> ## Documentation Index
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> Use this file to discover all available pages before exploring further.

# avgMergeState

> avgMergeState 组合器使用示例

<div id="description">
  ## 描述
</div>

[`MergeState`](/zh/reference/functions/aggregate-functions/combinators#-state) 组合器
可用于 [`avg`](/zh/reference/functions/aggregate-functions/avg)
函数，以合并类型为 `AverageFunction(avg, T)` 的部分聚合状态，并
返回一个新的中间聚合状态。

<div id="example-usage">
  ## 示例用法
</div>

`MergeState` 组合器在多级聚合
场景中特别有用：当你希望合并预聚合状态，并继续将其保留为
状态 (而不是将其最终化) 以便后续处理时，尤其如此。为了说明这一点，我们来看
一个示例：如何将单台服务器的性能指标
转换为跨多个层级的分层聚合：服务器级 → 区域级
→ 数据中心级。

首先，我们创建一个表来存储原始数据：

```sql theme={null}
CREATE TABLE raw_server_metrics
(
    timestamp DateTime DEFAULT now(),
    server_id UInt32,
    region String,
    datacenter String,
    response_time_ms UInt32
)
ENGINE = MergeTree()
ORDER BY (region, server_id, timestamp);
```

我们将创建一个服务器级聚合目标表，并定义一个充当该表插入触发器的 Incremental
materialized view：

```sql theme={null}
CREATE TABLE server_performance
(
    server_id UInt32,
    region String,
    datacenter String,
    avg_response_time AggregateFunction(avg, UInt32)
)
ENGINE = AggregatingMergeTree()
ORDER BY (region, server_id);

CREATE MATERIALIZED VIEW server_performance_mv
TO server_performance
AS SELECT
    server_id,
    region,
    datacenter,
    avgState(response_time_ms) AS avg_response_time
FROM raw_server_metrics
GROUP BY server_id, region, datacenter;
```

我们也将在区域和数据中心层级执行相同的操作：

```sql theme={null}
CREATE TABLE region_performance
(
    region String,
    datacenter String,
    avg_response_time AggregateFunction(avg, UInt32)
)
ENGINE = AggregatingMergeTree()
ORDER BY (datacenter, region);

CREATE MATERIALIZED VIEW region_performance_mv
TO region_performance
AS SELECT
    region,
    datacenter,
    avgMergeState(avg_response_time) AS avg_response_time
FROM server_performance
GROUP BY region, datacenter;

-- 数据中心级别的表和 materialized view

CREATE TABLE datacenter_performance
(
    datacenter String,
    avg_response_time AggregateFunction(avg, UInt32)
)
ENGINE = AggregatingMergeTree()
ORDER BY datacenter;

CREATE MATERIALIZED VIEW datacenter_performance_mv
TO datacenter_performance
AS SELECT
      datacenter,
      avgMergeState(avg_response_time) AS avg_response_time
FROM region_performance
GROUP BY datacenter;
```

然后，我们会将示例原始数据插入源表中：

```sql theme={null}
INSERT INTO raw_server_metrics (timestamp, server_id, region, datacenter, response_time_ms) VALUES
    (now(), 101, 'us-east', 'dc1', 120),
    (now(), 101, 'us-east', 'dc1', 130),
    (now(), 102, 'us-east', 'dc1', 115),
    (now(), 201, 'us-west', 'dc1', 95),
    (now(), 202, 'us-west', 'dc1', 105),
    (now(), 301, 'eu-central', 'dc2', 145),
    (now(), 302, 'eu-central', 'dc2', 155);
```

我们将为每个级别分别编写三个查询：

<Tabs>
  <Tab title="服务级别">
    ```sql theme={null}
    SELECT
        server_id,
        region,
        avgMerge(avg_response_time) AS avg_response_ms
    FROM server_performance
    GROUP BY server_id, region
    ORDER BY region, server_id;
    ```

    ```response theme={null}
    ┌─server_id─┬─region─────┬─avg_response_ms─┐
    │       301 │ eu-central │             145 │
    │       302 │ eu-central │             155 │
    │       101 │ us-east    │             125 │
    │       102 │ us-east    │             115 │
    │       201 │ us-west    │              95 │
    │       202 │ us-west    │             105 │
    └───────────┴────────────┴─────────────────┘
    ```
  </Tab>

  <Tab title="区域级别">
    ```sql theme={null}
    SELECT
        region,
        datacenter,
        avgMerge(avg_response_time) AS avg_response_ms
    FROM region_performance
    GROUP BY region, datacenter
    ORDER BY datacenter, region;
    ```

    ```response theme={null}
    ┌─region─────┬─datacenter─┬────avg_response_ms─┐
    │ us-east    │ dc1        │ 121.66666666666667 │
    │ us-west    │ dc1        │                100 │
    │ eu-central │ dc2        │                150 │
    └────────────┴────────────┴────────────────────┘
    ```
  </Tab>

  <Tab title="数据中心级别">
    ```sql theme={null}
    SELECT
        datacenter,
        avgMerge(avg_response_time) AS avg_response_ms
    FROM datacenter_performance
    GROUP BY datacenter
    ORDER BY datacenter;
    ```

    ```response theme={null}
    ┌─datacenter─┬─avg_response_ms─┐
    │ dc1        │             113 │
    │ dc2        │             150 │
    └────────────┴─────────────────┘
    ```
  </Tab>
</Tabs>

我们可以再插入更多数据：

```sql theme={null}
INSERT INTO raw_server_metrics (timestamp, server_id, region, datacenter, response_time_ms) VALUES
    (now(), 101, 'us-east', 'dc1', 140),
    (now(), 201, 'us-west', 'dc1', 85),
    (now(), 301, 'eu-central', 'dc2', 135);
```

我们再来看看数据中心层面的性能。请注意，整个
聚合链已自动更新：

```sql theme={null}
SELECT
    datacenter,
    avgMerge(avg_response_time) AS avg_response_ms
FROM datacenter_performance
GROUP BY datacenter
ORDER BY datacenter;
```

```response theme={null}
┌─datacenter─┬────avg_response_ms─┐
│ dc1        │ 112.85714285714286 │
│ dc2        │                145 │
└────────────┴────────────────────┘
```

<div id="see-also">
  ## 另请参见
</div>

* [`avg`](/zh/reference/functions/aggregate-functions/avg)
* [`AggregateFunction`](/zh/reference/data-types/aggregatefunction)
* [`Merge`](/zh/reference/functions/aggregate-functions/combinators#-merge)
* [`MergeState`](/zh/reference/functions/aggregate-functions/combinators#-mergestate)