# Mathematical functions

### ABS()

Returns the absolute value of the argument.

### ATAN2()

Returns the arctangent function of two arguments, expressed in radians.

### BITDOT()

`BITDOT(mask, w0, w1, ...)` returns the sum of products of each bit of a mask multiplied by its weight. `bit0*w0 + bit1*w1 + ...`

### CEIL()

Returns the smallest integer value greater than or equal to the argument.

### COS()

Returns the cosine of the argument.

### CRC32()

Returns the CRC32 value of a string argument.

### EXP()

Returns the exponent of the argument (e=2.718... to the power of the argument).

### FIBONACCI()

Returns the N-th Fibonacci number, where N is the integer argument. That is, arguments of 0 and up will generate the values 0, 1, 1, 2, 3, 5, 8, 13 and so on. Note that the computations are done using 32-bit integer math and thus numbers 48th and up will be returned modulo 2^32.

### FLOOR()

Returns the largest integer value lesser than or equal to the argument.

### GREATEST()

`GREATEST(attr_json.some_array)` function takes a JSON array as the argument, and returns the greatest value in that array. Also works for MVA.

### IDIV()

Returns the result of an integer division of the first argument by the second argument. Both arguments must be of an integer type.

### LEAST()

`LEAST(attr_json.some_array)` function takes a JSON array as the argument, and returns the least value in that array. Also works for MVA.

### LN()

Returns the natural logarithm of the argument (with the base of e=2.718...).

### LOG10()

Returns the common logarithm of the argument (with the base of 10).

### LOG2()

Returns the binary logarithm of the argument (with the base of 2).

### MAX()

Returns the larger of two arguments.

### MIN()

Returns the smaller of two arguments.

## POW()

Returns the first argument raised to the power of the second argument.

### RAND()

Returns a random float between 0 and 1. It can optionally accept a `seed`, which can be a constant integer or an integer attribute's name.

If you use a `seed`, keep in mind that it resets `rand()`'s starting point separately for each plain table, RT disk, RAM chunk, or pseudo shard. Therefore, queries to a distributed table in any form can return multiple identical random values.

### SIN()

Returns the sine of the argument.

### SQRT()

Returns the square root of the argument.

# Searching and ranking functions

### BM25A()

`BM25A(k1,b)` returns the exact `BM25A()` value. Requires the `expr` ranker and enabled `index_field_lengths`. Parameters `k1` and `b` must be floats.

### BM25F()

`BM25F(k1, b, {field=weight, ...})` returns the exact `BM25F()` value and requires `index_field_lengths` to be enabled. The `expr` ranker is also necessary. Parameters `k1` and `b` must be floats.

### EXIST()

Substitutes non-existent columns with default values. It returns either the value of an attribute specified by 'attr-name', or the 'default-value' if that attribute does not exist. STRING or MVA attributes are not supported. This function is useful when searching through multiple tables with different schemas.

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

### MIN_TOP_SORTVAL()

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

### MIN_TOP_WEIGHT()

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

### PACKEDFACTORS()

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

``````mysql> SELECT id, PACKEDFACTORS() FROM test1
-> WHERE MATCH('test one') OPTION ranker=expr('1') \G
*************************** 1\. row ***************************
id: 1
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
packedfactors({json=1}):
{

"bm25": 569,
"bm25a": 0.617197,
"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:

``````SELECT *, CUSTOM_RANK(PACKEDFACTORS()) AS r
FROM my_index
WHERE match('hello')
ORDER BY r DESC
OPTION ranker=expr('1');``````

Where `CUSTOM_RANK()` is a function implemented in a 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:

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

`PACKEDFACTORS()` data is available at all query stages, not just during the initial matching and ranking pass. This enables another particularly interesting application of `PACKEDFACTORS()`: re-ranking.

In the example 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. Now, let's assume for the sake of an example that our UDF is extremely expensive to compute, with a throughput of only 10,000 calls per second. If our query matches 1,000,000 documents, we would want to use a much simpler expression to do most of our ranking in order to maintain reasonable performance. Then, we would apply the expensive UDF to only a few top results, say, the top 100 results. In other words, we would build the top 100 results using a simpler ranking function and then re-rank those with a more complex one. This can be done with subselects:

``````SELECT * FROM (
SELECT *, CUSTOM_RANK(PACKEDFACTORS()) AS r
FROM my_index WHERE match('hello')
OPTION ranker=expr('sum(lcs)*1000+bm25')
ORDER BY WEIGHT() DESC
LIMIT 100
) ORDER BY r DESC LIMIT 10``````

In this example, the expression-based ranker is called for every matched document to compute `WEIGHT()`, so it gets called 1,000,000 times. However, the UDF computation can be postponed until the outer sort, and it will only be performed for the top 100 matches by `WEIGHT()`, according to the inner limit. This means the UDF will only be called 100 times. Finally, the top 10 matches by UDF value are selected and returned to the application.

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

When used in SQL but not called from any UDFs, the result of `PACKEDFACTORS()` is 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()

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

``SELECT REMOVE_REPEATS((SELECT * FROM dist1), gid, 0, 10)``

### WEIGHT()

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

The search above performs a simple matching, where all words need to be present. However, 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        |
| 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 four words, but a match can occur even if only three of the four words are found. The search will rank documents containing all words higher.

### ZONESPANLIST()

The `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()

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

Table functions are a mechanism for post-query result set processing. Table functions take an arbitrary result set as input and return a new, processed set as 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. Currently, only built-in table functions are supported. Table functions work for both outer `SELECT` and nested SELECT.

# Type Casting Functions

Type casting comprises three principal actions: conversion, reinterpretation, and promotion.

• Conversion. This refers to the process of changing the data type of a value to another data type. This involves additional computations and is exclusively performed by the `TO_STRING()` function.
• Reinterpretation. This involves treating the binary data representing a value as if it were of a different data type, without actually changing the underlying data. This is handled by `SINT()`, doesn't involve extra computations; instead, it merely reinterprets existing data.
• Promotion. This refers to the process of converting a value to a "larger" or more precise data type. It doesn't require extra computation either; it merely requests the argument to deliver a value of a different type. Only JSON fields and a few other functions can promote their values to integers. If an argument cannot yield a value of a different type, the promotion will fail. For instance, the `TIMEDIFF()` function usually returns a string, but can also return a number. So, `BIGINT(TIMEDIFF(1,2))` will execute successfully, compelling `TIMEDIFF()` to supply an integer value. Conversely, `DATE_FORMAT()` solely returns strings and can't yield a number, meaning that `BIGINT(DATE_FORMAT(...))` will fail.

### BIGINT()

This function promotes an integer argument to a 64-bit type, leaving floating-point arguments untouched. It's designed to ensure the evaluation of specific expressions (such as `a*b`) in 64-bit mode, even if all arguments are 32-bit.

### DOUBLE()

The `DOUBLE()` function promotes its argument to a floating-point type. This is designed to help enforce the evaluation of numeric JSON fields.

### INTEGER()

The `INTEGER()` function promotes its argument to a 64-bit signed type. This is designed to enforce the evaluation of numeric JSON fields.

### TO_STRING()

This function forcefully converts its argument to a string type.

### UINT()

The `UINT()` function promotes its argument to a 32-bit unsigned integer type.

### UINT64()

The `UINT64()` function promotes its argument to a 64-bit unsigned integer type.

### SINT()

The `SINT()` function forcefully reinterprets its 32-bit unsigned integer argument as signed and extends it to a 64-bit type (since the 32-bit type is unsigned). For instance, 1-2 ordinarily evaluates to 4294967295, but `SINT(1-2)` evaluates to -1.