That makes sense.  Hopefully the LTS support for 2.37 can be extended in 
the mean time.

On Wednesday, 1 February 2023 at 10:45:34 UTC Julien Pivotto wrote:

> On 01 Feb 02:00, Brian Candler wrote:
> > Aside: is 2.42.0 going to be an LTS version?
>
> Hello,
>
> I have not updated the website yet, but 2.42 will not be a LTS version.
>
> My feeling is that we still need a few releases so that the native
> histogram and OOO ingestion "stabilizes". It is not about waiting for
> them to be stable, but more making sure that the eventual bugs
> introduced in the codebase by those two major features are noticed and
> fixed.
>
>
> > 
> > On Wednesday, 1 February 2023 at 09:35:00 UTC sup...@gmail.com wrote:
> > 
> > > Or upgrade to 2.42.0. :)
> > >
> > > On Wed, Feb 1, 2023 at 9:48 AM Julien Pivotto <roidel...@prometheus.io> 
>
> > > wrote:
> > >
> > >> On 24 Jan 21:43, Victor Hadianto wrote:
> > >> > > Also, what version(s) of prometheus are these two instances?
> > >> > 
> > >> > They are both the same:
> > >> > prometheus, version 2.37.0 (branch: HEAD, revision:
> > >> > b41e0750abf5cc18d8233161560731de05199330)
> > >>
> > >> Please update to 2.37.5. There has been a memory leak fixed in 2.37.3.
> > >>
> > >>
> > >>
> > >> > 
> > >> > > The RAM usage of Prometheus depends on a number of factors. 
> There's a
> > >> > calculator embedded in this article, but it's pretty old now:
> > >> > 
> > >> 
> https://www.robustperception.io/how-much-ram-does-prometheus-2-x-need-for-cardinality-and-ingestion
> > >> > 
> > >> > Thanks for this, I'll read & play around with that calculator for 
> our
> > >> > Prometheus instances (we have 9 in various clusters now).
> > >> > 
> > >> > Regards,
> > >> > Victor
> > >> > 
> > >> > 
> > >> > On Tue, 24 Jan 2023 at 21:03, Brian Candler <b.ca...@pobox.com> 
> wrote:
> > >> > 
> > >> > > Also, what version(s) of prometheus are these two instances? 
> Different
> > >> > > versions of Prometheus are compiled using different versions of 
> Go, 
> > >> which
> > >> > > in turn have different degrees of aggressiveness in returning 
> unused 
> > >> RAM to
> > >> > > the operating system. Also remember Go is a garbage-collected 
> > >> language.
> > >> > >
> > >> > > The RAM usage of Prometheus depends on a number of factors. 
> There's a
> > >> > > calculator embedded in this article, but it's pretty old now:
> > >> > >
> > >> > > 
> > >> 
> https://www.robustperception.io/how-much-ram-does-prometheus-2-x-need-for-cardinality-and-ingestion
> > >> > >
> > >> > > On Tuesday, 24 January 2023 at 09:29:47 UTC sup...@gmail.com 
> wrote:
> > >> > >
> > >> > >> When you say "measured by Kubernetes", what metric specifically?
> > >> > >>
> > >> > >> There are several misleading metrics. What matters is
> > >> > >> `container_memory_rss` or `container_memory_working_set_bytes`. 
> The
> > >> > >> `container_memmory_usage_bytes` is misleading because it 
> includes 
> > >> page
> > >> > >> cache values.
> > >> > >>
> > >> > >> On Tue, Jan 24, 2023 at 10:20 AM Victor H <vhad...@gmail.com> 
> wrote:
> > >> > >>
> > >> > >>> Hi,
> > >> > >>>
> > >> > >>> We are running multiple Prometheus instances in Kubernetes 
> (deployed
> > >> > >>> using Prometheus Operator) and hope that someone can help us 
> > >> understanding
> > >> > >>> why the RAM usage in a few of our instances are unexpectedly 
> high 
> > >> (we think
> > >> > >>> it's cardinality but not sure where to look)
> > >> > >>>
> > >> > >>> In Prometheus A, we have the following stat:
> > >> > >>>
> > >> > >>> Number of Series: 56486
> > >> > >>> Number of Chunks: 56684
> > >> > >>> Number of Label Pairs: 678
> > >> > >>>
> > >> > >>> tsdb analyze has the following result:
> > >> > >>>
> > >> > >>> /bin $ ./promtool tsdb analyze /prometheus/
> > >> > >>> Block ID: 01GQGMKZAF548DPE2DFZTF1TRW
> > >> > >>> Duration: 1h59m59.368s
> > >> > >>> Series: 56470
> > >> > >>> Label names: 26
> > >> > >>> Postings (unique label pairs): 678
> > >> > >>> Postings entries (total label pairs): 338705
> > >> > >>>
> > >> > >>> This instance uses roughly between 4Gb - 5Gb of RAM (measured by
> > >> > >>> Kubernetes).
> > >> > >>>
> > >> > >>> From our reading, each time series should use around 8kb of RAM 
> so 
> > >> for
> > >> > >>> 56k series should be using a mere 500Mb.
> > >> > >>>
> > >> > >>> On a different Prometheus instance (let's call it Prometheus 
> > >> Central) we
> > >> > >>> have 1,1m series and it's using 9Gb - 10Gb which is roughly 
> what is
> > >> > >>> expected.
> > >> > >>>
> > >> > >>> We're curious about this instance and we believe it's 
> cardinality. 
> > >> We
> > >> > >>> have a lot more targets in Prometheus A. I also note that the 
> > >> Posting
> > >> > >>> entries (total label pairs) is 338k but I'm not sure where to 
> look 
> > >> for this.
> > >> > >>>
> > >> > >>> The top entries from tsdb analyze is right at the bottom of 
> this 
> > >> post.
> > >> > >>> The "most common label pairs" entries have alarmingly high 
> count, I 
> > >> wonder
> > >> > >>> if this contributes the high "total label pairs" and 
> consequently 
> > >> higher
> > >> > >>> than expected RAM usage.
> > >> > >>>
> > >> > >>> When calculating the expected RAM usage, is the "total label 
> pairs" 
> > >> is
> > >> > >>> the number we need to use rather than the "total series"
> > >> > >>>
> > >> > >>> Thanks,
> > >> > >>> Victor
> > >> > >>>
> > >> > >>>
> > >> > >>> Label pairs most involved in churning:
> > >> > >>> 296 activity_type=none
> > >> > >>> 258 workflow_type=PodUpdateWorkflow
> > >> > >>> 163 __name__=temporal_request_latency_bucket
> > >> > >>> 104 workflow_type=GenerateSPVarsWorkflow
> > >> > >>> 95 operation=RespondActivityTaskCompleted
> > >> > >>> 89 __name__=temporal_activity_execution_latency_bucket
> > >> > >>> 89 __name__=temporal_activity_schedule_to_start_latency_bucket
> > >> > >>> 65 workflow_type=PodInitWorkflow
> > >> > >>> 53 operation=RespondWorkflowTaskCompleted
> > >> > >>> 49 __name__=temporal_workflow_endtoend_latency_bucket
> > >> > >>> 49 
> __name__=temporal_workflow_task_schedule_to_start_latency_bucket
> > >> > >>> 49 __name__=temporal_workflow_task_execution_latency_bucket
> > >> > >>> 49 __name__=temporal_workflow_task_replay_latency_bucket
> > >> > >>> 39 activity_type=UpdatePodConnectionsActivity
> > >> > >>> 38 le=+Inf
> > >> > >>> 38 le=0.02
> > >> > >>> 38 le=0.1
> > >> > >>> 38 le=0.001
> > >> > >>> 38 activity_type=GenerateSPVarsActivity
> > >> > >>> 38 le=5
> > >> > >>>
> > >> > >>> Label names most involved in churning:
> > >> > >>> 734 __name__
> > >> > >>> 734 job
> > >> > >>> 724 instance
> > >> > >>> 577 activity_type
> > >> > >>> 577 workflow_type
> > >> > >>> 541 le
> > >> > >>> 177 operation
> > >> > >>> 95 datname
> > >> > >>> 53 datid
> > >> > >>> 31 mode
> > >> > >>> 29 namespace
> > >> > >>> 21 state
> > >> > >>> 12 quantile
> > >> > >>> 11 container
> > >> > >>> 11 service
> > >> > >>> 11 pod
> > >> > >>> 11 endpoint
> > >> > >>> 10 scrape_job
> > >> > >>> 4 alertname
> > >> > >>> 4 severity
> > >> > >>>
> > >> > >>> Most common label pairs:
> > >> > >>> 23012 activity_type=none
> > >> > >>> 20060 workflow_type=PodUpdateWorkflow
> > >> > >>> 12712 __name__=temporal_request_latency_bucket
> > >> > >>> 8092 workflow_type=GenerateSPVarsWorkflow
> > >> > >>> 7440 operation=RespondActivityTaskCompleted
> > >> > >>> 6944 __name__=temporal_activity_execution_latency_bucket
> > >> > >>> 6944 __name__=temporal_activity_schedule_to_start_latency_bucket
> > >> > >>> 5100 workflow_type=PodInitWorkflow
> > >> > >>> 4140 operation=RespondWorkflowTaskCompleted
> > >> > >>> 3864 __name__=temporal_workflow_task_replay_latency_bucket
> > >> > >>> 3864 __name__=temporal_workflow_endtoend_latency_bucket
> > >> > >>> 3864 
> > >> __name__=temporal_workflow_task_schedule_to_start_latency_bucket
> > >> > >>> 3864 __name__=temporal_workflow_task_execution_latency_bucket
> > >> > >>> 3080 activity_type=UpdatePodConnectionsActivity
> > >> > >>> 3004 le=0.5
> > >> > >>> 3004 le=0.01
> > >> > >>> 3004 le=0.1
> > >> > >>> 3004 le=1
> > >> > >>> 3004 le=0.001
> > >> > >>> 3004 le=0.002
> > >> > >>>
> > >> > >>> Label names with highest cumulative label value length:
> > >> > >>> 8312 scrape_job
> > >> > >>> 4279 workflow_type
> > >> > >>> 3994 rule_group
> > >> > >>> 2614 __name__
> > >> > >>> 2478 instance
> > >> > >>> 1564 job
> > >> > >>> 434 datname
> > >> > >>> 248 activity_type
> > >> > >>> 139 mode
> > >> > >>> 128 operation
> > >> > >>> 109 version
> > >> > >>> 97 pod
> > >> > >>> 88 state
> > >> > >>> 68 service
> > >> > >>> 45 le
> > >> > >>> 44 namespace
> > >> > >>> 43 slice
> > >> > >>> 31 container
> > >> > >>> 28 quantile
> > >> > >>> 18 alertname
> > >> > >>>
> > >> > >>> Highest cardinality labels:
> > >> > >>> 138 instance
> > >> > >>> 138 scrape_job
> > >> > >>> 84 __name__
> > >> > >>> 75 workflow_type
> > >> > >>> 71 datname
> > >> > >>> 70 job
> > >> > >>> 19 rule_group
> > >> > >>> 14 le
> > >> > >>> 10 activity_type
> > >> > >>> 9 mode
> > >> > >>> 9 quantile
> > >> > >>> 6 state
> > >> > >>> 6 operation
> > >> > >>> 5 datid
> > >> > >>> 4 slice
> > >> > >>> 2 container
> > >> > >>> 2 pod
> > >> > >>> 2 alertname
> > >> > >>> 2 version
> > >> > >>> 2 service
> > >> > >>>
> > >> > >>> Highest cardinality metric names:
> > >> > >>> 12712 temporal_request_latency_bucket
> > >> > >>> 6944 temporal_activity_execution_latency_bucket
> > >> > >>> 6944 temporal_activity_schedule_to_start_latency_bucket
> > >> > >>> 3864 temporal_workflow_task_schedule_to_start_latency_bucket
> > >> > >>> 3864 temporal_workflow_task_replay_latency_bucket
> > >> > >>> 3864 temporal_workflow_task_execution_latency_bucket
> > >> > >>> 3864 temporal_workflow_endtoend_latency_bucket
> > >> > >>> 2448 pg_locks_count
> > >> > >>> 1632 pg_stat_activity_count
> > >> > >>> 908 temporal_request
> > >> > >>> 690 prometheus_target_sync_length_seconds
> > >> > >>> 496 temporal_activity_execution_latency_count
> > >> > >>> 350 go_gc_duration_seconds
> > >> > >>> 340 pg_stat_database_tup_inserted
> > >> > >>> 340 pg_stat_database_temp_bytes
> > >> > >>> 340 pg_stat_database_xact_commit
> > >> > >>> 340 pg_stat_database_xact_rollback
> > >> > >>> 340 pg_stat_database_tup_updated
> > >> > >>> 340 pg_stat_database_deadlocks
> > >> > >>> 340 pg_stat_database_tup_returned
> > >> > >>>
> > >> > >>>
> > >> > >>>
> > >> > >>>
> > >> > >>>
> > >> > >>>
> > >> > >>> --
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> > >> > >>> Groups "Prometheus Users" group.
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> > >> send
> > >> > >>> an email to prometheus-use...@googlegroups.com.
> > >> > >>> To view this discussion on the web visit
> > >> > >>> 
> > >> 
> https://groups.google.com/d/msgid/prometheus-users/59f74cb9-3135-4fc3-a7e7-9bec02a3143an%40googlegroups.com
> > >> > >>> <
> > >> 
> https://groups.google.com/d/msgid/prometheus-users/59f74cb9-3135-4fc3-a7e7-9bec02a3143an%40googlegroups.com?utm_medium=email&utm_source=footer
> > >> >
> > >> > >>> .
> > >> > >>>
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> > >> 
> https://groups.google.com/d/msgid/prometheus-users/9a2d7848-4f4f-43b9-90f4-765367f33c47n%40googlegroups.com
> > >> > > <
> > >> 
> https://groups.google.com/d/msgid/prometheus-users/9a2d7848-4f4f-43b9-90f4-765367f33c47n%40googlegroups.com?utm_medium=email&utm_source=footer
> > >> >
> > >> > > .
> > >> > >
> > >> > 
> > >> > -- 
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> > >> > To view this discussion on the web visit 
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> > >> .
> > >>
> > >> -- 
> > >> Julien Pivotto
> > >> @roidelapluie
> > >>
> > >> -- 
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> > >>
> > > To view this discussion on the web visit 
> > >> 
> https://groups.google.com/d/msgid/prometheus-users/Y9onaJkBb8Quugae%40nixos
> > >> .
> > >>
> > >
> > 
> > -- 
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> .
>
>
> -- 
> Julien Pivotto
> @roidelapluie
>

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