7.3.58. select — Groonga v16.0.5 documentation

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# 7.3.58. `select`

## 7.3.58.1. Summary

`select` searches records that are matched to specified conditions from a table and then outputs them.

`select` is the most important command in groonga. You need to understand `select` to use the full power of Groonga.

## 7.3.58.2. Syntax

This command takes many parameters.

The required parameter is only `table`. Other parameters are optional:

    select table
           [match_columns=null]
           [query=null]
           [filter=null]
           [scorer=null]
           [sortby=null]
           [output_columns="_id, _key, *"]
           [offset=0]
           [limit=10]
           [drilldown=null]
           [drilldown_sortby=null]
           [drilldown_output_columns="_key, _nsubrecs"]
           [drilldown_offset=0]
           [drilldown_limit=10]
           [cache=yes]
           [match_escalation_threshold=0]
           [query_expansion=null]
           [query_flags=ALLOW_PRAGMA|ALLOW_COLUMN]
           [query_expander=null]
           [adjuster=null]
           [drilldown_calc_types=NONE]
           [drilldown_calc_target=null]
           [drilldown_filter=null]
           [sort_keys=null]
           [drilldown_sort_keys=null]
           [match_escalation=auto]
           [load_table=null]
           [load_columns=null]
           [load_values=null]
           [drilldown_max_n_target_records=-1]
           [n_workers=0]
           [fuzzy_max_distance_ratio=0]
           [fuzzy_max_distance=0]
           [fuzzy_max_expansions=10]
           [fuzzy_prefix_length=0]
           [fuzzy_with_transposition=yes]
           [fuzzy_tokenize=no]

This command has the following named parameters for dynamic columns:

> - `columns[${NAME}].stage=null`

> - `columns[${NAME}].flags=COLUMN_SCALAR`

> - `columns[${NAME}].type=null`

> - `columns[${NAME}].value=null`

> - `columns[${NAME}].window.sort_keys=null`

> - `columns[${NAME}].window.group_keys=null`

You can use one or more alphabets, digits, `_` for `${NAME}`. For example, `column1` is a valid `${NAME}`. This is the same rule as normal column. See also name.

Parameters that have the same `${NAME}` are grouped.

For example, the following parameters specify one dynamic column:

> - `--columns[name].stage initial`

> - `--columns[name].type UInt32`

> - `--columns[name].value 29`

The following parameters specify two dynamic columns:

> - `--columns[name1].stage initial`

> - `--columns[name1].type UInt32`

> - `--columns[name1].value 29`

> - `--columns[name2].stage filtered`

> - `--columns[name2].type Float`

> - `--columns[name2].value '_score * 0.1'`

This command has the following named parameters for advanced drilldown:

> - `drilldowns[${LABEL}].keys=null`

> - `drilldowns[${LABEL}].sort_keys=null`

> - `drilldowns[${LABEL}].output_columns="_key, _nsubrecs"`

> - `drilldowns[${LABEL}].offset=0`

> - `drilldowns[${LABEL}].limit=10`

> - `drilldowns[${LABEL}].calc_types=NONE`

> - `drilldowns[${LABEL}].calc_target=null`

> - `drilldowns[${LABEL}].filter=null`

> - `drilldowns[${LABEL}].max_n_target_records=-1`

> - `drilldowns[${LABEL}].columns[${NAME}].stage=null`

> - `drilldowns[${LABEL}].columns[${NAME}].flags=COLUMN_SCALAR`

> - `drilldowns[${LABEL}].columns[${NAME}].type=null`

> - `drilldowns[${LABEL}].columns[${NAME}].value=null`

> - `drilldowns[${LABEL}].columns[${NAME}].window.sort_keys=null`

> - `drilldowns[${LABEL}].columns[${NAME}].window.group_keys=null`

Deprecated since version 6.0.3: `drilldown[...]` syntax is deprecated, Use `drilldowns[...]` instead.

You can use one or more alphabets, digits, `_` and `.` for `${LABEL}`. For example, `parent.sub1` is a valid `${LABEL}`.

Parameters that have the same `${LABEL}` are grouped.

For example, the following parameters specify one drilldown:

> - `--drilldowns[label].keys column`

> - `--drilldowns[label].sort_keys -_nsubrecs`

The following parameters specify two drilldowns:

> - `--drilldowns[label1].keys column1`

> - `--drilldowns[label1].sort_keys -_nsubrecs`

> - `--drilldowns[label2].keys column2`

> - `--drilldowns[label2].sort_keys _key`

## 7.3.58.3. Usage

Let’s learn about `select` usage with examples. This section shows many popular usages.

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

Execution example:

    table_create Entries TABLE_HASH_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]
    column_create Entries tag COLUMN_SCALAR ShortText
    # [[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,
     "tag": "Hello"},
    {"_key":    "Groonga",
     "content": "I started to use Groonga. It's very fast!",
     "n_likes": 10,
     "tag": "Groonga"},
    {"_key":    "Mroonga",
     "content": "I also started to use Mroonga. It's also very fast! Really fast!",
     "n_likes": 15,
     "tag": "Groonga"},
    {"_key":    "Good-bye Senna",
     "content": "I migrated all Senna system!",
     "n_likes": 3,
     "tag": "Senna"},
    {"_key":    "Good-bye Tritonn",
     "content": "I also migrated all Tritonn system!",
     "n_likes": 3,
     "tag": "Senna"}
    ]
    # [[0,1337566253.89858,0.000355720520019531],5]

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

### 7.3.58.3.1. Simple usage

Here is the most simple usage with the above schema and data. It outputs all records in `Entries` table.

Execution example:

    select Entries
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "tag",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         1,
    #         "The first post!",
    #         "Welcome! This is my first post!",
    #         5,
    #         "Hello"
    #       ],
    #       [
    #         2,
    #         "Groonga",
    #         "I started to use Groonga. It's very fast!",
    #         10,
    #         "Groonga"
    #       ],
    #       [
    #         3,
    #         "Mroonga",
    #         "I also started to use Mroonga. It's also very fast! Really fast!",
    #         15,
    #         "Groonga"
    #       ],
    #       [
    #         4,
    #         "Good-bye Senna",
    #         "I migrated all Senna system!",
    #         3,
    #         "Senna"
    #       ],
    #       [
    #         5,
    #         "Good-bye Tritonn",
    #         "I also migrated all Tritonn system!",
    #         3,
    #         "Senna"
    #       ]
    #     ]
    #   ]
    # ]

Why does the command output all records? There are two reasons. The first reason is that the command doesn’t specify any search conditions. No search condition means all records are matched. The second reason is that the number of all records is 5. `select` command outputs 10 records at a maximum by default. There are only 5 records. It is less than 10. So the command outputs all records.

### 7.3.58.3.2. Search conditions

Search conditions are specified by `query` or `filter`. You can also specify both `query` and `filter`. It means that selected records must be matched against both `query` and `filter`.

#### 7.3.58.3.2.1. Search condition: `query`

`query` is designed for search box in Web page. Imagine a search box in google.com. You specify search conditions for `query` as space separated keywords. For example, `search engine` means a matched record should contain two words, `search` and `engine`.

Normally, `query` parameter is used for specifying fulltext search conditions. It can be used for non fulltext search conditions but `filter` is used for the propose.

`query` parameter is used with `match_columns` parameter when `query` parameter is used for specifying fulltext search conditions. `match_columns` specifies which columns and indexes are matched against `query`.

Here is a simple `query` usage example.

Execution example:

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

The `select` command searches records that contain a word `fast` in `content` column value from `Entries` table.

`query` has query syntax but its details aren’t described here. See Query syntax for details.

#### 7.3.58.3.2.2. Search condition: `filter`

`filter` is designed for complex search conditions. You specify search conditions for `filter` as ECMAScript like syntax.

Here is a simple `filter` usage example.

Execution example:

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

The `select` command searches records that contain a word `fast` in `content` column value and has `Groonga` as `_key` from `Entries` table. There are three operators in the command, `@`, `&&` and `==`. `@` is fulltext search operator. `&&` and `==` are the same as ECMAScript. `&&` is logical AND operator and `==` is equality operator.

`filter` has more operators and syntax like grouping by `(...)` its details aren’t described here. See Script syntax for details.

### 7.3.58.3.3. Paging

You can specify range of outputted records by `offset` and `limit`. Here is an example to output only the 2nd record.

Execution example:

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

`offset` is zero-based. `--offset 1` means output range is started from the 2nd record.

`limit` specifies the max number of output records. `--limit 1` means the number of output records is 1 at a maximum. If no records are matched, `select` command outputs no records.

### 7.3.58.3.4. The total number of records

You can use `--limit 0` to retrieve the total number of records without any contents of records.

Execution example:

    select Entries --limit 0
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "tag",
    #           "ShortText"
    #         ]
    #       ]
    #     ]
    #   ]
    # ]

`--limit 0` is also useful for retrieving only the number of matched records.

### 7.3.58.3.5. Drilldown

You can get additional grouped results against the search result in one `select`. You need to use two or more `SELECT` s in SQL but `select` in Groonga can do it in one `select`.

This feature is called as drilldown in Groonga. It’s also called as faceted search in other search engine.

For example, think about the following situation.

You search entries that has `fast` word:

Execution example:

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

You want to use `tag` for additional search condition like `--filter 'content @ "fast" && tag == "???"`. But you don’t know suitable tag until you see the result of `content @ "fast"`.

If you know the number of matched records of each available tag, you can choose suitable tag. You can use drilldown for the case:

Execution example:

    select Entries --filter 'content @ "fast"' --drilldown tag
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "tag",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         2,
    #         "Groonga",
    #         "I started to use Groonga. It's very fast!",
    #         10,
    #         "Groonga"
    #       ],
    #       [
    #         3,
    #         "Mroonga",
    #         "I also started to use Mroonga. It's also very fast! Really fast!",
    #         15,
    #         "Groonga"
    #       ]
    #     ],
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "_nsubrecs",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "Groonga",
    #         2
    #       ]
    #     ]
    #   ]
    # ]

`--drilldown tag` returns a list of pair of available tag and the number of matched records. You can avoid “no hit search” case by choosing a tag from the list. You can also avoid “too many search results” case by choosing a tag that the number of matched records is few from the list.

You can create the following UI with the drilldown results:

> - Links to narrow search results. (Users don’t need to input a search query by their keyboard. They just click a link.)

Most EC sites use the UI. See side menu at Amazon.

Groonga supports not only counting grouped records but also finding the maximum and/or minimum value from grouped records, summing values in grouped records and so on. See Drilldown related parameters for details.

### 7.3.58.3.6. Dynamic column

You can create zero or more columns dynamically while a `select` execution. You can use them for drilldown by computed value, window function and so on.

Here is an example that uses dynamic column for drilldown by computed value. This example creates a new column named `n_likes_class`. `n_likes_class` column has classified value of `Entry.n_likes` value. This example classifies `Entry.n_likes` column value `10` step and the lowest number in the class is the classified value. If a `Entry.n_likes` value is between `0` and `9` such as `3` and `5`, `n_likes_class` value (classified value) is `0`. If `Entry.n_likes` value is between `10` and `19` such as `10` and `15`, `n_likes_class` value (classified value) is `10`.

You can use number_classify function for the classification. You need to register `functions/number` plugin by plugin_register command to use number_classify function.

This example does drilldown by `n_likes_class` value. The drilldown result will help you to know data trend.

Execution example:

    plugin_register functions/number
    # [[0,1337566253.89858,0.000355720520019531],true]
    select \
      --table Entries \
      --columns[n_likes_class].stage initial \
      --columns[n_likes_class].type UInt32 \
      --columns[n_likes_class].value 'number_classify(n_likes, 10)' \
      --drilldown n_likes_class \
      --drilldown_sort_keys _nsubrecs \
      --output_columns n_likes,n_likes_class
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "n_likes_class",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         5,
    #         0
    #       ],
    #       [
    #         10,
    #         10
    #       ],
    #       [
    #         15,
    #         10
    #       ],
    #       [
    #         3,
    #         0
    #       ],
    #       [
    #         3,
    #         0
    #       ]
    #     ],
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_key",
    #           "UInt32"
    #         ],
    #         [
    #           "_nsubrecs",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         10,
    #         2
    #       ],
    #       [
    #         0,
    #         3
    #       ]
    #     ]
    #   ]
    # ]

See Dynamic column related parameters for details.

### 7.3.58.3.7. Window function

You can compute each record value from values of grouped records. For example, you can compute sums of each group and puts sums to each record. The difference against drilldown is drilldown can compute sums of each group but it puts sums to each group not record.

Here is the result with window function. Each record has sum:

Group No. | Target value | Sum result
--- | --- | ---
1 | 5 | 5
2 | 10 | 25
2 | 15 | 25
3 | 3 | 8
3 | 5 | 8

Here is the result with drilldown. Each group has sum:

Group No. | Target values | Sum result
--- | --- | ---
1 | 5 | 5
2 | 10, 15 | 25
3 | 3, 5 | 8

Window function is useful for data analysis.

Here is an example that sums `Entries.n_likes` per `Entries.tag`:

Execution example:

    plugin_register functions/number
    # [[0,1337566253.89858,0.000355720520019531],true]
    select \
      --table Entries \
      --columns[n_likes_sum_per_tag].stage initial \
      --columns[n_likes_sum_per_tag].type UInt32 \
      --columns[n_likes_sum_per_tag].value 'window_sum(n_likes)' \
      --columns[n_likes_sum_per_tag].window.group_keys tag \
      --output_columns tag,n_likes,n_likes_sum_per_tag
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "tag",
    #           "ShortText"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "n_likes_sum_per_tag",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         "Hello",
    #         5,
    #         5
    #       ],
    #       [
    #         "Groonga",
    #         10,
    #         25
    #       ],
    #       [
    #         "Groonga",
    #         15,
    #         25
    #       ],
    #       [
    #         "Senna",
    #         3,
    #         6
    #       ],
    #       [
    #         "Senna",
    #         3,
    #         6
    #       ]
    #     ]
    #   ]
    # ]

See Window function related parameters for details.

### 7.3.58.3.8. Typo tolerance

You can implement typo tolerance search by specifying how many characters to be accepted as typo. If no records are matched by the given query, Groonga searches with typo fixed query again automatically.

The number of accepted typo characters is 0 by default. So typo tolerance search isn’t enabled by default.

You can enable typo tolerance search by specifying fuzzy_max_distance_ratio or fuzzy_max_distance. In general, `--fuzzy_max_distance_ratio 0.34` will be a good parameter.

fuzzy_max_distance_ratio specifies how many typo characters is accepted based on the number of characters of each input term.

Here is a table that shows how many characters are accepted as typo with `--fuzzy_max_distance_ratio 0.34`:

The number of characters of a term | The number of accepted typo characters
--- | ---
1 | 0 (`floor(1 * 0.34) = floor(0.34) = 0`)
2 | 0 (`floor(2 * 0.34) = floor(0.68) = 0`)
3 | 1 (`floor(3 * 0.34) = floor(1.02) = 1`)
4 | 1 (`floor(4 * 0.34) = floor(1.36) = 1`)
5 | 1 (`floor(5 * 0.34) = floor(1.7) = 1`)
6 | 2 (`floor(6 * 0.34) = floor(2.04) = 2`)

In other words, Groonga doesn’t accept any typo for a short term (0-2 characters term), accepts 1 typo for a middle term (3-5 characters term) and accepts 2 or more typos for a long term (6- characters term).

Here is an example that shows that we can search `Groonga` with `Moronga` (2 typos):

Execution example:

    select \
      --table Entries \
      --fuzzy_max_distance_ratio 0.34 \
      --match_columns content \
      --query Moronga \
      --output_columns content,_score
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "I started to use Groonga. It's very fast!",
    #         1
    #       ],
    #       [
    #         "I also started to use Mroonga. It's also very fast! Really fast!",
    #         2
    #       ]
    #     ]
    #   ]
    # ]

You can specify the fixed number of typo accept characters by fuzzy_max_distance. For example, Groonga accepts 2 characters for all terms with `--fuzzy_max_distance 2`. But `--fuzzy_max_distance_ratio` will be better for many use cases.

You need correct terms for typo tolerance search. Groonga uses terms in a lexicon as correct terms. `Terms` is a lexicon for this case. Terms in a lexicon are generated by a tokenizer. If your data is alphabet based language such as English, you can use TokenNgram. Because TokenNgram tokenizes a text to (almost) words for alphabet based languages. If your data isn’t alphabet based language such as Japanese, you can’t use TokenNgram. Because TokenNgram tokenizes a text to N-characters for non alphabet based languages. You need to use morphological analyzer based tokenizer for non alphabet based languages. For example, you can use TokenMecab for Japanese. (You can use TokenMecab for non Japanese languages with suitable dictionary.)

Here is an example to use typo tolerance search with Japanese text. `--default_tokenizer TokenMecab` for `JapaneseTerms` is important. `JapaneseTerms` is a lexicon for this case.

Execution example:

    table_create JapaneseEntries TABLE_NO_KEY
    # [[0,1337566253.89858,0.000355720520019531],true]
    column_create JapaneseEntries content COLUMN_SCALAR Text
    # [[0,1337566253.89858,0.000355720520019531],true]
    table_create JapaneseTerms TABLE_PAT_KEY ShortText \
      --default_tokenizer TokenMecab \
      --normalizer NormalizerNFKC150
    # [[0,1337566253.89858,0.000355720520019531],true]
    column_create JapaneseTerms japanese_entries_content \
      COLUMN_INDEX|WITH_POSITION JapaneseEntries content
    # [[0,1337566253.89858,0.000355720520019531],true]
    load --table JapaneseEntries
    [
    {"content": "ようこそ！これが最初の投稿です！"},
    {"content": "Groongaを使い始めました。とても速いですね！"},
    {"content": "Mroongaも使い始めました。これもとても速いですね！本当に速い！"},
    {"content": "Sennaのシステムをすべて移行しました！"},
    {"content": "Tritonnのシステムもすべて移行しました！"}
    ]
    # [[0,1337566253.89858,0.000355720520019531],5]
    select \
      --table JapaneseEntries \
      --fuzzy_max_distance_ratio 0.34 \
      --match_columns content \
      --query ともて \
      --output_columns content,_score
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "Groongaを使い始めました。とても速いですね！",
    #         1
    #       ],
    #       [
    #         "Mroongaも使い始めました。これもとても速いですね！本当に速い！",
    #         1
    #       ]
    #     ]
    #   ]
    # ]

See Fuzzy query related parameters for details.

## 7.3.58.4. Parameters

This section describes all parameters. Parameters are categorized.

### 7.3.58.4.1. Required parameters

There is a required parameter, `table`.

#### 7.3.58.4.1.1. `table`

Specifies a table to be searched. `table` must be specified.

If nonexistent table is specified, an error is returned.

Execution example:

    select Nonexistent
    # [
    #   [
    #     -22,
    #     1337566253.89858,
    #     0.000355720520019531,
    #     "[select][table] invalid name: <Nonexistent>",
    #     [
    #       [
    #         "execute",
    #         "lib/proc/proc_select.cpp",
    #         2929
    #       ]
    #     ]
    #   ]
    # ]

### 7.3.58.4.2. Search related parameters

There are search related parameters. Typically, `match_columns` and `query` parameters are used for implementing a search box. `filter` parameters is used for implementing complex search feature.

If both `query` and `filter` are specified, selected records must be matched against both `query` and `filter`. If both `query` and `filter` aren’t specified, all records are selected.

#### 7.3.58.4.2.1. `match_columns`

Specifies the default target column for fulltext search by `query` parameter value. A target column for fulltext search can be specified in `query` parameter. The difference between `match_columns` and `query` is whether weight and score function are supported or not. `match_columns` supports them but `query` doesn’t.

Weight is relative importance of target column. A higher weight target column gets more hit score rather than a lower weight target column when a record is matched by fulltext search. The default weight is 1.

Here is a simple `match_columns` usage example.

Execution example:

    select Entries --match_columns content --query fast --output_columns '_key, _score'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "Groonga",
    #         1
    #       ],
    #       [
    #         "Mroonga",
    #         2
    #       ]
    #     ]
    #   ]
    # ]

`--match_columns content` means the default target column for fulltext search is `content` column and its weight is 1. `--output_columns '_key, _score'` means that the `select` command outputs `_key` value and `_score` value for matched records.

Pay attention to `_score` value. `_score` value is the number of matched counts against `query` parameter value. In the example, `query` parameter value is `fast`. The fact that `_score` value is 1 means that `fast` appears in `content` column only once. The fact that `_score` value is 2 means that `fast` appears in `content` column twice.

To specify weight, `column * weight` syntax is used. Here is a weight usage example.

Execution example:

    select Entries --match_columns 'content * 2' --query fast --output_columns '_key, _score'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "Groonga",
    #         2
    #       ],
    #       [
    #         "Mroonga",
    #         4
    #       ]
    #     ]
    #   ]
    # ]

`--match_columns 'content * 2'` means the default target column for fulltext search is `content` column and its weight is 2.

Pay attention to `_score` value. `_score` value is doubled because weight is 2.

You can specify one or more columns as the default target columns for fulltext search. If one or more columns are specified, fulltext search is done for all columns and scores are accumulated. If one of the columns is matched against `query` parameter value, the record is treated as matched.

To specify one or more columns, `column1 * weight1 || column2 * weight2 || ...` syntax is used. `* weight` can be omitted. If it is omitted, 1 is used for weight. Here is a one or more columns usage example.

Execution example:

    select Entries --match_columns '_key * 10 || content' --query groonga --output_columns '_key, _score'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "Groonga",
    #         11
    #       ]
    #     ]
    #   ]
    # ]

`--match_columns '_key * 10 || content'` means the default target columns for fulltext search are `_key` and `content` columns and `_key` column’s weight is 10 and `content` column’s weight is 1. This weight allocation means `_key` column value is more important rather than `content` column value. In this example, title of blog entry is more important rather than content of blog entry.

You can also specify score function. See Scorer for details.

Note that score function isn’t related to scorer parameter.

You can also specify index columns to `--match_columns` parameter. In such a case, you can control what index column should be used and what data column should be searched explicitly.

If data column is specified to `--match_columns`, Groonga searches related index column automatically, but it is not defined as a spec that which index column is actually selected. Thus, if multiple index columns are created against one data column, there is no way to know which index column will be selected in advance. (In most cases, latest created index column will be selected, but it is undocumented behavior.)

Here is a use case how to specify index column.

- index column for one data column

- index column for multiple data column

If you use index column for one data column, index column which corresponds to data column is paired. Thus, `TERMS_TABLE.INDEX_COLUMN` should be specified. On the other hand, if you use index column for multiple data column, all data column is searched by default. If you want to use specific multiple index column to search specific data column, Specify index column with data column name such as `TERMS_TABLE.INDEX_COLUMN.DATA_COLUMN`. See example use case Full text search with specific index name for details.

#### 7.3.58.4.2.2. `query`

Specifies the query text. Normally, it is used for fulltext search with `match_columns` parameter. `query` parameter is designed for a fulltext search form in a Web page. A query text should be formatted in Query syntax. The syntax is similar to common search form like Google’s search form. For example, `word1 word2` means that groonga searches records that contain both `word1` and `word2`. `word1 OR word2` means that groonga searches records that contain either `word1` or `word2`.

Here is a simple logical and search example.

Execution example:

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

The `select` command searches records that contain two words `fast` and `groonga` in `content` column value from `Entries` table.

Here is a simple logical or search example.

Execution example:

    select Entries --match_columns content --query "groonga OR mroonga"
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "tag",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         2,
    #         "Groonga",
    #         "I started to use Groonga. It's very fast!",
    #         10,
    #         "Groonga"
    #       ],
    #       [
    #         3,
    #         "Mroonga",
    #         "I also started to use Mroonga. It's also very fast! Really fast!",
    #         15,
    #         "Groonga"
    #       ]
    #     ]
    #   ]
    # ]

The `select` command searches records that contain one of two words `groonga` or `mroonga` in `content` column value from `Entries` table.

See Query syntax for other syntax.

It can be used for not only fulltext search but also other conditions. For example, `column:value` means the value of `column` column is equal to `value`. `column:<value` means the value of `column` column is less than `value`.

Here is a simple equality operator search example.

Execution example:

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

The `select` command searches records that `_key` column value is `Groonga` from `Entries` table.

Here is a simple less than operator search example.

Execution example:

    select Entries --query n_likes:<11
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         4
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "tag",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         1,
    #         "The first post!",
    #         "Welcome! This is my first post!",
    #         5,
    #         "Hello"
    #       ],
    #       [
    #         2,
    #         "Groonga",
    #         "I started to use Groonga. It's very fast!",
    #         10,
    #         "Groonga"
    #       ],
    #       [
    #         4,
    #         "Good-bye Senna",
    #         "I migrated all Senna system!",
    #         3,
    #         "Senna"
    #       ],
    #       [
    #         5,
    #         "Good-bye Tritonn",
    #         "I also migrated all Tritonn system!",
    #         3,
    #         "Senna"
    #       ]
    #     ]
    #   ]
    # ]

The `select` command searches records that `n_likes` column value is less than `11` from `Entries` table.

See Query syntax for other operations.

#### 7.3.58.4.2.3. `filter`

Specifies the filter text. Normally, it is used for complex search conditions. `filter` can be used with `query` parameter. If both `filter` and `query` are specified, there are combined with logical and. It means that matched records should be matched against both `filter` and `query`.

`filter` parameter is designed for complex conditions. A filter text should be formatted in Script syntax. The syntax is similar to ECMAScript. For example, `column == "value"` means that the value of `column` column is equal to `"value"`. `column < value` means that the value of `column` column is less than `value`.

Here is a simple equality operator search example.

Execution example:

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

The `select` command searches records that `_key` column value is `Groonga` from `Entries` table.

Here is a simple less than operator search example.

Execution example:

    select Entries --filter 'n_likes < 11'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         4
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "tag",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         1,
    #         "The first post!",
    #         "Welcome! This is my first post!",
    #         5,
    #         "Hello"
    #       ],
    #       [
    #         2,
    #         "Groonga",
    #         "I started to use Groonga. It's very fast!",
    #         10,
    #         "Groonga"
    #       ],
    #       [
    #         4,
    #         "Good-bye Senna",
    #         "I migrated all Senna system!",
    #         3,
    #         "Senna"
    #       ],
    #       [
    #         5,
    #         "Good-bye Tritonn",
    #         "I also migrated all Tritonn system!",
    #         3,
    #         "Senna"
    #       ]
    #     ]
    #   ]
    # ]

The `select` command searches records that `n_likes` column value is less than `11` from `Entries` table.

See Script syntax for other operators.

#### 7.3.58.4.2.4. `load_table`

Added in version 9.0.1.

You can store specified a table a result of `select` with `--load_table`, `--load-columns` and `--load_values` arguments. `--load_table` specifies a table name for storing a result of `select`.

You must specify a table that already exists.

This argument must use with load_columns and load_values.

Here is an example that can store `_id` and `timestamp` that a result of `select` in a Logs table specified by `--load_table`.

Execution example:

    table_create Logs_20150203 TABLE_HASH_KEY ShortText
    # [[0,1337566253.89858,0.000355720520019531],true]
    column_create Logs_20150203 timestamp COLUMN_SCALAR Time
    # [[0,1337566253.89858,0.000355720520019531],true]
    table_create Logs TABLE_HASH_KEY ShortText
    # [[0,1337566253.89858,0.000355720520019531],true]
    column_create Logs original_id COLUMN_SCALAR UInt32
    # [[0,1337566253.89858,0.000355720520019531],true]
    column_create Logs timestamp_text COLUMN_SCALAR ShortText
    # [[0,1337566253.89858,0.000355720520019531],true]
    load --table Logs_20150203
    [
    {
      "_key": "2015-02-03:1",
      "timestamp": "2015-02-03 10:49:00"
    },
    {
      "_key": "2015-02-03:2",
      "timestamp": "2015-02-03 12:49:00"
    }
    ]
    # [[0,1337566253.89858,0.000355720520019531],2]
    select \
      --table Logs_20150203 \
      --load_table Logs \
      --load_columns "_key, original_id, timestamp_text" \
      --load_values "_key, _id, timestamp"
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "timestamp",
    #           "Time"
    #         ]
    #       ],
    #       [
    #         1,
    #         "2015-02-03:1",
    #         1422928140.0
    #       ],
    #       [
    #         2,
    #         "2015-02-03:2",
    #         1422935340.0
    #       ]
    #     ]
    #   ]
    # ]
    select --table Logs
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "original_id",
    #           "UInt32"
    #         ],
    #         [
    #           "timestamp_text",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         1,
    #         "2015-02-03:1",
    #         1,
    #         "1422928140000000"
    #       ],
    #       [
    #         2,
    #         "2015-02-03:2",
    #         2,
    #         "1422935340000000"
    #       ]
    #     ]
    #   ]
    # ]

#### 7.3.58.4.2.5. `load_columns`

Added in version 9.0.1.

Specifies columns of a table that specifying `--load-table`. Stores value of columns that specified with load_values in columns that specified with this argument. You must specify columns that already exists.

This argument must use with load_table and load_values.

See example of `--load_table` for how to use this argument.

#### 7.3.58.4.2.6. `load_values`

Added in version 9.0.1.

Specifies columns of result of `select`. Specifies columns for storing values into columns that specified with load_columns. You must specify columns that already exists.

This argument must use with load_table and load_columns.

See example of `--load_table` for how to use this argument.

### 7.3.58.4.3. Advanced search parameters

#### 7.3.58.4.3.1. `match_escalation_threshold`

Added in version 8.0.1.

Specifies threshold to determine whether search strategy escalation is used or not. The threshold is compared against the number of matched records. If the number of matched records is equal to or less than the threshold, the search strategy escalation is used. See 検索 about the search strategy escalation.

The default threshold is 0. It means that search strategy escalation is used only when no records are matched.

The default threshold can be customized by one of the followings.

> - `--with-match-escalation-threshold` option of configure

> - `--match-escalation-threshold` option of groonga command

> - `match-escalation-threshold` configuration item in configuration file

Here is a simple `match_escalation_threshold` usage example. The first `select` doesn’t have `match_escalation_threshold` parameter. The second `select` has `match_escalation_threshold` parameter.

Execution example:

    select Entries --match_columns content --query groo
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "tag",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         2,
    #         "Groonga",
    #         "I started to use Groonga. It's very fast!",
    #         10,
    #         "Groonga"
    #       ]
    #     ]
    #   ]
    # ]
    select Entries --match_columns content --query groo --match_escalation_threshold -1
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         0
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "tag",
    #           "ShortText"
    #         ]
    #       ]
    #     ]
    #   ]
    # ]

The first `select` command searches records that contain a word `groo` in `content` column value from `Entries` table. But no records are matched because the `TokenBigram` tokenizer tokenizes `groonga` to `groonga` not `gr|ro|oo|on|ng|ga`. (The `TokenBigramSplitSymbolAlpha` tokenizer tokenizes `groonga` to `gr|ro|oo|on|ng|ga`. See Tokenizers for details.) It means that `groonga` is indexed but `groo` isn’t indexed. So no records are matched against `groo` by exact match. In the case, the search strategy escalation is used because the number of matched records (0) is equal to `match_escalation_threshold` (0). One record is matched against `groo` by unsplit search.

The second `select` command also searches records that contain a word `groo` in `content` column value from `Entries` table. And it also doesn’t find matched records. In this case, the search strategy escalation is not used because the number of matched records (0) is larger than `match_escalation_threshold` (-1). So no more searches aren’t executed. And no records are matched.

#### 7.3.58.4.3.2. `match_escalation`

Specifies how to use match escalation. See also match_escalation and 検索 about the match escalation.

Here are available values:

Value | Description
--- | ---
`auto` | Groonga uses match_escalation_threshold to determine whether match escalation is used or not. This is the default.
`yes` | Groonga always uses match escalation.
`no` | Groonga never use match escalation.

`--match_escalation yes` is stronger than `--match_escalation_threshold 9999...999`. `--filter 'true && column @ "query"` with `--match_escalation yes` uses match escalation. `--filter 'true && column @ "query"` with `--match_escalation_threshold 9999...999` doesn’t use match escalation.

Here is a simple `match_escalation` usage example. The first `select` doesn’t have `match_escalation` parameter. The second `select` has `match_escalation` parameter.

Execution example:

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

The first `select` command searches records that contain a word `groo` in `content` column value from `Entries` table. But no records are matched because the `TokenBigram` tokenizer tokenizes `groonga` to `groonga` not `gr|ro|oo|on|ng|ga`.

The second `select` command also searches records that contain a word `groo` in `content` column value from `Entries` table. And it uses match escalation. So it can find matched records.

#### 7.3.58.4.3.3. `query_expansion`

Deprecated since version 3.0.2: Use query_expander instead.

#### 7.3.58.4.3.4. `query_flags`

It customs `query` parameter syntax. You cannot update column value by `query` parameter by default. But if you specify `ALLOW_COLUMN|ALLOW_UPDATE` as `query_flags`, you can update column value by `query`.

Here are available values:

- `ALLOW_PRAGMA`

- `ALLOW_COLUMN`

- `ALLOW_UPDATE`

- `ALLOW_LEADING_NOT`

- `QUERY_NO_SYNTAX_ERROR`

- `NONE`

`ALLOW_PRAGMA` enables pragma at the head of `query`. This is not implemented yet.

`ALLOW_COLUMN` enables search against columns that are not included in `match_columns`. To specify column, there are `COLUMN:...` syntaxes.

`ALLOW_UPDATE` enables column update by `query` with `COLUMN:=NEW_VALUE` syntax. `ALLOW_COLUMN` is also required to update column because the column update syntax specifies column.

`ALLOW_LEADING_NOT` enables leading NOT condition with `-WORD` syntax. The query searches records that doesn’t match `WORD`. Leading NOT condition query is heavy query in many cases because it matches many records. So this flag is disabled by default. Be careful about it when you use the flag.

`QUERY_NO_SYNTAX_ERROR` enables never causes syntax error for query. This flag is useful when an application uses user input directly and doesn’t want to show syntax error to the user and in a log. This flag is disabled by default.

`NONE` is just ignores. You can use `NONE` for specifying no flags.

They can be combined by separated `|` such as `ALLOW_COLUMN|ALLOW_UPDATE`.

The default value is `ALLOW_PRAGMA|ALLOW_COLUMN`.

Here is a usage example of `ALLOW_COLUMN`.

Execution example:

    select Entries --query content:@mroonga --query_flags ALLOW_COLUMN
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "tag",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         3,
    #         "Mroonga",
    #         "I also started to use Mroonga. It's also very fast! Really fast!",
    #         15,
    #         "Groonga"
    #       ]
    #     ]
    #   ]
    # ]

The `select` command searches records that contain `mroonga` in `content` column value from `Entries` table.

Here is a usage example of `ALLOW_UPDATE`.

Execution example:

    table_create Users TABLE_HASH_KEY ShortText
    # [[0,1337566253.89858,0.000355720520019531],true]
    column_create Users age COLUMN_SCALAR UInt32
    # [[0,1337566253.89858,0.000355720520019531],true]
    load --table Users
    [
    {"_key": "alice", "age": 18},
    {"_key": "bob",   "age": 20}
    ]
    # [[0,1337566253.89858,0.000355720520019531],2]
    select Users --query age:=19 --query_flags ALLOW_COLUMN|ALLOW_UPDATE
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "age",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         1,
    #         "alice",
    #         19
    #       ],
    #       [
    #         2,
    #         "bob",
    #         19
    #       ]
    #     ]
    #   ]
    # ]
    select Users
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "age",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         1,
    #         "alice",
    #         19
    #       ],
    #       [
    #         2,
    #         "bob",
    #         19
    #       ]
    #     ]
    #   ]
    # ]

The first `select` command sets `age` column value of all records to `19`. The second `select` command outputs updated `age` column values.

Here is a usage example of `ALLOW_LEADING_NOT`.

Execution example:

    select Entries --match_columns content --query -mroonga --query_flags ALLOW_LEADING_NOT
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         4
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "tag",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         1,
    #         "The first post!",
    #         "Welcome! This is my first post!",
    #         5,
    #         "Hello"
    #       ],
    #       [
    #         2,
    #         "Groonga",
    #         "I started to use Groonga. It's very fast!",
    #         10,
    #         "Groonga"
    #       ],
    #       [
    #         4,
    #         "Good-bye Senna",
    #         "I migrated all Senna system!",
    #         3,
    #         "Senna"
    #       ],
    #       [
    #         5,
    #         "Good-bye Tritonn",
    #         "I also migrated all Tritonn system!",
    #         3,
    #         "Senna"
    #       ]
    #     ]
    #   ]
    # ]

The `select` command searches records that don’t contain `mroonga` in `content` column value from `Entries` table.

Here are a schema definition and sample data to describe other flags:

Execution example:

    table_create --name Magazine --flags TABLE_HASH_KEY --key_type ShortText
    # [[0,1337566253.89858,0.000355720520019531],true]
    column_create --table Magazine --name title --type ShortText
    # [[0,1337566253.89858,0.000355720520019531],true]
    load --table Magazine
    [
    {"_key":"http://test.jp/magazine/webplus","title":"WEB+"},
    {"_key":"http://test.jp/magazine/database","title":"DataBase"},
    ]
    # [[0,1337566253.89858,0.000355720520019531],2]

Here is an example of `QUERY_NO_SYNTAX_ERROR`:

Execution example:

    select Magazine --match_columns title --query 'WEB +'  --query_flags ALLOW_PRAGMA|ALLOW_COLUMN|QUERY_NO_SYNTAX_ERROR
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "title",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         1,
    #         "http://test.jp/magazine/webplus",
    #         "WEB+"
    #       ]
    #     ]
    #   ]
    # ]

If you don’t specify this flag, the query causes a syntax error as below.

Execution example:

    select Magazine --match_columns title --query 'WEB +'  --query_flags ALLOW_PRAGMA|ALLOW_COLUMN
    # [
    #   [
    #     -63,
    #     1337566253.89858,
    #     0.000355720520019531,
    #     "Syntax error: <WEB +||>",
    #     [
    #       [
    #         "yy_syntax_error",
    #         "grn_ecmascript.lemon",
    #         2929
    #       ]
    #     ]
    #   ]
    # ]

Here is a usage example of `NONE`.

Execution example:

    select Entries --match_columns content --query 'mroonga OR _key:Groonga' --query_flags NONE
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "tag",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         3,
    #         "Mroonga",
    #         "I also started to use Mroonga. It's also very fast! Really fast!",
    #         15,
    #         "Groonga"
    #       ]
    #     ]
    #   ]
    # ]

The `select` command searches records that contain one of two words `mroonga` or `_key:Groonga` in `content` from `Entries` table. Note that `_key:Groonga` doesn’t mean that the value of `_key` column is equal to `Groonga`. Because `ALLOW_COLUMN` flag is not specified.

See also Query syntax.

#### 7.3.58.4.3.5. `query_expander`

It’s for query expansion. Query expansion substitutes specific words to another words in query. Normally, it’s used for synonym search.

It specifies a column that is used to substitute `query` parameter value. The format of this parameter value is “`${TABLE}.${COLUMN}`”. For example, “`Terms.synonym`” specifies `synonym` column in `Terms` table.

Table for query expansion is called “substitution table”. Substitution table’s key must be `ShortText`. So array table (`TABLE_NO_KEY`) can’t be used for query expansion. Because array table doesn’t have key.

Column for query expansion is called “substitution column”. Substitution column’s value type must be `ShortText`. Column type must be vector (`COLUMN_VECTOR`).

Query expansion substitutes key of substitution table in query with values in substitution column. If a word in `query` is a key of substitution table, the word is substituted with substitution column value that is associated with the key. Substitution isn’t performed recursively. It means that substitution target words in substituted query aren’t substituted.

Here is a sample substitution table to show a simple `query_expander` usage example.

Execution example:

    table_create Thesaurus TABLE_PAT_KEY ShortText --normalizer NormalizerAuto
    # [[0,1337566253.89858,0.000355720520019531],true]
    column_create Thesaurus synonym COLUMN_VECTOR ShortText
    # [[0,1337566253.89858,0.000355720520019531],true]
    load --table Thesaurus
    [
    {"_key": "mroonga", "synonym": ["mroonga", "tritonn", "groonga mysql"]},
    {"_key": "groonga", "synonym": ["groonga", "senna"]}
    ]
    # [[0,1337566253.89858,0.000355720520019531],2]

`Thesaurus` substitution table has two synonyms, `"mroonga"` and `"groonga"`. If an user searches with `"mroonga"`, Groonga searches with `"((mroonga) OR (tritonn) OR (groonga mysql))"`. If an user searches with `"groonga"`, Groonga searches with `"((groonga) OR (senna))"`.

Normally, it’s good idea that substitution table uses a normalizer. For example, if normalizer is used, substitute target word is matched in case insensitive manner. See Normalizers for available normalizers.

Note that those synonym values include the key value such as `"mroonga"` and `"groonga"`. It’s recommended that you include the key value. If you don’t include key value, substituted value doesn’t include the original substitute target value. Normally, including the original value is better search result. If you have a word that you don’t want to be searched, you should not include the original word. For example, you can implement “stop words” by an empty vector value.

Here is a simple `query_expander` usage example.

Execution example:

    select Entries --match_columns content --query "mroonga"
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "tag",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         3,
    #         "Mroonga",
    #         "I also started to use Mroonga. It's also very fast! Really fast!",
    #         15,
    #         "Groonga"
    #       ]
    #     ]
    #   ]
    # ]
    select Entries --match_columns content --query "mroonga" --query_expander Thesaurus.synonym
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "tag",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         3,
    #         "Mroonga",
    #         "I also started to use Mroonga. It's also very fast! Really fast!",
    #         15,
    #         "Groonga"
    #       ],
    #       [
    #         5,
    #         "Good-bye Tritonn",
    #         "I also migrated all Tritonn system!",
    #         3,
    #         "Senna"
    #       ]
    #     ]
    #   ]
    # ]
    select Entries --match_columns content --query "((mroonga) OR (tritonn) OR (groonga mysql))"
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "tag",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         3,
    #         "Mroonga",
    #         "I also started to use Mroonga. It's also very fast! Really fast!",
    #         15,
    #         "Groonga"
    #       ],
    #       [
    #         5,
    #         "Good-bye Tritonn",
    #         "I also migrated all Tritonn system!",
    #         3,
    #         "Senna"
    #       ]
    #     ]
    #   ]
    # ]

The first `select` command doesn’t use query expansion. So a record that has `"tritonn"` isn’t found. The second `select` command uses query expansion. So a record that has `"tritonn"` is found. The third `select` command doesn’t use query expansion but it is same as the second `select` command. The third one uses expanded query.

Each substitute value can contain any Query syntax syntax such as `(...)` and `OR`. You can use complex substitution by using those syntax.

Here is a complex substitution usage example that uses query syntax.

Execution example:

    load --table Thesaurus
    [
    {"_key": "popular", "synonym": ["popular", "n_likes:>=10"]}
    ]
    # [[0,1337566253.89858,0.000355720520019531],1]
    select Entries --match_columns content --query "popular" --query_expander Thesaurus.synonym
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "tag",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         2,
    #         "Groonga",
    #         "I started to use Groonga. It's very fast!",
    #         10,
    #         "Groonga"
    #       ],
    #       [
    #         3,
    #         "Mroonga",
    #         "I also started to use Mroonga. It's also very fast! Really fast!",
    #         15,
    #         "Groonga"
    #       ]
    #     ]
    #   ]
    # ]

The `load` command registers a new synonym `"popular"`. It is substituted with `((popular) OR (n_likes:>=10))`. The substituted query means that “popular” is containing the word “popular” or 10 or more liked entries.

The `select` command outputs records that `n_likes` column value is equal to or more than `10` from `Entries` table.

#### 7.3.58.4.3.6. `n_workers`

Added in version 12.0.5.

Note

This is an experimental feature. Currently, this feature is still not stable.

This feature requires Command version 3 or later.

This feature requires that Apache Arrow is enabled in Groonga.

It depends on package provider whether Apache Arrow is enabled or not.

To check whether Apache Arrow is enabled, you can use status command that show the result of `apache_arrow` is `true` or not.

If Apache Arrow is disabled, you should build Groonga from the source code with enabling Apache Arrow following the steps in Install or request to enable Apache Arrow to the package provider.

drilldown , drilldowns and slices are executed in parallel when this parameter is specified `-1` or `2` or more.

In a default setting, `drilldown`, `drilldowns` and `slices` are executed in serial. In other words, a next process is executed after a current process is finished. So, queries tend to take a long time if there are a lot of `drilldown`, `drilldowns` and `slices`.

`n_workers` enables to execute independent `drilldown`, `drilldowns` and `slices` in parallel. The execution time of the total sum of processes can be shourtend by executing them in parallel. This parallel execution is done for each `select` command.

“independent” means not using `drilldowns.table` to reference the results of other drilldowns or slices.

If there are dependencies as same meaning as using `drilldowns.table`, it wait for finish the dependent drilldowns or slices. Therefore, the degree of parallelism is reduced if they have dependencies.

Executing in parallel means using multiple CPUs at the same time. If executing in parallel without free CPU resource, it may actually slow down the execution time. This is because they have to wait for the other process being executed by the target CPU to finish.

It depends on a system configuration whether or not there are free CPU resources and how many `n_workers` should be specified.

For example, consider using Groonga HTTP server on a system with 6 CPUs.

Groonga HTTP server allocates 1 thread (= 1CPU) for each request.

When the average number of concurrent connections is 6, there are no free CPU resources because 6 CPUs are already in use. All the CPU is used to process each request.

When the average number of concurrent connections is 2, there are 4 free CPU resources because only 2 CPUs are already in use. When specifying `2` for `n_workers`, the `select` command will use at most 3 CPUs, including the thread for processing requests. Therefore, if two `select` commands with `2` specified for `n_workers` are requested at the same time, they will use at most 6 CPUs in total and will be processed fastly by using all of the resources. When specifying greater than `2`, the degree of parallelism can be higher than the CPU resources, so it may actually slow down the execution time.

`n_workers` behaves as follows depending on the specified value.

- When specifying `0` or `1`

  - Executes the select command in serial

- When specifying `2` or more

  - Executes the select command in parallel with at most the specified number of threads.

- When specifying `-1` or less

  - Executes the select command in parallel with the threads of at most the number of CPU cores.

The default value of this parameter is `0` . It means that the select command is executed in serial in default.

Note

The default value can be changed by specifying the environment variable `GRN_SELECT_N_WORKERS_DEFAULT`.

### 7.3.58.4.4. Output related parameters

#### 7.3.58.4.4.1. `output_columns`

Specifies output columns separated by `,`.

Here is a simple `output_columns` usage example.

Execution example:

    select Entries --output_columns '_id, _key' --limit 1
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         1,
    #         "The first post!"
    #       ]
    #     ]
    #   ]
    # ]

The `select` command just outputs `_id` and `_key` column values.

`*` is a special value. It means that all columns that are not Pseudo column.

Here is a `*` usage example.

Execution example:

    select Entries --output_columns '_key, *' --limit 1
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "tag",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         "The first post!",
    #         "Welcome! This is my first post!",
    #         5,
    #         "Hello"
    #       ]
    #     ]
    #   ]
    # ]

The `select` command outputs `_key` pseudo column, `content` column and `n_likes` column values but doesn’t output `_id` pseudo column value.

The default value is `_id, _key, *`. It means that all column values except `_score` are outputted.

#### 7.3.58.4.4.2. `sortby`

Deprecated since version 6.0.3: Use sort_keys instead.

#### 7.3.58.4.4.3. `sort_keys`

Specifies sort keys separated by `,`. Each sort key is column name.

Here is a simple `sort_keys` usage example.

Execution example:

    select Entries --sort_keys 'n_likes, _id'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "tag",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         4,
    #         "Good-bye Senna",
    #         "I migrated all Senna system!",
    #         3,
    #         "Senna"
    #       ],
    #       [
    #         5,
    #         "Good-bye Tritonn",
    #         "I also migrated all Tritonn system!",
    #         3,
    #         "Senna"
    #       ],
    #       [
    #         1,
    #         "The first post!",
    #         "Welcome! This is my first post!",
    #         5,
    #         "Hello"
    #       ],
    #       [
    #         2,
    #         "Groonga",
    #         "I started to use Groonga. It's very fast!",
    #         10,
    #         "Groonga"
    #       ],
    #       [
    #         3,
    #         "Mroonga",
    #         "I also started to use Mroonga. It's also very fast! Really fast!",
    #         15,
    #         "Groonga"
    #       ]
    #     ]
    #   ]
    # ]

The `select` command sorts by `n_likes` column value in ascending order. For records that has the same `n_likes` are sorted by `_id` in ascending order. `"Good-bye Senna"` and `"Good-bye Tritonn"` are the case.

If you want to sort in descending order, add `-` before column name.

Here is a descending order `sort_keys` usage example.

Execution example:

    select Entries --sort_keys '-n_likes, _id'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "tag",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         3,
    #         "Mroonga",
    #         "I also started to use Mroonga. It's also very fast! Really fast!",
    #         15,
    #         "Groonga"
    #       ],
    #       [
    #         2,
    #         "Groonga",
    #         "I started to use Groonga. It's very fast!",
    #         10,
    #         "Groonga"
    #       ],
    #       [
    #         1,
    #         "The first post!",
    #         "Welcome! This is my first post!",
    #         5,
    #         "Hello"
    #       ],
    #       [
    #         4,
    #         "Good-bye Senna",
    #         "I migrated all Senna system!",
    #         3,
    #         "Senna"
    #       ],
    #       [
    #         5,
    #         "Good-bye Tritonn",
    #         "I also migrated all Tritonn system!",
    #         3,
    #         "Senna"
    #       ]
    #     ]
    #   ]
    # ]

The `select` command sorts by `n_likes` column value in descending order. But ascending order is used for sorting by `_id`.

You can use `_score` pseudo column in `sort_keys` if you use `query` or `filter` parameter.

Execution example:

    select Entries --match_columns content --query fast --sort_keys -_score --output_columns '_key, _score'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "Mroonga",
    #         2
    #       ],
    #       [
    #         "Groonga",
    #         1
    #       ]
    #     ]
    #   ]
    # ]

The `select` command sorts matched records by hit score in descending order and outputs record key and hit score.

If you use `_score` without `query` nor `filter` parameters, it’s just ignored but get a warning in log file.

#### 7.3.58.4.4.4. `offset`

Specifies offset to determine output records range. Offset is zero-based. `--offset 1` means output range is started from the 2nd record.

Execution example:

    select Entries --sort_keys _id --offset 3 --output_columns _key
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         "Good-bye Senna"
    #       ],
    #       [
    #         "Good-bye Tritonn"
    #       ]
    #     ]
    #   ]
    # ]

The `select` command outputs from the 4th record.

You can specify negative value. It means that `the number of matched records + offset`. If you have 3 matched records and specify `--offset -2`, you get records from the 2nd (`3 + -2 = 1`. `1` means 2nd. Remember that offset is zero-based.) record to the 3rd record.

Execution example:

    select Entries --sort_keys _id --offset -2 --output_columns _key
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         "Good-bye Senna"
    #       ],
    #       [
    #         "Good-bye Tritonn"
    #       ]
    #     ]
    #   ]
    # ]

The `select` command outputs from the 4th record because the total number of records is `5`.

The default value is `0`.

#### 7.3.58.4.4.5. `limit`

Specifies the max number of output records. If the number of matched records is less than `limit`, all records are outputted.

Here is a simple `limit` usage example.

Execution example:

    select Entries --sort_keys _id --offset 2 --limit 3 --output_columns _key
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         "Mroonga"
    #       ],
    #       [
    #         "Good-bye Senna"
    #       ],
    #       [
    #         "Good-bye Tritonn"
    #       ]
    #     ]
    #   ]
    # ]

The `select` command outputs the 3rd, the 4th and the 5th records.

You can specify negative value. It means that `the number of matched records + limit + 1`. For example, `--limit -1` outputs all records. It’s very useful value to show all records.

Here is a simple negative `limit` value usage example.

Execution example:

    select Entries --limit -1
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "tag",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         1,
    #         "The first post!",
    #         "Welcome! This is my first post!",
    #         5,
    #         "Hello"
    #       ],
    #       [
    #         2,
    #         "Groonga",
    #         "I started to use Groonga. It's very fast!",
    #         10,
    #         "Groonga"
    #       ],
    #       [
    #         3,
    #         "Mroonga",
    #         "I also started to use Mroonga. It's also very fast! Really fast!",
    #         15,
    #         "Groonga"
    #       ],
    #       [
    #         4,
    #         "Good-bye Senna",
    #         "I migrated all Senna system!",
    #         3,
    #         "Senna"
    #       ],
    #       [
    #         5,
    #         "Good-bye Tritonn",
    #         "I also migrated all Tritonn system!",
    #         3,
    #         "Senna"
    #       ]
    #     ]
    #   ]
    # ]

The `select` command outputs all records.

The default value is `10`.

#### 7.3.58.4.4.6. `scorer`

TODO: write in English and add example.

検索条件にマッチする全てのレコードに対して適用するgrn_exprをscript形式で指定します。

scorerは、検索処理が完了し、ソート処理が実行される前に呼び出されます。従って、各レコードのスコアを操作する式を指定しておけば、検索結果のソート順序をカスタマイズできるようになります。

### 7.3.58.4.5. Fuzzy query related parameters

Added in version 13.0.8.

Note

This is an experimental feature. Currently, this feature is still not stable.

This section describes fuzzy query related parameters. See also Typo tolerance as a use case of fuzzy query.

You need to specify at least fuzzy_max_distance_ratio or fuzzy_max_distance to use fuzzy query.

Fuzzy query is executed automatically when no record is matched with the original query. It means that fuzzy query is implemented as one of match escalation methods. See also match_escalation_threshold for match escalation.

#### 7.3.58.4.5.1. `fuzzy_max_distance_ratio`

Added in version 13.0.8.

Note

This is an experimental feature. Currently, this feature is still not stable.

The default value is `0`.

You need to specify fuzzy_max_distance_ratio or fuzzy_max_distance to enable fuzzy query. If you specify both of them, fuzzy_max_distance_ratio is used.

You can specify how long edit distance is accepted based on the target query. For most use cases, this parameter is more suitable than fuzzy_max_distance.

In general, long edit distance for short query isn’t suitable. Because it will increase noisy results. For example, `hye` with edit distance `3` accepts the following terms:

- `hey` (preferable result)

- `eye` (preferable result)

- `bye` (preferable result)

- `hyper` (preferable result?)

- `hyphen` (preferable result?)

fuzzy_max_distance specifies a fixed size edit distance for all queries (both of short queries and long queries).

You can specify an edit distance for each query based on the number of characters of the target query by this parameter. For example, edit distance `1` is used for `hye` with `--fuzzy_max_distance_ratio 0.34`. Edit distance `2` is used for `hypehn` with `--fuzzy_max_distance_ratio 0.34`. It’s calculated by `floor(${THE_NUMBER_OF_CHARACTERS} * ${FUZZY_MAX_DISTANCE_RATIO})`:

- `hye`: `floor(3 * 0.34) = floor(1.02) = 1`

- `hypehn`: `floor(6 * 0.34) = floor(2.04) = 2`

In general, `--fuzzy_max_distance_ratio 0.34` is a good value. If the value doesn’t fix your use case, you can change the value.

Here is a table that shows how many characters are accepted as typo with `--fuzzy_max_distance_ratio 0.34`:

The number of characters of a term | The number of accepted typo characters
--- | ---
1 | 0 (`floor(1 * 0.34) = floor(0.34) = 0`)
2 | 0 (`floor(2 * 0.34) = floor(0.68) = 0`)
3 | 1 (`floor(3 * 0.34) = floor(1.02) = 1`)
4 | 1 (`floor(4 * 0.34) = floor(1.36) = 1`)
5 | 1 (`floor(5 * 0.34) = floor(1.7) = 1`)
6 | 2 (`floor(6 * 0.34) = floor(2.04) = 2`)

Here is an example that accepts 1 typo character for `vary` and 2 typo characters for `Gnoonag`. This example specifies `yes` as match_escalation to enable fuzzy query for all queries (`vary` and `Gnoonag`). In general, you should not specify `--match_escalation yes` because it may increase noisy results.

Execution example:

    select \
      --table Entries \
      --fuzzy_max_distance_ratio 0.34 \
      --match_columns content \
      --query 'vary Gnoonag' \
      --match_escalation yes \
      --output_columns content,_score
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "I started to use Groonga. It's very fast!",
    #         2
    #       ]
    #     ]
    #   ]
    # ]

#### 7.3.58.4.5.2. `fuzzy_max_distance`

Added in version 13.0.8.

Note

This is an experimental feature. Currently, this feature is still not stable.

The default value is `0`.

You need to specify fuzzy_max_distance_ratio or fuzzy_max_distance to enable fuzzy query. If you specify both of them, fuzzy_max_distance_ratio is used.

You can specify a fixed edit distance to be accepted by this parameter. For most use cases, fuzzy_max_distance is more suitable than this parameter.

Here is an example that accepts 1 typo character for `vary`:

Execution example:

    select \
      --table Entries \
      --fuzzy_max_distance 1 \
      --match_columns content \
      --query vary \
      --output_columns content,_score
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "I started to use Groonga. It's very fast!",
    #         1
    #       ],
    #       [
    #         "I also started to use Mroonga. It's also very fast! Really fast!",
    #         1
    #       ]
    #     ]
    #   ]
    # ]

#### 7.3.58.4.5.3. `fuzzy_max_expansions`

Added in version 13.0.8.

Note

This is an experimental feature. Currently, this feature is still not stable.

The default value is `10`.

You can specify the max number of terms as fixed terms. If `hye` is the given query, this parameter is `2` and `hey`, `eye` and `hyper` are candidates of fixed terms, `hey` and `eye` (2 terms) are only used as fixed terms.

Here is an example that uses 1 fixed terms for `alx`. `all` is only used. `also` isn’t used.

Execution example:

    select \
      --table Entries \
      --fuzzy_max_distance 2 \
      --fuzzy_max_expansions 1 \
      --match_columns content \
      --query alx \
      --output_columns content,_score
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "I migrated all Senna system!",
    #         2
    #       ],
    #       [
    #         "I also migrated all Tritonn system!",
    #         2
    #       ]
    #     ]
    #   ]
    # ]

#### 7.3.58.4.5.4. `fuzzy_prefix_length`

Added in version 13.0.8.

Note

This is an experimental feature. Currently, this feature is still not stable.

The default value is `0`.

You can specify the number of prefix characters. If this value is `1` and the given term is `hye`, the prefix is `h`. Fixed terms must be started with `h`. For example, `hey` can be used as a fixed term but `eye` and `bye` can’t be used as a fixed terms.

This option will improve performance when a lexicon has many terms.

Here is an example that requires `gr` prefix for fixed terms with `groonag` query. `Groonga` (case insensitive for this case because you use NormalizerAuto for this case) can be used for a fixed term but `Mroonga` can’t be used for a fixed term. Because `Mroonga` isn’t started with `gr`.

Execution example:

    select \
      --table Entries \
      --fuzzy_max_distance 2 \
      --fuzzy_prefix_length 2 \
      --match_columns content \
      --query groonag \
      --output_columns content,_score
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "I started to use Groonga. It's very fast!",
    #         2
    #       ]
    #     ]
    #   ]
    # ]

#### 7.3.58.4.5.5. `fuzzy_with_transposition`

Added in version 13.0.9.

Note

This is an experimental feature. Currently, this feature is still not stable.

The default value is `yes`.

You can choose edit distance `1` or `2` for the transposition case. An example of the transposition case is `hello` and `ehllo`. `h` and `e` is transposed. If this parameter is `yes`, the edit distance of this case is `1`. It’s `2` (because insertion and deletion are needed) otherwise.

Here is an example that uses edit distance `2` for transposition. In this example, you can’t use `Mroonga` as a fixed term for `groonag` because it has edit distance `3`:

1. Substitute `g` with `M`: `groonag` -> `Mroonag`

2. Add `a`: `Mroonag` -> `Mroonaga`

3. Remove `a`: `Mroonaga` -> `Mroonga`

Execution example:

    select \
      --table Entries \
      --fuzzy_max_distance 2 \
      --fuzzy_with_transposition no \
      --match_columns content \
      --query groonag \
      --output_columns content,_score
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "I started to use Groonga. It's very fast!",
    #         1
    #       ]
    #     ]
    #   ]
    # ]

#### 7.3.58.4.5.6. `fuzzy_with_tokenize`

Added in version 13.0.9.

Note

This is an experimental feature. Currently, this feature is still not stable.

The default value is `no`.

You can choose whether tokenize the given term or not before fuzzy query. If tokenizer is TokenNgram and the given term is `he11o`, it’s tokenized to `he`, `11` and `o`. If this value is `yes`, fixed terms are searched for each of them. For example, `hi` is found as a fixed term of `he`, `12` is found as a fixed term of `11` and `x` is found as a fixed term of `o`. And `hi12x` is searched. If this value is `no`, fixed terms are searched for `he11o`. For example, `hello` is found as a fixed term of `he11o` and `hello` is searched.

Note that each term is separated by ore or more space characters before fuzzy query is executed. For example, `hello world` are separated to `hello` and `world` by query parser. (See also Query syntax.) `hello` and `world` are processed separately.

For morphological analyzer based tokenizer such as TokenMecab, `no` is suitable. Because typo-ed term isn’t tokenized as you expected for most cases. For example, `ともて` (a typo of `とても`) may be tokenized to `とも `` (adjective) and ``て` (conjunctive particle) not `ともて` (adverb).

Here is an example that tokenizes the given term before fuzzy query. In this example, you can’t use `Groonga` as a fixed term for `gr00nga` because `gr`, `00` and `nga` are processed separately.

Execution example:

    select \
      --table Entries \
      --fuzzy_max_distance 2 \
      --fuzzy_tokenize yes \
      --match_columns content \
      --query gr00nga \
      --output_columns content,_score
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         0
    #       ],
    #       [
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ]
    #       ]
    #     ]
    #   ]
    # ]

### 7.3.58.4.6. Dynamic column related parameters

Added in version 6.0.6.

This section describes dynamic column related parameters. You can use dynamic column for window function but this section doesn’t describe window function. See Window function related parameters for window function.

You can create zero or more columns and fill values into these columns while a `select` execution. These columns are called as “dynamic columns”. You can use dynamic columns as same as normal columns after dynamic columns are created.

Dynamic column has performance merit because its values are computed at once and reused computed values.

Dynamic column increases memory usage because its values are kept while the `select` execution.

You need to use dynamic column in the following cases:

> - You want to name values like `AS` in SQL.

> - You want to use computed values for drilldown. Groonga doesn’t support drilldown target value computation in drilldown.

> - You want to use window function.

There are some points to create dynamic columns. You must specify `stage` to each dynamic column to control dynamic columns creation points. It’s important that you choose proper point to get better performance.

For example, it’s not a good idea that you create a dynamic column that is only used for output for all records. The number of output records will be a little even if there are many records in a table. Because you will filter, sort and limit all records and output only the limited records in many cases.

See columns[${NAME}].stage for `stage` detail.

Here are parameters for dynamic column. They don’t include window function related parameters. See Window function related parameters for window function related parameters:

Name | Default value | Required or optional
--- | --- | ---
`columns[${NAME}].stage` | `null` | Required
`columns[${NAME}].flags` | `COLUMN_SCALAR` | Optional
`columns[${NAME}].type` | `null` | Required
`columns[${NAME}].value` | `null` | Required

You need to specify multiple parameters for a dynamic column. `${NAME}` is the name for each dynamic column. Parameters that use the same `${NAME}` are treated as parameters for the same dynamic column. Here is an example to specify parameters for 2 dynamic columns (`name1` and `name2`):

    --columns[name1].stage initial
    --columns[name1].type UInt32
    --columns[name1].value 29

    --columns[name2].stage filtered
    --columns[name2].type ShortText
    --columns[name2].value "29"

#### 7.3.58.4.6.1. `columns[${NAME}].stage`

Added in version 6.0.6.

Specifies when the dynamic column is created. This is a required parameter to create a dynamic column.

Here are available stages:

Name | Description
--- | ---
`initial` | Dynamic column is created at first.
`filtered` | Dynamic column is created after query and filter are evaluated.
`output` | Dynamic column is created before output_columns is evaluated.

Here is the `select` process flow with dynamic column creation points. You should choose stage as late as possible:

> 1. Creates dynamic columns for `initial` stage. All records have these dynamic columns.

> 2. Evaluates query and filter. You can use dynamic columns created in `initial` stage.

> 3. Creates dynamic columns for `filtered` stage. Only filtered records have these dynamic columns.

> 4. Evaluates adjuster. You can use dynamic columns created in `initial` stage and `filtered` stage.

> 5. Evaluates scorer. You can use dynamic columns created in `initial` stage and `filtered` stage.

> 6. Evaluates sort_keys, offset and limit. You can use dynamic columns created in `initial` stage and `filtered` stage.

> 7. Evaluates Slice related parameters. You can use dynamic columns created in `initial` stage and `filtered` stage.

> 8. Evaluates Drilldown related parameters and Advanced drilldown related parameters. You can use dynamic columns created in `initial` stage and `filtered` stage. Note that you can create dynamic columns in each drilldown.

> 9. Creates dynamic columns for `output` stage. Only limit records have these dynamic columns.

> 10. Evaluates output_columns. You can use dynamic columns created in `initial` stage, `filtered` stage and `output` stage.

Here is an example that creates `is_popular` column at `initial` stage. You can use `is_popular` in all parameters such as `filter` and `output_columns`:

Execution example:

    select Entries \
      --columns[is_popular].stage initial \
      --columns[is_popular].type Bool \
      --columns[is_popular].value 'n_likes >= 10' \
      --filter is_popular \
      --output_columns _id,is_popular,n_likes
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "is_popular",
    #           "Bool"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         2,
    #         true,
    #         10
    #       ],
    #       [
    #         3,
    #         true,
    #         15
    #       ]
    #     ]
    #   ]
    # ]

#### 7.3.58.4.6.2. `columns[${NAME}].flags`

Added in version 6.0.6.

Specifies flags for the dynamic column. It’s the same as `flags` parameter for column_create. See flags for available flags.

The default value is `COLUMN_SCALAR`.

Here is a `columns[${NAME}].flags` example. It creates a vector column by `COLUMN_VECTOR` flags. `plugin_register functions/vector` is for using vector_new function:

Execution example:

    plugin_register functions/vector
    # [[0,1337566253.89858,0.000355720520019531],true]
    select Entries \
      --columns[vector].stage initial \
      --columns[vector].flags COLUMN_VECTOR \
      --columns[vector].type UInt32 \
      --columns[vector].value 'vector_new(1, 2, 3)' \
      --output_columns _id,vector
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "vector",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         1,
    #         [
    #           1,
    #           2,
    #           3
    #         ]
    #       ],
    #       [
    #         2,
    #         [
    #           1,
    #           2,
    #           3
    #         ]
    #       ],
    #       [
    #         3,
    #         [
    #           1,
    #           2,
    #           3
    #         ]
    #       ],
    #       [
    #         4,
    #         [
    #           1,
    #           2,
    #           3
    #         ]
    #       ],
    #       [
    #         5,
    #         [
    #           1,
    #           2,
    #           3
    #         ]
    #       ]
    #     ]
    #   ]
    # ]

#### 7.3.58.4.6.3. `columns[${NAME}].type`

Added in version 6.0.6.

Specifies value type for the dynamic column. It’s the same as `type` parameter for column_create. See type for available types.

This is a required parameter.

Here is an example that creates a `ShortText` type column. Stored value is casted to `ShortText` automatically. In this example, number is casted to `ShortText`:

Execution example:

    select Entries \
      --columns[n_likes_string].stage initial \
      --columns[n_likes_string].type ShortText \
      --columns[n_likes_string].value n_likes \
      --output_columns _id,n_likes,n_likes_string
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "n_likes_string",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         1,
    #         5,
    #         "5"
    #       ],
    #       [
    #         2,
    #         10,
    #         "10"
    #       ],
    #       [
    #         3,
    #         15,
    #         "15"
    #       ],
    #       [
    #         4,
    #         3,
    #         "3"
    #       ],
    #       [
    #         5,
    #         3,
    #         "3"
    #       ]
    #     ]
    #   ]
    # ]

#### 7.3.58.4.6.4. `columns[${NAME}].value`

Added in version 6.0.6.

Specifies expression that generates values for the dynamic column. The expression uses Script syntax. It’s the same syntax in filter. For example, `1 + 1`, `string_length("Hello")`, `column * 1.08` and so on are valid expressions.

You need to specify Window function as `value` value and other window function related parameters when you use window function. See Window function related parameters for details.

This is a required parameter.

Here is an example that creates a new dynamic column that stores the number of characters of content. This example uses string_length function in `functions/string` plugin to compute the number of characters in a string. plugin_register is used to register `functions/string` plugin:

Execution example:

    plugin_register functions/string
    # [[0,1337566253.89858,0.000355720520019531],true]
    select Entries \
      --columns[content_length].stage initial \
      --columns[content_length].type UInt32 \
      --columns[content_length].value 'string_length(content)' \
      --output_columns _id,content,content_length
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "content_length",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         1,
    #         "Welcome! This is my first post!",
    #         31
    #       ],
    #       [
    #         2,
    #         "I started to use Groonga. It's very fast!",
    #         41
    #       ],
    #       [
    #         3,
    #         "I also started to use Mroonga. It's also very fast! Really fast!",
    #         64
    #       ],
    #       [
    #         4,
    #         "I migrated all Senna system!",
    #         28
    #       ],
    #       [
    #         5,
    #         "I also migrated all Tritonn system!",
    #         35
    #       ]
    #     ]
    #   ]
    # ]

### 7.3.58.4.7. Window function related parameters

Added in version 6.0.6.

This section describes window function related parameters. You need to use dynamic column for using window function. See Dynamic column related parameters for dynamic column.

Window function in Groonga is similar to window function in SQL. Normal function computes its result with only the current record. On the other hand, window function computes its result with multiple records. Window function is useful to data analysis because it can process multiple records.

You can find supported window functions at Window function. For example, window_sum is a window function. It sums numbers in the target records.

Window function processes records that are grouped by the specified group keys. For example, window function processes three groups (`Hello` group, `Groonga` group and `Senna` group) in the following case. window_sum sums `n_likes` values in each group:

Group No. | Group key value | `n_likes` value | window_sum result
--- | --- | --- | ---
1 | `Hello` | 5 | 5
2 | `Groonga` | 10 | 25
2 | `Groonga` | 15 | 25
3 | `Senna` | 3 | 6
3 | `Senna` | 3 | 6

You can specify no group keys. In the case, window function processes only one group that includes all records. window_sum sums all `n_likes` values in the following case:

Group No. | `n_likes` value | window_sum result
--- | --- | ---
1 | 5 | 36
1 | 10 | 36
1 | 15 | 36
1 | 3 | 36
1 | 3 | 36

Window function processes records in each group in the specified order. You can specify no sort keys like the above group keys example.

The behavior when you specify no sort keys depends on each window function specification. For example, window_sum uses different behavior whether sort keys are specified or not. If you specify not sort keys, window_sum sums values of all records in the group and puts it to all target records like the above group keys example. If you specify sort keys, window_sum behaves as cumulative sum. window_sum sums values of all records in the group in sequence and puts the current sum to the current record like the following:

Group No. | Group key value | Sort key value | `n_likes` value | window_sum result | Note
--- | --- | --- | --- | --- | ---
1 | `Hello` | 1 | 5 | 5 | The first record in group No. 1. (`5 = 5`)
2 | `Groonga` | 90 | 10 | 10 | The first record in group No. 2. (`10 = 10`)
2 | `Groonga` | 91 | 15 | 25 | The second record in group No. 2. (`10 + 15 = 25`)
3 | `Senna` | 200 | 3 | 8 | The second record in group No. 3. (`5 + 3 = 8`)
3 | `Senna` | 100 | 5 | 5 | The first record in group No. 3. (`5 = 5`)

Here are parameters for window function. You need to specify both window function related parameters and required dynamic columns parameters. Because window function is implemented based on dynamic column. See Dynamic column related parameters for dynamic column related parameters:

Name | Required or optional | Note
--- | --- | ---
`columns[${NAME}].value` | Required | Use Window function.
`columns[${NAME}].window.sort_keys` | Required if `columns[${NAME}].window.group_keys` isn’t specified. |
`columns[${NAME}].window.group_keys` | Required if `columns[${NAME}].window.sort_keys` isn’t specified. |

#### 7.3.58.4.7.1. `columns[${NAME}].window.sort_keys`

Added in version 6.0.6.

Specifies sort keys in each group. Window function processes records in each group in the specified order.

Sort keys are separated by `,`. Each sort key is column name. It’s the same as sort_keys.

You must specify columns[${NAME}].window.sort_keys or columns[${NAME}].window.group_keys to use window function.

Here is an example that computes cumulative sum per `Entries.tag`. Each group is sorted by `Entries._key`:

Execution example:

    select \
      --table Entries \
      --columns[n_likes_cumulative_sum_per_tag].stage initial \
      --columns[n_likes_cumulative_sum_per_tag].type UInt32 \
      --columns[n_likes_cumulative_sum_per_tag].value 'window_sum(n_likes)' \
      --columns[n_likes_cumulative_sum_per_tag].window.sort_keys _key \
      --columns[n_likes_cumulative_sum_per_tag].window.group_keys tag \
      --sort_keys _key \
      --output_columns tag,_key,n_likes,n_likes_cumulative_sum_per_tag
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "tag",
    #           "ShortText"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "n_likes_cumulative_sum_per_tag",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         "Senna",
    #         "Good-bye Senna",
    #         3,
    #         3
    #       ],
    #       [
    #         "Senna",
    #         "Good-bye Tritonn",
    #         3,
    #         6
    #       ],
    #       [
    #         "Groonga",
    #         "Groonga",
    #         10,
    #         10
    #       ],
    #       [
    #         "Groonga",
    #         "Mroonga",
    #         15,
    #         25
    #       ],
    #       [
    #         "Hello",
    #         "The first post!",
    #         5,
    #         5
    #       ]
    #     ]
    #   ]
    # ]

#### 7.3.58.4.7.2. `columns[${NAME}].window.group_keys`

Added in version 7.0.0.

Specifies group keys. Window function processes records in each group. If you specify no group keys, window function processes one group that includes all records.

Group keys are separated by `,`. Each group key is column name. It’s the same as drilldown.

You must specify columns[${NAME}].window.sort_keys or columns[${NAME}].window.group_keys to use window function.

Here is an example that computes sum per `Entries.tag`:

Execution example:

    select \
      --table Entries \
      --columns[n_likes_sum_per_tag].stage initial \
      --columns[n_likes_sum_per_tag].type UInt32 \
      --columns[n_likes_sum_per_tag].value 'window_sum(n_likes)' \
      --columns[n_likes_sum_per_tag].window.group_keys tag \
      --sort_keys _key \
      --output_columns tag,_key,n_likes,n_likes_sum_per_tag
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "tag",
    #           "ShortText"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "n_likes_sum_per_tag",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         "Senna",
    #         "Good-bye Senna",
    #         3,
    #         6
    #       ],
    #       [
    #         "Senna",
    #         "Good-bye Tritonn",
    #         3,
    #         6
    #       ],
    #       [
    #         "Groonga",
    #         "Groonga",
    #         10,
    #         25
    #       ],
    #       [
    #         "Groonga",
    #         "Mroonga",
    #         15,
    #         25
    #       ],
    #       [
    #         "Hello",
    #         "The first post!",
    #         5,
    #         5
    #       ]
    #     ]
    #   ]
    # ]

### 7.3.58.4.8. Drilldown related parameters

This section describes basic drilldown related parameters. Advanced drilldown related parameters are described in another section.

#### 7.3.58.4.8.1. `drilldown`

Specifies keys for grouping separated by `,`.

Matched records by specified search conditions are grouped by each key. If you specify no search condition, all records are grouped by each key.

Here is a simple `drilldown` example:

Execution example:

    select Entries \
      --output_columns _key,tag \
      --drilldown tag
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "tag",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         "The first post!",
    #         "Hello"
    #       ],
    #       [
    #         "Groonga",
    #         "Groonga"
    #       ],
    #       [
    #         "Mroonga",
    #         "Groonga"
    #       ],
    #       [
    #         "Good-bye Senna",
    #         "Senna"
    #       ],
    #       [
    #         "Good-bye Tritonn",
    #         "Senna"
    #       ]
    #     ],
    #     [
    #       [
    #         3
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "_nsubrecs",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "Hello",
    #         1
    #       ],
    #       [
    #         "Groonga",
    #         2
    #       ],
    #       [
    #         "Senna",
    #         2
    #       ]
    #     ]
    #   ]
    # ]

The `select` command outputs the following information:

> - There is one record that has “Hello” tag.

> - There is two records that has “Groonga” tag.

> - There is two records that has “Senna” tag.

Here is a `drilldown` with search condition example:

Execution example:

    select Entries \
      --output_columns _key,tag \
      --filter 'n_likes >= 5' \
      --drilldown tag
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         3
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "tag",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         "The first post!",
    #         "Hello"
    #       ],
    #       [
    #         "Groonga",
    #         "Groonga"
    #       ],
    #       [
    #         "Mroonga",
    #         "Groonga"
    #       ]
    #     ],
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "_nsubrecs",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "Hello",
    #         1
    #       ],
    #       [
    #         "Groonga",
    #         2
    #       ]
    #     ]
    #   ]
    # ]

The `select` command outputs the following information:

> - In records that have 5 or larger as `n_likes` value:

>   - There is one record that has “Hello” tag.

>   - There is two records that has “Groonga” tag.

Here is a `drilldown` with multiple group keys example:

Execution example:

    select Entries \
      --limit 0 \
      --output_columns _id \
      --drilldown tag,n_likes
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ]
    #       ]
    #     ],
    #     [
    #       [
    #         3
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "_nsubrecs",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "Hello",
    #         1
    #       ],
    #       [
    #         "Groonga",
    #         2
    #       ],
    #       [
    #         "Senna",
    #         2
    #       ]
    #     ],
    #     [
    #       [
    #         4
    #       ],
    #       [
    #         [
    #           "_key",
    #           "UInt32"
    #         ],
    #         [
    #           "_nsubrecs",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         5,
    #         1
    #       ],
    #       [
    #         10,
    #         1
    #       ],
    #       [
    #         15,
    #         1
    #       ],
    #       [
    #         3,
    #         2
    #       ]
    #     ]
    #   ]
    # ]

The `select` command outputs the following information:

> - About `tag`:

>   - There is one record that has “Hello” tag.

>   - There is two records that has “Groonga” tag.

>   - There is two records that has “Senna” tag.

> - About `n_likes`:

>   - There is one record that has “Hello” tag.

>   - There is two records that has “Groonga” tag.

>   - There is two records that has “Senna” tag.

#### 7.3.58.4.8.2. `drilldown_sortby`

Deprecated since version 6.0.3: Use drilldown_sort_keys instead.

#### 7.3.58.4.8.3. `drilldown_sort_keys`

Specifies sort keys for drilldown outputs separated by `,`. Each sort key is column name.

You can refer the number of grouped records by `_nsubrecs` Pseudo column.

Here is a simple `drilldown_sort_keys` example:

Execution example:

    select Entries \
      --limit 0 \
      --output_columns _id \
      --drilldown 'tag, n_likes' \
      --drilldown_sort_keys '-_nsubrecs, _key'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ]
    #       ]
    #     ],
    #     [
    #       [
    #         3
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "_nsubrecs",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "Groonga",
    #         2
    #       ],
    #       [
    #         "Senna",
    #         2
    #       ],
    #       [
    #         "Hello",
    #         1
    #       ]
    #     ],
    #     [
    #       [
    #         4
    #       ],
    #       [
    #         [
    #           "_key",
    #           "UInt32"
    #         ],
    #         [
    #           "_nsubrecs",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         3,
    #         2
    #       ],
    #       [
    #         5,
    #         1
    #       ],
    #       [
    #         10,
    #         1
    #       ],
    #       [
    #         15,
    #         1
    #       ]
    #     ]
    #   ]
    # ]

Drilldown result is sorted by the number of grouped records (= `_nsubrecs` ) in descending order. If there are grouped results that the number of records in the group are the same, these grouped results are sorted by grouped key (= `_key` ) in ascending order.

The sort keys are used in all group keys specified in `drilldown`:

Execution example:

    select Entries \
      --limit 0 \
      --output_columns _id \
      --drilldown 'tag, n_likes' \
      --drilldown_sort_keys '-_nsubrecs, _key'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ]
    #       ]
    #     ],
    #     [
    #       [
    #         3
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "_nsubrecs",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "Groonga",
    #         2
    #       ],
    #       [
    #         "Senna",
    #         2
    #       ],
    #       [
    #         "Hello",
    #         1
    #       ]
    #     ],
    #     [
    #       [
    #         4
    #       ],
    #       [
    #         [
    #           "_key",
    #           "UInt32"
    #         ],
    #         [
    #           "_nsubrecs",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         3,
    #         2
    #       ],
    #       [
    #         5,
    #         1
    #       ],
    #       [
    #         10,
    #         1
    #       ],
    #       [
    #         15,
    #         1
    #       ]
    #     ]
    #   ]
    # ]

The same sort keys are used in `tag` drilldown and `n_likes` drilldown.

If you want to use different sort keys for each drilldown, use Advanced drilldown related parameters.

#### 7.3.58.4.8.4. `drilldown_output_columns`

Specifies output columns for drilldown separated by `,`.

Here is a `drilldown_output_columns` example:

Execution example:

    select Entries \
      --limit 0 \
      --output_columns _id \
      --drilldown tag \
      --drilldown_output_columns _key
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ]
    #       ]
    #     ],
    #     [
    #       [
    #         3
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         "Hello"
    #       ],
    #       [
    #         "Groonga"
    #       ],
    #       [
    #         "Senna"
    #       ]
    #     ]
    #   ]
    # ]

The `select` command just outputs grouped key.

If grouped key is a referenced type column (= column that its type is a table), you can access column of the table referenced by the referenced type column.

Here are a schema definition and sample data to show drilldown against referenced type column:

Execution example:

    table_create Tags TABLE_HASH_KEY ShortText --normalizer NormalizerAuto
    # [[0,1337566253.89858,0.000355720520019531],true]
    column_create Tags label COLUMN_SCALAR ShortText
    # [[0,1337566253.89858,0.000355720520019531],true]
    column_create Tags priority COLUMN_SCALAR Int32
    # [[0,1337566253.89858,0.000355720520019531],true]
    table_create Items TABLE_HASH_KEY ShortText
    # [[0,1337566253.89858,0.000355720520019531],true]
    column_create Items tag COLUMN_SCALAR Tags
    # [[0,1337566253.89858,0.000355720520019531],true]
    load --table Tags
    [
    {"_key": "groonga", label: "Groonga", priority: 10},
    {"_key": "mroonga", label: "Mroonga", priority: 5}
    ]
    # [[0,1337566253.89858,0.000355720520019531],2]
    load --table Items
    [
    {"_key": "A", "tag": "groonga"},
    {"_key": "B", "tag": "groonga"},
    {"_key": "C", "tag": "mroonga"}
    ]
    # [[0,1337566253.89858,0.000355720520019531],3]

`Tags` table is a referenced table. `Items.tag` is a referenced type column.

You can refer `Tags.label` by `label` in `drilldown_output_columns`:

Execution example:

    select Items \
      --limit 0 \
      --output_columns _id \
      --drilldown tag \
      --drilldown_output_columns '_key, label'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         3
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ]
    #       ]
    #     ],
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "label",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         "groonga",
    #         "Groonga"
    #       ],
    #       [
    #         "mroonga",
    #         "Mroonga"
    #       ]
    #     ]
    #   ]
    # ]

You can use `*` to refer all columns in referenced table (= `Tags`):

Execution example:

    select Items \
      --limit 0 \
      --output_columns _id \
      --drilldown tag \
      --drilldown_output_columns '_key, *'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         3
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ]
    #       ]
    #     ],
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "label",
    #           "ShortText"
    #         ],
    #         [
    #           "priority",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "groonga",
    #         "Groonga",
    #         10
    #       ],
    #       [
    #         "mroonga",
    #         "Mroonga",
    #         5
    #       ]
    #     ]
    #   ]
    # ]

`*` is expanded to `label, priority`.

The default value of `drilldown_output_columns` is `_key, _nsubrecs`. It means that grouped key and the number of records in the group are output.

You can use more Pseudo column in `drilldown_output_columns` such as `_max`, `_min`, `_sum` and `_avg` when you use drilldown_calc_types. See `drilldown_calc_types` document for details.

#### 7.3.58.4.8.5. `drilldown_offset`

Specifies offset to determine range of drilldown output records. Offset is zero-based. `--drilldown_offset 1` means output range is started from the 2nd record.

Here is a `drilldown_offset` example:

Execution example:

    select Entries \
      --limit 0 \
      --output_columns _id \
      --drilldown tag \
      --drilldown_sort_keys _key \
      --drilldown_offset 1
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ]
    #       ]
    #     ],
    #     [
    #       [
    #         3
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "_nsubrecs",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "Hello",
    #         1
    #       ],
    #       [
    #         "Senna",
    #         2
    #       ]
    #     ]
    #   ]
    # ]

The `select` command outputs from the 2nd record.

You can specify negative value. It means that `the number of grouped results + offset`. If you have 3 grouped results and specify `--drilldown_offset -2`, you get grouped results from the 2st (`3 + -2 = 1`. `1` means 2nd. Remember that offset is zero-based.) grouped result to the 3rd grouped result.

Execution example:

    select Entries \
      --limit 0 \
      --output_columns _id \
      --drilldown tag \
      --drilldown_sort_keys _key \
      --drilldown_offset -2
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ]
    #       ]
    #     ],
    #     [
    #       [
    #         3
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "_nsubrecs",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "Hello",
    #         1
    #       ],
    #       [
    #         "Senna",
    #         2
    #       ]
    #     ]
    #   ]
    # ]

The `select` command outputs from the 2nd grouped result because the total number of grouped results is `3`.

The default value of `drilldown_offset` is `0`.

#### 7.3.58.4.8.6. `drilldown_limit`

Specifies the max number of groups in a drilldown. If the number of groups is less than `drilldown_limit`, all groups are outputted.

Here is a `drilldown_limit` example:

Execution example:

    select Entries \
      --limit 0 \
      --output_columns _id \
      --drilldown tag \
      --drilldown_sort_keys _key \
      --drilldown_offset 1 \
      --drilldown_limit 2
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ]
    #       ]
    #     ],
    #     [
    #       [
    #         3
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "_nsubrecs",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "Hello",
    #         1
    #       ],
    #       [
    #         "Senna",
    #         2
    #       ]
    #     ]
    #   ]
    # ]

The `select` command outputs the 2rd and the 3rd groups.

You can specify negative value. It means that `the number of groups + drilldown_limit + 1`. For example, `--drilldown_limit -1` outputs all groups. It’s very useful value to show all groups.

Here is a negative `drilldown_limit` value example.

Execution example:

    select Entries \
      --limit 0 \
      --output_columns _id \
      --drilldown tag \
      --drilldown_sort_keys _key \
      --drilldown_limit -1
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ]
    #       ]
    #     ],
    #     [
    #       [
    #         3
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "_nsubrecs",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "Groonga",
    #         2
    #       ],
    #       [
    #         "Hello",
    #         1
    #       ],
    #       [
    #         "Senna",
    #         2
    #       ]
    #     ]
    #   ]
    # ]

The `select` command outputs all groups.

The default value of `drilldown_limit` is `10`.

#### 7.3.58.4.8.7. `drilldown_calc_types`

Specifies how to calculate (aggregate) values in grouped records by a drilldown. You can specify multiple calculation types separated by “`,`”. For example, `MAX,MIN`.

Calculation target values are read from a column of grouped records. The column is specified by drilldown_calc_target.

You can read calculated value by Pseudo column such as `_max` and `_min` in drilldown_output_columns.

You can use the following calculation types:

Type name | Pseudo column name | Need drilldown_calc_target | Description
--- | --- | --- | ---
`NONE` | Nothing. | Not needs. | Just ignored.
`COUNT` | `_nsubrecs` | Not needs. | Counting grouped records. It’s always enabled. So you don’t need to specify it.
`MAX` | `_max` | Needs. | Finding the maximum integer value from integer values in grouped records.
`MIN` | `_min` | Needs. | Finding the minimum integer value from integer values in grouped records.
`SUM` | `_sum` | Needs. | Summing integer values in grouped records.
`AVG` | `_avg` | Needs. | Averaging integer/float values in grouped records.

Here is a `MAX` example:

Execution example:

    select Entries \
      --limit -1 \
      --output_columns _id,n_likes \
      --drilldown tag \
      --drilldown_calc_types MAX \
      --drilldown_calc_target n_likes \
      --drilldown_output_columns _key,_max
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         1,
    #         5
    #       ],
    #       [
    #         2,
    #         10
    #       ],
    #       [
    #         3,
    #         15
    #       ],
    #       [
    #         4,
    #         3
    #       ],
    #       [
    #         5,
    #         3
    #       ]
    #     ],
    #     [
    #       [
    #         3
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "_max",
    #           "Int64"
    #         ]
    #       ],
    #       [
    #         "Hello",
    #         5
    #       ],
    #       [
    #         "Groonga",
    #         15
    #       ],
    #       [
    #         "Senna",
    #         3
    #       ]
    #     ]
    #   ]
    # ]

The `select` command groups all records by `tag` column value, finding the maximum `n_likes` column value for each group and outputs pairs of grouped key and the maximum `n_likes` column value for the group. It uses `_max` Pseudo column to read the maximum `n_likes` column value.

Here is a `MIN` example:

Execution example:

    select Entries \
      --limit -1 \
      --output_columns _id,n_likes \
      --drilldown tag \
      --drilldown_calc_types MIN \
      --drilldown_calc_target n_likes \
      --drilldown_output_columns _key,_min
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         1,
    #         5
    #       ],
    #       [
    #         2,
    #         10
    #       ],
    #       [
    #         3,
    #         15
    #       ],
    #       [
    #         4,
    #         3
    #       ],
    #       [
    #         5,
    #         3
    #       ]
    #     ],
    #     [
    #       [
    #         3
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "_min",
    #           "Int64"
    #         ]
    #       ],
    #       [
    #         "Hello",
    #         5
    #       ],
    #       [
    #         "Groonga",
    #         10
    #       ],
    #       [
    #         "Senna",
    #         3
    #       ]
    #     ]
    #   ]
    # ]

The `select` command groups all records by `tag` column value, finding the minimum `n_likes` column value for each group and outputs pairs of grouped key and the minimum `n_likes` column value for the group. It uses `_min` Pseudo column to read the minimum `n_likes` column value.

Here is a `SUM` example:

Execution example:

    select Entries \
      --limit -1 \
      --output_columns _id,n_likes \
      --drilldown tag \
      --drilldown_calc_types SUM \
      --drilldown_calc_target n_likes \
      --drilldown_output_columns _key,_sum
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         1,
    #         5
    #       ],
    #       [
    #         2,
    #         10
    #       ],
    #       [
    #         3,
    #         15
    #       ],
    #       [
    #         4,
    #         3
    #       ],
    #       [
    #         5,
    #         3
    #       ]
    #     ],
    #     [
    #       [
    #         3
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "_sum",
    #           "Int64"
    #         ]
    #       ],
    #       [
    #         "Hello",
    #         5
    #       ],
    #       [
    #         "Groonga",
    #         25
    #       ],
    #       [
    #         "Senna",
    #         6
    #       ]
    #     ]
    #   ]
    # ]

The `select` command groups all records by `tag` column value, sums all `n_likes` column values for each group and outputs pairs of grouped key and the summed `n_likes` column values for the group. It uses `_sum` Pseudo column to read the summed `n_likes` column values.

Here is a `AVG` example:

Execution example:

    select Entries \
      --limit -1 \
      --output_columns _id,n_likes \
      --drilldown tag \
      --drilldown_calc_types AVG \
      --drilldown_calc_target n_likes \
      --drilldown_output_columns _key,_avg
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         1,
    #         5
    #       ],
    #       [
    #         2,
    #         10
    #       ],
    #       [
    #         3,
    #         15
    #       ],
    #       [
    #         4,
    #         3
    #       ],
    #       [
    #         5,
    #         3
    #       ]
    #     ],
    #     [
    #       [
    #         3
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "_avg",
    #           "Float"
    #         ]
    #       ],
    #       [
    #         "Hello",
    #         5.0
    #       ],
    #       [
    #         "Groonga",
    #         12.5
    #       ],
    #       [
    #         "Senna",
    #         3.0
    #       ]
    #     ]
    #   ]
    # ]

The `select` command groups all records by `tag` column value, averages all `n_likes` column values for each group and outputs pairs of grouped key and the averaged `n_likes` column values for the group. It uses `_avg` Pseudo column to read the averaged `n_likes` column values.

Here is an example that uses all calculation types:

Execution example:

    select Entries \
      --limit -1 \
      --output_columns _id,n_likes \
      --drilldown tag \
      --drilldown_calc_types MAX,MIN,SUM,AVG \
      --drilldown_calc_target n_likes \
      --drilldown_output_columns _key,_nsubrecs,_max,_min,_sum,_avg
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         1,
    #         5
    #       ],
    #       [
    #         2,
    #         10
    #       ],
    #       [
    #         3,
    #         15
    #       ],
    #       [
    #         4,
    #         3
    #       ],
    #       [
    #         5,
    #         3
    #       ]
    #     ],
    #     [
    #       [
    #         3
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "_nsubrecs",
    #           "Int32"
    #         ],
    #         [
    #           "_max",
    #           "Int64"
    #         ],
    #         [
    #           "_min",
    #           "Int64"
    #         ],
    #         [
    #           "_sum",
    #           "Int64"
    #         ],
    #         [
    #           "_avg",
    #           "Float"
    #         ]
    #       ],
    #       [
    #         "Hello",
    #         1,
    #         5,
    #         5,
    #         5,
    #         5.0
    #       ],
    #       [
    #         "Groonga",
    #         2,
    #         15,
    #         10,
    #         25,
    #         12.5
    #       ],
    #       [
    #         "Senna",
    #         2,
    #         3,
    #         3,
    #         6,
    #         3.0
    #       ]
    #     ]
    #   ]
    # ]

The `select` command specifies multiple calculation types separated by “`,`” like `MAX,MIN,SUM,AVG`. You can use `_nsubrecs` Pseudo column in drilldown_output_columns without specifying `COUNT` in `drilldown_calc_types`. Because `COUNT` is always enabled.

The default value of `drilldown_calc_types` is `NONE`. It means that only `COUNT` is enabled. Because `NONE` is just ignored and `COUNT` is always enabled.

#### 7.3.58.4.8.8. `drilldown_calc_target`

Added in version 6.0.3.

Specifies the target column for drilldown_calc_types.

If you specify a calculation type that needs a target column such as `MAX` in drilldown_calc_types but you omit `drilldown_calc_target`, the calculation result is always `0`.

You can specify only one column name like `--drilldown_calc_target n_likes`. You can’t specify multiple column name like `--drilldown_calc_target _key,n_likes`.

You can use referenced value from the target record by combining “`.`” like `--drilldown_calc_target reference_column.nested_reference_column.value`.

See drilldown_calc_types to know how to use `drilldown_calc_target`.

The default value of `drilldown_calc_target` is `null`. It means that no calculation target column is specified.

#### 7.3.58.4.8.9. `drilldown_filter`

Added in version 6.0.3.

Specifies the filter condition against the drilled down result.

The syntax is Script syntax. It’s the same as filter.

Here is an example to suppress tags that are occurred only once:

Execution example:

    select Entries \
      --limit -1 \
      --output_columns _id,tag \
      --drilldown tag \
      --drilldown_filter '_nsubrecs > 1' \
      --drilldown_output_columns _key,_nsubrecs
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "tag",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         1,
    #         "Hello"
    #       ],
    #       [
    #         2,
    #         "Groonga"
    #       ],
    #       [
    #         3,
    #         "Groonga"
    #       ],
    #       [
    #         4,
    #         "Senna"
    #       ],
    #       [
    #         5,
    #         "Senna"
    #       ]
    #     ],
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "_nsubrecs",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "Groonga",
    #         2
    #       ],
    #       [
    #         "Senna",
    #         2
    #       ]
    #     ]
    #   ]
    # ]

#### 7.3.58.4.8.10. `drilldown_max_n_target_records`

Added in version 12.0.0.

Specifies the max number of records of the drilldown target table (filtered result) to use drilldown. If the number of filtered result is larger than the specified value, some records in filtered result aren’t used for drilldown.

If the specified value is negative, it’s processed same as limit. For example, `-1` uses all records. The default value is `-1`. It means that all filtered records are used by default.

This feature is useful when filtered result may be very large. A drilldown against large filtered result may be slow. You can limit the max number of records to be used for drilldown by this feature.

Here is an example to limit the max number of records to be used for drilldown. The last 2 records, `{"_id": 4, "tag": "Senna"}` and `{"_id": 5, "tag": "Senna"}`, aren’t used:

Execution example:

    select Entries \
      --limit -1 \
      --output_columns _id,tag \
      --drilldown tag \
      --drilldown_max_n_target_records 3
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "tag",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         1,
    #         "Hello"
    #       ],
    #       [
    #         2,
    #         "Groonga"
    #       ],
    #       [
    #         3,
    #         "Groonga"
    #       ],
    #       [
    #         4,
    #         "Senna"
    #       ],
    #       [
    #         5,
    #         "Senna"
    #       ]
    #     ],
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "_nsubrecs",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "Hello",
    #         1
    #       ],
    #       [
    #         "Groonga",
    #         2
    #       ]
    #     ]
    #   ]
    # ]

### 7.3.58.4.9. Advanced drilldown related parameters

Added in version 4.0.8.

You can get multiple drilldown results by specifying multiple group keys by drilldown. But you need to use the same configuration for all drilldowns. For example, drilldown_output_columns is used by all drilldowns.

You can use a configuration for each drilldown by the following parameters:

> - `drilldowns[${LABEL}].keys`

> - `drilldowns[${LABEL}].table`

> - `drilldowns[${LABEL}].sort_keys`

> - `drilldowns[${LABEL}].output_columns`

> - `drilldowns[${LABEL}].offset`

> - `drilldowns[${LABEL}].limit`

> - `drilldowns[${LABEL}].calc_types`

> - `drilldowns[${LABEL}].calc_target`

> - `drilldowns[${LABEL}].filter`

> - `drilldowns[${LABEL}].max_n_target_records`

> - `drilldowns[${LABEL}].key_vector_expansion`

> - `drilldowns[${LABEL}].columns[${NAME}].stage=null`

> - `drilldowns[${LABEL}].columns[${NAME}].flags=COLUMN_SCALAR`

> - `drilldowns[${LABEL}].columns[${NAME}].type=null`

> - `drilldowns[${LABEL}].columns[${NAME}].value=null`

> - `drilldowns[${LABEL}].columns[${NAME}].window.sort_keys=null`

> - `drilldowns[${LABEL}].columns[${NAME}].window.group_keys=null`

`${LABEL}` is a variable. You can use the following characters for `${LABEL}`:

> - Alphabets

> - Digits

> - `.`

> - `_`

`${NAME}` is a variable. You can use the following characters for `${NAME}`:

> - Alphabets

> - Digits

> - `_`

Note

You can use more characters but it’s better that you use only these characters.

Parameters that has the same `${LABEL}` value are grouped. Grouped parameters are used for one drilldown.

For example, there are 2 groups for the following parameters:

> - `--drilldowns[label1].keys _key`

> - `--drilldowns[label1].output_columns _nsubrecs`

> - `--drilldowns[label2].keys tag`

> - `--drilldowns[label2].output_columns _key,_nsubrecs`

`drilldowns[label1].keys` and `drilldowns[label1].output_columns` are grouped. `drilldowns[label2].keys` and `drilldowns[label2].output_columns` are also grouped.

In `label1` group, `_key` is used for group key and `_nsubrecs` is used for output columns.

In `label2` group, `tag` is used for group key and `_key,_nsubrecs` is used for output columns.

See document for corresponding `drilldown_XXX` parameter to know how to use it for the following parameters:

> - `drilldowns[${LABEL}].sort_keys`: drilldown_sort_keys

> - `drilldowns[${LABEL}].offset`: drilldown_offset

> - `drilldowns[${LABEL}].limit`: drilldown_limit

> - `drilldowns[${LABEL}].calc_types`: drilldown_calc_types

> - `drilldowns[${LABEL}].calc_target`: drilldown_calc_target

> - `drilldowns[${LABEL}].filter`: drilldown_filter

> - `drilldowns[${LABEL}].max_n_target_records`: drilldown_max_n_target_records

See document for corresponding `columns[${NAME}].XXX` parameter to know how to use it for the following parameters:

> - `drilldowns[${LABEL}].columns[${NAME}].flags=COLUMN_SCALAR`: columns[${NAME}].flags

> - `drilldowns[${LABEL}].columns[${NAME}].type=null`: columns[${NAME}].type

> - `drilldowns[${LABEL}].columns[${NAME}].value=null`: columns[${NAME}].value

> - `drilldowns[${LABEL}].columns[${NAME}].window.sort_keys=null`: columns[${NAME}].window.sort_keys

> - `drilldowns[${LABEL}].columns[${NAME}].window.group_keys=null`: columns[${NAME}].window.group_keys

The following parameters are needed more description:

> - `drilldowns[${LABEL}].keys`

> - `drilldowns[${LABEL}].table`

> - `drilldowns[${LABEL}].output_columns`

> - `drilldowns[${LABEL}].columns[${NAME}].stage=null`

Output format is different a bit. It’s also needed more description.

#### 7.3.58.4.9.1. `drilldowns[${LABEL}].keys`

Added in version 4.0.8.

drilldown can specify multiple keys for multiple drilldowns. But it can’t specify multiple keys for one drilldown.

`drilldowns[${LABEL}].keys` can’t specify multiple keys for multiple drilldowns. But it can specify multiple keys for one drilldown.

You can specify multiple keys separated by “`,`”.

Here is an example to group by multiple keys, `tag` and `n_likes` column values:

Execution example:

    select Entries \
      --limit -1 \
      --output_columns tag,n_likes \
      --drilldowns[tag.n_likes].keys tag,n_likes \
      --drilldowns[tag.n_likes].output_columns _value.tag,_value.n_likes,_nsubrecs
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "tag",
    #           "ShortText"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ]
    #       ],
    #       [
    #         "Hello",
    #         5
    #       ],
    #       [
    #         "Groonga",
    #         10
    #       ],
    #       [
    #         "Groonga",
    #         15
    #       ],
    #       [
    #         "Senna",
    #         3
    #       ],
    #       [
    #         "Senna",
    #         3
    #       ]
    #     ],
    #     {
    #       "tag.n_likes": [
    #         [
    #           4
    #         ],
    #         [
    #           [
    #             "tag",
    #             "ShortText"
    #           ],
    #           [
    #             "n_likes",
    #             "UInt32"
    #           ],
    #           [
    #             "_nsubrecs",
    #             "Int32"
    #           ]
    #         ],
    #         [
    #           "Hello",
    #           5,
    #           1
    #         ],
    #         [
    #           "Groonga",
    #           10,
    #           1
    #         ],
    #         [
    #           "Groonga",
    #           15,
    #           1
    #         ],
    #         [
    #           "Senna",
    #           3,
    #           2
    #         ]
    #       ]
    #     }
    #   ]
    # ]

`tag.n_likes` is used as the label for the drilldown parameters group. You can refer grouped keys by `_value.${KEY_NAME}` syntax in drilldowns[${LABEL}].output_columns. `${KEY_NAME}` is a column name to be used by group key. `tag` and `n_likes` are `${KEY_NAME}` in this case.

Note that you can’t use `_value.${KEY_NAME}` syntax when you just specify one key as `drilldowns[${LABEL}].keys` like `--drilldowns[tag].keys tag`. You should use `_key` for the case. It’s the same rule in drilldown_output_columns.

#### 7.3.58.4.9.2. `drilldowns[${LABEL}].table`

Added in version 6.0.2.

Specify `${LABEL}` of other `drilldowns` or `slices`.

You can drilldown the result of specified `${LABEL}`. It means that this parameter enables a nested drilldown.

Here is an example to execute the nested drilldown. The final result takes first drilldown by `tag` and then 2nd drilldown by `category` against first result.

Execution example:

    table_create NestedDrilldownTags TABLE_PAT_KEY ShortText
    # [[0,1337566253.89858,0.000355720520019531],true]
    column_create NestedDrilldownTags category COLUMN_SCALAR ShortText
    # [[0,1337566253.89858,0.000355720520019531],true]
    table_create NestedDrilldownMemos TABLE_HASH_KEY ShortText
    # [[0,1337566253.89858,0.000355720520019531],true]
    column_create NestedDrilldownMemos tag COLUMN_SCALAR NestedDrilldownTags
    # [[0,1337566253.89858,0.000355720520019531],true]
    load --table NestedDrilldownMemos
    [
    {"_key": "Groonga is fast!", "tag": "Groonga"},
    {"_key": "Groonga sticker!", "tag": "Groonga"},
    {"_key": "Mroonga sticker!", "tag": "Mroonga"},
    {"_key": "Rroonga is fast!", "tag": "Rroonga"}
    ]
    # [[0,1337566253.89858,0.000355720520019531],4]
    load --table NestedDrilldownTags
    [
    {"_key": "Groonga", "category": "C/C++"},
    {"_key": "Mroonga", "category": "C/C++"},
    {"_key": "PGroonga", "category": "C/C++"},
    {"_key": "Rroonga", "category": "Ruby"}
    ]
    # [[0,1337566253.89858,0.000355720520019531],4]
    select NestedDrilldownMemos \
      --drilldowns[Tag].keys tag \
      --drilldowns[Tag].output_columns _key \
      --drilldowns[Category].table Tag \
      --drilldowns[Category].keys category \
      --drilldowns[Category].output_columns _key,_nsubrecs
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         4
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "tag",
    #           "NestedDrilldownTags"
    #         ]
    #       ],
    #       [
    #         1,
    #         "Groonga is fast!",
    #         "Groonga"
    #       ],
    #       [
    #         2,
    #         "Groonga sticker!",
    #         "Groonga"
    #       ],
    #       [
    #         3,
    #         "Mroonga sticker!",
    #         "Mroonga"
    #       ],
    #       [
    #         4,
    #         "Rroonga is fast!",
    #         "Rroonga"
    #       ]
    #     ],
    #     {
    #       "Tag": [
    #         [
    #           3
    #         ],
    #         [
    #           [
    #             "_key",
    #             "ShortText"
    #           ]
    #         ],
    #         [
    #           "Groonga"
    #         ],
    #         [
    #           "Mroonga"
    #         ],
    #         [
    #           "Rroonga"
    #         ]
    #       ],
    #       "Category": [
    #         [
    #           2
    #         ],
    #         [
    #           [
    #             "_key",
    #             "ShortText"
    #           ],
    #           [
    #             "_nsubrecs",
    #             "Int32"
    #           ]
    #         ],
    #         [
    #           "C/C++",
    #           2
    #         ],
    #         [
    #           "Ruby",
    #           1
    #         ]
    #       ]
    #     }
    #   ]
    # ]

In this example; The schema contains the table named as `NestedDrilldownMemo` which has the column named as `tag`, the table named as `NestedDrilldownTags` which has the column named as `category`.

`Tag` drilldowns `NestedDrilldownMemos` by `tag`. Thus, the result of `Tag` contains one row each for `Groonga`, `Mroonga` and `Rroonga`. And then, `Category` drilldowns `Tag` by `category`. Thus the result of `Category` contains two records has `C/C++` and one records has `Ruby`.

#### 7.3.58.4.9.3. `drilldowns[${LABEL}].key_vector_expansion`

Added in version 12.1.1.

This specifies how to expand key when keys for drilldown are vector. Currently, `NONE` or `POWER_SET` are able to be specified.

This work only time when one key is target for drilldown. This doesn’t work time when more than 2 keys are target for drilldown.

##### 7.3.58.4.9.3.1. `NONE`

This works as same as `key_vector_expansion` is not specified.

Keys would not be expanded. Each element within a vector works as each key.

Following is a sample to aggregate total number of individual and combination occurrence for 3 tags, `Groonga`, `Mroonga`, and `PGroonga`.

Execution example:

    table_create NoneExpantionDrilldownMemos TABLE_HASH_KEY ShortText
    # [[0,1337566253.89858,0.000355720520019531],true]
    column_create NoneExpantionDrilldownMemos tags COLUMN_VECTOR ShortText
    # [[0,1337566253.89858,0.000355720520019531],true]
    load --table NoneExpantionDrilldownMemos
    [
    {"_key": "Groonga is fast!", "tags": ["Groonga"]},
    {"_key": "Mroonga uses Groonga!", "tags": ["Groonga", "Mroonga"]},
    {"_key": "PGroonga uses Groonga!", "tags": ["Groonga", "PGroonga"]},
    {"_key": "Mroonga and PGroonga are Groonga family", "tags": ["Groonga", "Mroonga", "PGroonga"]}
    ]
    # [[0,1337566253.89858,0.000355720520019531],4]
    select NoneExpantionDrilldownMemos \
      --drilldowns[tags].keys tags \
      --drilldowns[tags].key_vector_expansion NONE \
      --drilldowns[tags].columns[none_expantion].stage initial \
      --drilldowns[tags].columns[none_expantion].value _key \
      --drilldowns[tags].columns[none_expantion].flags COLUMN_VECTOR \
      --drilldowns[tags].sort_keys 'none_expantion' \
      --drilldowns[tags].output_columns 'none_expantion, _nsubrecs' \
      --limit 0
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         4
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "tags",
    #           "ShortText"
    #         ]
    #       ]
    #     ],
    #     {
    #       "tags": [
    #         [
    #           3
    #         ],
    #         [
    #           [
    #             "none_expantion",
    #             "Text"
    #           ],
    #           [
    #             "_nsubrecs",
    #             "Int32"
    #           ]
    #         ],
    #         [
    #           [
    #             "Groonga"
    #           ],
    #           4
    #         ],
    #         [
    #           [
    #             "Mroonga"
    #           ],
    #           2
    #         ],
    #         [
    #           [
    #             "PGroonga"
    #           ],
    #           2
    #         ]
    #       ]
    #     }
    #   ]
    # ]

Following are facts from the results.

tag | number of occurrence（ `_nsubrecs` ）
`Groonga` | 4
`Mroonga` | 2
`PGroonga` | 2

##### 7.3.58.4.9.3.2. `POWER_SET`

This aggregates total with expanding vectors to the power set. In this case, a target vector is considered as multi set. Thus, each element is considered as an individual element when there are multiple elements with same value.

For example, there is a vector `[A, B, C]`. In this case, a target set is `{A, B, C}`. The power set is aggregation for all of subsets within a set. Following shows all of subset within a set `{A, B, C}`. However, Groonga does not use empty set, that number of elements is 0. It is because empty set is not useful for results of drilldown. Please report at issue, if you find a case to use empty set.

- Subset with 1 element

  - `{A}`

  - `{B}`

  - `{C}`

- Subset with 2 elements

  - `{A, B}`

  - `{B, C}`

  - `{A, C}`

- Subset with 3 elements

  - `{A, B, C}`

Those are all subsets for `{A, B, C}`. Since the power set is aggregation of those subsets, `{{A}, {B}, {C}, {A, B}, {B, C}, {A, C}, {A, B, C}}` is a power set for the vector.

`POWER_SET` aggregates with each subset for `{{A}, {B}, {C}, {A, B}, {B, C}, {A, C}, {A, B, C}}`.

For example, there is a case to aggregate `[A, B, C]` and `[B, C, D]` with the power set.

A power set for `[A, B, C]` is `{{A}, {B}, {C}, {A, B}, {B, C}, {A, C}, {A, B, C}}` as previously explained. Likewise a power set for `[B, C, D]` is `{{B}, {C}, {D}, {B, C}, {C, D}, {B, D}, {B, C, D}}`.

Aggregating occurrence of each subset in each power set is shown in result as follows.

subset | number of occurrence（ `_nsubrecs` ）
`{A}` | 1
`{B}` | 2
`{C}` | 2
`{D}` | 1
`{A, B}` | 1
`{A, C}` | 1
`{B, C}` | 2
`{B, D}` | 1
`{C, D}` | 1
`{A, B, C}` | 1
`{B, C, D}` | 1

This aggregating methods is useful when it is requirement to sum total number of occurrence for individual tags and combination of tags at once.

Following is an example to aggregate total occurrence for individual and combination of 3 tags, `Groonga`, `Mroonga`, and `PGroonga`.

Execution example:

    table_create PowerSetDrilldownMemos TABLE_HASH_KEY ShortText
    # [[0,1337566253.89858,0.000355720520019531],true]
    column_create PowerSetDrilldownMemos tags COLUMN_VECTOR ShortText
    # [[0,1337566253.89858,0.000355720520019531],true]
    load --table PowerSetDrilldownMemos
    [
    {"_key": "Groonga is fast!", "tags": ["Groonga"]},
    {"_key": "Mroonga uses Groonga!", "tags": ["Groonga", "Mroonga"]},
    {"_key": "PGroonga uses Groonga!", "tags": ["Groonga", "PGroonga"]},
    {"_key": "Mroonga and PGroonga are Groonga family", "tags": ["Groonga", "Mroonga", "PGroonga"]}
    ]
    # [[0,1337566253.89858,0.000355720520019531],4]
    select PowerSetDrilldownMemos \
      --drilldowns[tags].keys tags \
      --drilldowns[tags].key_vector_expansion POWER_SET \
      --drilldowns[tags].columns[power_set].stage initial \
      --drilldowns[tags].columns[power_set].value _key \
      --drilldowns[tags].columns[power_set].flags COLUMN_VECTOR \
      --drilldowns[tags].sort_keys 'power_set' \
      --drilldowns[tags].output_columns 'power_set, _nsubrecs' \
      --limit 0
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         4
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "tags",
    #           "ShortText"
    #         ]
    #       ]
    #     ],
    #     {
    #       "tags": [
    #         [
    #           7
    #         ],
    #         [
    #           [
    #             "power_set",
    #             "Text"
    #           ],
    #           [
    #             "_nsubrecs",
    #             "Int32"
    #           ]
    #         ],
    #         [
    #           [
    #             "Groonga"
    #           ],
    #           4
    #         ],
    #         [
    #           [
    #             "Mroonga"
    #           ],
    #           2
    #         ],
    #         [
    #           [
    #             "PGroonga"
    #           ],
    #           2
    #         ],
    #         [
    #           [
    #             "Groonga",
    #             "Mroonga"
    #           ],
    #           2
    #         ],
    #         [
    #           [
    #             "Groonga",
    #             "PGroonga"
    #           ],
    #           2
    #         ],
    #         [
    #           [
    #             "Mroonga",
    #             "PGroonga"
    #           ],
    #           1
    #         ],
    #         [
    #           [
    #             "Groonga",
    #             "Mroonga",
    #             "PGroonga"
    #           ],
    #           1
    #         ]
    #       ]
    #     }
    #   ]
    # ]

With this aggregation result, following information would be known.

tag | number of occurrence（ `_nsubrecs` ）
`Groonga` | 4
`Mroonga` | 2
`PGroonga` | 2
`Groonga` and `Mroonga` | 2
`Groonga` and `PGroonga` | 2
`Mroonga` and `PGroonga` | 1
`Groonga` and `Mroonga` and `PGroonga` | 1

This result allows to analyze correlation between each tag and combination such as whether often used. For example with this sample, it can be analyzed that `Groonga` and `Mroonga` are used twice at same time and within those twice, `PGroonga` is also used at same time for once.

#### 7.3.58.4.9.4. `drilldowns[${LABEL}].output_columns`

Added in version 4.0.8.

It’s almost same as drilldown_output_columns. The difference between drilldown_output_columns and `drilldowns[${LABEL}].output_columns` is how to refer group keys.

drilldown_output_columns uses `_key` Pseudo column to refer group key. `drilldowns[${LABEL}].output_columns` also uses `_key` Pseudo column to refer group key when you specify only one group key by drilldowns[${LABEL}].keys.

Here is an example to refer single group key by `_key` Pseudo column:

Execution example:

    select Entries \
      --limit 0 \
      --output_columns _id \
      --drilldowns[tag.n_likes].keys tag \
      --drilldowns[tag.n_likes].output_columns _key
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ]
    #       ]
    #     ],
    #     {
    #       "tag.n_likes": [
    #         [
    #           3
    #         ],
    #         [
    #           [
    #             "_key",
    #             "ShortText"
    #           ]
    #         ],
    #         [
    #           "Hello"
    #         ],
    #         [
    #           "Groonga"
    #         ],
    #         [
    #           "Senna"
    #         ]
    #       ]
    #     }
    #   ]
    # ]

But you can’t refer each group key by `_key` Pseudo column in `drilldowns[${LABEL}].output_columns`. You need to use `_value.${KEY_NAME}` syntax. `${KEY_NAME}` is a column name that is used for group key in drilldowns[${LABEL}].keys.

Here is an example to refer each group key in multiple group keys by `_value.${KEY_NAME}` syntax:

Execution example:

    select Entries \
      --limit 0 \
      --output_columns _id \
      --drilldowns[tag.n_likes].keys tag,n_likes \
      --drilldowns[tag.n_likes].output_columns _value.tag,_value.n_likes
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ]
    #       ]
    #     ],
    #     {
    #       "tag.n_likes": [
    #         [
    #           4
    #         ],
    #         [
    #           [
    #             "tag",
    #             "ShortText"
    #           ],
    #           [
    #             "n_likes",
    #             "UInt32"
    #           ]
    #         ],
    #         [
    #           "Hello",
    #           5
    #         ],
    #         [
    #           "Groonga",
    #           10
    #         ],
    #         [
    #           "Groonga",
    #           15
    #         ],
    #         [
    #           "Senna",
    #           3
    #         ]
    #       ]
    #     }
    #   ]
    # ]

Tip

Why `_value.${KEY_NAME}` syntax?

It’s implementation specific information.

`_key` is a vector value. The vector value is consists of all group keys. You can see byte sequence of the vector value by referring `_key` in `drilldowns[${LABEL}].output_columns`.

There is one grouped record in `_value` to refer each grouped values when you specify multiple group keys to drilldowns[${LABEL}].keys. So you can refer each group key by `_value.${KEY_NAME}` syntax.

On the other hand, there is no grouped record in `_value` when you specify only one group key to drilldowns[${LABEL}].keys. So you can’t refer group key by `_value.${KEY_NAME}` syntax.

#### 7.3.58.4.9.5. `drilldowns[${LABEL}].columns[${NAME}].stage`

Added in version 6.0.5.

Specifies when the dynamic column is created. This is a required parameter to create a dynamic column.

Here are available stages:

Name | Description
--- | ---
`initial` | Dynamic column is created at first.
`filtered` | Dynamic column is created after `drilldowns[${LABEL}].filter` is evaluated.
`output` | Dynamic column is created before drilldowns[${LABEL}].output_columns is evaluated.

Note

`filtered` stage and `output` stage will be able to use from 10.0.3 or later.

Here is one drilldown process flow with dynamic column creation points. You should choose stage as late as possible:

> 1. Evaluates drilldowns[${LABEL}].keys, `drilldowns[${LABEL}].calc_types` and `drilldowns[${LABEL}].calc_target`.

> 2. Creates dynamic columns for `initial` stage. All drilldown result records have these dynamic columns.

> 3. Evaluates `drilldowns[${LABEL}].filter`. You can use dynamic columns created in `initial` stage.

> 4. Creates dynamic columns for `filtered` stage. Only filtered records have these dynamic columns.

> 5. Evaluates `drilldowns[${LABEL}].sort_keys`, `drilldowns[${LABEL}].offset` and `drilldowns[${LABEL}].limit`. You can use dynamic columns created in `initial` stage and `filtered` stage.

> 6. Creates dynamic columns for `output` stage. Only `drilldowns[${LABEL}].limit` records have these dynamic columns.

> 7. Evaluates drilldowns[${LABEL}].output_columns. You can use dynamic columns created in `initial` stage, `filtered` stage, and `output` stage.

Here is a `drilldowns[${LABEL}].columns[${NAME}].stage` example. It creates `is_popular` column at `initial` stage. You can use `is_popular` in all parameters such as `drilldowns[${LABEL}].filter` and `drilldowns[${LABEL}].output_columns`:

Execution example:

    select Entries \
      --drilldowns[tag].keys tag \
      --drilldowns[tag].columns[is_popular].stage initial \
      --drilldowns[tag].columns[is_popular].type Bool \
      --drilldowns[tag].columns[is_popular].value '_nsubrecs > 1' \
      --drilldowns[tag].filter is_popular \
      --drilldowns[tag].output_columns _key,is_popular,_nsubrecs
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "tag",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         1,
    #         "The first post!",
    #         "Welcome! This is my first post!",
    #         5,
    #         "Hello"
    #       ],
    #       [
    #         2,
    #         "Groonga",
    #         "I started to use Groonga. It's very fast!",
    #         10,
    #         "Groonga"
    #       ],
    #       [
    #         3,
    #         "Mroonga",
    #         "I also started to use Mroonga. It's also very fast! Really fast!",
    #         15,
    #         "Groonga"
    #       ],
    #       [
    #         4,
    #         "Good-bye Senna",
    #         "I migrated all Senna system!",
    #         3,
    #         "Senna"
    #       ],
    #       [
    #         5,
    #         "Good-bye Tritonn",
    #         "I also migrated all Tritonn system!",
    #         3,
    #         "Senna"
    #       ]
    #     ],
    #     {
    #       "tag": [
    #         [
    #           2
    #         ],
    #         [
    #           [
    #             "_key",
    #             "ShortText"
    #           ],
    #           [
    #             "is_popular",
    #             "Bool"
    #           ],
    #           [
    #             "_nsubrecs",
    #             "Int32"
    #           ]
    #         ],
    #         [
    #           "Groonga",
    #           true,
    #           2
    #         ],
    #         [
    #           "Senna",
    #           true,
    #           2
    #         ]
    #       ]
    #     }
    #   ]
    # ]

Added in version 4.0.8.

#### 7.3.58.4.9.6. Output format for `drilldowns[${LABEL}]` style

There is a difference in output format between drilldown and drilldowns[${LABEL}].keys. drilldown uses array to output multiple drilldown results. drilldowns[${LABEL}].keys uses pairs of label and drilldown result.

drilldown uses the following output format:

    [
      HEADER,
      [
        SEARCH_RESULT,
        DRILLDOWN_RESULT1,
        DRILLDOWN_RESULT2,
        ...
      ]
    ]

drilldowns[${LABEL}].keys uses the following output format:

    [
      HEADER,
      [
        SEARCH_RESULT,
        {
          "LABEL1": DRILLDOWN_RESULT1,
          "LABEL2": DRILLDOWN_RESULT2,
          ...
        }
      ]
    ]

### 7.3.58.4.10. Slice related parameters

Added in version 6.0.3.

This section describes slice related parameters.

TODO

Here are parameters for slice:

Name | Required
--- | ---
`--slices[${LABEL}].match_columns` | Optional
`--slices[${LABEL}].query` | Required if `--slices[${LABEL}].filter` isn’t specified.
`--slices[${LABEL}].filter` | Required if `--slices[${LABEL}].query` isn’t specified.
`--slices[${LABEL}].query_expander` | Optional
`--slices[${LABEL}].query_flags` | Optional
`--slices[${LABEL}].sort_keys` | Optional
`--slices[${LABEL}].output_columns` | Optional
`--slices[${LABEL}].offset` | Optional
`--slices[${LABEL}].limit` | Optional

#### 7.3.58.4.10.1. `slices[${LABEL}].match_columns`

TODO

#### 7.3.58.4.10.2. `slices[${LABEL}].query`

TODO

#### 7.3.58.4.10.3. `slices[${LABEL}].filter`

TODO

#### 7.3.58.4.10.4. `slices[${LABEL}].query_expander`

TODO

#### 7.3.58.4.10.5. `slices[${LABEL}].query_flags`

TODO

#### 7.3.58.4.10.6. `slices[${LABEL}].sort_keys`

TODO

#### 7.3.58.4.10.7. `slices[${LABEL}].output_columns`

TODO

#### 7.3.58.4.10.8. `slices[${LABEL}].offset`

TODO

#### 7.3.58.4.10.9. `slices[${LABEL}].limit`

TODO

### 7.3.58.4.11. Cache related parameter

#### 7.3.58.4.11.1. `cache`

Specifies whether caching the result of this query or not.

If the result of this query is cached, the next same query returns response quickly by using the cache.

It doesn’t control whether existing cached result is used or not.

Here are available values:

Value | Description
--- | ---
`no` | Don’t cache the output of this query.
`yes` | Cache the output of this query. It’s the default value.

Here is an example to disable caching the result of this query:

Execution example:

    select Entries --cache no
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "n_likes",
    #           "UInt32"
    #         ],
    #         [
    #           "tag",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         1,
    #         "The first post!",
    #         "Welcome! This is my first post!",
    #         5,
    #         "Hello"
    #       ],
    #       [
    #         2,
    #         "Groonga",
    #         "I started to use Groonga. It's very fast!",
    #         10,
    #         "Groonga"
    #       ],
    #       [
    #         3,
    #         "Mroonga",
    #         "I also started to use Mroonga. It's also very fast! Really fast!",
    #         15,
    #         "Groonga"
    #       ],
    #       [
    #         4,
    #         "Good-bye Senna",
    #         "I migrated all Senna system!",
    #         3,
    #         "Senna"
    #       ],
    #       [
    #         5,
    #         "Good-bye Tritonn",
    #         "I also migrated all Tritonn system!",
    #         3,
    #         "Senna"
    #       ]
    #     ]
    #   ]
    # ]

The default value is `yes`.

### 7.3.58.4.12. Score related parameters

There is a score related parameter, `adjuster`.

#### 7.3.58.4.12.1. `adjuster`

Specifies one or more score adjust expressions. You need to use `adjuster` with `query` or `filter`. `adjuster` doesn’t work with not searched request.

You can increase score of specific records by `adjuster`. You can use `adjuster` to set high score for important records.

For example, you can use `adjuster` to increase score of records that have `groonga` tag.

Here is the syntax:

    --adjuster "SCORE_ADJUST_EXPRESSION1 + SCORE_ADJUST_EXPRESSION2 + ..."

Here is the `SCORE_ADJUST_EXPRESSION` syntax:

    COLUMN @ "KEYWORD" * FACTOR

Note the following:

> - `COLUMN` must be indexed.

> - `"KEYWORD"` must be a string.

> - `FACTOR` must be a positive integer.

Here is a sample `adjuster` usage example that uses just one `SCORE_ADJUST_EXPRESSION`:

Execution example:

    select Entries \
      --filter true \
      --adjuster 'content @ "groonga" * 5' \
      --output_columns _key,content,_score
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "The first post!",
    #         "Welcome! This is my first post!",
    #         1
    #       ],
    #       [
    #         "Groonga",
    #         "I started to use Groonga. It's very fast!",
    #         6
    #       ],
    #       [
    #         "Mroonga",
    #         "I also started to use Mroonga. It's also very fast! Really fast!",
    #         1
    #       ],
    #       [
    #         "Good-bye Senna",
    #         "I migrated all Senna system!",
    #         1
    #       ],
    #       [
    #         "Good-bye Tritonn",
    #         "I also migrated all Tritonn system!",
    #         1
    #       ]
    #     ]
    #   ]
    # ]

The `select` command matches all records. Then it applies `adjuster`. The adjuster increases score of records that have `"groonga"` in `Entries.content` column by 5. There is only one record that has `"groonga"` in `Entries.content` column. So the record that its key is `"Groonga"` has score 6 (`= 1 + 5`).

You can omit `FACTOR`. If you omit `FACTOR`, it is treated as 1.

Here is a sample `adjuster` usage example that omits `FACTOR`:

Execution example:

    select Entries \
      --filter true \
      --adjuster 'content @ "groonga"' \
      --output_columns _key,content,_score
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "The first post!",
    #         "Welcome! This is my first post!",
    #         1
    #       ],
    #       [
    #         "Groonga",
    #         "I started to use Groonga. It's very fast!",
    #         2
    #       ],
    #       [
    #         "Mroonga",
    #         "I also started to use Mroonga. It's also very fast! Really fast!",
    #         1
    #       ],
    #       [
    #         "Good-bye Senna",
    #         "I migrated all Senna system!",
    #         1
    #       ],
    #       [
    #         "Good-bye Tritonn",
    #         "I also migrated all Tritonn system!",
    #         1
    #       ]
    #     ]
    #   ]
    # ]

The `adjuster` in the `select` command doesn’t have `FACTOR`. So the factor is treated as 1. There is only one record that has `"groonga"` in `Entries.content` column. So the record that its key is `"Groonga"` has score 2 (`= 1 + 1`).

Here is a sample `adjuster` usage example that uses multiple `SCORE_ADJUST_EXPRESSION`:

Execution example:

    select Entries \
      --filter true \
      --adjuster 'content @ "groonga" * 5 + content @ "started" * 3' \
      --output_columns _key,content,_score
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         5
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ]
    #       ],
    #       [
    #         "The first post!",
    #         "Welcome! This is my first post!",
    #         1
    #       ],
    #       [
    #         "Groonga",
    #         "I started to use Groonga. It's very fast!",
    #         9
    #       ],
    #       [
    #         "Mroonga",
    #         "I also started to use Mroonga. It's also very fast! Really fast!",
    #         4
    #       ],
    #       [
    #         "Good-bye Senna",
    #         "I migrated all Senna system!",
    #         1
    #       ],
    #       [
    #         "Good-bye Tritonn",
    #         "I also migrated all Tritonn system!",
    #         1
    #       ]
    #     ]
    #   ]
    # ]

The `adjuster` in the `select` command has two `SCORE_ADJUST_EXPRESSION` s. The final increased score is sum of scores of these `SCORE_ADJUST_EXPRESSION` s. All `SCORE_ADJUST_EXPRESSION` s in the `select` command are applied to a record that its key is `"Groonga"`. So the final increased score of the record is sum of scores of all `SCORE_ADJUST_EXPRESSION` s.

The first `SCORE_ADJUST_EXPRESSION` is `content @ "groonga" * 5`. It increases score by 5.

The second `SCORE_ADJUST_EXPRESSION` is `content @ "started" * 3`. It increases score by 3.

The final increased score is 9 (`= 1 + 5 + 3`).

A `SCORE_ADJUST_EXPRESSION` has a factor for `"KEYWORD"`. This means that increased scores of all records that has `"KEYWORD"` are the same value. You can change increase score for each record that has the same `"KEYWORD"`. It is useful to tune search score. See Weight vector column for details.

## 7.3.58.5. Return value

The command returns a response with the following format:

    [
      HEADER,
      [
        SEARCH_RESULT,
        DRILLDOWN_RESULT_1,
        DRILLDOWN_RESULT_2,
        ...,
        DRILLDOWN_RESULT_N
      ]
    ]

If the command fails, error details are in `HEADER`.

See Output format for `HEADER`.

There are zero or more `DRILLDOWN_RESULT`. If no `drilldown` and `drilldowns[${LABEL}].keys` are specified, they are omitted like the following:

    [
      HEADER,
      [
        SEARCH_RESULT
      ]
    ]

If `drilldown` has two or more keys like `--drilldown "_key, column1, column2"`, multiple `DRILLDOWN_RESULT` exist:

    [
      HEADER,
      [
        SEARCH_RESULT,
        DRILLDOWN_RESULT_FOR_KEY,
        DRILLDOWN_RESULT_FOR_COLUMN1,
        DRILLDOWN_RESULT_FOR_COLUMN2
      ]
    ]

If `drilldowns[${LABEL}].keys` is used, only one `DRILLDOWN_RESULT` exist:

    [
      HEADER,
      [
        SEARCH_RESULT,
        DRILLDOWN_RESULT_FOR_LABELED_DRILLDOWN
      ]
    ]

`DRILLDOWN_RESULT` format is different between `drilldown` and `drilldowns[${LABEL}].keys`. It’s described later.

`SEARCH_RESULT` is the following format:

    [
      [N_HITS],
      COLUMNS,
      RECORDS
    ]

See Simple usage for concrete example of the format.

`N_HITS` is the number of matched records before limit is applied.

`COLUMNS` describes about output columns specified by output_columns. It uses the following format:

    [
      [COLUMN_NAME_1, COLUMN_TYPE_1],
      [COLUMN_NAME_2, COLUMN_TYPE_2],
      ...,
      [COLUMN_NAME_N, COLUMN_TYPE_N]
    ]

`COLUMNS` includes one or more output column information. Each output column information includes the followings:

> - Column name as string

> - Column type as string or `null`

Column name is extracted from value specified as output_columns.

Column type is Groonga’s type name or `null`. It doesn’t describe whether the column value is vector or scalar. You need to determine it by whether real column value is array or not.

See Data types for type details.

`null` is used when column value type isn’t determined. For example, function call in output_columns such as `--output_columns "snippet_html(content)"` uses `null`.

Here is an example of `COLUMNS`:

    [
      ["_id",     "UInt32"],
      ["_key",    "ShortText"],
      ["n_likes", "UInt32"],
    ]

`RECORDS` includes column values for each matched record. Included records are selected by offset and limit. It uses the following format:

    [
      [
        RECORD_1_COLUMN_1,
        RECORD_1_COLUMN_2,
        ...,
        RECORD_1_COLUMN_N
      ],
      [
        RECORD_2_COLUMN_1,
        RECORD_2_COLUMN_2,
        ...,
        RECORD_2_COLUMN_N
      ],
      ...
      [
        RECORD_N_COLUMN_1,
        RECORD_N_COLUMN_2,
        ...,
        RECORD_N_COLUMN_N
      ]
    ]

Here is an example `RECORDS`:

    [
      [
        1,
        "The first post!",
        5
      ],
      [
        2,
        "Groonga",
        10
      ],
      [
        3,
        "Mroonga",
        15
      ]
    ]

`DRILLDOWN_RESULT` format is different between `drilldown` and `drilldowns[${LABEL}].keys`.

`drilldown` uses the same format as `SEARCH_RESULT`:

    [
      [N_HITS],
      COLUMNS,
      RECORDS
    ]

And `drilldown` generates one or more `DRILLDOWN_RESULT` when drilldown has one ore more keys.

`drilldowns[${LABEL}].keys` uses the following format. Multiple `drilldowns[${LABEL}].keys` are mapped to one object (key-value pairs):

    {
      "LABEL_1": [
        [N_HITS],
        COLUMNS,
        RECORDS
      ],
      "LABEL_2": [
        [N_HITS],
        COLUMNS,
        RECORDS
      ],
      ...,
      "LABEL_N": [
        [N_HITS],
        COLUMNS,
        RECORDS
      ]
    }

Each `drilldowns[${LABEL}].keys` corresponds to the following:

    "LABEL": [
      [N_HITS],
      COLUMNS,
      RECORDS
    ]

The following value part is the same format as `SEARCH_RESULT`:

    [
      [N_HITS],
      COLUMNS,
      RECORDS
    ]

See also Output format for drilldowns[${LABEL}] style for `drilldowns[${LABEL}]` style drilldown output format.

## 7.3.58.6. See also

> - Query syntax

> - Script syntax