Keeping older data can useful for later analysis but is often avoided due to If fields always occur in the same order. It is more likely to find longer duplicate strings in those _source documents Put fields in the same order in documents editĭue to the fact that multiple documents are compressed together into blocks, Structure, fields, and values together should improve the compression ratio. Then instead they are compressed in sorted order. Share some field values, especially on fields that have a low cardinality orīy default documents are compressed together in the order that they are added For instance it is veryĬommon that documents share the same field names, and quite common that they In order to improve the overall compression ratio.
When Elasticsearch stores _source, it compresses multiple documents at once Use index sorting to colocate similar documents edit Use-case: using float over double, or half_float over float will help Stored in a scaled_float if appropriate or in the smallest type that fits the ( byte, short, integer or long) and floating points should either be In particular, integers should be stored using an integer type The type that you pick for numeric data can have a significant impact Use the smallest numeric type that is sufficient edit Together with the force merge API above, this can significantly reduce the number of shards and segments of an index. The shrink API allows you to reduce the number of shards in an index.
Remain in the index which can result in increased disk usage and worse search Merge policy will never consider these segments for future merges until they Force merge can cause very large (>5GB) segments toīe produced, and if you continue to write to such an index then the automatic In many cases, the number of segments can be reduced to one per shard by setting max_num_segments=1.įorce merge should only be called against an index after you haveįinished writing to it. The force merge API can be used to reduce the number of segments per shard. Larger segments are more efficient for storing data. Each shard is a Lucene index and made up of one or more segments - the actual files on disk. Indices in Elasticsearch are stored in one or more shards. They can be compressed more aggressively by using the best_compression codec. The _source and stored fields can easily take a non negligible amount of disk
However, APIs that needs access to _source such as update and reindex won’t work. If you don’t need access to it you can disable it. The _source field stores the original JSON body of the document.
Keep in mind that large shard sizes come with drawbacks, such as long full recovery times. by leveraging the Rollover API), or modifying an existing index using the Shrink API. To increase the size of your shards, you can decrease the number of primary shards in an index by creating indices with fewer primary shards, creating fewer indices (e.g. Larger shards are going to be more efficient at storing data.