Container Platform 4.14 Operator 在 OpenShift Container Platform 中使用 Operator Last Updated: 2024-02-23 OpenShift Container Platform 4.14 Operator 在 OpenShift Container Platform 中使用 Operator 法律通告 法律通告 Copyright 本文档提供有关在 OpenShift Container Platform 中使用 Operator 的信息。文中为集群管理员提供 了 Operator 的安装和管理说明，为开发人员提供了如何通过所安装的 Operator 创建应用程序的信 息。另外还提供了一些使用 Operator SDK 构建自用 Operator 的指南。 . . . . . . . . . . . . . . . . . 目录 录 第 第 1 章 章 OPERATOR 概述 概述 1.1. 对于开发人员 1.2. 对于管理员 1.3. 后续步骤 第 第 2 章 章 了解 了解 OPERATOR 2.1. 什么是 OPERATOR? 2.2. OPERATOR FRAMEWORK 打包格式 2.3. OPERATOR FRAMEWORK 常用术语表 2.4. OPERATOR LIFECYCLE MANAGER

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

Node Operator: Kubernetes Node Management Made Simple 陈俊(Joe), Ant Financial Agenda • Background and Motivation • Introduction of Operators • Node-Operator • Advanced Topic: Topic: Kube-on-Kube-Operator • Achievement • Q&A Background: DC/OS From Sigma 2.0(Swarm) to Sigma 3.1(Kubernetes) Background: Cluster Scale • Production environment: • Dozens of Cluster • 5k+ Nodes / Cluster architecture Work order deployment system can not meet the requirements of resource management. Operator Observe Action Analyze • Observe: watch desired resource and actual resource • Analyze: difference

© 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