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

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# 7.3.11. `column_create`

## 7.3.11.1. Summary

`column_create` creates a new column in a table.

You need to create one or more columns to store multiple data in one record.

Groonga provides an index as a column. It’s different from other systems. An index is just an index in other systems. Implementing an index as a column provides flexibility. For example, you can add metadata to each token.

See Column for column details.

## 7.3.11.2. Syntax

This command takes many parameters.

Most parameters are required:

    column_create table
                  name
                  flags
                  type
                  [source=null]
                  [path=null]

## 7.3.11.3. Usage

This section describes about the followings:

> - Create a scalar column

> - Create a vector column

> - Create a weight vector column

> - Create a column that refers a table’s record

> - Create an index column

> - Create an index column for full text search

> - Create a multiple columns index column

> - Create a small index column

> - Create a medium index column

> - Create a large index column

Here is the `People` table definition. The `People` table is used in examples:

Execution example:

    table_create \
      --name People \
      --flags TABLE_HASH_KEY \
      --key_type ShortText
    # [[0, 1337566253.89858, 0.000355720520019531], true]

### 7.3.11.3.1. Create a scalar column

Groonga provides scalar column to store one value. For example, scalar column should be used for storing age into a person record. Because a person record must have only one age.

If you want to store multiple values into a record, scalar column isn’t suitable. Use Create a vector column instead.

You must specify `COLUMN_SCALAR` to the `flags` parameter to create a scalar column.

Here is an example to create the `age` column to the `People` table. `age` column is a scalar column. It can store one `UInt8` (`0-255`) value as its value:

Execution example:

    column_create \
      --table People \
      --name age \
      --flags COLUMN_SCALAR \
      --type UInt8
    # [[0, 1337566253.89858, 0.000355720520019531], true]

You can store one value (`7`) by the following load command:

Execution example:

    load --table People
    [
    {"_key": "alice", "age": 7}
    ]
    # [[0, 1337566253.89858, 0.000355720520019531], 1]

You can confirm the stored one value (`7`) by the following select command:

Execution example:

    select --table People
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "age",
    #           "UInt8"
    #         ]
    #       ],
    #       [
    #         1,
    #         "alice",
    #         7
    #       ]
    #     ]
    #   ]
    # ]

### 7.3.11.3.2. Create a vector column

Groonga provides vector column to store multiple values. For example, vector column may be used for storing roles into a person record. Because a person record may have multiple roles.

If you want to store only one value into a record, vector column isn’t suitable. Use Create a scalar column instead.

You must specify `COLUMN_VECTOR` to the `flags` parameter to create a vector column.

Here is an example to create the `roles` column to the `People` table. `roles` column is a vector column. It can store zero or more `ShortText` values as its value:

Execution example:

    column_create \
      --table People \
      --name roles \
      --flags COLUMN_VECTOR \
      --type ShortText
    # [[0, 1337566253.89858, 0.000355720520019531], true]

You can store multiple values (`["adventurer", "younger-sister"]`) by the following load command:

Execution example:

    load --table People
    [
    {"_key": "alice", "roles": ["adventurer", "younger-sister"]}
    ]
    # [[0, 1337566253.89858, 0.000355720520019531], 1]

You can confirm the stored multiple values (`["adventurer", "younger-sister"]`) by the following select command:

Execution example:

    select --table People
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "age",
    #           "UInt8"
    #         ],
    #         [
    #           "roles",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         1,
    #         "alice",
    #         7,
    #         [
    #           "adventurer",
    #           "younger-sister"
    #         ]
    #       ]
    #     ]
    #   ]
    # ]

### 7.3.11.3.3. Create a weight vector column

TODO: See also Weight vector column and adjuster.

### 7.3.11.3.4. Create a column that refers a table’s record

Both scalar column and vector column can store reference to record of an existing table as column value. It’s useful to store relationship between records.

For example, using a column that refers a person record is better for storing a character into a book record. Because one person may be appeared in some books.

You must specify table name to be referenced to the `type` parameter to create a column that refers a table’s record.

Here is an example to create the `character` column to the `Books` table. The `character` column refers the `People` table. It can store one `People` table’s record.

Here is the `Books` table definition:

Execution example:

    table_create \
      --name Books \
      --flags TABLE_HASH_KEY \
      --key_type ShortText
    # [[0, 1337566253.89858, 0.000355720520019531], true]

Here is the `character` column definition in the `Books` table. `--type People` is important:

Execution example:

    column_create \
      --table Books \
      --name character \
      --flags COLUMN_SCALAR \
      --type People
    # [[0, 1337566253.89858, 0.000355720520019531], true]

You can store one reference (`"alice"`) by the following load command. You can use key value (`People._key` value) for referring a record:

Execution example:

    load --table Books
    [
    {"_key": "Alice's Adventure in Wonderland", "character": "alice"}
    ]
    # [[0, 1337566253.89858, 0.000355720520019531], 1]

You can confirm the stored reference (`"alice"` record) by the following select command. It retrieves the `age` column and the `roles` column values:

Execution example:

    select \
     --table Books \
     --output_columns _key,character._key,character.age,character.roles
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "character._key",
    #           "ShortText"
    #         ],
    #         [
    #           "character.age",
    #           "UInt8"
    #         ],
    #         [
    #           "character.roles",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         "Alice's Adventure in Wonderland",
    #         "alice",
    #         7,
    #         [
    #           "adventurer",
    #           "younger-sister"
    #         ]
    #       ]
    #     ]
    #   ]
    # ]

### 7.3.11.3.5. Create an index column

Groonga provides index column for fast search. It doesn’t store your data. It stores data for fast search.

You don’t need to update index column by yourself. Index column is updated automatically when you store data into a data column (scalar column or vector column) that is marked as index target column. You can set multiple columns as index target columns to one index column.

If you make a new index, it is invisible until finishing of index build.

If Groonga has an index column for the `age` column of the `People` table, Groonga can do fast equal search, fast comparison search and fast range search against `age` column values.

You must specify the following parameters to create an index column:

> - The `flags` parameter: `COLUMN_INDEX`

> - The `type` parameter: The table name of index target column such as `People`

> - The `source` parameter: The index target column name such as `age`

You don’t need additional flags to the `flags` parameter for equal search, comparison search and range search index column. You need additional flags to the `flags` parameter for full text search index column or multiple column index column. See Create an index column for full text search and Create a multiple columns index column for details.

Here is an example to create an index column for the `age` column of the `People` table.

First, you need to create a table for range index column. See Create range index table for details. This example creates the `Ages` table as TABLE_PAT_KEY:

Execution example:

    table_create \
      --name Ages \
      --flags TABLE_PAT_KEY \
      --key_type UInt8
    # [[0, 1337566253.89858, 0.000355720520019531], true]

Now, you can create an index column for the `age` column of the `People` table. `COLUMN_INDEX` in the `flags` parameter, `People` in the `type` parameter and `age` in the `source` parameter are important:

Execution example:

    column_create \
      --table Ages \
      --name people_age_index \
      --flags COLUMN_INDEX \
      --type People \
      --source age
    # [[0, 1337566253.89858, 0.000355720520019531], true]

You can confirm that `age > 5` is evaluated by the `Ages.people_age_index` newly created index column from log. Groonga reports used index columns in `info` log level. You can change log level dynamically by log_level command.

Execution example:

    log_level --level info
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    select \
      --table People \
      --filter 'age > 5'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "age",
    #           "UInt8"
    #         ],
    #         [
    #           "roles",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         1,
    #         "alice",
    #         7,
    #         [
    #           "adventurer",
    #           "younger-sister"
    #         ]
    #       ]
    #     ]
    #   ]
    # ]
    # log: 2019-04-02 18:35:32.349471|i| [table][select][index][range] <Ages.people_age_index>
    log_level --level notice
    # [[0, 1337566253.89858, 0.000355720520019531], true]

You can confirm that the `Ages.people_age_index` is used from the following log:

    [table][select][index][range] <Ages.people_age_index>

The log says `Ages.people_age_index` index column is used for range search.

### 7.3.11.3.6. Create an index column for full text search

There is a difference between for non full text search (equal search, comparison search or range search) index column and for full text search index column. You need to add `WITH_POSITION` to the `flags` parameter. It means that you need to specify `COLUMN_INDEX|WITH_POSITION` to the `flags` parameter. It’s the difference.

Here is an example to create a full text search index column for the key of the `People` table.

First, you need to create a table for full text search index column. See Create lexicon for details. This example creates the `Terms` table as TABLE_PAT_KEY with TokenBigram tokenizer and NormalizerAuto normalizer:

Execution example:

    table_create \
      --name Terms \
      --flags TABLE_PAT_KEY \
      --key_type ShortText \
      --default_tokenizer TokenBigram \
      --normalizer NormalizerAuto
    # [[0, 1337566253.89858, 0.000355720520019531], true]

Now, you can create a full text search index column for the key of the `People` table. `COLUMN_INDEX|WITH_POSITION` in the `flags` parameter, `People` in the `type` parameter and `_key` in the `source` parameter are important:

Execution example:

    column_create \
      --table Terms \
      --name people_key_index \
      --flags COLUMN_INDEX|WITH_POSITION \
      --type People \
      --source _key
    # [[0, 1337566253.89858, 0.000355720520019531], true]

You can confirm that `--match_columns _key` and `--query Alice` are evaluated by the `Terms.people_key_index` newly created full text search index column from log. Groonga reports used index columns in `info` log level. You can change log level dynamically by log_level command.

Execution example:

    log_level --level info
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    select \
      --table People \
      --match_columns _key \
      --query Alice
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "age",
    #           "UInt8"
    #         ],
    #         [
    #           "roles",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         1,
    #         "alice",
    #         7,
    #         [
    #           "adventurer",
    #           "younger-sister"
    #         ]
    #       ]
    #     ]
    #   ]
    # ]
    # log: 2019-04-02 18:35:33.044069|i| [object][search][index][key][exact] <Terms.people_key_index>
    # log: 2019-04-02 18:35:33.044114|i| grn_ii_sel > (Alice)
    # log: 2019-04-02 18:35:33.044226|i| n=1 (Alice)
    # log: 2019-04-02 18:35:33.044305|i| exact: 1
    # log: 2019-04-02 18:35:33.044318|i| hits=1
    log_level --level notice
    # [[0, 1337566253.89858, 0.000355720520019531], true]

You can confirm that the `Terms.people_key_index` is used from the following log:

    [object][search][index][key][exact] <Terms.people_key_index>

The log says `Terms.people_key_index` index column is used for full text search. (To be precise, the index column is used for exact term search by inverted index.)

### 7.3.11.3.7. Create a multiple columns index column

You can create an index column for multiple columns. It means that you can do fast search for multiple columns with one index column. Multiple columns index column has better space efficiency than single column index column only when multiple columns have many same tokens. Multiple columns index column may be slower than single column index column. Because multiple columns index column will be a bigger index column.

You can’t use multiples columns in different tables as index target columns in the same multiple columns index column. You must specify columns in the same tables as index target columns to one multiple columns index column. For example, you can’t create a multiple columns index for `People._key` and `Books._key` because they are columns of different tables. You can create a multiple columns index column for `People._key` and `People.roles` because they are columns of the same table.

There is a difference between for single column index column and for multiple columns index column. You need to add `WITH_SECTION` to the `flags` parameter. It means that you need to specify `COLUMN_INDEX|WITH_SECTION` to the `flags` parameter. It’s the difference.

If you want to create a multiple columns index column for full text search, you need to specify `COLUMN_INDEX|WITH_POSITION|WITH_SECTION` to the `flags` parameter. See Create an index column for full text search for full text search index column details.

Here is an example to create a multiple columns index column for the key of the `People` table and the `roles` column of the `People` table.

There is no difference between index table for single column index column and multiple columns index column.

In this example, `Names` table is created for equal search and prefix search. It uses `TABLE_PAT_KEY` because `TABLE_PAT_KEY` supports prefix search. See Tables for details.

Execution example:

    table_create \
      --name Names \
      --flags TABLE_PAT_KEY \
      --key_type ShortText \
      --normalizer NormalizerAuto
    # [[0, 1337566253.89858, 0.000355720520019531], true]

You can create a multiple columns index column for the key of the `People` table and `roles` column of the `People` table. `COLUMN_INDEX|WITH_SECTION` in the `flags` parameter, `People` in the `type` parameter and `_key,roles` in the `source` parameter are important:

Execution example:

    column_create \
      --table Names \
      --name people_key_roles_index \
      --flags COLUMN_INDEX|WITH_SECTION \
      --type People \
      --source _key,roles
    # [[0, 1337566253.89858, 0.000355720520019531], true]

You can confirm that `--filter 'roles @^ "Younger"` is evaluated by the `Names.people_key_roles_index` newly created multiple columns index column from log. Groonga reports used index columns in `info` log level. You can change log level dynamically by log_level command.

Execution example:

    log_level --level info
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    select \
      --table People \
      --filter 'roles @^ "Younger"'
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "age",
    #           "UInt8"
    #         ],
    #         [
    #           "roles",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         1,
    #         "alice",
    #         7,
    #         [
    #           "adventurer",
    #           "younger-sister"
    #         ]
    #       ]
    #     ]
    #   ]
    # ]
    # log: 2019-04-02 18:35:33.728665|i| [table][select][index][prefix] <Names.people_key_roles_index>
    log_level --level notice
    # [[0, 1337566253.89858, 0.000355720520019531], true]

You can confirm that the `Names.people_key_roles_index` is used from the following log:

    [table][select][index][prefix] <Names.people_key_roles_index>

The log says `Names.people_key_roles_index` index column is used for prefix search.

### 7.3.11.3.8. Create a small index column

If you know index target data are small, you can reduce memory usage for the index column. Memory usage is `1/256` of the default index column.

How many data are small? It depends on data. Small index column can’t handle 1 billion records at least. If index target is only one scalar column with no text family type (`ShortText`, `Text` or `LongText`), the maximum handleable records are depends of the number of kinds of index target data. If index target column has `1`, `1`, `2` and `3`, the number of kinds of them are `3` (`1` and `2` and `3`). The following table shows the relationship between the number of kinds of index target data and the number of handleable records:

The number of kinds of index target data and the number of handleable records in a small index column
The number of kinds of index target data | The number of hanleable records
--- | ---
1 | 16779234
2 | 4648070
4 | 7238996
8 | 8308622
16 | 11068624
32 | 12670817
64 | 18524211
128 | 38095511
256 | 51265384

You need to add `INDEX_SMALL` to the `flags` parameter such as `COLUMN_INDEX|INDEX_SMALL` to create a small index column.

If the `People` table has only 1 million records, you can use a small index column for the `age` column:

Execution example:

    column_create \
      --table Ages \
      --name people_age_small_index \
      --flags COLUMN_INDEX|INDEX_SMALL \
      --type People \
      --source age
    # [[0, 1337566253.89858, 0.000355720520019531], true]

### 7.3.11.3.9. Create a medium index column

If you know index target data are medium, you can reduce memory usage for the index column. Memory usage is `5/24` of the default index column.

How many data are medium? It depends on data.

If index target is only one scalar column, a medium index column can handle all records.

A medium index column may not handle all records at the following cases:

> - Index target is one text family (`ShortText`, `Text` or `LongText`) scalar column

> - Index target is one vector column

> - Index targets are multiple columns

> - Index table has tokenizer

You need to add `INDEX_MEDIUM` to the `flags` parameter such as `COLUMN_INDEX|INDEX_MEDIUM` to create a medium index column.

You can use a medium index column for an index column of the `age` column of the `People` table safely. Because it’s one scalar column with `UInt8` type.

Here is an example to create a medium index column:

Execution example:

    column_create \
      --table Ages \
      --name people_age_medium_index \
      --flags COLUMN_INDEX|INDEX_MEDIUM \
      --type People \
      --source age
    # [[0, 1337566253.89858, 0.000355720520019531], true]

### 7.3.11.3.10. Create a large index column

If you know index target data are large, you need to use large index column. It uses increases memory usage for the index column but it can accept more data. Memory usage is 2 times larger than the default index column.

How many data are large? It depends on data.

If index target is only one scalar column, it’s not large data.

Large data must have many records (normally at least 10 millions records) and at least one of the following features:

> - Index targets are multiple columns

> - Index table has tokenizer

You need to add `INDEX_LARGE` to the `flags` parameter such as `COLUMN_INDEX|INDEX_LARGE` to create a large index column.

You can use a large index column for an index column of the `_key` of the `People` table and the `role` column of the `People` table.

Here is an example to create a large index column:

Execution example:

    column_create \
      --table Terms \
      --name people_roles_large_index \
      --flags COLUMN_INDEX|WITH_POSITION|WITH_SECTION|INDEX_LARGE \
      --type People \
      --source roles
    # [[0, 1337566253.89858, 0.000355720520019531], true]

## 7.3.11.4. Parameters

This section describes all parameters.

### 7.3.11.4.1. Required parameters

There are some required parameters.

#### 7.3.11.4.1.1. `table`

Specifies an existing table name for the new column.

#### 7.3.11.4.1.2. `name`

Specifies the column name to be created.

The column name must be unique in the same table.

Here are available characters:

- `0` .. `9` (digit)

- `a` .. `z` (alphabet, lower case)

- `A` .. `Z` (alphabet, upper case)

- `#` (hash)

- `@` (at mark)

- `-` (hyphen)

- `_` (underscore) (NOTE: Underscore can’t be used as the first character.)

You need to create a name with one or more the above characters. Note that you can’t use `_` as the first character such as `_name`.

#### 7.3.11.4.1.3. `flags`

Specifies the column type and column customize options.

Here are available flags:

Flag | Description
--- | ---
`COLUMN_SCALAR` | Scalar column. It can store one value. See also Scalar column or Create a scalar column.
`COLUMN_VECTOR` | Vector column. It can store multiple values. See also Vector column or Create a vector column.
`COLUMN_INDEX` | Index column. It stores data for fast search. See also Index column or Create an index column.
`COMPRESS_ZLIB` | It enables column value compression by zlib. You need Groonga that enables zlib support. Compression by zlib is higher space efficiency than compression by LZ4. But compression by zlib is slower than compression by LZ4. This flag is available only for `COLUMN_SCALAR` and `COLUMN_VECTOR`.
`COMPRESS_LZ4` | It enables column value compression by LZ4. You need Groonga that enables LZ4 support. Compression by LZ4 is faster than compression by zlib. But compression by LZ4 is lower space efficiency than compression by zlib. This flag is available only for `COLUMN_SCALAR` and `COLUMN_VECTOR`.
`COMPRESS_ZSTD` | It enables column value compression by Zstandard. You need Groonga that enables Zstandard support. Compression by Zstandard is faster than compression by zlib and the same space efficiency as zlib. This flag is available only for `COLUMN_SCALAR` and `COLUMN_VECTOR`.
`WITH_SECTION` | It enables section support to index column. If section support is enabled, you can support multiple documents in the same index column. You must specify this flag to create a multiple columns index column. See also Create a multiple columns index column for details. Section support requires additional spaces. If you don’t need section support, you should not enable section support. This flag is available only for `COLUMN_INDEX`.
`WITH_WEIGHT` | It enables weight support to vector column or index column. If weight support is enabled for vector column, you can add weight for each element. If weight support is enabled for index column, you can add weight for each posting. They are useful to compute suitable search score. You must specify this flag to use adjuster. See also Create a weight vector column for details. Weight support requires additional spaces. If you don’t need weight support, you should not enable weight support. This flag is available only for `COLUMN_VECTOR` or `COLUMN_INDEX`.
`WEIGHT_FLOAT32` | New in version 10.0.3. You can use 32bit floating point instead of 32bit unsigned integer for weight value. You also need to specify `WITH_WEIGHT`. This flag is available only for `COLUMN_VECTOR` or `COLUMN_INDEX`.
`WITH_POSITION` | It enables position support to index column. It means that the index column is full inverted index. (Index column is implemented as inverted index.) If position support is enabled, you can add position in the document for each posting. It’s required for phrase search. It means that index column for full text search must enable position support because most full text search uses phrase search. You must specify this flag to create a full text search index column. See also Create an index column for full text search for details. Position support requires additional spaces. If you don’t need position support, you should not enable position support. This flag is available only for `COLUMN_INDEX`.
`INDEX_SMALL` | New in version 6.0.8. It requires to create a small index column. If index target data are small, small index column is enough. Small index column uses fewer memory than a normal index column or a medium index column. See also Create a small index column for knowing what are “small data” and how to use this flag. This flag is available only for `COLUMN_INDEX`.
`INDEX_MEDIUM` | New in version 6.0.8. It requires to create a medium index column. If index target data are medium, medium index column is enough. Medium index column uses fewer memory than a normal index column. See also Create a medium index column for knowing what are “medium data” and how to use this flag. This flag is available only for `COLUMN_INDEX`.
`INDEX_LARGE` | New in version 9.0.2. It requires to create a large index column. If index target data are large, you need to use large index column. Large index column uses more memory than a normal index column but accepts more data than a normal index column. See also Create a large index column for knowing what are “large data” and how to use this flag. This flag is available only for `COLUMN_INDEX`.

You must specify one of `COLUMN_${TYPE}` flags. You can’t specify two or more `COLUMN_${TYPE}` flags. For example, `COLUMN_SCALAR|COLUMN_VECTOR` is invalid.

You can combine flags with `|` (vertical bar) such as `COLUMN_INDEX|WITH_SECTION|WITH_POSITION`.

#### 7.3.11.4.1.4. `type`

Specifies type of the column value.

If the column is scalar column or vector column, here are available types:

> - Builtin types described in Data types

> - Tables defined by users

If the column is index column, here are available types:

> - Tables defined by users

See also the followings:

> - Create a scalar column

> - Create a vector column

> - Create a column that refers a table’s record

> - Create an index column

### 7.3.11.4.2. Optional parameters

There is an optional parameter.

#### 7.3.11.4.2.1. `source`

Specifies index target columns. You can specify one or more columns to the `source` parameter.

This parameter is only available for index column.

You can only specify columns of the table specified as type. You can also use the `_key` pseudo column for specifying the table key as index target.

If you specify multiple columns to the `source` parameter, separate columns with `,` (comma) such as `_key,roles`.

#### 7.3.11.4.2.2. `path`

New in version 10.0.7.

Specifies a path for storing a column.

This option is useful if you want to store a column that you often use to fast storage (e.g. SSD) and store it that you don’t often use to slow storage (e.g. HDD).

You can use a relative path or an absolute path in this option. If you specify a relative path, it is resolved from the current directory for the `groonga` process.

The default value is none.

## 7.3.11.5. Return value

`column_create` returns `true` as body on success such as:

    [HEADER, true]

If `column_create` fails, `column_create` returns `false` as body:

    [HEADER, false]

Error details are in `HEADER`.

See Output format for `HEADER`.

## 7.3.11.6. See also

> - Column

> - table_create

> - Output format