w3ll1ngt commented on code in PR #13130:
URL: https://github.com/apache/ignite/pull/13130#discussion_r3627510164
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docs/_docs/perf-and-troubleshooting/general-perf-tips.adoc:
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@@ -47,3 +47,393 @@ queries with JOINs at massive scale and expect significant
performance benefits.
* Adjust link:data-rebalancing[data rebalancing settings] to ensure that
rebalancing completes faster when your cluster topology changes.
+== How to assess cluster health
+
+Cluster health is a complex thing. Apache Ignite is capable of demonstrating
great performance across different scenarios with varying loads. Therefore, in
general terms, a healthy cluster is one whose behavior aligns with your
expectations. However, there are some universal aspects that apply to all
deployments and warrant attention.
+
+It is important to understand that a healthy cluster may undergo planned
topology changes or temporary load spikes.
+
+The key properties are:
+
+* The cluster is in the intended link:monitoring-metrics/cluster-states[state]
and serves only the operations allowed by that state.
+* Baseline topology, when it is used or managed manually, matches the expected
data-bearing server nodes.
+* Data remains consistent:
link:tools/control-script#verifying-partition-checksums[`idle_verify`] reports
no partition conflicts when the cluster is idle.
+* Expected nodes are present, and no node segmentation or repeated membership
churn is reported.
+* link:configuring-caches/partition-loss-policy[Lost partitions] are absent.
+* Long-running transactions, Partition Map Exchange (PME), rebalancing,
checkpointing, and executor queues converge instead of accumulating.
+
+There is no single command or metric that proves cluster health for every
deployment.
+
+Use several signals together.
+A simple client connection or SQL liveness check can prove only that a
particular client or query path is reachable; it does not check user
partitions, backup consistency, baseline membership, or all server nodes.
+Similarly, `control.(sh|bat) --cache idle_verify` is an important consistency
check, but it is still not a complete health check.
+
+=== Check the Intended State and Node Membership
+
+Start with the link:monitoring-metrics/cluster-states[cluster state].
+
+Run:
+
+[source,shell]
+----
+control.(sh|bat) --state
+----
+
+Relevant output:
+
+[source,text]
+----
+Command [STATE] started
+Arguments: --state
+--------------------------------------------------------------------------------
+Cluster state: ACTIVE
+Command [STATE] finished with code: 0
+----
+
+`ACTIVE` is expected for normal read-write operation.
+`ACTIVE_READ_ONLY` is normal when read-only operation was intentionally
enabled.
+`INACTIVE` is acceptable only when it matches the current operation, for
example planned maintenance; an inactive cluster does not serve the data
workload.
+The criterion is whether the actual state matches the state that was
intentionally set for the deployment.
+
+Check baseline topology when the cluster uses persistence, when baseline
autoadjustment is disabled, or when you intentionally manage the set of
data-bearing nodes.
+In pure in-memory clusters with the default immediate autoadjustment, baseline
topology normally follows the current server topology automatically.
+If autoadjustment is disabled, the baseline changes only after an operator
changes it.
+If autoadjustment is configured with a non-zero timeout, the baseline is
updated only after the topology remains unchanged for that timeout.
+In both cases, run `control.(sh|bat) --baseline` and compare `Baseline nodes`
and `Other nodes` with the expected set of server nodes.
+
+Run:
+
+[source,shell]
+----
+control.(sh|bat) --baseline
+----
+
+Relevant output for a cluster where all baseline nodes are online:
+
+[source,text]
+----
+Cluster state: ACTIVE
+Current topology version: 3
+Baseline auto adjustment disabled: softTimeout=300000
+
+Current topology version: 3 (Coordinator: ConsistentId=node-1, Order=1)
+
+Baseline nodes:
+ ConsistentId=node-1, State=ONLINE, Order=1
+ ConsistentId=node-2, State=ONLINE, Order=2
+ ConsistentId=node-3, State=ONLINE, Order=3
+--------------------------------------------------------------------------------
+Number of baseline nodes: 3
+
+Other nodes not found.
+----
+
+Example: one baseline node is offline:
+
+[source,text]
+----
+Baseline nodes:
+ ConsistentId=node-1, State=ONLINE, Order=1
+ ConsistentId=node-2, State=OFFLINE, Order=2
+ ConsistentId=node-3, State=ONLINE, Order=3
+--------------------------------------------------------------------------------
+Number of baseline nodes: 3
+----
+
+If a baseline node is `OFFLINE`, an expected data-bearing server is missing.
+If other primary or backup copies are available, its absence does not cause
partition loss.
+To check for partition loss, query the partition states as described in
<<confirm-that-rebalancing-converges,Confirm That Rebalancing Converges>>.
+
+If an online server node has joined the cluster but is not in the baseline,
the command shows it under `Other nodes`:
+
+[source,text]
+----
+Other nodes:
+ ConsistentId=node-4, Order=4
+Number of other nodes: 1
+----
+
+The baseline contains server nodes that are intended to store data.
+Client nodes are not part of the baseline.
+An online server node in `Other nodes` is not always an error: the node may
have been prepared intentionally but not yet introduced into the data topology.
+If the node is expected to store data, first check the
link:clustering/baseline-topology#baseline-topology-autoadjustment[baseline
auto-adjustment policy] and the current maintenance or scale-out procedure,
then use the documented baseline change procedure.
+Changing the baseline can start link:data-rebalancing[rebalancing]: partitions
are redistributed according to the new affinity assignment.
+Plan for the additional network, CPU, and storage load, especially in clusters
with persistence.
+
+Use topology changes to distinguish planned activity from instability.
+A server `JOIN`, `LEFT`, or `FAIL` event changes cluster membership and
triggers PME.
+Cache or SQL schema changes can also trigger PME without server node loss.
+Therefore, a topology version or PME metric change is useful only when
interpreted together with maintenance actions and node logs.
+
+Run `control.(sh|bat) --baseline` repeatedly or monitor topology metrics to
confirm that membership is stable when no maintenance is in progress.
+In logs, look for repeated node join, left, fail, segmentation, and
exchange-worker messages.
+Investigate unexpected repeated membership churn, node segmentation, network
failures, or a PME that does not finish.
+For PME metrics and transaction checks, see
<<check-transactions-and-sql-queries,Check Transactions and SQL Queries>>.
+
+=== Verify Partition Consistency
+
+When the cluster is expected to be idle, run:
+
+[source,shell]
+----
+control.(sh|bat) --cache idle_verify
+----
+
+Successful result:
+
+[source,text]
+----
+The check procedure has finished, no conflicts have been found.
+----
+
+The beginning of a conflict result uses this format:
+
+[source,text]
+----
+The check procedure has failed, conflict partitions has been found:
[counterConflicts=1, hashConflicts=0]
+Update counter conflicts:
+Conflict partition: PartitionKey [grpId=1544803905, grpName=default, partId=5]
+----
+
+The command compares partition update counters and partition hashes between
primary and backup copies.
+Run it only when data updates are stopped.
+If updates are active, the command can report false conflicts because copies
are changing while hashes are being calculated.
+Partitions in `MOVING` or `LOST` state may be skipped, so the result can be
incomplete.
+A successful `idle_verify` result is an important confirmation of consistency,
but it still does not prove overall cluster health check success.
+
+[#confirm-that-rebalancing-converges]
+=== Confirm That Rebalancing Converges
+
+After a topology event, such as a new node join, transient rebalancing is
expected, but it should converge. Rebalance progress should move toward
completion.
+
+Use the
link:monitoring-metrics/system-views#partition_states[PARTITION_STATES] system
view to check partition states:
+
+* `OWNING`: the node is the current primary or backup owner.
+* `MOVING`: a partition copy is being loaded on the node during rebalance.
+* `RENTING`: an old copy is being removed after ownership changes.
+* `EVICTED`: the partition is absent on a node that is no longer an owner;
this is not an error by itself.
+* `LOST`: the partition is unavailable and must not be used; investigate
immediately.
+
+[source,sql]
+----
+SELECT CACHE_GROUP_ID, PARTITION_ID, NODE_ID, STATE, IS_PRIMARY
+FROM SYS.PARTITION_STATES
+WHERE STATE IN ('MOVING', 'RENTING', 'LOST')
+ORDER BY STATE, CACHE_GROUP_ID, PARTITION_ID, NODE_ID;
+----
+
+In steady state, this query usually should not return `MOVING`, `RENTING`, or
`LOST` rows.
+`MOVING` and `RENTING` are expected right after an intended topology or cache
event, but their count should decrease.
+`LOST` is not a normal transient state.
+
+Example: one partition is lost:
+
+[source,text]
+----
+CACHE_GROUP_ID | PARTITION_ID | NODE_ID | STATE |
IS_PRIMARY
+1544803905 | 5 | 0f4d6f30-3e04-4f68-b6a2-6b89f1795c0d | LOST |
true
+----
+
+If the query returns `LOST`, follow the
link:configuring-caches/partition-loss-policy[Partition Loss Policy] recovery
procedure.
+If a failed node returns, its persistent data may become available again, but
the affected partitions remain in the `LOST` state.
+If the required data is available again, reset the lost partitions:
+
+[source,shell]
+----
+control.(sh|bat) --cache reset_lost_partitions cacheName1,cacheName2,...
+----
+
+If data is still missing, resetting the partitions does not restore it; the
command only clears the `LOST` state.
+
+=== Check Execution Queues
+
+Ignite has several internal executors. A regular thread pool executes tasks
from a shared queue. These queues may grow for a short time under load, but
they should not grow continuously. Sustained queue growth means that a node is
not keeping up with the workload or that message processing is impaired. The
same logic applies to the striped executor.
+
+The striped executor divides internal cache and transaction tasks between
independent stripes: tasks in the same stripe run sequentially, while different
stripes can run in parallel.
+If a stripe is blocked, tasks related to that stripe can accumulate even when
overall CPU usage does not look high.
+
+Check queue metrics on every server node:
+
+[source,sql]
+----
+SELECT NAME, VALUE
+FROM SYS.METRICS
+WHERE NAME IN ('io.communication.OutboundMessagesQueueSize'
+,'io.discovery.MessageWorkerQueueSize'
+,'threadPools.StripedExecutor.TotalQueueSize'
+,'threadPools.StripedExecutor.DetectStarvation'
+)
+ OR NAME LIKE 'threadPools.%.QueueSize'
+ORDER BY NAME;
+----
+
+Inspect queued striped tasks when the striped queue does not drain:
+
+[source,sql]
+----
+SELECT STRIPE_INDEX, THREAD_NAME, TASK_NAME, DESCRIPTION
+FROM SYS.STRIPED_THREADPOOL_QUEUE
+ORDER BY STRIPE_INDEX, THREAD_NAME;
+----
+
+As mentioned above, short non-zero queues are acceptable under load.
+
+On an idle node, queues usually return to zero.
+Investigate continuous growth, lack of drain after load stops, repeated
`DetectStarvation=true`, or repeated starvation warnings in logs.
+There is no universal absolute threshold.
+Queue metrics are node-local, so collect them from all server nodes.
+
+JMX uses the metric registry name to build `group` and `name` in the MBean
object name.
+The following mappings are useful for queue checks:
+
+* Registry `io.communication` is exposed as JMX group `io`, bean name
`communication`; the attribute is `OutboundMessagesQueueSize`.
+* Registry `io.discovery` is exposed as JMX group `io`, bean name `discovery`;
the attribute is `MessageWorkerQueueSize`.
+* Registry `threadPools.StripedExecutor` is exposed as JMX group
`threadPools`, bean name `StripedExecutor`; the attributes include
`TotalQueueSize`, `StripesQueueSizes`, and `DetectStarvation`.
+* Regular pools such as `threadPools.GridSystemExecutor` expose `QueueSize`.
+
+For JMX object names and SQL metric access, see
link:monitoring-metrics/new-metrics-system#jmx[JMX] and
link:monitoring-metrics/new-metrics-system#sql-view[SQL View].
+
+.JConsole view of node-local striped executor queue metrics
+image::perf-and-troubleshooting/images/healthy-cluster-queues-jconsole.png[JConsole
MBeans view showing threadPools/StripedExecutor and queue-related attributes]
+
+[#check-transactions-and-sql-queries]
+=== Check Transactions and SQL Queries
+
+A transaction or query is not unhealthy merely because it runs for some time.
+Investigate when the number or age of active operations continues to increase
after the load drops, or when the same operations repeatedly block other work.
+
+Use the transaction command to list long transactions:
+
+[source,shell]
+----
+control.(sh|bat) --tx --min-duration 60 --servers --order DURATION
+----
+
+The value `60` is only an example diagnostic filter in seconds, not a
universal production threshold.
+
+Use the system views for current transactions and SQL queries:
+
+[source,sql]
+----
+SELECT XID, STATE, START_TIME, DURATION, KEYS_COUNT, LABEL
+FROM SYS.TRANSACTIONS
+ORDER BY DURATION DESC;
+----
+
+[source,sql]
+----
+SELECT QUERY_ID, START_TIME, DURATION, INITIATOR_ID, SQL
+FROM SYS.SQL_QUERIES
+ORDER BY DURATION DESC;
+----
+
+Track related metrics:
+
+[source,sql]
+----
+SELECT NAME, VALUE
+FROM SYS.METRICS
+WHERE NAME IN (
+ 'tx.OwnerTransactionsNumber',
+ 'tx.TransactionsHoldingLockNumber',
+ 'tx.LockedKeysNumber',
+ 'pme.Duration',
+ 'pme.CacheOperationsBlockedDuration'
+)
+ORDER BY NAME;
+----
+
+Non-zero transaction counters are normal while work is running.
+The problem is sustained growth, increasing age of the oldest operations, and
failure to return to the usual range after the workload drops.
+For view definitions and metrics, see
link:monitoring-metrics/system-views#transactions[TRANSACTIONS],
link:monitoring-metrics/system-views#sql_queries[SQL_QUERIES],
link:monitoring-metrics/new-metrics#transactions[transaction metrics], and
link:monitoring-metrics/new-metrics#partition-map-exchange[Partition Map
Exchange metrics].
+
+Partition Map Exchange (PME) synchronizes partition distribution after
topology and cache changes.
+At one stage, PME waits for incomplete transactions to finish.
+A long transaction can delay a node join, cache start, and other operations
that depend on exchange.
+
+`TransactionConfiguration.setTxTimeoutOnPartitionMapExchange(...)` is
described in
link:key-value-api/transactions#long-running-transactions-termination[Long
Running Transactions Termination].
+The default is `0`, which means transactions are not rolled back because of a
PME timeout.
+The timeout is applied only when PME starts.
+Incomplete transactions that exceed the configured value can be rolled back.
+Applications must handle `TransactionRollbackException` and retry where
appropriate; see
link:key-value-api/transactions#handling-failed-transactions[Handling Failed
Transactions].
+Do not use a universal timeout value.
+Choose a value above the normal duration of legitimate transactions with a
justified safety margin, and test application behavior when rollback happens.
+
+=== Check Checkpoint Pressure When Persistence Is Enabled
+
+This check applies only to data regions with Native Persistence enabled.
+A pure in-memory cluster does not perform persistence checkpoints for its
in-memory regions.
+
+A link:persistence/native-persistence#checkpointing[checkpoint] writes dirty
pages from RAM to partition files.
+Checkpointing itself is a normal background operation.
+The problem starts when the application write rate exceeds the effective
storage write speed.
+Under checkpoint-buffer or dirty-page pressure, Ignite can throttle update
threads.
+If the checkpoint buffer is exhausted, update processing can stop until the
checkpoint completes.
+
+Monitor these metrics for persistent data regions and data storage:
+
+* `io.dataregion.<region>.DirtyPages`
+* `io.dataregion.<region>.CheckpointBufferSize`
+* `io.dataregion.<region>.UsedCheckpointBufferSize`
+* `io.dataregion.<region>.TotalThrottlingTime`
+* `io.datastorage.LastCheckpointStart`
+* `io.datastorage.LastCheckpointDuration`
+* `io.datastorage.LastCheckpointPagesWriteDuration`
+* `io.datastorage.LastCheckpointTotalPagesNumber`
+* `io.datastorage.LastCheckpointFsyncDuration`
+
+Planned checkpoints run according to
`DataStorageConfiguration.checkpointFrequency`.
+Dirty-page pressure, checkpoint-buffer pressure, and some administrative
operations can trigger an earlier checkpoint.
+Successive `LastCheckpointStart` values let you estimate the real interval.
+A single early checkpoint does not prove a problem.
+A regularly shortening interval together with high `DirtyPages`, high
`UsedCheckpointBufferSize`, or growing `TotalThrottlingTime` indicates storage
or write pressure.
+
+Approximate checkpoint page-write throughput in MiB/s:
+
+[source,text]
+----
+LastCheckpointTotalPagesNumber
+* configured DataStorageConfiguration.pageSize in bytes
+* 1000
+/ LastCheckpointPagesWriteDuration in milliseconds
+/ 1048576
+----
+
+Do not calculate this when `LastCheckpointPagesWriteDuration` is zero.
+Use the actually configured `DataStorageConfiguration.pageSize`; do not assume
it is always 4 KiB.
+This is an approximate estimate, not a full disk benchmark.
+Compare several checkpoints and check whether there is enough headroom for the
normal write workload.
+
+If headroom is insufficient, use checks and changes that can be verified:
+
+* check storage latency and saturation together with
link:monitoring-metrics/new-metrics-system#monitoring-checkpointing-operations[checkpoint
metrics];
+* use faster production storage devices, as discussed in
link:persistence/persistence-tuning#purchase-production-level-ssds[Purchase
Production-Level SSDs];
+* place data files and WAL on separate physical devices, not just separate
directories on the same disk; see
link:persistence/persistence-tuning#keep-wals-separately[Keep WALs Separately];
+* review
link:persistence/persistence-tuning#adjusting-checkpointing-buffer-size[checkpoint
buffer] and link:persistence/persistence-tuning#pages-writes-throttling[page
write throttling] configuration;
+* distribute write load or add server nodes when operational constraints allow
it;
+* check link:persistence/native-persistence#wal-archive[WAL archive] I/O
contention.
+
+JMX checkpoint and data-region metrics are node-local.
+Open the same charts or attributes on each server node that owns persistent
data.
+
+.JConsole view of node-local persistence checkpoint metrics
+image::perf-and-troubleshooting/images/healthy-cluster-checkpoint-jconsole.png[JConsole
MBeans view showing io/datastorage checkpoint attributes and a persistent
dataregion bean]
+
+=== Check Logs and Optional Features
+
+Investigate logs and failure handling for:
+
+* node segmentation;
+* repeated system-worker blockage;
+* failure-handler activations;
+* `OutOfMemoryError` and `IgniteOutOfMemoryException`;
+* repeated striped-pool starvation warnings;
+* unexpected node restarts.
+
+Ignite reports critical failures to the configured failure handler; depending
on the handler, the result can be node invalidation, failover handling, node
stop, or JVM termination.
Review Comment:
done with crosslinking
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