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High-disk watermark

As explained in Elasticsearch in Action, Second Edition

An Elasticsearch threshold on available disk space that controls shard allocation and signals storage pressure.

A high-disk watermark protects an Elasticsearch node when storage pressure becomes unsafe. At the default 90% threshold, Elasticsearch alerts and works to relocate shards to nodes with adequate disk space. For example, a node with 1 TB allocated and 850 GB consumed approaches the configured protection limits. section 15.3.3

Listing 15.15 Setting the high-disk watermark on the cluster
PUT _cluster/settings
{
  "transient": {
    "cluster.routing.allocation.disk.watermark.high":"0.85"
  }
}

Change the high-disk watermark dynamically through the cluster settings API; the example expresses an 85% threshold as 0.85. section 15.3.3

Relate the high-disk watermark to the surrounding disk-protection thresholds:

  • Use the low-disk watermark to manage shard allocation and signal emerging storage pressure. section 15.3.3 section 15.6
  • Expect shard relocation when the high-disk watermark is crossed. section 15.3.3
  • Treat the flood-stage watermark as the emergency threshold: affected shards become read-only and indexing stops. section 15.3.3 section 15.6

How Elasticsearch in Action, Second Edition covers it

How deep it goes
Explains the default threshold, operational response, and dynamic update path without detailing shard-allocation internals.
Examples
Uses a 1 TB node with 850 GB consumed and a REST setting that changes the threshold to 0.85.
What it assumes
Assumes Elasticsearch cluster administration concepts, including nodes, shards, disk allocation, and the cluster settings API.
What you can do after
Operate/configure the threshold dynamically through Elasticsearch’s cluster settings API and anticipate its relocation response.
How it connects
Connects high-disk watermark with low-disk watermark, flood-stage watermark, shard allocation, shard relocation, and read-only shards.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Elasticsearch in Action, Second Edition. The section links point to the parts of the book it draws on.