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：
- 表名
- 表结构：列名以及对应数据类型
- 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：
- 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
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：
- 列式存储数据库
- 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：
- 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
- 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：
- 减少了数据扫描范围：有效减少了所需扫描的数据量
- 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：
- 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
- 针对不同场景使用了最优的算法，使性能最优化
- 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
- 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://*.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