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https://issues.apache.org/jira/browse/KAFKA-12472?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Guozhang Wang updated KAFKA-12472:
----------------------------------
Description:
Today to trouble shoot a rebalance issue operators need to do a lot of manual
steps: locating the problematic members, search in the log entries, and look
for related metrics. It would be great to add a single metric that covers all
these manual steps and operators would only need to check this single signal to
check what is the root cause. A concrete idea is to expose two enum gauge
metrics on consumer and streams, respectively:
* Consumer level (the order below is by-design, see Streams level for details):
0. *None* => there is no rebalance on going.
1. *CoordinatorRequested* => any of the coordinator response contains a
RebalanceInProgress error code.
2. *NewMember* => when the join group response has a MemberIdRequired error
code.
3. *UnknownMember* => when any of the coordinator response contains an
UnknownMember error code, indicating this member is already kicked out of the
group.
4. *StaleMember* => when any of the coordinator response contains an
IllegalGeneration error code.
5. *DroppedGroup* => when hb thread decides to call leaveGroup due to hb
expired.
6. *UserRequested* => when leaveGroup upon the shutdown / unsubscribeAll API,
as well as upon calling the enforceRebalance API.
7. *MetadataChanged* => requestRejoin triggered since metadata has changed.
8. *SubscriptionChanged* => requestRejoin triggered since subscription has
changed.
9. *RetryOnError* => when join/syncGroup response contains a retriable error
which would cause the consumer to backoff and retry.
10. *RevocationNeeded* => requestRejoin triggered since revoked partitions is
not empty.
The transition rule is that a non-zero status code can only transit to zero or
to a higher code, but not to a lower code (same for streams, see rationales
below).
* Streams level: today a streams client can have multiple consumers. We
introduced some new enum states as well as aggregation rules across consumers:
if there's no streams-layer events as below that transits its status (i.e.
streams layer think it is 0), then we aggregate across all the embedded
consumers and take the largest status code value as the streams metric; if
there are streams-layer events that determines its status should be in 10+,
then its overrides all embedded consumer layer status code. In addition, when
create aggregated metric across streams instance within an app, we also follow
the same aggregation rule, e.g. if there are two streams instance where one
instance's status code is 1), and the other is 10), then the app's status is
10).
10. *RevocationNeeded* => the definition of this is changed to the original
10) defined in consumer above, OR leader decides to revoke either
active/standby tasks and hence schedule follow-ups.
11. *AssignmentProbing* => leader decides to schedule follow-ups since the
current assignment is unstable.
12. *VersionProbing* => leader decides to schedule follow-ups due to version
probing.
13. *EndpointUpdate* => anyone decides to schedule follow-ups due to endpoint
updates.
The main motivations of the above proposed precedence order are the following:
1. When a rebalance is triggered by one member, all other members would only
know it is due to CoordinatorRequested from coordinator error codes, and hence
CoordinatorRequested should be overridden by any other status when aggregating
across clients.
2. DroppedGroup could cause unknown/stale members that would fail and retry
immediately, and hence should take higher precedence.
3. Revocation definition is extended in Streams, and hence it needs to take the
highest precedence among all consumer-only status so that it would not be
overridden by any of the consumer-only status.
4. In general, more rare events get higher precedence.
Any comments on the precedence rules / categorization are more than welcomed!
was:
Today to trouble shoot a rebalance issue operators need to do a lot of manual
steps: locating the problematic members, search in the log entries, and look
for related metrics. It would be great to add a single metric that covers all
these manual steps and operators would only need to check this single signal to
check what is the root cause. A concrete idea is to expose two enum gauge
metrics on consumer and streams, respectively:
* Consumer level (the order below is by-design, see Streams level for details):
0. None => there is no rebalance on going.
1. *CoordinatorRequested* => any of the coordinator response contains a
RebalanceInProgress error code.
2. *NewMember* => when the join group response has a MemberIdRequired error
code.
3. *UnknownMember* => when any of the coordinator response contains an
UnknownMember error code, indicating this member is already kicked out of the
group.
4. *StaleMember* => when any of the coordinator response contains an
IllegalGeneration error code.
5. *DroppedGroup* => when hb thread decides to call leaveGroup due to hb
expired.
6. *UserRequested* => when leaveGroup upon the shutdown / unsubscribeAll API,
as well as upon calling the enforceRebalance API.
7. *MetadataChanged* => requestRejoin triggered since metadata has changed.
8. *SubscriptionChanged* => requestRejoin triggered since subscription has
changed.
9. *RetryOnError* => when join/syncGroup response contains a retriable error
which would cause the consumer to backoff and retry.
10. *RevocationNeeded* => requestRejoin triggered since revoked partitions is
not empty.
The transition rule is that a non-zero status code can only transit to zero or
to a higher code, but not to a lower code (same for streams, see rationales
below).
* Streams level: today a streams client can have multiple consumers. We
introduced some new enum states as well as aggregation rules across consumers:
if there's no streams-layer events as below that transits its status (i.e.
streams layer think it is 0), then we aggregate across all the embedded
consumers and take the largest status code value as the streams metric; if
there are streams-layer events that determines its status should be in 10+,
then its overrides all embedded consumer layer status code. In addition, when
create aggregated metric across streams instance within an app, we also follow
the same aggregation rule, e.g. if there are two streams instance where one
instance's status code is 1), and the other is 10), then the app's status is
10).
10. *RevocationNeeded* => the definition of this is changed to the original
10) defined in consumer above, OR leader decides to revoke either
active/standby tasks and hence schedule follow-ups.
11. *AssignmentProbing* => leader decides to schedule follow-ups since the
current assignment is unstable.
12. *VersionProbing* => leader decides to schedule follow-ups due to version
probing.
13. *EndpointUpdate* => anyone decides to schedule follow-ups due to endpoint
updates.
The main motivations of the above proposed precedence order are the following:
1. When a rebalance is triggered by one member, all other members would only
know it is due to CoordinatorRequested from coordinator error codes, and hence
CoordinatorRequested should be overridden by any other status when aggregating
across clients.
2. DroppedGroup could cause unknown/stale members that would fail and retry
immediately, and hence should take higher precedence.
3. Revocation definition is extended in Streams, and hence it needs to take the
highest precedence among all consumer-only status so that it would not be
overridden by any of the consumer-only status.
4. In general, more rare events get higher precedence.
Any comments on the precedence rules / categorization are more than welcomed!
> Add a Consumer / Streams metric to indicate the current rebalance status
> ------------------------------------------------------------------------
>
> Key: KAFKA-12472
> URL: https://issues.apache.org/jira/browse/KAFKA-12472
> Project: Kafka
> Issue Type: Improvement
> Components: consumer, streams
> Reporter: Guozhang Wang
> Priority: Major
> Labels: needs-kip
>
> Today to trouble shoot a rebalance issue operators need to do a lot of manual
> steps: locating the problematic members, search in the log entries, and look
> for related metrics. It would be great to add a single metric that covers all
> these manual steps and operators would only need to check this single signal
> to check what is the root cause. A concrete idea is to expose two enum gauge
> metrics on consumer and streams, respectively:
> * Consumer level (the order below is by-design, see Streams level for
> details):
> 0. *None* => there is no rebalance on going.
> 1. *CoordinatorRequested* => any of the coordinator response contains a
> RebalanceInProgress error code.
> 2. *NewMember* => when the join group response has a MemberIdRequired error
> code.
> 3. *UnknownMember* => when any of the coordinator response contains an
> UnknownMember error code, indicating this member is already kicked out of the
> group.
> 4. *StaleMember* => when any of the coordinator response contains an
> IllegalGeneration error code.
> 5. *DroppedGroup* => when hb thread decides to call leaveGroup due to hb
> expired.
> 6. *UserRequested* => when leaveGroup upon the shutdown / unsubscribeAll
> API, as well as upon calling the enforceRebalance API.
> 7. *MetadataChanged* => requestRejoin triggered since metadata has changed.
> 8. *SubscriptionChanged* => requestRejoin triggered since subscription has
> changed.
> 9. *RetryOnError* => when join/syncGroup response contains a retriable
> error which would cause the consumer to backoff and retry.
> 10. *RevocationNeeded* => requestRejoin triggered since revoked partitions
> is not empty.
> The transition rule is that a non-zero status code can only transit to zero
> or to a higher code, but not to a lower code (same for streams, see
> rationales below).
> * Streams level: today a streams client can have multiple consumers. We
> introduced some new enum states as well as aggregation rules across
> consumers: if there's no streams-layer events as below that transits its
> status (i.e. streams layer think it is 0), then we aggregate across all the
> embedded consumers and take the largest status code value as the streams
> metric; if there are streams-layer events that determines its status should
> be in 10+, then its overrides all embedded consumer layer status code. In
> addition, when create aggregated metric across streams instance within an
> app, we also follow the same aggregation rule, e.g. if there are two streams
> instance where one instance's status code is 1), and the other is 10), then
> the app's status is 10).
> 10. *RevocationNeeded* => the definition of this is changed to the original
> 10) defined in consumer above, OR leader decides to revoke either
> active/standby tasks and hence schedule follow-ups.
> 11. *AssignmentProbing* => leader decides to schedule follow-ups since the
> current assignment is unstable.
> 12. *VersionProbing* => leader decides to schedule follow-ups due to version
> probing.
> 13. *EndpointUpdate* => anyone decides to schedule follow-ups due to
> endpoint updates.
> The main motivations of the above proposed precedence order are the following:
> 1. When a rebalance is triggered by one member, all other members would only
> know it is due to CoordinatorRequested from coordinator error codes, and
> hence CoordinatorRequested should be overridden by any other status when
> aggregating across clients.
> 2. DroppedGroup could cause unknown/stale members that would fail and retry
> immediately, and hence should take higher precedence.
> 3. Revocation definition is extended in Streams, and hence it needs to take
> the highest precedence among all consumer-only status so that it would not be
> overridden by any of the consumer-only status.
> 4. In general, more rare events get higher precedence.
> Any comments on the precedence rules / categorization are more than welcomed!
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