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E-raamat: Pro Apache Phoenix: An SQL Driver for HBase

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  • Formaat: EPUB+DRM
  • Ilmumisaeg: 29-Dec-2016
  • Kirjastus: APress
  • Keel: eng
  • ISBN-13: 9781484223703
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Pro Apache Phoenix: An SQL Driver for HBaseSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 29-Dec-2016
  • Kirjastus: APress
  • Keel: eng
  • ISBN-13: 9781484223703
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This book gives a deep insight into leveraging Phoenix as an ANSI SQL engine built on top of highly distributed scalable No-SQL framework HBase. The author takes deep dive into phoenix while starting with basics and describes the best practices that are being adopted in Phoenix to enable a high write and read throughput in big data space. 

The book will be written with many real world use cases like IOT devices sending continuous streams to Phoenix  and through which explain how key features like Joins, Indexes, Transactions and Functions enable readers understand the simple, flexible yet powerful API that Phoenix provides. The examples will look into use cases like Real time data, Data driven businesses, which need to collect, analyze and act in seconds.  

The book will also delve into the nuances of setting up a distributed HBase cluster with Phoenix libraries, running performance benchmarks, configuring parameters for production scenarios and the ability to view the results.  

Finally, We will be covering how Phoenix plays well with other key frameworks in the Hadoop ecosystem like Apache Spark, Pig, Flume and Sqoop.


Readership
Data Engineers, Big Data Administrators and Architects  

What you will learn:
1. How to handle petabyte data store by applying familiar SQL techniques
2. Storing, Analyzing and manipulating data in NoSQL Hadoop echo system with HBase
3. Applying Best Practices while working with scalable data store on Hadoop and HBase
4. Integrating popular framework (Apache Spark, Pig, Flume) to simplify big data analysis
5. Demonstrating Real time use cases and big data modeling techniques

About the Authors xiii
About the Technical Reviewers xv
Chapter 1 Introduction
1(14)
1.1 Big Data Lake and Its Representation
2(1)
1.2 Modern Applications and Big Data
3(1)
1.2.1 Fraud Detection in Banking
3(1)
1.2.2 Log Data Analysis
3(1)
1.2.3 Recommendation Engines
4(1)
1.3 Analyzing Big Data
4(1)
1.4 An Overview of Hadoop and MapReduce
5(1)
1.5 Hadoop Ecosystem
5(7)
1.5.1 HDFS
6(1)
1.5.2 MapReduce
7(2)
1.5.3 HBase
9(1)
1.5.4 Hive
10(1)
1.5.5 YARN
11(1)
1.5.6 Spark
11(1)
1.5.7 PIG
11(1)
1.5.8 ZooKeeper
11(1)
1.6 Phoenix in the Hadoop Ecosystem
12(1)
1.7 Phoenix's Place in Big Data Systems
12(1)
1.8 Importance of Traditional SQL-Based Tools and the Role of Phoenix
12(2)
1.8.1 Traditional DBA Problems for Big Data Systems-
13(1)
1.8.2 Which Tool Should I Use for Big Data?
13(1)
1.8.3 Massive Data Storage and Challenges
13(1)
1.8.4 A Traditional Data Warehouse and Querying
13(1)
1.9 Apache Phoenix in Big Data Analytics
14(1)
1.10 Summary
14(1)
Chapter 2 Using Phoenix
15(22)
2.1 What is Apache Phoenix?
15(1)
2.2 Architecture
16(2)
2.2.1 Installing Apache Phoenix
17(1)
2.2.2 Installing Java
17(1)
2.3 Installing HBase
18(1)
2.4 Installing Apache Phoenix
19(1)
2.5 Installing Phoenix on Hortonworks HDP
20(10)
2.5.1 Downloading Hortonworks Sandbox
21(6)
2.5.2 Start HBase
27(1)
2.5.3 Testing Your Phoenix Installation
28(2)
2.6 Installing Phoenix on Cloudera Hadoop
30(1)
2.7 Capabilities
31(1)
2.8 Hadoop Ecosystem and the Role of Phoenix
32(1)
2.9 Brief Description of Phoenix's Key Features
33(2)
2.9.1 Transactions
33(1)
2.9.2 User-Defined Functions
33(1)
2.9.3 Secondary Indexes
34(1)
2.9.4 Skip Scan
34(1)
2.9.5 Views
34(1)
2.9.6 Multi-Tenancy
34(1)
2.9.7 Query Server
35(1)
2.10 Summary
35(2)
Chapter 3 CRUD with Phoenix
37(14)
3.1 Data Types in Phoenix
37(1)
3.1.1 Primitive Data Types
37(1)
3.1.2 Complex Data Types
37(1)
3.2 Data Model
38(1)
3.2.1 Steps in data modeling
39(1)
3.3 Phoenix Write Path
39(1)
3.4 Phoenix Read Path
39(1)
3.5 Basic Commands
39(4)
3.5.1 HELP
40(1)
3.5.2 CREATE
41(1)
3.5.3 UPSERT
41(1)
3.5.4 SELECT
41(1)
3.5.5 ALTER
42(1)
3.5.6 DELETE
42(1)
3.5.7 DESCRIBE
42(1)
3.5.8 LIST
43(1)
3.6 Working with Phoenix API
43(6)
3.6.1 Environment setup
43(6)
3.7 Summary
49(2)
Chapter 4 Querying Data
51(12)
4.1 Constraints
51(1)
4.1.1 NOT NULL
51(1)
4.2 Creating Tables
52(1)
4.3 Salted Tables
53(2)
4.4 Dropping Tables
55(1)
4.5 ALTER Tables
55(2)
4.5.1 Adding Columns
56(1)
4.5.2 Deleting or Replacing Columns
56(1)
4.5.3 Renaming a Column
57(1)
4.6 Clauses
57(3)
4.6.1 LIMIT
57(1)
4.6.2 WHERE
58(1)
4.6.3 GROUP BY
58(1)
4.6.4 HAVING
59(1)
4.6.5 ORDER BY
59(1)
4.7 Logical Operators
60(1)
4.7.1 AND
60(1)
4.7.2 OR
60(1)
4.7.3 IN
60(1)
4.7.4 LIKE
61(1)
4.7.5 BETWEEN
61(1)
4.8 Summary
61(2)
Chapter 5 Advanced Querying
63(16)
5.1 Joins
63(1)
5.2 Inner Join
63(1)
5.3 Outer Join
64(3)
5.3.1 Left Outer Join
64(1)
5.3.2 Right Outer Join
65(1)
5.3.3 Full Outer Join
66(1)
5.4 Grouped Joins
67(1)
5.5 Hash Join
68(1)
5.6 Sort Merge Join
69(1)
5.7 Join Query Optimizations
69(2)
5.7.1 Optimizing Through Configuration Properties
70(1)
5.7.2 Optimizing Query
70(1)
5.8 Subqueries
71(3)
5.8.1 IN and NOT IN in Subqueries
72(1)
5.8.2 EXISTS and NOT EXISTS Clauses
72(1)
5.8.3 ANY, SOME, and ALL Operators with Subqueries
73(1)
5.8.4 UPSERT Using Subqueries
73(1)
5.9 Views
74(1)
5.9.1 Creating Views
74(1)
5.9.2 Dropping Views
75(1)
5.10 Paged Queries
75(2)
5.10.1 LIMIT and OFFSET
76(1)
5.10.2 Row Value Constructor
76(1)
5.11 Summary
77(2)
Chapter 6 Transactions
79(12)
6.1 SQL Transactions
79(1)
6.2 Transaction Properties
79(1)
6.2.1 Atomicity
80(1)
6.2.2 Consistency
80(1)
6.2.3 Isolation
80(1)
6.2.4 Durability
80(1)
6.3 Transaction Control
80(1)
6.3.1 COMMIT
80(1)
6.3.2 ROLLBACK
80(1)
6.3.3 SAVEPOINT
81(1)
6.3.4 SET TRANSACTION
81(1)
6.4 Transactions in HBase
81(4)
6.4.1 Integrating HBase with Transaction Manager
81(1)
6.4.2 Components of Transaction Manager
82(2)
6.4.3 Transaction Lifecycle
84(1)
6.4.4 Concurrency Control
84(1)
6.4.5 Multiversion Concurrency Control
85(1)
6.4.6 Optimistic Concurrency Control
85(1)
6.5 Apache Tephra As a Transaction Manager
85(1)
6.6 Phoenix Transactions
86(4)
6.6.1 Enabling Transactions for Tables
89(1)
6.6.2 Committing Transactions
89(1)
6.7 Transaction Limitations in Phoenix
90(1)
6.8 Summary
90(1)
Chapter 7 Advanced Phoenix Concepts
91(20)
7.1 Secondary Indexes
91(11)
7.1.1 Global Index
92(4)
7.1.2 Local Index
96(3)
7.1.3 Covered Index
99(1)
7.1.4 Functional Indexes
100(1)
7.1.5 Index Consistency
100(2)
7.2 User Defined Functions
102(4)
7.2.1 Writing Custom User Defined Functions
102(4)
7.3 Phoenix Query Server
106(3)
7.3.1 Download
107(1)
7.3.2 Installation
107(1)
7.3.3 Setup
107(1)
7.3.4 Starting PQS
107(1)
7.3.5 Client
107(1)
7.3.6 Usage
108(1)
7.3.7 Additional PQS Features
109(1)
7.4 Summary
109(2)
Chapter 8 Integrating Phoenix with Other Frameworks
111(12)
8.1 Hadoop Ecosystem
111(1)
8.2 MapReduce Integration
111(4)
8.2.1 Setup
112(3)
8.3 Apache Spark Integration
115(3)
8.3.1 Setup
116(1)
8.3.2 Reading and Writing Using Dataframe
117(1)
8.4 Apache Hive Integration
118(2)
8.4.1 Setup
118(1)
8.4.2 Table Creation
119(1)
8.5 Apache Pig Integration
120(1)
8.5.1 Setup
120(1)
8.5.2 Accessing Data from Phoenix
120(1)
8.5.3 Storing Data to Phoenix
120(1)
8.6 Apache Flume Integration
121(1)
8.6.1 Setup
121(1)
8.6.2 Configuration
121(1)
8.6.3 Running the Above Setup
122(1)
8.7 Summary
122(1)
Chapter 9 Tools & Tuning
123(14)
9.1 Phoenix Tracing Server
123(4)
9.1.1 Trace
123(1)
9.1.2 Span
124(1)
9.1.3 Span Receivers
124(1)
9.1.4 Setup
124(3)
9.2 Phoenix Bulk Loading
127(2)
9.2.1 Setup
127(1)
9.2.2 Gotchas
128(1)
9.3 Index Load Async
129(1)
9.4 Pherf
129(6)
9.4.1 Setup to Run the Test
133(1)
9.4.2 Gotchas
134(1)
9.5 Summary
135(2)
Index 137
Shakil Akhtar is TOGAF 9 Certified Enterprise Architect passionate about Digital Transformation, Cloud Computing, Big Data and Internet of Things technologies. He holds many certifications including Oracle Certified Master Java Enterprise Architect (OCMJEA). He worked with Cisco, Oracle, CA Technologies and various other organizations. Where he developed and architected large-scale complex enterprise software, creating frameworks and scaling systems to petabyte datasets. He is an enthusiastic open source user and longtime fan. When not working, he can be found playing guitar and doing some jamming sessions with his friends. Ravi Mugham, an engineer passionate about data and data-driven engineering, experienced with working and scaling solutions to petabyte datasets. In his past experience, he has worked with CA Technologies, Bazaarvoice and various other startups. Actively involved in open source projects and is a PMC member to Apache Phoenix. Currently, his interests are in Distributed Data stream processing
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