Fulltext search

The MATCH predicate performs a fulltext search on one or more indexed columns or indices and supports different matching techniques. It can also be used to perform geographical searches on indices.

The actual applicability of the MATCH predicate depends on the index’s type. In fact, the availability of certain match_types and match_parameters depend on the index. This section however, only covers the usage of the MATCH predicate on fulltext indices on text columns. To use MATCH on geo_shape indices, see .

In order to use fulltext searches on a column, a fulltext index with an analyzer must be created for this column. See for details. There are different types of Fulltext indices with different goals, however it’s not possible to query multiple index columns with different index types within the same MATCH predicate.

To get the relevance of a matching row, a specific system column can be selected. It contains a numeric score relative to the other rows: The higher, the more relevant the row:

cr> select name, _score from locations
... where match(name_description_ft, 'time') order by _score desc;
+-----------+------------+
| name      |     _score |
+-----------+------------+
| Bartledan | 0.75782394 |
| Altair    | 0.63013375 |
+-----------+------------+
SELECT 2 rows in set (... sec)

The MATCH predicate in its simplest form performs a fulltext search against a single column. It takes the query_term and, if no analyzer was provided, analyzes the term with the analyzer configured on column_or_idx_ident. The resulting tokens are then matched against the index at column_or_idx_ident and if one of them matches, MATCH returns TRUE.

The MATCH predicate can be also used to perform a fulltext search on multiple columns with a single query_term and to add weight to specific columns it’s possible to add a boost argument to each column_or_idx_ident. Matches on columns with a higher boost result in a higher _score value for that document.

The match_type argument determines how a single query_term is applied and and how the resulting is computed. For more information see Match Types.

Results are ordered by by default, but can be overridden by adding an ORDER BY clause.

column_or_idx_ident

A reference to a column or an index.

If the column has an implicit index (e.g. created with something like TEXT column_a INDEX USING FULLTEXT) this should be the name of the column.

If the column has an explicit index (e.g. created with something like INDEX "column_a_idx" USING FULLTEXT ("column_a") WITH (...)) this should be the name of the index.

By default every column is indexed but only the raw data is stored, so matching against a text column without a fulltext index is equivalent to using the = operator. To perform real fulltext searches use a .

boost

A column ident can have a boost attached. That is a weight factor that increases the relevance of a column in respect to the other columns. The default boost is 1.

query_term

This string is analyzed (using the explicitly given analyzer or the analyzer of the columns to perform the search on) and the resulting tokens are compared to the index. The tokens used for search are combined using the boolean OR operator unless stated otherwise using the operator option.

match_type

Optional. Defaults to best_fields for fulltext indices. For details see Match Types.

Note

The MATCH predicate can only be used in the and on user-created tables. Using the MATCH predicate on system tables is not supported.

One MATCH predicate cannot combine columns of both relations of a join.

Additionally, MATCH predicates cannot be used on columns of both relations of a join if they cannot be logically applied to each of them separately. For example:

This is allowed:

FROM t1, t2 WHERE match(t1.txt, 'foo') AND match(t2.txt, 'bar');``

But this is not:

The match type determines how the query_term is applied and the _score is created, thereby influencing which documents are considered more relevant. The default match_type for fulltext indices is best_fields.

best_fields

Use the of the column that matched best. For example if a column contains all the tokens of the query_term it’s considered more relevant than other columns containing only one.

most_fields

This match type takes the _score of every matching column and averages their scores.

cross_fields

This match type analyzes the query_term into tokens and searches all tokens in all given columns at once as if they were one big column (given they have the same analyzer). All tokens have to be present in at least one column, so querying for foo bar should have the tokens foo in one column and bar in the same or any other.

phrase

This match type differs from best_fields in that it constructs a phrase query from the query_term. A phrase query will only match if the tokens in the columns are exactly in the same order as the analyzed columns from the query_term. So, querying for foo bar (analyzed tokens: foo and bar) will only match if one of the columns contains those two token in that order - without any other tokens in between.

phrase_prefix

This match type is roughly the same than phrase but it allows to match by prefix on the last token of the . For example if your query for foo ba, one of the columns has to contain foo and a token that starts with ba in that order. So a column containing foo baz would match and foo bar too.

Options

The match options further distinguish the way the matching process using a certain match type works. Not all options are applicable to all match types. See the options below for details.

analyzer

The analyzer used to convert the query_term into tokens.

boost

This numeric value is multiplied with the resulting of this match call.

If this match call is used with other conditions in the where clause a value above 1.0 will increase its influence on the overall _score of the whole query, a value below 1.0 will decrease it.

cutoff_frequency

The token frequency is the number of occurrences of a token in a column.

This option specifies a minimum token frequency that excludes matching tokens with a higher frequency from the overall . Their _score is only included if another token with a lower frequency also matches. This can be used to suppress results where only high frequency terms like the would cause a match.

fuzziness

Can be used to perform fuzzy full text search.

On numeric columns use a numeric, on timestamp columns a long indicating milliseconds, on strings use a number indicating the maximum allowed Levenshtein Edit Distance. Use prefix_length, fuzzy_rewrite and max_expansions to fine tune the fuzzy matching process.

fuzzy_rewrite

The same than rewrite but only applies to queries using fuzziness.

max_expansions

When using fuzziness or phrase_prefix this options controls to how many different possible tokens a search token will be expanded. The fuzziness controls how big the distance or difference between the original token and the set of tokens it is expanded to can be. This option controls how big this set can get.

minimum_should_match

The number of tokens from the query_term to match when or is used. Defaults to 1.

operator

Can be or or and. The default is or. It is used to combine the tokens of the query_term. If and is used, every token from the query_term has to match. If or is used only the number of minimum_should_match have to match.

When used with fuzziness option or with phrase_prefix this options controls how long the common prefix of the tokens that are considered as similar (same prefix or fuzziness distance/difference)has to be.

rewrite

When using phrase_prefix the prefix query is constructed using all possible terms and rewriting them into another kind of query to compute the score. Possible values are constant_score_auto, constant_score_boolean, constant_score_filter, scoring_boolean,``top_terms_N``, top_terms_boost_N. The constant_... values can be used together with the boost option to set a constant _score for rows with a matching prefix or fuzzy match.

slop

When matching for phrases this option controls how exact the phrase match should be (proximity search). If set to 0 (the default), the terms must be in the exact order. If two transposed terms should match, a minimum slop of 2 has to be set. Only applicable to phrase and phrase_prefix queries. As an example with slop 2, querying for foo bar will not only match foo bar but also foo what a bar.

tie_breaker

When using best_fields, phrase or phrase_prefix the of every other column will be multiplied with this value and added to the _score of the best matching column.

Defaults to 0.0.

Not applicable to match type most_fields as this type is executed as if it had a tie_breaker of .

zero_terms_query

If no tokens are generated analyzing the query_term then no documents are matched. If all is given here, all documents are matched.

A fulltext search is done using the predicate:

cr> select name from locations where match(name_description_ft, 'time') order by _score desc;
+-----------+
+-----------+
| Bartledan |
| Altair    |
+-----------+
SELECT 2 rows in set (... sec)

It returns TRUE for rows which match the search string. To get more detailed information about the quality of a match, the relevance of the row, the _score can be selected:

cr> select name, _score
... from locations where match(name_description_ft, 'time') order by _score desc;
+-----------+------------+
| name      |     _score |
+-----------+------------+
| Bartledan | 0.75782394 |
| Altair    | 0.63013375 |
+-----------+------------+
SELECT 2 rows in set (... sec)

Note

The _score is not an absolute value. It just sets a row in relation to the other ones.

There are two possibilities if a search should span the contents of multiple columns:

use a composite index column on your table. See .
use the MATCH Predicate predicate on multiple columns.

When querying multiple columns, there are many ways how the relevance a.k.a. can be computed. These different techniques are called Match Types.

To increase the relevance of rows where one column matches extremely well, use best_fields (the default).

If rows with good matches spread over all included columns should be more relevant, use most_fields. If searching multiple columns as if they were one, use cross_fields.

For searching of matching phrases (tokens are in the exact same order) use phrase or phrase_prefix:

cr> select name, description, _score from locations
... where match(
...     (name_description_ft), 'end of the galaxy'
... ) using phrase with (analyzer='english', slop=4);
+------+-------------------------+-----------+
+------+-------------------------+-----------+
| NULL | The end of the Galaxy.% | 1.5614427 |
+------+-------------------------+-----------+
SELECT 1 row in set (... sec)

A vast amount of options exist to fine-tune your fulltext search. A detailed reference can be found here .

A negative fulltext search can be done using a NOT clause:

cr> select name, _score from locations
... where not match(name_description_ft, 'time')
... order by _score, name asc;
+------------------------------------+--------+
| name                               | _score |
+------------------------------------+--------+
|                                    |    1.0 |
| Aldebaran                          |    1.0 |
| Algol                              |    1.0 |
| Allosimanius Syneca                |    1.0 |
| Alpha Centauri                     |    1.0 |
| Argabuthon                         |    1.0 |
| Arkintoofle Minor                  |    1.0 |
| Galactic Sector QQ7 Active J Gamma |    1.0 |
| North West Ripple                  |    1.0 |
| Outer Eastern Rim                  |    1.0 |
| NULL                               |    1.0 |

It is possible to filter results by the _score column but as its value is a computed value relative to the highest score of all results and consequently never absolute or comparable across searches the usefulness outside of sorting is very limited.

Although possible, filtering by the greater-than-or-equals (>=) on the _score column would not make much sense and can lead to unpredictable result sets.

Anyway let’s do it here for demonstration purpose:

As you might have noticed, the value has changed for the same query text and document because it’s a ratio relative to all results, and by filtering on _score, ‘all results’ has already changed.

As noted above is a relative number and not comparable across searches. Filtering is therefore greatly discouraged.