Re: RocksDB's state size discrepancy with what's seen with state processor API
Hi Alexis, Sorry for the late response. I come from the reply in FLINK-27504[1]. The MAINFEST file in RocksDB records history of version changes. In other words, once a new SST file created or an old file deleted via compaction, it will create a new version in RocksDB, which will update the MAINFEST file. The default value for max MAINFEST file size is 1GB [2], since you create the checkpoint every 30 seconds, files might be flushed on that time, and that's why the MAINFEST file grows. You can limit the max file size via DBOptions#setMaxManifestFileSize [3]. [1] https://issues.apache.org/jira/browse/FLINK-27504?focusedCommentId=17537788=com.atlassian.jira.plugin.system.issuetabpanels%3Acomment-tabpanel#comment-17537788 [2] https://github.com/ververica/frocksdb/blob/8608d75d85f8e1b3b64b73a4fb6d19baec61ba5c/include/rocksdb/options.h#L636 [3] https://github.com/ververica/frocksdb/blob/8608d75d85f8e1b3b64b73a4fb6d19baec61ba5c/java/src/main/java/org/rocksdb/DBOptions.java#L520 Best Yun Tang From: Alexis Sarda-Espinosa Sent: Tuesday, May 3, 2022 8:47 To: Peter Brucia Cc: user@flink.apache.org Subject: RE: RocksDB's state size discrepancy with what's seen with state processor API Ok Regards, Alexis. From: Peter Brucia Sent: Freitag, 22. April 2022 15:31 To: Alexis Sarda-Espinosa Subject: Re: RocksDB's state size discrepancy with what's seen with state processor API No Sent from my iPhone
RE: RocksDB's state size discrepancy with what's seen with state processor API
Ok Regards, Alexis. From: Peter Brucia Sent: Freitag, 22. April 2022 15:31 To: Alexis Sarda-Espinosa Subject: Re: RocksDB's state size discrepancy with what's seen with state processor API No Sent from my iPhone
Re: RocksDB's state size discrepancy with what's seen with state processor API
Hi David, I don't find it troublesome per se, I was rather trying to understand what should be expected (and documented) for my application. Before I restarted the job and changed some configurations, it ran for around 10 days and ended up with a state size of about 1.8GB, so I'm still not sure what is the upper bound in my scenario, or if that amount of "uncompacted garbage" is normal or not (for our throughput). This is important for us because we need to know how to size (disk space) the infrastructure, although it is also having a big impact on timings because each checkpoint ends up requiring 30+ seconds to complete, and they will eventually time out for sure. I understand RocksDB has different sophisticated mechanisms, so I certainly don't expect one magic button that does exactly what I want, but ideally there would be a way to tune configuration in a way that a rough upper bound estimate of disk space can be deduced. Having some expired state for a while is expected, what I find odd is that it grows so fast, the size of the state quickly outpaces the size of processed events, even though the state only persists a subset of information (some integer ids, string ids, longs for epochs). At this point I think I can conclude that the "live" state from my operators is not growing indefinitely (based on what I see with the state processor API), so is there a way to get a better estimate of disk utilization other than letting the job run and wait? I've been reading through RocksDB documentation as well, but that might not be enough because I don't know how Flink handles its own framework state internally. Regards, Alexis. From: David Anderson Sent: Friday, April 22, 2022 9:57 AM To: Alexis Sarda-Espinosa Cc: ro...@apache.org ; user@flink.apache.org Subject: Re: RocksDB's state size discrepancy with what's seen with state processor API Alexis, Compaction isn't an all-at-once procedure. RocksDB is organized as a series of levels, each 10x larger than the one below. There are a few different compaction algorithms available, and they are tunable, but what's typically happening during compaction is that one SST file at level n is being merged into the relevant SST files at level n+1. During this compaction procedure, obsolete and deleted entries are cleaned up. And several such compactions can be occurring concurrently. (Not to mention that each TM has its own independent RocksDB instance.) It's not unusual for jobs with a small amount of state to end up with checkpoints of a few hundred MBs in size, where a lot of that is uncompacted garbage. If you find this troublesome, you could configure RocksDB to compact more frequently. David On Thu, Apr 21, 2022 at 12:49 PM Alexis Sarda-Espinosa mailto:alexis.sarda-espin...@microfocus.com>> wrote: Hello, I enabled some of the RocksDB metrics and I noticed some additional things. After changing the configuration YAML, I restarted the cluster with a savepoint, and I can see that it only used 5.6MB on disk. Consequently, after the job switched to running state, the new checkpoints were also a few MB in size. After running for 1 day, checkpoint size is now around 100MB. From the metrics I can see with the Prometheus reporter: - All entries for num-live-versions show 1 - All entries for compaction-pending show 0 - Most entries for estimate-num-keys are in the range of 0 to 100, although I see a few with 151 coming from flink_taskmanager_job_task_operator__timer_state_event_window_timers_rocksdb_estimate_num_keys Is compaction expected after only 100MB? I imagine not, but if the savepoint shows that the effective amount of data is so low, size growth still seems far too large. In fact, if I only look at the UI, Bytes Received for the relevant SubTasks is about 14MB, yet the latest checkpoint already shows a Data Size of 75MB for said SubTasks. Regards, Alexis. -Original Message- From: Roman Khachatryan mailto:ro...@apache.org>> Sent: Mittwoch, 20. April 2022 10:37 To: Alexis Sarda-Espinosa mailto:alexis.sarda-espin...@microfocus.com>> Cc: user@flink.apache.org<mailto:user@flink.apache.org> Subject: Re: RocksDB's state size discrepancy with what's seen with state processor API State Processor API works on a higher level and is not aware of any RocksDB specifics (in fact, it can be used with any backend). Regards, Roman On Tue, Apr 19, 2022 at 10:52 PM Alexis Sarda-Espinosa mailto:alexis.sarda-espin...@microfocus.com>> wrote: > > I can look into RocksDB metrics, I need to configure Prometheus at some point > anyway. However, going back to the original question, is there no way to gain > more insight into this with the state processor API? You've mentioned > potential issues (too many states, missing compaction) but, with my > admittedly limited understanding of the way RocksDB is used in Flink,
Re: RocksDB's state size discrepancy with what's seen with state processor API
Alexis, Compaction isn't an all-at-once procedure. RocksDB is organized as a series of levels, each 10x larger than the one below. There are a few different compaction algorithms available, and they are tunable, but what's typically happening during compaction is that one SST file at level n is being merged into the relevant SST files at level n+1. During this compaction procedure, obsolete and deleted entries are cleaned up. And several such compactions can be occurring concurrently. (Not to mention that each TM has its own independent RocksDB instance.) It's not unusual for jobs with a small amount of state to end up with checkpoints of a few hundred MBs in size, where a lot of that is uncompacted garbage. If you find this troublesome, you could configure RocksDB to compact more frequently. David On Thu, Apr 21, 2022 at 12:49 PM Alexis Sarda-Espinosa < alexis.sarda-espin...@microfocus.com> wrote: > Hello, > > I enabled some of the RocksDB metrics and I noticed some additional > things. After changing the configuration YAML, I restarted the cluster with > a savepoint, and I can see that it only used 5.6MB on disk. Consequently, > after the job switched to running state, the new checkpoints were also a > few MB in size. After running for 1 day, checkpoint size is now around > 100MB. From the metrics I can see with the Prometheus reporter: > > - All entries for num-live-versions show 1 > - All entries for compaction-pending show 0 > - Most entries for estimate-num-keys are in the range of 0 to 100, > although I see a few with 151 coming from > flink_taskmanager_job_task_operator__timer_state_event_window_timers_rocksdb_estimate_num_keys > > Is compaction expected after only 100MB? I imagine not, but if the > savepoint shows that the effective amount of data is so low, size growth > still seems far too large. In fact, if I only look at the UI, Bytes > Received for the relevant SubTasks is about 14MB, yet the latest checkpoint > already shows a Data Size of 75MB for said SubTasks. > > Regards, > Alexis. > > -Original Message- > From: Roman Khachatryan > Sent: Mittwoch, 20. April 2022 10:37 > To: Alexis Sarda-Espinosa > Cc: user@flink.apache.org > Subject: Re: RocksDB's state size discrepancy with what's seen with state > processor API > > State Processor API works on a higher level and is not aware of any > RocksDB specifics (in fact, it can be used with any backend). > > Regards, > Roman > > On Tue, Apr 19, 2022 at 10:52 PM Alexis Sarda-Espinosa > > wrote: > > > > I can look into RocksDB metrics, I need to configure Prometheus at some > point anyway. However, going back to the original question, is there no way > to gain more insight into this with the state processor API? You've > mentioned potential issues (too many states, missing compaction) but, with > my admittedly limited understanding of the way RocksDB is used in Flink, I > would have thought that such things would be visible when using the state > processor. Is there no way for me to "parse" those MANIFEST files with some > of Flink's classes and get some more hints? > > > > Regards, > > Alexis. > > > > ____________ > > From: Roman Khachatryan > > Sent: Tuesday, April 19, 2022 5:51 PM > > To: Alexis Sarda-Espinosa > > Cc: user@flink.apache.org > > Subject: Re: RocksDB's state size discrepancy with what's seen with > > state processor API > > > > > I assume that when you say "new states", that is related to new > descriptors with different names? Because, in the case of windowing for > example, each window "instance" has its own scoped (non-global and keyed) > state, but that's not regarded as a separate column family, is it? > > Yes, that's what I meant, and that's regarded as the same column family. > > > > Another possible reason is that SST files aren't being compacted and > > that increases the MANIFEST file size. > > I'd check the total number of loaded SST files and the creation date > > of the oldest one. > > > > You can also see whether there are any compactions running via RocksDB > > metrics [1] [2] (a reporter needs to be configured [3]). > > > > [1] > > https://nightlies.apache.org/flink/flink-docs-master/docs/deployment/c > > onfig/#state-backend-rocksdb-metrics-num-running-compactions > > [2] > > https://nightlies.apache.org/flink/flink-docs-master/docs/deployment/c > > onfig/#state-backend-rocksdb-metrics-compaction-pending > > [3] > > https://nightlies.apache.org/flink/flink-docs-master/docs/deployment/m > > etric_reporters/#reporters > > > > Regards, > > Roman > > > > On
RE: RocksDB's state size discrepancy with what's seen with state processor API
Hello, I enabled some of the RocksDB metrics and I noticed some additional things. After changing the configuration YAML, I restarted the cluster with a savepoint, and I can see that it only used 5.6MB on disk. Consequently, after the job switched to running state, the new checkpoints were also a few MB in size. After running for 1 day, checkpoint size is now around 100MB. From the metrics I can see with the Prometheus reporter: - All entries for num-live-versions show 1 - All entries for compaction-pending show 0 - Most entries for estimate-num-keys are in the range of 0 to 100, although I see a few with 151 coming from flink_taskmanager_job_task_operator__timer_state_event_window_timers_rocksdb_estimate_num_keys Is compaction expected after only 100MB? I imagine not, but if the savepoint shows that the effective amount of data is so low, size growth still seems far too large. In fact, if I only look at the UI, Bytes Received for the relevant SubTasks is about 14MB, yet the latest checkpoint already shows a Data Size of 75MB for said SubTasks. Regards, Alexis. -Original Message- From: Roman Khachatryan Sent: Mittwoch, 20. April 2022 10:37 To: Alexis Sarda-Espinosa Cc: user@flink.apache.org Subject: Re: RocksDB's state size discrepancy with what's seen with state processor API State Processor API works on a higher level and is not aware of any RocksDB specifics (in fact, it can be used with any backend). Regards, Roman On Tue, Apr 19, 2022 at 10:52 PM Alexis Sarda-Espinosa wrote: > > I can look into RocksDB metrics, I need to configure Prometheus at some point > anyway. However, going back to the original question, is there no way to gain > more insight into this with the state processor API? You've mentioned > potential issues (too many states, missing compaction) but, with my > admittedly limited understanding of the way RocksDB is used in Flink, I would > have thought that such things would be visible when using the state > processor. Is there no way for me to "parse" those MANIFEST files with some > of Flink's classes and get some more hints? > > Regards, > Alexis. > > > From: Roman Khachatryan > Sent: Tuesday, April 19, 2022 5:51 PM > To: Alexis Sarda-Espinosa > Cc: user@flink.apache.org > Subject: Re: RocksDB's state size discrepancy with what's seen with > state processor API > > > I assume that when you say "new states", that is related to new descriptors > > with different names? Because, in the case of windowing for example, each > > window "instance" has its own scoped (non-global and keyed) state, but > > that's not regarded as a separate column family, is it? > Yes, that's what I meant, and that's regarded as the same column family. > > Another possible reason is that SST files aren't being compacted and > that increases the MANIFEST file size. > I'd check the total number of loaded SST files and the creation date > of the oldest one. > > You can also see whether there are any compactions running via RocksDB > metrics [1] [2] (a reporter needs to be configured [3]). > > [1] > https://nightlies.apache.org/flink/flink-docs-master/docs/deployment/c > onfig/#state-backend-rocksdb-metrics-num-running-compactions > [2] > https://nightlies.apache.org/flink/flink-docs-master/docs/deployment/c > onfig/#state-backend-rocksdb-metrics-compaction-pending > [3] > https://nightlies.apache.org/flink/flink-docs-master/docs/deployment/m > etric_reporters/#reporters > > Regards, > Roman > > On Tue, Apr 19, 2022 at 1:38 PM Alexis Sarda-Espinosa > wrote: > > > > Hi Roman, > > > > I assume that when you say "new states", that is related to new descriptors > > with different names? Because, in the case of windowing for example, each > > window "instance" has its own scoped (non-global and keyed) state, but > > that's not regarded as a separate column family, is it? > > > > For the 3 descriptors I mentioned before, they are only instantiated once > > and used like this: > > > > - Window list state: each call to process() executes > > context.windowState().getListState(...).get() > > - Global map state: each call to process() executes > > context.globalState().getMapState(...) > > - Global list state: within open(), runtimeContext.getListState(...) is > > executed once and used throughout the life of the operator. > > > > According to [1], the two ways of using global state should be equivalent. > > > > I will say that some of the operators instantiate the state descriptor in > > their constructors, i.e. before they are serialized to the TM, but the > > descriptors are Serializabl
Re: RocksDB's state size discrepancy with what's seen with state processor API
State Processor API works on a higher level and is not aware of any RocksDB specifics (in fact, it can be used with any backend). Regards, Roman On Tue, Apr 19, 2022 at 10:52 PM Alexis Sarda-Espinosa wrote: > > I can look into RocksDB metrics, I need to configure Prometheus at some point > anyway. However, going back to the original question, is there no way to gain > more insight into this with the state processor API? You've mentioned > potential issues (too many states, missing compaction) but, with my > admittedly limited understanding of the way RocksDB is used in Flink, I would > have thought that such things would be visible when using the state > processor. Is there no way for me to "parse" those MANIFEST files with some > of Flink's classes and get some more hints? > > Regards, > Alexis. > > > From: Roman Khachatryan > Sent: Tuesday, April 19, 2022 5:51 PM > To: Alexis Sarda-Espinosa > Cc: user@flink.apache.org > Subject: Re: RocksDB's state size discrepancy with what's seen with state > processor API > > > I assume that when you say "new states", that is related to new descriptors > > with different names? Because, in the case of windowing for example, each > > window "instance" has its own scoped (non-global and keyed) state, but > > that's not regarded as a separate column family, is it? > Yes, that's what I meant, and that's regarded as the same column family. > > Another possible reason is that SST files aren't being compacted and > that increases the MANIFEST file size. > I'd check the total number of loaded SST files and the creation date > of the oldest one. > > You can also see whether there are any compactions running via RocksDB > metrics [1] [2] (a reporter needs to be configured [3]). > > [1] > https://nightlies.apache.org/flink/flink-docs-master/docs/deployment/config/#state-backend-rocksdb-metrics-num-running-compactions > [2] > https://nightlies.apache.org/flink/flink-docs-master/docs/deployment/config/#state-backend-rocksdb-metrics-compaction-pending > [3] > https://nightlies.apache.org/flink/flink-docs-master/docs/deployment/metric_reporters/#reporters > > Regards, > Roman > > On Tue, Apr 19, 2022 at 1:38 PM Alexis Sarda-Espinosa > wrote: > > > > Hi Roman, > > > > I assume that when you say "new states", that is related to new descriptors > > with different names? Because, in the case of windowing for example, each > > window "instance" has its own scoped (non-global and keyed) state, but > > that's not regarded as a separate column family, is it? > > > > For the 3 descriptors I mentioned before, they are only instantiated once > > and used like this: > > > > - Window list state: each call to process() executes > > context.windowState().getListState(...).get() > > - Global map state: each call to process() executes > > context.globalState().getMapState(...) > > - Global list state: within open(), runtimeContext.getListState(...) is > > executed once and used throughout the life of the operator. > > > > According to [1], the two ways of using global state should be equivalent. > > > > I will say that some of the operators instantiate the state descriptor in > > their constructors, i.e. before they are serialized to the TM, but the > > descriptors are Serializable, so I imagine that's not relevant. > > > > [1] https://stackoverflow.com/a/50510054/5793905 > > > > Regards, > > Alexis. > > > > -Original Message- > > From: Roman Khachatryan > > Sent: Dienstag, 19. April 2022 11:48 > > To: Alexis Sarda-Espinosa > > Cc: user@flink.apache.org > > Subject: Re: RocksDB's state size discrepancy with what's seen with state > > processor API > > > > Hi Alexis, > > > > Thanks a lot for the information, > > > > MANIFEST files list RocksDB column families (among other info); ever > > growing size of these files might indicate that some new states are > > constantly being created. > > Could you please confirm that the number of state names is constant? > > > > > Could you confirm if Flink's own operators could be creating state in > > > RocksDB? I assume the window operators save some information in the state > > > as well. > > That's correct, window operators maintain a list of elements per window and > > a set of timers (timestamps). These states' names should be fixed > > (something like "window-contents" and "window-timers"). > > > > > is that related to managed s
Re: RocksDB's state size discrepancy with what's seen with state processor API
I can look into RocksDB metrics, I need to configure Prometheus at some point anyway. However, going back to the original question, is there no way to gain more insight into this with the state processor API? You've mentioned potential issues (too many states, missing compaction) but, with my admittedly limited understanding of the way RocksDB is used in Flink, I would have thought that such things would be visible when using the state processor. Is there no way for me to "parse" those MANIFEST files with some of Flink's classes and get some more hints? Regards, Alexis. From: Roman Khachatryan Sent: Tuesday, April 19, 2022 5:51 PM To: Alexis Sarda-Espinosa Cc: user@flink.apache.org Subject: Re: RocksDB's state size discrepancy with what's seen with state processor API > I assume that when you say "new states", that is related to new descriptors > with different names? Because, in the case of windowing for example, each > window "instance" has its own scoped (non-global and keyed) state, but that's > not regarded as a separate column family, is it? Yes, that's what I meant, and that's regarded as the same column family. Another possible reason is that SST files aren't being compacted and that increases the MANIFEST file size. I'd check the total number of loaded SST files and the creation date of the oldest one. You can also see whether there are any compactions running via RocksDB metrics [1] [2] (a reporter needs to be configured [3]). [1] https://nightlies.apache.org/flink/flink-docs-master/docs/deployment/config/#state-backend-rocksdb-metrics-num-running-compactions [2] https://nightlies.apache.org/flink/flink-docs-master/docs/deployment/config/#state-backend-rocksdb-metrics-compaction-pending [3] https://nightlies.apache.org/flink/flink-docs-master/docs/deployment/metric_reporters/#reporters Regards, Roman On Tue, Apr 19, 2022 at 1:38 PM Alexis Sarda-Espinosa wrote: > > Hi Roman, > > I assume that when you say "new states", that is related to new descriptors > with different names? Because, in the case of windowing for example, each > window "instance" has its own scoped (non-global and keyed) state, but that's > not regarded as a separate column family, is it? > > For the 3 descriptors I mentioned before, they are only instantiated once and > used like this: > > - Window list state: each call to process() executes > context.windowState().getListState(...).get() > - Global map state: each call to process() executes > context.globalState().getMapState(...) > - Global list state: within open(), runtimeContext.getListState(...) is > executed once and used throughout the life of the operator. > > According to [1], the two ways of using global state should be equivalent. > > I will say that some of the operators instantiate the state descriptor in > their constructors, i.e. before they are serialized to the TM, but the > descriptors are Serializable, so I imagine that's not relevant. > > [1] https://stackoverflow.com/a/50510054/5793905 > > Regards, > Alexis. > > -Original Message----- > From: Roman Khachatryan > Sent: Dienstag, 19. April 2022 11:48 > To: Alexis Sarda-Espinosa > Cc: user@flink.apache.org > Subject: Re: RocksDB's state size discrepancy with what's seen with state > processor API > > Hi Alexis, > > Thanks a lot for the information, > > MANIFEST files list RocksDB column families (among other info); ever growing > size of these files might indicate that some new states are constantly being > created. > Could you please confirm that the number of state names is constant? > > > Could you confirm if Flink's own operators could be creating state in > > RocksDB? I assume the window operators save some information in the state > > as well. > That's correct, window operators maintain a list of elements per window and a > set of timers (timestamps). These states' names should be fixed (something > like "window-contents" and "window-timers"). > > > is that related to managed state used by my functions? Or does that > > indicate size growth is elsewhere? > The same mechanism is used for both Flink internal state and operator state, > so it's hard to say without at least knowing the state names. > > > Regards, > Roman > > > On Tue, Apr 12, 2022 at 2:06 PM Roman Khachatryan wrote: > > > > /shared folder contains keyed state that is shared among different > > checkpoints [1]. Most of state should be shared in your case since > > you're using keyed state and incremental checkpoints. > > > > When a checkpoint is loaded, the state that it shares with older > > checkpoints is loaded as well. I suggest
Re: RocksDB's state size discrepancy with what's seen with state processor API
> I assume that when you say "new states", that is related to new descriptors > with different names? Because, in the case of windowing for example, each > window "instance" has its own scoped (non-global and keyed) state, but that's > not regarded as a separate column family, is it? Yes, that's what I meant, and that's regarded as the same column family. Another possible reason is that SST files aren't being compacted and that increases the MANIFEST file size. I'd check the total number of loaded SST files and the creation date of the oldest one. You can also see whether there are any compactions running via RocksDB metrics [1] [2] (a reporter needs to be configured [3]). [1] https://nightlies.apache.org/flink/flink-docs-master/docs/deployment/config/#state-backend-rocksdb-metrics-num-running-compactions [2] https://nightlies.apache.org/flink/flink-docs-master/docs/deployment/config/#state-backend-rocksdb-metrics-compaction-pending [3] https://nightlies.apache.org/flink/flink-docs-master/docs/deployment/metric_reporters/#reporters Regards, Roman On Tue, Apr 19, 2022 at 1:38 PM Alexis Sarda-Espinosa wrote: > > Hi Roman, > > I assume that when you say "new states", that is related to new descriptors > with different names? Because, in the case of windowing for example, each > window "instance" has its own scoped (non-global and keyed) state, but that's > not regarded as a separate column family, is it? > > For the 3 descriptors I mentioned before, they are only instantiated once and > used like this: > > - Window list state: each call to process() executes > context.windowState().getListState(...).get() > - Global map state: each call to process() executes > context.globalState().getMapState(...) > - Global list state: within open(), runtimeContext.getListState(...) is > executed once and used throughout the life of the operator. > > According to [1], the two ways of using global state should be equivalent. > > I will say that some of the operators instantiate the state descriptor in > their constructors, i.e. before they are serialized to the TM, but the > descriptors are Serializable, so I imagine that's not relevant. > > [1] https://stackoverflow.com/a/50510054/5793905 > > Regards, > Alexis. > > -Original Message----- > From: Roman Khachatryan > Sent: Dienstag, 19. April 2022 11:48 > To: Alexis Sarda-Espinosa > Cc: user@flink.apache.org > Subject: Re: RocksDB's state size discrepancy with what's seen with state > processor API > > Hi Alexis, > > Thanks a lot for the information, > > MANIFEST files list RocksDB column families (among other info); ever growing > size of these files might indicate that some new states are constantly being > created. > Could you please confirm that the number of state names is constant? > > > Could you confirm if Flink's own operators could be creating state in > > RocksDB? I assume the window operators save some information in the state > > as well. > That's correct, window operators maintain a list of elements per window and a > set of timers (timestamps). These states' names should be fixed (something > like "window-contents" and "window-timers"). > > > is that related to managed state used by my functions? Or does that > > indicate size growth is elsewhere? > The same mechanism is used for both Flink internal state and operator state, > so it's hard to say without at least knowing the state names. > > > Regards, > Roman > > > On Tue, Apr 12, 2022 at 2:06 PM Roman Khachatryan wrote: > > > > /shared folder contains keyed state that is shared among different > > checkpoints [1]. Most of state should be shared in your case since > > you're using keyed state and incremental checkpoints. > > > > When a checkpoint is loaded, the state that it shares with older > > checkpoints is loaded as well. I suggested to load different > > checkpoints (i.e. chk-* folders) and compare the numbers of objects in > > their states. To prevent the job from discarding the state, it can > > either be stopped for some time and then restarted from the latest > > checkpoint; or the number of retained checkpoints can be increased > > [2]. Copying isn't necessary. > > > > Besides that, you can also check state sizes of operator in Flink Web > > UI (but not the sizes of individual states). If the operators are > > chained then their combined state size will be shown. To prevent this, > > you can disable chaining [3] (although this will have performance > > impact). > > > > Individual checkpoint folders should be eventually removed (when the > > checkpoint i
RE: RocksDB's state size discrepancy with what's seen with state processor API
Hi Roman, I assume that when you say "new states", that is related to new descriptors with different names? Because, in the case of windowing for example, each window "instance" has its own scoped (non-global and keyed) state, but that's not regarded as a separate column family, is it? For the 3 descriptors I mentioned before, they are only instantiated once and used like this: - Window list state: each call to process() executes context.windowState().getListState(...).get() - Global map state: each call to process() executes context.globalState().getMapState(...) - Global list state: within open(), runtimeContext.getListState(...) is executed once and used throughout the life of the operator. According to [1], the two ways of using global state should be equivalent. I will say that some of the operators instantiate the state descriptor in their constructors, i.e. before they are serialized to the TM, but the descriptors are Serializable, so I imagine that's not relevant. [1] https://stackoverflow.com/a/50510054/5793905 Regards, Alexis. -Original Message- From: Roman Khachatryan Sent: Dienstag, 19. April 2022 11:48 To: Alexis Sarda-Espinosa Cc: user@flink.apache.org Subject: Re: RocksDB's state size discrepancy with what's seen with state processor API Hi Alexis, Thanks a lot for the information, MANIFEST files list RocksDB column families (among other info); ever growing size of these files might indicate that some new states are constantly being created. Could you please confirm that the number of state names is constant? > Could you confirm if Flink's own operators could be creating state in > RocksDB? I assume the window operators save some information in the state as > well. That's correct, window operators maintain a list of elements per window and a set of timers (timestamps). These states' names should be fixed (something like "window-contents" and "window-timers"). > is that related to managed state used by my functions? Or does that indicate > size growth is elsewhere? The same mechanism is used for both Flink internal state and operator state, so it's hard to say without at least knowing the state names. Regards, Roman On Tue, Apr 12, 2022 at 2:06 PM Roman Khachatryan wrote: > > /shared folder contains keyed state that is shared among different > checkpoints [1]. Most of state should be shared in your case since > you're using keyed state and incremental checkpoints. > > When a checkpoint is loaded, the state that it shares with older > checkpoints is loaded as well. I suggested to load different > checkpoints (i.e. chk-* folders) and compare the numbers of objects in > their states. To prevent the job from discarding the state, it can > either be stopped for some time and then restarted from the latest > checkpoint; or the number of retained checkpoints can be increased > [2]. Copying isn't necessary. > > Besides that, you can also check state sizes of operator in Flink Web > UI (but not the sizes of individual states). If the operators are > chained then their combined state size will be shown. To prevent this, > you can disable chaining [3] (although this will have performance > impact). > > Individual checkpoint folders should be eventually removed (when the > checkpoint is subsumed). However, this is not guaranteed: if there is > any problem during deletion, it will be logged, but the job will not > fail. > > [1] > https://nightlies.apache.org/flink/flink-docs-master/docs/ops/state/ch > eckpoints/#directory-structure > [2] > https://nightlies.apache.org/flink/flink-docs-master/docs/deployment/c > onfig/#state-checkpoints-num-retained > [3] > https://nightlies.apache.org/flink/flink-docs-master/docs/dev/datastre > am/operators/overview/#disable-chaining > > Regards, > Roman > > On Tue, Apr 12, 2022 at 12:58 PM Alexis Sarda-Espinosa > wrote: > > > > Hi Roman, > > > > Maybe I'm misunderstanding the structure of the data within the checkpoint. > > You suggest comparing counts of objects in different checkpoints, I assume > > you mean copying my "checkpoints" folder at different times and comparing, > > not comparing different "chk-*" folders in the same snapshot, right? > > > > I haven't executed the processor program with a newer checkpoint, but I did > > look at the folder in the running system, and I noticed that most of the > > chk-* folders have remained unchanged, there's only 1 or 2 new folders > > corresponding to newer checkpoints. I would think this makes sense since > > the configuration specifies that only 1 completed checkpoint should be > > retained, but then why are the older chk-* folders still there? I did > > trigger a manual r
Re: RocksDB's state size discrepancy with what's seen with state processor API
Hi Alexis, Thanks a lot for the information, MANIFEST files list RocksDB column families (among other info); ever growing size of these files might indicate that some new states are constantly being created. Could you please confirm that the number of state names is constant? > Could you confirm if Flink's own operators could be creating state in > RocksDB? I assume the window operators save some information in the state as > well. That's correct, window operators maintain a list of elements per window and a set of timers (timestamps). These states' names should be fixed (something like "window-contents" and "window-timers"). > is that related to managed state used by my functions? Or does that indicate > size growth is elsewhere? The same mechanism is used for both Flink internal state and operator state, so it's hard to say without at least knowing the state names. Regards, Roman On Tue, Apr 12, 2022 at 2:06 PM Roman Khachatryan wrote: > > /shared folder contains keyed state that is shared among different > checkpoints [1]. Most of state should be shared in your case since > you're using keyed state and incremental checkpoints. > > When a checkpoint is loaded, the state that it shares with older > checkpoints is loaded as well. I suggested to load different > checkpoints (i.e. chk-* folders) and compare the numbers of objects in > their states. To prevent the job from discarding the state, it can > either be stopped for some time and then restarted from the latest > checkpoint; or the number of retained checkpoints can be increased > [2]. Copying isn't necessary. > > Besides that, you can also check state sizes of operator in Flink Web > UI (but not the sizes of individual states). If the operators are > chained then their combined state size will be shown. To prevent this, > you can disable chaining [3] (although this will have performance > impact). > > Individual checkpoint folders should be eventually removed (when the > checkpoint is subsumed). However, this is not guaranteed: if there is > any problem during deletion, it will be logged, but the job will not > fail. > > [1] > https://nightlies.apache.org/flink/flink-docs-master/docs/ops/state/checkpoints/#directory-structure > [2] > https://nightlies.apache.org/flink/flink-docs-master/docs/deployment/config/#state-checkpoints-num-retained > [3] > https://nightlies.apache.org/flink/flink-docs-master/docs/dev/datastream/operators/overview/#disable-chaining > > Regards, > Roman > > On Tue, Apr 12, 2022 at 12:58 PM Alexis Sarda-Espinosa > wrote: > > > > Hi Roman, > > > > Maybe I'm misunderstanding the structure of the data within the checkpoint. > > You suggest comparing counts of objects in different checkpoints, I assume > > you mean copying my "checkpoints" folder at different times and comparing, > > not comparing different "chk-*" folders in the same snapshot, right? > > > > I haven't executed the processor program with a newer checkpoint, but I did > > look at the folder in the running system, and I noticed that most of the > > chk-* folders have remained unchanged, there's only 1 or 2 new folders > > corresponding to newer checkpoints. I would think this makes sense since > > the configuration specifies that only 1 completed checkpoint should be > > retained, but then why are the older chk-* folders still there? I did > > trigger a manual restart of the Flink cluster in the past (before starting > > the long-running test), but if my policy is to CLAIM the checkpoint, > > Flink's documentation states that it would be cleaned eventually. > > > > Moreover, just by looking at folder sizes with "du", I can see that most of > > the state is held in the "shared" folder, and that has grown for sure; I'm > > not sure what "shared" usually holds, but if that's what's growing, maybe I > > can rule out expired state staying around?. My pipeline doesn't use timers, > > although I guess Flink itself may use them. Is there any way I could get > > some insight into which operator holds larger states? > > > > Regards, > > Alexis. > > > > -Original Message- > > From: Roman Khachatryan > > Sent: Dienstag, 12. April 2022 12:37 > > To: Alexis Sarda-Espinosa > > Cc: user@flink.apache.org > > Subject: Re: RocksDB's state size discrepancy with what's seen with state > > processor API > > > > Hi Alexis, > > > > Thanks a lot for sharing this. I think the program is correct. > > Although it doesn't take timers into account; and to estimate the state > > size more accurately, you could also use
RE: RocksDB's state size discrepancy with what's seen with state processor API
Hello,
There was a network issue in my environment and the job had to restart. After
the job came back up, the logs showed a lot of lines like this:
RocksDBIncrementalRestoreOperation ... Starting to restore from state handle:
...
Interestingly, those entries include information about sizes in bytes:
-
445163.sst=ByteStreamStateHandle{handleName='file:/opt/flink/state/checkpoints//shared/18f95afa-dc66-467d-bd05-779895f24960',
dataBytes=1328}
- privateState={MANIFEST-04=File State:
file:/opt/flink/state/checkpoints//shared/bd7fde24-3ef6-4e05-bbd6-1474f8051d5d
[80921331 bytes]
I extracted a lot of that information and I can see that:
- If I sum all dataBytes from sharedState, that only accounts for a couple MB.
- Most of the state comes from privateState, specifically from the entries
referring to MANIFEST File State; that accounts for almost 1.5GB.
I believe that is one of the files RocksDB uses internally, but is that related
to managed state used by my functions? Or does that indicate size growth is
elsewhere?
Regards,
Alexis.
-Original Message-
From: Alexis Sarda-Espinosa
Sent: Dienstag, 12. April 2022 15:39
To: ro...@apache.org
Cc: user@flink.apache.org
Subject: RE: RocksDB's state size discrepancy with what's seen with state
processor API
Thanks for all the pointers. The UI does show combined state for a chain, but
the only state descriptors inside that chain are the 3 I mentioned before. Its
size is still increasing today, and duration is now around 30 seconds (I can't
use unaligned checkpoints because I use partitionCustom).
I've executed the state processor program for all of the 50 chk-* folders, but
I don't see anything weird. The counts go up and down depending on which one I
load, but even the bigger ones have around 500-700 entries, which should only
be a couple hundred KB; it's not growing monotonically.
The chain of operators is relatively simple:
timestampedStream = inputStream -> keyBy -> assignTimestampsAndWatermarks
windowedStream = timestampedStream -> reinterpretAsKeyedStream ->
window (SlidingEventTimeWindows)
windowedStream -> process1 -> sink1
windowedStream -> process2 -> sink2
windowedStream -> process3 -> map
And according to the Low Watermark I see in the UI, event time is advancing
correctly.
Could you confirm if Flink's own operators could be creating state in RocksDB?
I assume the window operators save some information in the state as well.
Regards,
Alexis.
-Original Message-
From: Roman Khachatryan
Sent: Dienstag, 12. April 2022 14:06
To: Alexis Sarda-Espinosa
Cc: user@flink.apache.org
Subject: Re: RocksDB's state size discrepancy with what's seen with state
processor API
/shared folder contains keyed state that is shared among different checkpoints
[1]. Most of state should be shared in your case since you're using keyed state
and incremental checkpoints.
When a checkpoint is loaded, the state that it shares with older checkpoints is
loaded as well. I suggested to load different checkpoints (i.e. chk-* folders)
and compare the numbers of objects in their states. To prevent the job from
discarding the state, it can either be stopped for some time and then restarted
from the latest checkpoint; or the number of retained checkpoints can be
increased [2]. Copying isn't necessary.
Besides that, you can also check state sizes of operator in Flink Web UI (but
not the sizes of individual states). If the operators are chained then their
combined state size will be shown. To prevent this, you can disable chaining
[3] (although this will have performance impact).
Individual checkpoint folders should be eventually removed (when the checkpoint
is subsumed). However, this is not guaranteed: if there is any problem during
deletion, it will be logged, but the job will not fail.
[1]
https://nightlies.apache.org/flink/flink-docs-master/docs/ops/state/checkpoints/#directory-structure
[2]
https://nightlies.apache.org/flink/flink-docs-master/docs/deployment/config/#state-checkpoints-num-retained
[3]
https://nightlies.apache.org/flink/flink-docs-master/docs/dev/datastream/operators/overview/#disable-chaining
Regards,
Roman
On Tue, Apr 12, 2022 at 12:58 PM Alexis Sarda-Espinosa
wrote:
>
> Hi Roman,
>
> Maybe I'm misunderstanding the structure of the data within the checkpoint.
> You suggest comparing counts of objects in different checkpoints, I assume
> you mean copying my "checkpoints" folder at different times and comparing,
> not comparing different "chk-*" folders in the same snapshot, right?
>
> I haven't executed the processor program with a newer checkpoint, but I did
> look at the folder in the running system, and I noticed that most of the
> chk-* folders have remained unchanged, there's only 1 or 2 new folders
> corresp
RE: RocksDB's state size discrepancy with what's seen with state processor API
Thanks for all the pointers. The UI does show combined state for a chain, but the only state descriptors inside that chain are the 3 I mentioned before. Its size is still increasing today, and duration is now around 30 seconds (I can't use unaligned checkpoints because I use partitionCustom). I've executed the state processor program for all of the 50 chk-* folders, but I don't see anything weird. The counts go up and down depending on which one I load, but even the bigger ones have around 500-700 entries, which should only be a couple hundred KB; it's not growing monotonically. The chain of operators is relatively simple: timestampedStream = inputStream -> keyBy -> assignTimestampsAndWatermarks windowedStream = timestampedStream -> reinterpretAsKeyedStream -> window (SlidingEventTimeWindows) windowedStream -> process1 -> sink1 windowedStream -> process2 -> sink2 windowedStream -> process3 -> map And according to the Low Watermark I see in the UI, event time is advancing correctly. Could you confirm if Flink's own operators could be creating state in RocksDB? I assume the window operators save some information in the state as well. Regards, Alexis. -Original Message- From: Roman Khachatryan Sent: Dienstag, 12. April 2022 14:06 To: Alexis Sarda-Espinosa Cc: user@flink.apache.org Subject: Re: RocksDB's state size discrepancy with what's seen with state processor API /shared folder contains keyed state that is shared among different checkpoints [1]. Most of state should be shared in your case since you're using keyed state and incremental checkpoints. When a checkpoint is loaded, the state that it shares with older checkpoints is loaded as well. I suggested to load different checkpoints (i.e. chk-* folders) and compare the numbers of objects in their states. To prevent the job from discarding the state, it can either be stopped for some time and then restarted from the latest checkpoint; or the number of retained checkpoints can be increased [2]. Copying isn't necessary. Besides that, you can also check state sizes of operator in Flink Web UI (but not the sizes of individual states). If the operators are chained then their combined state size will be shown. To prevent this, you can disable chaining [3] (although this will have performance impact). Individual checkpoint folders should be eventually removed (when the checkpoint is subsumed). However, this is not guaranteed: if there is any problem during deletion, it will be logged, but the job will not fail. [1] https://nightlies.apache.org/flink/flink-docs-master/docs/ops/state/checkpoints/#directory-structure [2] https://nightlies.apache.org/flink/flink-docs-master/docs/deployment/config/#state-checkpoints-num-retained [3] https://nightlies.apache.org/flink/flink-docs-master/docs/dev/datastream/operators/overview/#disable-chaining Regards, Roman On Tue, Apr 12, 2022 at 12:58 PM Alexis Sarda-Espinosa wrote: > > Hi Roman, > > Maybe I'm misunderstanding the structure of the data within the checkpoint. > You suggest comparing counts of objects in different checkpoints, I assume > you mean copying my "checkpoints" folder at different times and comparing, > not comparing different "chk-*" folders in the same snapshot, right? > > I haven't executed the processor program with a newer checkpoint, but I did > look at the folder in the running system, and I noticed that most of the > chk-* folders have remained unchanged, there's only 1 or 2 new folders > corresponding to newer checkpoints. I would think this makes sense since the > configuration specifies that only 1 completed checkpoint should be retained, > but then why are the older chk-* folders still there? I did trigger a manual > restart of the Flink cluster in the past (before starting the long-running > test), but if my policy is to CLAIM the checkpoint, Flink's documentation > states that it would be cleaned eventually. > > Moreover, just by looking at folder sizes with "du", I can see that most of > the state is held in the "shared" folder, and that has grown for sure; I'm > not sure what "shared" usually holds, but if that's what's growing, maybe I > can rule out expired state staying around?. My pipeline doesn't use timers, > although I guess Flink itself may use them. Is there any way I could get some > insight into which operator holds larger states? > > Regards, > Alexis. > > -Original Message- > From: Roman Khachatryan > Sent: Dienstag, 12. April 2022 12:37 > To: Alexis Sarda-Espinosa > Cc: user@flink.apache.org > Subject: Re: RocksDB's state size discrepancy with what's seen with > state processor API > > Hi Alexis, > > Thanks a lot for sharing this. I think the program is correct. > Although it doesn'
Re: RocksDB's state size discrepancy with what's seen with state processor API
/shared folder contains keyed state that is shared among different checkpoints [1]. Most of state should be shared in your case since you're using keyed state and incremental checkpoints. When a checkpoint is loaded, the state that it shares with older checkpoints is loaded as well. I suggested to load different checkpoints (i.e. chk-* folders) and compare the numbers of objects in their states. To prevent the job from discarding the state, it can either be stopped for some time and then restarted from the latest checkpoint; or the number of retained checkpoints can be increased [2]. Copying isn't necessary. Besides that, you can also check state sizes of operator in Flink Web UI (but not the sizes of individual states). If the operators are chained then their combined state size will be shown. To prevent this, you can disable chaining [3] (although this will have performance impact). Individual checkpoint folders should be eventually removed (when the checkpoint is subsumed). However, this is not guaranteed: if there is any problem during deletion, it will be logged, but the job will not fail. [1] https://nightlies.apache.org/flink/flink-docs-master/docs/ops/state/checkpoints/#directory-structure [2] https://nightlies.apache.org/flink/flink-docs-master/docs/deployment/config/#state-checkpoints-num-retained [3] https://nightlies.apache.org/flink/flink-docs-master/docs/dev/datastream/operators/overview/#disable-chaining Regards, Roman On Tue, Apr 12, 2022 at 12:58 PM Alexis Sarda-Espinosa wrote: > > Hi Roman, > > Maybe I'm misunderstanding the structure of the data within the checkpoint. > You suggest comparing counts of objects in different checkpoints, I assume > you mean copying my "checkpoints" folder at different times and comparing, > not comparing different "chk-*" folders in the same snapshot, right? > > I haven't executed the processor program with a newer checkpoint, but I did > look at the folder in the running system, and I noticed that most of the > chk-* folders have remained unchanged, there's only 1 or 2 new folders > corresponding to newer checkpoints. I would think this makes sense since the > configuration specifies that only 1 completed checkpoint should be retained, > but then why are the older chk-* folders still there? I did trigger a manual > restart of the Flink cluster in the past (before starting the long-running > test), but if my policy is to CLAIM the checkpoint, Flink's documentation > states that it would be cleaned eventually. > > Moreover, just by looking at folder sizes with "du", I can see that most of > the state is held in the "shared" folder, and that has grown for sure; I'm > not sure what "shared" usually holds, but if that's what's growing, maybe I > can rule out expired state staying around?. My pipeline doesn't use timers, > although I guess Flink itself may use them. Is there any way I could get some > insight into which operator holds larger states? > > Regards, > Alexis. > > -Original Message- > From: Roman Khachatryan > Sent: Dienstag, 12. April 2022 12:37 > To: Alexis Sarda-Espinosa > Cc: user@flink.apache.org > Subject: Re: RocksDB's state size discrepancy with what's seen with state > processor API > > Hi Alexis, > > Thanks a lot for sharing this. I think the program is correct. > Although it doesn't take timers into account; and to estimate the state size > more accurately, you could also use the same serializers used by the job. > But maybe it makes more sense to compare the counts of objects in different > checkpoints and see which state is growing. > > If the number of keys is small, compaction should eventually clean up the old > values, given that the windows eventually expire. I think it makes sense to > check that watermarks in all windows are making progress. > > Setting ExecutionEnvironment#setParallelism(1) shouldn't affect the results > of the State Processor program. > > Regards, > Roman > > On Mon, Apr 11, 2022 at 12:28 PM Alexis Sarda-Espinosa > wrote: > > > > Some additional information that I’ve gathered: > > > > > > > > The number of unique keys in the system is 10, and that is correctly > > reflected in the state. > > TTL for global window state is set to update on read and write, but the > > code has logic to remove old state based on event time. > > Not sure it’s relevant, but the Flink cluster does run with jemalloc > > enabled. > > GitHub gist with the whole processor setup since it’s not too long: > > https://gist.github.com/asardaes/eaf21f18860ec39b325a40acef2db678 > > > > > > > > Relevant configuration entries (explicitly set, others are left with > > defaults)
RE: RocksDB's state size discrepancy with what's seen with state processor API
Hi Roman,
Maybe I'm misunderstanding the structure of the data within the checkpoint. You
suggest comparing counts of objects in different checkpoints, I assume you mean
copying my "checkpoints" folder at different times and comparing, not comparing
different "chk-*" folders in the same snapshot, right?
I haven't executed the processor program with a newer checkpoint, but I did
look at the folder in the running system, and I noticed that most of the chk-*
folders have remained unchanged, there's only 1 or 2 new folders corresponding
to newer checkpoints. I would think this makes sense since the configuration
specifies that only 1 completed checkpoint should be retained, but then why are
the older chk-* folders still there? I did trigger a manual restart of the
Flink cluster in the past (before starting the long-running test), but if my
policy is to CLAIM the checkpoint, Flink's documentation states that it would
be cleaned eventually.
Moreover, just by looking at folder sizes with "du", I can see that most of the
state is held in the "shared" folder, and that has grown for sure; I'm not sure
what "shared" usually holds, but if that's what's growing, maybe I can rule out
expired state staying around?. My pipeline doesn't use timers, although I guess
Flink itself may use them. Is there any way I could get some insight into which
operator holds larger states?
Regards,
Alexis.
-Original Message-
From: Roman Khachatryan
Sent: Dienstag, 12. April 2022 12:37
To: Alexis Sarda-Espinosa
Cc: user@flink.apache.org
Subject: Re: RocksDB's state size discrepancy with what's seen with state
processor API
Hi Alexis,
Thanks a lot for sharing this. I think the program is correct.
Although it doesn't take timers into account; and to estimate the state size
more accurately, you could also use the same serializers used by the job.
But maybe it makes more sense to compare the counts of objects in different
checkpoints and see which state is growing.
If the number of keys is small, compaction should eventually clean up the old
values, given that the windows eventually expire. I think it makes sense to
check that watermarks in all windows are making progress.
Setting ExecutionEnvironment#setParallelism(1) shouldn't affect the results of
the State Processor program.
Regards,
Roman
On Mon, Apr 11, 2022 at 12:28 PM Alexis Sarda-Espinosa
wrote:
>
> Some additional information that I’ve gathered:
>
>
>
> The number of unique keys in the system is 10, and that is correctly
> reflected in the state.
> TTL for global window state is set to update on read and write, but the code
> has logic to remove old state based on event time.
> Not sure it’s relevant, but the Flink cluster does run with jemalloc enabled.
> GitHub gist with the whole processor setup since it’s not too long:
> https://gist.github.com/asardaes/eaf21f18860ec39b325a40acef2db678
>
>
>
> Relevant configuration entries (explicitly set, others are left with
> defaults):
>
>
>
> state.backend: rocksdb
>
> state.backend.incremental: true
>
> execution.checkpointing.interval: 30 s
>
> execution.checkpointing.min-pause: 25 s
>
> execution.checkpointing.timeout: 5 min
>
> execution.savepoint-restore-mode: CLAIM
>
> execution.checkpointing.externalized-checkpoint-retention:
> RETAIN_ON_CANCELLATION
>
>
>
> Over the weekend, state size has grown to 1.23GB with the operators
> referenced in the processor program taking 849MB, so I’m still pretty
> puzzled. I thought it could be due to expired state being retained, but I
> think that doesn’t make sense if I have finite keys, right?
>
>
>
> Regards,
>
> Alexis.
>
>
>
> From: Alexis Sarda-Espinosa
> Sent: Samstag, 9. April 2022 01:39
> To: ro...@apache.org
> Cc: user@flink.apache.org
> Subject: Re: RocksDB's state size discrepancy with what's seen with
> state processor API
>
>
>
> Hi Roman,
>
>
>
> Here's an example of a WindowReaderFunction:
>
>
>
> public class StateReaderFunction extends
> WindowReaderFunction {
>
> private static final ListStateDescriptor LSD = new
> ListStateDescriptor<>(
>
> "descriptorId",
>
> Integer.class
>
> );
>
>
>
> @Override
>
> public void readWindow(String s, Context context,
> Iterable elements, Collector out) throws Exception {
>
> int count = 0;
>
> for (Integer i :
> context.windowState().getListState(LSD).get()) {
>
> count++;
>
> }
>
> out.collect(count);
>
> }
>
> }
>
>
>
> That's for the operator that uses window state.
Re: RocksDB's state size discrepancy with what's seen with state processor API
Hi Alexis,
Thanks a lot for sharing this. I think the program is correct.
Although it doesn't take timers into account; and to estimate the
state size more accurately, you could also use the same serializers
used by the job.
But maybe it makes more sense to compare the counts of objects in
different checkpoints and see which state is growing.
If the number of keys is small, compaction should eventually clean up
the old values, given that the windows eventually expire. I think it
makes sense to check that watermarks in all windows are making
progress.
Setting ExecutionEnvironment#setParallelism(1) shouldn't affect the
results of the State Processor program.
Regards,
Roman
On Mon, Apr 11, 2022 at 12:28 PM Alexis Sarda-Espinosa
wrote:
>
> Some additional information that I’ve gathered:
>
>
>
> The number of unique keys in the system is 10, and that is correctly
> reflected in the state.
> TTL for global window state is set to update on read and write, but the code
> has logic to remove old state based on event time.
> Not sure it’s relevant, but the Flink cluster does run with jemalloc enabled.
> GitHub gist with the whole processor setup since it’s not too long:
> https://gist.github.com/asardaes/eaf21f18860ec39b325a40acef2db678
>
>
>
> Relevant configuration entries (explicitly set, others are left with
> defaults):
>
>
>
> state.backend: rocksdb
>
> state.backend.incremental: true
>
> execution.checkpointing.interval: 30 s
>
> execution.checkpointing.min-pause: 25 s
>
> execution.checkpointing.timeout: 5 min
>
> execution.savepoint-restore-mode: CLAIM
>
> execution.checkpointing.externalized-checkpoint-retention:
> RETAIN_ON_CANCELLATION
>
>
>
> Over the weekend, state size has grown to 1.23GB with the operators
> referenced in the processor program taking 849MB, so I’m still pretty
> puzzled. I thought it could be due to expired state being retained, but I
> think that doesn’t make sense if I have finite keys, right?
>
>
>
> Regards,
>
> Alexis.
>
>
>
> From: Alexis Sarda-Espinosa
> Sent: Samstag, 9. April 2022 01:39
> To: ro...@apache.org
> Cc: user@flink.apache.org
> Subject: Re: RocksDB's state size discrepancy with what's seen with state
> processor API
>
>
>
> Hi Roman,
>
>
>
> Here's an example of a WindowReaderFunction:
>
>
>
> public class StateReaderFunction extends WindowReaderFunction Integer, String, TimeWindow> {
>
> private static final ListStateDescriptor LSD = new
> ListStateDescriptor<>(
>
> "descriptorId",
>
> Integer.class
>
> );
>
>
>
> @Override
>
> public void readWindow(String s, Context context,
> Iterable elements, Collector out) throws Exception {
>
> int count = 0;
>
> for (Integer i : context.windowState().getListState(LSD).get()) {
>
> count++;
>
> }
>
> out.collect(count);
>
> }
>
> }
>
>
>
> That's for the operator that uses window state. The other readers do
> something similar but with context.globalState(). That should provide the
> number of state entries for each key+window combination, no? And after
> collecting all results, I would get the number of state entries across all
> keys+windows for an operator.
>
>
>
> And yes, I do mean ProcessWindowFunction.clear(). Therein I call
> context.windowState().getListState(...).clear().
>
>
>
> Side note: in the state processor program I call
> ExecutionEnvironment#setParallelism(1) even though my streaming job runs with
> parallelism=4, this doesn't affect the result, does it?
>
>
>
> Regards,
>
> Alexis.
>
>
>
>
>
> From: Roman Khachatryan
> Sent: Friday, April 8, 2022 11:06 PM
> To: Alexis Sarda-Espinosa
> Cc: user@flink.apache.org
> Subject: Re: RocksDB's state size discrepancy with what's seen with state
> processor API
>
>
>
> Hi Alexis,
>
> If I understand correctly, the provided StateProcessor program gives
> you the number of stream elements per operator. However, you mentioned
> that these operators have collection-type states (ListState and
> MapState). That means that per one entry there can be an arbitrary
> number of state elements.
>
> Have you tried estimating the state sizes directly via readKeyedState[1]?
>
> > The other operator does override and call clear()
> Just to make sure, you mean ProcessWindowFunction.clear() [2], right?
>
> [1]
> https://nightlies.apache.org/flink/flink-docs-release-1.14/api/java/org/apache/fl
RE: RocksDB's state size discrepancy with what's seen with state processor API
Some additional information that I've gathered:
* The number of unique keys in the system is 10, and that is correctly
reflected in the state.
* TTL for global window state is set to update on read and write, but the
code has logic to remove old state based on event time.
* Not sure it's relevant, but the Flink cluster does run with jemalloc
enabled.
* GitHub gist with the whole processor setup since it's not too long:
https://gist.github.com/asardaes/eaf21f18860ec39b325a40acef2db678
Relevant configuration entries (explicitly set, others are left with defaults):
state.backend: rocksdb
state.backend.incremental: true
execution.checkpointing.interval: 30 s
execution.checkpointing.min-pause: 25 s
execution.checkpointing.timeout: 5 min
execution.savepoint-restore-mode: CLAIM
execution.checkpointing.externalized-checkpoint-retention:
RETAIN_ON_CANCELLATION
Over the weekend, state size has grown to 1.23GB with the operators referenced
in the processor program taking 849MB, so I'm still pretty puzzled. I thought
it could be due to expired state being retained, but I think that doesn't make
sense if I have finite keys, right?
Regards,
Alexis.
From: Alexis Sarda-Espinosa
Sent: Samstag, 9. April 2022 01:39
To: ro...@apache.org
Cc: user@flink.apache.org
Subject: Re: RocksDB's state size discrepancy with what's seen with state
processor API
Hi Roman,
Here's an example of a WindowReaderFunction:
public class StateReaderFunction extends WindowReaderFunction {
private static final ListStateDescriptor LSD = new
ListStateDescriptor<>(
"descriptorId",
Integer.class
);
@Override
public void readWindow(String s, Context context,
Iterable elements, Collector out) throws Exception {
int count = 0;
for (Integer i : context.windowState().getListState(LSD).get()) {
count++;
}
out.collect(count);
}
}
That's for the operator that uses window state. The other readers do something
similar but with context.globalState(). That should provide the number of state
entries for each key+window combination, no? And after collecting all results,
I would get the number of state entries across all keys+windows for an operator.
And yes, I do mean ProcessWindowFunction.clear(). Therein I call
context.windowState().getListState(...).clear().
Side note: in the state processor program I call
ExecutionEnvironment#setParallelism(1) even though my streaming job runs with
parallelism=4, this doesn't affect the result, does it?
Regards,
Alexis.
From: Roman Khachatryan mailto:ro...@apache.org>>
Sent: Friday, April 8, 2022 11:06 PM
To: Alexis Sarda-Espinosa
mailto:alexis.sarda-espin...@microfocus.com>>
Cc: user@flink.apache.org<mailto:user@flink.apache.org>
mailto:user@flink.apache.org>>
Subject: Re: RocksDB's state size discrepancy with what's seen with state
processor API
Hi Alexis,
If I understand correctly, the provided StateProcessor program gives
you the number of stream elements per operator. However, you mentioned
that these operators have collection-type states (ListState and
MapState). That means that per one entry there can be an arbitrary
number of state elements.
Have you tried estimating the state sizes directly via readKeyedState[1]?
> The other operator does override and call clear()
Just to make sure, you mean ProcessWindowFunction.clear() [2], right?
[1]
https://nightlies.apache.org/flink/flink-docs-release-1.14/api/java/org/apache/flink/state/api/ExistingSavepoint.html#readKeyedState-java.lang.String-org.apache.flink.state.api.functions.KeyedStateReaderFunction-
[2]
https://nightlies.apache.org/flink/flink-docs-release-1.4/api/java/org/apache/flink/streaming/api/functions/windowing/ProcessWindowFunction.html#clear-org.apache.flink.streaming.api.functions.windowing.ProcessWindowFunction.Context-
Regards,
Roman
On Fri, Apr 8, 2022 at 4:19 PM Alexis Sarda-Espinosa
mailto:alexis.sarda-espin...@microfocus.com>>
wrote:
>
> Hello,
>
>
>
> I have a streaming job running on Flink 1.14.4 that uses managed state with
> RocksDB with incremental checkpoints as backend. I've been monitoring a dev
> environment that has been running for the last week and I noticed that state
> size and end-to-end duration have been increasing steadily. Currently,
> duration is 11 seconds and size is 917MB (as shown in the UI). The tasks with
> the largest state (614MB) come from keyed sliding windows. Some attributes of
> this job's setup:
>
>
>
> Windows are 11 minutes in size.
> Slide time is 1 minute.
> Throughput is approximately 20 events per minute.
>
>
>
> I have 3 operators with these states:
>
>
>
> Window state with ListState and no TTL.
> Global window state with MapState> and a TTL o
Re: RocksDB's state size discrepancy with what's seen with state processor API
Hi Roman,
Here's an example of a WindowReaderFunction:
public class StateReaderFunction extends WindowReaderFunction {
private static final ListStateDescriptor LSD = new
ListStateDescriptor<>(
"descriptorId",
Integer.class
);
@Override
public void readWindow(String s, Context context,
Iterable elements, Collector out) throws Exception {
int count = 0;
for (Integer i : context.windowState().getListState(LSD).get()) {
count++;
}
out.collect(count);
}
}
That's for the operator that uses window state. The other readers do something
similar but with context.globalState(). That should provide the number of state
entries for each key+window combination, no? And after collecting all results,
I would get the number of state entries across all keys+windows for an operator.
And yes, I do mean ProcessWindowFunction.clear(). Therein I call
context.windowState().getListState(...).clear().
Side note: in the state processor program I call
ExecutionEnvironment#setParallelism(1) even though my streaming job runs with
parallelism=4, this doesn't affect the result, does it?
Regards,
Alexis.
From: Roman Khachatryan
Sent: Friday, April 8, 2022 11:06 PM
To: Alexis Sarda-Espinosa
Cc: user@flink.apache.org
Subject: Re: RocksDB's state size discrepancy with what's seen with state
processor API
Hi Alexis,
If I understand correctly, the provided StateProcessor program gives
you the number of stream elements per operator. However, you mentioned
that these operators have collection-type states (ListState and
MapState). That means that per one entry there can be an arbitrary
number of state elements.
Have you tried estimating the state sizes directly via readKeyedState[1]?
> The other operator does override and call clear()
Just to make sure, you mean ProcessWindowFunction.clear() [2], right?
[1]
https://nightlies.apache.org/flink/flink-docs-release-1.14/api/java/org/apache/flink/state/api/ExistingSavepoint.html#readKeyedState-java.lang.String-org.apache.flink.state.api.functions.KeyedStateReaderFunction-
[2]
https://nightlies.apache.org/flink/flink-docs-release-1.4/api/java/org/apache/flink/streaming/api/functions/windowing/ProcessWindowFunction.html#clear-org.apache.flink.streaming.api.functions.windowing.ProcessWindowFunction.Context-
Regards,
Roman
On Fri, Apr 8, 2022 at 4:19 PM Alexis Sarda-Espinosa
wrote:
>
> Hello,
>
>
>
> I have a streaming job running on Flink 1.14.4 that uses managed state with
> RocksDB with incremental checkpoints as backend. I’ve been monitoring a dev
> environment that has been running for the last week and I noticed that state
> size and end-to-end duration have been increasing steadily. Currently,
> duration is 11 seconds and size is 917MB (as shown in the UI). The tasks with
> the largest state (614MB) come from keyed sliding windows. Some attributes of
> this job’s setup:
>
>
>
> Windows are 11 minutes in size.
> Slide time is 1 minute.
> Throughput is approximately 20 events per minute.
>
>
>
> I have 3 operators with these states:
>
>
>
> Window state with ListState and no TTL.
> Global window state with MapState> and a TTL of 1 hour
> (with cleanupInRocksdbCompactFilter(1000L)).
> Global window state with ListState where the Pojo has an int and a
> long, a TTL of 1 hour, and configured with
> cleanupInRocksdbCompactFilter(1000L) as well.
>
>
>
> Both operators with global window state have logic to manually remove old
> state in addition to configured TTL. The other operator does override and
> call clear().
>
>
>
> I have now analyzed the checkpoint folder with the state processor API, and
> I’ll note here that I see 50 folders named chk-*** even though I don’t set
> state.checkpoints.num-retained and the default should be 1. I loaded the data
> from the folder with the highest chk number and I see that my operators have
> these amounts respectively:
>
>
>
> 10 entries
> 80 entries
> 200 entries
>
>
>
> I got those numbers with something like this:
>
>
>
> savepoint
>
> .window(SlidingEventTimeWindows.of(Time.minutes(11L),
> Time.minutes(1L)))
>
> .process(...)
>
> .collect()
>
> .parallelStream()
>
> .reduce(0, Integer::sum);
>
>
>
> Where my WindowReaderFunction classes just count the number of entries in
> each call to readWindow.
>
>
>
> Those amounts cannot possibly account for 614MB, so what am I missing?
>
>
>
> Regards,
>
> Alexis.
>
>
Re: RocksDB's state size discrepancy with what's seen with state processor API
Hi Alexis, If I understand correctly, the provided StateProcessor program gives you the number of stream elements per operator. However, you mentioned that these operators have collection-type states (ListState and MapState). That means that per one entry there can be an arbitrary number of state elements. Have you tried estimating the state sizes directly via readKeyedState[1]? > The other operator does override and call clear() Just to make sure, you mean ProcessWindowFunction.clear() [2], right? [1] https://nightlies.apache.org/flink/flink-docs-release-1.14/api/java/org/apache/flink/state/api/ExistingSavepoint.html#readKeyedState-java.lang.String-org.apache.flink.state.api.functions.KeyedStateReaderFunction- [2] https://nightlies.apache.org/flink/flink-docs-release-1.4/api/java/org/apache/flink/streaming/api/functions/windowing/ProcessWindowFunction.html#clear-org.apache.flink.streaming.api.functions.windowing.ProcessWindowFunction.Context- Regards, Roman On Fri, Apr 8, 2022 at 4:19 PM Alexis Sarda-Espinosa wrote: > > Hello, > > > > I have a streaming job running on Flink 1.14.4 that uses managed state with > RocksDB with incremental checkpoints as backend. I’ve been monitoring a dev > environment that has been running for the last week and I noticed that state > size and end-to-end duration have been increasing steadily. Currently, > duration is 11 seconds and size is 917MB (as shown in the UI). The tasks with > the largest state (614MB) come from keyed sliding windows. Some attributes of > this job’s setup: > > > > Windows are 11 minutes in size. > Slide time is 1 minute. > Throughput is approximately 20 events per minute. > > > > I have 3 operators with these states: > > > > Window state with ListState and no TTL. > Global window state with MapState> and a TTL of 1 hour > (with cleanupInRocksdbCompactFilter(1000L)). > Global window state with ListState where the Pojo has an int and a > long, a TTL of 1 hour, and configured with > cleanupInRocksdbCompactFilter(1000L) as well. > > > > Both operators with global window state have logic to manually remove old > state in addition to configured TTL. The other operator does override and > call clear(). > > > > I have now analyzed the checkpoint folder with the state processor API, and > I’ll note here that I see 50 folders named chk-*** even though I don’t set > state.checkpoints.num-retained and the default should be 1. I loaded the data > from the folder with the highest chk number and I see that my operators have > these amounts respectively: > > > > 10 entries > 80 entries > 200 entries > > > > I got those numbers with something like this: > > > > savepoint > > .window(SlidingEventTimeWindows.of(Time.minutes(11L), > Time.minutes(1L))) > > .process(...) > > .collect() > > .parallelStream() > > .reduce(0, Integer::sum); > > > > Where my WindowReaderFunction classes just count the number of entries in > each call to readWindow. > > > > Those amounts cannot possibly account for 614MB, so what am I missing? > > > > Regards, > > Alexis. > >
RocksDB's state size discrepancy with what's seen with state processor API
Hello, I have a streaming job running on Flink 1.14.4 that uses managed state with RocksDB with incremental checkpoints as backend. I've been monitoring a dev environment that has been running for the last week and I noticed that state size and end-to-end duration have been increasing steadily. Currently, duration is 11 seconds and size is 917MB (as shown in the UI). The tasks with the largest state (614MB) come from keyed sliding windows. Some attributes of this job's setup: * Windows are 11 minutes in size. * Slide time is 1 minute. * Throughput is approximately 20 events per minute. I have 3 operators with these states: 1. Window state with ListState and no TTL. 2. Global window state with MapState> and a TTL of 1 hour (with cleanupInRocksdbCompactFilter(1000L)). 3. Global window state with ListState where the Pojo has an int and a long, a TTL of 1 hour, and configured with cleanupInRocksdbCompactFilter(1000L) as well. Both operators with global window state have logic to manually remove old state in addition to configured TTL. The other operator does override and call clear(). I have now analyzed the checkpoint folder with the state processor API, and I'll note here that I see 50 folders named chk-*** even though I don't set state.checkpoints.num-retained and the default should be 1. I loaded the data from the folder with the highest chk number and I see that my operators have these amounts respectively: 1. 10 entries 2. 80 entries 3. 200 entries I got those numbers with something like this: savepoint .window(SlidingEventTimeWindows.of(Time.minutes(11L), Time.minutes(1L))) .process(...) .collect() .parallelStream() .reduce(0, Integer::sum); Where my WindowReaderFunction classes just count the number of entries in each call to readWindow. Those amounts cannot possibly account for 614MB, so what am I missing? Regards, Alexis.
