2017-04-10 14:24:53 +00:00
<!DOCTYPE html>
< html lang = "en" >
< head >
< meta charset = "utf-8" / >
< meta http-equiv = "X-UA-Compatible" content = "IE=edge" / >
< title > ClickHouse — open source distributed column-oriented DBMS< / title >
< link rel = "shortcut icon" href = "favicon.ico" / >
< meta property = "og:title" content = "ClickHouse DBMS" / >
< meta property = "og:description"
content="ClickHouse is an open source column-oriented database management system that allows generating analytical data reports in real time using SQL queries."/>
< meta property = "og:type" content = "website" / >
< meta property = "og:url" content = "https://clickhouse.yandex" / >
2017-06-08 14:43:12 +00:00
< meta property = "og:image" content = "https://clickhouse.yandex/images/logo.png" / >
2017-04-10 14:24:53 +00:00
< meta property = "twitter:title" content = "ClickHouse DBMS" / >
< meta name = "description"
content="ClickHouse is an open source distributed column-oriented database management system that allows generating analytical data reports in real time using SQL queries. С
< meta name = "keywords"
content="ClickHouse, DBMS, OLAP, relational, analytics, analytical, big data, open-source, SQL, web-analytics" />
2017-06-08 14:43:12 +00:00
< link href = "index.css" media = "all" rel = "stylesheet" / >
2017-04-10 14:24:53 +00:00
< / head >
< body >
< div id = "navbar" >
< div id = "navbar-inner" >
< div id = "top-menu" class = "desktop-only" >
< a class = "menu_item" href = "#quick-start" > Quick Start< / a >
< a class = "menu_item" href = "#performance" > Performance< / a >
2017-05-30 09:45:54 +00:00
< a class = "menu_item" href = "docs/en/" > Documentation< / a >
2017-04-10 14:24:53 +00:00
< a class = "menu_item" href = "#contacts" > Contacts< / a >
< / div >
< a id = "logo" href = "#" >
< h1 id = "main-title" >
< svg id = "title-logo" xmlns = "http://www.w3.org/2000/svg" width = "48" height = "44" viewBox = "0 0 9 8" >
< path class = "red" d = "M0,7 h1 v1 h-1 z" > < / path >
< path class = "orange" d = "M0,0 h1 v7 h-1 z" > < / path >
< path class = "orange" d = "M2,0 h1 v8 h-1 z" > < / path >
< path class = "orange" d = "M4,0 h1 v8 h-1 z" > < / path >
< path class = "orange" d = "M6,0 h1 v8 h-1 z" > < / path >
< path class = "orange" d = "M8,3.25 h1 v1.5 h-1 z" > < / path >
< / svg >
ClickHouse
< / h1 >
< / a >
< / div >
< / div >
< div id = "hero" >
< div class = "page" >
< div class = "block-70" >
< p id = "short-description" > ClickHouse is an < span class = "orange" > open source< / span > column-oriented
database management system
capable of < span class = "orange" > real time< / span > generation of analytical data reports using < span
class="orange">SQL< / span > queries.< / p >
< a id = "call_to_action" href = "#quick-start" >
Quick Start
< / a >
< / div >
< div class = "block-30" >
< ul id = "index_ul" class = "dashed" >
< li >
< a class = "index_item" href = "#blazing-fast" > Blazing Fast< / a >
< / li >
< li >
< a class = "index_item" href = "#linearly-scalable" > Linearly Scalable< / a >
< / li >
< li >
< a class = "index_item" href = "#hardware-efficient" > Hardware Efficient< / a >
< / li >
< li >
< a class = "index_item" href = "#fault-tolerant" > Fault Tolerant< / a >
< / li >
< li >
2017-06-08 14:08:08 +00:00
< a class = "index_item" href = "#key-features" > Feature Rich< / a >
2017-04-10 14:24:53 +00:00
< / li >
< li >
2017-06-08 14:08:08 +00:00
< a class = "index_item" href = "#highly-reliable" > Highly Reliable< / a >
2017-04-10 14:24:53 +00:00
< / li >
< li >
2017-06-08 14:08:08 +00:00
< a class = "index_item" href = "#simple-and-handy" > Simple and Handy< / a >
2017-04-10 14:24:53 +00:00
< / li >
< / ul >
< / div >
< div class = "clear" > < / div >
< / div >
< / div >
< div class = "page" >
< h2 id = "slogan" > ClickHouse. Just makes you think faster.< / h2 >
< div class = "block-70" >
< ul class = "dashed" >
< li > Run more queries in the same amount of time< / li >
< li > Test more hypotheses< / li >
< li > Slice and dice your data in many more new ways< / li >
< li > Look at your data from new angles< / li >
< li > Discover new dimensions< / li >
< / ul >
< / div >
< div class = "block-30" >
< svg id = "placeholder" class = "desktop-only" viewBox = "0 0 76 76" xmlns = "http://www.w3.org/2000/svg" >
< defs >
< rect id = "path-1" x = "0" y = "16" width = "60" height = "60" rx = "1" > < / rect >
< mask id = "mask-2" maskContentUnits = "userSpaceOnUse" maskUnits = "objectBoundingBox" x = "0" y = "0" width = "60" height = "60" fill = "white" >
< use xlink:href = "#path-1" > < / use >
< / mask >
< rect id = "path-3" x = "16" y = "0" width = "60" height = "60" rx = "1" > < / rect >
< mask id = "mask-4" maskContentUnits = "userSpaceOnUse" maskUnits = "objectBoundingBox" x = "0" y = "0" width = "60" height = "60" fill = "white" >
< use xlink:href = "#path-3" > < / use >
< / mask >
< rect id = "path-5" x = "0" y = "8" width = "20" height = "20" rx = "1" > < / rect >
< mask id = "mask-6" maskContentUnits = "userSpaceOnUse" maskUnits = "objectBoundingBox" x = "0" y = "0" width = "20" height = "20" fill = "white" >
< use xlink:href = "#path-5" > < / use >
< / mask >
< rect id = "path-7" x = "8" y = "0" width = "20" height = "20" rx = "1" > < / rect >
< mask id = "mask-8" maskContentUnits = "userSpaceOnUse" maskUnits = "objectBoundingBox" x = "0" y = "0" width = "20" height = "20" fill = "white" >
< use xlink:href = "#path-7" > < / use >
< / mask >
< / defs >
< g id = "Page-1" stroke = "none" stroke-width = "1" fill = "none" fill-rule = "evenodd" stroke-linecap = "round" >
< g id = "Clickhouse_2" transform = "translate(-558.000000, -1293.000000)" >
< g id = "Group-11" transform = "translate(558.000000, 1293.000000)" >
< use id = "Rectangle-33" stroke = "#FFCC00" mask = "url(#mask-2)" stroke-width = "4" xlink:href = "#path-1" > < / use >
< use id = "Rectangle-33" stroke = "#FFCC00" mask = "url(#mask-4)" stroke-width = "4" xlink:href = "#path-3" > < / use >
< path d = "M0.989013672,17.017334 L16.8210449,1.16748047" id = "Path-26" stroke = "#FFCC00" stroke-width = "2" > < / path >
< path d = "M59.0788574,74.9973145 L74.7983398,59.2650146" id = "Path-26" stroke = "#FFCC00" stroke-width = "2" > < / path >
< path d = "M59.1091309,17.1687012 L74.9368896,1.10351562" id = "Path-26-Copy" stroke = "#FFCC00" stroke-width = "2" > < / path >
< path d = "M1.07910156,17.2504883 L26.0395508,33.4033203" id = "Path-26" stroke = "#FFCC00" stroke-width = "2" > < / path >
< path d = "M17.2602539,1.18457031 L34.0175781,25.1796875" id = "Path-26" stroke = "#FFCC00" stroke-width = "2" > < / path >
< path d = "M51.2958984,25.4736328 L58.8277588,17" id = "Path-26-Copy" stroke = "#FFCC00" stroke-width = "2" > < / path >
< path d = "M1.01904297,50.942627 L25.9216309,75.064209" id = "Path-26" stroke = "#FFCC00" stroke-width = "2" transform = "translate(13.470337, 63.003418) scale(-1, 1) translate(-13.470337, -63.003418) " > < / path >
< path d = "M44.1804199,51.300293 L58.9638672,75.010498" id = "Path-26" stroke = "#FFCC00" stroke-width = "2" > < / path >
< path d = "M52.0131836,43.1345215 L75.0227051,58.9299316" id = "Path-26" stroke = "#FFCC00" stroke-width = "2" > < / path >
< g id = "Group-3" transform = "translate(25.000000, 24.000000)" stroke = "#444444" >
< use id = "Rectangle-33" mask = "url(#mask-6)" stroke-width = "4" xlink:href = "#path-5" > < / use >
< use id = "Rectangle-33" mask = "url(#mask-8)" stroke-width = "4" xlink:href = "#path-7" > < / use >
< path d = "M19.2587891,1.08825684 L26.7729492,8.8046875" id = "Path-26" stroke-width = "2" transform = "translate(23.015869, 4.946472) scale(-1, 1) translate(-23.015869, -4.946472) " > < / path >
< path d = "M1.05773926,1.04125977 L8.82080078,8.9654541" id = "Path-26" stroke-width = "2" transform = "translate(4.939270, 5.003357) scale(-1, 1) translate(-4.939270, -5.003357) " > < / path >
< path d = "M1.12487793,18.887207 L9.26220703,26.8897705" id = "Path-26" stroke-width = "2" transform = "translate(5.193542, 22.888489) scale(-1, 1) translate(-5.193542, -22.888489) " > < / path >
< path d = "M19.038208,19.1968994 L26.9085693,26.9760742" id = "Path-26" stroke-width = "2" transform = "translate(22.973389, 23.086487) scale(-1, 1) translate(-22.973389, -23.086487) " > < / path >
< / g >
< / g >
< / g >
< / g >
< / svg >
< / div >
< div class = "clear" > < / div >
< h2 id = "blazing-fast" > Blazing Fast< / h2 >
< p > ClickHouse's performance < a href = "benchmark.html" > exceeds< / a > comparable column-oriented DBMS currently available
on the market processes hundreds of millions to more than a billion rows and tens of gigabytes of data
per single server per second.< / p >
2017-06-08 15:54:09 +00:00
< p > ClickHouse uses all available hardware to it's full potential to process each query as fast as possible. The peak
processing performance for a single query < span class = "grey" > (after decompression, only used columns)< / span >
stands at more than 2 terabytes per second.< / p >
2017-04-10 14:24:53 +00:00
< / div >
< div id = "performance" class = "colored-block" >
< div class = "page" >
< h2 > ClickHouse works 100-1,000x faster than traditional approaches< / h2 >
2017-06-08 15:54:09 +00:00
< p > In contrast to common data management methods, where vast amounts of raw data in its native format are available as
a "data lake" for any given query,
ClickHouse offers instant results in most cases: the data is processed faster than it takes
to create a query. Follow the link below to see detailed benchmarks by Yandex of ClickHouse in comparison
with other database management systems. Also there are some links on third-party benchmarks in the following section.< / p >
2017-04-10 14:24:53 +00:00
< a id = "benchmark_learn_more" href = "benchmark.html" >
Learn more
< / a >
< div class = "clear" > < / div >
< / div >
< / div >
< div class = "page" >
2017-06-08 14:08:08 +00:00
< h2 > Independent Benchmarks< / h2 >
< ul class = "dashed" >
< li > < a href = "https://www.percona.com/blog/2017/02/13/clickhouse-new-opensource-columnar-database/"
rel="external nofollow">ClickHouse: New Open Source Columnar Database< / a > by Percona< / li >
< li > < a href = "https://www.percona.com/blog/2017/03/17/column-store-database-benchmarks-mariadb-columnstore-vs-clickhouse-vs-apache-spark/"
title="MariaDB ColumnStore vs. Clickhouse vs. Apache Spark"
rel="external nofollow">Column Store Database Benchmarks< / a > by Percona< / li >
< li > < a href = "http://tech.marksblogg.com/billion-nyc-taxi-clickhouse.html"
rel="external nofollow">1.1 Billion Taxi Rides on ClickHouse & an Intel Core i5< / a > by Mark Litwintschik< / li >
< li > < a href = "https://carto.com/blog/inside/geospatial-processing-with-clickhouse"
rel="external nofollow">Geospatial processing with Clickhouse< / a > by Carto< / li >
2017-06-08 17:04:47 +00:00
< li > < a href = "https://translate.yandex.com/translate?url=http%3A%2F%2Fverynull.com%2F2016%2F08%2F22%2Finfinidb%E4%B8%8Eclickhouse%E5%AF%B9%E6%AF%94%2F&lang=zh-en"
rel="external nofollow">ClickHouse and InfiniDB comparison< / a > by RamboLau < span class = "grey" > (machine translation from Chinese)< / span > < / li >
2017-06-08 14:08:08 +00:00
< / ul >
2017-04-10 14:24:53 +00:00
< h2 id = "linearly-scalable" > Linearly Scalable< / h2 >
< p > ClickHouse allows companies to add servers to their clusters when necessary without investing time or money into
2017-06-08 15:54:09 +00:00
any additional DBMS modification. The system has been successfully serving
< a href = "https://metrica.yandex.com/" rel = "external nofollow" > Yandex.Metrica< / a > ,
while the count of servers in it's main production cluster have grown from 60 to 394 in two years,
which are by the way located in six geographically distributed datacenters.< / p >
2017-04-10 14:24:53 +00:00
2017-06-08 15:54:09 +00:00
< p > ClickHouse scales well both vertically and horizontally. ClickHouse is easily adaptable to perform either on
cluster with hundreds of nodes, or on a single server or even on a tiny virtual machine. Currently there are
installations with more than two trillion rows per single node,
as well as installations with 100Tb of storage per single node.< / p >
2017-04-10 14:24:53 +00:00
< h2 id = "hardware-efficient" > Hardware Efficient< / h2 >
< p > ClickHouse processes typical analytical queries two to three orders of magnitude faster
2017-06-08 15:54:09 +00:00
row-oriented systems with the same available I/O throughput. The system's columnar storage format allows fitting
more hot data in RAM, which leads to a shorter response times.< / p >
2017-04-10 14:24:53 +00:00
2017-06-08 15:54:09 +00:00
< p > ClickHouse allows to minimize the number of seeks for range queries, which increases efficiency of using rotational
disk drives, as it maintains locality of reference for continually stored data.< / p >
2017-04-10 14:24:53 +00:00
2017-06-08 15:54:09 +00:00
< p > ClickHouse is CPU efficient because of it's vectorized query execution involving relevant processor instructions
and runtime code generation.< / p >
2017-04-10 14:24:53 +00:00
< p > By minimizing data transfers for most types of queries, ClickHouse enables companies to manage their data and
2017-06-08 15:54:09 +00:00
create reports without using specialized networks that are aimed at high-performance computing.< / p >
2017-06-08 14:08:08 +00:00
< h2 id = "fault-tolerant" > Fault Tolerant< / h2 >
< p > ClickHouse supports multi-master asynchronous replication and can be deployed across multiple datacenters.
2017-06-08 15:54:09 +00:00
Downtime of a single node or the whole datacenter won't affect the system's availability for both reads and
2017-06-08 14:08:08 +00:00
writes.
2017-06-08 15:54:09 +00:00
Distributed reads are automatically balanced to live replicas to avoid increasing latency. Replicated data
are synchronized automatically or semi-automatically after server downtime.< / p >
2017-06-08 14:08:08 +00:00
< / div >
2017-04-10 14:24:53 +00:00
< / div >
2017-06-08 14:08:08 +00:00
2017-04-10 14:24:53 +00:00
< div id = "grey-block" class = "colored-block" >
< div class = "page" >
2017-06-08 14:08:08 +00:00
< h2 id = "key-features" > Key Features< / h2 >
< div class = "block-50" >
< ul class = "dashed" >
< li > True column-oriented storage< / li >
< li > Vectorized query execution< / li >
< li > Data compression< / li >
< li > Parallel and distributed query execution< / li >
< li > Real time query processing< / li >
< li > Real time data ingestion< / li >
< li > On-disk locality of reference< / li >
< li > Cross-datacenter replication< / li >
< li > High availability< / li >
< li > SQL support< / li >
< / ul >
< / div >
< div class = "block-50" >
< ul class = "dashed" >
< li > Local and distributed joins< / li >
< li > Pluggable external dimension tables< / li >
< li > Arrays and nested data types< / li >
< li > Approximate query processing< / li >
< li > Probabilistic data structures< / li >
< li > Full support of IPv6< / li >
< li > Features for web analytics< / li >
< li > State-of-the-art algorithms< / li >
< li > Detailed documentation< / li >
< li > Clean documented code< / li >
< / ul >
< / div >
< div class = "clear" > < / div >
2017-04-10 14:24:53 +00:00
< / div >
< / div >
< div class = "page" >
< h2 id = "feature-rich" > Feature Rich< / h2 >
2017-06-08 15:54:09 +00:00
< p > ClickHouse features a user-friendly SQL query dialect with a number of built-in analytics capabilities.
For example, it includes probabilistic data
structures for fast and memory-efficient calculation of cardinalities and quantiles. There are functions for
working dates, times and time zones, as well as some specialized ones like addressing URLs and IPs
(both IPv4 and IPv6) and many more.< / p >
2017-04-10 14:24:53 +00:00
2017-06-08 15:54:09 +00:00
< p > Data organizing options available in ClickHouse, such as arrays, array joins, tuples and nested data structures, are
2017-04-10 14:24:53 +00:00
extremely efficient for managing denormalized data.< / p >
< p > Using ClickHouse allows joining both distributed data and co-located data, as the system supports local joins and
distributed joins. It also offers an opportunity to use external dictionaries, dimension tables loaded from
2017-06-08 15:54:09 +00:00
an external source, for seamless joins with simple syntax.< / p >
2017-04-10 14:24:53 +00:00
< p > ClickHouse supports approximate query processing –
indispensable when dealing with terabytes and petabytes of data.< / p >
2017-06-08 15:54:09 +00:00
< p > The system's conditional aggregate functions, calculation of totals and extremes, allow getting results with a
2017-04-10 14:24:53 +00:00
single query without having to run a number of them.< / p >
2017-06-08 14:08:08 +00:00
< h2 > Success Stories< / h2 >
< ul class = "dashed" >
< li > < a href = "docs/en/introduction/ya_metrika_task.html" > Yandex.Metrica< / a > < / li >
< li > < a href = "https://blog.cloudflare.com/how-cloudflare-analyzes-1m-dns-queries-per-second/"
rel="external nofollow">CloudFlare DNS Analytics< / a > < / li >
< li > < a href = "https://translate.yandex.com/translate?url=https%3A%2F%2Fhabrahabr.ru%2Fpost%2F322620%2F&lang=ru-en"
2017-06-08 17:04:47 +00:00
rel="external nofollow">Migrating to Yandex ClickHouse< / a > by LifeStreet < span class = "grey" > (machine translation from Russian)< / span > < / li >
2017-06-08 14:08:08 +00:00
< li > < a href = "https://translate.yandex.com/translate?url=https%3A%2F%2Fhabrahabr.ru%2Fcompany%2Fsmi2%2Fblog%2F314558%2F&lang=ru-en"
2017-06-08 17:04:47 +00:00
rel="external nofollow">How to start ClickHouse up and win the jackpot< / a > by SMI2 < span class = "grey" > (machine translation from Russian)< / span > < / li >
2017-06-08 14:08:08 +00:00
< li > < a href = "https://www.yandex.com/company/press_center/press_releases/2012/2012-04-10/"
rel="external nofollow">LHCb experiment< / a > by CERN< / li >
< / ul >
2017-06-08 17:25:55 +00:00
< h2 > When to use ClickHouse< / h2 >
< p > For analytics over stream of clean, well structured and immutable events or logs.
It is recommended to put each such stream into a single wide fact table with pre-joined dimensions.
< / p >
< p > Some examples of viable applications:< / p >
2017-06-08 14:08:08 +00:00
< ul class = "dashed" >
< li > Web and App analytics< / li >
< li > Advertising networks and RTB< / li >
< li > Telecommunications< / li >
2017-06-08 15:54:09 +00:00
< li > E-commerce and finance< / li >
2017-06-08 14:08:08 +00:00
< li > Information security< / li >
< li > Monitoring and telemetry< / li >
< li > Business intelligence< / li >
< li > Online games< / li >
< li > Internet of Things< / li >
< / ul >
2017-04-10 14:24:53 +00:00
2017-06-08 17:25:55 +00:00
< h2 > When < span class = "red" > NOT< / span > to use ClickHouse< / h2 >
2017-06-08 17:04:47 +00:00
< ul class = "dashed" >
2017-06-08 17:25:55 +00:00
< li > Transactional workloads (OLTP)< / li >
< li > Key-value access with high request rate< / li >
< li > Blob or document storage< / li >
< li > Over-normalized data< / li >
2017-06-08 17:04:47 +00:00
< / ul >
2017-04-10 14:24:53 +00:00
< h2 id = "highly-reliable" > Highly Reliable< / h2 >
2017-06-08 15:54:09 +00:00
< p > ClickHouse has been managing petabytes of data serving a number of highload mass audience services of
< a href = "https://www.yandex.com/company/"
rel="external nofollow">Yandex< / a > , Russia's
leading search provider and one of largest European IT companies.
Since 2012, ClickHouse has been providing robust database management for the company's < a
2017-04-10 14:24:53 +00:00
href="https://metrica.yandex.com/" rel="external nofollow">web analytics service< / a > , comparison
2017-06-08 15:54:09 +00:00
e-commerce platform, public email service, online advertising platform, business intelligence tools
and infrastructure monitoring.< / p >
2017-04-10 14:24:53 +00:00
2017-06-08 15:54:09 +00:00
< p > ClickHouse can be configured as purely distributed system located on independent nodes,
without any single points of failure.< / p >
2017-04-10 14:24:53 +00:00
2017-06-08 15:54:09 +00:00
< p > Software and hardware failures or misconfigurations do not result in loss of data. Instead of deleting "broken"
data, ClickHouse saves it or asks you what to do before a startup. All data is checksummed before every
read or write to disk or network. It is virtually impossible to delete data by accident as there are safeguards
even for human errors.< / p >
2017-04-10 14:24:53 +00:00
2017-06-08 15:54:09 +00:00
< p > ClickHouse offers flexible limits on query complexity and resource usage, which can be fine-tuned with settings.
2017-04-10 14:24:53 +00:00
It is possible to simultaneously serve both a number of high priority low-latency requests and some
2017-06-08 15:54:09 +00:00
long-running queries with background priority.< / p >
2017-04-10 14:24:53 +00:00
2017-06-08 14:08:08 +00:00
< h2 id = "simple-and-handy" > Simple and Handy< / h2 >
2017-06-08 15:54:09 +00:00
< p > ClickHouse streamlines all your data processing. It's easy to use: ingest all your structured data into the
2017-06-08 14:08:08 +00:00
system, and it is instantly available for reports. New columns for new properties or dimensions can be
easily added to the system at any time without slowing it down.< / p >
< p > ClickHouse is simple and works out-of-the-box. As well as performing on hundreds of node clusters, this system
can be easily installed on a single server or even a virtual machine. No development experience or code-writing
skills are required to install ClickHouse.< / p >
2017-04-10 14:24:53 +00:00
< h2 id = "quick-start" > Quick Start< / h2 >
< p > System requirements: Linux, x86_64 with SSE 4.2.< / p >
< p > Install packages for
< span class = "distributive_not_selected" id = "ubuntu_xenial" > Ubuntu 16.04 Xenial< / span > , < span
class="distributive_selected" id="ubuntu_trusty">Ubuntu 14.04 Trusty< / span > or < span
class="distributive_not_selected" id="ubuntu_precise">Ubuntu 12.04 Precise< / span > :
< / p >
< code id = "ubuntu-install" >
< pre >
sudo apt-key adv --keyserver keyserver.ubuntu.com --recv E0C56BD4 # optional
2017-05-15 19:42:14 +00:00
sudo apt-add-repository "deb http://repo.yandex.ru/clickhouse/< span id = "distributive" > trusty< / span > stable main"
2017-04-10 14:24:53 +00:00
sudo apt-get update
2017-05-15 19:42:14 +00:00
sudo apt-get install clickhouse-server-common clickhouse-client -y
2017-04-10 14:24:53 +00:00
sudo service clickhouse-server start
clickhouse-client
< / pre >
< / code >
< p > For other operating systems the easiest way to get started is using
< a href = "https://hub.docker.com/r/yandex/clickhouse-server/" rel = "external nofollow"
target="_blank">
official Docker images of ClickHouse< / a >
. Alternatively you can build ClickHouse from < a
href="https://github.com/yandex/ClickHouse" rel="external nofollow"
target="_blank">sources< / a >
according to the < a
href="https://github.com/yandex/ClickHouse/blob/master/doc/build.md" rel="external nofollow"
target="_blank">instruction< / a > .< / p >
2017-05-30 09:45:54 +00:00
< p > After installation proceed to < strong > < a href = "tutorial.html" > tutorial< / a > < / strong > or < strong > < a href = "docs/en/" > full
2017-04-10 14:24:53 +00:00
documentation< / a > < / strong > .< / p >
< h2 id = "contacts" > Contacts< / h2 >
< ul class = "dashed" >
2017-06-08 14:28:39 +00:00
< li > Subscribe on < a href = "https://yandex.com/blog/clickhouse"
rel="external nofollow" target="_blank">official ClickHouse blog< / a >
and it's < a href = "https://yandex.ru/blog/clickhouse"
2017-06-08 17:04:47 +00:00
rel="external nofollow" target="_blank">Russian branch< / a > .< / li >
2017-06-08 14:28:39 +00:00
< li > Ask any questions on < a href = "https://stackoverflow.com/questions/tagged/clickhouse"
rel="external nofollow" target="_blank">Stack Overflow< / a > or
< a href = "https://groups.google.com/group/clickhouse"
rel="external nofollow" target="_blank">Google Group< / a > .
2017-04-10 14:24:53 +00:00
< / li >
2017-06-08 14:28:39 +00:00
< li > Join Telegram chat to discuss with real users in < a
href="https://telegram.me/clickhouse_en"
rel="external nofollow" target="_blank">English< / a > or in
< a href = "https://telegram.me/clickhouse_ru"
rel="external nofollow" target="_blank">Russian< / a > .< / li >
2017-04-10 14:24:53 +00:00
< / ul >
2017-06-08 15:06:20 +00:00
< p > Or email ClickHouse team at Yandex directly:
< a id = "feedback_email" href = "" > turn on JavaScript to see email address< / a > ,
for example if you are interested in commercial support.< / p >
< p > Friendly reminder: check out the documentation in < a href = "docs/en/" > English< / a > or < a href = "docs/ru/" > Russian< / a > first — maybe your question is already covered.
< / p >
2017-04-10 14:24:53 +00:00
< h2 > Like ClickHouse?< / h2 >
< p > Help to spread the word about it via < a rel = "external nofollow" target = "_blank" href = "https://www.facebook.com/sharer.php?u=https://clickhouse.yandex" > Facebook< / a > ,
< a rel = "external nofollow" target = "_blank" href = "https://twitter.com/intent/tweet?url=https://clickhouse.yandex" > Twitter< / a > and
< a rel = "external nofollow" target = "_blank" href = "https://www.linkedin.com/shareArticle?url=https://clickhouse.yandex" > LinkedIn< / a > !< / p >
2017-06-08 14:36:04 +00:00
< 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
2017-04-10 14:24:53 +00:00
KIND, either express or implied.< / p >
< p id = "footer" > © 2016– < a href = "https://yandex.com/company/" rel = "external nofollow" > YANDEX< / a > LLC< / p >
< / div >
2017-06-09 10:01:40 +00:00
< a id = "github_link"
href="https://github.com/yandex/ClickHouse"
rel="external nofollow"
target="_blank"
>< div id = "github" > Fork me on GitHub< / div > < / a >
2017-05-30 09:45:54 +00:00
< script type = "text/javascript" src = "//yastatic.net/jquery/3.1.1/jquery.min.js" > < / script >
2017-04-10 14:24:53 +00:00
< script type = "text/javascript" >
$(document).ready(function () {
var name = $('#main-title').text().trim().toLowerCase();
var feedback_address = name + '-feedback' + '@yandex-team.com';
var feedback_email = $('#feedback_email');
feedback_email.attr('href', 'mailto:' + feedback_address);
feedback_email.html(feedback_address);
$("a[href^='#']").on('click', function (e) {
e.preventDefault();
var selector = $(e.target).attr('href');
var offset = 0;
if (selector) {
offset = $(selector).offset().top - $('#logo').height() * 1.5;
}
$('html, body').animate({
scrollTop: offset
}, 500);
2017-04-11 04:38:43 +00:00
window.history.replaceState('', document.title, window.location.href.replace(location.hash, '') + this.hash);
2017-04-10 14:24:53 +00:00
});
2017-06-02 13:24:31 +00:00
var hostParts = window.location.host.split('.');
2017-06-07 16:04:39 +00:00
if (hostParts.length > 2 & & hostParts[0] != 'test') {
2017-06-02 13:24:31 +00:00
window.location.host = hostParts[0] + '.' + hostParts[1];
}
2017-04-10 14:24:53 +00:00
var available_distributives = ['xenial', 'trusty', 'precise'];
available_distributives.forEach(function (name) {
$('#ubuntu_' + name).on('click', function () {
$('#distributive').html(name);
available_distributives.forEach(function (distr) {
$('#ubuntu_' + distr).attr('class', (name == distr) ? 'distributive_selected' : 'distributive_not_selected');
});
});
});
});
< / script >
<!-- Yandex.Metrika counter -->
< script type = "text/javascript" >
2017-05-16 07:51:05 +00:00
(function (d, w, c) {
(w[c] = w[c] || []).push(function() {
try {
w.yaCounter18343495 = new Ya.Metrika2({
id:18343495,
clickmap:true,
trackLinks:true,
accurateTrackBounce:true,
webvisor:true
});
} catch(e) { }
2017-04-10 14:24:53 +00:00
});
2017-05-16 07:51:05 +00:00
var n = d.getElementsByTagName("script")[0],
s = d.createElement("script"),
f = function () { n.parentNode.insertBefore(s, n); };
s.type = "text/javascript";
s.async = true;
s.src = "https://mc.yandex.ru/metrika/tag.js";
if (w.opera == "[object Opera]") {
d.addEventListener("DOMContentLoaded", f, false);
} else { f(); }
})(document, window, "yandex_metrika_callbacks2");
2017-04-10 14:24:53 +00:00
< / script >
< noscript >
< div > < img src = "https://mc.yandex.ru/watch/18343495" style = "position:absolute; left:-9999px;" alt = "" / > < / div >
< / noscript >
<!-- /Yandex.Metrika counter -->
< / body >
< / html >
