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Elasticsearch index

As explained in Learn Docker in a Month of Lunches, Second Edition

An Elasticsearch index is a named collection of documents that can be searched and queried as a unit.

An Elasticsearch index is a named collection of documents searched and queried as a unit. In centralized logging, each entry becomes a structured document with searchable fields, while different entries can use different fields because no fixed database schema is required. section 19.3

Daily container-log indexes

Fluentd saves each collected log entry as a document in an index named fluentd-{date}. In Kibana, create a data view named logs with the pattern fluentd* and use @timestamp as the timestamp field; Discover then searches and filters the collected documents. section 19.3

How Learn Docker in a Month of Lunches, Second Edition covers it

How deep it goes
Defines indexes as document collections and demonstrates naming, data views, and searches, but does not detail Elasticsearch internals.
Examples
Uses Fluentd-created fluentd-{date} indexes containing timecheck and other container log documents.
What it assumes
Assumes Docker containers and Fluentd logging, while teaching the index terminology through a working logging setup.
What you can do after
Operate/configure an EFK exercise, create a Kibana data view, and search collected container-log documents.
How it connects
Ties indexes to documents, Fluentd, Kibana, data views, and centralized logging.

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