Data ingestion in Elasticsearch refers to the process of indexing data into an Elasticsearch cluster. When data is ingested, it is analyzed, transformed, and stored in a way that makes it searchable and accessible through Elasticsearch.
The process of data ingestion typically involves several steps:
1. Defining the index mapping: An index mapping defines the fields and data types that are used to store the data in Elasticsearch. The mapping can be defined manually or automatically generated by Elasticsearch.
2. Indexing the data: The data is indexed into Elasticsearch using the index mapping. This involves transforming the data into a format that can be stored in Elasticsearch, and storing it in one or more shards.
3. Analyzing the data: Elasticsearch analyzes the text data to create an inverted index, which allows for efficient searching of the data. This involves tokenizing the text into individual words, removing stop words, and performing stemming and other text analysis techniques.
4. Querying the data: Once the data is indexed, it can be searched and queried using Elasticsearch. Queries can be simple keyword searches or complex aggregations that combine multiple search criteria.
Data ingestion is a critical process in Elasticsearch, as it determines how the data is stored and made searchable within the cluster. By properly defining the index mapping, indexing the data, analyzing the data, and querying the data, Elasticsearch can efficiently store and retrieve large amounts of data and provide fast and accurate search results.