up an environment to develop Flink programs • Implement streaming data processing pipelines • Flink managed state • Event time Streaming in Apache Flink • Streams are natural • Events of any type

GPU Tile 1 Tile 0 Xe LinkProject Goals - Offer high-level, standard C++ distributed data structures - Support distributed algorithms - Achieve high performance for both multi-GPU, NUMA, and multi-node reduce(par_unseq, z, 0, std::plus()); }Outline - Background (Ranges, Parallelism, Distributed Data Structures) - Distributed Ranges (Concepts) - Implementation (Algorithms and views) - Complex sparse matrices) - Lessons learnedOutline - Background (Ranges, Parallelism, Distributed Data Structures) - Distributed Ranges (Concepts) - Implementation (Algorithms and views) - Complex

© 2018 Percona. 1 Peter Zaitsev Using MySQL for Distributed Database Architectures CEO, Percona PingCAP Infra Meetup, Shanghai, China, May 26, 2018 © 2018 Percona. 2 About Percona Solutions enterprises © 2018 Percona. 3 Presentation Cover Basics Why Going Distributed How to do it © 2018 Percona. 4 Distributed ? MySQL Deployment on More than one System © 2018 Percona. 5 Modern Active Users Possible 15M of Daily Active Users counting time of day skew © 2018 Percona. 8 Distributed Systems Tend To be More Complicated to Develop Against More Complicated to Operate Have

Scalable Stream Processing - Spark Streaming and Flink Amir H. Payberah payberah@kth.se 05/10/2018 The Course Web Page https://id2221kth.github.io 1 / 79 Where Are We? 2 / 79 Stream Processing Systems Spark streaming ▶ Flink 4 / 79 Spark Streaming 5 / 79 Contribution ▶ Design issues • Continuous vs. micro-batch processing • Record-at-a-Time vs. declarative APIs 6 / 79 Spark Streaming ▶ Run Run a streaming computation as a series of very small, deterministic batch jobs. • Chops up the live stream into batches of X seconds. • Treats each batch as RDDs and processes them using RDD operations

Updates to a shared chat / drawing canvas – Game events © Joe Mertz – Mobile to Cloud: Building Distributed Applications • Workarounds have been devised • E.g. Polling – Client continuously polls the server stamp for the next time he wants to send a letter. © Joe Mertz – Mobile to Cloud: Building Distributed Applications • Provides for true two-way ongoing communication between a client and server. Note: – Some old browsers don't implement WebSockets © Joe Mertz – Mobile to Cloud: Building Distributed Applications // Create a new WebSocket var wSocket = new WebSocket("ws://www.example.com/socketserver")

program for a supercomputer? Introduce PGAS Model, RDMA Building Remote Pointer Types Building Distributed Data Structures Extending to GPUsThis Talk Background: how do we write a program for a supercomputer supercomputer? Introduce PGAS Model, RDMA Building Remote Pointer Types Building Distributed Data Structures Extending to GPUsThis Talk Background: how do we write a program for a supercomputer? Introduce Introduce PGAS Model, RDMA Building Remote Pointer Types Building Distributed Data Structures Extending to GPUsThis Talk Background: how do we write a program for a supercomputer? Introduce PGAS Model

4/14: Stream processing optimizations ??? Vasiliki Kalavri | Boston University 2020 2 • Costs of streaming operator execution • state, parallelism, selectivity • Dataflow optimizations • plan translation Pipeline: A || B Task: B || C Data: A || A ??? Vasiliki Kalavri | Boston University 2020 8 Distributed execution in Flink ??? Vasiliki Kalavri | Boston University 2020 9 Identify the most efficient ??? Vasiliki Kalavri | Boston University 2020 12 • What does efficient mean in the context of streaming? • queries run continuously • streams are unbounded • In traditional ad-hoc database queries

OpenShift Container Platform 4.3 架构 OpenShift Container Platform 架构概述 Last Updated: 2020-11-30 OpenShift Container Platform 4.3 架构 OpenShift Container Platform 架构概述 法律通告 法律通告 Copyright © 2020 Red All other trademarks are the property of their respective owners. 摘要 摘要 本文概述 OpenShift Container Platform 中的平台和应用架构。 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 章 章 OPENSHIFT CONTAINER PLATFORM 架 架构 构 1.1. OPENSHIFT CONTAINER PLATFORM 简介 第 第 2 章 章 安装和更新 安装和更新 2.1. OPENSHIFT CONTAINER PLATFORM 安装概述 2.2. 关于 OPENSHIFT CONTAINER PLATFORM 更新服务 2.3. 支持非受管 OPERATOR

OpenShift Container Platform 4.10 监控 在 OpenShift Container Platform 中配置和使用监控堆栈 Last Updated: 2023-10-17 OpenShift Container Platform 4.10 监控 在 OpenShift Container Platform 中配置和使用监控堆栈 法律通告 法律通告 Copyright other trademarks are the property of their respective owners. 摘要 摘要 本文提供有关在 OpenShift Container Platform 中配置和使用 Prometheus 监控堆栈的说明。 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 目 目录 录 第 第 1 章 章 监 监控概述 控概述 1.1. 关于 OPENSHIFT CONTAINER PLATFORM 监控 1.2. 了解监控堆栈 1.3. OPENSHIFT CONTAINER PLATFORM 监控的常见术语表 1.4. 其他资源 1.5. 后续步骤 第 第 2 章 章 配置 配置监 监控堆 控堆栈 栈 2.1. 先决条件 2.2

OpenShift Container Platform 4.2 支持 获取 OpenShift Container Platform 4.2 支持 Last Updated: 2020-08-21 OpenShift Container Platform 4.2 支持 获取 OpenShift Container Platform 4.2 支持 法律通告 法律通告 Copyright trademarks are the property of their respective owners. 摘要 摘要 本文档提供了有关从红帽获取 OpenShift Container Platform 支持的信息。文中还包含有关通过 Telemetry 和 Insights Operator 进行远程健康监控的信息。 . . . . . . . . . . . . . . . . . . 关于远程健康监控 3.2. 显示远程健康监控收集的数据 3.3. 不使用远程健康报告功能 3 3 4 4 4 5 6 6 7 9 目 目录 录 1 OpenShift Container Platform 4.2 支持 支持 2 第 1 章 获取支持 1.1. 获取支持 如果您在执行本文档所述的某个流程时遇到问题，请访问红帽客户门户。您可通过该客户门户： 搜索或浏览红帽知识库，了解有关红帽产品的技术支持文章。