[[index-modules-codec]] == Codec module Codecs define how documents are written to disk and read from disk. The postings format is the part of the codec that responsible for reading and writing the term dictionary, postings lists and positions, payloads and offsets stored in the postings list. The doc values format is responsible for reading column-stride storage for a field and is typically used for sorting or faceting. When a field doesn't have doc values enabled, it is still possible to sort or facet by loading field values from the inverted index into main memory. Configuring custom postings or doc values formats is an expert feature and most likely using the builtin formats will suit your needs as is described in the <>. ********************************** Only the default codec, postings format and doc values format are supported: other formats may break backward compatibility between minor versions of Elasticsearch, requiring data to be reindexed. ********************************** [float] [[custom-postings]] === Configuring a custom postings format Custom postings format can be defined in the index settings in the `codec` part. The `codec` part can be configured when creating an index or updating index settings. An example on how to define your custom postings format: [source,js] -------------------------------------------------- curl -XPUT 'http://localhost:9200/twitter/' -d '{ "settings" : { "index" : { "codec" : { "postings_format" : { "my_format" : { "type" : "pulsing", "freq_cut_off" : "5" } } } } } }' -------------------------------------------------- Then we defining your mapping your can use the `my_format` name in the `postings_format` option as the example below illustrates: [source,js] -------------------------------------------------- { "person" : { "properties" : { "second_person_id" : {"type" : "string", "postings_format" : "my_format"} } } } -------------------------------------------------- [float] === Available postings formats [float] [[direct-postings]] ==== Direct postings format Wraps the default postings format for on-disk storage, but then at read time loads and stores all terms & postings directly in RAM. This postings format makes no effort to compress the terms and posting list and therefore is memory intensive, but because of this it gives a substantial increase in search performance. Because this holds all term bytes as a single byte[], you cannot have more than 2.1GB worth of terms in a single segment. This postings format offers the following parameters: `min_skip_count`:: The minimum number terms with a shared prefix to allow a skip pointer to be written. The default is *8*. `low_freq_cutoff`:: Terms with a lower document frequency use a single array object representation for postings and positions. The default is *32*. Type name: `direct` [float] [[memory-postings]] ==== Memory postings format A postings format that stores terms & postings (docs, positions, payloads) in RAM, using an FST. This postings format does write to disk, but loads everything into memory. The memory postings format has the following options: `pack_fst`:: A boolean option that defines if the in memory structure should be packed once its build. Packed will reduce the size for the data-structure in memory but requires more memory during building. Default is *false*. `acceptable_overhead_ratio`:: The compression ratio specified as a float, that is used to compress internal structures. Example ratios `0` (Compact, no memory overhead at all, but the returned implementation may be slow), `0.5` (Fast, at most 50% memory overhead, always select a reasonably fast implementation), `7` (Fastest, at most 700% memory overhead, no compression). Default is `0.2`. Type name: `memory` [float] [[bloom-postings]] ==== Bloom filter posting format The bloom filter postings format wraps a delegate postings format and on top of this creates a bloom filter that is written to disk. During opening this bloom filter is loaded into memory and used to offer "fast-fail" reads. This postings format is useful for low doc-frequency fields such as primary keys. The bloom filter postings format has the following options: `delegate`:: The name of the configured postings format that the bloom filter postings format will wrap. `fpp`:: The desired false positive probability specified as a floating point number between 0 and 1.0. The `fpp` can be configured for multiple expected insertions. Example expression: *10k=0.01,1m=0.03*. If number docs per index segment is larger than *1m* then use *0.03* as fpp and if number of docs per segment is larger than *10k* use *0.01* as fpp. The last fallback value is always *0.03*. This example expression is also the default. Type name: `bloom` [float] [[pulsing-postings]] ==== Pulsing postings format The pulsing implementation in-lines the posting lists for very low frequent terms in the term dictionary. This is useful to improve lookup performance for low-frequent terms. This postings format offers the following parameters: `min_block_size`:: The minimum block size the default Lucene term dictionary uses to encode on-disk blocks. Defaults to *25*. `max_block_size`:: The maximum block size the default Lucene term dictionary uses to encode on-disk blocks. Defaults to *48*. `freq_cut_off`:: The document frequency cut off where pulsing in-lines posting lists into the term dictionary. Terms with a document frequency less or equal to the cutoff will be in-lined. The default is *1*. Type name: `pulsing` [float] [[default-postings]] ==== Default postings format The default postings format has the following options: `min_block_size`:: The minimum block size the default Lucene term dictionary uses to encode on-disk blocks. Defaults to *25*. `max_block_size`:: The maximum block size the default Lucene term dictionary uses to encode on-disk blocks. Defaults to *48*. Type name: `default` [float] === Configuring a custom doc values format Custom doc values format can be defined in the index settings in the `codec` part. The `codec` part can be configured when creating an index or updating index settings. An example on how to define your custom doc values format: [source,js] -------------------------------------------------- curl -XPUT 'http://localhost:9200/twitter/' -d '{ "settings" : { "index" : { "codec" : { "doc_values_format" : { "my_format" : { "type" : "disk" } } } } } }' -------------------------------------------------- Then we defining your mapping your can use the `my_format` name in the `doc_values_format` option as the example below illustrates: [source,js] -------------------------------------------------- { "product" : { "properties" : { "price" : {"type" : "integer", "doc_values_format" : "my_format"} } } } -------------------------------------------------- [float] === Available doc values formats [float] ==== Memory doc values format A doc values format that stores all values in a FST in RAM. This format does write to disk but the whole data-structure is loaded into memory when reading the index. The memory postings format has no options. Type name: `memory` [float] ==== Disk doc values format A doc values format that stores and reads everything from disk. Although it may be slightly slower than the default doc values format, this doc values format will require almost no memory from the JVM. The disk doc values format has no options. Type name: `disk` [float] ==== Default doc values format The default doc values format tries to make a good compromise between speed and memory usage by only loading into memory data-structures that matter for performance. This makes this doc values format a good fit for most use-cases. The default doc values format has no options. Type name: `default`