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SELECT foo FROM distributed_table
SELECT foo FROM local_table GROUP BY col1
• Server 1
SELECT foo FROM local_table GROUP BY col1
• Server 2
SELECT foo FROM local_table GROUP BY col1
• Server 3
N Servers 1 3 140
Time, sec 1.224 0.438 0.043
Speedup x2.8 x28.5
https://www.altinity.com/blog/2017/6/5/clickhouse-data-distribution
ClickHouse: A DBMS for interactive analytics at scale by Vadim Tkachenko
ClickHouse: A DBMS for interactive analytics at scale by Vadim Tkachenko
ClickHouse: A DBMS for interactive analytics at scale by Vadim Tkachenko
ClickHouse: A DBMS for interactive analytics at scale by Vadim Tkachenko
ClickHouse: A DBMS for interactive analytics at scale by Vadim Tkachenko
ClickHouse: A DBMS for interactive analytics at scale by Vadim Tkachenko
ClickHouse: A DBMS for interactive analytics at scale by Vadim Tkachenko
ClickHouse: A DBMS for interactive analytics at scale by Vadim Tkachenko

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ClickHouse: A DBMS for interactive analytics at scale by Vadim Tkachenko

Notas del editor

  1. Analytic database landscape: Commerical -- fast and expensive: Vertica RedShift Teradata etc. Open Source -- somewhat slow, buggy but free InfiniDB (part of MariaDB now) InfoBright GreenPlum (started as commerical) Hadoop systems ClickHouse: fast as free! ClickHouse story: Yandex -- Russian Google Yandex Metrika -- Russian Google Analytics Interactive Ad Hoc reports at multiple petabytes That's why they developed ClickHouse ClickHouse is extremelly fast and scalable. Why ClickHouse is so fast Popular Yandex answer -- because they had no choice Techical details Vectorised processing (see VectorWise) True MPP  True shared nothing True column store with late materialization (like C-Store and Vertica but unlike many others): Data compression Column locality No random reads Some technical details (in Russian): https://clickhouse.yandex/presentations/meetup7/internals.pdf  What is column store (I think it is important to explain) Why it is good for quries like range scan + aggregation (look at Yandex presentation above, there are examples) Conclusion -- such an architecture allows very fast queries on a single table with filters and group by's Not enough speed -- let's see how data distribution works  Care about reliability -- let's see how replication is set up (again, can use Yandex slides above as the source) Benchmark 1 Benchmark 2 Benchmark 3 Few words on limitations: Custom SQL dialect As a consequence -- limited ecosystem (can not fit to standard one) No deletes/updates: but there are mutable table types (engines) there is a way to connect to external updateble data (dictionaries) Somewhat hard to manage -- no tools Final word: Potential to be MySQL for Analytics Invite to try Need more info -- http://clickhouse.yandex Need consulting/support -- http://www.altinity.com