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

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

## 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

..... 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 5.1. 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 Master for Standby Servers ..... 504 25.2.4. Setting Up a Standby Server ..... 504 25.2.5. Streaming Replication ..... 505 25.2.5.1. Authentication ..... 506 25.2.5.2. Monitoring ..... 506

jpg)  RocketMQ streaming ecosystem ## CONTENT  ## 01 ## RocketMQ streaming ecosystem Apache RocketMQ – A distributed streaming platform. Not only open source distributed messaging and streaming data platform. Latest release v4.3.1 Star 5,688 Fork 2,646 Getting Started Apache RocketMQ – A distributed streaming platform. Not only messaging. ![

..... 32 PyFlink is a Python API for Apache Flink that allows you to build scalable batch and streaming workloads, such as real-time data processing pipelines, large-scale exploratory data analysis, Machine context for creating Table and SQL API programs. Flink is an unified streaming and batch computing engine, which provides unified streaming and batch API to create a TableEnvironment. TableEnvironment is responsible table_environment.tableenvironment at="" 0x7fcd16342ac8=""> [2]: # Create a streaming TableEnvironment env_settings = EnvironmentSettings.in_streaming_mode() table_env = TableEnvironment.create(env_settings) table_env

..... 32 PyFlink is a Python API for Apache Flink that allows you to build scalable batch and streaming workloads, such as real-time data processing pipelines, large-scale exploratory data analysis, Machine context for creating Table and SQL API programs. Flink is an unified streaming and batch computing engine, which provides unified streaming and batch API to create a TableEnvironment. TableEnvironment is responsible table_environment.tableenvironment at="" 0x7fcd16342ac8=""> [2]: # Create a streaming TableEnvironment env_settings = EnvironmentSettings.in_streaming_mode() table_env = TableEnvironment.create(env_settings) table_env