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Troubleshooting an unstable cluster

Normally, a node will only leave a cluster if deliberately shut down. If a node leaves the cluster unexpectedly, it’s important to address the cause. A cluster in which nodes leave unexpectedly is unstable and can create several issues. For instance:

The cluster health may be yellow or red.
Some shards will be initializing and other shards may be failing.
Search, indexing, and monitoring operations may fail and report exceptions in logs.
The .security index may be unavailable, blocking access to the cluster.
The master may appear busy due to frequent cluster state updates.

To troubleshoot a cluster in this state, first ensure the cluster has a stable master. Next, focus on the nodes unexpectedly leaving the cluster ahead of all other issues. It will not be possible to solve other issues until the cluster has a stable master node and stable node membership.

Diagnostics and statistics are usually not useful in an unstable cluster. These tools only offer a view of the state of the cluster at a single point in time. Instead, look at the cluster logs to see the pattern of behaviour over time. Focus particularly on logs from the elected master. When a node leaves the cluster, logs for the elected master include a message like this (with line breaks added to make it easier to read):

[2022-03-21T11:02:35,513][INFO ][o.e.c.c.NodeLeftExecutor] [instance-0000000000]
    node-left: [{instance-0000000004}{bfcMDTiDRkietFb9v_di7w}{aNlyORLASam1ammv2DzYXA}{172.27.47.21}{172.27.47.21:19054}{m}]
    with reason [disconnected]

This message says that the NodeLeftExecutor on the elected master (instance-0000000000) processed a node-left task, identifying the node that was removed and the reason for its removal. When the node joins the cluster again, logs for the elected master will include a message like this (with line breaks added to make it easier to read):

[2022-03-21T11:02:59,892][INFO ][o.e.c.c.NodeJoinExecutor] [instance-0000000000]
    node-join: [{instance-0000000004}{bfcMDTiDRkietFb9v_di7w}{UNw_RuazQCSBskWZV8ID_w}{172.27.47.21}{172.27.47.21:19054}{m}]
    with reason [joining after restart, removed [24s] ago with reason [disconnected]]

This message says that the NodeJoinExecutor on the elected master (instance-0000000000) processed a node-join task, identifying the node that was added to the cluster and the reason for the task.

Other nodes may log similar messages, but report fewer details:

[2020-01-29T11:02:36,985][INFO ][o.e.c.s.ClusterApplierService]
    [instance-0000000001] removed {
        {instance-0000000004}{bfcMDTiDRkietFb9v_di7w}{aNlyORLASam1ammv2DzYXA}{172.27.47.21}{172.27.47.21:19054}{m}
        {tiebreaker-0000000003}{UNw_RuazQCSBskWZV8ID_w}{bltyVOQ-RNu20OQfTHSLtA}{172.27.161.154}{172.27.161.154:19251}{mv}
    }, term: 14, version: 1653415, reason: Publication{term=14, version=1653415}

These messages are not especially useful for troubleshooting, so focus on the ones from the NodeLeftExecutor and NodeJoinExecutor which are only emitted on the elected master and which contain more details. If you don’t see the messages from the NodeLeftExecutor and NodeJoinExecutor, check that:

You’re looking at the logs for the elected master node.
The logs cover the correct time period.
Logging is enabled at INFO level.

Nodes will also log a message containing master node changed whenever they start or stop following the elected master. You can use these messages to determine each node’s view of the state of the master over time.

If a node restarts, it will leave the cluster and then join the cluster again. When it rejoins, the NodeJoinExecutor will log that it processed a node-join task indicating that the node is joining after restart. If a node is unexpectedly restarting, look at the node’s logs to see why it is shutting down.

The Health API on the affected node will also provide some useful information about the situation.

If the node did not restart then you should look at the reason for its departure more closely. Each reason has different troubleshooting steps, described below. There are three possible reasons:

disconnected: The connection from the master node to the removed node was closed.
lagging: The master published a cluster state update, but the removed node did not apply it within the permitted timeout. By default, this timeout is 2 minutes. Refer to Discovery and cluster formation settings for information about the settings which control this mechanism.
followers check retry count exceeded: The master sent a number of consecutive health checks to the removed node. These checks were rejected or timed out. By default, each health check times out after 10 seconds and Elasticsearch removes the node removed after three consecutively failed health checks. Refer to Discovery and cluster formation settings for information about the settings which control this mechanism.

Diagnosing `disconnected` nodes

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Nodes typically leave the cluster with reason disconnected when they shut down, but if they rejoin the cluster without restarting then there is some other problem.

Elasticsearch is designed to run on a fairly reliable network. It opens a number of TCP connections between nodes and expects these connections to remain open forever. If a connection is closed then Elasticsearch will try and reconnect, so the occasional blip may fail some in-flight operations but should otherwise have limited impact on the cluster. In contrast, repeatedly-dropped connections will severely affect its operation.

The connections from the elected master node to every other node in the cluster are particularly important. The elected master never spontaneously closes its outbound connections to other nodes. Similarly, once an inbound connection is fully established, a node never spontaneously closes it unless the node is shutting down.

If you see a node unexpectedly leave the cluster with the disconnected reason, something other than Elasticsearch likely caused the connection to close. A common cause is a misconfigured firewall with an improper timeout or another policy that’s incompatible with Elasticsearch. It could also be caused by general connectivity issues, such as packet loss due to faulty hardware or network congestion. If you’re an advanced user, configure the following loggers to get more detailed information about network exceptions:

logger.org.elasticsearch.transport.TcpTransport: DEBUG
logger.org.elasticsearch.xpack.core.security.transport.netty4.SecurityNetty4Transport: DEBUG

If these logs do not show enough information to diagnose the problem, obtain a packet capture simultaneously from the nodes at both ends of an unstable connection and analyse it alongside the Elasticsearch logs from those nodes to determine if traffic between the nodes is being disrupted by another device on the network.

Diagnosing `lagging` nodes

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Elasticsearch needs every node to process cluster state updates reasonably quickly. If a node takes too long to process a cluster state update, it can be harmful to the cluster. The master will remove these nodes with the lagging reason. Refer to Discovery and cluster formation settings for information about the settings which control this mechanism.

Lagging is typically caused by performance issues on the removed node. However, a node may also lag due to severe network delays. To rule out network delays, ensure that net.ipv4.tcp_retries2 is configured properly. Log messages that contain warn threshold may provide more information about the root cause.

If you’re an advanced user, you can get more detailed information about what the node was doing when it was removed by configuring the following logger:

logger.org.elasticsearch.cluster.coordination.LagDetector: DEBUG

When this logger is enabled, Elasticsearch will attempt to run the Nodes hot threads API on the faulty node and report the results in the logs on the elected master. The results are compressed, encoded, and split into chunks to avoid truncation:

[DEBUG][o.e.c.c.LagDetector      ] [master] hot threads from node [{node}{g3cCUaMDQJmQ2ZLtjr-3dg}{10.0.0.1:9300}] lagging at version [183619] despite commit of cluster state version [183620] [part 1]: H4sIAAAAAAAA/x...
[DEBUG][o.e.c.c.LagDetector      ] [master] hot threads from node [{node}{g3cCUaMDQJmQ2ZLtjr-3dg}{10.0.0.1:9300}] lagging at version [183619] despite commit of cluster state version [183620] [part 2]: p7x3w1hmOQVtuV...
[DEBUG][o.e.c.c.LagDetector      ] [master] hot threads from node [{node}{g3cCUaMDQJmQ2ZLtjr-3dg}{10.0.0.1:9300}] lagging at version [183619] despite commit of cluster state version [183620] [part 3]: v7uTboMGDbyOy+...
[DEBUG][o.e.c.c.LagDetector      ] [master] hot threads from node [{node}{g3cCUaMDQJmQ2ZLtjr-3dg}{10.0.0.1:9300}] lagging at version [183619] despite commit of cluster state version [183620] [part 4]: 4tse0RnPnLeDNN...
[DEBUG][o.e.c.c.LagDetector      ] [master] hot threads from node [{node}{g3cCUaMDQJmQ2ZLtjr-3dg}{10.0.0.1:9300}] lagging at version [183619] despite commit of cluster state version [183620] (gzip compressed, base64-encoded, and split into 4 parts on preceding log lines)

To reconstruct the output, base64-decode the data and decompress it using gzip. For instance, on Unix-like systems:

cat lagdetector.log | sed -e 's/.*://' | base64 --decode | gzip --decompress

Diagnosing `follower check retry count exceeded` nodes

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Nodes sometimes leave the cluster with reason follower check retry count exceeded when they shut down, but if they rejoin the cluster without restarting then there is some other problem.

Elasticsearch needs every node to respond to network messages successfully and reasonably quickly. If a node rejects requests or does not respond at all then it can be harmful to the cluster. If enough consecutive checks fail then the master will remove the node with reason follower check retry count exceeded and will indicate in the node-left message how many of the consecutive unsuccessful checks failed and how many of them timed out. Refer to Discovery and cluster formation settings for information about the settings which control this mechanism.

Timeouts and failures may be due to network delays or performance problems on the affected nodes. Ensure that net.ipv4.tcp_retries2 is configured properly to eliminate network delays as a possible cause for this kind of instability. Log messages containing warn threshold may give further clues about the cause of the instability.

If the last check failed with an exception then the exception is reported, and typically indicates the problem that needs to be addressed. If any of the checks timed out then narrow down the problem as follows.

GC pauses are recorded in the GC logs that Elasticsearch emits by default, and also usually by the JvmMonitorService in the main node logs. Use these logs to confirm whether or not the node is experiencing high heap usage with long GC pauses. If so, the troubleshooting guide for high heap usage has some suggestions for further investigation but typically you will need to capture a heap dump and the garbage collector logs during a time of high heap usage to fully understand the problem.
VM pauses also affect other processes on the same host. A VM pause also typically causes a discontinuity in the system clock, which Elasticsearch will report in its logs. If you see evidence of other processes pausing at the same time, or unexpected clock discontinuities, investigate the infrastructure on which you are running Elasticsearch.
Packet captures will reveal system-level and network-level faults, especially if you capture the network traffic simultaneously at the elected master and the faulty node and analyse it alongside the Elasticsearch logs from those nodes. The connection used for follower checks is not used for any other traffic so it can be easily identified from the flow pattern alone, even if TLS is in use: almost exactly every second there will be a few hundred bytes sent each way, first the request by the master and then the response by the follower. You should be able to observe any retransmissions, packet loss, or other delays on such a connection.
Long waits for particular threads to be available can be identified by taking stack dumps of the main Elasticsearch process (for example, using jstack) or a profiling trace (for example, using Java Flight Recorder) in the few seconds leading up to the relevant log message.

The Nodes hot threads API sometimes yields useful information, but bear in mind that this API also requires a number of transport_worker and generic threads across all the nodes in the cluster. The API may be affected by the very problem you’re trying to diagnose. jstack is much more reliable since it doesn’t require any JVM threads.

The threads involved in discovery and cluster membership are mainly transport_worker and cluster_coordination threads, for which there should never be a long wait. There may also be evidence of long waits for threads in the Elasticsearch logs, particularly looking at warning logs from org.elasticsearch.transport.InboundHandler. See Networking threading model for more information.

By default the follower checks will time out after 30s, so if node departures are unpredictable then capture stack dumps every 15s to be sure that at least one stack dump was taken at the right time.

Diagnosing `ShardLockObtainFailedException` failures

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If a node leaves and rejoins the cluster then Elasticsearch will usually shut down and re-initialize its shards. If the shards do not shut down quickly enough then Elasticsearch may fail to re-initialize them due to a ShardLockObtainFailedException.

To gather more information about the reason for shards shutting down slowly, configure the following logger:

logger.org.elasticsearch.env.NodeEnvironment: DEBUG

When this logger is enabled, Elasticsearch will attempt to run the Nodes hot threads API whenever it encounters a ShardLockObtainFailedException. The results are compressed, encoded, and split into chunks to avoid truncation:

[DEBUG][o.e.e.NodeEnvironment    ] [master] hot threads while failing to obtain shard lock for [index][0] [part 1]: H4sIAAAAAAAA/x...
[DEBUG][o.e.e.NodeEnvironment    ] [master] hot threads while failing to obtain shard lock for [index][0] [part 2]: p7x3w1hmOQVtuV...
[DEBUG][o.e.e.NodeEnvironment    ] [master] hot threads while failing to obtain shard lock for [index][0] [part 3]: v7uTboMGDbyOy+...
[DEBUG][o.e.e.NodeEnvironment    ] [master] hot threads while failing to obtain shard lock for [index][0] [part 4]: 4tse0RnPnLeDNN...
[DEBUG][o.e.e.NodeEnvironment    ] [master] hot threads while failing to obtain shard lock for [index][0] (gzip compressed, base64-encoded, and split into 4 parts on preceding log lines)

To reconstruct the output, base64-decode the data and decompress it using gzip. For instance, on Unix-like systems:

cat shardlock.log | sed -e 's/.*://' | base64 --decode | gzip --decompress

Diagnosing other network disconnections

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Elasticsearch nodes will only actively close an outbound connection to another node if the other node leaves the cluster. See Troubleshooting an unstable cluster for further information about identifying and troubleshooting this situation. If an outbound connection closes for some other reason, nodes will log a message such as the following:

[INFO ][o.e.t.ClusterConnectionManager] [node-1] transport connection to [{node-2}{g3cCUaMDQJmQ2ZLtjr-3dg}{10.0.0.1:9300}] closed by remote

Similarly, once an inbound connection is fully established, a node never spontaneously closes it unless the node is shutting down.

Therefore if you see a node report that a connection to another node closed unexpectedly, something other than Elasticsearch likely caused the connection to close. A common cause is a misconfigured firewall with an improper timeout or another policy that’s incompatible with Elasticsearch. It could also be caused by general connectivity issues, such as packet loss due to faulty hardware or network congestion. If you’re an advanced user, configure the following loggers to get more detailed information about network exceptions:

logger.org.elasticsearch.transport.TcpTransport: DEBUG
logger.org.elasticsearch.xpack.core.security.transport.netty4.SecurityNetty4Transport: DEBUG

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Troubleshooting an unstable cluster