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

- 日本語版はこちら

### Navigation

- index
- next |
- previous |
- Groonga v4.0.4-214-gb1aaec1 documentation »
- 4. Tutorial »

# 4.7. match_columns parameter

## 4.7.1. Full-text search against multiple columns

Groonga supports full-text search against multiple columns. Let's consider blog site. Usually, blog site has a table which contains title column and content column. How do you search the blog entry which contains specified keywords in title or content?

In such a case, there are two ways to create indexes. One way is creating column index against each column. The other way is creating one column index against multiple columns. Either way, Groonga supports similar full-text search syntax.

### 4.7.1.1. Creating column index against each column

Here is the example which create column index against each column.

First, create `Blog1` table, add `title` column which stores title string, `message` column which stores content of blog entry.

Then create `IndexBlog1` table for column indexes, add `index_title` column for `title` column, `index_message` column for `message` column.

Execution example:

    table_create --name Blog1 --flags TABLE_HASH_KEY --key_type ShortText
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create --table Blog1 --name title --flags COLUMN_SCALAR --type ShortText
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create --table Blog1 --name message --flags COLUMN_SCALAR --type ShortText
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    table_create --name IndexBlog1 --flags TABLE_PAT_KEY|KEY_NORMALIZE --key_type ShortText --default_tokenizer TokenBigram
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create --table IndexBlog1 --name index_title --flags COLUMN_INDEX|WITH_POSITION --type Blog1 --source title
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create --table IndexBlog1 --name index_message --flags COLUMN_INDEX|WITH_POSITION --type Blog1 --source message
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    load --table Blog1
    [
    {"_key":"grn1","title":"groonga test","message":"groonga message"},
    {"_key":"grn2","title":"baseball result","message":"rakutan eggs 4 - 4 groonga moritars"},
    {"_key":"grn3","title":"groonga message","message":"none"}
    ]
    # [[0, 1337566253.89858, 0.000355720520019531], 3]

`match_columns` option of `select` command accepts multiple columns as search target. Specify query string to `query` option. Then you can do full-text search title and content of blog entries.

Let's try to search blog entries.

Execution example:

    select --table Blog1 --match_columns title||message --query groonga
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         3
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "message",
    #           "ShortText"
    #         ],
    #         [
    #           "title",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         1,
    #         "grn1",
    #         "groonga message",
    #         "groonga test"
    #       ],
    #       [
    #         3,
    #         "grn3",
    #         "none",
    #         "groonga message"
    #       ],
    #       [
    #         2,
    #         "grn2",
    #         "rakutan eggs 4 - 4 groonga moritars",
    #         "baseball result"
    #       ]
    #     ]
    #   ]
    # ]
    select --table Blog1 --match_columns title||message --query message
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "message",
    #           "ShortText"
    #         ],
    #         [
    #           "title",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         3,
    #         "grn3",
    #         "none",
    #         "groonga message"
    #       ],
    #       [
    #         1,
    #         "grn1",
    #         "groonga message",
    #         "groonga test"
    #       ]
    #     ]
    #   ]
    # ]
    select --table Blog1 --match_columns title --query message
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "message",
    #           "ShortText"
    #         ],
    #         [
    #           "title",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         3,
    #         "grn3",
    #         "none",
    #         "groonga message"
    #       ]
    #     ]
    #   ]
    # ]

### 4.7.1.2. Creating one column index against multiple columns

Groonga also supports one column index against multiple columns.

The difference for previous example is only one column index exists. Thus, There is one common column index against title and message column.

Even though same column index is used, Groonga supports to search against title column only, message column only and title or message column.

Execution example:

    table_create --name Blog2 --flags TABLE_HASH_KEY --key_type ShortText
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create --table Blog2 --name title --flags COLUMN_SCALAR --type ShortText
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create --table Blog2 --name message --flags COLUMN_SCALAR --type ShortText
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    table_create --name IndexBlog2 --flags TABLE_PAT_KEY|KEY_NORMALIZE --key_type ShortText --default_tokenizer TokenBigram
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create --table IndexBlog2 --name index_blog --flags COLUMN_INDEX|WITH_POSITION|WITH_SECTION --type Blog2 --source title,message
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    load --table Blog2
    [
    {"_key":"grn1","title":"groonga test","message":"groonga message"},
    {"_key":"grn2","title":"baseball result","message":"rakutan eggs 4 - 4 groonga moritars"},
    {"_key":"grn3","title":"groonga message","message":"none"}
    ]
    # [[0, 1337566253.89858, 0.000355720520019531], 3]

Let's search same query in previous section. You can get same search results.

Execution example:

    select --table Blog2 --match_columns title||message --query groonga
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         3
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "message",
    #           "ShortText"
    #         ],
    #         [
    #           "title",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         1,
    #         "grn1",
    #         "groonga message",
    #         "groonga test"
    #       ],
    #       [
    #         2,
    #         "grn2",
    #         "rakutan eggs 4 - 4 groonga moritars",
    #         "baseball result"
    #       ],
    #       [
    #         3,
    #         "grn3",
    #         "none",
    #         "groonga message"
    #       ]
    #     ]
    #   ]
    # ]
    select --table Blog2 --match_columns title||message --query message
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "message",
    #           "ShortText"
    #         ],
    #         [
    #           "title",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         1,
    #         "grn1",
    #         "groonga message",
    #         "groonga test"
    #       ],
    #       [
    #         3,
    #         "grn3",
    #         "none",
    #         "groonga message"
    #       ]
    #     ]
    #   ]
    # ]
    select --table Blog2 --match_columns title --query message
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_key",
    #           "ShortText"
    #         ],
    #         [
    #           "message",
    #           "ShortText"
    #         ],
    #         [
    #           "title",
    #           "ShortText"
    #         ]
    #       ],
    #       [
    #         3,
    #         "grn3",
    #         "none",
    #         "groonga message"
    #       ]
    #     ]
    #   ]
    # ]

Note

There may be a question that "which is the better solution for indexing." It depends on the case.

- Indexes for each column - The update performance tends to be better than multiple colum index because there is enough buffer for updating. On the other hand, the efficiency of disk usage is not so good.
- Indexes for multiple column - It saves disk usage because it shares common buffer. On the other hand, the update performance is not so good.

## 4.7.2. Full text search with specific index name

TODO

## 4.7.3. Nested index search among related table by column index

If there are relationships among multiple table with column index, you can search multiple table by specifying reference column name.

Here is the concrete example.

There are tables which store blog articles, comments for articles. The table which stores articles has columns for article and comment. And the comment column refers Comments table. The table which stores comments has columns for comment and column index to article table.

if you want to search the articles which contain specified keyword in comment, you need to execute fulltext search for table of comment, then search the records which contains fulltext search results.

But, you can search the records by specifying the reference column index at once.

Here is the sample schema.

Execution example:

    table_create Comments TABLE_HASH_KEY UInt32
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Comments content COLUMN_SCALAR ShortText
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    table_create Articles TABLE_NO_KEY
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Articles content COLUMN_SCALAR Text
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Articles comment COLUMN_SCALAR Comments
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    table_create Lexicon TABLE_PAT_KEY|KEY_NORMALIZE ShortText --default_tokenizer TokenBigram
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Lexicon articles_content COLUMN_INDEX|WITH_POSITION Articles content
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Lexicon comments_content COLUMN_INDEX|WITH_POSITION Comments content
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Comments article COLUMN_INDEX Articles comment
    # [[0, 1337566253.89858, 0.000355720520019531], true]

Here is the sample data.

Execution example:

    load --table Comments
    [
    {"_key": 1, "content": "I'm using groonga too!"},
    {"_key": 2, "content": "I'm using groonga and mroonga!"},
    {"_key": 3, "content": "I'm using mroonga too!"}
    ]
    # [[0, 1337566253.89858, 0.000355720520019531], 3]
    load --table Articles
    [
    {"content": "Groonga is fast!", "comment": 1},
    {"content": "Groonga is useful!"},
    {"content": "Mroonga is fast!", "comment": 3}
    ]
    # [[0, 1337566253.89858, 0.000355720520019531], 3]

You can write the query that search the records which contains specified keyword as a comment, then fetch the articles which refers to it.

Query for searching the records described above:

    select Articles --match_columns comment.content --query groonga --output_columns "_id, _score, *"

You need to concatenate comment column of Articles table and content column of Comments table with period(.) as --match_columns arguments.

At first, this query execute fulltext search from content of Comments table, then fetch the records of Articles table which refers to already searched records of Comments table. (Because of this, if you comment out the query which create column index 'article' of Comments table, you can't get intended search results.)

Execution example:

    select Articles --match_columns comment.content --query groonga --output_columns "_id, _score, *"
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ],
    #         [
    #           "comment",
    #           "Comments"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ]
    #       ],
    #       [
    #         1,
    #         1,
    #         1,
    #         "Groonga is fast!"
    #       ]
    #     ]
    #   ]
    # ]

Now, you can search articles which contains specific keywords as a comment.

The feature of nested index search is not limited to the relationship between two table only.

Here is the sample schema similar to previous one. The difference is added table which express 'Reply' and relationship is extended to three tables.

Execution example:

    table_create Replies2 TABLE_HASH_KEY UInt32
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Replies2 content COLUMN_SCALAR ShortText
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    table_create Comments2 TABLE_HASH_KEY UInt32
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Comments2 content COLUMN_SCALAR ShortText
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Comments2 comment COLUMN_SCALAR Replies2
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    table_create Articles2 TABLE_NO_KEY
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Articles2 content COLUMN_SCALAR Text
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Articles2 comment COLUMN_SCALAR Comments2
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    table_create Lexicon2 TABLE_PAT_KEY|KEY_NORMALIZE ShortText --default_tokenizer TokenBigram
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Lexicon2 articles_content COLUMN_INDEX|WITH_POSITION Articles2 content
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Lexicon2 comments_content COLUMN_INDEX|WITH_POSITION Comments2 content
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Lexicon2 replies_content COLUMN_INDEX|WITH_POSITION Replies2 content
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Comments2 article COLUMN_INDEX Articles2 comment
    # [[0, 1337566253.89858, 0.000355720520019531], true]
    column_create Replies2 reply_to COLUMN_INDEX Comments2 comment
    # [[0, 1337566253.89858, 0.000355720520019531], true]

Here is the sample data.

Execution example:

    load --table Replies2
    [
    {"_key": 1, "content": "I'm using rroonga too!"},
    {"_key": 2, "content": "I'm using groonga and mroonga and rroonga!"},
    {"_key": 3, "content": "I'm using nroonga too!"}
    ]
    # [[0, 1337566253.89858, 0.000355720520019531], 3]
    load --table Comments2
    [
    {"_key": 1, "content": "I'm using groonga too!", "comment": 1},
    {"_key": 2, "content": "I'm using groonga and mroonga!", "comment": 2},
    {"_key": 3, "content": "I'm using mroonga too!"}
    ]
    # [[0, 1337566253.89858, 0.000355720520019531], 3]
    load --table Articles2
    [
    {"content": "Groonga is fast!", "comment": 1},
    {"content": "Groonga is useful!", "comment": 2},
    {"content": "Mroonga is fast!", "comment": 3}
    ]
    # [[0, 1337566253.89858, 0.000355720520019531], 3]

Query for searching the records described above:

    select Articles2 --match_columns comment.content --query mroonga --output_columns "_id, _score, *"
    select Articles2 --match_columns comment.comment.content --query mroonga --output_columns "_id, _score, *"

The first query searches 'mroonga' from Comments2 table, the second one searches 'mroonga' from Replies2 and Comment2 table by using reference column index.

Execution example:

    select Articles2 --match_columns comment.content --query mroonga --output_columns "_id, _score, *"
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         2
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ],
    #         [
    #           "comment",
    #           "Comments2"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ]
    #       ],
    #       [
    #         2,
    #         1,
    #         2,
    #         "Groonga is useful!"
    #       ],
    #       [
    #         3,
    #         1,
    #         3,
    #         "Mroonga is fast!"
    #       ]
    #     ]
    #   ]
    # ]
    select Articles2 --match_columns comment.comment.content --query mroonga --output_columns "_id, _score, *"
    # [
    #   [
    #     0,
    #     1337566253.89858,
    #     0.000355720520019531
    #   ],
    #   [
    #     [
    #       [
    #         1
    #       ],
    #       [
    #         [
    #           "_id",
    #           "UInt32"
    #         ],
    #         [
    #           "_score",
    #           "Int32"
    #         ],
    #         [
    #           "comment",
    #           "Comments2"
    #         ],
    #         [
    #           "content",
    #           "Text"
    #         ]
    #       ],
    #       [
    #         2,
    #         1,
    #         2,
    #         "Groonga is useful!"
    #       ]
    #     ]
    #   ]
    # ]

As a result, the first query matches two article because of Comments2 table has two records which contains 'mroonga' as keyword.

On the other hand, the second one matches one article only because of Replies2 table has only one record which contains 'mroonga' as keyword, and there is one record which contains same keyword and refers to the record in Comments2 table.

## 4.7.4. Indexes with Weight

TODO

### Navigation

- index
- next |
- previous |
- Groonga v4.0.4-214-gb1aaec1 documentation »
- 4. Tutorial »