# groonga - An open-source fulltext search engine and column store.

- 日本語

# 7.13.2. Script syntax

Script syntax is a syntax to specify complex search condition. It is similar to ECMAScript. For example, `_key == "book"` means that groonga searches records that `_key` value is `"book"`. All values are string in Query syntax but its own type in script syntax. For example, `"book"` is string, `1` is integer, `TokenBigram` is the object whose name is `TokenBigram` and so on.

Script syntax doesn’t support full ECMAScript syntax. For example, script syntax doesn’t support statement such as `if` control statement, `for` iteration statement and variable definition statement. Function definion is not supported too. But script syntax addes the original additional operators. They are described after ECMAScript syntax is described.

## 7.13.2.1. Security

For security reason, you should not pass an input from users to Groonga directly. If there is an evil user, the user may input a query that retrieves records that should not be shown to the user.

Think about the following case.

A Groonga application constructs a Groonga request by the following program:

    filter = "column @ \"#{user_input}\""
    select_options = {
      # ...
      :filter => filter,
    }
    groonga_client.select(select_options)

`user_input` is an input from user. If the input is `query`, here is the constructed filter parameter:

    column @ "query"

If the input is `x" || true || "`, here is the constructed filter parameter:

    column @ "x" || true || ""

This query matches to all records. The user will get all records from your database. The user may be evil.

It’s better that you just receive an user input as a value. It means that you don’t accept that user input can contain operator such as `@` and `&&`. If you accept operator, user can create evil query.

If user input has only value, you blocks evil query by escaping user input value. Here is a list how to escape user input value:

> - True value: Convert it to `true`.

> - False value: Convert it to `false`.

> - Numerical value: Convert it to Integer or Float. For example, `1.2`, `-10`, `314e-2` and so on.

> - String value: Replace `"` with `\"` and `\` with `\\` in the string value and surround substituted string value by `"`. For example, `double " quote and back \ slash` should be converted to `"double \" quote and back \\ slash"`.

## 7.13.2.2. Sample data

Here are a schema definition and sample data to show usage.

Execution example:

    table_create Entries TABLE_PAT_KEY ShortText
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Entries content COLUMN_SCALAR Text
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Entries n_likes COLUMN_SCALAR UInt32
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    table_create Terms TABLE_PAT_KEY ShortText --default_tokenizer TokenBigram --normalizer NormalizerAuto
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Terms entries_key_index COLUMN_INDEX|WITH_POSITION Entries _key
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Terms entries_content_index COLUMN_INDEX|WITH_POSITION Entries content
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    load --table Entries
    [
    {"_key":    "The first post!",
     "content": "Welcome! This is my first post!",
     "n_likes": 5},
    {"_key":    "Groonga",
     "content": "I started to use Groonga. It's very fast!",
     "n_likes": 10},
    {"_key":    "Mroonga",
     "content": "I also started to use Mroonga. It's also very fast! Really fast!",
     "n_likes": 15},
    {"_key":    "Good-bye Senna",
     "content": "I migrated all Senna system!",
     "n_likes": 3},
    {"_key":    "Good-bye Tritonn",
     "content": "I also migrated all Tritonn system!",
     "n_likes": 3}
    ]
    # [[0, 1337566253.89858, 0.000355720520019531], 5]

There is a table, `Entries`, for blog entries. An entry has title, content and the number of likes for the entry. Title is key of `Entries`. Content is value of `Entries.content` column. The number of likes is value of `Entries.n_likes` column.

`Entries._key` column and `Entries.content` column are indexed using `TokenBigram` tokenizer. So both `Entries._key` and `Entries.content` are fulltext search ready.

OK. The schema and data for examples are ready.

## 7.13.2.3. Literals

### 7.13.2.3.1. Integer

Integer literal is sequence of `0` to `9` such as `1234567890`. `+` or `-` can be prepended as sign such as `+29` and `-29`. Integer literal must be decimal. Octal notation, hex and so on can’t be used.

The maximum value of integer literal is `9223372036854775807` (`= 2 ** 63 - 1`). The minimum value of integer literal is `-9223372036854775808` (`= -(2 ** 63)`).

### 7.13.2.3.2. Float

Float literal is sequence of `0` to `9`, `.` and `0` to `9` such as `3.14`. `+` or `-` can be prepended as sign such as `+3.14` and `-3.14`. `${RADIX}e${EXPORNENTIAL}` and `${RADIX}E${EXPORNENTIAL}` formats are also supported. For example, `314e-2` is the same as `3.14`.

### 7.13.2.3.3. String

String literal is `"..."`. You need to escape `"` in literal by prepending `\\'' such as ``\"`. For example, `"Say \"Hello!\"."` is a literal for `Say "Hello!".` string.

String encoding must be the same as encoding of database. The default encoding is UTF-8. It can be changed by `--with-default-encoding` configure option, `--encodiong` groonga executable file option and so on.

### 7.13.2.3.4. Boolean

Boolean literal is `true` and `false`. `true` means true and `false` means false.

### 7.13.2.3.5. Null

Null literal is `null`. Groonga doesn’t support null value but null literal is supported.

### 7.13.2.3.6. Time

Note

This is the groonga original notation.

Time literal doesn’t exit. There are string time notation, integer time notation and float time notation.

String time notation is `"YYYY/MM/DD hh:mm:ss.uuuuuu"` or `"YYYY-MM-DD hh:mm:ss.uuuuuu"`. `YYYY` is year, `MM` is month, `DD` is day, `hh` is hour, `mm` is minute, `ss` is second and `uuuuuu` is micro second. It is local time. For example, `"2012/07/23 02:41:10.436218"` is `2012-07-23T02:41:10.436218` in ISO 8601 format.

Integer time notation is the number of seconds that have elapsed since midnight UTC, January 1, 1970. It is also known as POSIX time. For example, `1343011270` is `2012-07-23T02:41:10Z` in ISO 8601 format.

Float time notation is the number of seconds and micro seconds that have elapsed since midnight UTC, January 1, 1970. For example, `1343011270.436218` is `2012-07-23T02:41:10.436218Z` in ISO 8601 format.

### 7.13.2.3.7. Geo point

Note

This is the groonga original notation.

Geo point literal doesn’t exist. There is string geo point notation.

String geo point notation has the following patterns:

> - `"LATITUDE_IN_MSECxLONGITUDE_IN_MSEC"`

> - `"LATITUDE_IN_MSEC,LONGITUDE_IN_MSEC"`

> - `"LATITUDE_IN_DEGREExLONGITUDE_IN_DEGREE"`

> - `"LATITUDE_IN_DEGREE,LONGITUDE_IN_DEGREE"`

`x` and `,` can be used for separator. Latitude and longitude can be represented in milliseconds or degree.

### 7.13.2.3.8. Array

Array literal is `[element1, element2, ...]`.

### 7.13.2.3.9. Object literal

Object literal is `{name1: value1, name2: value2, ...}`. Groonga doesn’t support object literal yet.

## 7.13.2.4. Control syntaxes

Script syntax doesn’t support statement. So you cannot use control statement such as `if`. You can only use `A ? B : C` expression as control syntax.

`A ? B : C` returns `B` if `A` is true, `C` otherwise.

Here is a simple example.

Execution example:

    select Entries --filter 'n_likes == (_id == 1 ? 5 : 3)'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         3
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         4,
    #         "Good-bye Senna",
    #         "I migrated all Senna system!",
    #         3
    #       ],
    #       [
    #         5,
    #         "Good-bye Tritonn",
    #         "I also migrated all Tritonn system!",
    #         3
    #       ],
    #       [
    #         1,
    #         "The first post!",
    #         "Welcome! This is my first post!",
    #         5
    #       ]
    #     ]
    #   ]
    # ]

The expression matches records that `_id` column value is equal to `1` and `n_likes` column value is equal to `5` or `_id` column value is not equal to 1 and `n_likes` column value is equal to `3`.

## 7.13.2.5. Grouping

Its syntax is `(...)`. `...` is comma separated expression list.

`(...)` groups one ore more expressions and they can be processed as an expression. `a && b || c` means that `a` and `b` are matched or `c` is matched. `a && (b || c)` means that `a` and one of `b` and `c` are matched.

Here is a simple example.

Execution example:

    select Entries --filter 'n_likes < 5 && content @ "senna" || content @ "fast"'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         3
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         4,
    #         "Good-bye Senna",
    #         "I migrated all Senna system!",
    #         3
    #       ],
    #       [
    #         2,
    #         "Groonga",
    #         "I started to use Groonga. It's very fast!",
    #         10
    #       ],
    #       [
    #         3,
    #         "Mroonga",
    #         "I also started to use Mroonga. It's also very fast! Really fast!",
    #         15
    #       ]
    #     ]
    #   ]
    # ]
    select Entries --filter 'n_likes < 5 && (content @ "senna" || content @ "fast")'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         4,
    #         "Good-bye Senna",
    #         "I migrated all Senna system!",
    #         3
    #       ]
    #     ]
    #   ]
    # ]

The first expression doesn’t use grouping. It matches records that `n_likes < 5` and `content @ "senna"` are matched or `content @ "fast"` is matched.

The second expression uses grouping. It matches records that `n_likes < 5` and one of `content @ "senna"` or `content @ "fast"` are matched.

## 7.13.2.6. Function call

Its syntax is `name(arugment1, argument2, ...)`.

`name(argument1, argument2, ...)` calls a function that is named `name` with arguments `argument1`, `argument2` and `...`.

See Function for available functin list.

Here is a simple example.

Execution example:

    select Entries --filter 'edit_distance(_key, "Groonga") <= 1'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         2,
    #         "Groonga",
    #         "I started to use Groonga. It's very fast!",
    #         10
    #       ],
    #       [
    #         3,
    #         "Mroonga",
    #         "I also started to use Mroonga. It's also very fast! Really fast!",
    #         15
    #       ]
    #     ]
    #   ]
    # ]

The expression uses edit_distance. It matches records that `_key` column value is similar to `"Groonga"`. Similality of `"Groonga"` is computed as edit distance. If edit distance is less than or equal to 1, the value is treated as similar. In this case, `"Groonga"` and `"Mroonga"` are treated as similar.

## 7.13.2.7. Basic operators

Groonga supports operators defined in ECMAScript.

### 7.13.2.7.1. Arithmetic operators

Here are arithmetic operators.

#### 7.13.2.7.1.1. Addition operator

Its syntax is `number1 + number2`.

The operator adds `number1` and `number2` and returns the result.

Here is a simple example.

Execution example:

    select Entries --filter 'n_likes == 10 + 5'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         3,
    #         "Mroonga",
    #         "I also started to use Mroonga. It's also very fast! Really fast!",
    #         15
    #       ]
    #     ]
    #   ]
    # ]

The expression matches records that `n_likes` column value is equal to `15` (= `10 + 5`).

#### 7.13.2.7.1.2. Subtraction operator

Its syntax is `number1 - number2`.

The operator subtracts `number2` from `number1` and returns the result.

Here is a simple example.

Execution example:

    select Entries --filter 'n_likes == 20 - 5'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         3,
    #         "Mroonga",
    #         "I also started to use Mroonga. It's also very fast! Really fast!",
    #         15
    #       ]
    #     ]
    #   ]
    # ]

The expression matches records that `n_likes` column value is equal to `15` (= `20 - 5`).

#### 7.13.2.7.1.3. Multiplication operator

Its syntax is `number1 * number2`.

The operator multiplies `number1` and `number2` and returns the result.

Here is a simple example.

Execution example:

    select Entries --filter 'n_likes == 3 * 5'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         3,
    #         "Mroonga",
    #         "I also started to use Mroonga. It's also very fast! Really fast!",
    #         15
    #       ]
    #     ]
    #   ]
    # ]

The expression matches records that `n_likes` column value is equal to `15` (= `3 * 5`).

#### 7.13.2.7.1.4. Division operator

Its syntax is `number1 / number2` and `number1 % number2`.

The operator divides `number2` by `number1`. `/` returns the quotient of result. `%` returns the remainder of result.

Here is simple examples.

Execution example:

    select Entries --filter 'n_likes == 26 / 7'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         4,
    #         "Good-bye Senna",
    #         "I migrated all Senna system!",
    #         3
    #       ],
    #       [
    #         5,
    #         "Good-bye Tritonn",
    #         "I also migrated all Tritonn system!",
    #         3
    #       ]
    #     ]
    #   ]
    # ]

The expression matches records that `n_likes` column value is equal to `3` (= `26 / 7`).

Execution example:

    select Entries --filter 'n_likes == 26 % 7'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         1,
    #         "The first post!",
    #         "Welcome! This is my first post!",
    #         5
    #       ]
    #     ]
    #   ]
    # ]

The expression matches records that `n_likes` column value is equal to `5` (= `26 % 7`).

### 7.13.2.7.2. Logical operators

Here are logical operators.

#### 7.13.2.7.2.1. Logical NOT operator

Its syntax is `!condition`.

The operator inverts boolean value of `condition`.

Here is a simple example.

Execution example:

    select Entries --filter '!(n_likes == 5)'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         4
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         4,
    #         "Good-bye Senna",
    #         "I migrated all Senna system!",
    #         3
    #       ],
    #       [
    #         5,
    #         "Good-bye Tritonn",
    #         "I also migrated all Tritonn system!",
    #         3
    #       ],
    #       [
    #         2,
    #         "Groonga",
    #         "I started to use Groonga. It's very fast!",
    #         10
    #       ],
    #       [
    #         3,
    #         "Mroonga",
    #         "I also started to use Mroonga. It's also very fast! Really fast!",
    #         15
    #       ]
    #     ]
    #   ]
    # ]

The expression matches records that `n_likes` column value is not equal to `5`.

#### 7.13.2.7.2.2. Logical AND operator

Its syntax is `condition1 && condition2`.

The operator returns true if both of `condition1` and `condition2` are true, false otherwise.

Here is a simple example.

Execution example:

    select Entries --filter 'content @ "fast" && n_likes >= 10'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         2,
    #         "Groonga",
    #         "I started to use Groonga. It's very fast!",
    #         10
    #       ],
    #       [
    #         3,
    #         "Mroonga",
    #         "I also started to use Mroonga. It's also very fast! Really fast!",
    #         15
    #       ]
    #     ]
    #   ]
    # ]

The expression matches records that `content` column value has the word `fast` and `n_likes` column value is greater or equal to `10`.

#### 7.13.2.7.2.3. Logical OR operator

Its syntax is `condition1 || condition2`.

The operator returns true if either `condition1` or `condition2` is true, false otherwise.

Here is a simple example.

Execution example:

    select Entries --filter 'n_likes == 5 || n_likes == 10'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         1,
    #         "The first post!",
    #         "Welcome! This is my first post!",
    #         5
    #       ],
    #       [
    #         2,
    #         "Groonga",
    #         "I started to use Groonga. It's very fast!",
    #         10
    #       ]
    #     ]
    #   ]
    # ]

The expression matches records that `n_likes` column value is equal to `5` or `10`.

#### 7.13.2.7.2.4. Logical AND NOT operator

Its syntax is `condition1 &! condition2`.

The operator returns true if `condition1` is true but `condition2` is false, false otherwise. It returns difference set.

Here is a simple example.

Execution example:

    select Entries --filter 'content @ "fast" &! content @ "mroonga"'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         2,
    #         "Groonga",
    #         "I started to use Groonga. It's very fast!",
    #         10
    #       ]
    #     ]
    #   ]
    # ]

The expression matches records that `content` column value has the word `fast` but doesn’t have the word `mroonga`.

### 7.13.2.7.3. Bitwise operators

Here are bitwise operators.

#### 7.13.2.7.3.1. Bitwise NOT operator

Its syntax is `~number`.

The operator returns bitwise NOT of `number`.

Here is a simple example.

Execution example:

    select Entries --filter '~n_likes == -6'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         1,
    #         "The first post!",
    #         "Welcome! This is my first post!",
    #         5
    #       ]
    #     ]
    #   ]
    # ]

The expression matches records that `n_likes` column value is equal to `5` because bitwise NOT of `5` is equal to `-6`.

#### 7.13.2.7.3.2. Bitwise AND operator

Its syntax is `number1 & number2`.

The operator returns bitwise AND between `number1` and `number2`.

Here is a simple example.

Execution example:

    select Entries --filter '(n_likes & 1) == 1'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         4
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         4,
    #         "Good-bye Senna",
    #         "I migrated all Senna system!",
    #         3
    #       ],
    #       [
    #         5,
    #         "Good-bye Tritonn",
    #         "I also migrated all Tritonn system!",
    #         3
    #       ],
    #       [
    #         3,
    #         "Mroonga",
    #         "I also started to use Mroonga. It's also very fast! Really fast!",
    #         15
    #       ],
    #       [
    #         1,
    #         "The first post!",
    #         "Welcome! This is my first post!",
    #         5
    #       ]
    #     ]
    #   ]
    # ]

The expression matches records that `n_likes` column value is even number because bitwise AND between an even number and `1` is equal to `1` and bitwise AND between an odd number and `1` is equal to `0`.

### 7.13.2.7.4. Bitwise OR operator

Its syntax is `number1 | number2`.

The operator returns bitwise OR between `number1` and `number2`.

Here is a simple example.

Execution example:

    select Entries --filter 'n_likes == (1 | 4)'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         1,
    #         "The first post!",
    #         "Welcome! This is my first post!",
    #         5
    #       ]
    #     ]
    #   ]
    # ]

The expression matches records that `n_likes` column value is equal to `5` (= `1 | 4`).

### 7.13.2.7.5. Bitwise XOR operator

Its syntax is `number1 ^ number2`.

The operator returns bitwise XOR between `number1` and `number2`.

Here is a simple example.

Execution example:

    select Entries --filter 'n_likes == (10 ^ 15)'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         1,
    #         "The first post!",
    #         "Welcome! This is my first post!",
    #         5
    #       ]
    #     ]
    #   ]
    # ]

The expression matches records that `n_likes` column value is equal to `5` (= `10 ^ 15`).

### 7.13.2.7.6. Shift operators

Here are shift operators.

#### 7.13.2.7.6.1. Left shift operator

Its syntax is `number1 << number2`.

The operator performs a bitwise left shift operation on `number1` by `number2`.

Here is a simple example.

Execution example:

    select Entries --filter 'n_likes == (5 << 1)'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         2,
    #         "Groonga",
    #         "I started to use Groonga. It's very fast!",
    #         10
    #       ]
    #     ]
    #   ]
    # ]

The expression matches records that `n_likes` column value is equal to `10` (= `5 << 1`).

#### 7.13.2.7.6.2. Signed right shift operator

Its syntax is `number1 >> number2`.

The operator shifts bits of `number1` to right by `number2`. The sign of the result is the same as `number1`.

Here is a simple example.

Execution example:

    select Entries --filter 'n_likes == -(-10 >> 1)'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         1,
    #         "The first post!",
    #         "Welcome! This is my first post!",
    #         5
    #       ]
    #     ]
    #   ]
    # ]

The expression matches records that `n_likes` column value is equal to `5` (= `-(-10 >> 1)` = `-(-5)`).

#### 7.13.2.7.6.3. Unsigned right shift operator

Its syntax is `number1 >>> number2`.

The operator shifts bits of `number1` to right by `number2`. The leftmost `number2` bits are filled by `0`.

Here is a simple example.

Execution example:

    select Entries --filter 'n_likes == (2147483648 - (-10 >>> 1))'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         1,
    #         "The first post!",
    #         "Welcome! This is my first post!",
    #         5
    #       ]
    #     ]
    #   ]
    # ]

The expression matches records that `n_likes` column value is equal to `5` (= `2147483648 - (-10 >>> 1)` = `2147483648 - 2147483643`).

### 7.13.2.7.7. Comparison operators

Here are comparison operators.

#### 7.13.2.7.7.1. Equal operator

Its syntax is `object1 == object2`.

The operator returns true if `object1` equals to `object2`, false otherwise.

Here is a simple example.

Execution example:

    select Entries --filter 'n_likes == 5'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         1,
    #         "The first post!",
    #         "Welcome! This is my first post!",
    #         5
    #       ]
    #     ]
    #   ]
    # ]

The expression matches records that `n_likes` column value is equal to `5`.

#### 7.13.2.7.7.2. Not equal operator

Its syntax is `object1 != object2`.

The operator returns true if `object1` does not equal to `object2`, false otherwise.

Here is a simple example.

Execution example:

    select Entries --filter 'n_likes != 5'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         4
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         4,
    #         "Good-bye Senna",
    #         "I migrated all Senna system!",
    #         3
    #       ],
    #       [
    #         5,
    #         "Good-bye Tritonn",
    #         "I also migrated all Tritonn system!",
    #         3
    #       ],
    #       [
    #         2,
    #         "Groonga",
    #         "I started to use Groonga. It's very fast!",
    #         10
    #       ],
    #       [
    #         3,
    #         "Mroonga",
    #         "I also started to use Mroonga. It's also very fast! Really fast!",
    #         15
    #       ]
    #     ]
    #   ]
    # ]

The expression matches records that `n_likes` column value is not equal to `5`.

#### 7.13.2.7.7.3. Less than operator

TODO: …

#### 7.13.2.7.7.4. Less than or equal to operator

TODO: …

#### 7.13.2.7.7.5. Greater than operator

TODO: …

#### 7.13.2.7.7.6. Greater than or equal to operator

TODO: …

## 7.13.2.8. Assignment operators

### 7.13.2.8.1. Addition assignment operator

Its syntax is `column1 += column2`.

The operator performs addition assignment operation on column1 by column2.

Execution example:

    select Entries --output_columns _key,n_likes,_score --filter true --scorer '_score += n_likes'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "Good-bye Senna",
    #         3,
    #         4
    #       ],
    #       [
    #         "Good-bye Tritonn",
    #         3,
    #         4
    #       ],
    #       [
    #         "Groonga",
    #         10,
    #         11
    #       ],
    #       [
    #         "Mroonga",
    #         15,
    #         16
    #       ],
    #       [
    #         "The first post!",
    #         5,
    #         6
    #       ]
    #     ]
    #   ]
    # ]

The value of `_score` by `--filter` is always 1 in this case, then performs addition assignment operation such as ‘_score = _score + n_likes’ for each records.

For example, the value of `_score` about the record which stores “Good-bye Senna” as the `_key` is 3.

So the expression `1 + 3` is evaluated and stored to `_score` column as the execution result.

### 7.13.2.8.2. Subtraction assignment operator

Its syntax is `column1 -= column2`.

The operator performs subtraction assignment operation on column1 by column2.

Execution example:

    select Entries --output_columns _key,n_likes,_score --filter true --scorer '_score -= n_likes'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "Good-bye Senna",
    #         3,
    #         -2
    #       ],
    #       [
    #         "Good-bye Tritonn",
    #         3,
    #         -2
    #       ],
    #       [
    #         "Groonga",
    #         10,
    #         -9
    #       ],
    #       [
    #         "Mroonga",
    #         15,
    #         -14
    #       ],
    #       [
    #         "The first post!",
    #         5,
    #         -4
    #       ]
    #     ]
    #   ]
    # ]

The value of `_score` by `--filter` is always 1 in this case, then performs subtraction assignment operation such as ‘_score = _score - n_likes’ for each records.

For example, the value of `_score` about the record which stores “Good-bye Senna” as the `_key` is 3.

So the expression `1 - 3` is evaluated and stored to `_score` column as the execution result.

### 7.13.2.8.3. Multiplication assignment operator

Its syntax is `column1 *= column2`.

The operator performs multiplication assignment operation on column1 by column2.

Execution example:

    select Entries --output_columns _key,n_likes,_score --filter true --scorer '_score *= n_likes'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "Good-bye Senna",
    #         3,
    #         3
    #       ],
    #       [
    #         "Good-bye Tritonn",
    #         3,
    #         3
    #       ],
    #       [
    #         "Groonga",
    #         10,
    #         10
    #       ],
    #       [
    #         "Mroonga",
    #         15,
    #         15
    #       ],
    #       [
    #         "The first post!",
    #         5,
    #         5
    #       ]
    #     ]
    #   ]
    # ]

The value of `_score` by `--filter` is always 1 in this case, then performs subtraction assignment operation such as ‘_score = _score * n_likes’ for each records.

For example, the value of `_score` about the record which stores “Good-bye Senna” as the `_key` is 3.

So the expression `1 * 3` is evaluated and stored to `_score` column as the execution result.

### 7.13.2.8.4. Division assignment operator

Its syntax is `column1 /= column2`.

The operator performs division assignment operation on column1 by column2.

Execution example:

    select Entries --output_columns _key,n_likes,_score --filter true --scorer '_score /= n_likes'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "Good-bye Senna",
    #         3,
    #         0
    #       ],
    #       [
    #         "Good-bye Tritonn",
    #         3,
    #         0
    #       ],
    #       [
    #         "Groonga",
    #         10,
    #         0
    #       ],
    #       [
    #         "Mroonga",
    #         15,
    #         0
    #       ],
    #       [
    #         "The first post!",
    #         5,
    #         0
    #       ]
    #     ]
    #   ]
    # ]

The value of `_score` by `--filter` is always 1 in this case, then performs subtraction assignment operation such as ‘_score = _score / n_likes’ for each records.

For example, the value of `_score` about the record which stores “Good-bye Senna” as the `_key` is 3.

So the expression `1 / 3` is evaluated and stored to `_score` column as the execution result.

### 7.13.2.8.5. Modulo assignment operator

Its syntax is `column1 %= column2`.

The operator performs modulo assignment operation on column1 by column2.

Execution example:

    select Entries --output_columns _key,n_likes,_score --filter true --scorer '_score %= n_likes'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "Good-bye Senna",
    #         3,
    #         1
    #       ],
    #       [
    #         "Good-bye Tritonn",
    #         3,
    #         1
    #       ],
    #       [
    #         "Groonga",
    #         10,
    #         1
    #       ],
    #       [
    #         "Mroonga",
    #         15,
    #         1
    #       ],
    #       [
    #         "The first post!",
    #         5,
    #         1
    #       ]
    #     ]
    #   ]
    # ]

The value of `_score` by `--filter` is always 1 in this case, then performs subtraction assignment operation such as ‘_score = _score % n_likes’ for each records.

For example, the value of `_score` about the record which stores “Good-bye Senna” as the `_key` is 3.

So the expression `1 % 3` is evaluated and stored to `_score` column as the execution result.

### 7.13.2.8.6. Bitwise left shift assignment operator

Its syntax is `column1 <<= column2`.

The operator performs left shift assignment operation on column1 by column2.

Execution example:

    select Entries --output_columns _key,n_likes,_score --filter true --scorer '_score <<= n_likes'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "Good-bye Senna",
    #         3,
    #         8
    #       ],
    #       [
    #         "Good-bye Tritonn",
    #         3,
    #         8
    #       ],
    #       [
    #         "Groonga",
    #         10,
    #         1024
    #       ],
    #       [
    #         "Mroonga",
    #         15,
    #         32768
    #       ],
    #       [
    #         "The first post!",
    #         5,
    #         32
    #       ]
    #     ]
    #   ]
    # ]

The value of `_score` by `--filter` is always 1 in this case, then performs subtraction assignment operation such as ‘_score = _score << n_likes’ for each records.

For example, the value of `_score` about the record which stores “Good-bye Senna” as the `_key` is 3.

So the expression `1 << 3` is evaluated and stored to `_score` column as the execution result.

### 7.13.2.8.7. Bitwise signed right shift assignment operator

Its syntax is `column2 >>= column2`.

The operator performs signed right shift assignment operation on column1 by column2.

### 7.13.2.8.8. Bitwise unsigned right shift assignment operator

Its syntax is `column1 >>>= column2`.

The operator performs unsigned right shift assignment operation on column1 by column2.

### 7.13.2.8.9. Bitwise AND assignment operator

Its syntax is `column1 &= column2`.

The operator performs bitwise AND assignment operation on column1 by column2.

Execution example:

    select Entries --output_columns _key,n_likes,_score --filter true --scorer '_score &= n_likes'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "Good-bye Senna",
    #         3,
    #         1
    #       ],
    #       [
    #         "Good-bye Tritonn",
    #         3,
    #         1
    #       ],
    #       [
    #         "Groonga",
    #         10,
    #         0
    #       ],
    #       [
    #         "Mroonga",
    #         15,
    #         1
    #       ],
    #       [
    #         "The first post!",
    #         5,
    #         1
    #       ]
    #     ]
    #   ]
    # ]

The value of `_score` by `--filter` is always 1 in this case, then performs subtraction assignment operation such as ‘_score = _score & n_likes’ for each records.

For example, the value of `_score` about the record which stores “Groonga” as the `_key` is 10.

So the expression `1 & 10` is evaluated and stored to `_score` column as the execution result.

### 7.13.2.8.10. Bitwise OR assignment operator

Its syntax is `column1 |= column2`.

The operator performs bitwise OR assignment operation on column1 by column2.

Execution example:

    select Entries --output_columns _key,n_likes,_score --filter true --scorer '_score |= n_likes'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "Good-bye Senna",
    #         3,
    #         3
    #       ],
    #       [
    #         "Good-bye Tritonn",
    #         3,
    #         3
    #       ],
    #       [
    #         "Groonga",
    #         10,
    #         11
    #       ],
    #       [
    #         "Mroonga",
    #         15,
    #         15
    #       ],
    #       [
    #         "The first post!",
    #         5,
    #         5
    #       ]
    #     ]
    #   ]
    # ]

The value of `_score` by `--filter` is always 1 in this case, then performs subtraction assignment operation such as ‘_score = _score | n_likes’ for each records.

For example, the value of `_score` about the record which stores “Groonga” as the `_key` is 10.

So the expression `1 | 10` is evaluated and stored to `_score` column as the execution result.

### 7.13.2.8.11. Bitwise XOR assignment operator

Its syntax is `column1 ^= column2`.

The operator performs bitwise XOR assignment operation on column1 by column2.

Execution example:

    select Entries --output_columns _key,n_likes,_score --filter true --scorer '_score ^= n_likes'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "Good-bye Senna",
    #         3,
    #         2
    #       ],
    #       [
    #         "Good-bye Tritonn",
    #         3,
    #         2
    #       ],
    #       [
    #         "Groonga",
    #         10,
    #         11
    #       ],
    #       [
    #         "Mroonga",
    #         15,
    #         14
    #       ],
    #       [
    #         "The first post!",
    #         5,
    #         4
    #       ]
    #     ]
    #   ]
    # ]

The value of `_score` by `--filter` is always 1 in this case, then performs subtraction assignment operation such as ‘_score = _score ^ n_likes’ for each records.

For example, the value of `_score` about the record which stores “Good-bye Senna” as the `_key` is 3.

So the expression `1 ^ 3` is evaluated and stored to `_score` column as the execution result.

## 7.13.2.9. Original operators

Script syntax adds the original binary opearators to ECMAScript syntax. They operate search specific operations. They are starts with `@` or `*`.

### 7.13.2.9.1. Match operator

Its syntax is `column @ value`.

The operator searches `value` by inverted index of `column`. Normally, full text search is operated but tag search can be operated. Because tag search is also implemented by inverted index.

Query syntax uses this operator by default.

Here is a simple example.

Execution example:

    select Entries --filter 'content @ "fast"' --output_columns content
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "content",
    #           "Text"
    #         ]
    #       ],
    #       [
    #         "I started to use Groonga. It's very fast!"
    #       ],
    #       [
    #         "I also started to use Mroonga. It's also very fast! Really fast!"
    #       ]
    #     ]
    #   ]
    # ]

The expression matches records that contain a word `fast` in `content` column value.

### 7.13.2.9.2. Prefix search operator

Its syntax is `column @^ value`.

The operator does prefix search with `value`. Prefix search searches records that contain a word that starts with `value`.

You can use fast prefix search against a column. The column must be indexed and index table must be patricia trie table (`TABLE_PAT_KEY`) or double array trie table (`TABLE_DAT_KEY`). You can also use fast prefix search against `_key` pseudo column of patricia trie table or double array trie table. You don’t need to index `_key`.

Prefix search can be used with other table types but it causes all records scan. It’s not problem for small records but it spends more time for large records.

Here is a simple example.

Execution example:

    select Entries --filter '_key @^ "Goo"' --output_columns _key
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         "Good-bye Senna"
    #       ],
    #       [
    #         "Good-bye Tritonn"
    #       ]
    #     ]
    #   ]
    # ]

The expression matches records that contain a word that starts with `Goo` in `_key` pseudo column value. `Good-bye Senna` and `Good-bye Tritonn` are matched with the expression.

### 7.13.2.9.3. Suffix search operator

Its syntax is `column @$ value`.

This operator does suffix search with `value`. Suffix search searches records that contain a word that ends with `value`.

You can use fast suffix search against a column. The column must be indexed and index table must be patricia trie table (`TABLE_PAT_KEY`) with `KEY_WITH_SIS` flag. You can also use fast suffix search against `_key` pseudo column of patricia trie table (`TABLE_PAT_KEY`) with `KEY_WITH_SIS` flag. You don’t need to index `_key`. We recommended that you use index column based fast suffix search instead of `_key` based fast suffix search. `_key` based fast suffix search returns automatically registered substrings. (TODO: write document about suffix search and link to it from here.)

Note

Fast suffix search can be used only for non-ASCII characters such as hiragana in Japanese. You cannot use fast suffix search for ASCII character.

Suffix search can be used with other table types or patricia trie table without `KEY_WITH_SIS` flag but it causes all records scan. It’s not problem for small records but it spends more time for large records.

Here is a simple example. It uses fast suffix search for hiragana in Japanese that is one of non-ASCII characters.

Execution example:

    table_create Titles TABLE_NO_KEY
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Titles content COLUMN_SCALAR ShortText
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    table_create SuffixSearchTerms TABLE_PAT_KEY|KEY_WITH_SIS ShortText
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create SuffixSearchTerms index COLUMN_INDEX Titles content
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    load --table Titles
    [
    {"content": "ぐるんが"},
    {"content": "むるんが"},
    {"content": "せな"},
    {"content": "とりとん"}
    ]
    # [[0, 1337566253.89858, 0.000355720520019531], 4]
    select Titles --query 'content:$んが'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "content",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         2,
    #         "むるんが"
    #       ],
    #       [
    #         1,
    #         "ぐるんが"
    #       ]
    #     ]
    #   ]
    # ]

The expression matches records that have value that ends with `んが` in `content` column value. `ぐるんが` and `むるんが` are matched with the expression.

### 7.13.2.9.4. Near search operator

Its syntax is one of them:

    column *N "word1 word2 ..."
    column *N${MAX_INTERVAL} "word1 word2 ..."

Here are the examples of the second form:

    column *N29 "word1 word2 ..."
    column *N-1 "word1 word2 ..."

The first example means that `29` is used for the max interval.

The second example means that `-1` is used for the max interval. `-1` max interval means no limit.

The operator does near search with words `word1 word2 ...`. Near search searches records that contain the words and the words are appeared in the specified order and the max interval.

The max interval is `10` by default. The unit of the max interval is the number of characters in N-gram family tokenizers and the number of words in morphological analysis family tokenizers.

However, `TokenBigram` doesn’t split ASCII only word into tokens. Because `TokenBigram` uses white-space-separate like tokenize method for ASCII characters in this case.

So the unit for ASCII words with `TokenBigram` is the number of words even if `TokenBigram` is a N-gram family tokenizer.

Note that an index column for full text search must be defined for `column`.

Here is a simple example.

Execution example:

    select Entries --filter 'content *N "I fast"'      --output_columns content
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "content",
    #           "Text"
    #         ]
    #       ],
    #       [
    #         "I started to use Groonga. It's very fast!"
    #       ]
    #     ]
    #   ]
    # ]
    select Entries --filter 'content *N "I Really"'    --output_columns content
    # [[0, 1337566253.89858, 0.000355720520019531], [[[0], [["content", "Text"]]]]]
    select Entries --filter 'content *N "also Really"' --output_columns content
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "content",
    #           "Text"
    #         ]
    #       ],
    #       [
    #         "I also started to use Mroonga. It's also very fast! Really fast!"
    #       ]
    #     ]
    #   ]
    # ]

The first expression matches records that contain `I` and `fast` and the max interval of those words are in 10 words. So the record that its content is `I also started to use mroonga. It's also very fast! ...` is matched. The number of words between `I` and `fast` is just 10.

The second expression matches records that contain `I` and `Really` and the max interval of those words are in 10 words. So the record that its content is `I also started to use mroonga. It's also very fast! Really fast!` is not matched. The number of words between `I` and `Really` is 11.

The third expression matches records that contain `also` and `Really` and the max interval of those words are in 10 words. So the record that its content is `I also st arted to use mroonga. It's also very fast! Really fast!` is matched. The number of words between `also` and `Really` is 10.

### 7.13.2.9.5. Near phrase search operator

Its syntax is one of them:

    column *NP "phrase1 phrase2 ..."
    column *NP${MAX_INTERVAL} "phrase1 phrase2 ..."

Here are the examples of the second form:

    column *NP29 "phrase1 phrase2 ..."
    column *NP-1 "phrase1 phrase2 ..."

The first example means that `29` is used for the max interval.

The second example means that `-1` is used for the max interval. `-1` max interval means no limit.

The operator does near phrase search with phrases `phrase1 phrase2 ...`. Near phrase search searches records that contain the phrases and the phrases are appeared in the specified order and the max interval.

The max interval is `10` by default. The unit of the max interval is the number of characters in N-gram family tokenizers and the number of words in morphological analysis family tokenizers.

However, `TokenBigram` doesn’t split ASCII only word into tokens. Because `TokenBigram` uses white-space-separate like tokenize method for ASCII characters in this case.

So the unit for ASCII words with `TokenBigram` is the number of words even if `TokenBigram` is a N-gram family tokenizer.

Note that an index column for full text search must be defined for `column`.

TODO: Use index that has `TokenNgram("unify_alphabet", false)` tokenizer to show difference with near search with English text.

Here is a simple example.

Execution example:

    select Entries --filter 'content *NP "I fast"'      --output_columns content
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "content",
    #           "Text"
    #         ]
    #       ],
    #       [
    #         "I started to use Groonga. It's very fast!"
    #       ]
    #     ]
    #   ]
    # ]
    select Entries --filter 'content *NP "I Really"'    --output_columns content
    # [[0, 1337566253.89858, 0.000355720520019531], [[[0], [["content", "Text"]]]]]
    select Entries --filter 'content *NP "also Really"' --output_columns content
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "content",
    #           "Text"
    #         ]
    #       ],
    #       [
    #         "I also started to use Mroonga. It's also very fast! Really fast!"
    #       ]
    #     ]
    #   ]
    # ]

The first expression matches records that contain `I` and `fast` and the max interval of those words are in 10 words. So the record that its content is `I also started to use mroonga. It's also very fast! ...` is matched. The number of words between `I` and `fast` is just 10.

The second expression matches records that contain `I` and `Really` and the max interval of those words are in 10 words. So the record that its content is `I also started to use mroonga. It's also very fast! Really fast!` is not matched. The number of words between `I` and `Really` is 11.

The third expression matches records that contain `also` and `Really` and the max interval of those words are in 10 words. So the record that its content is `I also st arted to use mroonga. It's also very fast! Really fast!` is matched. The number of words between `also` and `Really` is 10.

### 7.13.2.9.6. Similar search

Its syntax is `column *S "document"`.

The operator does similar search with document `document`. Similar search searches records that have similar content to `document`.

Note that an index column for full text search must be defined for `column`.

Here is a simple example.

Execution example:

    select Entries --filter 'content *S "I migrated all Solr system!"' --output_columns content
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "content",
    #           "Text"
    #         ]
    #       ],
    #       [
    #         "I migrated all Senna system!"
    #       ],
    #       [
    #         "I also migrated all Tritonn system!"
    #       ]
    #     ]
    #   ]
    # ]

The expression matches records that have similar content to `I migrated all Solr system!`. In this case, records that have `I migrated all XXX system!` content are matched.

You should use `TokenMecab` tokenizer for similar search against Japanese documents. Because `TokenMecab` will tokenize target documents to almost words, it improves similar search precision.

### 7.13.2.9.7. Term extract operator

Its syntax is `_key *T "document"`.

The operator extracts terms from `document`. Terms must be registered as keys of the table of `_key`.

Note that the table must be patricia trie (`TABLE_PAT_KEY`) or double array trie (`TABLE_DAT_KEY`). You can’t use hash table (`TABLE_HASH_KEY`) and array (`TABLE_NO_KEY`) because they don’t support longest common prefix search. Longest common prefix search is used to implement the operator.

Here is a simple example.

Execution example:

    table_create Words TABLE_PAT_KEY ShortText --normalizer NormalizerAuto
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    load --table Words
    [
    {"_key": "groonga"},
    {"_key": "mroonga"},
    {"_key": "Senna"},
    {"_key": "Tritonn"}
    ]
    # [[0, 1337566253.89858, 0.000355720520019531], 4]
    select Words --filter '_key *T "Groonga is the successor project to Senna."' --output_columns _key
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         "groonga"
    #       ],
    #       [
    #         "senna"
    #       ]
    #     ]
    #   ]
    # ]

The expression extrcts terms that included in document `Groonga is the successor project to Senna.`. In this case, `NormalizerAuto` normalizer is specified to `Words`. So `Groonga` can be extracted even if it is loaded as `groonga` into `Words`. All of extracted terms are also normalized.

### 7.13.2.9.8. Regular expression operator

New in version 5.0.1.

Its syntax is `column @~ "pattern"`.

The operator searches records by the regular expression `pattern`. If a record’s `column` value is matched to `pattern`, the record is matched.

`pattern` must be valid regular expression syntax. See Regular expression about regular expression syntax details.

The following example uses `.roonga` as pattern. It matches `Groonga`, `Mroonga` and so on.

Execution example:

    select Entries --filter 'content @~ ".roonga"'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         2,
    #         "Groonga",
    #         "I started to use Groonga. It's very fast!",
    #         10
    #       ],
    #       [
    #         3,
    #         "Mroonga",
    #         "I also started to use Mroonga. It's also very fast! Really fast!",
    #         15
    #       ]
    #     ]
    #   ]
    # ]

In most cases, regular expression is evaluated sequentially. So it may be slow against many records.

In some cases, Groonga evaluates regular expression by index. It’s very fast. See Regular expression for details.