Learning apache apex : real-time streaming applications with apex / Thomas Weise [and three others].

Format:

Book

Author/Creator:

Weise, Thomas, author.

Language:

English

Subjects (All):

Apache Apex.

Application software--Development.

Application software.

Physical Description:

1 online resource (1 volume) : illustrations

Edition:

1st edition

Place of Publication:

Birmingham, [England] ; Mumbai, [India] : Packt Publishing, 2017.

System Details:

Mode of access: World Wide Web.

text file

Biography/History:

Gundabattula Ananth: Ananth is a senior application architect in the Decisioning and Advanced Analytics architecture team for Commonwealth Bank of Australia. Ananth holds a Ph. D degree in the domain of computer science security and is interested in all things data including low latency distributed processing systems, machine learning and data engineering domains. He holds 3 patents granted by USPTO and has one application pending. Prior to joining to CBA, he was an architect at Threatmetrix and the member of the core team that scaled Threatmetrix architecture to 100 million transactions per day that runs at very low latencies using Cassandra, Zookeeper and Kafka. He also migrated Threatmetrix data warehouse into the next generation architecture based on Hadoop and Impala. Prior to Threatmetrix, he worked for the IBM software labs and IBM CIO labs enabling some of the first IBM CIO projects onboarding HBase, Hadoop and Mahout stack. Ananth is a committer for Apache Apex and is currently working for the next generation architectures for CBA fraud platform and Advanced Analytics Omnia platform at CBA. Weise Thomas: Thomas Weise is the Apache Apex PMC Chair and cofounder at Atrato. Earlier, he worked at a number of other technology companies in the San Francisco Bay Area, including DataTorrent, where he was a cofounder of the Apex project. Thomas is also a committer to Apache Beam and has contributed to several more of the ecosystem projects. He has been working on distributed systems for 20 years and has been a speaker at international big data conferences. Thomas received the degree of Diplom-Informatiker (MSc in computer science) from TU Dresden, Germany. He can be reached on Twitter at: @thweise. V. Ramanath Munagala: Dr. Munagala V. Ramanath got his PhD in Computer Science from the University of Wisconsin, USA and an MSc in Mathematics from Carleton University, Ottawa, Canada. After that, he taught Computer Science courses as Assistant/Associate Professor at the University of Western Ontario in Canada for a few years, before transitioning to the corporate sphere. Since then, he has worked as a senior software engineer at a number of technology companies in California including SeeBeyond, EMC, Sun Microsystems, DataTorrent, and Cloudera. He has published papers in peer reviewed journals in several areas including code optimization, graph theory, and image processing. Yan David: David Yan is based in the Silicon Valley, California. He is a senior software engineer at Google. Prior to Google, he worked at DataTorrent, Yahoo! , and the Jet Propulsion Laboratory. David holds a master of science in Computer Science from Stanford University and a bachelor of science in Electrical Engineering and Computer Science from the University of California at BerkeleyKnowles Kenneth: Kenneth Knowles is a founding PMC member of Apache Beam. Kenn has been working on Google Cloud DataflowGoogle's Beam backendsince 2014. Prior to that, he built backends for startups such as Cityspan, Inkling, and Dimagi. Kenn holds a PhD in Programming Language Theory from the University of California, Santa Cruz.

Summary:

Designing and writing a real-time streaming publication with Apache Apex About This Book Get a clear, practical approach to real-time data processing Program Apache Apex streaming applications This book shows you Apex integration with the open source Big Data ecosystem Who This Book Is For This book assumes knowledge of application development with Java and familiarity with distributed systems. Familiarity with other real-time streaming frameworks is not required, but some practical experience with other big data processing utilities might be helpful. What You Will Learn Put together a functioning Apex application from scratch Scale an Apex application and configure it for optimal performance Understand how to deal with failures via the fault tolerance features of the platform Use Apex via other frameworks such as Beam Understand the DevOps implications of deploying Apex In Detail Apache Apex is a next-generation stream processing framework designed to operate on data at large scale, with minimum latency, maximum reliability, and strict correctness guarantees. Half of the book consists of Apex applications, showing you key aspects of data processing pipelines such as connectors for sources and sinks, and common data transformations. The other half of the book is evenly split into explaining the Apex framework, and tuning, testing, and scaling Apex applications. Much of our economic world depends on growing streams of data, such as social media feeds, financial records, data from mobile devices, sensors and machines (the Internet of Things - IoT). The projects in the book show how to process such streams to gain valuable, timely, and actionable insights. Traditional use cases, such as ETL, that currently consume a significant chunk of data engineering resources are also covered. The final chapter shows you future possibilities emerging in the streaming space, and how Apache Apex can contribute to it. Style and approach This book is divided into two major parts: first it explains what Apex is, what its relevant parts are, and how to write well-built Apex applications. The second part is entirely application-driven, walking you through Apex applications of increasing complexity.

Contents:

Cover

Title Page

Copyright

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Customer Feedback

Table of Contents

Preface

Chapter 1: Introduction to Apex

Unbounded data and continuous processing

Stream processing

Stream processing systems

What is Apex and why is it important?

Use cases and case studies

Real-time insights for Advertising Tech (PubMatic)

Industrial IoT applications (GE)

Real-time threat detection (Capital One)

Silver Spring Networks (SSN)

Application Model and API

Directed Acyclic Graph (DAG)

Apex DAG Java API

High-level Stream Java API

SQL

JSON

Windowing and time

Value proposition of Apex

Low latency and stateful processing

Native streaming versus micro-batch

Performance

Where Apex excels

Where Apex is not suitable

Summary

Chapter 2: Getting Started with Application Development

Development process and methodology

Setting up the development environment

Creating a new Maven project

Application specifications

Custom operator development

The Apex operator model

CheckpointListener/CheckpointNotificationListener

ActivationListener

IdleTimeHandler

Application configuration

Testing in the IDE

Writing the integration test

Running the application on YARN

Execution layer components

Installing Apex Docker sandbox

Running the application

Working on the cluster

YARN web UI

Apex CLI

Logging

Dynamically adjusting logging levels

Chapter 3: The Apex Library

An overview of the library

Integrations

Apache Kafka

Kafka input

Kafka output

Other streaming integrations

JMS (ActiveMQ, SQS, and so on)

Kinesis streams

Files

File input

File splitter and block reader

File writer

Databases

JDBC input

JDBC output

Other databases.

Transformations

Parser

Filter

Enrichment

Map transform

Custom functions

Windowed transformations

Windowing

Global Window

Time Windows

Sliding Time Windows

Session Windows

Window propagation

State

Accumulation

Accumulation Mode

State storage

Watermarks

Allowed lateness

Triggering

Merging of streams

The windowing example

Dedup

Join

State Management

Chapter 4: Scalability, Low Latency, and Performance

Partitioning and how it works

Elasticity

Partitioning toolkit

Configuring and triggering partitioning

StreamCodec

Unifier

Custom dynamic partitioning

Performance optimizations

Affinity and anti-affinity

Low-latency versus throughput

Sample application for dynamic partitioning

Performance - other aspects for custom operators

Chapter 5: Fault Tolerance and Reliability

Distributed systems need to be resilient

Fault-tolerance components and mechanism in Apex

Checkpointing

When to checkpoint

How to checkpoint

What to checkpoint

Incremental state saving

Incremental recovery

Processing guarantees

Example - exactly-once counting

The exactly-once output to JDBC

Chapter 6: Example Project - Real-Time Aggregation and Visualization

Streaming ETL and beyond

The application pattern in a real-world use case

Analyzing Twitter feed

Top Hashtags

TweetStats

Configuring Twitter API access

Enabling WebSocket output

The Pub/Sub server

Grafana visualization

Installing Grafana

Installing Grafana Simple JSON Datasource

The Grafana Pub/Sub adapter server

Setting up the dashboard

Chapter 7: Example Project - Real-Time Ride Service Data Processing

The goal

Datasource

The pipeline.

Simulation of a real-time feed using historical data

Parsing the data

Looking up of the zip code and preparing for the windowing operation

Windowed operator configuration

Serving the data with WebSocket

Running the application on GCP Dataproc

Chapter 8: Example Project - ETL Using SQL

The application pipeline

Building and running the application

The application code

Partitioning

Application testing

Understanding application logs

Calcite integration

Chapter 9: Introduction to Apache Beam

Introduction to Apache Beam

Beam concepts

Pipelines, PTransforms, and PCollections

ParDo - elementwise computation

GroupByKey/CombinePerKey - aggregation across elements

Windowing, watermarks, and triggering in Beam

Windowing in Beam

Watermarks in Beam

Triggering in Beam

Advanced topic - stateful ParDo

WordCount in Apache Beam

Setting up your pipeline

Reading the works of Shakespeare in parallel

Splitting each line on spaces

Eliminating empty strings

Counting the occurrences of each word

Format your results

Writing to a sharded text file in parallel

Testing the pipeline at small scale with DirectRunner

Running Apache Beam WordCount on Apache Apex

Chapter 10: The Future of Stream Processing

Lower barrier for building streaming pipelines

Visual development tools

Streaming SQL

Better programming API

Bridging the gap between data science and engineering

Machine learning integration

State management

State query and data consistency

Containerized infrastructure

Management tools

Index.

Notes:

Includes bibliographical references and index.

Description based on online resource; title from PDF title page (EBC, viewed January 3, 2018).

ISBN:

9781788294119

1788294114

OCLC:

1019128795