Release 11.0.4 - 2021-06-29¶

Known Issues¶

• Currently, Groonga has a bug that there is possible that data is corrupt when we execute many additions, delete, and update data to vector column.

• [The browser based administration tool] Currently, Groonga has a bug that a search query that is inputted to non-administration mode is sent even if we input checks to the checkbox for the administration mode of a record list.

• *< and *> only valid when we use query() the right side of filter condition. If we specify as below, *< and *> work as &&.

  • 'content @ "Groonga" *< content @ "Mroonga"'

Thanks¶

• Takashi Hashida

Improvements¶

• [query] Added support for ignoring TokenFilterStem by the query.

  • TokenFilterStem can search by using a stem. For example, all of develop, developing, developed and develops tokens are stemmed as develop. So we can find develop, developing and developed by develops query.

  • In this release, we are able to search without TokenFilterStem in only a specific query as below.

    plugin_register token_filters/stem
    
    table_create Memos TABLE_NO_KEY
    column_create Memos content COLUMN_SCALAR ShortText
    
    table_create Terms TABLE_PAT_KEY ShortText \
      --default_tokenizer TokenBigram \
      --normalizer NormalizerAuto \
      --token_filters 'TokenFilterStem("keep_original", true)'
    column_create Terms memos_content COLUMN_INDEX|WITH_POSITION Memos content
    
    load --table Memos
    [
    {"content": "I develop Groonga"},
    {"content": "I'm developing Groonga"},
    {"content": "I developed Groonga"}
    ]
    
    select Memos \
      --match_columns content \
      --query '"developed groonga"' \
      --query_options '{"TokenFilterStem.enable": false}'
    [
      [
        0,
        0.0,
        0.0
      ],
      [
        [
          [
            1
          ],
          [
            [
              "_id",
              "UInt32"
            ],
            [
              "content",
              "ShortText"
            ]
          ],
          [
            3,
            "I developed Groonga"
          ]
        ]
      ]
    ]
    

  • This feature is useful when users want to search by a stemmed word generally but users sometimes want to search by a exact (not stemmed) word as below.

    • If Groonga returns many results when searching by a stemmed word.

    • If TokenFilterStem returns the wrong result of stemming.

    • If we want to find only records that have an exact (not stemmed) word.

• [query] Added support for ignoring TokenFilterStopWord by the query.

  • TokenFilterStopWord searched without stop word that we registered beforehand. It uses for reducing noise of search by ignoring frequently word (e.g., and, is, and so on.).

  • However, we sometimes want to search include these words only a specific query. In this release, we are able to search without TokenFilterStopWord in only a specific query as below.

    plugin_register token_filters/stop_word
    
    table_create Memos TABLE_NO_KEY
    column_create Memos content COLUMN_SCALAR ShortText
    
    table_create Terms TABLE_PAT_KEY ShortText \
      --default_tokenizer TokenBigram \
      --normalizer NormalizerAuto \
      --token_filters TokenFilterStopWord
    column_create Terms memos_content COLUMN_INDEX|WITH_POSITION Memos content
    column_create Terms is_stop_word COLUMN_SCALAR Bool
    
    load --table Terms
    [
    {"_key": "and", "is_stop_word": true}
    ]
    
    load --table Memos
    [
    {"content": "Hello"},
    {"content": "Hello and Good-bye"},
    {"content": "Good-bye"}
    ]
    
    select Memos \
      --match_columns content \
      --query "Hello and" \
      --query_options '{"TokenFilterStopWord.enable": false}' \
      --match_escalation_threshold -1 \
      --sort_keys -_score
    [
      [
        0,
        0.0,
        0.0
      ],
      [
        [
          [
            1
          ],
          [
            [
              "_id",
              "UInt32"
            ],
            [
              "content",
              "ShortText"
            ]
          ],
          [
            2,
            "Hello and Good-bye"
          ]
        ]
      ]
    ]
    

  • In the above example, we specify TokenFilterStopWord.enable by using --query-options, but we also specify it by using {"options": {"TokenFilterStopWord.enable": false}} as below.

    plugin_register token_filters/stop_word
    
    table_create Memos TABLE_NO_KEY
    column_create Memos content COLUMN_SCALAR ShortText
    
    table_create Terms TABLE_PAT_KEY ShortText \
      --default_tokenizer TokenBigram \
      --normalizer NormalizerAuto \
      --token_filters TokenFilterStopWord
    column_create Terms memos_content COLUMN_INDEX|WITH_POSITION Memos content
    column_create Terms is_stop_word COLUMN_SCALAR Bool
    
    load --table Terms
    [
    {"_key": "and", "is_stop_word": true}
    ]
    
    load --table Memos
    [
    {"content": "Hello"},
    {"content": "Hello and Good-bye"},
    {"content": "Good-bye"}
    ]
    
    select Memos \
      --filter 'query("content", \
                      "Hello and", \
                      {"options": {"TokenFilterStopWord.enable": false}})' \
      --match_escalation_threshold -1 \
      --sort_keys -_score
    [
      [
        0,
        0.0,
        0.0
      ],
      [
        [
          [
            1
          ],
          [
            [
              "_id",
              "UInt32"
            ],
            [
              "content",
              "ShortText"
            ]
          ],
          [
            2,
            "Hello and Good-bye"
          ]
        ]
      ]
    ]
    

  • This feature is useful if that Groonga can’t return results correctly if we don’t search by keywords include commonly used words (e.g., if a search for a song title, a shop name, and so on.).

• [Normalizers][NormalizerNFKC] Added a new option remove_new_line.

  • If we want to normalize the key of a table that stores data, we set a normalizer to it. However, normally, normalizers remove a new line.

  • Groonga can’t handle a key that is only a new line.

  • We can register data that is only a new line as key by this option.

• [string_slice] Added a new function string_slice(). [Github#1177][Patched by Takashi Hashida]

  • string_slice() extracts a substring of a string.

  • To enable this function, we need to register functions/string plugin.

  • We can use two different extraction methods depending on the arguments as below.

    • Extraction by position:

      plugin_register functions/string
      table_create Memos TABLE_HASH_KEY ShortText
      
      load --table Memos
      [
        {"_key": "Groonga"}
      ]
      select Memos --output_columns '_key, string_slice(_key, 2, 3)'
      [
        [
          0,
          1337566253.89858,
          0.000355720520019531
        ],
        [
          [
            [
              1
            ],
            [
              [
                "_key",
                "ShortText"
              ],
              [
                "string_slice",
                null
              ]
            ],
            [
              "Groonga",
              "oon"
            ]
          ]
        ]
      ]
      

    • Extraction by regular expression:

      plugin_register functions/string
      table_create Memos TABLE_HASH_KEY ShortText
      
      load --table Memos
      [
        {"_key": "Groonga"}
      ]
      select Memos --output_columns '_key, string_slice(_key, "(Gro+)(.*)", 2)'
      [
        [p
          0,
          1337566253.89858,
          0.000355720520019531
        ],
        [
          [
            [
              1
            ],
            [
              [
                "_key",
                "ShortText"
              ],
              [
                "string_slice",
                null
              ]
            ],
            [
              "Groonga",
              "nga"
            ]
          ]
        ]
      ]
      

• [Ubuntu] Dropped support for Ubuntu 16.04 LTS (Xenial Xerus).

• Added EditorConfig for Visual Studio. [GitHub#1191][Patched by Takashi Hashida]

  • Most settings are for Visual Studio only.

• [httpd] Updated bundled nginx to 1.20.1.

  • Contains security fix of CVE-2021-23017.

Fixes¶

• Fixed a bug that Groonga may not have returned a result of a search query if we sent many search queries when tokenizer, normalizer, or token_filters that support options were used.

Known Issues¶

• Currently, Groonga has a bug that there is possible that data is corrupt when we execute many additions, delete, and update data to vector column.

• [The browser based administration tool] Currently, Groonga has a bug that a search query that is inputted to non-administration mode is sent even if we input checks to the checkbox for the administration mode of a record list.

Thanks¶

• Takashi Hashida

Improvements¶

• [Documentation] Removed a reference about ruby_load command. [GitHub#1172][Patched by Anthony M. Cook]

  • Because this command has already deleted.

• [Debian GNU/Linux] Added support for Debian 11(Bullseye).

• [select] Added support for --post_filter.

  • We can use post_filter to filter by filtered stage dynamic columns as below.

    table_create Items TABLE_NO_KEY
    column_create Items price COLUMN_SCALAR UInt32
    
    load --table Items
    [
    {"price": 100},
    {"price": 150},
    {"price": 200},
    {"price": 250},
    {"price": 300}
    ]
    
    select Items \
      --filter "price >= 150" \
      --columns[price_with_tax].stage filtered \
      --columns[price_with_tax].type UInt32 \
      --columns[price_with_tax].flags COLUMN_SCALAR \
      --columns[price_with_tax].value "price * 1.1" \
      --post_filter "price_with_tax <= 250"
    [
      [
        0,
        0.0,
        0.0
      ],
      [
        [
          [
            2
          ],
          [
            [
              "_id",
              "UInt32"
            ],
            [
              "price_with_tax",
              "UInt32"
            ],
            [
              "price",
              "UInt32"
            ]
          ],
          [
            2,
            165,
            150
          ],
          [
            3,
            220,
            200
          ]
        ]
      ]
    ]
    

• [select] Added support for --slices[].post_filter.

  • We can use post_filter to filter by --slices[].filter as below.

    table_create Items TABLE_NO_KEY
    column_create Items price COLUMN_SCALAR UInt32
    
    load --table Items
    [
    {"price": 100},
    {"price": 200},
    {"price": 300},
    {"price": 1000},
    {"price": 2000},
    {"price": 3000}
    ]
    
    select Items \
      --slices[expensive].filter 'price >= 1000' \
      --slices[expensive].post_filter 'price < 3000'
    [
      [
        0,
        0.0,
        0.0
      ],
      [
        [
          [
            6
          ],
          [
            [
              "_id",
              "UInt32"
            ],
            [
              "price",
              "UInt32"
            ]
          ],
          [
            1,
            100
          ],
          [
            2,
            200
          ],
          [
            3,
            300
          ],
          [
            4,
            1000
          ],
          [
            5,
            2000
          ],
          [
            6,
            3000
          ]
        ],
        {
          "expensive": [
            [
              2
            ],
            [
              [
                "_id",
                "UInt32"
              ],
              [
                "price",
                "UInt32"
              ]
            ],
            [
              4,
              1000
            ],
            [
              5,
              2000
            ]
          ]
        }
      ]
    ]
    

• [select] Added support for describing expression into --sort_keys.

  • We can describe the expression into --sort_keys.

    • If nonexistent keys into expression as a --sort_keys, they are ignored and outputted warns into a log.

  • By this, for example, we can specify a value of an element of VECTOR COLUMN to --sort_keys. And we can sort a result with it.

  • We can sort a result with an element of VECTOR COLUMN even if the before version by using dynamic column. However, we can sort a result with an element of VECTOR COLUMN without using dynamic column by this feature.

    table_create Values TABLE_NO_KEY
    column_create Values numbers COLUMN_VECTOR Int32
    load --table Values
    [
    {"numbers": [127, 128, 129]},
    {"numbers": [126, 255]},
    {"numbers": [128, -254]}
    ]
    select Values --sort_keys 'numbers[1]' --output_columns numbers
    [
      [
        0,
        0.0,
        0.0
      ],
      [
        [
          [
            3
          ],
          [
            [
              "numbers",
              "Int32"
            ]
          ],
          [
            [
              128,
              -254
            ]
          ],
          [
            [
              127,
              128,
              129
            ]
          ],
          [
            [
              126,
              255
            ]
          ]
        ]
      ]
    ]
    

• [Token filters] Added support for multiple token filters with options.

  • We can specify multiple token filters with options like --token_filters 'TokenFilterStopWord("column", "ignore"), TokenFilterNFKC130("unify_kana", true)'. [Github#mroonga/mroonga#399][Reported by MASUDA Kazuhiro]

• [query] Added support a dynamic column of result_set stage with complex expression.

  • Complex expression is that it needs temporary result sets internally like a following expression.

    '(true && query("name * 10", "ali", {"score_column": "ali_score"})) || \
     (true && query("name * 2", "li", {"score_column": "li_score"}))'
    

    • In the above expressions, the temporary result sets are used to store the result of evaluating the true.

    • Therefore, for example, in the following expression, we can use a value of dynamic column of result_set stage in expression. Because temporary result sets internally are needless as below expression.

      '(query("name * 10", "ali", {"score_column": "ali_score"})) || \
       (query("name * 2", "li", {"score_column": "li_score"}))'
      

  • In this release, for example, we can set a value to li_score as below. (The value of li_score had been 0 in before version. Because the second expression could not get dynamic column.)

    table_create Users TABLE_NO_KEY
    column_create Users name COLUMN_SCALAR ShortText
    
    table_create Lexicon TABLE_HASH_KEY ShortText \
      --default_tokenizer TokenBigramSplitSymbolAlphaDigit \
      --normalizer NormalizerAuto
    column_create Lexicon users_name COLUMN_INDEX|WITH_POSITION Users name
    
    load --table Users
    [
    {"name": "Alice"},
    {"name": "Alisa"},
    {"name": "Bob"}
    ]
    
    select Users \
      --columns[ali_score].stage result_set \
      --columns[ali_score].type Float \
      --columns[ali_score].flags COLUMN_SCALAR \
      --columns[li_score].stage result_set \
      --columns[li_score].type Float \
      --columns[li_score].flags COLUMN_SCALAR \
      --output_columns name,_score,ali_score,li_score \
      --filter '(true && query("name * 10", "ali", {"score_column": "ali_score"})) || \
                (true && query("name * 2", "li", {"score_column": "li_score"}))'
    [
      [
        0,
        0.0,
        0.0
      ],
      [
        [
          [
            2
          ],
          [
            [
              "name",
              "ShortText"
            ],
            [
              "_score",
              "Int32"
            ],
            [
              "ali_score",
              "Float"
            ],
            [
              "li_score",
              "Float"
            ]
          ],
          [
            "Alice",
            14,
            10.0,
            2.0
          ],
          [
            "Alisa",
            14,
            10.0,
            2.0
          ]
        ]
      ]
    ]
    

  • We also supported a dynamic vector column of result_set stage as below.

    table_create Users TABLE_NO_KEY
    column_create Users name COLUMN_SCALAR ShortText
    
    table_create Lexicon TABLE_HASH_KEY ShortText \
      --default_tokenizer TokenBigramSplitSymbolAlphaDigit \
      --normalizer NormalizerAuto
    column_create Lexicon users_name COLUMN_INDEX|WITH_POSITION Users name
    
    load --table Users
    [
    {"name": "Alice"},
    {"name": "Alisa"},
    {"name": "Bob"}
    ]
    
    select Users \
      --columns[tags].stage result_set \
      --columns[tags].type ShortText \
      --columns[tags].flags COLUMN_VECTOR \
      --output_columns name,tags \
      --filter '(true && query("name", "al", {"tags": ["al"], "tags_column": "tags"})) || \
                (true && query("name", "sa", {"tags": ["sa"], "tags_column": "tags"}))'
    [
      [
        0,
        0.0,
        0.0
      ],
      [
        [
          [
            2
          ],
          [
            [
              "name",
              "ShortText"
            ],
            [
              "tags",
              "ShortText"
            ]
          ],
          [
            "Alice",
            [
              "al"
            ]
          ],
          [
            "Alisa",
            [
              "al",
              "sa"
            ]
          ]
        ]
      ]
    ]
    

    • If we use a dynamic vector column, the storing values are appended values of each element.

• [Ubuntu] Added support for Ubuntu 21.04 (Hirsute Hippo).

• [httpd] Updated bundled nginx to 1.19.10.

Known Issues¶

• Currently, Groonga has a bug that there is possible that data is corrupt when we execute many additions, delete, and update data to vector column.

• [The browser based administration tool] Currently, Groonga has a bug that a search query that is inputted to non-administration mode is sent even if we input checks to the checkbox for the administration mode of a record list. [Github#1186][Reported by poti]

Thanks¶

• Anthony M. Cook

• MASUDA Kazuhiro

• poti

Improvements¶

• [Debian GNU/Linux] Added support for a ARM64 package.

• [select] Added support for customizing adjust weight every key word.

  • We need to specify < or > to all keywords to adjust scores until now. Because the default adjustment of weight (6 or 4) is larger than the default score (1).

    • Therefore, for example, “A“‘s weight is 1 and “B“‘s weight is 4 in A <B. Decremented “B“‘s weight (4) is larger than not decremented “A“‘s weight (1). This is not works as expected. we need to specify >A <B to use smaller weight than “A” for “B”. “A“‘s weight is 6 and “B“‘s weight is 4 in >A <B.

  • We can customize adjustment of weight every key word by only specifying <${WEIGHT} or >${WEIGHT} to target keywords since this release. For example, “A“‘s weight is 1 and “B“‘s weight is 0.9 in A <0.1B (“B“‘s weight decrement 0.1).

  • However, note that these forms ( >${WEIGHT}..., <${WEIGHT}..., and ~${WEIGHT}... ) are incompatible.

• [select] Added support for outputting Float and Float32 value in Apache Arrow format.

  • For example, Groonga output as below.

  table_create Data TABLE_NO_KEY
  column_create Data float COLUMN_SCALAR Float
  
  load --table Data
  [
  {"float": 1.1}
  ]
  
  select Data \
    --command_version 3 \
    --output_type apache-arrow
  
    return_code: int32
    start_time: timestamp[ns]
    elapsed_time: double
    -- metadata --
    GROONGA:data_type: metadata
     return_code                    start_time       elapsed_time
    0                  0     1970-01-01T09:00:00+09:00           0.000000
    ========================================
    _id: uint32
    float: double
    -- metadata --
    GROONGA:n_hits: 1
     _id          float
    0          1       1.100000
  

• [select] Added support for getting a reference destination data via index column when we output a result.

  • Until now, Groonga had returned involuntary value when we specified output value like index_column.xxx. For example, A value of --columns[tags].value purchases.tag was ["apple",["many"]],["banana",["man"]],["cacao",["man"]] in the following example. In this case, the expected values was ["apple",["man","many"]],["banana",["man"]],["cacao",["woman"]]. In this release, we can get a correct reference destination data via index column as below.

    table_create Products TABLE_PAT_KEY ShortText
    
    table_create Purchases TABLE_NO_KEY
    column_create Purchases product COLUMN_SCALAR Products
    column_create Purchases tag COLUMN_SCALAR ShortText
    
    column_create Products purchases COLUMN_INDEX Purchases product
    
    load --table Products
    [
    {"_key": "apple"},
    {"_key": "banana"},
    {"_key": "cacao"}
    ]
    
    load --table Purchases
    [
    {"product": "apple",  "tag": "man"},
    {"product": "banana", "tag": "man"},
    {"product": "cacao",  "tag": "woman"},
    {"product": "apple",  "tag": "many"}
    ]
    
    select Products \
      --columns[tags].stage output \
      --columns[tags].flags COLUMN_VECTOR \
      --columns[tags].type ShortText \
      --columns[tags].value purchases.tag \
      --output_columns _key,tags
    [
      [
        0,
        0.0,
        0.0
      ],
      [
        [
          [
            3
          ],
          [
            [
              "_key",
              "ShortText"
            ],
            [
              "tags",
              "ShortText"
            ]
          ],
          [
            "apple",
            [
              "man",
              "many"
            ]
          ],
          [
            "banana",
            [
              "man"
            ]
          ],
          [
            "cacao",
            [
              "woman"
            ]
          ]
        ]
      ]
    ]
    

• [select] Added support for specifying index column directly as a part of nested index.

  • We can search source table after filtering by using index_column.except_source_column. For example, we specify comments.content when searching in the following example. In this case, at first, this query execute full text search from content column of Commentts table, then fetch the records of Articles table which refers to already searched records of Comments table.

    table_create Articles TABLE_HASH_KEY ShortText
    
    table_create Comments TABLE_NO_KEY
    column_create Comments article COLUMN_SCALAR Articles
    column_create Comments content COLUMN_SCALAR ShortText
    
    column_create Articles content COLUMN_SCALAR Text
    column_create Articles comments COLUMN_INDEX Comments article
    
    table_create Terms TABLE_PAT_KEY ShortText \
      --default_tokenizer TokenBigram \
      --normalizer NormalizerNFKC130
    column_create Terms articles_content COLUMN_INDEX|WITH_POSITION \
      Articles content
    column_create Terms comments_content COLUMN_INDEX|WITH_POSITION \
      Comments content
    
    load --table Articles
    [
    {"_key": "article-1", "content": "Groonga is fast!"},
    {"_key": "article-2", "content": "Groonga is useful!"},
    {"_key": "article-3", "content": "Mroonga is fast!"}
    ]
    
    load --table Comments
    [
    {"article": "article-1", "content": "I'm using Groonga too!"},
    {"article": "article-3", "content": "I'm using Mroonga!"},
    {"article": "article-1", "content": "I'm using PGroonga!"}
    ]
    
    select Articles --match_columns comments.content --query groonga \
      --output_columns "_key, _score, comments.content
    [
      [
        0,
        0.0,
        0.0
      ],
      [
        [
          [
            1
          ],
          [
            [
              "_key",
              "ShortText"
            ],
            [
              "_score",
              "Int32"
            ],
            [
              "comments.content",
              "ShortText"
            ]
          ],
          [
            "article-1",
            1,
            [
              "I'm using Groonga too!",
              "I'm using PGroonga!"
            ]
          ]
        ]
      ]
    ]
    

• [load] Added support for loading reference vector with inline object literal.

  • For example, we can load data like "key" : "[ { "key" : "value", ..., "key" : "value" } ]" as below.

    table_create Purchases TABLE_NO_KEY
    column_create Purchases item COLUMN_SCALAR ShortText
    column_create Purchases price COLUMN_SCALAR UInt32
    
    table_create Settlements TABLE_HASH_KEY ShortText
    column_create Settlements purchases COLUMN_VECTOR Purchases
    column_create Purchases settlements_purchases COLUMN_INDEX Settlements purchases
    
    load --table Settlements
    [
    {
      "_key": "super market",
      "purchases": [
         {"item": "apple", "price": 100},
         {"item": "milk",  "price": 200}
      ]
    },
    {
      "_key": "shoes shop",
      "purchases": [
         {"item": "sneakers", "price": 3000}
      ]
    }
    ]
    

  • It makes easier to add JSON data into reference columns by this feature.

  • Currently, this feature only support with JSON input.

• [load] Added support for loading reference vector from JSON text.

  • We can load data to reference vector from source table with JSON text as below.

    table_create Purchases TABLE_HASH_KEY ShortText
    column_create Purchases item COLUMN_SCALAR ShortText
    column_create Purchases price COLUMN_SCALAR UInt32
    
    table_create Settlements TABLE_HASH_KEY ShortText
    column_create Settlements purchases COLUMN_VECTOR Purchases
    
    column_create Purchases settlements_purchases COLUMN_INDEX Settlements purchases
    
    load --table Settlements
    [
    {
      "_key": "super market",
      "purchases": "[{\"_key\": \"super market-1\", \"item\": \"apple\", \"price\": 100}, {\"_key\": \"super market-2\", \"item\": \"milk\",  \"price\": 200}]"
    },
    {
      "_key": "shoes shop",
      "purchases": "[{\"_key\": \"shoes shop-1\", \"item\": \"sneakers\", \"price\": 3000}]"
    }
    ]
    
    dump \
      --dump_plugins no \
      --dump_schema no
    load --table Purchases
    [
    ["_key","item","price"],
    ["super market-1","apple",100],
    ["super market-2","milk",200],
    ["shoes shop-1","sneakers",3000]
    ]
    
    load --table Settlements
    [
    ["_key","purchases"],
    ["super market",["super market-1","super market-2"]],
    ["shoes shop",["shoes shop-1"]]
    ]
    
    column_create Purchases settlements_purchases COLUMN_INDEX Settlements purchases
    

  • Currently, this feature doesn’t support nested reference record.

• [Windows] Added support for UNIX epoch for time_classify_* functions.

  • Groonga handles timestamps on local time. Therefore, for example, if we input the UNIX epoch in Japan, inputting time is 9 hours behind the UNIX epoch.

  • The Windows API outputs an error when we input the time before the UNIX epoch.

  • We can use the UNIX epoch in time_classify_* functions as below in this release.

    plugin_register functions/time
    
    table_create Timestamps TABLE_PAT_KEY Time
    load --table Timestamps
    [
    {"_key": 0},
    {"_key": "2016-05-06 00:00:00.000001"},
    {"_key": "2016-05-06 23:59:59.999999"},
    {"_key": "2016-05-07 00:00:00.000000"},
    {"_key": "2016-05-07 00:00:00.000001"},
    {"_key": "2016-05-08 23:59:59.999999"},
    {"_key": "2016-05-08 00:00:00.000000"}
    ]
    
    select Timestamps \
      --sortby _id \
      --limit -1 \
      --output_columns '_key, time_classify_day_of_week(_key)'
    [
      [
        0,
        0.0,
        0.0
      ],
      [
        [
          [
            7
          ],
          [
            [
              "_key",
              "Time"
            ],
            [
              "time_classify_day_of_week",
              null
            ]
          ],
          [
            0.0,
            4
          ],
          [
            1462460400.000001,
            5
          ],
          [
            1462546799.999999,
            5
          ],
          [
            1462546800.0,
            6
          ],
          [
            1462546800.000001,
            6
          ],
          [
            1462719599.999999,
            0
          ],
          [
            1462633200.0,
            0
          ]
        ]
      ]
    ]
    

• [query_parallel_or] Added a new function for processing queries in parallel.

  • query_parallel_or requires Apache Arrow for processing queries in parallel. If it does not enable, query_parallel_or processes queries in sequence.

  • query_parallel_or processes combination of match_columns and query_string in parallel.

  • Syntax of query_parallel_or is as follow:

    query_parallel_or(match_columns, query_string1,
                                     query_string2,
                                     .
                                     .
                                     .
                                     query_stringN,
                                     {"option": "value", ...})
    

• [select] Added support for ignoring nonexistent sort keys.

  • Groonga had been outputted error when we specified nonexistent sort keys until now. However, Groonga ignore nonexistent sort keys since this release. (Groonga doesn’t output error.)

  • This feature implements for consistency. Because we just ignore invalid values in output_columns and most of invalid values in sort_keys.

• [select] Added support for ignoring nonexistent tables in drilldowns[].table. [GitHub#1169][Reported by naoa]

  • Groonga had been outputted error when we specified nonexistent tables in drilldowns[].table until now. However, Groonga ignore nonexistent tables in drilldowns[].table since this release. (Groonga doesn’t output error.)

  • This feature implements for consistency. Because we just ignore invalid values in output_columns and most of invalid values in sort_keys.

• [httpd] Updated bundled nginx to 1.19.8.

Fixes¶

• [reference_acquire] Fixed a bug that Groonga crash when a table’s reference is acquired and a column is added to the table before auto release is happened.

  • Because the added column’s reference isn’t acquired but it’s released on auto release.

• [Windows] Fixed a bug that one or more processes fail an output backtrace on SEGV when a new backtrace logging process starts when another backtrace logging process is running in another thread.

Known Issues¶

• Currently, Groonga has a bug that there is possible that data is corrupt when we execute many additions, delete, and update data to vector column.

Thanks¶

• naoa

Improvements¶

• [select] Added support for outputting values of scalar column and vector column via nested index.

  • The nested index is that has structure as below.

    table_create Products TABLE_PAT_KEY ShortText
    
    table_create Purchases TABLE_NO_KEY
    column_create Purchases product COLUMN_SCALAR Products
    column_create Purchases tag COLUMN_SCALAR ShortText
    
    column_create Products purchases COLUMN_INDEX Purchases product
    

  • The Products.purchases column is a index of Purchases.product column in the above example. Also, Purchases.product is a reference to Products table.

  • We had not got the correct search result when we search via nested index until now.

  • The result had been as follows until now. We can see that {"product": "apple",  "tag": "man"} is not output.

    table_create Products TABLE_PAT_KEY ShortText
    
    table_create Purchases TABLE_NO_KEY
    column_create Purchases product COLUMN_SCALAR Products
    column_create Purchases tag COLUMN_SCALAR ShortText
    
    column_create Products purchases COLUMN_INDEX Purchases product
    
    load --table Products
    [
    {"_key": "apple"},
    {"_key": "banana"},
    {"_key": "cacao"}
    ]
    
    load --table Purchases
    [
    {"product": "apple",  "tag": "man"},
    {"product": "banana", "tag": "man"},
    {"product": "cacao",  "tag": "woman"},
    {"product": "apple",  "tag": "many"}
    ]
    
    select Products \
      --output_columns _key,purchases.tag
    [
      [
        0,
        1612504193.380738,
        0.0002026557922363281
      ],
      [
        [
          [
            3
          ],
          [
            [
              "_key",
              "ShortText"
            ],
            [
              "purchases.tag",
              "ShortText"
            ]
          ],
          [
            "apple",
            "many"
          ],
          [
            "banana",
            "man"
          ],
          [
            "cacao",
            "man"
          ]
        ]
      ]
    ]
    

  • The result will be as follows from this release. We can see that {"product": "apple",  "tag": "man"} is output.

    select Products \
      --output_columns _key,purchases.tag
    [
      [
        0,
        0.0,
        0.0
      ],
      [
        [
          [
            3
          ],
          [
            [
              "_key",
              "ShortText"
            ],
            [
              "purchases.tags",
              "Tags"
            ]
          ],
          [
            "apple",
            [
              [
                "man",
                "one"
              ],
              [
                "child",
                "many"
              ]
            ]
          ],
          [
            "banana",
            [
              [
                "man",
                "many"
              ]
            ]
          ],
          [
            "cacao",
            [
              [
                "woman"
              ]
            ]
          ]
        ]
      ]
    ]
    

• [Windows] Dropped support for packages of Windows version that we had cross compiled by using MinGW on Linux.

  • Because there aren’t probably many people who use that.

  • These above packages are that We had provided as below name until now.

    • groonga-x.x.x-x86.exe

    • groonga-x.x.x-x86.zip

    • groonga-x.x.x-x64.exe

    • groonga-x.x.x-x86.zip

  • From now on, we use the following packages for Windows.

    • groonga-latest-x86-vs2019-with-vcruntime.zip

    • groonga-latest-x64-vs2019-with-vcruntime.zip

  • If a system already has installed Microsoft Visual C++ Runtime Library, we suggest that we use the following packages.

    • groonga-latest-x86-vs2019.zip

    • groonga-latest-x64-vs2019.zip

Fixes¶

• Fixed a bug that there is possible that index is corrupt when Groonga executes many additions, delete, and update information in it.

  • This bug occurs when we only execute many delete information from index. However, it doesn’t occur when we only execute many additions information into index.

  • We can repair the index that is corrupt by this bug using reconstruction of it.

  • This bug doesn’t detect unless we reference the broken index. Therefore, the index in our indexes may has already broken.

  • We can use [index_column_diff] command to confirm whether the index has already been broken or not.

Improvements¶

• [select] Added support for outputting UInt64 value in Apache Arrow format.

• [select] Added support for outputting a number of hits in Apache Arrow format as below.

  -- metadata --
  GROONGA:n-hits: 10
  

• [query] Added support for optimization of “order by estimated size”.

  • Normally, we can search at high speed when we first execute a condition which number of hits is a little.

    • The “B (There are few) && A (There are many)” faster than “A (There are many) && B (There are few)”.

  • This is a known optimization. However we need reorder conditions by ourselves.

  • Groonga executes this reorder automatically by using optimization of “order by estimated size”.

  • This optimization is valid by GRN_ORDER_BY_ESTIMATED_SIZE_ENABLE=yes.

• [between] Improved performance by the following improvements.

  • Improved the accuracy of a decision whether the between() use sequential search or not.

  • Improved that we set a result of between() into a result set in bulk.

• [select] Improved performance for a prefix search.

  • For example, the performance of the following prefix search by using “*” improved.

    table_create Memos TABLE_PAT_KEY ShortText
    table_create Contents TABLE_PAT_KEY ShortText   --normalizer NormalizerAuto
    column_create Contents entries_key_index COLUMN_INDEX Memos _key
    
    load --table Memos
    [
    {"_key": "(groonga) Start to try!"},
    {"_key": "(mroonga) Installed"},
    {"_key": "(groonga) Upgraded!"}
    ]
    
    select \
      --table Memos \
      --match_columns _key \
      --query '\\(groonga\\)*'
    

• [Tokenizers][TokenMecab] Improved performance for parallel construction fo token column. [GitHub#1158][Patched by naoa]

Fixes¶

• [sub_filter] Fixed a bug that sub_filter doesn’t work in slices[].filter.

  • For example, the result of sub_filter was 0 records by the following query by this bug.

    table_create Users TABLE_HASH_KEY ShortText
    column_create Users age COLUMN_SCALAR UInt8
    
    table_create Memos TABLE_NO_KEY
    column_create Memos user COLUMN_SCALAR Users
    column_create Memos content COLUMN_SCALAR Text
    
    load --table Users
    [
    {"_key": "alice", "age": 9},
    {"_key": "bob",   "age": 29}
    ]
    
    load --table Memos
    [
    {"user": "alice", "content": "hello"},
    {"user": "bob",   "content": "world"},
    {"user": "alice", "content": "bye"},
    {"user": "bob",   "content": "yay"}
    ]
    
    select \
      --table Memos \
      --slices[adult].filter '_id > 1 && sub_filter(user, "age >= 18")'
    

• Fixed a bug that it is possible that we can’t add data and Groonga crash when we repeat much addition of data and deletion of data against a hash table.

Thanks¶

• naoa

Improvements¶

• [highlight_html] Added support for removing leading full width spaces from highlight target. [PGroonga#GitHub#155][Reported by Hung Nguyen V]

  • Until now, leading full width spaces had also included in the target of highlight as below.

    table_create Entries TABLE_NO_KEY
    column_create Entries body COLUMN_SCALAR ShortText
    
    table_create Terms TABLE_PAT_KEY ShortText --default_tokenizer TokenBigram --normalizer NormalizerAuto
    column_create Terms document_index COLUMN_INDEX|WITH_POSITION Entries body
    
    load --table Entries
    [
    {"body": "Groonga　高速！"}
    ]
    
    select Entries --output_columns \
      --match_columns body --query '高速' \
      --output_columns 'highlight_html(body)'
    [
      [
        0,
        0.0,
        0.0
      ],
      [
        [
          [
            1
          ],
          [
            [
              "highlight_html",
              null
            ]
          ],
          [
            "Groonga<span class=\"keyword\">　高速</span>！"
          ]
        ]
      ]
    ]
    

  • However, this is needless as the target of highlight. Therefore, in this release, highlight_html() removes leading full width spaces.

• [status] Added a new item features.

  • We can display which Groonga’s features are enabled as below.

    status --output_pretty yes
    [
      [
        0,
        0.0,
        0.0
      ],
      {
        "alloc_count": 361,
        "starttime": 1608087311,
        "start_time": 1608087311,
        "uptime": 35,
        "version": "10.1.0",
        "n_queries": 0,
        "cache_hit_rate": 0.0,
        "command_version": 1,
        "default_command_version": 1,
        "max_command_version": 3,
        "n_jobs": 0,
        "features": {
          "nfkc": true,
          "mecab": true,
          "message_pack": true,
          "mruby": true,
          "onigmo": true,
          "zlib": true,
          "lz4": false,
          "zstandard": false,
          "kqueue": false,
          "epoll": true,
          "poll": false,
          "rapidjson": false,
          "apache_arrow": false,
          "xxhash": false
        }
      }
    ]
    

• [status] Added a new item apache_arrow.

  • We can display Apache Arrow version that Groonga use as below.

  [
    [
      0,
      1608088628.440753,
      0.0006628036499023438
    ],
    {
      "alloc_count": 360,
      "starttime": 1608088617,
      "start_time": 1608088617,
      "uptime": 11,
      "version": "10.0.9-39-g5a4c6f3",
      "n_queries": 0,
      "cache_hit_rate": 0.0,
      "command_version": 1,
      "default_command_version": 1,
      "max_command_version": 3,
      "n_jobs": 0,
      "features": {
        "nfkc": true,
        "mecab": true,
        "message_pack": true,
        "mruby": true,
        "onigmo": true,
        "zlib": true,
        "lz4": true,
        "zstandard": false,
        "kqueue": false,
        "epoll": true,
        "poll": false,
        "rapidjson": false,
        "apache_arrow": true,
        "xxhash": false
      },
      "apache_arrow": {
        "version_major": 2,
        "version_minor": 0,
        "version_patch": 0,
        "version": "2.0.0"
      }
    }
  ]
  

  • This item only display when Apache Arrow is valid in Groonga.

• [Window function] Added support for processing all tables at once even if target tables straddle a shard. (experimental)

  • If the target tables straddle a shard, the window function had processed each shard until now.

    • Therefore, if we used multiple group keys in windows functions, the value of the group keys from the second had to be one kind of value.

    • However, we can use multiple kind of values for it as below by this improvement.

      plugin_register sharding
      plugin_register functions/time
      
      table_create Logs_20170315 TABLE_NO_KEY
      column_create Logs_20170315 timestamp COLUMN_SCALAR Time
      column_create Logs_20170315 price COLUMN_SCALAR UInt32
      column_create Logs_20170315 item COLUMN_SCALAR ShortText
      
      table_create Logs_20170316 TABLE_NO_KEY
      column_create Logs_20170316 timestamp COLUMN_SCALAR Time
      column_create Logs_20170316 price COLUMN_SCALAR UInt32
      column_create Logs_20170316 item COLUMN_SCALAR ShortText
      
      table_create Logs_20170317 TABLE_NO_KEY
      column_create Logs_20170317 timestamp COLUMN_SCALAR Time
      column_create Logs_20170317 price COLUMN_SCALAR UInt32
      column_create Logs_20170317 item COLUMN_SCALAR ShortText
      
      load --table Logs_20170315
      [
      {"timestamp": "2017/03/15 10:00:00", "price": 1000, "item": "A"},
      {"timestamp": "2017/03/15 11:00:00", "price":  900, "item": "A"},
      {"timestamp": "2017/03/15 12:00:00", "price":  300, "item": "B"},
      {"timestamp": "2017/03/15 13:00:00", "price":  200, "item": "B"}
      ]
      
      load --table Logs_20170316
      [
      {"timestamp": "2017/03/16 10:00:00", "price":  530, "item": "A"},
      {"timestamp": "2017/03/16 11:00:00", "price":  520, "item": "B"},
      {"timestamp": "2017/03/16 12:00:00", "price":  110, "item": "A"},
      {"timestamp": "2017/03/16 13:00:00", "price":  410, "item": "A"},
      {"timestamp": "2017/03/16 14:00:00", "price":  710, "item": "B"}
      ]
      
      load --table Logs_20170317
      [
      {"timestamp": "2017/03/17 10:00:00", "price":  800, "item": "A"},
      {"timestamp": "2017/03/17 11:00:00", "price":  400, "item": "B"},
      {"timestamp": "2017/03/17 12:00:00", "price":  500, "item": "B"},
      {"timestamp": "2017/03/17 13:00:00", "price":  300, "item": "A"}
      ]
      
      table_create Times TABLE_PAT_KEY Time
      column_create Times logs_20170315 COLUMN_INDEX Logs_20170315 timestamp
      column_create Times logs_20170316 COLUMN_INDEX Logs_20170316 timestamp
      column_create Times logs_20170317 COLUMN_INDEX Logs_20170317 timestamp
      
      logical_range_filter Logs \
        --shard_key timestamp \
        --filter 'price >= 300' \
        --limit -1 \
        --columns[offsetted_timestamp].stage filtered \
        --columns[offsetted_timestamp].type Time \
        --columns[offsetted_timestamp].flags COLUMN_SCALAR \
        --columns[offsetted_timestamp].value 'timestamp - 39600000000' \
        --columns[offsetted_day].stage filtered \
        --columns[offsetted_day].type Time \
        --columns[offsetted_day].flags COLUMN_SCALAR \
        --columns[offsetted_day].value 'time_classify_day(offsetted_timestamp)' \
        --columns[n_records_per_day_and_item].stage filtered \
        --columns[n_records_per_day_and_item].type UInt32 \
        --columns[n_records_per_day_and_item].flags COLUMN_SCALAR \
        --columns[n_records_per_day_and_item].value 'window_count()' \
        --columns[n_records_per_day_and_item].window.group_keys 'offsetted_day,item' \
        --output_columns "_id,time_format_iso8601(offsetted_day),item,n_records_per_day_and_item"
      [
        [
          0,
          0.0,
          0.0
        ],
        [
          [
            [
              "_id",
              "UInt32"
            ],
            [
              "time_format_iso8601",
              null
            ],
            [
              "item",
              "ShortText"
            ],
            [
              "n_records_per_day_and_item",
              "UInt32"
            ]
          ],
          [
            1,
            "2017-03-14T00:00:00.000000+09:00",
            "A",
            1
          ],
          [
            2,
            "2017-03-15T00:00:00.000000+09:00",
            "A",
            2
          ],
          [
            3,
            "2017-03-15T00:00:00.000000+09:00",
            "B",
            1
          ],
          [
            1,
            "2017-03-15T00:00:00.000000+09:00",
            "A",
            2
          ],
          [
            2,
            "2017-03-16T00:00:00.000000+09:00",
            "B",
            2
          ],
          [
            4,
            "2017-03-16T00:00:00.000000+09:00",
            "A",
            2
          ],
          [
            5,
            "2017-03-16T00:00:00.000000+09:00",
            "B",
            2
          ],
          [
            1,
            "2017-03-16T00:00:00.000000+09:00",
            "A",
            2
          ],
          [
            2,
            "2017-03-17T00:00:00.000000+09:00",
            "B",
            2
          ],
          [
            3,
            "2017-03-17T00:00:00.000000+09:00",
            "B",
            2
          ],
          [
            4,
            "2017-03-17T00:00:00.000000+09:00",
            "A",
            1
          ]
        ]
      ]
      

  • This feature requires Apache Arrow 3.0.0 that is not released yet.

• Added support for sequential search against reference column.

  • This feature is only used if an index search will match many records and the current result set is enough small.

    • Because the sequential search is faster than the index search in the above case.

  • It is invalid by default.

  • It is valid if we set GRN_II_SELECT_TOO_MANY_INDEX_MATCH_RATIO_REFERENCE environment variable.

  • GRN_II_SELECT_TOO_MANY_INDEX_MATCH_RATIO_REFERENCE environment variable is a threshold to switch the sequential search from the index search.

    • For example, if we set GRN_II_SELECT_TOO_MANY_INDEX_MATCH_RATIO_REFERENCE=0.7 as below, if the number of records for the result set less than 70 % of total records, a search is executed by a sequential search.

      $ export GRN_II_SELECT_TOO_MANY_INDEX_MATCH_RATIO_REFERENCE=0.7
      
      table_create Tags TABLE_HASH_KEY ShortText
      table_create Memos TABLE_HASH_KEY ShortText
      column_create Memos tag COLUMN_SCALAR Tags
      
      load --table Memos
      [
      {"_key": "Rroonga is fast!", "tag": "Rroonga"},
      {"_key": "Groonga is fast!", "tag": "Groonga"},
      {"_key": "Mroonga is fast!", "tag": "Mroonga"},
      {"_key": "Groonga sticker!", "tag": "Groonga"},
      {"_key": "Groonga is good!", "tag": "Groonga"}
      ]
      
      column_create Tags memos_tag COLUMN_INDEX Memos tag
      
      select Memos --query '_id:>=3 tag:@Groonga' --output_columns _id,_score,_key,tag
      [
        [
          0,
          0.0,
          0.0
        ],
        [
          [
            [
              2
            ],
            [
              [
                "_id",
                "UInt32"
              ],
              [
                "_score",
                "Int32"
              ],
              [
                "_key",
                "ShortText"
              ],
              [
                "tag",
                "Tags"
              ]
            ],
            [
              4,
              2,
              "Groonga sticker!",
              "Groonga"
            ],
            [
              5,
              2,
              "Groonga is good!",
              "Groonga"
            ]
          ]
        ]
      ]
      

• [tokenizers] Added support for the token column into TokenDocumentVectorTFIDF and TokenDocumentVectorBM25.

  • If there is the token column that has the same source as the index column, these tokenizer use the token id of the token column.

    • The token column has already had data that has already finished tokenize.

    • Therefore, these tokenizer are improved performance by using a token column.

  • For example, we can use this feature by making a token column named content_tokens as below.

    table_create Memos TABLE_NO_KEY
    column_create Memos content COLUMN_SCALAR Text
    
    load --table Memos
    [
    {"content": "a b c a"},
    {"content": "c b c"},
    {"content": "b b a"},
    {"content": "a c c"},
    {"content": "a"}
    ]
    
    table_create Tokens TABLE_PAT_KEY ShortText \
      --normalizer NormalizerNFKC121 \
      --default_tokenizer TokenNgram
    column_create Tokens memos_content COLUMN_INDEX|WITH_POSITION Memos content
    
    column_create Memos content_tokens COLUMN_VECTOR Tokens content
    
    table_create DocumentVectorBM25 TABLE_HASH_KEY Tokens \
      --default_tokenizer \
        'TokenDocumentVectorBM25("index_column", "memos_content", \
                                 "df_column", "df")'
    column_create DocumentVectorBM25 df COLUMN_SCALAR UInt32
    
    column_create Memos content_feature COLUMN_VECTOR|WITH_WEIGHT|WEIGHT_FLOAT32 \
      DocumentVectorBM25 content
    
    select Memos
    [
      [
        0,
        0.0,
        0.0
      ],
      [
        [
          [
            5
          ],
          [
            [
              "_id",
              "UInt32"
            ],
            [
              "content",
              "Text"
            ],
            [
              "content_feature",
              "DocumentVectorBM25"
            ],
            [
              "content_tokens",
              "Tokens"
            ]
          ],
          [
            1,
            "a b c a",
            {
              "a": 0.5095787,
              "b": 0.6084117,
              "c": 0.6084117
            },
            [
              "a",
              "b",
              "c",
              "a"
            ]
          ],
          [
            2,
            "c b c",
            {
              "c": 0.8342565,
              "b": 0.5513765
            },
            [
              "c",
              "b",
              "c"
            ]
          ],
          [
            3,
            "b b a",
            {
              "b": 0.9430448,
              "a": 0.3326656
            },
            [
              "b",
              "b",
              "a"
            ]
          ],
          [
            4,
            "a c c",
            {
              "a": 0.3326656,
              "c": 0.9430448
            },
            [
              "a",
              "c",
              "c"
            ]
          ],
          [
            5,
            "a",
            {
              "a": 1.0
            },
            [
              "a"
            ]
          ]
        ]
      ]
    ]
    

  • TokenDocumentVectorTFIDF and TokenDocumentVectorBM25 give a weight against each tokens.

    • TokenDocumentVectorTFIDF calculate a weight for token by using TF-IDF.

      • Please refer to https://radimrehurek.com/gensim/models/tfidfmodel.html about TF-IDF.

    • TokenDocumentVectorBM25 calculate a weight for token by using Okapi BM25.

      • Please refer to https://en.wikipedia.org/wiki/Okapi_BM25 about Okapi BM25.

• Improved performance when below case.

  • (column @ "value") && (column @ "value")

• [Ubuntu] Added support for Ubuntu 20.10 (Groovy Gorilla).

• [Debian GNU/Linux] Dropped stretch support.

  • It reached EOL.

• [CentOS] Dropped CentOS 6.

  • It reached EOL.

• [httpd] Updated bundled nginx to 1.19.6.

Fixes¶

• Fixed a bug that Groonga crash when we use multiple keys drilldown and use multiple accessor as below. [GitHub#1153][Patched by naoa]

  table_create Tags TABLE_PAT_KEY ShortText
  
  table_create Memos TABLE_HASH_KEY ShortText
  column_create Memos tags COLUMN_VECTOR Tags
  column_create Memos year COLUMN_SCALAR Int32
  
  load --table Memos
  [
  {"_key": "Groonga is fast!", "tags": ["full-text-search"], "year": 2019},
  {"_key": "Mroonga is fast!", "tags": ["mysql", "full-text-search"], "year": 2019},
  {"_key": "Groonga sticker!", "tags": ["full-text-search", "sticker"], "year": 2020},
  {"_key": "Rroonga is fast!", "tags": ["full-text-search", "ruby"], "year": 2020},
  {"_key": "Groonga is good!", "tags": ["full-text-search"], "year": 2020}
  ]
  
  select Memos \
    --filter '_id > 0' \
    --drilldowns[tags].keys 'tags,year >= 2020' \
    --drilldowns[tags].output_columns _key[0],_key[1],_nsubrecs
  
  select Memos \
    --filter '_id > 0' \
    --drilldowns[tags].keys 'tags,year >= 2020' \
    --drilldowns[tags].output_columns _key[1],_nsubrecs
  

• Fixed a bug that the near phrase search did not match when the same phrase occurs multiple times as below.

  table_create Entries TABLE_NO_KEY
  column_create Entries content COLUMN_SCALAR Text
  
  table_create Terms TABLE_PAT_KEY ShortText \
    --default_tokenizer TokenNgram \
    --normalizer NormalizerNFKC121
  column_create Terms entries_content COLUMN_INDEX|WITH_POSITION Entries content
  
  load --table Entries
  [
  {"content": "a x a x b x x"},
  {"content": "a x x b x"}
  ]
  
  select Entries \
    --match_columns content \
    --query '*NP2"a b"' \
    --output_columns '_score, content'
  

Thanks¶

• Hung Nguyen V

• naoa

• timgates42 [Provided the patch at GitHub#1155]

Improvements¶

• Improved performance when we specified -1 to limit.

• [reference_acquire] Added a new option --auto_release_count.

  • Groonga reduces a reference count automatically when a request reaching the number of value that is specified in --auto_release_count.

  • For example, the acquired reference of Users is released automatically after the second status is processed as below.

    reference_acquire --target_name Users --auto_release_count 2
    status # Users is still referred.
    status # Users' reference is released after this command is processed.
    

  • We can prevent a leak of the release of acquired reference by this option.

• Modify behavior when Groonga evaluated empty vector and uvector.

  • Empty vector and uvector are evaluated to false in command version 3.

    • This behavior is valid for only command version 3.

    • Note that the different behavior until now.

• [Normalizers] Added a new Normalizer NormalizerNFKC130 based on Unicode NFKC (Normalization Form Compatibility Composition) for Unicode 13.0.

• [Token filters] Added a new TokenFilter TokenFilterNFKC130 based on Unicode NFKC (Normalization Form Compatibility Composition) for Unicode 13.0.

• [select] Improved performance for "_score = column - X".

• [reference_acquire] Improved that --reference_acquire doesn’t get unnecessary reference of index column when we specify the --recursive dependent option.

  • From this release, the targets of --recursive dependent are only the target table’s key and the index column that is set to data column.

• [select] Add support for ordered near phrase search.

  • Until now, the near phrase search have only looked for records that the distance of between specified phrases near.

  • This feature look for satisfy the following conditions records.

    • If the distance of between specified phrases is near.

    • If the specified phrases are in line with specified order.

  • This feature use *ONP as an operator. (Note that the operator isn’t *NP.)

  • $ is handled as itself in the query syntax. Note that it isn’t a special character in the query syntax.

  • If we use script syntax, this feature use as below.

    table_create Entries TABLE_NO_KEY
    column_create Entries content COLUMN_SCALAR Text
    
    table_create Terms TABLE_PAT_KEY ShortText \
      --default_tokenizer 'TokenNgram("unify_alphabet", false, \
                                      "unify_digit", false)' \
      --normalizer NormalizerNFKC121
    column_create Terms entries_content COLUMN_INDEX|WITH_POSITION Entries content
    
    load --table Entries
    [
    {"content": "abcXYZdef"},
    {"content": "abebcdXYZdef"},
    {"content": "abcdef"},
    {"content": "defXYZabc"},
    {"content": "XYZabc"},
    {"content": "abc123456789def"},
    {"content": "abc12345678def"},
    {"content": "abc1de2def"}
    ]
    
    select Entries --filter 'content *ONP "abc def"' --output_columns '_score, content'
    [
      [
        0,
        0.0,
        0.0
      ],
      [
        [
          [
            4
          ],
          [
            [
              "_score",
              "Int32"
            ],
            [
              "content",
              "Text"
            ]
          ],
          [
            1,
            "abcXYZdef"
          ],
          [
            1,
            "abcdef"
          ],
          [
            1,
            "abc12345678def"
          ],
          [
            1,
            "abc1de2def"
          ]
        ]
      ]
    ]
    

  • If we use query syntax, this feature use as below.

    select Entries --query 'content:*ONP "abc def"' --output_columns '_score, content'
    [
      [
        0,
        0.0,
        0.0
      ],
      [
        [
          [
            4
          ],
          [
            [
              "_score",
              "Int32"
            ],
            [
              "content",
              "Text"
            ]
          ],
          [
            1,
            "abcXYZdef"
          ],
          [
            1,
            "abcdef"
          ],
          [
            1,
            "abc12345678def"
          ],
          [
            1,
            "abc1de2def"
          ]
        ]
      ]
    ]
    

• [httpd] Updated bundled nginx to 1.19.5.

Fixes¶

• [Groonga HTTP server] Fixed that Groonga HTTP server finished without waiting all woker threads finished completely.

  • Until now, Groonga HTTP server had started the process of finish after worker threads finished itself process. However, worker threads may not finish completely at this timing. Therefore, Groonga HTTP server may crashed according to timing. Because Groonga HTTP server may free area of memory that worker threads are using them yet.

Improvements¶

• [select] Added support for large drilldown keys.

  • The maximum on the key size of Groonga’s tables are 4KiB. However, if we specify multiple keys in drilldown, the size of drilldown keys may be larger than 4KiB.

    • For example, if the total size for tag key and n_like key is lager than 4KiB in the following case, the drilldown had failed.

    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
    

    • Because the drilldown packes specifying all keys for drilldown. So, if each the size of drilldown key is large, the size of the packed drilldown keys is larger than 4KiB.

  • This feature requires xxHash .

    • However, if we install Groonga from package, we can use this feature without doing anything special. Because Groonga’s package already include xxHash .

• [select] Added support for handling as the same dynamic column even if columns refer to different tables.

  • We can’t have handled the same dynamic column if columns refer to different tables until now. Because the type of columns is different.

  • However, from this version, we can handle the same dynamic column even if columns refer to different tables by casting to built-in types as below.

    table_create Store_A TABLE_HASH_KEY ShortText
    table_create Store_B TABLE_HASH_KEY ShortText
    
    table_create Customers TABLE_HASH_KEY Int32
    column_create Customers customers_A COLUMN_VECTOR Store_A
    column_create Customers customers_B COLUMN_VECTOR Store_B
    
    load --table Customers
    [
      {"_key": 1604020694, "customers_A": ["A", "B", "C"]},
      {"_key": 1602724694, "customers_B": ["Z", "V", "Y", "T"]},
    ]
    
    select Customers \
      --filter '_key == 1604020694' \
      --columns[customers].stage output \
      --columns[customers].flags COLUMN_VECTOR \
      --columns[customers].type ShortText \
      --columns[customers].value 'customers_A' \
      --output_columns '_key, customers'
    

  • We have needed to set Store_A or Store_B in the type of customers column until now.

  • The type of customers_A column cast to ShortText in the above example.

  • By this, we can also set the value of customers_B in the value of customers column. Because both the key of customers_A and customers_B are ShortText type.

• [select] Improved performance when the number of records for search result are huge.

  • This optimization works when below cases.

    • --filter 'column <= "value"' or --filter 'column >= "value"'

    • --filter 'column == "value"'

    • --filter 'between(...)' or --filter 'between(_key, ...)'

    • --filter 'sub_filter(reference_column, ...)'

    • Comparing against _key such as --filter '_key > "value"'.

    • --filter 'geo_in_circle(...)'

• Updated bundled LZ4 to 1.9.2 from 1.8.2.

• Added support xxHash 0.8

• [httpd] Updated bundled nginx to 1.19.4.

Fixes¶

• Fixed the following bugs related the browser based administration tool. [GitHub#1139][Reported by sutamin]

  • The problem that Groonga’s logo had not been displayed.

  • The problem that the throughput chart had not been displayed on the index page for Japanese.

• [between] Fixed a bug that between(_key, ...) is always evaluated by sequential search.

Thanks¶

• sutamin

Improvements¶

• [highlight], [highlight_full] Added support for normalizer options.

• [Return code] Added a new return code GRN_CONNECTION_RESET for resetting connection.

  • it is returned when an existing connection was forcibly close by the remote host.

• Dropped Ubuntu 19.10 (Eoan Ermine).

  • Because this version has been EOL.

• [httpd] Updated bundled nginx to 1.19.2.

• [grndb] Added support for detecting duplicate keys.

  • grndb check is also able to detect duplicate keys since this release.

  • This check valid except a table of TABLE_NO_KEY.

  • If the table that was detected duplicate keys by grndb check has only index columns, we can recover by grndb recover.

• [table_create], [column_create] Added a new option --path

  • We can store specified a table or a column to any path using this option.

  • This option is useful if we want to store a table or a column that we often use to fast storage (e.g. SSD) and store them that we don’t often use to slow storage (e.g. HDD).

  • We can specify both relative path and absolute path in this option.

    • If we specify relative path in this option, the path is resolved the path of groonga process as the origin.

  • However, if we specify --path, the result of dump command includes --path informations.

    • Therefore, if we specify --path, we can’t restore to host in different enviroment.

      • Because the directory configuration and the location of groonga process in different by each enviroment.

    • If we don’t want include --path informations to a dump, we need specify --dump_paths no in dump command.

• [dump] Added a new option --dump_paths.

  • --dump_paths option control whether --path is dumped or not.

  • The default value of it is yes.

  • If we specify --path when we create tables or columns and we don’t want include --path informations to a dump, we specify no into --dump_paths when we execute dump command.

• [functions] Added a new function string_toknize().

  • It tokenizes the column value that is specified in the second argument with the tokenizer that is specified in the first argument.

• [tokenizers] Added a new tokenizer TokenDocumentVectorTFIDF (experimental).

  • It generates automatically document vector by TF-IDF.

• [tokenizers] Added a new tokenizer TokenDocumentVectorBM25 (experimental).

  • It generates automatically document vector by BM25.

• [select] Added support for near search in same sentence.

Fixes¶

• [load] Fixed a bug that load didn’t a return response when we executed it against 257 columns.

  • This bug may occur from 10.0.4 or later.

  • This bug only occur when we load data by using [a, b, c, ...] format.

    • If we load data by using [{...}], this bug doesn’t occur.

• [MessagePack] Fixed a bug that float32 value wasn’t unpacked correctly.

• Fixed the following bugs related multi column index.

  • _score may be broken with full text search.

  • The records that couldn’t hit might hit.

  • Please refer to the following URL for the details about occurrence conditionin of this bug.

    • https://groonga.org/en/blog/2020/09/29/groonga-10.0.7.html#multi-column-index

Improvements¶

• [logical_range_filter] Improved search plan for large data.

  • Normally, logical_range_filter is faster than logical_select. However, it had been slower than logical_select in the below case.

    • If Groonga can’t get the number of required records easily, it has the feature that switches index search from sequential search. (Normally, logical_range_filter uses a sequential search when records of search target are many.)

    • The search process for it is almost the same as logical_select if the above switching occurred. So, logical_range_filter is severalfold slower than logical_select in the above case if the search target is large data. Because logical_range_filter executes sort after the search.

  • If we search for large data, Groonga easily use sequential search than until now since this release.

  • Therefore, logical_range_filter will improve performance. Because the case of the search process almost the same as logical_select decreases.

• [httpd] Updated bundled nginx to 1.19.1.

• Modify how to install into Debian GNU/Linux.

  • We modify to use groonga-apt-source instead of groonga-archive-keyring. Because the lintian command recommends using apt-source if a package that it puts files under the /etc/apt/sources.lists.d/.

    • The lintian command is the command which checks for many common packaging errors.

    • Please also refer to the following URL for the details about installation procedures.

      • https://groonga.org/docs/install/debian.html

• [logical_select] Added a support for highlight_html and highlight_full .

• Added support for recycling the IDs of records that are deleted when an array without value space delete.[GitHub#mroonga/mroonga#327][Reported by gaeeyo]

  • If an array that doesn’t have value space is deleted, deleted IDs are never recycled.

  • Groonga had used large storage space by large ID. Because it uses large storage space by itself.

    • For example, large ID is caused after many adds and deletes like Mroonga’s mroonga_operations

• [select] Improved performance of full-text-search without index.

• [Function] Improved performance for calling of function that all arguments a variable reference or literal.

• [Indexing] Improved performance of offline index construction by using token column. [GitHub#1126][Patched by naoa]

• Improved performance for "_score = func(...)".

  • The performance when the _score value calculate by using only function like "_score = func(...)" improved.

Fixes¶

• Fixed a bug that garbage may be included in response after response send error.

  • It may occur if a client didn’t read all responses and closed the connection.

Thanks¶

• gaeeyo

• naoa

Improvements¶

• [select] Added support for storing reference in table that we specify with --load_table.

  • --load_table is a feature that stores search results to the table in a prepared.

    • If the searches are executed multiple times, we can cache the search results by storing them to this table.

    • We can shorten the search times that the search after the first time by using this table.

  • We can store a reference to other tables into the key of this table as below since this release.

    • We can make a smaller size of this table. Because we only store references without store column values.

    • If we search against this table, we can search by using indexes for reference destination.

    table_create Logs TABLE_HASH_KEY ShortText
    column_create Logs timestamp COLUMN_SCALAR Time
    
    table_create Times TABLE_PAT_KEY Time
    column_create Times logs_timestamp COLUMN_INDEX Logs timestamp
    
    table_create LoadedLogs TABLE_HASH_KEY Logs
    
    load --table Logs
    [
    {
      "_key": "2015-02-03:1",
      "timestamp": "2015-02-03 10:49:00"
    },
    {
      "_key": "2015-02-03:2",
      "timestamp": "2015-02-03 12:49:00"
    },
    {
      "_key": "2015-02-04:1",
      "timestamp": "2015-02-04 00:00:00"
    }
    ]
    
    select \
      Logs \
      --load_table LoadedLogs \
      --load_columns "_key" \
      --load_values "_key" \
      --limit 0
    
    select \
      --table LoadedLogs \
      --filter 'timestamp >= "2015-02-03 12:49:00"'
    [
      [
        0,
        0.0,
        0.0
      ],
      [
        [
          [
            2
          ],
          [
            [
              "_id",
              "UInt32"
            ],
            [
              "_key",
              "ShortText"
            ],
            [
              "timestamp",
              "Time"
            ]
          ],
          [
            2,
            "2015-02-03:2",
            1422935340.0
          ],
          [
            3,
            "2015-02-04:1",
            1422975600.0
          ]
        ]
      ]
    ]
    

• [select] Improved sort performance on below cases.

  • When many sort keys need ID resolution.

    • For example, the following expression needs ID resolution.

      • --filter true --sort_keys column

    • For example, the following expression doesn’t need ID resolution. Because the _score pseudo column exists in the result table not the source table.

      • --filter true --sort_keys _score

  • When a sort target table has a key.

    • Therefore, TABLE_NO_KEY isn’t supported this improvement.

• [select] Improved performance a bit on below cases.

  • A case of searching for many records matches.

  • A case of drilldown for many records.

• [aggregator] Added support for score accessor for target. [GitHub#1120][Patched by naoa]

  • For example, we can _score subject to aggregator_* as below.

    table_create Items TABLE_HASH_KEY ShortText
    column_create Items price COLUMN_SCALAR UInt32
    column_create Items tag COLUMN_SCALAR ShortText
    
    load --table Items
    [
    {"_key": "Book",  "price": 1000, "tag": "A"},
    {"_key": "Note",  "price": 1000, "tag": "B"},
    {"_key": "Box",   "price": 500,  "tag": "B"},
    {"_key": "Pen",   "price": 500,  "tag": "A"},
    {"_key": "Food",  "price": 500,  "tag": "C"},
    {"_key": "Drink", "price": 300,  "tag": "B"}
    ]
    
    select Items \
      --filter true \
      --drilldowns[tag].keys tag \
      --drilldowns[tag].output_columns _key,_nsubrecs,score_mean \
      --drilldowns[tag].columns[score_mean].stage group \
      --drilldowns[tag].columns[score_mean].type Float \
      --drilldowns[tag].columns[score_mean].flags COLUMN_SCALAR \
      --drilldowns[tag].columns[score_mean].value 'aggregator_mean(_score)'
    [
      [
        0,
        0.0,
        0.0
      ],
      [
        [
          [
            6
          ],
          [
            [
              "_id",
              "UInt32"
            ],
            [
              "_key",
              "ShortText"
            ],
            [
              "price",
              "UInt32"
            ],
            [
              "tag",
              "ShortText"
            ]
          ],
          [
            1,
            "Book",
            1000,
            "A"
          ],
          [
            2,
            "Note",
            1000,
            "B"
          ],
          [
            3,
            "Box",
            500,
            "B"
          ],
          [
            4,
            "Pen",
            500,
            "A"
          ],
          [
            5,
            "Food",
            500,
            "C"
          ],
          [
            6,
            "Drink",
            300,
            "B"
          ]
        ],
        {
          "tag": [
            [
              3
            ],
            [
              [
                "_key",
                "ShortText"
              ],
              [
                "_nsubrecs",
                "Int32"
              ],
              [
                "score_mean",
                "Float"
              ]
            ],
            [
              "A",
              2,
              1.0
            ],
            [
              "B",
              3,
              1.0
            ],
            [
              "C",
              1,
              1.0
            ]
          ]
        }
      ]
    ]
    

• [Indexing] Improved performance of offline index construction on VC++ version.

• [select] Use null instead NaN, Infinity, and -Infinity when Groonga outputs result for JSON format.

  • Because JSON doesn’t support them.

• [select] Add support fot aggregating standard deviation value.

  • For example, we can calculate a standard deviation for every group as below.

    table_create Items TABLE_HASH_KEY ShortText
    column_create Items price COLUMN_SCALAR UInt32
    column_create Items tag COLUMN_SCALAR ShortText
    
    load --table Items
    [
    {"_key": "Book",  "price": 1000, "tag": "A"},
    {"_key": "Note",  "price": 1000, "tag": "B"},
    {"_key": "Box",   "price": 500,  "tag": "B"},
    {"_key": "Pen",   "price": 500,  "tag": "A"},
    {"_key": "Food",  "price": 500,  "tag": "C"},
    {"_key": "Drink", "price": 300,  "tag": "B"}
    ]
    
    select Items \
      --drilldowns[tag].keys tag \
      --drilldowns[tag].output_columns _key,_nsubrecs,price_sd \
      --drilldowns[tag].columns[price_sd].stage group \
      --drilldowns[tag].columns[price_sd].type Float \
      --drilldowns[tag].columns[price_sd].flags COLUMN_SCALAR \
      --drilldowns[tag].columns[price_sd].value 'aggregator_sd(price)' \
      --output_pretty yes
    [
      [
        0,
        1594339851.924836,
        0.002813816070556641
      ],
      [
        [
          [
            6
          ],
          [
            [
              "_id",
              "UInt32"
            ],
            [
              "_key",
              "ShortText"
            ],
            [
              "price",
              "UInt32"
            ],
            [
              "tag",
              "ShortText"
            ]
          ],
          [
            1,
            "Book",
            1000,
            "A"
          ],
          [
            2,
            "Note",
            1000,
            "B"
          ],
          [
            3,
            "Box",
            500,
            "B"
          ],
          [
            4,
            "Pen",
            500,
            "A"
          ],
          [
            5,
            "Food",
            500,
            "C"
          ],
          [
            6,
            "Drink",
            300,
            "B"
          ]
        ],
        {
          "tag": [
            [
              3
            ],
            [
              [
                "_key",
                "ShortText"
              ],
              [
                "_nsubrecs",
                "Int32"
              ],
              [
                "price_sd",
                "Float"
              ]
            ],
            [
              "A",
              2,
              250.0
            ],
            [
              "B",
              3,
              294.3920288775949
            ],
            [
              "C",
              1,
              0.0
            ]
          ]
        }
      ]
    ]
    

    • We can also calculate sample standard deviation to specifing aggregate_sd(target, {"unbiased": true}).

• [Windows] Dropped Visual Studio 2013 support.

Fixes¶

• [Groonga HTTP server] Fixed a bug that a request can’t halt even if we execute shutdown?mode=immediate when the response was halted by error occurrence.

• Fixed a crash bug when an error occurs while a request.

  • It only occurs when we use Apache Arrow Format.

  • Groonga crashes when we send request to Groonga again after the previous request was halted by error occurrence.

• [between] Fixed a crash bug when temporary table is used.

  • For example, if we specify a dynamic column in the first argument for between, Groonga had crashed.

• Fixed a bug that procedure created by plugin is freed unexpectedly.

  • It’s only occurred in reference count mode.

  • It’s not occurred if we don’t use plugin_register.

  • It’s not occurred in the process that executes plugin_register.

  • It’s occurred in the process that doesn’t execute plugin_register.

• Fixed a bug that normalization error occurred while static index construction by token_column. [GitHub#1122][Reported by naoa]

Thanks¶

• naoa

Improvements¶

• [Tables] Added support for registering 400M records into a hash table.

• [select] Improve scorer performance when the _score doesn’t get recursively values.

  • Groonga get recursively value of _score when search result is search target.

  • For example, the search targets of slices are search result. Therefore, if we use slice in a query, this improvement doesn’t ineffective.

• [Log] Improved that we output drilldown keys in query-log.

• [reference_acquire], [reference_release] Added new commands for reference count mode.

  • If we need to call multiple load in a short time, auto close by the reference count mode will degrade performance.

  • We can avoid the performance degrading by calling reference_acquire before multiple load and calling reference_release after multiple load. Between reference_acquire and reference_release, auto close is disabled.

    • Because reference_acquire acquires a reference of target objects.

  • We can must call reference_release after you finish performance impact operations.

  • If we don’t call reference_release, the reference count mode doesn’t work.

• [select] Added support for aggregating multiple groups on one time drilldown.

  • We came to be able to calculate sum or arithmetic mean every different multiple groups on one time drilldown as below.

    table_create Items TABLE_HASH_KEY ShortText
    column_create Items price COLUMN_SCALAR UInt32
    column_create Items quantity COLUMN_SCALAR UInt32
    column_create Items tag COLUMN_SCALAR ShortText
    
    load --table Items
    [
    {"_key": "Book",  "price": 1000, "quantity": 100, "tag": "A"},
    {"_key": "Note",  "price": 1000, "quantity": 10,  "tag": "B"},
    {"_key": "Box",   "price": 500,  "quantity": 15,  "tag": "B"},
    {"_key": "Pen",   "price": 500,  "quantity": 12,  "tag": "A"},
    {"_key": "Food",  "price": 500,  "quantity": 111, "tag": "C"},
    {"_key": "Drink", "price": 300,  "quantity": 22,  "tag": "B"}
    ]
    
    select Items \
      --drilldowns[tag].keys tag \
      --drilldowns[tag].output_columns _key,_nsubrecs,price_sum,quantity_sum \
      --drilldowns[tag].columns[price_sum].stage group \
      --drilldowns[tag].columns[price_sum].type UInt32 \
      --drilldowns[tag].columns[price_sum].flags COLUMN_SCALAR \
      --drilldowns[tag].columns[price_sum].value 'aggregator_sum(price)' \
      --drilldowns[tag].columns[quantity_sum].stage group \
      --drilldowns[tag].columns[quantity_sum].type UInt32 \
      --drilldowns[tag].columns[quantity_sum].flags COLUMN_SCALAR \
      --drilldowns[tag].columns[quantity_sum].value 'aggregator_sum(quantity)'
    [
      [
        0,
        0.0,
        0.0
      ],
      [
        [
          [
            6
          ],
          [
            [
              "_id",
              "UInt32"
            ],
            [
              "_key",
              "ShortText"
            ],
            [
              "price",
              "UInt32"
            ],
            [
              "quantity",
              "UInt32"
            ],
            [
              "tag",
              "ShortText"
            ]
          ],
          [
            1,
            "Book",
            1000,
            100,
            "A"
          ],
          [
            2,
            "Note",
            1000,
            10,
            "B"
          ],
          [
            3,
            "Box",
            500,
            15,
            "B"
          ],
          [
            4,
            "Pen",
            500,
            12,
            "A"
          ],
          [
            5,
            "Food",
            500,
            111,
            "C"
          ],
          [
            6,
            "Drink",
            300,
            22,
            "B"
          ]
        ],
        {
          "tag": [
            [
              3
            ],
            [
              [
                "_key",
                "ShortText"
              ],
              [
                "_nsubrecs",
                "Int32"
              ],
              [
                "price_sum",
                "UInt32"
              ],
              [
                "quantity_sum",
                "UInt32"
              ]
            ],
            [
              "A",
              2,
              1500,
              112
            ],
            [
              "B",
              3,
              1800,
              47
            ],
            [
              "C",
              1,
              500,
              111
            ]
          ]
        }
      ]
    ]
    

• [groonga executable file] Added support for --pid-path in standalone mode.

  • Because --pid-path had been ignored in standalone mode in before version.

• [io_flush] Added support for reference count mode.

• [logical_range_filter], [logical_count] Added support for reference count mode.

• [Groonga HTTP server] We didn’t add header after the last chunk.

  • Because there is a possibility to exist that the HTTP client ignores header after the last chunk.

• [vector_slice] Added support for a vector that has the value of the Float32 type. [GitHub#1112 patched by naoa]

• Added support for parallel offline index construction using token column.

  • We came to be able to construct an offline index on parallel threads from data that are tokenized in advance.

  • We can tune parameters of parallel offline construction by the following environment variables

    • GRN_TOKEN_COLUMN_PARALLEL_CHUNK_SIZE : We specify how many records are processed per thread.

      • The default value is 1024 records.

    • GRN_TOKEN_COLUMN_PARALLEL_TABLE_SIZE_THRESHOLD : We specify how many source records are required for parallel offline construction.

      • The default value is 102400 records.

• [select] Improved performance for load_table on the reference count mode.

Fixes¶

• Fixed a bug that the database of Groonga was broken when we search by using the dynamic columns that don’t specify a --filter and stridden over shard.

• Fixed a bug that Float32 type had not displayed on a result of schema command.

• Fixed a bug that we count in surplus to _nsubrecs when the reference uvector hasn’t element.

Thanks¶

• naoa

Improvements¶

• We came to be able to construct an inverted index from data that are tokenized in advance.

  • The construct of an index is speeded up from this.

  • We need to prepare token column to use this improvement.

  • token column is an auto generated value column like an index column.

  • token column value is generated from source column value by tokenizing the source column value.

  • We can create a token column by setting the source column as below.

    table_create Terms TABLE_PAT_KEY ShortText \
      --normalizer NormalizerNFKC121 \
      --default_tokenizer TokenNgram
    
    table_create Notes TABLE_NO_KEY
    column_create Notes title COLUMN_SCALAR Text
    
    # The last "title" is the source column.
    column_create Notes title_terms COLUMN_VECTOR Terms title
    

• [select] We came to be able to specify a vector for the argument of a function.

  • For example, flags options of query can describe by a vector as below.

    select \
      --table Memos \
      --filter 'query("content", "-content:@mroonga", \
                      { \
                        "expander": "QueryExpanderTSV", \
                        "flags": ["ALLOW_LEADING_NOT", "ALLOW_COLUMN"] \
                      })'
    

• [select] Added a new stage result_set for dynamic columns.

  • This stage generates a column into a result set table. Therefore, it is not generated if query or filter doesn’t exist

    • Because if query or filter doesn’t exist, Groonga doesn’t make a result set table.

  • We can’t use _value for the stage. The result_set stage is for storing value by score_column.

• [vector_slice] Added support for weight vector that has weight of Float32 type. [GitHub#1106 patched by naoa]

• [select] Added support for filtered stage and output stage of dynamic columns on drilldowns. [GitHub#1101 patched by naoa][GitHub#1100 patched by naoa]

  • We can use filtered and output stage of dynamic columns on drilldowns as with drilldowns[Label].stage filtered and drilldowns[Label].stage output.

• [select] Added support for Float type value in aggregating on drilldown.

  • We can aggregate max value, min value, and sum value for Float type value using MAX, MIN, and SUM.

• [query] [geo_in_rectangle] [geo_in_circle] Added a new option score_column for query(), geo_in_rectangle(), and geo_in_circle().

  • We can store a score value by condition using score_column.

  • Normally, Groonga calculate a score by adding scores of all conditions. However, we sometimes want to get a score value by condition.

  • For example, if we want to only use how near central coordinate as score as below, we use score_column.

  table_create LandMarks TABLE_NO_KEY
  column_create LandMarks name COLUMN_SCALAR ShortText
  column_create LandMarks category COLUMN_SCALAR ShortText
  column_create LandMarks point COLUMN_SCALAR WGS84GeoPoint
  
  table_create Points TABLE_PAT_KEY WGS84GeoPoint
  column_create Points land_mark_index COLUMN_INDEX LandMarks point
  
  load --table LandMarks
  [
    {"name": "Aries"      , "category": "Tower"     , "point": "11x11"},
    {"name": "Taurus"     , "category": "Lighthouse", "point": "9x10" },
    {"name": "Gemini"     , "category": "Lighthouse", "point": "8x8"  },
    {"name": "Cancer"     , "category": "Tower"     , "point": "12x12"},
    {"name": "Leo"        , "category": "Tower"     , "point": "11x13"},
    {"name": "Virgo"      , "category": "Temple"    , "point": "22x10"},
    {"name": "Libra"      , "category": "Tower"     , "point": "14x14"},
    {"name": "Scorpio"    , "category": "Temple"    , "point": "21x9" },
    {"name": "Sagittarius", "category": "Temple"    , "point": "43x12"},
    {"name": "Capricorn"  , "category": "Tower"     , "point": "33x12"},
    {"name": "Aquarius"   , "category": "mountain"  , "point": "55x11"},
    {"name": "Pisces"     , "category": "Tower"     , "point": "9x9"  },
    {"name": "Ophiuchus"  , "category": "mountain"  , "point": "21x21"}
  ]
  
  select LandMarks \
    --sort_keys 'distance' \
    --columns[distance].stage initial \
    --columns[distance].type Float \
    --columns[distance].flags COLUMN_SCALAR \
    --columns[distance].value 0.0 \
    --output_columns 'name, category, point, distance, _score' \
    --limit -1 \
    --filter 'geo_in_circle(point, "11x11", "11x1", {"score_column": distance}) && category == "Tower"'
  [
    [
      0,
      1590647445.406149,
      0.0002503395080566406
    ],
    [
      [
        [
          5
        ],
        [
          [
            "name",
            "ShortText"
          ],
          [
            "category","ShortText"
          ],
          [
            "point",
            "WGS84GeoPoint"
          ],
          [
            "distance",
            "Float"
          ],
          [
            "_score",
            "Int32"
          ]
        ],
        [
          "Aries",
          "Tower",
          "11x11",
          0.0,
          1
        ],
        [
          "Cancer",
          "Tower",
          "12x12",
          0.0435875803232193,
          1
        ],
        [
          "Leo",
          "Tower",
          "11x13",
          0.06164214760065079,
          1
        ],
        [
          "Pisces",
          "Tower",
          "9x9",
          0.0871751606464386,
          1
        ],
        [
          "Libra",
          "Tower",
          "14x14",
          0.1307627409696579,
          1
        ]
      ]
    ]
  ]
  

  • The sort by _score is meaningless in the above example. Because the value of _score is all 1 by category == "Tower". However, we can sort distance from central coordinate using score_column.

• [Windows] Groonga came to be able to output backtrace when it occurs error even if it doesn’t crash.

• [Windows] Dropped support for old Windows.

  • Groonga for Windows come to require Windows 8 (Windows Server 2012) or later from 10.0.3.

• [select] Improved sort performance when sort keys were mixed referable sort keys and the other sort keys.

  • We improved sort performance if mixed referable sort keys and the other and there are referable keys two or more.

    • Referable sort keys are sort keys that except below them.

      • Compressed columns

      • _value against the result of drilldown that is specified multiple values to the key of drilldown.

      • _key against patricia trie table that has not the key of ShortText type.

      • _score

  • The more sort keys that except string, a decrease in the usage of memory for sort.

• [select] Improved sort performance when sort keys are all referable keys case.

• [select] Improve scorer performance as a _socre = column1*X + column2*Y + ... case.

  • This optimization effective when there are many + or * in _score.

  • At the moment, it has only effective against + and *.

• [select] Added support for phrase near search.

  • We can search phrase by phrase by a near search.

    • Query syntax for near phrase search is *NP"Phrase1 phrase2 ...".

    • Script syntax for near phrase search is column *NP "phrase1 phrase2 ...".

    • If the search target phrase includes space, we can search for it by surrounding it with " as below.

      table_create Entries TABLE_NO_KEY
      column_create Entries content COLUMN_SCALAR Text
      
      table_create Terms TABLE_PAT_KEY ShortText \
        --default_tokenizer 'TokenNgram("unify_alphabet", false, \
                                        "unify_digit", false)' \
        --normalizer NormalizerNFKC121
      column_create Terms entries_content COLUMN_INDEX|WITH_POSITION Entries content
      
      load --table Entries
      [
      {"content": "I started to use Groonga. It's very fast!"},
      {"content": "I also started to use Groonga. It's also very fast! Really fast!"}
      ]
      
      select Entries --filter 'content *NP "\\"I started\\" \\"use Groonga\\""' --output_columns 'content'
      [
        [
          0,
          1590469700.715882,
          0.03997230529785156
        ],
        [
          [
            [
              1
            ],
            [
              [
                "content",
                "Text"
              ]
            ],
            [
              "I started to use Groonga. It's very fast!"
            ]
          ]
        ]
      ]
      

• [Vector column] Added support for float32 weight vector.

  • We can store weight as float32 instead of uint32.

  • We need to add WEIGHT_FLOAT32 flag when execute column_create to use this feature.

    column_create Records tags COLUMN_VECTOR|WITH_WEIGHT|WEIGHT_FLOAT32 Tags
    

  • However, WEIGHT_FLOAT32 flag isn’t available with COLUMN_INDEX flag for now.

• Added following APIs

  • Added grn_obj_is_xxx functions. For more information as below.

    • grn_obj_is_weight_vector(grn_ctx *ctx, grn_obj *obj)

      • It returns as a bool whether the object is a weight vector.

    • grn_obj_is_uvector(grn_ctx *ctx, grn_obj *obj)

      • It returns as a bool whether the object is a uvector.

        • uvector is a vector that size of elements for vector are fixed.

    • grn_obj_is_weight_uvector(grn_ctx *ctx, grn_obj *obj)

      • It returns as a bool whether the object is a weight uvector.

  • Added grn_type_id_size(grn_ctx *ctx, grn_id id).

    • It returns the size of Groonga data type as a size_t.

  • Added grn_selector_data_get_xxx functions. For more information as below.

    • These functions return selector related data.

      • These functions are supposed to call in selector. If they are called except in selector, they return NULL.

        • grn_selector_data_get(grn_ctx *ctx)

          • It returns all information that relating calling selector as grn_selector_data * structure.

        • grn_selector_data_get_selector(grn_ctx *ctx, grn_selector_data *data)

          • It returns selector itself as grn_obj *.

        • grn_selector_data_get_expr(grn_ctx *ctx, grn_selector_data *data)

          • It returns selector is used --filter condition and --query condition as grn_obj *.

        • grn_selector_data_get_table(grn_ctx *ctx, grn_selector_data *data)

          • It returns target table as grn_obj *

        • grn_selector_data_get_index(grn_ctx *ctx, grn_selector_data *data)

          • It returns index is used by selector as grn_obj *.

        • grn_selector_data_get_args(grn_ctx *ctx, grn_selector_data *data, size_t *n_args)

          • It returns arguments of function that called selector as grn_obj **.

        • grn_selector_data_get_result_set(grn_ctx *ctx, grn_selector_data *data)

          • It returns result table as grn_obj *.

        • grn_selector_data_get_op(grn_ctx *ctx, grn_selector_data *data)

          • It returns how to perform the set operation on existing result set as grn_operator.

  • Added grn_plugin_proc_xxx functions. For more information as below.

    • grn_plugin_proc_get_value_operator(grn_ctx *ctx, grn_obj *value, grn_operator default_operator, const char *context)

      • It returns the operator of a query as a grn_operator.

        • For example, && is returned as a GRN_OP_AND.

    • grn_plugin_proc_get_value_bool(grn_ctx *ctx, grn_obj *value, bool default_value, const char *tag)

      • It returns the value that is specified true or false like with_transposition argument of the below function as a bool (bool is the data type of C language).

        fuzzy_search(column, query, {"max_distance": 1, "prefix_length": 0, "max_expansion": 0, "with_transposition": true})
        

  • Added grn_proc_options_xxx functions. For more information as below.

    • query() only uses them for now.

      • grn_proc_options_parsev(grn_ctx *ctx, grn_obj *options, const char *tag, const char *name, va_list args)

        • This function execute parse options.

        • We had to implement parsing to options ourselves until now, however, we can parse them by just call this function from this version.

      • grn_proc_options_parse(grn_ctx *ctx, grn_obj *options, const char *tag, const char *name, ...)

        • It calls grn_proc_options_parsev(). Therefore, features of this function same grn_proc_options_parsev().

        • It only differs in the interface compare with grn_proc_options_parsev().

  • Added grn_text_printfv(grn_ctx *ctx, grn_obj *bulk, const char *format, va_list args)

    • grn_text_vprintf is deprecated from 10.0.3. We use grn_text_printfv instead.

  • Added grn_type_id_is_float_family(grn_ctx *ctx, grn_id id).

    • It returns whether grn_type_id is GRN_DB_FLOAT32 or GRN_DB_FLOAT or not as a bool.

  • Added grn_dat_cursor_get_max_n_records(grn_ctx *ctx, grn_dat_cursor *c).

    • It returns the number of max records the cursor can have as a size_t. (This API is for the DAT table)

  • Added grn_table_cursor_get_max_n_records(grn_ctx *ctx, grn_table_cursor *cursor).

    • It returns the number of max records the cursor can have as a size_t.

    • It can use against all table type (TABLE_NO_KEY, TABLE_HASH_KEY, TABLE_DAT_KEY, and TABLE_PAT_KEY).

  • Added grn_result_set_add_xxx functions. For more information as below.

    • grn_result_set_add_record(grn_ctx *ctx, grn_hash *result_set, grn_posting *posting, grn_operator op)

      • It adds a record into the table of result sets.

      • grn_ii_posting_add_float is deprecated from 10.0.3. We use grn_rset_add_records() instead.

    • grn_result_set_add_table(grn_ctx *ctx, grn_hash *result_set, grn_obj *table, double score, grn_operator op)

      • It adds a table into the result sets.

    • grn_result_set_add_table_cursor(grn_ctx *ctx, grn_hash *result_set, grn_table_cursor *cursor, double score, grn_operator op)

      • It adds records that a table cursor has into the result sets.

  • Added grn_vector_copy(grn_ctx *ctx, grn_obj *src, grn_obj *dest).

    • It copies a vector object. It returns whether success copy a vector object.

  • Added grn_obj_have_source(grn_ctx *ctx, grn_obj *obj).

    • It returns whether the column has a source column as a bool.

  • Added grn_obj_is_token_column(grn_ctx *ctx, grn_obj *obj).

    • It returns whether the column is a token column as a bool.

  • Added grn_hash_add_table_cursor(grn_ctx *ctx, grn_hash *hash, grn_table_cursor *cursor, double score).

    • It’s for bulk result set insert. It’s faster than inserting records by grn_ii_posting_add().