ClickHouse数据查询

川长思鸟来 2022-12-11 12:16 240阅读 0赞

### 文章目录 ###

*  ClickHouse数据查询
 *   *  前言
     *  查询语法
     *  查询子句
     *   *  WITH子句
         *  SAMPLE子句
         *  PREWHERE子句
         *  ARRAY JOIN子句
         *  LIMIT BY子句
         *  INTO OUTFILE子句
     *  执行计划
     *   *  查看执行计划
         *  执行计划示例

# ClickHouse数据查询 #

## 前言 ##

在[ClickHouse数据导入][ClickHouse] 一文中介绍了导入数据到ClickHouse数据表中，本文描述如何对ClickHouse中的数据进行查询。

## 查询语法 ##

参见：

*  [https://clickhouse.tech/docs/en/sql-reference/statements/select/][https_clickhouse.tech_docs_en_sql-reference_statements_select]

[WITH expr_list|(subquery)]
    SELECT [DISTINCT] expr_list
    [FROM [db.]table | (subquery) | table_function] [FINAL]
    [SAMPLE sample_coeff]
    [ARRAY JOIN ...]
    [GLOBAL] [ANY|ALL|ASOF] [INNER|LEFT|RIGHT|FULL|CROSS] [OUTER|SEMI|ANTI] JOIN (subquery)|table (ON <expr_list>)|(USING <column_list>)
    [PREWHERE expr]
    [WHERE expr]
    [GROUP BY expr_list] [WITH TOTALS]
    [HAVING expr]
    [ORDER BY expr_list] [WITH FILL] [FROM expr] [TO expr] [STEP expr]
    [LIMIT [offset_value, ]n BY columns]
    [LIMIT [n, ]m] [WITH TIES]
    [UNION ALL ...]
    [INTO OUTFILE filename]
    [FORMAT format]

可以看到ClickHouse的SELECT语句的语法和通用的SQL的SELECT语句非常类似，包括：

*  `SELECT`: 指定返回结果字段
 *  `DISTINCT`：去重
 *  `FROM`: 指定要查询的表或子查询
 *  `JOIN`：表连接，支持内连接和外连接、左连接和右连接
 *  `WHERE`：筛选条件
 *  `GROUP BY`：分组，和聚合函数一起使用
 *  `HAVING`：分组后筛选
 *  `ORDER BY`：排序
 *  `LIMIT`：限制返回记录数
 *  `UNION ALL`：并集；ClickHouse目前只支持`UNION ALL`，还不支持`UNION`

ClickHouse的SELECT语句中也有一些特殊的用法：

*  `WITH`: 设置查询中要用到的变量
 *  `SAMPLE`: 数据取样，类似Pandas库的sample()函数
 *  `PREWHERE`: 预筛选，起到提升性能作用
 *  `ARRAY JOIN`：数组连接，用来展开数组或嵌套字段，一行变多行
 *  `LIMIT BY`: 分组，再从每组中取前n条记录
 *  `INTO OUTFILE`: 到处表数据到文件，再用`FORMAT`指定文件格式

## 查询子句 ##

下面只说明ClickHouse中特有的数据查询子句。

### WITH子句 ###

参见：

*  [https://clickhouse.tech/docs/en/sql-reference/statements/select/with/][https_clickhouse.tech_docs_en_sql-reference_statements_select_with]

-- 在WITH子句中定义一个变量并赋值，然后在SELECT子句中通过别名使用该变量
    with '2014-03-17' as dt \
    select count(1) from hits_v1 where EventDate = dt;
    
    
    -- 在WITH子句中定义一个函数，然后在SELECT子句中通过别名使用该函数
    with round(Duration / 60)as duration_minutes \
    select StartDate, max(duration_minutes) as max_duration_minutes from visits_v1 \
    group by StartDate, Duration \
    order by max_duration_minutes desc \
    limit 10;
    
    
    -- 在WITH子句中定义一个子查询，然后在SELECT子句中通过别名使用该子查询
    -- 该子查询只能返回一行数据
    with ( \
      select sum(Duration) from visits_v1 \
    ) as total_duration \
    select StartDate, sum(Duration) / total_duration as duration_percentage from visits_v1 \
    group by StartDate, Duration \
    limit 10;

### SAMPLE子句 ###

参见：

*  [https://clickhouse.tech/docs/en/sql-reference/statements/select/sample/][https_clickhouse.tech_docs_en_sql-reference_statements_select_sample]

对使用了MergeTree表引擎的表，并且设置了SAMPLE BY的表，可以使用SAMPLE子句来对数据进行抽样。

表例子：

CREATE TABLE tutorial.hits_v1 \ 
    ( 
      ...
    )
    ENGINE = MergeTree() \ 
    PARTITION BY toYYYYMM(EventDate) \ 
    ORDER BY (CounterID, EventDate, intHash32(UserID)) \ 
    SAMPLE BY intHash32(UserID) \ 
    SETTINGS index_granularity = 8192;

hits\_v1表使用MergeTree表引擎，并且SAMPLE BY为将UserID转换成32位的Hash值。

SAMPLE子句分为：

<table> 
 <thead> 
  <tr> 
   <th>分类</th> 
   <th>语法</th> 
   <th>描述</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td>按比例采样</td> 
   <td><code>SAMPLE k</code></td> 
   <td>k值为0到1，比如k为0.1时表示采样10%的表数据。</td> 
  </tr> 
  <tr> 
   <td>按记录数采样</td> 
   <td><code>SAMPLE n</code></td> 
   <td>n为一个足够大的数，一般大于索引粒度index_granularity；<br>n较小时，采样结果数据可能为0条记录。</td> 
  </tr> 
  <tr> 
   <td>按比例和偏移量采样</td> 
   <td><code>SAMPLE k OFFSET m</code></td> 
   <td>k值为0到1，比如k为0.1时表示采样10%的表数据。<br>m值为0到1，比如0.3表示从从后面70%的表数据中采样。</td> 
  </tr> 
 </tbody> 
</table>

SAMPLE子句示例：

-- 按比例采样
    -- 采样结果记录数
    select count(1) from hits_v1 sample 0.1
    -- 采样数据，默认限制返回10000条
    select CounterID, UserID, EventDate, EventTime  from hits_v1 sample 0.1
    -- 采样数据，限制返回10条
    select CounterID, UserID, EventDate, EventTime  from hits_v1 sample 0.1 limit 10
    
    
    -- 按记录数采样
    -- 采样记录数较小时，采样结果数据为0条
    select count(1) from hits_v1 sample 100
    -- 采样记录数大过索引粒度时，采样结果数据记录数接近采样记录数
    elect count(1) from hits_v1 sample 10000
    -- 采样数据，默认限制返回10000条
    select CounterID, UserID, EventDate, EventTime  from hits_v1 sample 20000
    -- 采样数据，限制返回10条
    select CounterID, UserID, EventDate, EventTime  from hits_v1 sample 20000 limit 10
    
    
    -- 按比例和偏移量采样，类似于按比例采样
    select CounterID, UserID, EventDate, EventTime  from hits_v1 sample 0.1 offset 0.3 limit 10

SAMPLE子句数据采样具有幂等性和近似性的特点：

*  幂等性：采样条件不变时，两次采样的结果**可能**一样
 *  近似性：采样范围和采样结果不保证精确

### PREWHERE子句 ###

参见：

*  [https://clickhouse.tech/docs/en/sql-reference/statements/select/prewhere/][https_clickhouse.tech_docs_en_sql-reference_statements_select_prewhere]

只有MergeTree表引擎的表才能使用PREWHERE子句，可以将PREWHERE看作是ClickHouse对WHERE子句的优化。

ClickHouse默认将WHERE自动优化为PREWHERE:

-- optimize_move_to_prewhere为1时，表示开始PREWHERE自动优化
    select name, value from system.settings where name like '%prewhere%'

### ARRAY JOIN子句 ###

参见：

*  [https://clickhouse.tech/docs/en/sql-reference/statements/select/array-join/][https_clickhouse.tech_docs_en_sql-reference_statements_select_array-join]

可以用ARRAY JOIN子句来对数组（Array）或嵌套（Nested）类型做链接查询，可以将一行数组展成多行。

ARRAY JOIN数组示例：

-- 不使用ARRAY JOIN
    select WatchID, RefererCategories from hits_v1 where WatchID = 4944118417295196513
    
    -- 结果：
    ┌─────────────WatchID─┬─RefererCategories─┐
    │ 4944118417295196513 │ [6,98,456,8586]   │
    └─────────────────────┴───────────────────┘
    
    
    -- 使用ARRAY JOIN
    select WatchID, RefererCategories \
    from hits_v1 \
    array join RefererCategories \
    where WatchID = 4944118417295196513;
    
    -- 结果：
    ─────────────WatchID─┬─RefererCategories─┐
    │ 4944118417295196513 │                 6 │
    │ 4944118417295196513 │                98 │
    │ 4944118417295196513 │               456 │
    │ 4944118417295196513 │              8586 │
    └─────────────────────┴───────────────────┘

ARRAY JOIN嵌套类型示例：

-- 不使用ARRAY JOIN
    select WatchID, ParsedParams.Key1, ParsedParams.Key2 from hits_v1 where WatchID = 5024825574842900819
    
    -- 结果：
    ┌─────────────WatchID─┬─ParsedParams.Key1───────────┬─ParsedParams.Key2───┐
    │ 5024825574842900819 │ ['gen_timestamp','Toolbar'] │ ['group','true" /'] │
    └─────────────────────┴─────────────────────────────┴─────────────────────┘
    
    
    -- 使用ARRAY JOIN
    select WatchID, ParsedParams.Key1, ParsedParams.Key2 \
    from hits_v1 \
    array join ParsedParams \
    where WatchID = 5024825574842900819;
    
    -- 结果：
    ┌─────────────WatchID─┬─ParsedParams.Key1─┬─ParsedParams.Key2─┐
    │ 5024825574842900819 │ gen_timestamp     │ group             │
    │ 5024825574842900819 │ Toolbar           │ true" /           │
    └─────────────────────┴───────────────────┴───────────────────┘

ARRAY JOIN也支持左连接接`LEFT ARRAY JOIN`。

### LIMIT BY子句 ###

参见：

*  [https://clickhouse.tech/docs/en/sql-reference/statements/select/limit-by/][https_clickhouse.tech_docs_en_sql-reference_statements_select_limit-by]

LIMIT BY子句不同于通用SQL的LIMIT子句。

`LIMIT n BY expression` 对SELECT结果先按expression分组，再在每组里选出前n个，类似分类排行榜的概念。

示例：

CREATE TABLE limit_by(id Int, val Int) ENGINE = Memory;
    INSERT INTO limit_by VALUES (1, 10), (1, 11), (1, 12), (2, 20), (2, 21), (2, 22), (3, 31);

-- 排序
    SELECT * FROM limit_by ORDER BY id, val
    
    ┌─id─┬─val─┐
    │  1 │  10 │
    │  1 │  11 │
    │  1 │  12 │
    │  2 │  20 │
    │  2 │  21 │
    │  2 │  22 │
    │  3 │  31 │
    └────┴─────┘
    
    -- 分类排序，再在每个分组内取前2条记录
    SELECT * FROM limit_by ORDER BY id, val LIMIT 2 BY id
    
    ┌─id─┬─val─┐
    │  1 │  10 │
    │  1 │  11 │
    │  2 │  20 │
    │  2 │  21 │
    │  3 │  31 │
    └────┴─────┘
    
    
    -- 注意，与LIMIT 2的不同
    SELECT * FROM limit_by ORDER BY id, val LIMIT 2
    
    ┌─id─┬─val─┐
    │  1 │  10 │
    │  1 │  11 │
    └────┴─────┘

LIMIT BY子句也支持偏移量：`LIMIT n OFFSET m BY exression`

### INTO OUTFILE子句 ###

ClickHouse的INTO OUTFILE子句与MySQL的INTO OUTFILE子句类似。

-- 输出到当前目录
    -- 默认格式为TSV
    -- 注意文件名必须用单引号来括起来，且不能用双引号括起来，否则会报错：Expected string literal
    -- 目录下不能存在同名文件，否则会报错
    select WatchID, JavaEnable, EventDate  from hits_v1 limit 10 into outfile 'test.tsv'
    
    -- 设置格式为CSV，CSV需要为全大小
    select WatchID, JavaEnable, EventDate  from hits_v1 limit 10 into outfile 'out.csv' format CSV

## 执行计划 ##

### 查看执行计划 ###

可以通过以下方式查看执行计划：

1.  直接查看执行计划：

ch --send_logs_level=trace <<< 'select * from tutorial.hits_v1' > /dev/null

1.  可以将以下内容保持到一个脚本文件，比如`chx.sh`中：

#!/bin/bash
    
    clickhouse-client --send_logs_level=trace <<< "$1" > /dev/null

再通过`bash chi.sh "select * from tutorial.hits_v1"` 来查看执行计划。

1.  也可以在clickhouse-client执行sql同时，用`tail -f clickhouse-server.log` 查看clickhouse-server的日志。

### 执行计划示例 ###

`select * from tutorial.hits_v1`的执行计划日志示例：

<Debug> tutorial.hits_v1 (SelectExecutor): Key condition: unknown
    <Debug> tutorial.hits_v1 (SelectExecutor): MinMax index condition: unknown
    <Debug> tutorial.hits_v1 (SelectExecutor): Selected 9 parts by date, 9 parts by key, 1094 marks to read from 9 ranges
    <Trace> MergeTreeSelectProcessor: Reading 1 ranges from part 201403_1_6_1, approx. 1900000 rows starting from 0
    <Trace> MergeTreeSelectProcessor: Reading 1 ranges from part 201403_7_12_1, approx. 1900000 rows starting from 0
    <Trace> MergeTreeSelectProcessor: Reading 1 ranges from part 201403_13_18_1, approx. 1800000 rows starting from 0
    <Trace> MergeTreeSelectProcessor: Reading 1 ranges from part 201403_19_24_1, approx. 1800000 rows starting from 0
    <Trace> MergeTreeSelectProcessor: Reading 1 ranges from part 201403_25_25_0, approx. 303104 rows starting from 0
    <Trace> MergeTreeSelectProcessor: Reading 1 ranges from part 201403_26_26_0, approx. 303104 rows starting from 0
    <Trace> MergeTreeSelectProcessor: Reading 1 ranges from part 201403_27_27_0, approx. 303104 rows starting from 0
    <Trace> MergeTreeSelectProcessor: Reading 1 ranges from part 201403_28_28_0, approx. 303104 rows starting from 0
    <Trace> MergeTreeSelectProcessor: Reading 1 ranges from part 201403_29_29_0, approx. 278528 rows starting from 0
    <Trace> InterpreterSelectQuery: FetchColumns -> Complete
    <Information> executeQuery: Read 8873898 rows, 7.88 GiB in 47.964 sec., 185011 rows/sec., 168.22 MiB/sec.
    <Debug> MemoryTracker: Peak memory usage (for query): 204.54 MiB.

**说明：**

*  `Key condition: unknown`：没有使用主键索引。
 *  `MinMax index condition: unknown`： 没有使用分区索引。
 *  `Selected 9 parts by date, 9 parts by key, 1094 marks to read from 9 ranges`： 共扫描了9个分区，共1094个mark。
 *  `Read 8873898 rows, 7.88 GiB in 47.964 sec., 185011 rows/sec., 168.22 MiB/sec.`: 读取了8873898行数据，数据大小为7.88GB，用时47.964秒。
 *  `Peak memory usage (for query): 204.54 MiB.`: 内存峰值（最大内存使用量）为204.54MB。

将SQL改为只返回一个字段`select WatchID from tutorial.hits_v1`，再来看执行计划：

<Debug> tutorial.hits_v1 (SelectExecutor): Key condition: unknown
    <Debug> tutorial.hits_v1 (SelectExecutor): MinMax index condition: unknown
    <Debug> tutorial.hits_v1 (SelectExecutor): Selected 9 parts by date, 9 parts by key, 1094 marks to read from 9 ranges
    <Trace> MergeTreeSelectProcessor: Reading 1 ranges from part 201403_1_6_1, approx. 1900000 rows starting from 0
    <Trace> MergeTreeSelectProcessor: Reading 1 ranges from part 201403_7_12_1, approx. 1900000 rows starting from 0
    <Trace> MergeTreeSelectProcessor: Reading 1 ranges from part 201403_13_18_1, approx. 1800000 rows starting from 0
    <Trace> MergeTreeSelectProcessor: Reading 1 ranges from part 201403_19_24_1, approx. 1800000 rows starting from 0
    <Trace> MergeTreeSelectProcessor: Reading 1 ranges from part 201403_25_25_0, approx. 303104 rows starting from 0
    <Trace> MergeTreeSelectProcessor: Reading 1 ranges from part 201403_26_26_0, approx. 303104 rows starting from 0
    <Trace> MergeTreeSelectProcessor: Reading 1 ranges from part 201403_27_27_0, approx. 303104 rows starting from 0
    <Trace> MergeTreeSelectProcessor: Reading 1 ranges from part 201403_28_28_0, approx. 303104 rows starting from 0
    <Trace> MergeTreeSelectProcessor: Reading 1 ranges from part 201403_29_29_0, approx. 278528 rows starting from 0
    <Trace> InterpreterSelectQuery: FetchColumns -> Complete
    <Information> executeQuery: Read 8873898 rows, 67.70 MiB in 0.318 sec., 27892532 rows/sec., 212.80 MiB/sec.
    <Debug> MemoryTracker: Peak memory usage (for query): 12.32 MiB.

几个变化：

*  `Read 8873898 rows, 67.70 MiB in 0.318 sec., 27892532 rows/sec., 212.80 MiB/sec.`: 数据大小从7.88GB减小到67.70MB，用时从47.964秒减小到0.318秒。
 *  `Peak memory usage (for query): 12.32 MiB.`: 内存峰值从204.54MB减小到12.32MB。

再将SQL改为指定分区查询：

select WatchID from tutorial.hits_v1 where EventDate = '2014-03-17'

执行计划日志：

<Debug> executeQuery: (from 127.0.0.1:48102) SELECT WatchID FROM tutorial.hits_v1 WHERE EventDate = '2014-03-17'
    <Debug> InterpreterSelectQuery: MergeTreeWhereOptimizer: condition "EventDate = '2014-03-17'" moved to PREWHERE
    <Trace> ContextAccess (default): Access granted: SELECT(WatchID, EventDate) ON tutorial.hits_v1
    <Debug> tutorial.hits_v1 (SelectExecutor): Key condition: (column 1 in [16146, 16146])
    <Debug> tutorial.hits_v1 (SelectExecutor): MinMax index condition: (column 0 in [16146, 16146])
    <Debug> tutorial.hits_v1 (SelectExecutor): Selected 9 parts by date, 9 parts by key, 833 marks to read from 72 ranges
    <Trace> MergeTreeSelectProcessor: Reading 14 ranges from part 201403_1_6_1, approx. 818656, up to 818656 rows starting from 0
    <Trace> MergeTreeSelectProcessor: Reading 14 ranges from part 201403_7_12_1, approx. 1621472, up to 1621472 rows starting from 0
    <Trace> MergeTreeSelectProcessor: Reading 15 ranges from part 201403_13_18_1, approx. 1349440, up to 1349440 rows starting from 0
    <Trace> MergeTreeSelectProcessor: Reading 14 ranges from part 201403_19_24_1, approx. 1619776, up to 1619776 rows starting from 0
    <Trace> MergeTreeSelectProcessor: Reading 4 ranges from part 201403_25_25_0, approx. 278528, up to 278528 rows starting from 0
    <Trace> MergeTreeSelectProcessor: Reading 3 ranges from part 201403_26_26_0, approx. 286720, up to 286720 rows starting from 0
    <Trace> MergeTreeSelectProcessor: Reading 2 ranges from part 201403_27_27_0, approx. 294912, up to 294912 rows starting from 0
    <Trace> MergeTreeSelectProcessor: Reading 3 ranges from part 201403_28_28_0, approx. 229376, up to 229376 rows starting from 0
    <Trace> MergeTreeSelectProcessor: Reading 3 ranges from part 201403_29_29_0, approx. 253952, up to 253952 rows starting from 0
    <Trace> InterpreterSelectQuery: FetchColumns -> Complete
    <Information> executeQuery: Read 6735786 rows, 61.49 MiB in 0.097 sec., 69655325 rows/sec., 635.86 MiB/sec.
    <Debug> MemoryTracker: Peak memory usage (for query): 16.32 MiB.

**说明：**

*  `MergeTreeWhereOptimizer: condition "EventDate = '2014-03-17'" moved to PREWHERE`： 启动了PREWHERE优化。
 *  `Key condition: (column 1 in [16146, 16146])`： 用了主键索引。
 *  `MinMax index condition: (column 0 in [16146, 16146])`： 用了分区索引。
 *  `Selected 9 parts by date, 9 parts by key, 833 marks to read from 72 ranges`：从1094 marks减少到833 marks。
 *  `Read 6735786 rows, 61.49 MiB in 0.097 sec., 69655325 rows/sec., 635.86 MiB/sec.`： 数据大小从67.70MB变为61.49MB，用时从0.318秒减少到0.097秒。

[ClickHouse]: https://cookcode.blog.csdn.net/article/details/108864824
[https_clickhouse.tech_docs_en_sql-reference_statements_select]: https://clickhouse.tech/docs/en/sql-reference/statements/select/
[https_clickhouse.tech_docs_en_sql-reference_statements_select_with]: https://clickhouse.tech/docs/en/sql-reference/statements/select/with/
[https_clickhouse.tech_docs_en_sql-reference_statements_select_sample]: https://clickhouse.tech/docs/en/sql-reference/statements/select/sample/
[https_clickhouse.tech_docs_en_sql-reference_statements_select_prewhere]: https://clickhouse.tech/docs/en/sql-reference/statements/select/prewhere/
[https_clickhouse.tech_docs_en_sql-reference_statements_select_array-join]: https://clickhouse.tech/docs/en/sql-reference/statements/select/array-join/
[https_clickhouse.tech_docs_en_sql-reference_statements_select_limit-by]: https://clickhouse.tech/docs/en/sql-reference/statements/select/limit-by/