Token filter plugins

Token filter plugins let you implement a custom tokenizer that makes tokens according to custom rules. There are two type:

In the text processing pipeline, the token filters will run after the base tokenizer processing occurs (which processes the text from field or query and creates tokens out of them).

Index-time tokenizer

Index-time tokenizer gets created by indexer on indexing source data into a table or by an RT table on processing INSERT or REPLACE statements.

Plugin is declared as library name:plugin name:optional string of settings. The init functions of the plugin can accept arbitrary settings that can be passed as a string in format option1=value1;option2=value2;...


index_token_filter =;

The call workflow for index-time token filter is as follows:

  1. XXX_init() gets called right after indexer creates token filter with empty fields list then after indexer got table schema with actual fields list. It must return zero for successful initialization or error description otherwise.
  2. XXX_begin_document gets called only for RT table INSERT/REPLACE for every document. It must return zero for successful call or error description otherwise. Using OPTION token_filter_options additional parameters/settings can be passed to the function.
    INSERT INTO rt (id, title) VALUES (1, 'some text [email protected]') OPTION token_filter_options='.io'
  3. XXX_begin_field gets called once for each field prior to processing field with base tokenizer with field number as its parameter.
  4. XXX_push_token gets called once for each new token produced by base tokenizer with source token as its parameter. It must return token, count of extra tokens made by token filter and delta position for token.
  5. XXX_get_extra_token gets called multiple times in case XXX_push_token reports extra tokens. It must return token and delta position for that extra token.
  6. XXX_end_field gets called once right after source tokens from current field get over.
  7. XXX_deinit gets called in the very end of indexing.

The following functions are mandatory to be defined: XXX_begin_document and XXX_push_token and XXX_get_extra_token.

query-time token filter

Query-time tokenizer gets created on search each time full-text invoked by every table involved.

The call workflow for query-time token filter is as follows:

  1. XXX_init() gets called once per table prior to parsing query with parameters - max token length and string set by token_filter option
    SELECT * FROM index WHERE MATCH ('test') OPTION token_filter=''

    It must return zero for successful initialization or error description otherwise.

  2. XXX_push_token() gets called once for each new token produced by base tokenizer with parameters: token produced by base tokenizer, pointer to raw token at source query string and raw token length. It must return token and delta position for token.
  3. XXX_pre_morph() gets called once for token right before it got passed to morphology processor with reference to token and stopword flag. It might set stopword flag to mark token as stopword.
  4. XXX_post_morph() gets called once for token after it processed by morphology processor with reference to token and stopword flag. It might set stopword flag to mark token as stopword. It must return flag non-zero value of which means to use token prior to morphology processing.
  5. XXX_deinit() gets called in the very end of query processing.

Absence of any of the functions is tolerated.

Miscellaneous tools


indextool is a utility tool that helps to dump various information about a physical table (excluding template or distributedtables). The general syntax for using indextool is:

indextool <command> [options]

Options effective for all commands:

  • --config <file> (-c <file> for short) overrides the built-in config file names.
  • --quiet (-q for short) keep indextool quiet - it will not output banner, etc.
  • --help (-h for short) lists all of the parameters that can be called in your particular build of indextool.
  • -v show version information of your particular build of indextool.

The commands are as follows:

  • --checkconfig just loads and verifies the config file to check if it's valid, without syntax errors.
  • --buildidf DICTFILE1 [DICTFILE2 ...] --out IDFILE build IDF file from one or several dictionary dumps. Additional parameter --skip-uniq will skip unique (df=1) words.
  • --build-infixes TABLENAME build infixes for an existing dict=keywords table (upgrades .sph, .spi in place). You can use this option for legacy table files that already use dict=keywords, but now need to support infix searching too; updating the table files with indextool may prove easier or faster than regenerating them from scratch with indexer.
  • --dumpheader FILENAME.sph quickly dumps the provided table header file without touching any other table files or even the configuration file. The report provides a breakdown of all the table settings, in particular the entire attribute and field list.
  • --dumpconfig FILENAME.sph dumps the table definition from the given table header file in (almost) compliant sphinx.conf file format.
  • --dumpheader TABLENAME dumps table header by table name with looking up the header path in the configuration file.
  • --dumpdict TABLENAME dumps dictionary. Additional -stats switch will dump to dictionary the total number of documents. It is required for dictionary files that are used for creation of IDF files.
  • --dumpdocids TABLENAME dumps document IDs by table name.
  • --dumphitlist TABLENAME KEYWORD dumps all the hits (occurrences) of a given keyword in a given table, with keyword specified as text.
  • --dumphitlist TABLENAME --wordid ID dumps all the hits (occurrences) of a given keyword in a given table, with keyword specified as internal numeric ID.
  • --docextract TBL DOCID runs usual table check pass of whole dictionary/docs/hits, and collects all the words and hits belonging to requested document. Then all of the words are placed in the order according to their fields and positions, and result is printed, grouping by field.
  • --fold TABLENAME OPTFILE This options is useful too see how actually tokenizer proceeds input. You can feed indextool with text from file if specified or from stdin otherwise. The output will contain spaces instead of separators (accordingly to your charset_table settings) and lowercased letters in words.
  • --htmlstrip TABLENAME filters stdin using HTML stripper settings for a given table, and prints the filtering results to stdout. Note that the settings will be taken from sphinx.conf, and not the table header.
  • --mergeidf NODE1.idf [NODE2.idf ...] --out GLOBAL.idf merge several .idf files into a single one. Additional parameter --skip-uniq will skip unique (df=1) words.
  • --morph TABLENAME applies morphology to the given stdin and prints the result to stdout.
  • --check TABLENAME checks the table data files for consistency errors that might be introduced either by bugs in indexer and/or hardware faults. --check also works on RT tables, RAM and disk chunks. Additional options:
    • --check-id-dups checks if there are duplicate ids
    • --check-disk-chunk CHUNK_NAME checks only specific disk chunk of an RT table. The argument is a disk chunk numeric extension of the RT table to check.
  • --strip-path strips the path names from all the file names referenced from the table (stopwords, wordforms, exceptions, etc). This is useful for checking tables built on another machine with possibly different path layouts.
  • --rotate works only with --check and defines whether to check table waiting for rotation, i.e. with .new extension. This is useful when you want to check your table before actually using it.
  • --apply-killlists loads and applies kill-lists for all tables listed in the config file. Changes are saved in .SPM files. Kill-list files (.SPK) are deleted. This can be useful if you want to move applying tables from server startup to indexing stage.


spelldump is used to extract the contents of a dictionary file that uses the ispell or MySpell format, which can be useful in building word lists for wordforms - all of the possible forms are pre-built for you.

The general syntax is:

spelldump [options] <dictionary> <affix> [result] [locale-name]

The two main parameters are the dictionary's main file and its affix file; these are usually named [language-prefix].dict and [language-prefix].aff and can be found in most common Linux distributions and various online sources.

[result] is where the extracted dictionary data will be output, and [locale-name] specifies the locale details you wish to use.

There is also an optional -c [file] option, which specifies a file for case conversion details.

Examples of usage are:

spelldump en.dict en.aff
spelldump ru.dict ru.aff ru.txt ru_RU.CP1251
spelldump ru.dict ru.aff ru.txt .1251

The result file will contain a list of all the words in the dictionary, sorted alphabetically, in the format of a wordforms file. This can be used to tailor it to your specific needs. An example of what the result file could look like:

zone > zone
zoned > zoned
zoning > zoning


wordbreaker is used to split compound words, such as those commonly found in URLs, into their component words. For example, this tool can split "lordoftherings" into its four component words, or into "man of steel warner bros". This helps in searching, as it eliminates the need for prefixes or infixes. For example, searching for "sphinx" would not match "sphinxsearch", but if you break the compound word and index the separate components, you would get a match without the increased file sizes that come with using prefixes and infixes in full-text indexing.

Examples of usage include:

echo manofsteel | bin/wordbreaker -dict dict.txt split
man of steel

The input stream will be separated into words using the -dict dictionary file. If no dictionary is specified, wordbreaker looks in the working folder for a wordbreaker-dict.txt file. (The dictionary should match the language of the compound word.) The split command breaks words from the standard input and outputs the result to the standard output. There are also test and bench commands that allow you to test the splitting quality and benchmark the splitting functionality.

Wordbreaker requires a dictionary to recognize individual substrings within a string. To differentiate between different guesses, it uses the relative frequency of each word in the dictionary, with higher frequency meaning a higher split probability. You can generate such a file using the indexer tool:

indexer --buildstops dict.txt 100000 --buildfreqs myindex -c /path/to/sphinx.conf

which will write the 100,000 most frequent words along with their counts from myindex into dict.txt. The output file is a text file, so it can be edited by hand if necessary to add or remove words.

OpenAPI specification

The Manticore Search API is documented using the OpenAPI specification, which can be used to generate client SDKs. The machine-readable YAML file is available at

You can also view the specification visualized with the online Swagger Editor here.