In this blog, we explore how nested objects and parent-child relationships enable SQL-like join operations in Elasticsearch.
Elasticsearch is an open-source, distributed -based search and analytics engine built using Apache Lucene to provide fast real-time search functionality. It is a NoSQL data store that is document-oriented, scalable, and schemaless by default. Elasticsearch is designed to work at scale with large data sets. As a search engine, it provides fast indexing and search capabilities that can be horizontally scaled across multiple nodes.
Shameless plug: is a real-time indexing database in the cloud. It automatically builds indexes that are optimized not just for search but also aggregations and joins, making it fast and easy for your applications to query data, regardless of where it comes from and what format it is in. But this post is about highlighting some workarounds, in case you really want to do SQL-style joins in Elasticsearch.
objects. This, however, requires a thorough understanding of the data structure to efficiently query from it. The parent-child model leverages multiple filters to simplify the search functionality: query has_child Returns parent documents that have child documents matching the query. query has_parent Accepts a parent and returns child documents that associated parents have matched. query inner_hits Fetches relevant children information from the query.
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