See also Elasticsearch 6.6.0 release notes.
The index lifecycle management feature breaks the lifecycle of an index into four phases: hot, warm, cold, and delete phase. You can define an index lifecycle policy which enables you to:
- Have one primary shard on each hot node to maximize indexing throughput.
- Replace the hot index with a new empty index as soon as the existing index is “full” or after a time period.
- Move the old index to warm nodes, where it can be shrunk to a single shard and force-merged down to a single segment for optimized storage and querying.
- Later, move the index to cold nodes for cheaper storage.
Frozen indices allow for a much higher ratio of disk storage to heap memory, at the expense of search latency. When an index is frozen, it takes up no heap memory, allowing a single node to easily manage thousands of indices with very low overhead. When a search targets frozen indices, the query will fully open, search, and then close each index sequentially. Frozen indices are replicated, unlike closed indexes. Frozen indices provide a new set of choices for how to optimize your cluster cost and performance around your needs.
In 6.0, we introduced Bkd-backed geopoints, which resulted in significant storage, memory and performance improvements for querying geopoints. With 6.6.0, we bring the same Bkd-based benefits to geoshapes. Indexing is faster, it will take up less space on disk, and will use less memory.
In combination with this work, we are introducing a new experimental field,
geo, that combine the
geo_hash field. The
geo field is also backed by BKD trees.