Briefly, this error occurs when the disk usage on one or more Elasticsearch nodes exceeds the high watermark threshold, which is 85% by default. Elasticsearch will stop allocating new shards to the node to prevent disk fill up. To resolve this issue, you can either increase the disk space, delete unnecessary indices, or adjust the disk watermark thresholds. However, be cautious when adjusting the thresholds as it might lead to disk fill up and data loss.
This guide will help you check for common problems that cause the log ” high disk watermark exceeded on one or more nodes; rerouting shards ” to appear. To understand the issues related to this log, read the explanation below about the following Elasticsearch concepts: allocation, cluster, rest-high-level, routing and shards.
Overview
There are various “watermark” thresholds on your Elasticsearch cluster. As the disk fills up on a node, the first threshold to be crossed will be the “low disk watermark”. The second threshold will then be the “high disk watermark threshold”. Finally, the “disk flood stage” will be reached. Once this threshold is passed, the cluster will then block writing to ALL indices that have one shard (primary or replica) on the node which has passed the watermark. Reads (searches) will still be possible.
How to resolve this issue
Passing this threshold is a warning and you should not delay in taking action before the higher threshold flood_stage is reached. Here are possible actions you can take to resolve the issue:
- Delete old indices
- Remove documents from existing indices
- Reduce the number of replicas (on older indices)
- “Increase disk space on all nodes
- Add new nodes to the cluster
Although you may be reluctant to delete data, in a logging system it is often better to delete old indices (which you may be able to restore from a snapshot later if available) than to lose new data. However, this decision will depend upon the architecture of your system and the queueing mechanisms you have available.
Check the disk space on each node
You can see the space you have available on each node by running:
GET _nodes/stats/fs
Check if the cluster is rebalancing
If the high level watermark has been passed, then Elasticsearch should start rebalancing shards from that node to other nodes which are still below the low watermark. You can check to see if any rebalancing is going on by calling:
GET _cluster/health/
If you think that your cluster should be rebalancing shards to other nodes but it is not, there are probably some other cluster allocation rules which are preventing this from happening. The most likely causes are:
- The other nodes are already above the low disk watermark
- There are cluster allocation rules which govern the distribution of shards between nodes and conflict with the rebalancing requirements. (eg. zone awareness allocation).
- There are already too many rebalancing operations in progress
- The other nodes already contain the primary or replica shards of the shards that could be rebalanced.
Check the cluster settings
You can see the settings you have applied with this command:
GET _cluster/settings
If they are not appropriate, you can modify them using a command such as below:
PUT _cluster/settings
{
"transient": {
"cluster.routing.allocation.disk.watermark.low": "85%",
"cluster.routing.allocation.disk.watermark.high": "90%",
"cluster.routing.allocation.disk.watermark.flood_stage": "95%",
"cluster.info.update.interval": "1m"
}
}
Note: Threshold can be specified both as percentage and byte values, but the former is more flexible and easier to maintain (in case different nodes have different disk sizes, like in hot/warm deployments).
How to prevent
There are various mechanisms that allow you to automatically delete stale data.
How to automatically delete stale data:
- Apply ILM (Index Lifecycle Management)
Using ILM you can get Elasticsearch to automatically delete an index when your current index reaches a given age.
- Use date based indices
If your application uses date based indices, then it is easy to delete old indices using either a script, ILM or a tool such as Elasticsearch curator.
- Use snapshots to store data offline
It may be appropriate to store snapshotted data offline and restore it in the event that the archived data needs to be reviewed or studied.
- Automate / simplify process to add new data nodes
Use automation tools such as terraform to automate the addition of new nodes to the cluster. If this is not possible, at the very least ensure you have a clearly documented process to create new nodes, add TLS certificates and configuration and bring them into the Elasticsearch cluster in a short and predictable time frame.
Overview
There are various “watermark” thresholds on your Elasticsearch cluster. As the disk fills up on a node, the first threshold to be crossed will be the “low disk watermark”. The second threshold will then be the “high disk watermark threshold”. Finally, the “disk flood stage” will be reached. Once this threshold is passed, the cluster will then block writing to ALL indices that have one shard (primary or replica) on the node which has passed the watermark. Reads (searches) will still be possible.
Relevant settings
cluster.routing.allocation.disk.watermark have three thresholds of watermarks, it accepts absolute values as well as percentage values. The three watermarks are:
- Low disk watermark
- High disk watermark
- Flood stage disk watermark
How to fix log messages related to disk watermarks
Permanent fixes
1. Delete unused indices.
2. Merge segments to reduce the size of the shard on the affected node
3. Attach external disk or increase the disk used by the data node
Temporary hacks/fixes
1. Changed these settings values to a higher threshold by dynamically update settings using below update cluster API.
PUT _cluster/settings :
{
“transient”: {
“cluster.routing.allocation.disk.watermark.low”: “100gb”, –>adjust according to your situations
“cluster.routing.allocation.disk.watermark.high”: “50gb”,
“cluster.routing.allocation.disk.watermark.flood_stage”: “10gb”,
“cluster.info.update.interval”: “1m”
}
}2. Disable disk check by hitting below cluster update API
{
“transient”: {
“cluster.routing.allocation.disk.threshold_enabled” : false
}
}Even After all these fixes, Elasticsearch won’t bring indices in write mode for that this API needs to be activated
PUT _all/_settings
{
“index.blocks.read_only_allow_delete”: null
}Overview
Rest-high-level is built on top of low-level rest-client and is a method of communicating with Elasticsearch based on HTTP REST endpoints. This concept is majorly popular in the context of a Java-based Elasticsearch client. From day one, Elasticsearch supports transport clients for Java to communicate with Elasticsearch. In version 5.0, a low-level rest-client was released with lots of advantages over the existing transport client such as version independencies, increased stability, and lightweight JAR file libraries.
What it is used for
It is used for communicating with Elasticsearch HTTP REST endpoints in which marshalling and unmarshalling of response objects are handled by the Elasticsearch server itself.
Log Context
Log “high disk watermark exceeded on one or more nodes; rerouting shards” classname is DiskThresholdDecider.java.
We extracted the following from Elasticsearch source code for those seeking an in-depth context :
entry; DiskThresholdDecider.this.rerouteInterval);
}
}
}
if (reroute) {
logger.info("high disk watermark exceeded on one or more nodes; rerouting shards");
// Execute an empty reroute; but don't block on the response
client.admin().cluster().prepareReroute().execute();
}
}
}
