2018-11-30 19:26:35 +00:00
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# AggregatingMergeTree
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2019-05-20 02:49:08 +00:00
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该引擎继承自 [MergeTree](mergetree.md),并改变了数据片段的合并逻辑。 ClickHouse 会将相同主键的所有行(在一个数据片段内)替换为单个存储一系列聚合函数状态的行。
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2018-11-30 19:26:35 +00:00
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2019-05-20 02:49:08 +00:00
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可以使用 `AggregatingMergeTree` 表来做增量数据统计聚合,包括物化视图的数据聚合。
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2018-11-30 19:26:35 +00:00
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2019-05-20 02:49:08 +00:00
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引擎需使用 [AggregateFunction](../../data_types/nested_data_structures/aggregatefunction.md) 类型来处理所有列。
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2018-11-30 19:26:35 +00:00
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2019-05-20 02:49:08 +00:00
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如果要按一组规则来合并减少行数,则使用 `AggregatingMergeTree` 是合适的。
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2018-11-30 19:26:35 +00:00
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2019-05-20 02:49:08 +00:00
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## 建表
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2018-11-30 19:26:35 +00:00
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``` sql
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CREATE TABLE [IF NOT EXISTS] [db.]table_name [ON CLUSTER cluster]
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(
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name1 [type1] [DEFAULT|MATERIALIZED|ALIAS expr1],
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name2 [type2] [DEFAULT|MATERIALIZED|ALIAS expr2],
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...
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) ENGINE = AggregatingMergeTree()
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[PARTITION BY expr]
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[ORDER BY expr]
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[SAMPLE BY expr]
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[SETTINGS name=value, ...]
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```
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2019-05-20 02:49:08 +00:00
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语句参数的说明,请参阅 [语句描述](../../query_language/create.md)。
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2018-11-30 19:26:35 +00:00
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2019-05-20 02:49:08 +00:00
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**子句**
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2018-11-30 19:26:35 +00:00
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2019-05-20 02:49:08 +00:00
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创建 `AggregatingMergeTree` 表时,需用跟创建 `MergeTree` 表一样的[子句](mergetree.md)。
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2018-11-30 19:26:35 +00:00
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2019-05-20 02:49:08 +00:00
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<details markdown="1"><summary>已弃用的建表方法</summary>
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2018-11-30 19:26:35 +00:00
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2019-05-20 02:49:08 +00:00
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!!! 注意
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不要在新项目中使用该方法,可能的话,请将旧项目切换到上述方法。
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2018-11-30 19:26:35 +00:00
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```sql
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CREATE TABLE [IF NOT EXISTS] [db.]table_name [ON CLUSTER cluster]
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(
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name1 [type1] [DEFAULT|MATERIALIZED|ALIAS expr1],
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name2 [type2] [DEFAULT|MATERIALIZED|ALIAS expr2],
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...
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) ENGINE [=] AggregatingMergeTree(date-column [, sampling_expression], (primary, key), index_granularity)
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```
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2019-05-20 02:49:08 +00:00
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上面的所有参数跟 `MergeTree` 中的一样。
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2018-11-30 19:26:35 +00:00
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</details>
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2019-05-20 02:49:08 +00:00
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## SELECT 和 INSERT
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2018-11-30 19:26:35 +00:00
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2019-05-20 02:49:08 +00:00
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插入数据,需使用带有聚合 -State- 函数的 [INSERT SELECT](../../query_language/insert_into.md) 语句。
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从 `AggregatingMergeTree` 表中查询数据时,需使用 `GROUP BY` 子句并且要使用与插入时相同的聚合函数,但后缀要改为 `-Merge` 。
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2018-11-30 19:26:35 +00:00
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2019-05-20 02:49:08 +00:00
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在 `SELECT` 查询的结果中,对于 ClickHouse 的所有输出格式 `AggregateFunction` 类型的值都实现了特定的二进制表示法。如果直接用 `SELECT` 导出这些数据,例如如用 `TabSeparated` 格式,那么这些导出数据也能直接用 `INSERT` 语句加载导入。
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2018-11-30 19:26:35 +00:00
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2019-05-20 02:49:08 +00:00
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## 聚合物化视图的示例
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2018-11-30 19:26:35 +00:00
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2019-05-20 02:49:08 +00:00
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创建一个跟踪 `test.visits` 表的 `AggregatingMergeTree` 物化视图:
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``` sql
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CREATE MATERIALIZED VIEW test.basic
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ENGINE = AggregatingMergeTree() PARTITION BY toYYYYMM(StartDate) ORDER BY (CounterID, StartDate)
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AS SELECT
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CounterID,
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StartDate,
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sumState(Sign) AS Visits,
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uniqState(UserID) AS Users
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FROM test.visits
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GROUP BY CounterID, StartDate;
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```
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2019-05-20 02:49:08 +00:00
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向 `test.visits` 表中插入数据。
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2018-11-30 19:26:35 +00:00
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``` sql
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INSERT INTO test.visits ...
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```
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2019-05-20 02:49:08 +00:00
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数据会同时插入到表和视图中,并且视图 `test.basic` 会将里面的数据聚合。
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2018-11-30 19:26:35 +00:00
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2019-05-20 02:49:08 +00:00
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要获取聚合数据,我们需要在 `test.basic` 视图上执行类似 `SELECT ... GROUP BY ...` 这样的查询 :
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2018-11-30 19:26:35 +00:00
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``` sql
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SELECT
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StartDate,
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sumMerge(Visits) AS Visits,
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uniqMerge(Users) AS Users
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FROM test.basic
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GROUP BY StartDate
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ORDER BY StartDate;
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```
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2019-05-20 02:49:08 +00:00
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[来源文章](https://clickhouse.yandex/docs/en/operations/table_engines/aggregatingmergetree/) <!--hide-->
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