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E-raamat: Pro Spark Streaming: The Zen of Real-Time Analytics Using Apache Spark

  • Formaat: PDF+DRM
  • Ilmumisaeg: 13-Jun-2016
  • Kirjastus: APress
  • Keel: eng
  • ISBN-13: 9781484214794
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  • Formaat: PDF+DRM
  • Ilmumisaeg: 13-Jun-2016
  • Kirjastus: APress
  • Keel: eng
  • ISBN-13: 9781484214794
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Learn the right cutting-edge skills and knowledge to leverage Spark Streaming to implement a wide array of real-time, streaming applications. This book walks you through end-to-end real-time application development using real-world applications, data, and code. Taking an application-first approach, each chapter introduces use cases from a specific industry and uses publicly available datasets from that domain to unravel the intricacies of production-grade design and implementation. The domains covered in Pro Spark Streaming include social media, the sharing economy, finance, online advertising, telecommunication, and IoT.









In the last few years, Spark has become synonymous with big data processing. DStreams enhance the underlying Spark processing engine to support streaming analysis with a novel micro-batch processing model. Pro Spark Streaming by Zubair Nabi will enable you to become a specialist of latency sensitive applications by leveraging the key features of DStreams, micro-batch processing, and functional programming. To this end, the book includes ready-to-deploy examples and actual code. Pro Spark Streamingwill act as the bible of Spark Streaming.

What You'll Learn







Discover Spark Streaming application development and best practices Work with the low-level details of discretized streams Optimize production-grade deployments of Spark Streaming via configuration recipes and instrumentation using Graphite, collectd, and Nagios Ingest data from disparate sources including MQTT, Flume, Kafka, Twitter, and a custom HTTP receiver Integrate and couple with HBase, Cassandra, and Redis Take advantage of design patterns for side-effects and maintaining state across the Spark Streaming micro-batch model Implement real-time and scalable ETL using data frames, SparkSQL, Hive, and SparkR Use streaming machine learning, predictive analytics, and recommendations Mesh batch processing with stream processing via the Lambda architecture

Who This Book Is For

Data scientists, big data experts, BI analysts, and data architects.
About the Author xiii
About the Technical Reviewer xv
Acknowledgments xvii
Introduction xix
Chapter 1 The Hitchhiker's Guide to Big Data
1(8)
Before Spark
1(7)
The Era of Web 2.0
2(4)
Sensors, Sensors Everywhere
6(2)
Spark Streaming: At the Intersection of MapReduce and CEP
8(1)
Chapter 2 Introduction to Spark
9(20)
Installation
10(1)
Execution
11(1)
Standalone Cluster
11(1)
YARN
12(1)
First Application
12(5)
Build
14(1)
Execution
15(2)
SparkContext
17(3)
Creation of RDDs
17(1)
Handling Dependencies
18(1)
Creating Shared Variables
19(1)
Job execution
20(1)
RDD
20(7)
Persistence
21(1)
Transformations
22(4)
Actions
26(1)
Summary
27(2)
Chapter 3 DStreams: Real-Time RDDs
29(22)
From Continuous to Discretized Streams
29(1)
First Streaming Application
30(4)
Build and Execution
32(1)
StreamingContext
32(2)
DStreams
34(16)
The Anatomy of a Spark Streaming Application
36(4)
Transformations
40(10)
Summary
50(1)
Chapter 4 High-Velocity Streams: Parallelism and Other Stories
51(18)
One Giant Leap for Streaming Data
51(2)
Parallelism
53(6)
Worker
53(1)
Executor
54(2)
Task
56(3)
Batch Intervals
59(1)
Scheduling
60(3)
Inter-application Scheduling
60(1)
Batch Scheduling
61(1)
Inter-job Scheduling
61(1)
One Action, One Job
61(2)
Memory
63(3)
Serialization
63(2)
Compression
65(1)
Garbage Collection
65(1)
Every Day I'm Shuffling
66(1)
Early Projection and Filtering
66(1)
Always Use a Combiner
66(1)
Generous Parallelism
66(1)
File Consolidation
66(1)
More Memory
66(1)
Summary
67(2)
Chapter 5 Real-Time Route 66: Linking External Data Sources
69(30)
Smarter Cities, Smarter Planet, Smarter Everything
69(2)
Receiver lnputDStream
71(1)
Sockets
72(8)
MQTT
80(4)
Flume
84(2)
Push-Based Flume Ingestion
85(1)
Pull-Based Flume Ingestion
86(1)
Kafka
86(6)
Receiver-Based Kafka Consumer
89(2)
Direct Kafka Consumer
91(1)
Twitter
92(1)
Block Interval
93(1)
Custom Receiver
93(4)
HttplnputDStream
94(3)
Summary
97(2)
Chapter 6 The Art of Side Effects
99(26)
Taking Stock of the Stock Market
99(2)
foreachRDD
101(7)
Per-Record Connection
103(1)
Per-Partition Connection
103(1)
Static Connection
104(1)
Lazy Static Connection
105(1)
Static Connection Pool
106(2)
Scalable Streaming Storage
108(8)
HBase
108(2)
Stock Market Dashboard
110(2)
SparkOnHBase
112(1)
Cassandra
113(2)
Spark Cassandra Connector
115(1)
Global State
116(7)
Static Variables
116(2)
updateStateByKey()
118(1)
Accumulators
119(2)
External Solutions
121(2)
Summary
123(2)
Chapter 7 Getting Ready for Prime Time
125(26)
Every Click Counts
125(1)
Tachyon (Alluxio)
126(2)
Spark Web UI
128(15)
Historical Analysis
142(1)
RESTful Metrics
142(1)
Logging
143(1)
External Metrics
144(2)
System Metrics
146(1)
Monitoring and Alerting
147(2)
Summary
149(2)
Chapter 8 Real-Time ETL and Analytics Magic
151(26)
The Power of Transaction Data Records
151(2)
First Streaming Spark SQL Application
153(2)
SQLContext
155(6)
Data Frame Creation
155(3)
SQL Execution
158(1)
Configuration
158(1)
User-Defined Functions
159(1)
Catalyst: Query Execution and Optimization
160(1)
HiveContext
160(1)
Data Frame
161(9)
Types
162(1)
Query Transformations
162(6)
Actions
168(2)
RDD Operations
170(1)
Persistence
170(1)
Best Practices
170(1)
SparkR
170(1)
First SparkR Application
171(4)
Execution
172(1)
Streaming SparkR
173(2)
Summary
175(2)
Chapter 9 Machine Learning at Scale
177(22)
Sensor Data Storm
177(2)
Streaming MLlib Application
179(3)
MLlib
182(4)
Data Types
182(2)
Statistical Analysis
184(1)
Proprocessing
185(1)
Feature Selection and Extraction
186(1)
Chi-Square Selection
186(1)
Principal Component Analysis
187(1)
Learning Algorithms
187(7)
Classification
188(1)
Clustering
189(1)
Recommendation Systems
190(3)
Frequent Pattern Mining
193(1)
Streaming ML Pipeline Application
194(2)
ML
196(1)
Cross-Validation of Pipelines
197(1)
Summary
198(1)
Chapter 10 Of Clouds, Lambdas, and Pythons
199(28)
A Good Review Is Worth a Thousand Ads
200(1)
Google Dataproc
200(5)
First Spark on Dataproc Application
205(7)
PySpark
212(2)
Lambda Architecture
214(8)
Lambda Architecture using Spark Streaming on Google Cloud Platform
215(7)
Streaming Graph Analytics
222(3)
Summary
225(2)
Index 227
Zubair Nabi is one of the very few computer scientists who have solved Big Data problems in all three domains: academia, research, and industry. He currently works at Qubit, a London-based start up backed by Goldman Sachs, Accel Partners, Salesforce Ventures, and Balderton Capital. Qubit helps retailers understand their customers and provide personalized customer experience, and which has a rapidly growing client base that includes Staples, Emirates, Thomas Cook, and Topshop. Prior to Qubit, he was a researcher at IBM Research, where he worked at the intersection of Big Data systems and analytics to solve real-world problems in the telecommunication, electricity, and urban dynamics space. Zubairs work has been featured in MIT Technology Review, SciDev, CNET, and Asian Scientist, and on Swedish National Radio, among others. He has authored more than 20 research papers, published by some of the top publication venues in computer science including USENIX Middleware, ECML PKDD, and IEEE BigData; and he also has a number of patents to his credit. Zubair has an MPhil in computer science with distinction from Cambridge.