21st Athens Big Data Meetup - 1st Talk - Fast and simple data exploration with ClickHouse
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Clickhouse is a distributed columnar database management system optimized for analytics, supporting high-speed interactive data exploration and real-time reporting. Initially developed by Yandex for processing immense data volumes, it has become widely adopted across various industries for applications such as adtech, bioscience, and real-time analytics. Its performance is achieved through efficient data compression, parallel processing, and a rich SQL dialect, with strong support for integration and deployment across platforms.
21st Athens Big Data Meetup - 1st Talk - Fast and simple data exploration with ClickHouse
- 1. ClickHouse Enabling interactive data exploration Alexander Kuzmenkov, ClickHouse developer at Yandex
- 2. What is ClickHouse? ● RDBMS for analytics ○ SQL ○ FOSS ● Distributed ○ Cross-datacenter replication ○ Tolerant to a single-datacenter failure ● Linear scaling ○ 100s of servers ○ 100G-1000G records daily ● Blazing fast ○ interactive data exploration These slides have a lot of links! Download at https://clck.ru/MMWHA
- 3. History ● Yandex.Metrica (think Google Analytics) ○ 30B rows daily, 3PB total, 700 machines ○ Processing speed up to 2TB/s ● Used a MyISAM solution with pre-aggregation ○ Didn’t scale for the growing load ○ Could only build pre-defined reports ● Started developing own columnar DB in 2009 ● Enables “double real time” reporting ○ Add new events in real time ○ Build custom reports in real time ● Becomes the main engine in 2012 ● Open-sourced in 2016
- 4. Adoption ● Yandex-wide ○ business analytics ● Thousands of companies worldwide ○ analyzing 1M DNS queries per second (Cloudflare) ○ geospatial processing (Carto) ○ real-time ad analytics platform (LifeStreet) ○ storage performance analysis and monitoring (Infinidat) ○ AdTech, FinTech, sensors, logs, event streams, time series, etc. Unusual applications: ● Blockchain transaction history ○ blockchair.com ○ bloxy.info ● CERN LHCb experiment ● Bioinformatics
- 5. When to use ● stream of well-structured, immutable events ○ (mostly) fixed schema ○ append-only ○ heavy-weight ALTER UPDATE/DELETE as a “GDPR escape hatch” ● flexible real-time reporting ○ queries finish in seconds, not hours ○ no preprocessing needed ○ enables ad-hoc experiments
- 6. When NOT to use ● OLTP ○ no transactions ○ single INSERT is atomic, but no cross-query atomicity ○ very heavy UPDATEs ● key/value storage ○ sparse indexes ○ not suitable for point reads ● document/blob storage ○ optimized for records < 100 kB ● highly normalized data ○ optimized for star schema
- 7. How fast? Query 1 Query 2 Query 3 Query 4 Setup 0.005 0.01 0.10 0.188 BrytlytDB 2.1 & 5-node IBM Minsky cluster 0.051 0.15 0.05 0.794 kdb+/q & 4 Intel Xeon Phi 7210 CPUs 0.241 0.83 1.21 1.781 ClickHouse, 3 x c5d.9xlarge cluster 0.762 2.47 4.13 6.041 BrytlytDB 1.0 & 2-node p2.16xlarge cluster 1.034 3.06 5.35 12.748 ClickHouse, Intel Core i5 4670K 1.56 1.25 2.25 2.97 Redshift, 6-node ds2.8xlarge cluster 2 2.00 1.00 3 BigQuery 2.362 3.56 4.02 20.412 Spark 2.4 & 21 x m3.xlarge HDFS cluster 14.389 32.15 33.45 67.312 Vertica, Intel Core i5 4670K 1.1 Billion Taxi rides benchmark: 1.1G records, 51 columns, 500 GB uncompressed CSV More on performance at our site
- 8. Why so fast? Read less data ● Data locality ○ columnar storage ○ sorted by PK ● Compression Process data faster ● Parallelism ○ multithreading ○ distributed queries ● Efficient computation ○ >40 different GROUP BY algorithms ○ vectorized query execution with SIMD
- 9. Deployment ● Repos for major Linux distros ● Docker containers ● One self-contained binary ○ works everywhere ● ZooKeeper if you need replication ● Test it on your laptop ○ runs with minimal resources ● Stable release every two weeks ○ Thousands of scenarios tested for each change ● No data migration on update ○ Just run a new server
- 10. Data ingestion ● INSERTs ● Many data formats ○ CSV, JSON, Parquet, CapnProto, ORC, ... ● Batching for optimal performance ○ Buffer table engine ○ Kafka table engine ○ Third-party solutions — chproxy, kittenhouse etc.
- 11. Analyzing data ● A rich SQL dialect ○ strong typing ○ higher order functions like arrayMap ○ variety of aggregate function — quantiles, cardinality estimators etc. ○ sampling ○ Nested type for key/value records ○ LowCardinality type for dictionary encoding ● BI tools support ○ Tableau ○ Apache Superset ○ Holistics ○ others via ODBC/SQLAlchemy/...
- 12. clickhouse-local $ clickhouse local --file ~/hits_v1.tsv --structure 'WatchID UInt64, JavaEnable UInt8, ...' --query 'SELECT UserID, SearchPhrase, count() FROM table GROUP BY UserID, SearchPhrase' Read 8873898 rows, 7.88 GiB in 5.208 sec., 1704038 rows/sec., 1.51 GiB/sec. UserID SearchPhrase count() 8410854169855355129 пальные кость играть терхи 3 The full power of ClickHouse engine over a data file.
- 13. Interfaces Connect to ClickHouse ● Native binary protocol ○ Drivers for Python, Go, C++, ... ● RESTful HTTP ● ODBC ● JDBC ● MySQL wire protocol ● PostgreSQL ○ clickhouse_fdw ○ pg2ch (logical replication) Connect from ClickHouse ● File ● HDFS ● URL ● MySQL ● ODBC ● External dictionaries
- 14. Resources ● Check the docs at our site ● View the talks at our YouTube channel ● Create issues on github ● Ask on Stack Overflow ● Email us at clickhouse-feeback@yandex-team.ru ● Join English and Russian chats in Telegram ● Get commercial support from Altinity and others ● … and more
- 15. Thank you! Questions? ● https://clickhouse.tech ● https://github.com/ClickHouse/ClickHouse ● clickhouse-feedback@yandex-team.ru