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* Debian: rename packages * wip * wip * wip * move limits.d/clickhouse.conf to common * wip * fix * bad clang * update changelog * more Provides: Conflicts: * clickhouse-zookeeper-cli [#CLICKHOUSE-2947] * fix utils desc * Fix docs * use cmake links * fix doc * Fix test install * Test me * Test me * Test me. * Dont use pre-depends * fix * Minimize control changes * del * debian/clickhouse-common.install -> debian/clickhouse-common-static.install * debian/clickhouse-common.install -> debian/clickhouse-common-static.install
650 lines
26 KiB
HTML
650 lines
26 KiB
HTML
<!DOCTYPE html>
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<html lang="ru">
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<head>
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<meta charset="utf-8"/>
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<title>ClickHouse Quick Start Guide</title>
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<link rel="shortcut icon" href="favicon.ico"/>
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<meta name="description" content="Quick start guide to ClickHouse — open-source distributed column-oriented DBMS"/>
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<meta name="keywords"
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content="tutorial, ClickHouse, DBMS, OLAP, relational, analytics, analytical, big data, open-source, SQL, web-analytics"/>
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<link href="index.css" media="all" rel="stylesheet" />
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</head>
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<body>
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<div class="page">
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<div>
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<div id="tutorial_logo">
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<a href="/">
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<svg xmlns="http://www.w3.org/2000/svg" width="90" height="80" viewBox="0 0 9 8">
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<path class="red" d="M0,7 h1 v1 h-1 z"></path>
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<path class="orange" d="M0,0 h1 v7 h-1 z"></path>
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<path class="orange" d="M2,0 h1 v8 h-1 z"></path>
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<path class="orange" d="M4,0 h1 v8 h-1 z"></path>
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<path class="orange" d="M6,0 h1 v8 h-1 z"></path>
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<path class="orange" d="M8,3.25 h1 v1.5 h-1 z"></path>
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</svg>
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</a>
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</div>
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<h1 id="tutorial_title">ClickHouse</h1>
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<h2 id="tutorial_subtitle">Tutorial</h2>
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</div>
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<p>Let's get started with sample dataset from open sources. We will use USA civil flights data since 1987 till 2015.
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It's hard to call this sample a Big Data (contains 166 millions rows, 63 Gb of uncompressed data) but this
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allows us to quickly get to work. Dataset is available for download <a href="https://yadi.sk/d/pOZxpa42sDdgm">here</a>.
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Also you may download it from the original datasource <a
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href="docs/en/getting_started/example_datasets/ontime/"
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rel="external nofollow">as described here</a>.</p>
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<p>Firstly we will deploy ClickHouse to a single server. Below that we will also review the process of deployment to
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a cluster with support for sharding and replication.</p>
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<p>On Ubuntu and Debian Linux ClickHouse can be installed from <a href="/#quick-start">packages</a>.
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For other Linux distributions you can <a href="docs/en/development/build/"
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rel="external nofollow">compile
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ClickHouse from sources</a> and then install.</p>
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<p><b>clickhouse-client</b> package contains <a
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href="docs/en/interfaces/cli/">clickhouse-client</a> application —
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interactive ClickHouse client. <b>clickhouse-common</b> contains a clickhouse-server binary file. <b>clickhouse-server</b>
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— contains config files for the clickhouse-server.</p>
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<p>Server config files are located in /etc/clickhouse-server/. Before getting to work please notice the <b>path</b>
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element in config. <b>Path</b> determines the location for data storage. It's not really handy to directly
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edit <b>config.xml</b> file considering package updates. Recommended way is to override the config elements in
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<a href="docs/en/operations/configuration_files/">files of config.d directory</a>.
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Also you may want to <a href="docs/en/operations/access_rights/">set up access
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rights</a> at the start.</p>
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<p><b>clickhouse-server</b> won't be launched automatically after package installation. It won't be automatically
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restarted after updates either. Start the server with:
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<pre>sudo service clickhouse-server start</pre>
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Default location for server logs is /var/log/clickhouse-server/
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Server is ready to handle client connections once "Ready for connections" message was logged.</p>
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<p>Use <b>clickhouse-client</b> to connect to the server.</p>
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<div class="spoiler"><a class="spoiler_title">Tips for clickhouse-client</a>
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<div class="spoiler_body">
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Interactive mode:
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<pre>
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clickhouse-client
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clickhouse-client --host=... --port=... --user=... --password=...
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</pre>
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Enable multiline queries:
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<pre>
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clickhouse-client -m
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clickhouse-client --multiline
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</pre>
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Run queries in batch-mode:
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<pre>
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clickhouse-client --query='SELECT 1'
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echo 'SELECT 1' | clickhouse-client
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</pre>
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Insert data from file of a specified format:
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<pre>
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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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</pre>
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</div>
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</div>
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<h3>Create table for sample dataset</h3>
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<div class="spoiler"><a class="spoiler_title">Create table query</a>
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<div class="spoiler_body">
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<pre>
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$ clickhouse-client --multiline
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ClickHouse client version 0.0.53720.
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Connecting to localhost:9000.
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Connected to ClickHouse server version 0.0.53720.
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:) CREATE TABLE ontime
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(
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Year UInt16,
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Quarter UInt8,
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Month UInt8,
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DayofMonth UInt8,
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DayOfWeek UInt8,
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FlightDate Date,
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UniqueCarrier FixedString(7),
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AirlineID Int32,
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Carrier FixedString(2),
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TailNum String,
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FlightNum String,
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OriginAirportID Int32,
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OriginAirportSeqID Int32,
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OriginCityMarketID Int32,
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Origin FixedString(5),
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OriginCityName String,
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OriginState FixedString(2),
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OriginStateFips String,
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OriginStateName String,
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OriginWac Int32,
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DestAirportID Int32,
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DestAirportSeqID Int32,
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DestCityMarketID Int32,
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Dest FixedString(5),
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DestCityName String,
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DestState FixedString(2),
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DestStateFips String,
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DestStateName String,
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DestWac Int32,
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CRSDepTime Int32,
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DepTime Int32,
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DepDelay Int32,
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DepDelayMinutes Int32,
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DepDel15 Int32,
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DepartureDelayGroups String,
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DepTimeBlk String,
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TaxiOut Int32,
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WheelsOff Int32,
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WheelsOn Int32,
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TaxiIn Int32,
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CRSArrTime Int32,
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ArrTime Int32,
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ArrDelay Int32,
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ArrDelayMinutes Int32,
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ArrDel15 Int32,
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ArrivalDelayGroups Int32,
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ArrTimeBlk String,
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Cancelled UInt8,
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CancellationCode FixedString(1),
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Diverted UInt8,
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CRSElapsedTime Int32,
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ActualElapsedTime Int32,
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AirTime Int32,
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Flights Int32,
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Distance Int32,
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DistanceGroup UInt8,
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CarrierDelay Int32,
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WeatherDelay Int32,
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NASDelay Int32,
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SecurityDelay Int32,
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LateAircraftDelay Int32,
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FirstDepTime String,
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TotalAddGTime String,
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LongestAddGTime String,
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DivAirportLandings String,
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DivReachedDest String,
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DivActualElapsedTime String,
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DivArrDelay String,
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DivDistance String,
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Div1Airport String,
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Div1AirportID Int32,
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Div1AirportSeqID Int32,
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Div1WheelsOn String,
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Div1TotalGTime String,
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Div1LongestGTime String,
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Div1WheelsOff String,
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Div1TailNum String,
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Div2Airport String,
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Div2AirportID Int32,
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Div2AirportSeqID Int32,
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Div2WheelsOn String,
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Div2TotalGTime String,
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Div2LongestGTime String,
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Div2WheelsOff String,
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Div2TailNum String,
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Div3Airport String,
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Div3AirportID Int32,
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Div3AirportSeqID Int32,
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Div3WheelsOn String,
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Div3TotalGTime String,
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Div3LongestGTime String,
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Div3WheelsOff String,
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Div3TailNum String,
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Div4Airport String,
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Div4AirportID Int32,
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Div4AirportSeqID Int32,
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Div4WheelsOn String,
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Div4TotalGTime String,
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Div4LongestGTime String,
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Div4WheelsOff String,
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Div4TailNum String,
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Div5Airport String,
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Div5AirportID Int32,
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Div5AirportSeqID Int32,
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Div5WheelsOn String,
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Div5TotalGTime String,
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Div5LongestGTime String,
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Div5WheelsOff String,
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Div5TailNum String
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)
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ENGINE = MergeTree(FlightDate, (Year, FlightDate), 8192);
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</pre>
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</div>
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</div>
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<p>Now we have a table of <a href="docs/en/table_engines/mergetree/">MergeTree type</a>.
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MergeTree table type is recommended for usage in production. Table of this kind has a primary key used for
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incremental sort of table data. This allows fast execution of queries in ranges of a primary key.</p>
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<p><b>Note</b>
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We store ad network banners impressions logs in ClickHouse. Each table entry looks like:
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[Advertiser ID, Impression ID, attribute1, attribute2, …].
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Let assume that our aim is to provide a set of reports for each advertiser. Common and frequently demanded query
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would be to count impressions for a specific Advertiser ID. This means that table primary key should start with
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Advertiser ID. In this case ClickHouse needs to read smaller amount of data to perform the query for a
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given Advertiser ID.
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</p>
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<h3>Load data</h3>
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<pre>xz -v -c -d < ontime.csv.xz | clickhouse-client --query="INSERT INTO ontime FORMAT CSV"</pre>
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<p>ClickHouse INSERT query allows to load data in any <a href="docs/en/formats/">supported
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format</a>. Data load requires just O(1) RAM consumption. INSERT query can receive any data volume as input.
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It's strongly recommended to insert data with <a
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href="docs/en/introduction/performance/#performance-when-inserting-data">not too small
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size blocks</a>. Notice that insert of blocks with size up to max_insert_block_size (= 1 048 576
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rows by default) is an atomic operation: data block will be inserted completely or not inserted at all. In case
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of disconnect during insert operation you may not know if the block was inserted successfully. To achieve
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exactly-once semantics ClickHouse supports idempotency for <a
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href="docs/en/table_engines/replication/">replicated tables</a>. This means
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that you may retry insert of the same data block (possibly on a different replicas) but this block will be
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inserted just once. Anyway in this guide we will load data from our localhost so we may not take care about data
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blocks generation and exactly-once semantics.</p>
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<p>INSERT query into tables of MergeTree type is non-blocking (so does a SELECT query). You can execute SELECT
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queries right after of during insert operation.</p>
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<p>Our sample dataset is a bit not optimal. There are two reasons.</p>
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<p>The first is that String data type is used in cases when <a
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href="docs/en/data_types/enum/">Enum</a> or numeric type would fit best.</p>
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<p class="tip"><b>⚖</b> When set of possible values is determined and known to be small. (E.g. OS name, browser
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vendors etc.) it's recommended to use Enums or numbers to improve performance.
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When set of possible values is not limited (search query, URL, etc.) just go ahead with String.</p>
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<p>The second is that dataset contains redundant fields like Year, Quarter, Month, DayOfMonth, DayOfWeek. In fact a
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single FlightDate would be enough. Most likely they have been added to improve performance for other DBMS'es
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which DateTime handling functions may be not efficient.</p>
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<p class="tip"><b>✯</b> ClickHouse <a
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href="docs/en/functions/date_time_functions/">functions
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for operating with DateTime fields</a> are well-optimized so such redundancy is not required. Anyway much
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columns is not a reason to worry — ClickHouse is a <a href="https://en.wikipedia.org/wiki/Column-oriented_DBMS"
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rel="external nofollow">column-oriented
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DBMS</a>. This allows you to have as much fields as you need. Hundreds of columns in a table is fine for
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ClickHouse.</p>
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<h3>Querying the sample dataset</h3>
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<p>Here are some examples of the queries from our test data.</p>
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<ul>
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<li>
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<div class="spoiler"><a class="spoiler_title">the most popular destinations in 2015;</a>
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<div class="spoiler_body">
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<pre>
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SELECT
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OriginCityName,
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DestCityName,
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count(*) AS flights,
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bar(flights, 0, 20000, 40)
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FROM ontime WHERE Year = 2015 GROUP BY OriginCityName, DestCityName ORDER BY flights DESC LIMIT 20
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</pre>
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<img src="images/tutorial/a8518a200d6d405a95ee80ea1c8e1c90.png"/>
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<pre>
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SELECT
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OriginCityName < DestCityName ? OriginCityName : DestCityName AS a,
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OriginCityName < DestCityName ? DestCityName : OriginCityName AS b,
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count(*) AS flights,
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bar(flights, 0, 40000, 40)
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FROM ontime WHERE Year = 2015 GROUP BY a, b ORDER BY flights DESC LIMIT 20
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</pre>
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<img src="images/tutorial/d3578db55e304bd7b5eba818abdb53f5.png"/></div>
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</div>
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</li>
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<li>
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<div class="spoiler"><a class="spoiler_title">the most popular cities of departure;</a>
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<div class="spoiler_body">
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<pre>
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SELECT OriginCityName, count(*) AS flights
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FROM ontime GROUP BY OriginCityName ORDER BY flights DESC LIMIT 20
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</pre>
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<img src="images/tutorial/ef4141f348234773a5349c4bd3e8f804.png"/></div>
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</div>
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</li>
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<li>
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<div class="spoiler"><a class="spoiler_title">cities of departure which offer maximum variety of
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destinations;</a>
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<div class="spoiler_body">
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<pre>
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SELECT OriginCityName, uniq(Dest) AS u
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FROM ontime GROUP BY OriginCityName ORDER BY u DESC LIMIT 20
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</pre>
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<img src="images/tutorial/2409f49d11fb4aa1b8b5ff34cf9ca75d.png"/></div>
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</div>
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</li>
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<li>
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<div class="spoiler"><a class="spoiler_title">flight delay dependence on the day of week;</a>
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<div class="spoiler_body">
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<pre>
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SELECT DayOfWeek, count() AS c, avg(DepDelay > 60) AS delays
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FROM ontime GROUP BY DayOfWeek ORDER BY DayOfWeek
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</pre>
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<img src="images/tutorial/885e507930e34b7c8f788d25e7ca2bcf.png"/></div>
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</div>
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</li>
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<li>
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<div class="spoiler"><a class="spoiler_title">cities of departure with most frequent delays for 1 hour or
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longer;</a>
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<div class="spoiler_body">
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<pre>
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SELECT OriginCityName, count() AS c, avg(DepDelay > 60) AS delays
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FROM ontime
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GROUP BY OriginCityName
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HAVING c > 100000
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ORDER BY delays DESC
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LIMIT 20
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</pre>
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<img src="images/tutorial/ac292656d03946d0aba35c75783a31f2.png"/></div>
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</div>
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</li>
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<li>
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<div class="spoiler"><a class="spoiler_title">flights of maximum duration;</a>
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<div class="spoiler_body">
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<pre>
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SELECT OriginCityName, DestCityName, count(*) AS flights, avg(AirTime) AS duration
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FROM ontime
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GROUP BY OriginCityName, DestCityName
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ORDER BY duration DESC
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LIMIT 20
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</pre>
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<img src="images/tutorial/7b3c2e685832439b8c373bf2015131d2.png"/></div>
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</div>
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</li>
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<li>
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<div class="spoiler"><a class="spoiler_title">distribution of arrival time delays split by aircompanies;</a>
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<div class="spoiler_body">
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<pre>
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SELECT Carrier, count() AS c, round(quantileTDigest(0.99)(DepDelay), 2) AS q
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FROM ontime GROUP BY Carrier ORDER BY q DESC
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</pre>
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<img src="images/tutorial/49c332e3d93146ba8f46beef6b2b02b0.png"/></div>
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</div>
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</li>
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<li>
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<div class="spoiler"><a class="spoiler_title">aircompanies who stopped flights operation;</a>
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<div class="spoiler_body">
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<pre>
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SELECT Carrier, min(Year), max(Year), count()
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FROM ontime GROUP BY Carrier HAVING max(Year) < 2015 ORDER BY count() DESC
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</pre>
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<img src="images/tutorial/24956f1a2efc48d78212586958aa036c.png"/></div>
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</div>
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</li>
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<li>
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<div class="spoiler"><a class="spoiler_title">most trending destination cities in 2015;</a>
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<div class="spoiler_body">
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<pre>
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SELECT
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DestCityName,
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sum(Year = 2014) AS c2014,
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sum(Year = 2015) AS c2015,
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c2015 / c2014 AS diff
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FROM ontime
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WHERE Year IN (2014, 2015)
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GROUP BY DestCityName
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HAVING c2014 > 10000 AND c2015 > 1000 AND diff > 1
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ORDER BY diff DESC
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</pre>
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<img src="images/tutorial/f3132f4d1c0d42eab26d9111afe7771a.png"/></div>
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</div>
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</li>
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<li>
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<div class="spoiler"><a class="spoiler_title">destination cities with maximum popularity-season
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dependency.</a>
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<div class="spoiler_body">
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<pre>
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SELECT
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DestCityName,
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any(total),
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avg(abs(monthly * 12 - total) / total) AS avg_month_diff
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FROM
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(
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SELECT DestCityName, count() AS total
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FROM ontime GROUP BY DestCityName HAVING total > 100000
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)
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ALL INNER JOIN
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(
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SELECT DestCityName, Month, count() AS monthly
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FROM ontime GROUP BY DestCityName, Month HAVING monthly > 10000
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)
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USING DestCityName
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GROUP BY DestCityName
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ORDER BY avg_month_diff DESC
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LIMIT 20
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</pre>
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<img src="images/tutorial/26b2c7aae21a4c76800cb1c7a33a374d.png"/></div>
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</div>
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</li>
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</ul>
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<h3>ClickHouse deployment to cluster</h3>
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<p>ClickHouse cluster is a homogenous cluster. Steps to set up:
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<ol>
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<li>Install ClickHouse server on all machines of the cluster</li>
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<li>Set up cluster configs in configuration file</li>
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<li>Create local tables on each instance</li>
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<li>Create a <a href="docs/en/table_engines/distributed/">Distributed table</a></li>
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</ol>
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</p>
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<p><a href="docs/en/table_engines/distributed/">Distributed-table</a> is actually a kind of
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"view" to local tables of ClickHouse cluster. SELECT query from a distributed table will be executed using
|
||
resources of all cluster's shards. You may specify configs for multiple clusters and create multiple
|
||
Distributed-tables providing views to different clusters.</p>
|
||
|
||
<div class="spoiler"><a class="spoiler_title">Config for cluster of three shards. Each shard stores data on a single
|
||
replica</a>
|
||
<div class="spoiler_body">
|
||
<pre>
|
||
<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>
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
Creating a local table:
|
||
<pre>CREATE TABLE ontime_local (...) ENGINE = MergeTree(FlightDate, (Year, FlightDate), 8192);</pre>
|
||
Creating a distributed table providing a view into local tables of the cluster:
|
||
<pre>CREATE TABLE ontime_all AS ontime_local
|
||
ENGINE = Distributed(perftest_3shards_1replicas, default, ontime_local, rand());</pre>
|
||
|
||
<p>You can create a Distributed table on all machines in the cluster. This would allow to run distributed queries on
|
||
any machine of the cluster. Besides distributed table you can also use <a
|
||
href="docs/en/table_functions/remote/">*remote* table function</a>.</p>
|
||
|
||
<p>Let's run <a href="docs/en/query_language/queries/#insert">INSERT SELECT</a> into Distributed table
|
||
to spread the table to multiple servers.</p>
|
||
|
||
<pre>INSERT INTO ontime_all SELECT * FROM ontime;</pre>
|
||
|
||
<p class="tip"><b>⚠</b> Worth to notice that the approach given above wouldn't fit for sharding of large
|
||
tables.</p>
|
||
|
||
<p>As you could expect heavy queries are executed N times faster being launched on 3 servers instead of one.</p>
|
||
<div class="spoiler"><a class="spoiler_title">See here</a>
|
||
<div class="spoiler_body">
|
||
<img src="images/tutorial/ece020129fdf4a18a6e75daf2e699cb9.png"/>
|
||
|
||
<p>You may have noticed that quantiles calculation are slightly different. This happens due to <a
|
||
href="https://github.com/tdunning/t-digest/raw/master/docs/t-digest-paper/histo.pdf">t-digest</a>
|
||
algorithm implementation which is non-deterministic — it depends on the order of data processing.</p>
|
||
</div>
|
||
</div>
|
||
|
||
<p>In this case we have used a cluster with 3 shards each contains a single replica.</p>
|
||
|
||
<p>To provide for resilience in production environment we recommend that each shard should contain 2-3 replicas
|
||
distributed between multiple data-centers. Note that ClickHouse supports unlimited number of replicas.</p>
|
||
|
||
<div class="spoiler"><a class="spoiler_title">Config for cluster of one shard containing three replicas</a>
|
||
<div class="spoiler_body">
|
||
<pre>
|
||
<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>
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
|
||
<p>To enable replication <a href="http://zookeeper.apache.org/" rel="external nofollow">ZooKeeper</a> is required.
|
||
ClickHouse will take care of data consistency on all replicas and run restore procedure after failure
|
||
automatically. It's recommended to deploy ZooKeeper cluster to separate servers.</p>
|
||
|
||
<p>ZooKeeper is not a requirement — in some simple cases you can duplicate the data by writing it into all the
|
||
replicas from your application code. This approach is not recommended — in this case ClickHouse is not able to
|
||
guarantee data consistency on all replicas. This remains the responsibility of your application.</p>
|
||
|
||
<div class="spoiler"><a class="spoiler_title">Set ZooKeeper locations in configuration file</a>
|
||
<div class="spoiler_body">
|
||
<pre>
|
||
<zookeeper-servers>
|
||
<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-servers>
|
||
</pre>
|
||
</div>
|
||
</div>
|
||
|
||
<p>Also we need to set macros for identifying shard and replica — it will be used on table creation</p>
|
||
<pre>
|
||
<macros>
|
||
<shard>01</shard>
|
||
<replica>01</replica>
|
||
</macros>
|
||
</pre>
|
||
<p>If there are no replicas at the moment on replicated table creation — a new first replica will be instantiated.
|
||
If there are already live replicas — new replica will clone the data from existing ones. You have an option to
|
||
create all replicated tables first and that insert data to it. Another option is to create some replicas and add
|
||
the others after or during data insertion.</p>
|
||
|
||
<pre>
|
||
CREATE TABLE ontime_replica (...)
|
||
ENGINE = ReplicatedMergeTree(
|
||
'/clickhouse_perftest/tables/{shard}/ontime',
|
||
'{replica}',
|
||
FlightDate,
|
||
(Year, FlightDate),
|
||
8192);
|
||
</pre>
|
||
<p>Here we use <a href="docs/en/table_engines/replication/#replicatedmergetree">ReplicatedMergeTree</a>
|
||
table type. In parameters we specify ZooKeeper path containing shard and replica identifiers.</p>
|
||
|
||
<pre>INSERT INTO ontime_replica SELECT * FROM ontime;</pre>
|
||
<p>Replication operates in multi-master mode. Data can be loaded into any replica — it will be synced with other
|
||
instances automatically. Replication is asynchronous so at a given moment of time not all replicas may contain
|
||
recently inserted data. To allow data insertion at least one replica should be up. Others will sync up data and
|
||
repair consistency once they will become active again. Please notice that such scheme allows for the possibility
|
||
of just appended data loss.</p>
|
||
|
||
<p class="warranty"><a href="https://github.com/yandex/ClickHouse/blob/master/LICENSE"
|
||
rel="external nofollow" target="_blank">
|
||
ClickHouse source code is published under Apache 2.0 License.</a> Software is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
|
||
KIND, either express or implied.</p>
|
||
|
||
<p id="footer">© 2016–2018 <a href="https://yandex.com/company/" rel="external nofollow">YANDEX</a> LLC</p>
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