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 4.14 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 other trademarks are the property of their respective owners. 摘要 摘要 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 386 386 388 388 389 400 414 OpenShift Container Platform 4.14 Operator 2 目 目录 录 3 第 1 章 OPERATOR 概述 Operator 是 OpenShift Container Platform 中最重要的组件。Operator 是 control plane 上打包、部署和 管理服务的首选方法。

OpenShift Container Platform 4.12 Serverless OpenShift Serverless 的安装、使用与发行注记 Last Updated: 2024-02-16 OpenShift Container Platform 4.12 Serverless OpenShift Serverless 的安装、使用与发行注记 法律通告 法律通告 Copyright other trademarks are the property of their respective owners. 摘要 摘要 本文档提供有关如何在 OpenShift Container Platform 中使用 OpenShift Serverless 的信息. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . OpenShift Container Platform 4.12 Serverless 2 第 1 章 关于 SERVERLESS 1.1. {SERVERLESSPRODUCTNAME} 概述 OpenShift Serverless 提供 Kubernetes 原生构建块，供开发人员在 OpenShift Container Platform 中创 建和部署无服务器、事件驱动的应用程序。OpenShift

Processing and Analytics Vasiliki (Vasia) Kalavri
 vkalavri@bu.edu Spring 2020 4/28: Graph Streaming ??? Vasiliki Kalavri | Boston University 2020 Modeling the world as a graph 2 Social networks a vertex and all of its neighbors. Although this model can enable a theoretical analysis of streaming algorithms, it cannot adequately model real-world unbounded streams, as the neighbors cannot be continuously generated as a stream of edges? • How can we perform iterative computation in a streaming dataflow engine? How can we propagate watermarks? • Do we need to run the computation from scratch