IMPORTANT: No additional bug fixes or documentation updates will be released for this version. For the latest information, see the current release documentation.
« Publishing the cluster state Add and remove nodes in your cluster »
Elastic Docs ›Elasticsearch Guide [8.4] ›Set up Elasticsearch ›Discovery and cluster formation

Cluster fault detection

edit

Cluster fault detection

edit

The elected master periodically checks each of the nodes in the cluster to ensure that they are still connected and healthy. Each node in the cluster also periodically checks the health of the elected master. These checks are known respectively as follower checks and leader checks.

Elasticsearch allows these checks to occasionally fail or timeout without taking any action. It considers a node to be faulty only after a number of consecutive checks have failed. You can control fault detection behavior with cluster.fault_detection.* settings.

If the elected master detects that a node has disconnected, however, this situation is treated as an immediate failure. The master bypasses the timeout and retry setting values and attempts to remove the node from the cluster. Similarly, if a node detects that the elected master has disconnected, this situation is treated as an immediate failure. The node bypasses the timeout and retry settings and restarts its discovery phase to try and find or elect a new master.

Additionally, each node periodically verifies that its data path is healthy by writing a small file to disk and then deleting it again. If a node discovers its data path is unhealthy then it is removed from the cluster until the data path recovers. You can control this behavior with the monitor.fs.health settings.

The elected master node will also remove nodes from the cluster if nodes are unable to apply an updated cluster state within a reasonable time. The timeout defaults to 2 minutes starting from the beginning of the cluster state update. Refer to Publishing the cluster state for a more detailed description.

Troubleshooting an unstable cluster

edit

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 for clarity):

[2022-03-21T11:02:35,513][INFO ][o.e.c.s.MasterService    ]
    [instance-0000000000] node-left[
        {instance-0000000004}{bfcMDTiDRkietFb9v_di7w}{aNlyORLASam1ammv2DzYXA}{172.27.47.21}{172.27.47.21:19054}{m}
            reason: disconnected,
        {tiebreaker-0000000003}{UNw_RuazQCSBskWZV8ID_w}{bltyVOQ-RNu20OQfTHSLtA}{172.27.161.154}{172.27.161.154:19251}{mv}
            reason: disconnected
        ], term: 14, version: 1653415, ...

This message says that the MasterService on the elected master (instance-0000000000) is processing a node-left task. It lists the nodes that are being removed and the reasons for their removal. 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}

Focus on the one from the MasterService which is only emitted on the elected master, since it contains more details. If you don’t see the messages from the MasterService, 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 MasterService will log that it is processing a node-join task. You can tell from the master logs that the node was restarted because the node-join message will indicate that it is joining after restart. In older Elasticsearch versions, you can also determine that a node restarted by looking at the second "ephemeral" ID in the node-left and subsequent node-join messages. This ephemeral ID is different each time the node starts up. If a node is unexpectedly restarting, you’ll need to look at the node’s logs to see why it is shutting down.

If the node did not restart then you should look at the reason for its departure in the node-left message, which is reported after each node. 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

edit

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 should have limited impact on the cluster even if the affected node briefly leaves 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 a connection is fully established, a node never spontaneously close its inbound connections 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, you can get more detailed information about network exceptions by configuring the following loggers:

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

In extreme cases, you may need to take packet captures using tcpdump to determine whether messages between nodes are being dropped or rejected by some other device on the network.

Diagnosing lagging nodes

edit

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.

Diagnosing follower check retry count exceeded nodes

edit

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, it may be necessary to understand the detailed sequence of steps involved in a successful check. Here is an example of such a sequence:

  1. The master’s FollowerChecker, running on thread elasticsearch[master][scheduler][T#1], tells the TransportService to send the check request message to a follower node.
  2. The master’s TransportService running on thread elasticsearch[master][transport_worker][T#2] passes the check request message onto the operating system.
  3. The operating system on the master converts the message into one or more packets and sends them out over the network.
  4. Miscellaneous routers, firewalls, and other devices between the master node and the follower node forward the packets, possibly fragmenting or defragmenting them on the way.
  5. The operating system on the follower node receives the packets and notifies Elasticsearch that they’ve been received.
  6. The follower’s TransportService, running on thread elasticsearch[follower][transport_worker][T#3], reads the incoming packets. It then reconstructs and processes the check request. Usually, the check quickly succeeds. If so, the same thread immediately constructs a response and passes it back to the operating system.

  7. If the check doesn’t immediately succeed (for example, an election started recently) then:

    1. The follower’s FollowerChecker, running on thread elasticsearch[follower][cluster_coordination][T#4], processes the request. It constructs a response and tells the TransportService to send the response back to the master.
    2. The follower’s TransportService, running on thread elasticsearch[follower][transport_worker][T#3], passes the response to the operating system.
  8. The operating system on the follower converts the response into one or more packets and sends them out over the network.
  9. Miscellaneous routers, firewalls, and other devices between master and follower forward the packets, possibly fragmenting or defragmenting them on the way.
  10. The operating system on the master receives the packets and notifies Elasticsearch that they’ve been received.
  11. The master’s TransportService, running on thread elasticsearch[master][transport_worker][T#2], reads the incoming packets, reconstructs the check response, and processes it as long as the check didn’t already time out.

There are a lot of different things that can delay the completion of a check and cause it to time out. Here are some examples for each step:

  1. There may be a long garbage collection (GC) or virtual machine (VM) pause after passing the check request to the TransportService.
  2. There may be a long wait for the specific transport_worker thread to become available, or there may be a long GC or VM pause before passing the check request onto the operating system.
  3. A system fault (for example, a broken network card) on the master may delay sending the message over the network, possibly indefinitely.
  4. Intermediate devices may delay, drop, or corrupt packets along the way. The operating system for the master will wait and retransmit any unacknowledged or corrupted packets up to net.ipv4.tcp_retries2 times. We recommend reducing this value since the default represents a very long delay.
  5. A system fault (for example, a broken network card) on the follower may delay receiving the message from the network.
  6. There may be a long wait for the specific transport_worker thread to become available, or there may be a long GC or VM pause during the processing of the request on the follower.
  7. There may be a long wait for the cluster_coordination thread to become available, or for the specific transport_worker thread to become available again. There may also be a long GC or VM pause during the processing of the request.
  8. A system fault (for example, a broken network card) on the follower may delay sending the response from the network.
  9. Intermediate devices may delay, drop, or corrupt packets along the way again, causing retransmissions.
  10. A system fault (for example, a broken network card) on the master may delay receiving the message from the network.
  11. There may be a long wait for the specific transport_worker thread to become available to process the response, or a long GC or VM pause.

To determine why follower checks are timing out, we can narrow down the reason for the delay 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 GC is resulting in delays.
  • 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.
  • 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. 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 (for example, using jstack) or a profiling trace (for example, using Java Flight Recorder) in the few seconds leading up to a node departure. 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 the follower checks are 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. Refer to Networking threading model for more information.
« Publishing the cluster state Add and remove nodes in your cluster »