This functionality is in beta and is subject to change. The design and code is less mature than official GA features and is being provided as-is with no warranties. Beta features are not subject to the support SLA of official GA features.
A runtime field is a field that is evaluated at query time. Runtime fields enable you to:
- Add fields to existing documents without reindexing your data
- Start working with your data without understanding how it’s structured
- Override the value returned from an indexed field at query time
- Define fields for a specific use without modifying the underlying schema
You access runtime fields from the search API like any other field, and Elasticsearch sees runtime fields no differently. You can define runtime fields in the index mapping or in the search request. Your choice, which is part of the inherent flexibility of runtime fields.
Runtime fields are useful when working with log data (see examples), especially when you’re unsure about the data structure. Your search speed decreases, but your index size is much smaller and you can more quickly process logs without having to index them.
Because runtime fields aren’t indexed, adding a runtime field doesn’t increase the index size. You define runtime fields directly in the index mapping, saving storage costs and increasing ingestion speed. You can more quickly ingest data into the Elastic Stack and access it right away. When you define a runtime field, you can immediately use it in search requests, aggregations, filtering, and sorting.
If you make a runtime field an indexed field, you don’t need to modify any queries that refer to the runtime field. Better yet, you can refer to some indices where the field is a runtime field, and other indices where the field is an indexed field. You have the flexibility to choose which fields to index and which ones to keep as runtime fields.
At its core, the most important benefit of runtime fields is the ability to add fields to documents after you’ve ingested them. This capability simplifies mapping decisions because you don’t have to decide how to parse your data up front, and can use runtime fields to amend the mapping at any time. Using runtime fields allows for a smaller index and faster ingest time, which combined use less resources and reduce your operating costs.
Runtime fields use less disk space and provide flexibility in how you access your data, but can impact search performance based on the computation defined in the runtime script.
To balance search performance and flexibility, index fields that you’ll frequently search for, aggregate and filter on, such as a timestamp. Elasticsearch automatically uses these indexed fields first when running a query, resulting in a fast response time. You can then use runtime fields to limit the number of fields that Elasticsearch needs to calculate values for. Using indexed fields in tandem with runtime fields provides flexibility in the data that you index and how you define queries for other fields.
Use the asynchronous search API to run searches that include runtime fields. This method of search helps to offset the performance impacts of computing values for runtime fields in each document containing that field. If the query can’t return the result set synchronously, you’ll get results asynchronously as they become available.
Queries against runtime fields are considered expensive. If
search.allow_expensive_queries is set
false, expensive queries are not allowed and Elasticsearch will reject any queries
against runtime fields.
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