896.000mb (939524110 bytes), managedMemorySize=3.500gb (3758096440 bytes), jvmMetaspaceSize=256.000mb (268435456 bytes), jvmOverheadSize=1024.000mb (1073741824 bytes)}.

 

  

 which are as per defaults.

Now, after 25 days we started encountering the following yarn container kill error:

Association with remote system [akka.tcp://flink@...] has failed, address is now gated for [50] ms. Reason: [Association failed with [akka.tcp://flink@...]] Caused by: [java.net.ConnectException: Connection refused: .../...:37679]

2020-09-09 00:53:24 INFO Closing TaskExecutor connection container_e193_1592804717489_149347_01_000011 because: [2020-09-09 00:53:21.417]Container [pid=44371,containerID=container_e193_1592804717489_149347_01_000011] is running beyond physical memory limits. Current usage: 12.0 GB of 12 GB physical memory used; 14.4 GB of 25.2 GB virtual memory used. Killing container.

Yarn container size is 12GB as it is only allowed as a multiple of 3 GB (as per our settings).

Now, when the YARN reallocates a new container, the program starts again (without any issues) and after a few hours another container is killed with the same error and the cycle repeats.

At this point, I want to debug it as a running process without changing or playing around with various config options for memory as I don't think just to reproduce the error, I want to wait for ~1 month.

I have tried to figure out something from Graphite metrics (see attachments):

[1]: JVM Heap Memory (First 25 days) -> The memory goes up and after reaching a point goes does and again starts going up. (No container kills were encountered until 09/09/2020, program started on 14/08/2020)

[2]: JVM Heap Memory (Recent) -> The memory is still going up but it seems it doesn't even reaches its peak, but instead container is killed before that itself (within a few hours)

From [1] and [2], JVM heap memory should not rise up I think, but that doesn't explain container kill in [2] case if JVM heap memory was the issue causing container kill.

[3]: Direct Memory and Off heap Memory -> I don't think this is causing the issue as most of the network buffers are free and off heap memory is well below threshold.

At this point I thought RocksDB might be the culprit. I am aware that it uses the managed memory limits (I haven't changed any default config) which is completely off heap. But when I see the rocksDB size maintained at location:

/data_4/yarn-nm-local-dir/usercache/root/appcache/application_.../flink-io-a48d1127-58a1-41c5-a5f0-32c5180fe74d/job_0bff1881431b5774c3b496a98febed1a_op_WindowOperator_4061fbe16fb95459a1a8d207644e2e63__4_12__uuid_9fe0b2ff-24bc-4301-8044-3fe8e1b3a3a0/db/ 

 

It is only 17MB which doesn't seem much. I also took a heap dump of org.apache.flink.yarn.YarnTaskExecutorRunner process but it shows only 30MB of data is being used (not sure what I am missing here as it doesn't match with metrics shown by flink).

Although top -p 'pid' (for task manager process) does show RES = 10-12 GB for every container constantly going up and eventually dies.

Has someone encountered a similar situation or have guidelines that I can continue with to figure out and debug the issue? Let me know if there is anything else that you might wanna know.

--

Thanks & Regards

Shubham Kumar

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[Shubham Kumar]

@Kye , Thanks for your suggestions, we are using one yarn app per job mode and your point is still valid in Flink 1.10 as per docs, it does make sense to avoid dynamic classloading for such jobs. Also, we seemed to have enough off heap for resources mentioned and what turned out to be the issue was RocksDB memory usage (check below).

@Xintong, Yeah, I did try out the solution, the problem is definitely due to RocksDB, however the problem got solved by something else:

Short answer: 

Setting this property in flink-conf.yaml solves the issue:

state.backend.rocksdb.managed.memory : false

Long answer:

I observed that the OOM kills are a function of the number of restarts rather than the time for which the application is running. For every restart, the Taskmanager's RES memory rises by 3.5GB (which is the Flink managed memory allotted to TM). So, it could only withstand 2-3 restarts after which OOM kills become frequent as now the other TM will start getting killed. I enabled RocksDB  block-cache usage metric and it rises up until it reaches ~3.5 GB. 

At this point I tried setting

containerized.taskmanager.env.MALLOC_ARENA_MAX : 2

This did seem to reduce memory increase for few of the task managers(for e.g. if there are 12 task managers,  after a restart the RES memory increases by 3.5 GB for only few of them but not for others), but didn't solve the issue for me and OOM kills begin to occur after 4-5 restarts. I also tried setting it to 1, but got similar results. I didn't try using jemalloc because as per the JIRA issue [1], MALLOC_ARENA_MAX solution intends to produce similar results.

After setting state.backend.rocksdb.managed.memory: false, the TM RES memory doesn't increase after any number of restarts, infact after enabling RocksDB cache usage metrics, it shows around only ~100MB usage (ofcourse its dependent on the operators and state involved in the job). This might indicate that Flink is trying to allot more memory than required for RocksDB and also upon restart the RES memory rises again which is definitely not the intended behavior. 

[1]: https://issues.apache.org/jira/browse/FLINK-18712

Thanks 

Shubham

On Fri, Sep 25, 2020 at 8:46 PM Kye Bae <[hidden email]> wrote:

Not sure about Flink 1.10.x. Can share a few things up to Flink 1.9.x:

1. If your Flink cluster runs only one job, avoid using dynamic classloader for your job: start it from one of the Flink class paths. As of Flink 1.9.x, using the dynamic classloader results in the same classes getting loaded every time the job restarts (self-recovery or otherwise), and it could eat up all the JVM "off-heap" memory. Yarn seems to immediately kill the container when that happens.

2. Be sure to leave enough for the JVM "off-heap" area: GC + code cache + thread stacks + other Java internal resources end up there.

-K

On Sat, Sep 19, 2020 at 12:09 PM Shubham Kumar <[hidden email]> wrote:

Hey everyone, 

We had deployed a streaming job using Flink 1.10.1 one month back and now we are encountering a Yarn container killed due to memory issues very frequently. I am trying to figure out the root cause of this issue in order to fix it. 

 

We have a streaming job whose basic structure looks like this:

- Read 6 kafka streams and combine stats from them (union) to form a single stream

- stream.keyBy(MyKey)

             .window(TumblingEventTimeWindows.of(Time.minutes(1)))

             .reduce(MyReduceFunction)

             .addSink(new FlinkKafkaProducer011<>...);

We are using RocksDB as state backend. In flink-conf.yaml, we used taskmanager.memory.process.size = 10GB with a parallelism of 12 and only one slot per task manager.

So, a taskmanager process gets started with the following memory components as indicated in logs:

TaskExecutor container... will be started on ... with TaskExecutorProcessSpec {cpuCores=1.0, frameworkHeapSize=128.000mb (134217728 bytes), frameworkOffHeapSize=128.000mb (134217728 bytes), taskHeapSize=4.125gb (4429184954 bytes), taskOffHeapSize=0 bytes, networkMemSize=896.000mb (939524110 bytes), managedMemorySize=3.500gb (3758096440 bytes), jvmMetaspaceSize=256.000mb (268435456 bytes), jvmOverheadSize=1024.000mb (1073741824 bytes)}.

 

  

 which are as per defaults.

Now, after 25 days we started encountering the following yarn container kill error:

Association with remote system [akka.tcp://flink@...] has failed, address is now gated for [50] ms. Reason: [Association failed with [akka.tcp://flink@...]] Caused by: [java.net.ConnectException: Connection refused: .../...:37679]

2020-09-09 00:53:24 INFO Closing TaskExecutor connection container_e193_1592804717489_149347_01_000011 because: [2020-09-09 00:53:21.417]Container [pid=44371,containerID=container_e193_1592804717489_149347_01_000011] is running beyond physical memory limits. Current usage: 12.0 GB of 12 GB physical memory used; 14.4 GB of 25.2 GB virtual memory used. Killing container.

Yarn container size is 12GB as it is only allowed as a multiple of 3 GB (as per our settings).

Now, when the YARN reallocates a new container, the program starts again (without any issues) and after a few hours another container is killed with the same error and the cycle repeats.

At this point, I want to debug it as a running process without changing or playing around with various config options for memory as I don't think just to reproduce the error, I want to wait for ~1 month.

I have tried to figure out something from Graphite metrics (see attachments):

[1]: JVM Heap Memory (First 25 days) -> The memory goes up and after reaching a point goes does and again starts going up. (No container kills were encountered until 09/09/2020, program started on 14/08/2020)

[2]: JVM Heap Memory (Recent) -> The memory is still going up but it seems it doesn't even reaches its peak, but instead container is killed before that itself (within a few hours)

From [1] and [2], JVM heap memory should not rise up I think, but that doesn't explain container kill in [2] case if JVM heap memory was the issue causing container kill.

[3]: Direct Memory and Off heap Memory -> I don't think this is causing the issue as most of the network buffers are free and off heap memory is well below threshold.

At this point I thought RocksDB might be the culprit. I am aware that it uses the managed memory limits (I haven't changed any default config) which is completely off heap. But when I see the rocksDB size maintained at location:

/data_4/yarn-nm-local-dir/usercache/root/appcache/application_.../flink-io-a48d1127-58a1-41c5-a5f0-32c5180fe74d/job_0bff1881431b5774c3b496a98febed1a_op_WindowOperator_4061fbe16fb95459a1a8d207644e2e63__4_12__uuid_9fe0b2ff-24bc-4301-8044-3fe8e1b3a3a0/db/ 

 

It is only 17MB which doesn't seem much. I also took a heap dump of org.apache.flink.yarn.YarnTaskExecutorRunner process but it shows only 30MB of data is being used (not sure what I am missing here as it doesn't match with metrics shown by flink).

Although top -p 'pid' (for task manager process) does show RES = 10-12 GB for every container constantly going up and eventually dies.

Has someone encountered a similar situation or have guidelines that I can continue with to figure out and debug the issue? Let me know if there is anything else that you might wanna know.

--

Thanks & Regards

Shubham Kumar

---

The information contained in this e-mail is confidential and/or proprietary to Capital One and/or its affiliates and may only be used solely in performance of work or services for Capital One. The information transmitted herewith is intended only for use by the individual or entity to which it is addressed. If the reader of this message is not the intended recipient, you are hereby notified that any review, retransmission, dissemination, distribution, copying or other use of, or taking of any action in reliance upon this information is strictly prohibited. If you have received this communication in error, please contact the sender and delete the material from your computer.

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