Re: The partition tracker should support remote shuffle properly
Hi, Till ~ Thanks for your comments. Yes, I think we are on the same page – we are discussing how should JMPartitionTracker manage partitions properly ~ > If the ShuffleMaster should lose a result partition, then a reading task should fail with a PartitionException which will invalidate the partition on the JobMasterPartitionTracker so that it is reproduced. 1. True, the reproduction of a lost upstream partition could be triggered by the shuffle-read failure of downstream. But it tends to be unacceptable in the production environment for batch processing jobs. Say there are 100 upstream partitions lost due to failure of an external shuffle worker, if there's no notification from ShuffleMaster to JMPartitionTracker, 100 PartitionExceptions on downstream will happen and upstream partitions will be reproduced one by one sequentially. The time overhead will be unacceptable. From this point of view, I tend to think ShuffleMaster should have the ability to unregister partitions by locationID on JMPartitionTracker. > I think that deciding whether the partition is stored externally or not can be answered by using the ShuffleDescriptor.storesLocalResourcesOn method. 2. Yes, I agree it works (though it's not easy to access ShuffleDescriptor by tmID from JMPartitionTracker at this moment). But if we agree that my first point is valid, JMPartitionTracker should maintain the index from locationID to partition. Then it will be straightforward to check whether a partition is accommodated on remote by comparing its tmID and locationID; Best, Jin Till Rohrmann 于2021年6月1日周二 下午4:59写道： > Hi Jin, > > thanks for starting this discussion and the initiative to implement a > remote shuffle service. It has always been the idea of the ShuffleService > abstraction to make this possible and we probably have overlooked some > details. > > What I have understood from your description, you would like to introduce > the locationID which points to the location where the result partition is > stored (potentially external). Using the locationID and the tmID it is > possible to say whether the partition is stored externally or not. > > I think that deciding whether the partition is stored externally or not (or > more precisely whether the partition occupies resources on the TM) can be > answered by using the ShuffleDescriptor.storesLocalResourcesOn method. If > it returns some ResourceID then we have to tell the TM about the release. > If not, then we only tell the shuffle master about the partition release. > How the data can be accessed on the external system is then encapsulated by > the ShuffleMaster and the ShuffleDescriptor. The logic for releasing the > partitions on the TMs and the ShuffleMaster should already be implemented > in the JobMasterPartitionTrackerImpl. > > I think what we need to change is that we don't stop the tracking of > completed partitions when a TM on which the producers run disconnects and > if we store the result partition externally. This is required to make > partitions survive in case of TM failures. What this also requires is to > distinguish between finished and in-progress partitions. > > What indeed is currently not implemented is the channel from the > ShuffleMaster to the JobMasterPartitionTrackerImpl. This is, however, not a > big problem atm. If the ShuffleMaster should lose a result partition, then > a reading task should fail with a PartitionException which will invalidate > the partition on the JMPartitionTracker so that it is reproduced. Listening > to the ShuffleMaster would be an optimization to learn more quickly about > this fact and to avoid a restart cycle. > > Did I understand you correctly, Jin, and do my comments make sense? > > Cheers, > Till > > On Wed, May 26, 2021 at 5:52 AM XING JIN wrote: > > > Hi devs ~ > > Recently our team designed and started to build Flink remote shuffle > > service based on 'pluggable shuffle service framework'[1] for batch > > processing jobs. We found some potential enhancements could be made on > > 'pluggable shuffle service' and created an umbrella JIRA[2]. I raise this > > DISCUSSION and want to hear broader feedback / comments on one ticket [3] > > -- "The partition tracker should support remote shuffle properly". > > > > In current Flink, data partition is bound with the ResourceID of TM in > > Execution#startTrackingPartitions and JM partition tracker will stop > > tracking corresponding partitions when a TM > > disconnects(JobMaster#disconnectTaskManager), i.e. the lifecycle of > shuffle > > data is bound with computing resource (TM). It works fine for internal > > shuffle service, but doesn't for remote shuffle service. Note that > shuffle > > data is accommodated on remote, the lifecycle of a comp
Re: The partition tracker should support remote shuffle properly
Hi Jin, thanks for starting this discussion and the initiative to implement a remote shuffle service. It has always been the idea of the ShuffleService abstraction to make this possible and we probably have overlooked some details. What I have understood from your description, you would like to introduce the locationID which points to the location where the result partition is stored (potentially external). Using the locationID and the tmID it is possible to say whether the partition is stored externally or not. I think that deciding whether the partition is stored externally or not (or more precisely whether the partition occupies resources on the TM) can be answered by using the ShuffleDescriptor.storesLocalResourcesOn method. If it returns some ResourceID then we have to tell the TM about the release. If not, then we only tell the shuffle master about the partition release. How the data can be accessed on the external system is then encapsulated by the ShuffleMaster and the ShuffleDescriptor. The logic for releasing the partitions on the TMs and the ShuffleMaster should already be implemented in the JobMasterPartitionTrackerImpl. I think what we need to change is that we don't stop the tracking of completed partitions when a TM on which the producers run disconnects and if we store the result partition externally. This is required to make partitions survive in case of TM failures. What this also requires is to distinguish between finished and in-progress partitions. What indeed is currently not implemented is the channel from the ShuffleMaster to the JobMasterPartitionTrackerImpl. This is, however, not a big problem atm. If the ShuffleMaster should lose a result partition, then a reading task should fail with a PartitionException which will invalidate the partition on the JMPartitionTracker so that it is reproduced. Listening to the ShuffleMaster would be an optimization to learn more quickly about this fact and to avoid a restart cycle. Did I understand you correctly, Jin, and do my comments make sense? Cheers, Till On Wed, May 26, 2021 at 5:52 AM XING JIN wrote: > Hi devs ~ > Recently our team designed and started to build Flink remote shuffle > service based on 'pluggable shuffle service framework'[1] for batch > processing jobs. We found some potential enhancements could be made on > 'pluggable shuffle service' and created an umbrella JIRA[2]. I raise this > DISCUSSION and want to hear broader feedback / comments on one ticket [3] > -- "The partition tracker should support remote shuffle properly". > > In current Flink, data partition is bound with the ResourceID of TM in > Execution#startTrackingPartitions and JM partition tracker will stop > tracking corresponding partitions when a TM > disconnects(JobMaster#disconnectTaskManager), i.e. the lifecycle of shuffle > data is bound with computing resource (TM). It works fine for internal > shuffle service, but doesn't for remote shuffle service. Note that shuffle > data is accommodated on remote, the lifecycle of a completed partition is > capable to be decoupled with TM, i.e. TM is totally fine to be released > when no computing task is on it and further shuffle reading requests could > be directed to remote shuffle cluster. In addition, when a TM is lost, its > completed data partitions on remote shuffle cluster could avoid > reproducing. > > The issue mentioned above is because Flink JobMasterPartitionTracker mixed > up partition's locationID (where the partition is located) and tmID (which > TM the partition is produced from). In TM internal shuffle, partition's > locationID is the same with tmID, but it is not in remote shuffle; > JobMasterPartitionTracker as an independent component should be able to > differentiate locationID and tmID of a partition, thus to handle the > lifecycle of a partition properly; > > We propose that JobMasterPartitionTracker indexes partitions with both > locationID and tmID. The process of registration and unregistration will be > like below: > > A. Partition Registration > - Execution#registerProducedPartitions registers partition to ShuffleMaster > and gets a ShuffleDescriptor. Current > ShuffleDescriptor#storesLocalResourcesOn returns the location of the > producing TM ONLY IF the partition occupies local resources there. We > propose to change this method a proper name and always return the > locationID of the partition. It might be as below: > ResourceID getLocationID(); > - Execution#registerProducePartitions then registers partition to > JMPartitionTracker with tmID (ResourceID of TaskManager from > TaskManagerLocation) and the locationID (acquired in above step). > JobMasterPartitionTracker will indexes a partition with both tmID and > locationID; > > B. Invokes from JM and ShuffleMaster > JobMasterPartitionTracker listens invokes from both
The partition tracker should support remote shuffle properly
Hi devs ~ Recently our team designed and started to build Flink remote shuffle service based on 'pluggable shuffle service framework'[1] for batch processing jobs. We found some potential enhancements could be made on 'pluggable shuffle service' and created an umbrella JIRA[2]. I raise this DISCUSSION and want to hear broader feedback / comments on one ticket [3] -- "The partition tracker should support remote shuffle properly". In current Flink, data partition is bound with the ResourceID of TM in Execution#startTrackingPartitions and JM partition tracker will stop tracking corresponding partitions when a TM disconnects(JobMaster#disconnectTaskManager), i.e. the lifecycle of shuffle data is bound with computing resource (TM). It works fine for internal shuffle service, but doesn't for remote shuffle service. Note that shuffle data is accommodated on remote, the lifecycle of a completed partition is capable to be decoupled with TM, i.e. TM is totally fine to be released when no computing task is on it and further shuffle reading requests could be directed to remote shuffle cluster. In addition, when a TM is lost, its completed data partitions on remote shuffle cluster could avoid reproducing. The issue mentioned above is because Flink JobMasterPartitionTracker mixed up partition's locationID (where the partition is located) and tmID (which TM the partition is produced from). In TM internal shuffle, partition's locationID is the same with tmID, but it is not in remote shuffle; JobMasterPartitionTracker as an independent component should be able to differentiate locationID and tmID of a partition, thus to handle the lifecycle of a partition properly; We propose that JobMasterPartitionTracker indexes partitions with both locationID and tmID. The process of registration and unregistration will be like below: A. Partition Registration - Execution#registerProducedPartitions registers partition to ShuffleMaster and gets a ShuffleDescriptor. Current ShuffleDescriptor#storesLocalResourcesOn returns the location of the producing TM ONLY IF the partition occupies local resources there. We propose to change this method a proper name and always return the locationID of the partition. It might be as below: ResourceID getLocationID(); - Execution#registerProducePartitions then registers partition to JMPartitionTracker with tmID (ResourceID of TaskManager from TaskManagerLocation) and the locationID (acquired in above step). JobMasterPartitionTracker will indexes a partition with both tmID and locationID; B. Invokes from JM and ShuffleMaster JobMasterPartitionTracker listens invokes from both JM and ShuffleMaster. - When JMPartitionTracker hears from JobMaster#disconnectTaskManager that a TM disconnects, it will check whether the disconnected tmID equals a certain locationID of a partition. If so, tracking of the corresponding partition will be stopped. - When JobMasterPartitionTracker hears from ShuffleMaster that a data location gets lost, it will unregister corresponding partitions by locationID; C. Partition Unregistration When unregister a partition, JobMasterPartitionTracker removes the corresponding indexes to tmID and locationID firstly, and then release the partition according to shuffle service types: - If the locationID equals to the tmID, it indicates the partition is accommodated by TM internal shuffle service, JMPartitionTracker will invoke TaskExecutorGateway for the release; - If the locationID doesn't equal to tmID, it indicates the partition is accommodated by external shuffle service, JMPartitionTracker will invoke ShuffleMaster for the release; With the above change, JobMasterPartitionTracker can adapt with customized shuffle service properly for partition's lifecycle. Looking forward to inputs on this ~~ Best, Jin [1] https://cwiki.apache.org/confluence/display/FLINK/FLIP-31%3A+Pluggable+Shuffle+Service [2] https://issues.apache.org/jira/browse/FLINK-22672 [3] https://issues.apache.org/jira/browse/FLINK-22676
[jira] [Created] (FLINK-22676) The partition tracker should support remote shuffle properly
Jin Xing created FLINK-22676: Summary: The partition tracker should support remote shuffle properly Key: FLINK-22676 URL: https://issues.apache.org/jira/browse/FLINK-22676 Project: Flink Issue Type: Sub-task Components: Runtime / Network Reporter: Jin Xing In current Flink, data partition is bound with the ResourceID of TM in Execution#startTrackingPartitions and partition tracker will stop tracking corresponding partitions when a TM disconnects(JobMaster#disconnectTaskManager), i.e. the lifecycle of shuffle data is bound with computing resource (TM). It works fine for internal shuffle service, but doesn't for remote shuffle service. Note that shuffle data is accommodated on remote, the lifecycle of a completed partition is capable to be decoupled with TM, i.e. TM is totally fine to be released when no computing task on it and further shuffle reading requests could be directed to remote shuffle cluster. In addition, when a TM is lost, its completed data partitions on remote shuffle cluster could avoid reproducing. -- This message was sent by Atlassian Jira (v8.3.4#803005)
