mirror of
https://github.com/ClickHouse/ClickHouse.git
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672 lines
23 KiB
Markdown
672 lines
23 KiB
Markdown
---
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machine_translated: true
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machine_translated_rev: b111334d6614a02564cf32f379679e9ff970d9b1
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toc_priority: 12
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toc_title: "\u6559\u7A0B"
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---
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# 点击教程 {#clickhouse-tutorial}
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## 从本教程中可以期待什么？ {#what-to-expect-from-this-tutorial}
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通过本教程，您将学习如何设置一个简单的ClickHouse集群。 它会很小，但容错和可扩展。 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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为了推迟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通常是从安装 [黛布](index.md#install-from-deb-packages) 或 [rpm](index.md#from-rpm-packages) 包，但也有 [替代办法](index.md#from-docker-image) 对于不支持它们的操作系统。
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例如，您选择了 `deb` 包和执行:
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``` bash
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sudo apt-get install dirmngr
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sudo apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv E0C56BD4
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echo "deb http://repo.clickhouse.tech/deb/stable/ main/" | sudo tee /etc/apt/sources.list.d/clickhouse.list
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sudo apt-get update
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sudo apt-get install -y clickhouse-server clickhouse-client
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```
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我们在安装的软件包中有什么:
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- `clickhouse-client` 包包含 [ﾂ环板clientｮﾂ嘉ｯﾂ偲](../interfaces/cli.md) 应用程序，交互式ClickHouse控制台客户端。
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- `clickhouse-common` 包包含一个ClickHouse可执行文件。
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- `clickhouse-server` 包包含要作为服务器运行ClickHouse的配置文件。
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服务器配置文件位于 `/etc/clickhouse-server/`. 在进一步讨论之前，请注意 `<path>` 元素in `config.xml`. Path确定数据存储的位置，因此应该位于磁盘容量较大的卷上；默认值为 `/var/lib/clickhouse/`. 如果你想调整配置，直接编辑并不方便 `config.xml` 文件，考虑到它可能会在未来的软件包更新中被重写。 复盖配置元素的推荐方法是创建 [在配置文件。d目录](../operations/configuration_files.md) 它作为 “patches” 要配置。xml
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你可能已经注意到了, `clickhouse-server` 安装包后不会自动启动。 它也不会在更新后自动重新启动。 您启动服务器的方式取决于您的init系统，通常情况下，它是:
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``` bash
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sudo service clickhouse-server start
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```
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或
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``` bash
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sudo /etc/init.d/clickhouse-server start
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```
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服务器日志的默认位置是 `/var/log/clickhouse-server/`. 服务器已准备好处理客户端连接一旦它记录 `Ready for connections` 消息
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一旦 `clickhouse-server` 正在运行我们可以利用 `clickhouse-client` 连接到服务器并运行一些测试查询，如 `SELECT "Hello, world!";`.
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<details markdown="1">
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<summary>Clickhouse-客户端的快速提示</summary>
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交互模式:
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``` bash
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clickhouse-client
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clickhouse-client --host=... --port=... --user=... --password=...
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```
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启用多行查询:
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``` bash
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clickhouse-client -m
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clickhouse-client --multiline
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```
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以批处理模式运行查询:
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``` bash
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clickhouse-client --query='SELECT 1'
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echo 'SELECT 1' | clickhouse-client
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clickhouse-client <<< 'SELECT 1'
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```
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从指定格式的文件中插入数据:
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``` bash
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clickhouse-client --query='INSERT INTO table VALUES' < data.txt
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clickhouse-client --query='INSERT INTO table FORMAT TabSeparated' < data.tsv
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```
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</details>
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## 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服务器。 在本教程中，我们将使用Yandex的匿名数据。Metrica，在成为开源之前以生产方式运行ClickHouse的第一个服务（更多关于这一点 [历史科](../introduction/history.md)). 有 [多种导入Yandex的方式。梅里卡数据集](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}
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``` bash
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curl https://clickhouse-datasets.s3.yandex.net/hits/tsv/hits_v1.tsv.xz | unxz --threads=`nproc` > hits_v1.tsv
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curl https://clickhouse-datasets.s3.yandex.net/visits/tsv/visits_v1.tsv.xz | unxz --threads=`nproc` > visits_v1.tsv
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```
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提取的文件大小约为10GB。
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### 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 “databases”. 有一个 `default` 数据库，但我们将创建一个名为新的 `tutorial`:
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``` bash
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clickhouse-client --query "CREATE DATABASE IF NOT EXISTS tutorial"
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```
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与数据库相比，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。
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2. Table schema, i.e. list of columns and their [数据类型](../sql_reference/data_types/index.md).
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3. [表引擎](../engines/table_engines/index.md) 它是settings，它决定了如何物理执行对此表的查询的所有细节。
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YandexMetrica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:
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- `hits` 是一个表格，其中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。
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- `visits` 是一个包含预先构建的会话而不是单个操作的表。
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让我们看看并执行这些表的实际创建表查询:
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``` sql
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CREATE TABLE tutorial.hits_v1
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(
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`WatchID` UInt64,
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`JavaEnable` UInt8,
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`Title` String,
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`GoodEvent` Int16,
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`EventTime` DateTime,
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`EventDate` Date,
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`CounterID` UInt32,
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`ClientIP` UInt32,
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`ClientIP6` FixedString(16),
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`RegionID` UInt32,
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`UserID` UInt64,
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`CounterClass` Int8,
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`OS` UInt8,
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`UserAgent` UInt8,
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`URL` String,
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`Referer` String,
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`URLDomain` String,
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`RefererDomain` String,
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`Refresh` UInt8,
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`IsRobot` UInt8,
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`RefererCategories` Array(UInt16),
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`URLCategories` Array(UInt16),
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`URLRegions` Array(UInt32),
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`RefererRegions` Array(UInt32),
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`ResolutionWidth` UInt16,
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`ResolutionHeight` UInt16,
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`ResolutionDepth` UInt8,
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`FlashMajor` UInt8,
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`FlashMinor` UInt8,
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`FlashMinor2` String,
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`NetMajor` UInt8,
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`NetMinor` UInt8,
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`UserAgentMajor` UInt16,
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`UserAgentMinor` FixedString(2),
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`CookieEnable` UInt8,
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`JavascriptEnable` UInt8,
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`IsMobile` UInt8,
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`MobilePhone` UInt8,
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`MobilePhoneModel` String,
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`Params` String,
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`IPNetworkID` UInt32,
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`TraficSourceID` Int8,
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`SearchEngineID` UInt16,
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`SearchPhrase` String,
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`AdvEngineID` UInt8,
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`IsArtifical` UInt8,
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`WindowClientWidth` UInt16,
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`WindowClientHeight` UInt16,
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`ClientTimeZone` Int16,
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`ClientEventTime` DateTime,
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`SilverlightVersion1` UInt8,
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`SilverlightVersion2` UInt8,
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`SilverlightVersion3` UInt32,
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`SilverlightVersion4` UInt16,
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`PageCharset` String,
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`CodeVersion` UInt32,
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`IsLink` UInt8,
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`IsDownload` UInt8,
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`IsNotBounce` UInt8,
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`FUniqID` UInt64,
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`HID` UInt32,
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`IsOldCounter` UInt8,
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`IsEvent` UInt8,
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`IsParameter` UInt8,
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`DontCountHits` UInt8,
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`WithHash` UInt8,
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`HitColor` FixedString(1),
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`UTCEventTime` DateTime,
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`Age` UInt8,
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`Sex` UInt8,
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`Income` UInt8,
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`Interests` UInt16,
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`Robotness` UInt8,
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`GeneralInterests` Array(UInt16),
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`RemoteIP` UInt32,
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`RemoteIP6` FixedString(16),
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`WindowName` Int32,
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`OpenerName` Int32,
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`HistoryLength` Int16,
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`BrowserLanguage` FixedString(2),
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`BrowserCountry` FixedString(2),
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`SocialNetwork` String,
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`SocialAction` String,
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`HTTPError` UInt16,
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`SendTiming` Int32,
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`DNSTiming` Int32,
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`ConnectTiming` Int32,
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`ResponseStartTiming` Int32,
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`ResponseEndTiming` Int32,
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`FetchTiming` Int32,
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`RedirectTiming` Int32,
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`DOMInteractiveTiming` Int32,
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`DOMContentLoadedTiming` Int32,
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`DOMCompleteTiming` Int32,
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`LoadEventStartTiming` Int32,
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`LoadEventEndTiming` Int32,
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`NSToDOMContentLoadedTiming` Int32,
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`FirstPaintTiming` Int32,
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`RedirectCount` Int8,
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`SocialSourceNetworkID` UInt8,
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`SocialSourcePage` String,
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`ParamPrice` Int64,
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`ParamOrderID` String,
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`ParamCurrency` FixedString(3),
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`ParamCurrencyID` UInt16,
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`GoalsReached` Array(UInt32),
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`OpenstatServiceName` String,
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`OpenstatCampaignID` String,
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`OpenstatAdID` String,
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`OpenstatSourceID` String,
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`UTMSource` String,
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`UTMMedium` String,
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`UTMCampaign` String,
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`UTMContent` String,
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`UTMTerm` String,
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`FromTag` String,
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`HasGCLID` UInt8,
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`RefererHash` UInt64,
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`URLHash` UInt64,
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`CLID` UInt32,
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`YCLID` UInt64,
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`ShareService` String,
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`ShareURL` String,
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`ShareTitle` String,
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`ParsedParams` Nested(
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Key1 String,
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Key2 String,
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Key3 String,
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Key4 String,
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Key5 String,
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ValueDouble Float64),
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`IslandID` FixedString(16),
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`RequestNum` UInt32,
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`RequestTry` UInt8
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)
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ENGINE = MergeTree()
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PARTITION BY toYYYYMM(EventDate)
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ORDER BY (CounterID, EventDate, intHash32(UserID))
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SAMPLE BY intHash32(UserID)
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SETTINGS index_granularity = 8192
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```
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``` sql
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CREATE TABLE tutorial.visits_v1
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(
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`CounterID` UInt32,
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`StartDate` Date,
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`Sign` Int8,
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`IsNew` UInt8,
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`VisitID` UInt64,
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`UserID` UInt64,
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`StartTime` DateTime,
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`Duration` UInt32,
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`UTCStartTime` DateTime,
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`PageViews` Int32,
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`Hits` Int32,
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`IsBounce` UInt8,
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`Referer` String,
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`StartURL` String,
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`RefererDomain` String,
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`StartURLDomain` String,
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`EndURL` String,
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`LinkURL` String,
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`IsDownload` UInt8,
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`TraficSourceID` Int8,
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`SearchEngineID` UInt16,
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`SearchPhrase` String,
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`AdvEngineID` UInt8,
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`PlaceID` Int32,
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`RefererCategories` Array(UInt16),
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`URLCategories` Array(UInt16),
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`URLRegions` Array(UInt32),
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`RefererRegions` Array(UInt32),
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`IsYandex` UInt8,
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`GoalReachesDepth` Int32,
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`GoalReachesURL` Int32,
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`GoalReachesAny` Int32,
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`SocialSourceNetworkID` UInt8,
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`SocialSourcePage` String,
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`MobilePhoneModel` String,
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`ClientEventTime` DateTime,
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`RegionID` UInt32,
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`ClientIP` UInt32,
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`ClientIP6` FixedString(16),
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`RemoteIP` UInt32,
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`RemoteIP6` FixedString(16),
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`IPNetworkID` UInt32,
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`SilverlightVersion3` UInt32,
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`CodeVersion` UInt32,
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`ResolutionWidth` UInt16,
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`ResolutionHeight` UInt16,
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`UserAgentMajor` UInt16,
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`UserAgentMinor` UInt16,
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`WindowClientWidth` UInt16,
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`WindowClientHeight` UInt16,
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`SilverlightVersion2` UInt8,
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`SilverlightVersion4` UInt16,
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`FlashVersion3` UInt16,
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`FlashVersion4` UInt16,
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`ClientTimeZone` Int16,
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`OS` UInt8,
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`UserAgent` UInt8,
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`ResolutionDepth` UInt8,
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`FlashMajor` UInt8,
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`FlashMinor` UInt8,
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`NetMajor` UInt8,
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`NetMinor` UInt8,
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`MobilePhone` UInt8,
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`SilverlightVersion1` UInt8,
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`Age` UInt8,
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`Sex` UInt8,
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`Income` UInt8,
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`JavaEnable` UInt8,
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`CookieEnable` UInt8,
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`JavascriptEnable` UInt8,
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`IsMobile` UInt8,
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`BrowserLanguage` UInt16,
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`BrowserCountry` UInt16,
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`Interests` UInt16,
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`Robotness` UInt8,
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`GeneralInterests` Array(UInt16),
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`Params` Array(String),
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`Goals` Nested(
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ID UInt32,
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Serial UInt32,
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EventTime DateTime,
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Price Int64,
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OrderID String,
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CurrencyID UInt32),
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`WatchIDs` Array(UInt64),
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`ParamSumPrice` Int64,
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`ParamCurrency` FixedString(3),
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`ParamCurrencyID` UInt16,
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`ClickLogID` UInt64,
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`ClickEventID` Int32,
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`ClickGoodEvent` Int32,
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`ClickEventTime` DateTime,
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`ClickPriorityID` Int32,
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`ClickPhraseID` Int32,
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`ClickPageID` Int32,
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`ClickPlaceID` Int32,
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`ClickTypeID` Int32,
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`ClickResourceID` Int32,
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`ClickCost` UInt32,
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`ClickClientIP` UInt32,
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`ClickDomainID` UInt32,
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`ClickURL` String,
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`ClickAttempt` UInt8,
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`ClickOrderID` UInt32,
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`ClickBannerID` UInt32,
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`ClickMarketCategoryID` UInt32,
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`ClickMarketPP` UInt32,
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`ClickMarketCategoryName` String,
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`ClickMarketPPName` String,
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`ClickAWAPSCampaignName` String,
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`ClickPageName` String,
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`ClickTargetType` UInt16,
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`ClickTargetPhraseID` UInt64,
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`ClickContextType` UInt8,
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`ClickSelectType` Int8,
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`ClickOptions` String,
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`ClickGroupBannerID` Int32,
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`OpenstatServiceName` String,
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`OpenstatCampaignID` String,
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`OpenstatAdID` String,
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`OpenstatSourceID` String,
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`UTMSource` String,
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`UTMMedium` String,
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`UTMCampaign` String,
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`UTMContent` String,
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`UTMTerm` String,
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`FromTag` String,
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`HasGCLID` UInt8,
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`FirstVisit` DateTime,
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`PredLastVisit` Date,
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`LastVisit` Date,
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`TotalVisits` UInt32,
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`TraficSource` Nested(
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ID Int8,
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SearchEngineID UInt16,
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AdvEngineID UInt8,
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PlaceID UInt16,
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SocialSourceNetworkID UInt8,
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Domain String,
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SearchPhrase String,
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SocialSourcePage String),
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`Attendance` FixedString(16),
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`CLID` UInt32,
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`YCLID` UInt64,
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`NormalizedRefererHash` UInt64,
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`SearchPhraseHash` UInt64,
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`RefererDomainHash` UInt64,
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`NormalizedStartURLHash` UInt64,
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`StartURLDomainHash` UInt64,
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`NormalizedEndURLHash` UInt64,
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`TopLevelDomain` UInt64,
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`URLScheme` UInt64,
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`OpenstatServiceNameHash` UInt64,
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`OpenstatCampaignIDHash` UInt64,
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`OpenstatAdIDHash` UInt64,
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`OpenstatSourceIDHash` UInt64,
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`UTMSourceHash` UInt64,
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`UTMMediumHash` UInt64,
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`UTMCampaignHash` UInt64,
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`UTMContentHash` UInt64,
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`UTMTermHash` UInt64,
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`FromHash` UInt64,
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`WebVisorEnabled` UInt8,
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`WebVisorActivity` UInt32,
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`ParsedParams` Nested(
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Key1 String,
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Key2 String,
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Key3 String,
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Key4 String,
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Key5 String,
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ValueDouble Float64),
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`Market` Nested(
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Type UInt8,
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GoalID UInt32,
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OrderID String,
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OrderPrice Int64,
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PP UInt32,
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DirectPlaceID UInt32,
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DirectOrderID UInt32,
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DirectBannerID UInt32,
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GoodID String,
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GoodName String,
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GoodQuantity Int32,
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GoodPrice Int64),
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`IslandID` FixedString(16)
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)
|
||
ENGINE = CollapsingMergeTree(Sign)
|
||
PARTITION BY toYYYYMM(StartDate)
|
||
ORDER BY (CounterID, StartDate, intHash32(UserID), VisitID)
|
||
SAMPLE BY intHash32(UserID)
|
||
SETTINGS index_granularity = 8192
|
||
```
|
||
|
||
您可以使用以下交互模式执行这些查询 `clickhouse-client` （只需在终端中启动它，而不需要提前指定查询）或尝试一些 [替代接口](../interfaces/index.md) 如果你愿意的话
|
||
|
||
正如我们所看到的, `hits_v1` 使用 [基本MergeTree引擎](../engines/table_engines/mergetree_family/mergetree.md)，而 `visits_v1` 使用 [崩溃](../engines/table_engines/mergetree_family/collapsingmergetree.md) 变体。
|
||
|
||
### 导入数据 {#import-data}
|
||
|
||
数据导入到ClickHouse是通过以下方式完成的 [INSERT INTO](../sql_reference/statements/insert_into.md) 查询像许多其他SQL数据库。 然而，数据通常是在一个提供 [支持的序列化格式](../interfaces/formats.md) 而不是 `VALUES` 子句（也支持）。
|
||
|
||
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
|
||
```
|
||
|
||
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，我们可以看到 `--max_insert_block_size`. 找出可用的设置，它们意味着什么以及默认值的最简单方法是查询 `system.settings` 表:
|
||
|
||
``` 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."
|
||
```
|
||
|
||
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，而不是稍后进行一段时间:
|
||
|
||
``` bash
|
||
clickhouse-client --query "OPTIMIZE TABLE tutorial.hits_v1 FINAL"
|
||
clickhouse-client --query "OPTIMIZE TABLE tutorial.visits_v1 FINAL"
|
||
```
|
||
|
||
这些查询开始一个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。
|
||
|
||
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. 在群集的所有计算机上安装ClickHouse服务器
|
||
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
|
||
4. 创建一个 [分布式表](../engines/table_engines/special/distributed.md)
|
||
|
||
[分布式表](../engines/table_engines/special/distributed.md) 实际上是一种 “view” 到ClickHouse集群的本地表。 从分布式表中选择查询使用集群所有分片的资源执行。 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。
|
||
|
||
具有三个分片的集群的示例配置，每个分片一个副本:
|
||
|
||
``` 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>
|
||
```
|
||
|
||
为了进一步演示，让我们创建一个新的本地表 `CREATE TABLE` 我们用于查询 `hits_v1`，但不同的表名:
|
||
|
||
``` 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());
|
||
```
|
||
|
||
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。
|
||
|
||
我们走吧 [INSERT SELECT](../sql_reference/statements/insert_into.md) 将该表传播到多个服务器。
|
||
|
||
``` sql
|
||
INSERT INTO tutorial.hits_all SELECT * FROM tutorial.hits_v1;
|
||
```
|
||
|
||
!!! warning "碌莽禄Notice:"
|
||
这种方法不适合大型表的分片。 有一个单独的工具 [ﾂ环板-ｮﾂ嘉ｯﾂ偲](../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>
|
||
```
|
||
|
||
启用本机复制 [动物园管理员](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ClickHouse正在运行）。
|
||
|
||
!!! note "注"
|
||
ZooKeeper不是一个严格的requirement：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，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，它成为您的应用程序的责任。
|
||
|
||
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}'
|
||
)
|
||
...
|
||
```
|
||
|
||
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。
|
||
|
||
``` sql
|
||
INSERT INTO tutorial.hits_replica SELECT * FROM tutorial.hits_local;
|
||
```
|
||
|
||
复制在多主机模式下运行。 数据可以加载到任何副本中，然后系统会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。 至少应有一个副本允许数据摄取。 其他人将同步数据和修复一致性，一旦他们将再次变得活跃。 请注意，这种方法允许最近插入的数据丢失的可能性很低。
|
||
|
||
[原始文章](https://clickhouse.tech/docs/en/getting_started/tutorial/) <!--hide-->
|