One of the biggest features in the 6.0 release is sequence IDs, which allows for operations-based shard recovery. Previously, if a node disconnected from the cluster because of a network problem or a node restart, each shard on the node would have to be resynced by comparing segment files with the primary shard and copying over any segments that were different. This could be a long and costly process, that made even rolling restarts of nodes very slow. With sequence IDs, each shard will be able to replay just the operations missing from that shard making the recovery process much more efficient.

Doc-values provide a fast columnar data store - it’s part of the magic that makes aggregations so fast in Elasticsearch. Previously, a storage slot was reserved for every field in every column. If many fields occurred only in a few documents, this could result in a huge waste of disk space. Now, you pay for what you use. Dense fields will use the same amount of space as before, but sparse fields will be significantly smaller . Not only does this reduce disk space usage, it also reduces merge times and improves query throughput as the file system cache can be better utilised.

Faster Query Times with Sorted Indices

Imagine that you have a large search-heavy index. Searches should be super-fast, but a significant part of every search request is sorting the results into the correct order in order to return just the top 10 best hits. With index sorting, you can pay the price of sorting at index time (30-40% of throughput) instead of at search time. That way, a search can terminate as soon as it has gathered sufficient hits.

To take advantage of this, your documents need to be sorted at index time in the same order as will be used for your primary sort criterion at search time, e.g. by price or timestamp. This means that it won’t work well where your primary sort is on the relevance _score . It also isn’t suitable for searches with aggregations, as aggregations have to examine all documents regardless and can’t terminate early.