Data Access Version 5.3.36-SNAPSHOT ## Table of Contents 1. Transaction Management ..... 2 1.1. Advantages of the Spring Framework's Transaction Support Model ..... 2 1.1.1. Global Transactions Transactions ..... 2 1.1.2. Local Transactions ..... 3 1.1.3. Spring Framework's Consistent Programming Model ..... 3 1.2. Understanding the Spring Framework Transaction Abstraction ..... 4 1.2.1. Hibernate urceProxy ..... 11 1.4. Declarative Transaction Management ..... 11 1.4.1. Understanding the Spring Framework's Declarative Transaction Implementation ..... 12 1.4.2. Example of Declarative Transaction

frameworks day Spring Data JPA Agenda ORM and relational mapping. Technologies Spring Data and JPA overview API usage Queries and CRUD operations Performance & speed APPROVED Relational data mapping JDBC JDBC Spring JDBC jOOQ QueryDSL JPA Spring Data Sergiy Morenets, 2020 ORM. Mapped entities @Getter @Setter @Entity @Table public class Product { @Id private int id; private String name; private Set Morenets, 2020 JAKARTA EE Spring Data Started in 2008 Reduces programming effort and avoids boilerplate code Db-agnostic repository abstraction Dynamic query construction Spring Data JPA is abstraction layer

nts/7/d/6/8/7d68b4c74e48f9837e9ad490adbd9f8e/p1_1.jpg) ### Red Hat OpenShift Data Foundation 4.12 ## 更新 OpenShift Data Foundation 针对集群和存储管理员的有关升级的说明 Powered by TCPDF (www.tcpdf.org) 针对集群和存储管理员的有关升级的说明 本文档解释了如何更新以前的 Red Hat OpenShift Data Foundation 版本。 ## 目录 使开源包含更多 ..... 3 对红帽文档提供反馈 ..... 4 第 1 章 OPENSHIFT DATA FOUNDATION 更新过程概述 ..... 5 第 2 章 OPENSHIFT DATA FOUNDATION 升级频道和发行版本 ..... 6 第 将 RED HAT OPENSHIFT DATA FOUNDATION 4.11 更新至 4.12 ..... 7 第 4 章 将 RED HAT OPENSHIFT DATA FOUNDATION 4.12.X 更新至 4.12.Y ..... 9 第 5 章 更改更新批准策略 ..... 11 第 6 章 更新 OPENSHIFT DATA FOUNDATION 外部机密 ....

# Simple Data Storage; SQLite Duen Horng (Polo) Chau Associate Professor, College of Computing Associate Director, MS Analytics Georgia Tech ## How to store the data? What's the easiest way? ## ## Easiest Way to Store Data As comma-separated files (CSV) But may not be easy to parse. Why? 1997, Ford, E350 # Easiest Way to Store Data 1997, Ford, E350 • Any field may be quoted (that is, enclosed org/famous.html iPhone (iOS), Android, Chrome (browsers), Mac, etc. Self-contained: one file contains data + schema Serverless: database right on your computer Zero-configuration: no need to set up! See

# CS 591 K1: Data Stream Processing and Analytics Spring 2020 4/16: Skew mitigation Vasiliki (Vasia) Kalavri vkalavri@bu.edu ## Key partitioning  > δ*N, where N is the number of stream elements • The solution will randomized load balancing. IEEE TPDS 2001. • Manku, G.S., Motwani, R. Approximate frequency counts over data streams. VLDB 2002.

# CS 591 K1: Data Stream Processing and Analytics Spring 2020 2/25: State Management Vasiliki (Vasia) Kalavri vkalavri@bu.edu ## State in dataflow computations Any non-trivial streaming computation state types can you think of? • Count, sum, list, map, ... ## State management in Apache Flink All data maintained by a task and used to compute results: a local or instance variable that is accessed by com/blog/manage-rocksdb-memory-size-apache-flink ## RocksDB - RocksDB is a persistent key value store: data lives on disk, state can grow larger than available memory and will not be lost upon failure. - Keys

## CS 591 K1: Data Stream Processing and Analytics Spring 2020 ## 4 /14: Stream processing optimizations Vasiliki (Vasia) Kalavri vkalavri@bu.edu ## Topics covered in this lecture • Costs of streaming 10387ee7/p4_1.jpg) ## Dataflow graph • operators are nodes, data channels are edges • channels have FIFO semantics • streams of data elements flow continuously along edges ## Operators • receive selectivity always known at development time? ## Types of Parallelism Pipeline: A || B Task: B || C Data: A || A  ![Image

## CS 591 K1: Data Stream Processing and Analytics Spring 2020 2/11: Windows and Triggers Vasiliki (Vasia) Kalavri vkalavri@bu.edu ## Window operators • Practical way to perform operations on unbounded

## CS 591 K1: Data Stream Processing and Analytics Spring 2020 1/21: Introduction Vasiliki (Vasia) Kalavri vkalavri@bu.edu ## Course Information • Instructor: Vasiliki Kalavri • Office: MCS 206 • /syllabus.html • Class schedule: /lectures.html • including today’s slides • Piazza: piazza.com/bu/spring2020/cs591k1/home • For questions & discussions • Blackboard: learn.bu.edu/... • For quizzes 6d827594bab5473/p4_1.jpg) Fundamental Algorithms for representing, summarizing, and analyzing data streams ## Tools 

## CS 591 K1: Data Stream Processing and Analytics Spring 2020 2/06: Notions of time and progress Vasiliki (Vasia) Kalavri vkalavri@bu.edu ## Mobile game application • input stream: user activity captures the progress of the stage itself • minimum of input watermarks and event-times of non-late data ## Event-time update