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Terminology

This document explains about the terms used in the Handbook.

A | B | D | F | H | I | Q | T | V


A#

alias#

An alias is a virtual index name that can point to one or more indices.

aggregation#

Aggregations let you tap into OpenSearch’s powerful analytics engine to analyze your data and extract statistics from it.

Note: OpenSearch doesn’t support aggregations on a text field.

aggregation type#

There are three main types of aggregations:

  • Metric aggregations - Calculate metrics such as sum, min, max, and avg on numeric fields.
  • Bucket aggregations - Sort query results into groups based on some criteria.
  • Pipeline aggregations - Pipe the output of one aggregation as an input to another.

B#

bucket#

A set of documents in BAP that have certain characteristics in common. For example, matching documents might be bucketed by name, type, or date range.

D#

dashboard#

A collection of visualizations that provide insights into your data.

data source#

The data sources are the platforms and tools from where BAP can pull data to analyze.

document#

A JSON object containing data stored and indexed in OpenSearch.

F#

field#

A key-value pair in a document.

H#

Hatstall#

Hatstall is a web interface for SortingHat databases developed mainly with Django. It is available in https://[INSTANCE].biterg.io/identities. See more about affiliations here.

I#

index(es)/indices#

A collection of JSON documents.

index pattern#

A string containing a wildcard (*) pattern that can match multiple indices or aliases.

ingestion#

The process of collecting data from various data sources and sending it to OpenSearch.

Q#

query#

Request for information about your data. You can think of a query as a question, written in a way OpenSearch understands.

T#

text#

Unstructured content, such as a product description or log message which can be used for better analysis.

V#

visualization#

A graphical representation of query results in BAP's web user interface.