Searching and ranking functions


BM25A(k1,b) returns precise BM25A(). Requires ranker expr and enabled index_field_lengths. Parameters k and b must be float.


BM25F(k1,b, {field=weight, ...}) returns precise BM25F() and index_field_lengths to be enabled. Requires expr ranker. k and b parameters must be float.


Replaces non-existent columns with default values. It returns either a value of an attribute specified by 'attr-name', or 'default-value' if that attribute does not exist. It does not support STRING or MVA attributes. This function is handy when you are searching through several indexes with different schemas.

SELECT *, EXIST('gid', 6) as cnd FROM i1, i2 WHERE cnd>5


Returns sort key value of the worst found element in the current top-N matches if sort key is float and 0 otherwise.


Returns weight of the worst found element in the current top-N matches.


PACKEDFACTORS() can be used in queries, either to just see all the weighting factors calculated when doing the matching, or to provide a binary attribute that can be used to write a custom ranking UDF. This function works only if expression ranker is specified and the query is not a full scan, otherwise it will return an error. PACKEDFACTORS() can take an optional argument that disables ATC ranking factor calculation: PACKEDFACTORS({no_atc=1}) Calculating ATC slows down query processing considerably, so this option can be useful if you need to see the ranking factors, but do not need ATC. PACKEDFACTORS() can also be told to format its output as JSON: PACKEDFACTORS({json=1}) The respective outputs in either key-value pair or JSON format would look as follows below. (Note that the examples below are wrapped for readability; actual returned values would be single-line.)

    -> WHERE MATCH('test one') OPTION ranker=expr('1') \G
*************************** 1\. row ***************************
             id: 1
packedfactors(): bm25=569, bm25a=0.617197, field_mask=2, doc_word_count=2,
    field1=(lcs=1, hit_count=2, word_count=2, tf_idf=0.152356,
        min_idf=-0.062982, max_idf=0.215338, sum_idf=0.152356, min_hit_pos=4,
        min_best_span_pos=4, exact_hit=0, max_window_hits=1, min_gaps=2,
        exact_order=1, lccs=1, wlccs=0.215338, atc=-0.003974),
    word0=(tf=1, idf=-0.062982),
    word1=(tf=1, idf=0.215338)
1 row in set (0.00 sec)
mysql> SELECT id, PACKEDFACTORS({json=1}) FROM test1
    -> WHERE MATCH('test one') OPTION ranker=expr('1') \G
*************************** 1\. row ***************************
                     id: 1

    "bm25": 569,
    "bm25a": 0.617197,
    "field_mask": 2,
    "doc_word_count": 2,
    "fields": [
            "lcs": 1,
            "hit_count": 2,
            "word_count": 2,
            "tf_idf": 0.152356,
            "min_idf": -0.062982,
            "max_idf": 0.215338,
            "sum_idf": 0.152356,
            "min_hit_pos": 4,
            "min_best_span_pos": 4,
            "exact_hit": 0,
            "max_window_hits": 1,
            "min_gaps": 2,
            "exact_order": 1,
            "lccs": 1,
            "wlccs": 0.215338,
            "atc": -0.003974
    "words": [
            "tf": 1,
            "idf": -0.062982
            "tf": 1,
            "idf": 0.215338

1 row in set (0.01 sec)

This function can be used to implement custom ranking functions in UDFs, as in

FROM my_index
WHERE match('hello')
OPTION ranker=expr('1');

Where CUSTOM_RANK() is a function implemented in an UDF. It should declare a SPH_UDF_FACTORS structure (defined in sphinxudf.h), initialize this structure, unpack the factors into it before usage, and deinitialize it afterwards, as follows:

sphinx_factors_unpack((DWORD*)args->arg_values[0], &factors);
// ... can use the contents of factors variable here ...

PACKEDFACTORS() data is available at all query stages, not just when doing the initial matching and ranking pass. That enables another particularly interesting application of PACKEDFACTORS(), namely re-ranking.

In the example just above, we used an expression-based ranker with a dummy expression, and sorted the result set by the value computed by our UDF. In other words, we used the UDF to rank all our results. Assume now, for the sake of an example, that our UDF is extremely expensive to compute and has a throughput of just 10,000 calls per second. Assume that our query matches 1,000,000 documents. To maintain reasonable performance, we would then want to use a (much) simpler expression to do most of our ranking, and then apply the expensive UDF to only a few top results, say, top-100 results. Or, in other words, build top-100 results using a simpler ranking function and then re-rank those with a complex one. We can do that just as well with subselects:

    FROM my_index WHERE match('hello')
    OPTION ranker=expr('sum(lcs)*1000+bm25')
    LIMIT 100

In this example, expression-based ranker will be called for every matched document to compute WEIGHT(). So it will get called 1,000,000 times. But the UDF computation can be postponed until the outer sort. And it also will be done for just the top-100 matches by WEIGHT(), according to the inner limit. So the UDF will only get called 100 times. And then the final top-10 matches by UDF value will be selected and returned to the application.

For reference, in the distributed case PACKEDFACTORS() data gets sent from the agents to master in a binary format, too. This makes it technically feasible to implement additional re-ranking pass (or passes) on the master node, if needed.

If used in SQL, but not called from any UDFs, the result of PACKEDFACTORS() is simply formatted as plain text, which can be used to manually assess the ranking factors. Note that this feature is not currently supported by the Manticore API.


REMOVE_REPEATS ( result_set, column, offset, limit ) - removes repeated adjusted rows with the same 'column' value.



WEIGHT() function returns the calculated matching score. If no ordering specified, the result is sorted descending by the score provided by WEIGHT(). In this example we order first by weight and then by an integer attribute.

The search above does a simple matching, where all words need to be present. But we can do more (and this is just a simple example):

mysql> SELECT *,WEIGHT() FROM testrt WHERE MATCH('"list of business laptops"/3');
| id   | gid  | title                               | content                   | weight() |
|    1 |   10 | List of HP business laptops         | Elitebook Probook         |     2397 |
|    2 |   10 | List of Dell business laptops       | Latitude Precision Vostro |     2397 |
|    3 |   20 | List of Dell gaming laptops         | Inspirion Alienware       |     2375 |
|    5 |   30 | List of ASUS ultrabooks and laptops | Zenbook Vivobook          |     2375 |
4 rows in set (0.00 sec)

mysql> SHOW META;
| Variable_name  | Value    |
| total          | 4        |
| total_found    | 4        |
| total_relation | eq       |
| time           | 0.000    |
| keyword[0]     | list     |
| docs[0]        | 5        |
| hits[0]        | 5        |
| keyword[1]     | of       |
| docs[1]        | 4        |
| hits[1]        | 4        |
| keyword[2]     | business |
| docs[2]        | 2        |
| hits[2]        | 2        |
| keyword[3]     | laptops  |
| docs[3]        | 5        |
| hits[3]        | 5        |
16 rows in set (0.00 sec)

Here we search for 4 words, but we can have a match even if only 3 words (of 4) are found. The search will rank higher first the documents that contain all the words.


ZONESPANLIST() function returns pairs of matched zone spans. Each pair contains the matched zone span identifier, a colon, and the order number of the matched zone span. For example, if a document reads <emphasis role="bold"><i>text</i> the <i>text</i></emphasis>, and you query for 'ZONESPAN:(i,b) text', then ZONESPANLIST() will return the string "1:1 1:2 2:1" meaning that the first zone span matched "text" in spans 1 and 2, and the second zone span in span 1 only.


QUERY() returns the current search query. QUERY() is a postlimit expression and is intended to be used with SNIPPET().

Table functions is a mechanism of post-query result set processing. Table functions take an arbitrary result set as their input, and return a new, processed set as their output. The first argument should be the input result set, but a table function can optionally take and handle more arguments. Table functions can completely change the result set, including the schema. For now, only built in table functions are supported. Table functions work for both outer SELECT and nested SELECT.

Type casting functions


Forcibly promotes the integer argument to 64-bit type, and does nothing on floating point argument. It's intended to help enforce evaluation of certain expressions (such as a*b) in 64-bit mode even though all the arguments are 32-bit.


Forcibly promotes given argument to floating point type. Intended to help enforce evaluation of numeric JSON fields.


Forcibly promotes given argument to 64-bit signed type. Intended to help enforce evaluation of numeric JSON fields.


Forcibly promotes the argument to string type


Forcibly reinterprets given argument to 64-bit unsigned type.


Forcibly reinterprets its 32-bit unsigned integer argument as signed, and also expands it to 64-bit type (because 32-bit type is unsigned). It's easily illustrated by the following example: 1-2 normally evaluates to 4294967295, but SINT(1-2) evaluates to -1.

Arrays and conditions functions


ALL(cond FOR var IN json.array) applies to JSON arrays and returns 1 if condition is true for all elements in array and 0 otherwise. cond is a general expression which additionally can use var as current value of an array element within itself.

SELECT ALL(x>3 AND x<7 FOR x IN j.intarray) FROM test;

ALL(mva) is a special constructor for multi value attributes. When used in conjunction with comparison operators (including comparison with IN()) it returns 1 if all values from the mva attribute are found among the compared values.

SELECT * FROM test WHERE ALL(mymva)>10;

ALL(string list) is a special operation for filtering string tags.

SELECT * FROM test WHERE tags ALL('foo', 'bar', 'fake');
SELECT * FROM test WHERE tags NOT ALL('true', 'text', 'tag');

Here assumed that index 'test' has string attribute 'tags' with set of words (tags), separated by whitespace. If all of the words enumerated as arguments of ALL() present in the attribute, filter matches. Optional 'NOT' inverses the logic. For example, attr containing 'buy iphone cheap' will be matched by ALL('cheap', 'iphone'), but will not match ALL('iphone', '5s').

This filter internally uses doc-by-doc matching, so in case of full scan query it might be very slow. It is intended originally for attributes which are not indexed, like calculated expressions or tags in pq indexes.

if you like such filtering and want to use it in production, consider the solution to put the 'tags' attribute as full-text field, and then use FT operator match() which will invoke full-text indexed search.


ANY(cond FOR var IN json.array) works similar to ALL() except for it returns 1 if condition is true for any element in array.

ANY(mva) is a special constructor for multi value attributes. When used in conjunction with comparison operators (including comparison with IN()) it returns 1 if any of the mva values are found among the compared values. ANY is used by default if no constructor is used, however a warning will be raised about missing constructor.

ANY(string list) is a special operation for filtering string tags. Works similar to ALL() except if condition is true for the case when any tag of tested expression match.

SELECT * FROM test WHERE tags NOT ANY('true', 'text', 'tag');
SELECT TO_STRING(id*321) secret FROM test WHERE secret ANY('1000','3210');


CONTAINS(polygon, x, y) checks whether the (x,y) point is within the given polygon, and returns 1 if true, or 0 if false. The polygon has to be specified using either the POLY2D() function. The former function is intended for "small" polygons, meaning less than 500 km (300 miles) a side, and it doesn't take into account the Earth's curvature for speed. For larger distances, you should use GEOPOLY2D, which tessellates the given polygon in smaller parts, accounting for the Earth's curvature.


IF() behavior is slightly different than its MySQL counterpart. It takes 3 arguments, checks whether the 1st argument is equal to 0.0, returns the 2nd argument if it is not zero or the 3rd one when it is. Note that unlike comparison operators, IF() does not use a threshold! Therefore, it's safe to use comparison results as its 1st argument, but arithmetic operators might produce unexpected results. For instance, the following two calls will produce different results even though they are logically equivalent:

IF ( sqrt(3)*sqrt(3)-3<>0, a, b )
IF ( sqrt(3)*sqrt(3)-3, a, b )

In the first case, the comparison operator <> will return 0.0 (false) because of a threshold, and IF() will always return ** as a result. In the second one, the same sqrt(3)*sqrt(3)-3 expression will be compared with zero without threshold by the IF() function itself. But its value will be slightly different from zero because of limited floating point calculations precision. Because of that, the comparison with 0.0 done by IF() will not pass, and the second variant will return 'a' as a result.


IN(expr,val1,val2,...) takes 2 or more arguments, and returns 1 if 1st argument (expr) is equal to any of the other arguments (val1..valN), or 0 otherwise. Currently, all the checked values (but not the expression itself!) are required to be constant. The constants are pre-sorted and then binary search is used, so IN() even against a big arbitrary list of constants will be very quick. First argument can also be an MVA attribute. In that case, IN() will return 1 if any of the MVA values is equal to any of the other arguments. IN() also supports IN(expr,@uservar) syntax to check whether the value belongs to the list in the given global user variable. First argument can be JSON attribute.


INDEXOF(cond FOR var IN json.array) function iterates through all elements in array and returns index of first element for which 'cond' is true and -1 if 'cond' is false for every element in array.


INTERVAL(expr,point1,point2,point3,...), takes 2 or more arguments, and returns the index of the argument that is less than the first argument: it returns 0 if expr<point1, 1 if point1<=expr<point2, and so on. It is required that point1<point2<...<pointN for this function to work correctly.


LENGTH(attr_mva) function returns amount of elements in MVA set. It works with both 32-bit and 64-bit MVA attributes. LENGTH(attr_json) returns length of a field in JSON. Return value depends on type of a field. For example LENGTH(json_attr.some_int) always returns 1 and LENGTH(json_attr.some_array) returns number of elements in array. LENGTH(string_expr) function returns the length of the string resulted from an expression. TO_STRING() must enclose the expression, regardless if the expression returns a non-string or it's simply a string attribute.


REMAP(condition, expression, (cond1, cond2, ...), (expr1, expr2, ...)) function allows you to make some exceptions of an expression values depending on condition values. Condition expression should always result integer, expression can result in integer or float.


SELECT id, size, REMAP(size, 15, (5,6,7,8), (1,1,2,2)) s
FROM products

This will put documents with sizes 5 and 6 first, lower will go sizes 7 an 8. In case there's an original value not listed in the array (e.g. size 10) it will be defaulted to 15 and in this case will be put to the end.

More examples:

SELECT REMAP(userid, karmapoints, (1, 67), (999, 0)) FROM users;
SELECT REMAP(id%10, salary, (0), (0.0)) FROM employes;