## 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 Batch Data MapReduce, Dryad FlumeJava, Spark Structured Data Spark SQL Machine Learning Mliib Tensorflow Streaming Data Storm, SEEP, Naiad, Spark Streaming, Flink, Millwheel, Google Dataflow ## 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

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

# Spark 简介以及与 Hadoop 的对比 ## 1 Spark 简介 ### 1.1 Spark 概述 Spark 是 UC Berkeley AMP lab 所开源的类 Hadoop MapReduce 的通用的并行计算框架，Spark 基于 map reduce 算法实现的分布式计算，拥有 Hadoop MapReduce 所具有的优点；但不同于 MapReduce 的是 Job 中间输出和结果可以保存在内存中，从而不再需要读写 HDFS，因此 Spark 能更好地适用于数据挖掘与机器学习等需要迭代的 map reduce 的算法。 ### 1.2 Spark 核心概念 #### 1.2.1 弹性分布数据集（RDD） RDD 是 Spark 的最基本抽象, 是对分布式内存的抽象使用, 实现了以操作本地集合的方式来操作分布式数据集的抽象实现。RDD 是 Spark 最核心的东西, 它表示已被分区, 不可变的并能够被并行操作的数据集合 RDD 的操作不是马上执行，Spark 在遇到 Transformations 操作时只会记录需要这样的操作，并不会去执行，需要等到有 Actions 操作的时候才会真正启动计算过程进行计算。 2. 操作(Actions) (如 : count, collect, save 等), Actions 操作会返回结果或把 RDD 数据写到存储系统中。Actions 是触发 Spark 启动计算的动因。 ####

MATLAB与Spark/Hadoop相集成：实现大数据的处理和价值挖 马文辉  ## 内容 ## 大数据及其带来的挑战 ## ■ MATLAB大数据处理 tall数组 并行与分布式计算 ## ■ MATLAB与Spark/Hadoop集成 MATLAB与Spark/Hadoop集成 MATLAB访问HDFS(Hadoop分布式文件系统) 在Spark/Hadoop集群上运行MATLAB代码 ## 应用演示－汽车传感器数据分析 ## 大数据概述 大数据的"4V"特征： - Volumes - 数据规模，数据规模巨大 互联网、社交网络的普及，全社会的数字化转型，数据规模向PB级发展 Variety - 数据种类，数据种类繁多 结构化数据，半结构化数据，非结构化数据 ImageDatastore R2016a 编程 ■ Streaming ■ Block Processing Parallel-for loops ■ GPU Arrays SPMD and Distributed Arrays MapReduce R2014b MapReduce (MDCS/PCT) R2014b - MATLAB API for Spark API R2016b Tall Arrays

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

JDBC interface for large-scale data processing and analytics, built on top of Apache Spark $ ^{™} $ [http://spark.apache.org/]. In general, the complete ecosystem of Kyuubi falls into the hierarchies the above figure, with each layer loosely coupled to the other. For example, you can use Kyuubi, Spark and Apache Iceberg [https://iceberg.apache.org/] to build and manage Data Lake with pure SQL for multi-tenancy, and this is why we want to create this project despite that the Spark Thrift JDBC/ODBC server [http://spark.apache.org/docs/latest/sql-distributed-sql-engine.html#running-the-thrift-jdbcodbc-server]