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

## Scalable Stream Processing - Spark Streaming and Flink Amir H. Payberah payberah@kth.se 05/10/2018 https://id2221kth.github.io ## Data Processing Graph Data Pregel, GraphLab, PowerGraph GraphX FlumeJava, Spark Structured Data Spark SQL Machine Learning Mliib Tensorflow Streaming Data Storm, SEEP, Naiad, Spark Streaming, Flink, Millwheel, Google Dataflow ## Distributed File Systems ## Data Storage declarative APIs ▶ Spark streaming ▶ Flink ## Spark Streaming ## ▶ Design issues • Continuous vs. micro-batch processing • Record-at-a-Time vs. declarative APIs ▶ Run a streaming computation as a series

..364 14.4.6. Increase checkpoint_segments .....364 14.4.7. Disable WAL archival and streaming replication .....364 14.4.8. Run ANALYZE Afterwards .....364 14.4.9. Some Notes About pg_dump ... 5.1. Settings .....432 18.5.2. Checkpoints.....435 18.5.3. Archiving .....436 18.5.4. Streaming Replication.....437 18.5.5. Standby Servers .....438 18.6. Query Planning .....438 18.6.1. Planner 24.4.2. Other data migration methods.....533 25. High Availability, Load Balancing, and Replication.....534 25.1. Comparison of different solutions.....534 25.2. Log-Shipping Standby Servers

339 14.4.6. Increase checkpoint_segments ..... 339 14.4.7. Disable WAL archival and streaming replication ..... 339 14.4.8. Run ANALYZE Afterwards ..... 340 14.4.9. Some Notes About pg_dump . Settings ..... 403 18.5.2. Checkpoints ..... 406 18.5.3. Archiving ..... 407 18.5.4. Streaming Replication ..... 407 18.5.5. Standby Servers ..... 408 18.6. Query Planning ..... 409 18.6.1. Planner 24.4.2. Other data migration methods ..... 498 25. High Availability, Load Balancing, and Replication ..... 499 25.1. Comparison of different solutions ..... 499 25.2. Log-Shipping Standby

## Group Replication原理解析与实践经验 徐春阳 ## QCon 全球软件开发大会 10⽉17-19⽇上海·宝华万豪酒店  扫码锁定席位 ## 九 折即将结束 团购还享更多优惠，折扣有效期至9月17日 扫描右方二维码即可查看大会信息及购票 用户线程执行事务（sql）流程  group_replication_trans_before_commit 被唤醒后 将有关事务的日志写入到本地通道。后面的所有任务，例如，将这个事务日志利用paxos协议进行全局一致性广播，验证是否跟其他事务冲突等全部 CONTENTS ## MRG原理 MGR vs Semi-Sync MGR实践经验 ## TABLE OF CONTENTS ## 当心secondary节点变成可写节点 ## Group_replication_bootstrap_group参数须谨慎 规避大事务 DDL操作注意事项 ## Secondary节点变成可写节点 正常情况下，Single primary 模式下，secondary节点只读

optimizations Vasiliki (Vasia) Kalavri vkalavri@bu.edu ## Topics covered in this lecture • Costs of streaming operator execution • state, parallelism, selectivity • Dataflow optimizations • plan translation f8d9a883a0b9bacb2db614d10387ee7/p11_1.jpg) ## Challenges in streaming optimization • What does efficient mean in the context of streaming? • queries run continuously • streams are unbounded - In traditional on-the-fly. Different plans can be used for two consecutive executions of the same query. • A streaming dataflow is generated once and then scheduled for execution. - Changing execution strategy while

..379 14.4.6. Increase checkpoint_segments .....379 14.4.7. Disable WAL Archival and Streaming Replication .....379 14.4.8. Run ANALYZE Afterwards .....379 14.4.9. Some Notes About pg_dump ... 5.1. Settings ..... 449 18.5.2. Checkpoints ..... 453 18.5.3. Archiving ..... 453 18.6. Replication ..... 454 18.6.1. Master Server ..... 454 18.6.2. Standby Servers ..... 456 18.7. Query Planning archive_command Scripts .....555 24.3.6. Caveats .....555 25. High Availability, Load Balancing, and Replication .....557 25.1. Comparison of Different Solutions .....557 25.2. Log-Shipping Standby Servers

## CS 591 K1: Data Stream Processing and Analytics Spring 2020 4/28: Graph Streaming Vasiliki (Vasia) Kalavri vkalavri@bu.edu ## Modeling the world as a graph ![Image](/uploads/documents/d/c/6/7/d contain 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 is 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

# CS 591 K1: Data Stream Processing and Analytics Spring 2020 2/04: Streaming languages and operator semantics Vasiliki (Vasia) Kalavri vkalavri@bu.edu ## Languages for continuous data processing ## 10 is detected, followed (in a time interval of 5-15 s) by an item of type C with Z < 5. ## Streaming Operators ## Operator types (I) • Single-Item Operators process stream elements one-by-one. • condition. • not commonly supported • a termination condition must be defined, e.g. time limit ## Streaming Iteration Example timely::example(|scope| { let (handle, stream) = scope.loop_variable(100

things like persistent connections, represents query strings as collections, simplifies sending streaming POST requests with fields and files, and abstracts away the underlying HTTP transport layer. - $response->getBody(); while (!$body->eof()) { echo $body->read(1024); } ## Note Streaming response support must be implemented by the HTTP handler used by a client. This option might not things like persistent connections, represents query strings as collections, makes it simple to send streaming POST requests with fields and files, and abstracts away the underlying HTTP transport layer. By