ClickHouse介绍（一）初次使用

ClickHouse使用

ClickHouse是一个面向列存储的OLAP分析数据库，以其强大的分析速度而闻名。有关ClickHouse的介绍可以参考其官网说明[1]。本文主要介绍它的基本使用。

1. 安装

使用的环境为2台 AWS EC2，操作系统为Amazon Linux2。使用的ClickHouse为最新的stable版本v21.2.5.5-stable [2]。

export LATEST_VERSION=21.2.5.5

curl -O https://repo.clickhouse.tech/tgz/stable/clickhouse-common-static-$LATEST_VERSION.tgz
curl -O https://repo.clickhouse.tech/tgz/stable/clickhouse-common-static-dbg-$LATEST_VERSION.tgz
curl -O https://repo.clickhouse.tech/tgz/stable/clickhouse-server-$LATEST_VERSION.tgz
curl -O https://repo.clickhouse.tech/tgz/stable/clickhouse-client-$LATEST_VERSION.tgz

tar -xzvf clickhouse-common-static-$LATEST_VERSION.tgz
sudo clickhouse-common-static-$LATEST_VERSION/install/doinst.sh

tar -xzvf clickhouse-common-static-dbg-$LATEST_VERSION.tgz
sudo clickhouse-common-static-dbg-$LATEST_VERSION/install/doinst.sh

tar -xzvf clickhouse-server-$LATEST_VERSION.tgz
sudo clickhouse-server-$LATEST_VERSION/install/doinst.sh
sudo /etc/init.d/clickhouse-server start

tar -xzvf clickhouse-client-$LATEST_VERSION.tgz
sudo clickhouse-client-$LATEST_VERSION/install/doinst.sh

2. 初次使用

2.1. 数据

使用官网提供的数据：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：

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

# 上传到s3
aws s3 sync ./ s3://xxx-clickhouse/data/

2.2. 建表

与其他数据库一样，clickhouse也自带一个default数据库。这里先创建一个tutorial数据库：

ip-10-0-4-69.cn-north-1.compute.internal :) create database if not exists tutorial

> CREATE DATABASE IF NOT EXISTS tutorial

建表语句必须指定3个关键事情：

  1. 表名
  2. 表结构：列名以及对应数据类型
  3. 表引擎及其设置：决定了对此表的查询操作是如何在物理层执行的所有细节

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2个表可以创建：

  1. 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
  2. visits表：包含预先构建的会话，而不是单个操作

建表语句：

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)


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)

可以看到，hits_v1 使用的是MergeTree引擎；visits_v1使用的是Collapsing引擎。两者的partition格式均为toYYYYMM(EventDate)。

2.3. 导入数据并查询

导入本地数据：

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

优化表：

OPTIMIZE TABLE tutorial.hits_v1 FINAL
OPTIMIZE TABLE tutorial.visits_v1 FINAL

示例查询：

SELECT
    StartURL AS URL,
    AVG(Duration) AS AvgDuration
FROM tutorial.visits_v1
WHERE (StartDate >= '2014-03-23') AND (StartDate <= '2014-03-30')
GROUP BY URL
ORDER BY AvgDuration DESC
LIMIT 10

10 rows in set. Elapsed: 0.088 sec. Processed 1.45 million rows, 114.85 MB (16.56 million rows/s., 1.31 GB/s.)
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')

1 rows in set. Elapsed: 0.012 sec. Processed 13.05 thousand rows, 2.88 MB (1.10 million rows/s., 242.38 MB/s.)

从返回速度来看，基本上是立即返回，处理时间仅用 0.088 和 0.012 秒。

3. 为什么ClickHouse如此快

从各种公开文档来看，ClickHouse如此之快的原因主要有2点：

  1. 列式存储数据库
  2. 使用向量化引擎

3.1. 列式存储

列式存储与行式存储的区别已经有大量公开文档进行详细说明，在此不再赘述。简单来说，列式存储的优势在于：

  1. 只提取所需要的列的信息，避免了扫描不需要的其他列信息
  2. 对数据压缩的友好型：因为同一列拥有同样的数据类型和现实语义，重复项的可能性更高

这两点优势提供的是：

  1. 减少了数据扫描范围：有效减少了所需扫描的数据量
  2. 减少了数据传输的大小：数据压缩率越高，则数据体量越小，在网络中传输的数据量更少，所以对网络带宽和磁盘IO的压力也就越小，速度也就越快。

3.2. 向量化执行

向量化执行是合理利用CPU指令集的方式，它的必备条件是CPU支持SIMD（Single Instruction Multiple Data）指令，此指令的作用是：单条指定一次性操作多条数据。

在Stack Overflow[3] 上对此有一个较为具体的说明：

许多CPU都有“vector”或“SIMD”指令集，可以将同一个操作同时应用到2条、4条或是更多的数据条目上。向量化（vectorization）就是重写循环的操作，在一个循环中（例如while循环），一个长度为N的数组需要循环N次才能处理完。若是使用向量化操作，假设它一次能够处理4条数据，则对于长度为N的数据，仅需要N/4的时间即能处理完毕。

更具体的例子是，假设有以下循环语句：

for (int i=0; i<16; ++i)
    C[i] = A[i] + B[i];

传统处理方式是：一次循环处理A[i] 与 B[i] 相加，并赋值给C[i]。

此循环可以继续展开为：

for (int i=0; i<16; i+=4) {
    C[i]   = A[i]   + B[i];
    C[i+1] = A[i+1] + B[i+1];
    C[i+2] = A[i+2] + B[i+2];
    C[i+3] = A[i+3] + B[i+3];
}

对此使用向量化操作，则可以表示为：

for (int i=0; i<16; i+=4)
    addFourThingsAtOnceAndStoreResult(&C[i], &A[i], &B[i]);

此处addFourThingsAtOnceAndStoreResult() 为一个向量化操作，可以在一次循环中，同时处理4条数据。若是大家有了解过python 中 numpy的向量化操作，相信对此会有更深的了解。

3.3. 持续优化

根据朱凯[4] 在其书中提到的观点，ClickHouse如此之快的原因还包含：

  1. 开发人员注意到各种影响性能的细节，并进行优化，一点一滴的积累，使得性能越来越好
  2. 针对不同场景使用了最优的算法，使性能最优化
  3. 若是出现了更合适、更快的算法，开发人员会立即进行验证，若是效果理想则保留使用，否则将其抛弃
  4. ClickHouse更新迭代非常频繁，开发人员一直在对此进行不断的改进，追求更佳的性能

References

[1] https://clickhouse.tech/docs/zh/

[2] https://github.com/ClickHouse/ClickHouse/releases/tag/v21.1.8.30-stable

[3] https://stackoverflow.com/questions/1422149/what-is-vectorization

[4] 朱凯，ClickHouse原理解析与应用实践，机器工业出版社，2020年

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原文地址：https://www.cnblogs.com/zackstang/p/14660471.html