![]() ![]() Managing data in a flattened structure incurs additional overhead for data sets that are relational in nature.Duplication of data into flattened documents requires additional storage space. ![]() Many-to-many relationships can be handled by data flattening. You can avoid these expensive join operations by denormalizing data. While working with distributed systems, having to join data sets across the network can introduce significant latencies. Disk space is not an expensive commodity and thus little cause for concern. Though this increases the document size and results in the storage of duplicate data in each document. With denormalization, the data is stored in a flattened structure at the time of indexing. Each document is independent and contains all the required data, thus eliminating the need for expensive join operations. There are four common approaches to managing data in Elasticsearch:ĭenormalization provides the best query search performance in Elasticsearch, since joining data sets at query time isn’t necessary. Managing Your Data Model in Elasticsearch Want to learn more about Joins in Elasticsearch? Check out our post on common use cases The remainder of this blog will cover the other two scenarios in more detail. One-to-one object mappings are simple and will not be discussed much here.
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