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* update presentations * CLICKHOUSE-2936: redirect from clickhouse.yandex.ru and clickhouse.yandex.com * update submodule * lost files * CLICKHOUSE-2981: prefer sphinx docs over original reference * CLICKHOUSE-2981: docs styles more similar to main website + add flags to switch language links * update presentations * Less confusing directory structure (docs -> doc/reference/) * Minify sphinx docs too * Website release script: fail fast + pass docker hash on deploy * Do not underline links in docs * shorter * cleanup docker images * tune nginx config * CLICKHOUSE-3043: get rid of habrastorage links * Lost translation * CLICKHOUSE-2936: temporary client-side redirect * behaves weird in test * put redirect back * CLICKHOUSE-3047: copy docs txts to public too * move to proper file * remove old pages to avoid confusion * Remove reference redirect warning for now * Refresh README.md * Yellow buttons in docs * Use svg flags instead of unicode ones in docs * fix test website instance * Put flags to separate files * wrong flag * Copy Yandex.Metrica introduction from main page to docs * Yet another home page structure change, couple new blocks (CLICKHOUSE-3045) * Update Contacts section * CLICKHOUSE-2849: more detailed legal information * CLICKHOUSE-2978 preparation - split by files * More changes in Contacts block * Tune texts on index page * update presentations * One more benchmark * Add usage sections to index page, adapted from slides * Get the roadmap started, based on slides from last ClickHouse Meetup * CLICKHOUSE-2977: some rendering tuning * Get rid of excessive section in the end of getting started * Make headers linkable * CLICKHOUSE-2981: links to editing reference - https://github.com/yandex/ClickHouse/issues/849 * CLICKHOUSE-2981: fix mobile styles in docs * Ban crawling of duplicating docs * Open some external links in new tab * Ban old docs too * Lots of trivial fixes in english docs * Lots of trivial fixes in russian docs * Remove getting started copies in markdown * Add Yandex.Webmaster * Fix some sphinx warnings * More warnings fixed in english docs * More sphinx warnings fixed * Add code-block:: text * More code-block:: text * These headers look not that well * Better switch between documentation languages * merge use_case.rst into ya_metrika_task.rst * Edit the agg_functions.rst texts * Add lost empty lines * Lost blank lines * Add new logo sizes * update presentations * Next step in migrating to new documentation * Fix all warnings in en reference * Fix all warnings in ru reference * Re-arrange existing reference * Move operation tips to main reference * Fix typos noticed by milovidov@ * Get rid of zookeeper.md * Looks like duplicate of tutorial.html * Fix some mess with html tags in tutorial * No idea why nobody noticed this before, but it was completely not clear whet to get the data * Match code block styling between main and tutorial pages (in favor of the latter) * Get rid of some copypaste in tutorial * Normalize header styles * Move example_datasets to sphinx * Move presentations submodule to website * Move and update README.md * No point in duplicating articles from habrahabr here * Move development-related docs as is for now * doc/reference/ -> docs/ (to match the URL on website) * Adapt links to match the previous commit * Adapt development docs to rst (still lacks translation and strikethrough support) * clean on release * blacklist presentations in gulp * strikethrough support in sphinx * just copy development folder for now * fix weird introduction in style article * Style guide translation (WIP) * Finish style guide translation to English * gulp clean separately * Update year in LICENSE * Initial CONTRIBUTING.md * Fix remaining links to old docs in tutorial * Some tutorial fixes * Typo * Another typo * Update list of authors from yandex-team accoding to git log
63 lines
4.5 KiB
ReStructuredText
63 lines
4.5 KiB
ReStructuredText
Distinctive features of ClickHouse
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==================================
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True column-oriented DBMS
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-------------------------
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In a true column-oriented DBMS, there isn't any "garbage" stored with the values. For example, constant-length values must be supported, to avoid storing their length "number" next to the values. As an example, a billion UInt8-type values should actually consume around 1 GB uncompressed, or this will strongly affect the CPU use. It is very important to store data compactly (without any "garbage") even when uncompressed, since the speed of decompression (CPU usage) depends mainly on the volume of uncompressed data.
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This is worth noting because there are systems that can store values of separate columns separately, but that can't effectively process analytical queries due to their optimization for other scenarios. Example are HBase, BigTable, Cassandra, and HyperTable. In these systems, you will get throughput around a hundred thousand rows per second, but not hundreds of millions of rows per second.
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Also note that ClickHouse is a DBMS, not a single database. ClickHouse allows creating tables and databases in runtime, loading data, and running queries without reconfiguring and restarting the server.
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Data compression
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----------------
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Some column-oriented DBMSs (InfiniDB CE and MonetDB) do not use data compression. However, data compression really improves performance.
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Disk storage of data
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--------------------
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Many column-oriented DBMSs (SAP HANA, and Google PowerDrill) can only work in RAM. But even on thousands of servers, the RAM is too small for storing all the pageviews and sessions in Yandex.Metrica.
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Parallel processing on multiple cores
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-------------------------------------
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Large queries are parallelized in a natural way.
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Distributed processing on multiple servers
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------------------------------------------
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Almost none of the columnar DBMSs listed above have support for distributed processing.
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In ClickHouse, data can reside on different shards. Each shard can be a group of replicas that are used for fault tolerance. The query is processed on all the shards in parallel. This is transparent for the user.
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SQL support
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-----------
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If you are familiar with standard SQL, we can't really talk about SQL support.
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NULLs are not supported. All the functions have different names. However, this is a declarative query language based on SQL that can't be differentiated from SQL in many instances.
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JOINs are supported. Subqueries are supported in FROM, IN, JOIN clauses; and scalar subqueries.
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Correlated subqueries are not supported.
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Vector engine
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-------------
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Data is not only stored by columns, but is processed by vectors - parts of columns. This allows us to achieve high CPU performance.
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Real time data updates
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----------------------
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ClickHouse supports primary key tables. In order to quickly perform queries on the range of the primary key, the data is sorted incrementally using the merge tree. Due to this, data can continually be added to the table. There is no locking when adding data.
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Indexes
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-------
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Having a primary key allows, for example, extracting data for specific clients (Metrica counters) for a specific time range, with low latency less than several dozen milliseconds.
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Suitable for online queries
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---------------------------
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This lets us use the system as the back-end for a web interface. Low latency means queries can be processed without delay, while the Yandex.Metrica interface page is loading (in online mode).
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Support for approximated calculations
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-------------------------------------
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#. The system contains aggregate functions for approximated calculation of the number of various values, medians, and quantiles.
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#. Supports running a query based on a part (sample) of data and getting an approximated result. In this case, proportionally less data is retrieved from the disk.
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#. Supports running an aggregation for a limited number of random keys, instead of for all keys. Under certain conditions for key distribution in the data, this provides a reasonably accurate result while using fewer resources.
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Data replication and support for data integrity on replicas
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Uses asynchronous multi-master replication. After being written to any available replica, data is distributed to all the remaining replicas. The system maintains identical data on different replicas. Data is restored automatically after a failure, or using a "button" for complex cases.
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For more information, see the section "Data replication".
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