Rolling upgrades

Disable shard allocation.

When you shut down a node, the allocation process waits for index.unassigned.node_left.delayed_timeout (by default, one minute) before starting to replicate the shards on that node to other nodes in the cluster, which can involve a lot of I/O. Since the node is shortly going to be restarted, this I/O is unnecessary. You can avoid racing the clock by disabling allocation of replicas before shutting down the node:

PUT _cluster/settings
{
  "persistent": {
    "cluster.routing.allocation.enable": "primaries"
  }
}

Stop non-essential indexing and perform a synced flush. (Optional)

While you can continue indexing during the upgrade, shard recovery is much faster if you temporarily stop non-essential indexing and perform a synced-flush.

POST _flush/synced

When you perform a synced flush, check the response to make sure there are no failures. Synced flush operations that fail due to pending indexing operations are listed in the response body, although the request itself still returns a 200 OK status. If there are failures, reissue the request.

Stop any machine learning jobs that are running.

If your machine learning indices were created earlier than the previous major version, they must be reindexed. In those circumstances, there must be no machine learning jobs running during the upgrade.

In all other circumstances, there is no requirement to close your machine learning jobs. There are, however, advantages to doing so. If you choose to leave your jobs running during the upgrade, they are affected when you stop the machine learning nodes. The jobs move to another machine learning node and restore the model states. This scenario has the least disruption to the active machine learning jobs but incurs the highest load on the cluster.

To close all machine learning jobs before you upgrade, see Stopping machine learning. This method persists the model state at the moment of closure, which means that when you open your jobs after the upgrade, they use the exact same model. This scenario takes the most time, however, especially if you have many jobs or jobs with large model states.

To temporarily halt the tasks associated with your machine learning jobs and datafeeds and prevent new jobs from opening, use the set upgrade mode API:

POST _ml/set_upgrade_mode?enabled=true

This method does not persist the absolute latest model state, rather it uses the last model state that was automatically saved. By halting the tasks, you avoid incurring the cost of managing active jobs during the upgrade and it’s quicker than stopping datafeeds and closing jobs.

Shut down a single node.

Upgrade the node you shut down.

To upgrade using a Debian or RPM package:

To upgrade using a zip or compressed tarball:

Upgrade any plugins.

Use the elasticsearch-plugin script to install the upgraded version of each installed Elasticsearch plugin. All plugins must be upgraded when you upgrade a node.

Start the upgraded node.

Start the newly-upgraded node and confirm that it joins the cluster by checking the log file or by submitting a _cat/nodes request:

GET _cat/nodes

Reenable shard allocation.

Once the node has joined the cluster, remove the cluster.routing.allocation.enable setting to enable shard allocation and start using the node:

PUT _cluster/settings
{
  "persistent": {
    "cluster.routing.allocation.enable": null
  }
}

Wait for the node to recover.

Before upgrading the next node, wait for the cluster to finish shard allocation. You can check progress by submitting a _cat/health request:

GET _cat/health?v

Wait for the status column to switch from yellow to green. Once the node is green, all primary and replica shards have been allocated.

Shards that were not sync-flushed might take longer to recover. You can monitor the recovery status of individual shards by submitting a _cat/recovery request:

GET _cat/recovery

If you stopped indexing, it is safe to resume indexing as soon as recovery completes.

Repeat

When the node has recovered and the cluster is stable, repeat these steps for each node that needs to be updated.

Restart machine learning jobs.

If you closed all machine learning jobs before the upgrade, you must open them. Use Kibana or the open jobs API.

Alternatively, if you temporarily halted the tasks associated with your machine learning jobs, use the set upgrade mode API to return them to active states:

POST _ml/set_upgrade_mode?enabled=false