Storeedit

The store module allows you to control how index data is stored and accessed on disk.

File system storage typesedit

There are different file system implementations or storage types. The best one for the operating environment will be automatically chosen: mmapfs on Windows 64bit, simplefs on Windows 32bit, and default (hybrid niofs and mmapfs) for the rest.

This can be overridden for all indices by adding this to the config/elasticsearch.yml file:

index.store.type: niofs

It is a static setting that can be set on a per-index basis at index creation time:

PUT /my_index
{
  "settings": {
    "index.store.type": "niofs"
  }
}

This is an expert-only setting and may be removed in the future

The following sections lists all the different storage types supported.

simplefs
The Simple FS type is a straightforward implementation of file system storage (maps to Lucene SimpleFsDirectory) using a random access file. This implementation has poor concurrent performance (multiple threads will bottleneck). It is usually better to use the niofs when you need index persistence.
niofs
The NIO FS type stores the shard index on the file system (maps to Lucene NIOFSDirectory) using NIO. It allows multiple threads to read from the same file concurrently. It is not recommended on Windows because of a bug in the SUN Java implementation.
mmapfs
The MMap FS type stores the shard index on the file system (maps to Lucene MMapDirectory) by mapping a file into memory (mmap). Memory mapping uses up a portion of the virtual memory address space in your process equal to the size of the file being mapped. Before using this class, be sure you have allowed plenty of virtual address space.
default_fs
The default type is a hybrid of NIO FS and MMapFS, which chooses the best file system for each type of file. Currently only the Lucene term dictionary and doc values files are memory mapped to reduce the impact on the operating system. All other files are opened using Lucene NIOFSDirectory. Address space settings (Virtual memory) might also apply if your term dictionaries are large.