elasticsearch/docs/reference/tab-widgets/troubleshooting/disk/increase-other-node-capacity.asciidoc

// tag::cloud[]

. Log in to the {ess-console}[{ecloud} console].
+
. On the **Elasticsearch Service** panel, click the gear under the `Manage deployment` column that corresponds to the
name of your deployment.
+
. Go to `Actions > Edit deployment` and then go to the `Coordinating instances` or the `Machine Learning instances`
section depending on the roles listed in the diagnosis:
+
[role="screenshot"]
image::images/troubleshooting/disk/increase-disk-capacity-other-node.png[Increase disk capacity of other nodes,align="center"]

. Choose a larger than the pre-selected capacity configuration from the drop-down menu and click `save`. Wait for
the plan to be applied and the problem should be resolved.

// end::cloud[]

// tag::self-managed[]
In order to increase the disk capacity of any other node, you will need to replace the instance that has run out of
space with one of higher disk capacity.

. First, retrieve the disk threshold that will indicate how much disk space is needed. The relevant threshold is
the <<cluster-routing-watermark-high, high watermark>> and can be retrieved via the following command:
+
[source,console]
----
GET _cluster/settings?include_defaults&filter_path=*.cluster.routing.allocation.disk.watermark.high*
----
+
The response will look like this:
+
[source,console-result]
----
{
  "defaults": {
    "cluster": {
      "routing": {
        "allocation": {
          "disk": {
            "watermark": {
              "high": "90%",
              "high.max_headroom": "150GB"
            }
          }
        }
      }
    }
  }
----
// TEST[skip:illustration purposes only]
+
The above means that in order to resolve the disk shortage we need to either drop our disk usage below the 90% or have
more than 150GB available, read more how this threshold works <<cluster-routing-watermark-high, here>>.

. The next step is to find out the current disk usage, this will allow to calculate how much extra space is needed.
In the following example, we show only a machine learning node for readability purposes:
+
[source,console]
----
GET /_cat/nodes?v&h=name,node.role,disk.used_percent,disk.used,disk.avail,disk.total
----
+
The response will look like this:
+
[source,console-result]
----
name                node.role disk.used_percent disk.used disk.avail disk.total
instance-0000000000     l                 85.31    3.4gb     500mb       4gb
----
// TEST[skip:illustration purposes only]

. The desired situation is to drop the disk usage below the relevant threshold, in our example 90%. Consider adding
some padding, so it will not go over the threshold soon. Assuming you have the new node ready, add this node to the
cluster.

. Verify that the new node has joined the cluster:
+
[source,console]
----
GET /_cat/nodes?v&h=name,node.role,disk.used_percent,disk.used,disk.avail,disk.total
----
+
The response will look like this:
+
[source,console-result]
----
name                node.role disk.used_percent disk.used disk.avail disk.total
instance-0000000000     l                 85.31    3.4gb     500mb       4gb
instance-0000000001     l                 41.31    3.4gb     4.5gb       8gb
----
// TEST[skip:illustration purposes only]
. Now you can remove the out of disk space instance.
// end::self-managed[]