ClickHouse/docs/zh/getting-started/tutorial.md

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---
toc_priority: 12
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toc_title: 使用教程
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---
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# ClickHouse教程 {#clickhouse-tutorial}
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## 从本教程中可以获得什么? {#what-to-expect-from-this-tutorial}
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通过学习本教程您将了解如何设置一个简单的ClickHouse集群。它会很小但是可以容错和扩展。然后我们将使用其中一个示例数据集来填充数据并执行一些演示查询。
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## 单节点设置 {#single-node-setup}
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为了延迟演示分布式环境的复杂性我们将首先在单个服务器或虚拟机上部署ClickHouse。ClickHouse通常是从[deb](install.md#install-from-deb-packages)或[rpm](install.md#from-rpm-packages)包安装,但对于不支持它们的操作系统也有[其他方法](install.md#from-docker-image)。
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例如,您选择`deb`安装包,执行:
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``` bash
{% include 'install/deb.sh' %}
```
在我们安装的软件中包含这些包:
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- `clickhouse-client` 包,包含[clickhouse-client](../interfaces/cli.md)客户端它是交互式ClickHouse控制台客户端。
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- `clickhouse-common`包含一个ClickHouse可执行文件。
- `clickhouse-server`包含要作为服务端运行的ClickHouse配置文件。
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服务器配置文件位于`/etc/clickhouse-server/`。在继续之前,请注意`config.xml`中的`<path>`元素。它决定了数据存储的位置,因此它应该位于磁盘容量的卷上;默认值是`/var/lib/clickhouse/`。如果你想调整配置直接编辑config是不方便的。考虑到它可能会在将来的包更新中被重写。建议重写配置元素的方法是在配置中创建[config.d文件夹](../operations/configuration-files.md)作为config.xml的重写方式。
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你可能已经注意到了,`clickhouse-server`安装后不会自动启动。 它也不会在更新后自动重新启动。 您启动服务端的方式取决于您的初始系统,通常情况下是这样:
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``` bash
sudo service clickhouse-server start
```
``` bash
sudo /etc/init.d/clickhouse-server start
```
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服务端日志的默认位置是`/var/log/clickhouse-server/`。当服务端在日志中记录`Ready for connections`消息,即表示服务端已准备好处理客户端连接。
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一旦`clickhouse-server`启动并运行,我们可以利用`clickhouse-client`连接到服务端,并运行一些测试查询,如`SELECT "Hello, world!";`.
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<details markdown="1">
<summary>Clickhouse-client的快速提示</summary>
交互模式:
``` bash
clickhouse-client
clickhouse-client --host=... --port=... --user=... --password=...
```
启用多行查询:
``` bash
clickhouse-client -m
clickhouse-client --multiline
```
以批处理模式运行查询:
``` bash
clickhouse-client --query='SELECT 1'
echo 'SELECT 1' | clickhouse-client
clickhouse-client <<< 'SELECT 1'
```
从指定格式的文件中插入数据:
``` bash
clickhouse-client --query='INSERT INTO table VALUES' < data.txt
clickhouse-client --query='INSERT INTO table FORMAT TabSeparated' < data.tsv
```
</details>
## 导入示例数据集 {#import-sample-dataset}
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现在是时候用一些示例数据填充我们的ClickHouse服务端。 在本教程中我们将使用Yandex.Metrica的匿名数据它是在ClickHouse成为开源之前作为生产环境运行的第一个服务关于这一点的更多内容请参阅[ClickHouse历史](../introduction/history.md))。[多种导入Yandex.Metrica数据集方法](example-datasets/metrica.md),为了本教程,我们将使用最现实的一个。
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### 下载并提取表数据 {#download-and-extract-table-data}
``` bash
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curl https://datasets.clickhouse.tech/hits/tsv/hits_v1.tsv.xz | unxz --threads=`nproc` > hits_v1.tsv
curl https://datasets.clickhouse.tech/visits/tsv/visits_v1.tsv.xz | unxz --threads=`nproc` > visits_v1.tsv
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```
提取的文件大小约为10GB。
### 创建表 {#create-tables}
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与大多数数据库管理系统一样ClickHouse在逻辑上将表分组为数据库。包含一个`default`数据库,但我们将创建一个新的数据库`tutorial`:
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``` bash
clickhouse-client --query "CREATE DATABASE IF NOT EXISTS tutorial"
```
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与创建数据库相比,创建表的语法要复杂得多(请参阅[参考资料](../sql-reference/statements/create.md). 一般`CREATE TABLE`声明必须指定三个关键的事情:
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1. 要创建的表的名称。
2. 表结构,例如:列名和对应的[数据类型](../sql-reference/data-types/index.md)。
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3. [表引擎](../engines/table-engines/index.md)及其设置,这决定了对此表的查询操作是如何在物理层面执行的所有细节。
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Yandex.Metrica是一个网络分析服务样本数据集不包括其全部功能因此只有两个表可以创建:
- `hits` 表包含所有用户在服务所涵盖的所有网站上完成的每个操作。
- `visits` 表包含预先构建的会话,而不是单个操作。
让我们看看并执行这些表的实际创建表查询:
``` sql
CREATE TABLE tutorial.hits_v1
(
`WatchID` UInt64,
`JavaEnable` UInt8,
`Title` String,
`GoodEvent` Int16,
`EventTime` DateTime,
`EventDate` Date,
`CounterID` UInt32,
`ClientIP` UInt32,
`ClientIP6` FixedString(16),
`RegionID` UInt32,
`UserID` UInt64,
`CounterClass` Int8,
`OS` UInt8,
`UserAgent` UInt8,
`URL` String,
`Referer` String,
`URLDomain` String,
`RefererDomain` String,
`Refresh` UInt8,
`IsRobot` UInt8,
`RefererCategories` Array(UInt16),
`URLCategories` Array(UInt16),
`URLRegions` Array(UInt32),
`RefererRegions` Array(UInt32),
`ResolutionWidth` UInt16,
`ResolutionHeight` UInt16,
`ResolutionDepth` UInt8,
`FlashMajor` UInt8,
`FlashMinor` UInt8,
`FlashMinor2` String,
`NetMajor` UInt8,
`NetMinor` UInt8,
`UserAgentMajor` UInt16,
`UserAgentMinor` FixedString(2),
`CookieEnable` UInt8,
`JavascriptEnable` UInt8,
`IsMobile` UInt8,
`MobilePhone` UInt8,
`MobilePhoneModel` String,
`Params` String,
`IPNetworkID` UInt32,
`TraficSourceID` Int8,
`SearchEngineID` UInt16,
`SearchPhrase` String,
`AdvEngineID` UInt8,
`IsArtifical` UInt8,
`WindowClientWidth` UInt16,
`WindowClientHeight` UInt16,
`ClientTimeZone` Int16,
`ClientEventTime` DateTime,
`SilverlightVersion1` UInt8,
`SilverlightVersion2` UInt8,
`SilverlightVersion3` UInt32,
`SilverlightVersion4` UInt16,
`PageCharset` String,
`CodeVersion` UInt32,
`IsLink` UInt8,
`IsDownload` UInt8,
`IsNotBounce` UInt8,
`FUniqID` UInt64,
`HID` UInt32,
`IsOldCounter` UInt8,
`IsEvent` UInt8,
`IsParameter` UInt8,
`DontCountHits` UInt8,
`WithHash` UInt8,
`HitColor` FixedString(1),
`UTCEventTime` DateTime,
`Age` UInt8,
`Sex` UInt8,
`Income` UInt8,
`Interests` UInt16,
`Robotness` UInt8,
`GeneralInterests` Array(UInt16),
`RemoteIP` UInt32,
`RemoteIP6` FixedString(16),
`WindowName` Int32,
`OpenerName` Int32,
`HistoryLength` Int16,
`BrowserLanguage` FixedString(2),
`BrowserCountry` FixedString(2),
`SocialNetwork` String,
`SocialAction` String,
`HTTPError` UInt16,
`SendTiming` Int32,
`DNSTiming` Int32,
`ConnectTiming` Int32,
`ResponseStartTiming` Int32,
`ResponseEndTiming` Int32,
`FetchTiming` Int32,
`RedirectTiming` Int32,
`DOMInteractiveTiming` Int32,
`DOMContentLoadedTiming` Int32,
`DOMCompleteTiming` Int32,
`LoadEventStartTiming` Int32,
`LoadEventEndTiming` Int32,
`NSToDOMContentLoadedTiming` Int32,
`FirstPaintTiming` Int32,
`RedirectCount` Int8,
`SocialSourceNetworkID` UInt8,
`SocialSourcePage` String,
`ParamPrice` Int64,
`ParamOrderID` String,
`ParamCurrency` FixedString(3),
`ParamCurrencyID` UInt16,
`GoalsReached` Array(UInt32),
`OpenstatServiceName` String,
`OpenstatCampaignID` String,
`OpenstatAdID` String,
`OpenstatSourceID` String,
`UTMSource` String,
`UTMMedium` String,
`UTMCampaign` String,
`UTMContent` String,
`UTMTerm` String,
`FromTag` String,
`HasGCLID` UInt8,
`RefererHash` UInt64,
`URLHash` UInt64,
`CLID` UInt32,
`YCLID` UInt64,
`ShareService` String,
`ShareURL` String,
`ShareTitle` String,
`ParsedParams` Nested(
Key1 String,
Key2 String,
Key3 String,
Key4 String,
Key5 String,
ValueDouble Float64),
`IslandID` FixedString(16),
`RequestNum` UInt32,
`RequestTry` UInt8
)
ENGINE = MergeTree()
PARTITION BY toYYYYMM(EventDate)
ORDER BY (CounterID, EventDate, intHash32(UserID))
SAMPLE BY intHash32(UserID)
SETTINGS index_granularity = 8192
```
``` sql
CREATE TABLE tutorial.visits_v1
(
`CounterID` UInt32,
`StartDate` Date,
`Sign` Int8,
`IsNew` UInt8,
`VisitID` UInt64,
`UserID` UInt64,
`StartTime` DateTime,
`Duration` UInt32,
`UTCStartTime` DateTime,
`PageViews` Int32,
`Hits` Int32,
`IsBounce` UInt8,
`Referer` String,
`StartURL` String,
`RefererDomain` String,
`StartURLDomain` String,
`EndURL` String,
`LinkURL` String,
`IsDownload` UInt8,
`TraficSourceID` Int8,
`SearchEngineID` UInt16,
`SearchPhrase` String,
`AdvEngineID` UInt8,
`PlaceID` Int32,
`RefererCategories` Array(UInt16),
`URLCategories` Array(UInt16),
`URLRegions` Array(UInt32),
`RefererRegions` Array(UInt32),
`IsYandex` UInt8,
`GoalReachesDepth` Int32,
`GoalReachesURL` Int32,
`GoalReachesAny` Int32,
`SocialSourceNetworkID` UInt8,
`SocialSourcePage` String,
`MobilePhoneModel` String,
`ClientEventTime` DateTime,
`RegionID` UInt32,
`ClientIP` UInt32,
`ClientIP6` FixedString(16),
`RemoteIP` UInt32,
`RemoteIP6` FixedString(16),
`IPNetworkID` UInt32,
`SilverlightVersion3` UInt32,
`CodeVersion` UInt32,
`ResolutionWidth` UInt16,
`ResolutionHeight` UInt16,
`UserAgentMajor` UInt16,
`UserAgentMinor` UInt16,
`WindowClientWidth` UInt16,
`WindowClientHeight` UInt16,
`SilverlightVersion2` UInt8,
`SilverlightVersion4` UInt16,
`FlashVersion3` UInt16,
`FlashVersion4` UInt16,
`ClientTimeZone` Int16,
`OS` UInt8,
`UserAgent` UInt8,
`ResolutionDepth` UInt8,
`FlashMajor` UInt8,
`FlashMinor` UInt8,
`NetMajor` UInt8,
`NetMinor` UInt8,
`MobilePhone` UInt8,
`SilverlightVersion1` UInt8,
`Age` UInt8,
`Sex` UInt8,
`Income` UInt8,
`JavaEnable` UInt8,
`CookieEnable` UInt8,
`JavascriptEnable` UInt8,
`IsMobile` UInt8,
`BrowserLanguage` UInt16,
`BrowserCountry` UInt16,
`Interests` UInt16,
`Robotness` UInt8,
`GeneralInterests` Array(UInt16),
`Params` Array(String),
`Goals` Nested(
ID UInt32,
Serial UInt32,
EventTime DateTime,
Price Int64,
OrderID String,
CurrencyID UInt32),
`WatchIDs` Array(UInt64),
`ParamSumPrice` Int64,
`ParamCurrency` FixedString(3),
`ParamCurrencyID` UInt16,
`ClickLogID` UInt64,
`ClickEventID` Int32,
`ClickGoodEvent` Int32,
`ClickEventTime` DateTime,
`ClickPriorityID` Int32,
`ClickPhraseID` Int32,
`ClickPageID` Int32,
`ClickPlaceID` Int32,
`ClickTypeID` Int32,
`ClickResourceID` Int32,
`ClickCost` UInt32,
`ClickClientIP` UInt32,
`ClickDomainID` UInt32,
`ClickURL` String,
`ClickAttempt` UInt8,
`ClickOrderID` UInt32,
`ClickBannerID` UInt32,
`ClickMarketCategoryID` UInt32,
`ClickMarketPP` UInt32,
`ClickMarketCategoryName` String,
`ClickMarketPPName` String,
`ClickAWAPSCampaignName` String,
`ClickPageName` String,
`ClickTargetType` UInt16,
`ClickTargetPhraseID` UInt64,
`ClickContextType` UInt8,
`ClickSelectType` Int8,
`ClickOptions` String,
`ClickGroupBannerID` Int32,
`OpenstatServiceName` String,
`OpenstatCampaignID` String,
`OpenstatAdID` String,
`OpenstatSourceID` String,
`UTMSource` String,
`UTMMedium` String,
`UTMCampaign` String,
`UTMContent` String,
`UTMTerm` String,
`FromTag` String,
`HasGCLID` UInt8,
`FirstVisit` DateTime,
`PredLastVisit` Date,
`LastVisit` Date,
`TotalVisits` UInt32,
`TraficSource` Nested(
ID Int8,
SearchEngineID UInt16,
AdvEngineID UInt8,
PlaceID UInt16,
SocialSourceNetworkID UInt8,
Domain String,
SearchPhrase String,
SocialSourcePage String),
`Attendance` FixedString(16),
`CLID` UInt32,
`YCLID` UInt64,
`NormalizedRefererHash` UInt64,
`SearchPhraseHash` UInt64,
`RefererDomainHash` UInt64,
`NormalizedStartURLHash` UInt64,
`StartURLDomainHash` UInt64,
`NormalizedEndURLHash` UInt64,
`TopLevelDomain` UInt64,
`URLScheme` UInt64,
`OpenstatServiceNameHash` UInt64,
`OpenstatCampaignIDHash` UInt64,
`OpenstatAdIDHash` UInt64,
`OpenstatSourceIDHash` UInt64,
`UTMSourceHash` UInt64,
`UTMMediumHash` UInt64,
`UTMCampaignHash` UInt64,
`UTMContentHash` UInt64,
`UTMTermHash` UInt64,
`FromHash` UInt64,
`WebVisorEnabled` UInt8,
`WebVisorActivity` UInt32,
`ParsedParams` Nested(
Key1 String,
Key2 String,
Key3 String,
Key4 String,
Key5 String,
ValueDouble Float64),
`Market` Nested(
Type UInt8,
GoalID UInt32,
OrderID String,
OrderPrice Int64,
PP UInt32,
DirectPlaceID UInt32,
DirectOrderID UInt32,
DirectBannerID UInt32,
GoodID String,
GoodName String,
GoodQuantity Int32,
GoodPrice Int64),
`IslandID` FixedString(16)
)
ENGINE = CollapsingMergeTree(Sign)
PARTITION BY toYYYYMM(StartDate)
ORDER BY (CounterID, StartDate, intHash32(UserID), VisitID)
SAMPLE BY intHash32(UserID)
SETTINGS index_granularity = 8192
```
您可以使用`clickhouse-client`的交互模式执行这些查询(只需在终端中启动它,而不需要提前指定查询)。或者如果你愿意,可以尝试一些[替代接口](../interfaces/index.md)。
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正如我们所看到的, `hits_v1`使用 [MergeTree引擎](../engines/table-engines/mergetree-family/mergetree.md),而`visits_v1`使用 [Collapsing](../engines/table-engines/mergetree-family/collapsingmergetree.md)引擎。
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### 导入数据 {#import-data}
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数据导入到ClickHouse是通过[INSERT INTO](../sql-reference/statements/insert-into.md)方式完成的查询类似许多SQL数据库。然而数据通常是在一个提供[支持序列化格式](../interfaces/formats.md)而不是`VALUES`子句(也支持)。
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我们之前下载的文件是以制表符分隔的格式,所以这里是如何通过控制台客户端导入它们:
``` bash
clickhouse-client --query "INSERT INTO tutorial.hits_v1 FORMAT TSV" --max_insert_block_size=100000 < hits_v1.tsv
clickhouse-client --query "INSERT INTO tutorial.visits_v1 FORMAT TSV" --max_insert_block_size=100000 < visits_v1.tsv
```
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ClickHouse有很多[要调整的设置](../operations/settings/index.md)在控制台客户端中指定它们的一种方法是通过参数,就像我们看到上面语句中的`--max_insert_block_size`。找出可用的设置、含义及其默认值的最简单方法是查询`system.settings` 表:
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``` sql
SELECT name, value, changed, description
FROM system.settings
WHERE name LIKE '%max_insert_b%'
FORMAT TSV
max_insert_block_size 1048576 0 "The maximum block size for insertion, if we control the creation of blocks for insertion."
```
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您也可以[OPTIMIZE](../sql-reference/statements/misc.md#misc_operations-optimize)导入后的表。使用MergeTree-family引擎配置的表总是在后台合并数据部分以优化数据存储或至少检查是否有意义。 这些查询强制表引擎立即进行存储优化,而不是稍后一段时间执行:
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``` bash
clickhouse-client --query "OPTIMIZE TABLE tutorial.hits_v1 FINAL"
clickhouse-client --query "OPTIMIZE TABLE tutorial.visits_v1 FINAL"
```
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这些查询开始I/O和CPU密集型操作所以如果表一直接收到新数据最好不要管它让合并在后台运行。
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现在我们可以检查表导入是否成功:
``` bash
clickhouse-client --query "SELECT COUNT(*) FROM tutorial.hits_v1"
clickhouse-client --query "SELECT COUNT(*) FROM tutorial.visits_v1"
```
## 查询示例 {#example-queries}
``` sql
SELECT
StartURL AS URL,
AVG(Duration) AS AvgDuration
FROM tutorial.visits_v1
WHERE StartDate BETWEEN '2014-03-23' AND '2014-03-30'
GROUP BY URL
ORDER BY AvgDuration DESC
LIMIT 10
```
``` sql
SELECT
sum(Sign) AS visits,
sumIf(Sign, has(Goals.ID, 1105530)) AS goal_visits,
(100. * goal_visits) / visits AS goal_percent
FROM tutorial.visits_v1
WHERE (CounterID = 912887) AND (toYYYYMM(StartDate) = 201403) AND (domain(StartURL) = 'yandex.ru')
```
## 集群部署 {#cluster-deployment}
ClickHouse集群是一个同质集群。 设置步骤:
1. 在群集的所有机器上安装ClickHouse服务端
2. 在配置文件中设置群集配置
3. 在每个实例上创建本地表
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4. 创建一个[分布式表](../engines/table-engines/special/distributed.md)
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[分布式表](../engines/table-engines/special/distributed.md)实际上是一种`view`映射到ClickHouse集群的本地表。 从分布式表中执行**SELECT**查询会使用集群所有分片的资源。 您可以为多个集群指定configs并创建多个分布式表为不同的集群提供视图。
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具有三个分片,每个分片一个副本的集群的示例配置:
``` xml
<remote_servers>
<perftest_3shards_1replicas>
<shard>
<replica>
<host>example-perftest01j.yandex.ru</host>
<port>9000</port>
</replica>
</shard>
<shard>
<replica>
<host>example-perftest02j.yandex.ru</host>
<port>9000</port>
</replica>
</shard>
<shard>
<replica>
<host>example-perftest03j.yandex.ru</host>
<port>9000</port>
</replica>
</shard>
</perftest_3shards_1replicas>
</remote_servers>
```
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为了进一步演示,让我们使用和创建`hits_v1`表相同的`CREATE TABLE`语句创建一个新的本地表,但表名不同:
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``` sql
CREATE TABLE tutorial.hits_local (...) ENGINE = MergeTree() ...
```
创建提供集群本地表视图的分布式表:
``` sql
CREATE TABLE tutorial.hits_all AS tutorial.hits_local
ENGINE = Distributed(perftest_3shards_1replicas, tutorial, hits_local, rand());
```
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常见的做法是在集群的所有计算机上创建类似的分布式表。 它允许在群集的任何计算机上运行分布式查询。 还有一个替代选项可以使用以下方法为给定的SELECT查询创建临时分布式表[远程](../sql-reference/table-functions/remote.md)表功能。
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让我们运行[INSERT SELECT](../sql-reference/statements/insert-into.md)将该表传播到多个服务器。
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``` sql
INSERT INTO tutorial.hits_all SELECT * FROM tutorial.hits_v1;
```
!!! warning "注意:"
这种方法不适合大型表的分片。 有一个单独的工具 [clickhouse-copier](../operations/utilities/clickhouse-copier.md) 这可以重新分片任意大表。
正如您所期望的那样如果计算量大的查询使用3台服务器而不是一个则运行速度快N倍。
在这种情况下我们使用了具有3个分片的集群每个分片都包含一个副本。
为了在生产环境中提供弹性我们建议每个分片应包含分布在多个可用区或数据中心或至少机架之间的2-3个副本。 请注意ClickHouse支持无限数量的副本。
包含三个副本的一个分片集群的示例配置:
``` xml
<remote_servers>
...
<perftest_1shards_3replicas>
<shard>
<replica>
<host>example-perftest01j.yandex.ru</host>
<port>9000</port>
</replica>
<replica>
<host>example-perftest02j.yandex.ru</host>
<port>9000</port>
</replica>
<replica>
<host>example-perftest03j.yandex.ru</host>
<port>9000</port>
</replica>
</shard>
</perftest_1shards_3replicas>
</remote_servers>
```
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启用本机复制[Zookeeper](http://zookeeper.apache.org/)是必需的。 ClickHouse负责所有副本的数据一致性并在失败后自动运行恢复过程。建议将ZooKeeper集群部署在单独的服务器上其中没有其他进程包括运行的ClickHouse
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!!! note "注意"
ZooKeeper不是一个严格的要求在某些简单的情况下您可以通过将数据写入应用程序代码中的所有副本来复制数据。 这种方法是**不**建议的在这种情况下ClickHouse将无法保证所有副本上的数据一致性。 因此需要由您的应用来保证这一点。
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ZooKeeper位置在配置文件中指定:
``` xml
<zookeeper>
<node>
<host>zoo01.yandex.ru</host>
<port>2181</port>
</node>
<node>
<host>zoo02.yandex.ru</host>
<port>2181</port>
</node>
<node>
<host>zoo03.yandex.ru</host>
<port>2181</port>
</node>
</zookeeper>
```
此外,我们需要设置宏来识别每个用于创建表的分片和副本:
``` xml
<macros>
<shard>01</shard>
<replica>01</replica>
</macros>
```
如果在创建复制表时没有副本,则会实例化新的第一个副本。 如果已有实时副本,则新副本将克隆现有副本中的数据。 您可以选择首先创建所有复制的表,然后向其中插入数据。 另一种选择是创建一些副本,并在数据插入之后或期间添加其他副本。
``` sql
CREATE TABLE tutorial.hits_replica (...)
ENGINE = ReplcatedMergeTree(
'/clickhouse_perftest/tables/{shard}/hits',
'{replica}'
)
...
```
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在这里,我们使用[ReplicatedMergeTree](../engines/table-engines/mergetree-family/replication.md)表引擎。 在参数中我们指定包含分片和副本标识符的ZooKeeper路径。
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``` sql
INSERT INTO tutorial.hits_replica SELECT * FROM tutorial.hits_local;
```
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复制在多主机模式下运行。数据可以加载到任何副本中,然后系统自动将其与其他实例同步。复制是异步的,因此在给定时刻,并非所有副本都可能包含最近插入的数据。至少应该有一个副本允许数据摄入。另一些则会在重新激活后同步数据并修复一致性。请注意,这种方法允许最近插入的数据丢失的可能性很低。
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[原始文章](https://clickhouse.tech/docs/en/getting_started/tutorial/) <!--hide-->