WARNING: The 1.x versions of Elasticsearch have passed their EOL dates. If you are running a 1.x version, we strongly advise you to upgrade.
This documentation is no longer maintained and may be removed. For the latest information, see the current Elasticsearch documentation.
In-memory fielddata is limited by the size of your heap. While this is a problem that can be solved by scaling horizontally—you can always add more nodes—you will find that heavy use of aggregations and sorting can exhaust your heap space while other resources on the node are underutilized.
While fielddata defaults to loading values into memory on the fly, this is not the only option. It can also be written to disk at index time in a way that provides all the functionality of in-memory fielddata, but without the heap memory usage. This alternative format is called doc values.
Doc values were added to Elasticsearch in version 1.0.0 but, until recently, they were much slower than in-memory fielddata. By benchmarking and profiling performance, various bottlenecks have been identified—in both Elasticsearch and Lucene—and removed.
Doc values are now only about 10–25% slower than in-memory fielddata, and come with two major advantages:
They live on disk instead of in heap memory. This allows you to work with
quantities of fielddata that would normally be too large to fit into
memory. In fact, your heap space (
$ES_HEAP_SIZE) can now be set to a smaller size, which improves the speed of garbage collection and, consequently, node stability.
- Doc values are built at index time, not at search time. While in-memory fielddata has to be built on the fly at search time by uninverting the inverted index, doc values are prebuilt and much faster to initialize.
The trade-off is a larger index size and slightly slower fielddata access. Doc values are remarkably efficient, so for many queries you might not even notice the slightly slower speed. Combine that with faster garbage collections and improved initialization times and you may notice a net gain.
The more filesystem cache space that you have available, the better doc values will perform. If the files holding the doc values are resident in the filesystem cache, then accessing the files is almost equivalent to reading from RAM. And the filesystem cache is managed by the kernel instead of the JVM.
Doc values can be enabled for numeric, date, Boolean, binary, and geo-point
fields, and for
not_analyzed string fields. They do not currently work with
analyzed string fields. Doc values are enabled per field in the field
mapping, which means that you can combine in-memory fielddata with doc values:
That’s it! Queries, aggregations, sorting, and scripts will function as normal; they’ll just be using doc values now. There is no other configuration necessary.
Use doc values freely. The more you use them, the less stress you place on the heap. It is possible that doc values will become the default format in the near future.