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

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# 7.3.33. `logical_range_filter`

## 7.3.33.1. Summary

New in version 5.0.0.

`logical_range_filter` is a sharding version of range_filter. `logical_range_filter` searches records from multiple tables and outputs them.

`logical_range_filter` is similar to logical_select. Both of them searches records from multiples tables and outputs them. `logical_range_filter` stops searching when the number of matched records is requested the number of records. logical_select searches all records and outputs only needed records.

`logical_range_filter` has performance merit but some restrictions.

If many records are matched and requested records are small, `logical_range_filter` will be faster than logical_select.

`logical_range_filter` doesn't support drilldown because drilldown requires all matched records. `logical_range_filter` may not find all matched records. So `logical_range_filter` doesn't support drilldown.

`logical_range_filter` doesn't return the number of all matched records. Because `logical_range_filter` may not search all matched records.

You need to plugin_register `sharding` plugin because this command is included in `sharding` plugin.

## 7.3.33.2. Syntax

This command takes many parameters.

The required parameters are `logical_table` and `shard_key`:

    logical_range_filter
      logical_table
      shard_key
      [min=null]
      [min_border="include"]
      [max=null]
      [max_border="include"]
      [order="ascending"]
      [filter=null]
      [offset=0]
      [limit=10]
      [output_columns="_key, *"]
      [use_range_index=null]
      [post_filter=null]

There are some parameters that can be only used as named parameters. You can't use these parameters as ordered parameters. You must specify parameter name.

Here are parameters that can be only used as named parameters:

> - `cache=no`

New in version 7.0.9: 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'`

## 7.3.33.3. Usage

Let's learn about usage with examples. This section shows many popular usages.

You need to register `sharding` plugin because this command is included in `sharding` plugin.

Execution example:

    plugin_register sharding
    # [[0, 1337566253.89858, 0.000355720520019531], true]

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

Execution example:

    table_create Entries_20150708 TABLE_HASH_KEY ShortText
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Entries_20150708 created_at COLUMN_SCALAR Time
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Entries_20150708 content COLUMN_SCALAR Text
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Entries_20150708 n_likes COLUMN_SCALAR UInt32
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Entries_20150708 tag COLUMN_SCALAR ShortText
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    table_create Entries_20150709 TABLE_HASH_KEY ShortText
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Entries_20150709 created_at COLUMN_SCALAR Time
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Entries_20150709 content COLUMN_SCALAR Text
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Entries_20150709 n_likes COLUMN_SCALAR UInt32
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Entries_20150709 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_20150708 \
      COLUMN_INDEX|WITH_POSITION Entries_20150708 _key
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Terms entries_content_index_20150708 \
      COLUMN_INDEX|WITH_POSITION Entries_20150708 content
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Terms entries_key_index_20150709 \
      COLUMN_INDEX|WITH_POSITION Entries_20150709 _key
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Terms entries_content_index_20150709 \
      COLUMN_INDEX|WITH_POSITION Entries_20150709 content
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    load --table Entries_20150708
    [
    {"_key":       "The first post!",
     "created_at": "2015/07/08 00:00:00",
     "content":    "Welcome! This is my first post!",
     "n_likes":    5,
     "tag":        "Hello"},
    {"_key":       "Groonga",
     "created_at": "2015/07/08 01:00:00",
     "content":    "I started to use Groonga. It's very fast!",
     "n_likes":    10,
     "tag":        "Groonga"},
    {"_key":       "Mroonga",
     "created_at": "2015/07/08 02:00:00",
     "content":    "I also started to use Mroonga. It's also very fast! Really fast!",
     "n_likes":    15,
     "tag":        "Groonga"}
    ]
    # [[0, 1337566253.89858, 0.000355720520019531], 3]
    load --table Entries_20150709
    [
    {"_key":       "Good-bye Senna",
     "created_at": "2015/07/09 00:00:00",
     "content":    "I migrated all Senna system!",
     "n_likes":    3,
     "tag":        "Senna"},
    {"_key":       "Good-bye Tritonn",
     "created_at": "2015/07/09 01:00:00",
     "content":    "I also migrated all Tritonn system!",
     "n_likes":    3,
     "tag":        "Senna"}
    ]
    # [[0, 1337566253.89858, 0.000355720520019531], 2]

There are two tables, `Entries_20150708` and `Entries_20150709`, for blog entries.

Note

You need to use `${LOGICAL_TABLE_NAME}_${YYYYMMDD}` naming rule for table names. In this example, `LOGICAL_TABLE_NAME` is `Entries` and `YYYYMMDD` is `20150708` or `20150709`.

An entry has title, created time, content, the number of likes for the entry and tag. Title is key of `Entries_YYYYMMDD`. Created time is value of `Entries_YYYYMMDD.created_at` column. Content is value of `Entries_YYYYMMDD.content` column. The number of likes is value of `Entries_YYYYMMDD.n_likes` column. Tag is value of `Entries_YYYYMMDD.tag` column.

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

OK. The schema and data for examples are ready.

### 7.3.33.3.1. Simple usage

TODO

## 7.3.33.4. Parameters

This section describes parameters of `logical_range_filter`.

### 7.3.33.4.1. Required parameters

There are required parameters, `logical_table` and `shard_key`.

#### 7.3.33.4.1.1. `logical_table`

Specifies logical table name. It means table name without `_YYYYMMDD` postfix. If you use actual table such as `Entries_20150708`, `Entries_20150709` and so on, logical table name is `Entries`.

Execution example:

    logical_range_filter --logical_table Entries --shard_key created_at

    # [
    #   [
    #     0,
    #     1519131606.676006,
    #     0.0006020069122314453
    #   ],
    #   [
    #     [
    #       [
    #         "_key",
    #         "ShortText"
    #       ],
    #       [
    #         "content",
    #         "Text"
    #       ],
    #       [
    #         "created_at",
    #         "Time"
    #       ],
    #       [
    #         "n_likes",
    #         "UInt32"
    #       ],
    #       [
    #         "tag",
    #         "ShortText"
    #       ]
    #     ],
    #     [
    #       "The first post!",
    #       "Welcome! This is my first post!",
    #       1436281200.0,
    #       5,
    #       "Hello"
    #     ],
    #     [
    #       "Groonga",
    #       "I started to use Groonga. It's very fast!",
    #       1436284800.0,
    #       10,
    #       "Groonga"
    #     ],
    #     [
    #       "Mroonga",
    #       "I also started to use Mroonga. It's also very fast! Really fast!",
    #       1436288400.0,
    #       15,
    #       "Groonga"
    #     ],
    #     [
    #       "Good-bye Senna",
    #       "I migrated all Senna system!",
    #       1436367600.0,
    #       3,
    #       "Senna"
    #     ],
    #     [
    #       "Good-bye Tritonn",
    #       "I also migrated all Tritonn system!",
    #       1436371200.0,
    #       3,
    #       "Senna"
    #     ]
    #   ]
    # ]

If nonexistent table is specified, an error is returned.

Execution example:

    logical_range_filter --logical_table Nonexistent --shard_key created_at
    # [
    #   [
    #     -22,
    #     1519132441.586192,
    #     0.0005924701690673828,
    #     "[logical_range_filter] no shard exists: logical_table: <Nonexistent>: shard_key: <created_at>",
    #     [
    #       [
    #         "Groonga::Sharding::LogicalRangeFilterCommand::Executor.execute",
    #         "/tmp/local/lib/groonga/plugins/sharding/logical_range_filter.rb",
    #         193
    #       ]
    #     ]
    #   ]
    # ]

#### 7.3.33.4.1.2. `shard_key`

Specifies column name which is treated as shared key in each parted table.

TODO: Add examples

### 7.3.33.4.2. Optional parameters

There are optional parameters.

#### 7.3.33.4.2.1. `min`

Specifies the min value of `shard_key`

TODO: Add examples

#### 7.3.33.4.2.2. `min_border`

Specifies whether the min value of borderline must be include or not. Specify `include` or `exclude` as the value of this parameter.

TODO: Add examples

#### 7.3.33.4.2.3. `max`

Specifies the max value of `shard_key`.

TODO: Add examples

#### 7.3.33.4.2.4. `max_border`

Specifies whether the max value of borderline must be include or not. Specify `include` or `exclude` as the value of this parameter.

TODO: Add examples

#### 7.3.33.4.2.5. `order`

TODO

### 7.3.33.4.3. Search related parameters

This command provides select compatible search related parameters.

#### 7.3.33.4.3.1. `filter`

Corresponds to filter in select. See filter for details.

Here is an example:

TODO

#### 7.3.33.4.3.2. `post_filter`

New in version 7.1.1.

Specifies the filter text that is processed after `filtered` stage dynamic columns are generated. You can use `post_filter` to filter by `filtered` stage dynamic columns. Others are the same as filter.

If you use `post_filter`, "stop searching when enough records are matched in a table" feature is disabled. ("Stop searching against rest tables when enough records are matched" is still enabled.) Because `post_filter` needs to wait dynamic columns generation. See also Dynamic column related parameters.

Here is an example that shows entries only in popular tag. All target entries have `system` or `use` words:

Execution example:

    logical_range_filter \
      --logical_table Entries \
      --shard_key created_at \
      --columns[n_likes_sum_per_tag].stage filtered \
      --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' \
      --filter 'content @ "system" || content @ "use"' \
      --post_filter 'n_likes_sum_per_tag > 10' \
      --output_columns _key,n_likes,n_likes_sum_per_tag
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         "_key",
    #         "ShortText"
    #       ],
    #       [
    #         "n_likes",
    #         "UInt32"
    #       ],
    #       [
    #         "n_likes_sum_per_tag",
    #         "UInt32"
    #       ]
    #     ],
    #     [
    #       "Groonga",
    #       10,
    #       25
    #     ],
    #     [
    #       "Mroonga",
    #       15,
    #       25
    #     ]
    #   ]
    # ]

### 7.3.33.4.4. Output related parameters

#### 7.3.33.4.4.1. `output_columns`

Corresponds to output_columns in select. See output_columns for details.

Here is an example:

Execution example:

    logical_range_filter \
      --logical_table Entries \
      --shard_key created_at \
      --output_columns '_key, *'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         "_key",
    #         "ShortText"
    #       ],
    #       [
    #         "content",
    #         "Text"
    #       ],
    #       [
    #         "created_at",
    #         "Time"
    #       ],
    #       [
    #         "n_likes",
    #         "UInt32"
    #       ],
    #       [
    #         "tag",
    #         "ShortText"
    #       ]
    #     ],
    #     [
    #       "The first post!",
    #       "Welcome! This is my first post!",
    #       1436281200.0,
    #       5,
    #       "Hello"
    #     ],
    #     [
    #       "Groonga",
    #       "I started to use Groonga. It's very fast!",
    #       1436284800.0,
    #       10,
    #       "Groonga"
    #     ],
    #     [
    #       "Mroonga",
    #       "I also started to use Mroonga. It's also very fast! Really fast!",
    #       1436288400.0,
    #       15,
    #       "Groonga"
    #     ],
    #     [
    #       "Good-bye Senna",
    #       "I migrated all Senna system!",
    #       1436367600.0,
    #       3,
    #       "Senna"
    #     ],
    #     [
    #       "Good-bye Tritonn",
    #       "I also migrated all Tritonn system!",
    #       1436371200.0,
    #       3,
    #       "Senna"
    #     ]
    #   ]
    # ]

#### 7.3.33.4.4.2. `offset`

Corresponds to offset in select. See offset for details.

Here is an example:

Execution example:

    logical_range_filter \
      --logical_table Entries \
      --shard_key created_at \
      --offset 2
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         "_key",
    #         "ShortText"
    #       ],
    #       [
    #         "content",
    #         "Text"
    #       ],
    #       [
    #         "created_at",
    #         "Time"
    #       ],
    #       [
    #         "n_likes",
    #         "UInt32"
    #       ],
    #       [
    #         "tag",
    #         "ShortText"
    #       ]
    #     ],
    #     [
    #       "Mroonga",
    #       "I also started to use Mroonga. It's also very fast! Really fast!",
    #       1436288400.0,
    #       15,
    #       "Groonga"
    #     ],
    #     [
    #       "Good-bye Senna",
    #       "I migrated all Senna system!",
    #       1436367600.0,
    #       3,
    #       "Senna"
    #     ],
    #     [
    #       "Good-bye Tritonn",
    #       "I also migrated all Tritonn system!",
    #       1436371200.0,
    #       3,
    #       "Senna"
    #     ]
    #   ]
    # ]

#### 7.3.33.4.4.3. `limit`

Corresponds to limit in select. See limit for details.

The difference from select is that this command stops searching when all requested records specified by offset and limit are found.

Here is an example:

Execution example:

    logical_range_filter \
      --logical_table Entries \
      --shard_key created_at \
      --limit 2
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         "_key",
    #         "ShortText"
    #       ],
    #       [
    #         "content",
    #         "Text"
    #       ],
    #       [
    #         "created_at",
    #         "Time"
    #       ],
    #       [
    #         "n_likes",
    #         "UInt32"
    #       ],
    #       [
    #         "tag",
    #         "ShortText"
    #       ]
    #     ],
    #     [
    #       "The first post!",
    #       "Welcome! This is my first post!",
    #       1436281200.0,
    #       5,
    #       "Hello"
    #     ],
    #     [
    #       "Groonga",
    #       "I started to use Groonga. It's very fast!",
    #       1436284800.0,
    #       10,
    #       "Groonga"
    #     ]
    #   ]
    # ]

### 7.3.33.4.5. Test related parameters

#### 7.3.33.4.5.1. `use_range_index`

Specifies whether range_index is used or not. Note that it's a parameter for test. It should not be used for production.

TODO: Add examples

### 7.3.33.4.6. Dynamic column related parameters

New in version 7.0.9.

All dynamic column related parameters in select are supported. See Dynamic column related parameters for details.

If you use one or more dynamic columns, "stop searching when enough records are matched in a table" feature is disabled. ("Stop searching against rest tables when enough records are matched" is still enabled.) `logical_range_filter` searches all matched records in a table even when requested the number of matched records is small. It's for supporting window function. Running window function requires all target records in a table.

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

New in version 7.0.9.

Corresponds to columns[${NAME}].stage in select. See columns[${NAME}].stage for details.

This is a required parameter.

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:

    logical_range_filter \
      --logical_table Entries \
      --shard_key created_at \
      --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
    #   ],
    #   [
    #     [
    #       [
    #         "_id",
    #         "UInt32"
    #       ],
    #       [
    #         "is_popular",
    #         "Bool"
    #       ],
    #       [
    #         "n_likes",
    #         "UInt32"
    #       ]
    #     ],
    #     [
    #       2,
    #       true,
    #       10
    #     ],
    #     [
    #       3,
    #       true,
    #       15
    #     ]
    #   ]
    # ]

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

New in version 7.0.9.

Corresponds to columns[${NAME}].flags in select. See columns[${NAME}].flags for details.

The default value is `COLUMN_SCALAR`.

Here is an example that 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]
    logical_range_filter \
      --logical_table Entries \
      --shard_key created_at \
      --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
    #   ],
    #   [
    #     [
    #       [
    #         "_id",
    #         "UInt32"
    #       ],
    #       [
    #         "vector",
    #         "UInt32"
    #       ]
    #     ],
    #     [
    #       1,
    #       [
    #         1,
    #         2,
    #         3
    #       ]
    #     ],
    #     [
    #       2,
    #       [
    #         1,
    #         2,
    #         3
    #       ]
    #     ],
    #     [
    #       3,
    #       [
    #         1,
    #         2,
    #         3
    #       ]
    #     ],
    #     [
    #       1,
    #       [
    #         1,
    #         2,
    #         3
    #       ]
    #     ],
    #     [
    #       2,
    #       [
    #         1,
    #         2,
    #         3
    #       ]
    #     ]
    #   ]
    # ]

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

New in version 7.0.9.

Corresponds to columns[${NAME}].type in select. See columns[${NAME}].type for details.

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:

    logical_range_filter \
      --logical_table Entries \
      --shard_key created_at \
      --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
    #   ],
    #   [
    #     [
    #       [
    #         "_id",
    #         "UInt32"
    #       ],
    #       [
    #         "n_likes",
    #         "UInt32"
    #       ],
    #       [
    #         "n_likes_string",
    #         "ShortText"
    #       ]
    #     ],
    #     [
    #       1,
    #       5,
    #       "5"
    #     ],
    #     [
    #       2,
    #       10,
    #       "10"
    #     ],
    #     [
    #       3,
    #       15,
    #       "15"
    #     ],
    #     [
    #       1,
    #       3,
    #       "3"
    #     ],
    #     [
    #       2,
    #       3,
    #       "3"
    #     ]
    #   ]
    # ]

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

New in version 7.0.9.

Corresponds to columns[${NAME}].value in select. See columns[${NAME}].value for details.

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]
    logical_range_filter \
      --logical_table Entries \
      --shard_key created_at \
      --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
    #   ],
    #   [
    #     [
    #       [
    #         "_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
    #     ],
    #     [
    #       1,
    #       "I migrated all Senna system!",
    #       28
    #     ],
    #     [
    #       2,
    #       "I also migrated all Tritonn system!",
    #       35
    #     ]
    #   ]
    # ]

### 7.3.33.4.7. Window function related parameters

New in version 7.0.9.

All window function related parameters in select are supported. See Window function related parameters for details.

Note

Window function over multiple tables aren't supported yet.

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

New in version 7.0.9.

Corresponds to columns[${NAME}].window.sort_keys in select. See columns[${NAME}].window.sort_keys for details.

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:

    logical_range_filter \
      --logical_table Entries \
      --shard_key created_at \
      --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 \
      --output_columns tag,_key,n_likes,n_likes_cumulative_sum_per_tag
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         "tag",
    #         "ShortText"
    #       ],
    #       [
    #         "_key",
    #         "ShortText"
    #       ],
    #       [
    #         "n_likes",
    #         "UInt32"
    #       ],
    #       [
    #         "n_likes_cumulative_sum_per_tag",
    #         "UInt32"
    #       ]
    #     ],
    #     [
    #       "Hello",
    #       "The first post!",
    #       5,
    #       5
    #     ],
    #     [
    #       "Groonga",
    #       "Groonga",
    #       10,
    #       10
    #     ],
    #     [
    #       "Groonga",
    #       "Mroonga",
    #       15,
    #       25
    #     ],
    #     [
    #       "Senna",
    #       "Good-bye Senna",
    #       3,
    #       3
    #     ],
    #     [
    #       "Senna",
    #       "Good-bye Tritonn",
    #       3,
    #       6
    #     ]
    #   ]
    # ]

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

New in version 7.0.9.

Corresponds to columns[${NAME}].window.group_keys in select. See columns[${NAME}].window.group_keys for details.

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:

    logical_range_filter \
      --logical_table Entries \
      --shard_key created_at \
      --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,_key,n_likes,n_likes_sum_per_tag
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         "tag",
    #         "ShortText"
    #       ],
    #       [
    #         "_key",
    #         "ShortText"
    #       ],
    #       [
    #         "n_likes",
    #         "UInt32"
    #       ],
    #       [
    #         "n_likes_sum_per_tag",
    #         "UInt32"
    #       ]
    #     ],
    #     [
    #       "Hello",
    #       "The first post!",
    #       5,
    #       5
    #     ],
    #     [
    #       "Groonga",
    #       "Groonga",
    #       10,
    #       25
    #     ],
    #     [
    #       "Groonga",
    #       "Mroonga",
    #       15,
    #       25
    #     ],
    #     [
    #       "Senna",
    #       "Good-bye Senna",
    #       3,
    #       6
    #     ],
    #     [
    #       "Senna",
    #       "Good-bye Tritonn",
    #       3,
    #       6
    #     ]
    #   ]
    # ]

### 7.3.33.4.8. Cache related parameter

#### 7.3.33.4.8.1. `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:

    logical_range_filter \
      --logical_table Entries \
      --shard_key created_at \
      --cache no
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         "_key",
    #         "ShortText"
    #       ],
    #       [
    #         "content",
    #         "Text"
    #       ],
    #       [
    #         "created_at",
    #         "Time"
    #       ],
    #       [
    #         "n_likes",
    #         "UInt32"
    #       ],
    #       [
    #         "tag",
    #         "ShortText"
    #       ]
    #     ],
    #     [
    #       "The first post!",
    #       "Welcome! This is my first post!",
    #       1436281200.0,
    #       5,
    #       "Hello"
    #     ],
    #     [
    #       "Groonga",
    #       "I started to use Groonga. It's very fast!",
    #       1436284800.0,
    #       10,
    #       "Groonga"
    #     ],
    #     [
    #       "Mroonga",
    #       "I also started to use Mroonga. It's also very fast! Really fast!",
    #       1436288400.0,
    #       15,
    #       "Groonga"
    #     ],
    #     [
    #       "Good-bye Senna",
    #       "I migrated all Senna system!",
    #       1436367600.0,
    #       3,
    #       "Senna"
    #     ],
    #     [
    #       "Good-bye Tritonn",
    #       "I also migrated all Tritonn system!",
    #       1436371200.0,
    #       3,
    #       "Senna"
    #     ]
    #   ]
    # ]

The default value is `yes`.

## 7.3.33.5. Return value

The command returns a response with the following format:

    [
      HEADER,
      [
        COLUMNS,
        RECORDS
      ]
    ]

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

See Output format for `HEADER`.

`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
      ]
    ]