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Percolator index

As explained in Elasticsearch in Action, Second Edition

A percolator index is an Elasticsearch index that stores queries for later matching against documents through percolation.

A percolator index stores queries that should be checked later against newly indexed documents. In a bookstore search, an unsuccessful search for “Python” can be saved so the user can be notified when “Python in Action” becomes available. section 12.9.4

Create and use the index in three steps:

  • Define a query field with the percolator data type; mirror relevant document fields such as name and tags in the mapping. section 12.9.4
  • Index the failed user query as the query value rather than as an ordinary document. section 12.9.4
  • Run a percolate query with the newly available document to find stored queries that match it. section 12.9.4

How Elasticsearch in Action, Second Edition covers it

How deep it goes
Explains mapping, query storage, and document matching, but omits application-side collection of failed queries.
Examples
Uses technical-book documents, a failed Python search, and a later Python in Action document.
What it assumes
Assumes basic Elasticsearch indexing and match-query operations while defining the percolator-specific mapping and workflow.
What you can do after
Operate/configure an Elasticsearch percolator index and test stored queries against a candidate document.
How it connects
Connects percolator indexes with percolate queries, mappings, stored queries, and document indexing.
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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.