Integrating ClickHouse into Your IT Ecosystem Alexander Sapin, Software Engineer ClickHouse in Production ClickHouse DBMS › Blazing fast › Linearly scalable › Flexible SQL dialect › Store petabytes Fault-tolerant › 1000+ companies using in production › Open-source › Hundreds of contributors 1 / 97 ClickHouse is NOT Good for › Frequent small inserts › Regular updates › Key-value access with high request etcd) › NoSQL DBMS (MongoDB, Couchbase) › OLAP Database (ClickHouse!) https://github.com/donnemartin/system-design-primer 8 / 97 ClickHouse in Production: Yandex.Metrika › Third web analytics service

ClickHouse on Kubernetes! Alexander Zaitsev Altinity Background ● Premier provider of software and services for ClickHouse ● Incorporated in UK with distributed team in US/Canada/Europe ● US/Europe sponsor of ClickHouse community ● Offerings: ○ 24x7 support for ClickHouse deployments ○ Software (Kubernetes, cluster manager, tools & utilities) ○ POCs/Training What is Kubernetes ● allocate machine resources efficiently ● automate application deployment Why run ClickHouse on Kubernetes? Other applications are already there Easier to manage than deployment on hosts

ClickHouse on Kubernetes! Alexander Zaitsev, Altinity Limassol, May 7th 2019 Altinity Background ● Premier provider of software and services for ClickHouse ● Incorporated in UK with with distributed team in US/Canada/Europe ● US/Europe sponsor of ClickHouse community ● Offerings: ○ 24x7 support for ClickHouse deployments ○ Software (Kubernetes, cluster manager, tools & utilities) Why run ClickHouse on Kubernetes? 1. Other applications are already there 2. Portability 3. Bring up data warehouses quickly 4. Easier to manage than deployment on hosts What does ClickHouse look like

© 2018 Percona. 1 Peter Zaitsev Analyzing MySQL Logs with ClickHouse CEO, Percona April 27,2018 © 2018 Percona. 2 ClickHouse is my love at the first sight © 2018 Percona. 3 Why ? Fast and Expensive Logs can Consume a lot of Space Logs can be expensive to query © 2018 Percona. 7 Clickhouse Answers • 10x+ times space reduction compared to Raw Text Log Files High Compression MySQL Wire Protocol Compatibility with ProxySQL Extra Bonus © 2018 Percona. 9 Logs to ClickHouse © 2018 Percona. 10 Several Options Logstash (ELK Stack) Kafka Do it yourself © 2018

reserved. STRICTLY CONFIDENTIAL Begin Content Area = 16,30 $ ￥ € $ €￥ $ £ ￥ ￡ ￥ UDF in ClickHouse Concept, Develpoment, and Application in ML Systems Begin Content Area = 16,30 2 About CraiditX Interested in computer system and language stuff • 8 organizations, 90+ repos, 600+ followers ClickHouse Contributor Begin Content Area = 16,30 4 OLAP in ML Systems Begin Content Area = 16,30 5 TABLE ... AS SELECT ...” A Database System and A ML Pipeline Begin Content Area = 16,30 10 Why ClickHouse Limited hardware resources & time → efficiency matters Performance • Each node is able to handle

Sync Clickhouse with MySQL/MongoDB Company: Xiaoxin Tech. Industry: Education Team: Big Data Leader: wangchao@xiaoheiban.cn About 100 billion data this year till now 30 million users We use use Clickhouse in our daily tasks Chanllenges Complex Datasource Chanllenges Frequent Updates Chanllenges Possible Solutions 1. Replay binlog/oplog CRUD directly Can’t update/delete table frequently frequently in Clickhouse Possible Solutions 2. MySQL Engine Not suitable for big tables Not suitable for MongoDB Possible Solutions 3. Reinit whole table every day…… Possible Solutions 4. CollapsingMergeTree

MaterializeMySQL Database engine in ClickHouse WinterZhang(张健) About me • Active ClickHouse Contributor • MaterializeMySQL Database Engine • Custom HTTP Handler • MySQL Database Engine • BloomFilter

Machine Learning with ClickHouse Nikolai Kochetov, ClickHouse developer Experimental dataset NYC Taxi and Uber Trips › Where to download: https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page page › How to import data into ClickHouse: https://clickhouse.yandex/docs/en/getting_started/example_datasets/nyc_taxi/ › What you can also read: https://toddwschneider.com/posts/ analyzing-1-1-bi Tools you got used to Small sample of data is enough to start All you need is to get it from ClickHouse Couple of lines for Python + Pandas import requests import io import pandas as pd url = 'http://127

Machine Learning with ClickHouse Nikolai Kochetov, ClickHouse developer Experimental dataset NYC Taxi and Uber Trips › Where to download: https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page page › How to import data into ClickHouse: https://clickhouse.yandex/docs/en/getting_started/example_datasets/nyc_taxi/ › What you can also read: https://toddwschneider.com/posts/ analyzing-1-1-bi Tools you got used to Small sample of data is enough to start All you need is to get it from ClickHouse Couple of lines for Python + Pandas import requests import io import pandas as pd url = 'http://127

Continue to use ClickHouse as TSDB 邰翀 青云QingCloud 数据库研发工程师 ► Look back: Why we choose it ► Now: How we do ► Future: What we do Content Why we choose it Why we choose it Why we choose it Scaned 没有最好的解决方案 Why we choose it 没有最好的解决方案 小孩子才做选择 “好的”我们都想要 ! Why we choose it How we do ► ClickHouse 实现方式 ► (1) Column-Orient Model ► (2) Time-Series-Orient Model How we do ► Column-Orient Model