WARNING: The 2.x versions of Elasticsearch have passed their EOL dates. If you are running a 2.x version, we strongly advise you to upgrade.
This documentation is no longer maintained and may be removed. For the latest information, see the current Elasticsearch documentation.
In Getting Started we covered the basic tools in just enough detail to allow you to start searching your data with Elasticsearch. It won’t take long, though, before you find that you want more: more flexibility when matching user queries, more-accurate ranking of results, more-specific searches to cover different problem domains.
To move to the next level, it is not enough to just use the
match query. You
need to understand your data and how you want to be able to search it. The
chapters in this part explain how to index and query your data to allow
you to take advantage of word proximity, partial matching, fuzzy matching, and
Understanding how each query contributes to the relevance
_score will help
you to tune your queries: to ensure that the documents you consider to be the
best results appear on the first page, and to trim the “long tail” of barely
Search is not just about full-text search: a large portion of your data will be structured values like dates and numbers. We will start by explaining how to combine structured search with full-text search in the most efficient way.
Intro to Kibana
ELK for Logs & Metrics