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Part Ⅰ．Architectural Considerations for Hadoop Applications 1

1．Data Modeling in Hadoop 1

Data Storage Options 2

Standard File Formats 4

Hadoop File Types 5

Serialization Formats 7

Columnar Formats 9

Compression 12

HDFS Schema Design 14

Location of HDFS Files 16

Advanced HDFS Schema Design 17

HDFS Schema Design Summary 21

HBase Schema Design 21

Row Key 22

Timestamp 25

Hops 25

Tables and Regions 26

Using Columns 28

Using Column Families 30

Time-to-Live 30

Managing Metadata 31

What Is Metadata？ 31

Why Care About Metadata？ 32

Where to Store Metadata？ 32

Examples of Managing Metadata 34

Limitations of the Hive Metastore and HCatalog 34

Other Ways of Storing Metadata 35

Conclusion 36

2．Data Movement 39

Data Ingestion Considerations 39

Timeliness of Data Ingestion 40

Incremental Updates 42

Access Patterns 43

Original Source System and Data Structure 44

Transformations 47

Network Bottlenecks 48

Network Security 49

Push or Pull 49

Failure Handling 50

Level of Complexity 51

Data Ingestion Options 51

File Transfers 52

Considerations for File Transfers versus Other Ingest Methods 55

Sqoop：Batch Transfer Between Hadoop and Relational Databases 56

Flume：Event-Based Data Collection and Processing 61

Kafka 71

Data Extraction 76

Conclusion 77

3．Processing Data in Hadoop 79

MapReduce 80

MapReduce Overview 80

Example for MapReduce 88

When to Use MapReduce 94

Spark 95

Spark Overview 95

Overview of Spark Components 96

Basic Spark Concepts 97

Benefits of Using Spark 100

Spark Example 102

When to Use Spark 104

Abstractions 104

Pig 106

Pig Example 106

When to Use Pig 109

Crunch 110

Crunch Example 110

When to Use Crunch 115

Cascading 115

Cascading Example 116

When to Use Cascading 119

Hive 119

Hive Overview 119

Example of Hive Code 121

When to Use Hive 125

Impala 126

Impala Overview 127

Speed-Oriented Design 128

Impala Example 130

When to Use Impala 131

Conclusion 132

4．Common Hadoop Processing Patterns 135

Pattern：Removing Duplicate Records by Primary Key 135

Data Generation for Deduplication Example 136

Code Example：Spark Deduplication in Scala 137

Code Example：Deduplication in SQL 139

Pattern：Windowing Analysis 140

Data Generation for Windowing Analysis Example 141

Code Example：Peaks and Valleys in Spark 142

Code Example：Peaks and Valleys in SQL 146

Pattern：Time Series Modifications 147

Use HBase and Versioning 148

Use HBase with a RowKey of RecordKey and StartTime 149

Use HDFS and Rewrite the Whole Table 149

Use Partitions on HDFS for Current and Historical Records 150

Data Generation for Time Series Example 150

Code Example：Time Series in Spark 151

Code Example：Time Series in SQL 154

Conclusion 157

5．Graph Processing on Hadoop 159

What Is a Graph？ 159

What Is Graph Processing？ 161

How Do You Process a Graph in a Distributed System？ 162

The Bulk Synchronous Parallel Model 163

BSP by Example 163

Giraph 165

Read and Partition the Data 166

Batch Process the Graph with BSP 168

Write the Graph Back to Disk 172

Putting It All Together 173

When Should You Use Giraph？ 174

GraphX 174

Just Another RDD 175

GraphX Pregel Interface 177

vprog（） 178

sendMessage（） 179

mergeMessage（） 179

Which Tool to Use？ 180

Conclusion 180

6．Orchestration 183

Why We Need Workflow Orchestration 183

The Limits of Scripting 184

The Enterprise Job Scheduler and Hadoop 186

Orchestration Frameworks in the Hadoop Ecosystem 186

Oozie Terminology 188

Oozie Overview 188

Oozie Workflow 191

Workflow Patterns 194

Point-to-Point Workflow 194

Fan-Out Workflow 196

Capture-and-Decide Workflow 198

Parameterizing Workflows 201

Classpath Definition 203

Scheduling Patterns 204

Frequency Scheduling 205

Time and Data Triggers 205

Executing Workflows 210

Conclusion 210

7．Near-Real-Time Processing with Hadoop 213

Stream Processing 215

Apache Storm 217

Storm High-Level Architecture 218

Storm Topologies 219

Tuples and Streams 221

Spouts and Bolts 221

Stream Groupings 222

Reliability of Storm Applications 223

Exactly-Once Processing 223

Fault Tolerance 224

Integrating Storm with HDFS 225

Integrating Storm with HBase 225

Storm Example：Simple Moving Average 226

Evaluating Storm 233

Trident 233

Trident Example：Simple Moving Average 234

Evaluating Trident 237

Spark Streaming 237

Overview of Spark Streaming 238

Spark Streaming Example：Simple Count 238

Spark Streaming Example：Multiple Inputs 240

Spark Streaming Example：Maintaining State 241

Spark Streaming Example：Windowing 243

Spark Streaming Example：Streaming versus ETL Code 244

Evaluating Spark Streaming 245

Flume Interceptors 246

Which Tool to Use？ 247

Low-Latency Enrichment,Validation,Alerting,and Ingestion 247

NRT Counting,Rolling Averages,and Iterative Processing 248

Complex Data Pipelines 249

Conclusion 250

Part Ⅱ．Case Studies 250

8．Clickstream Analysis 253

Defining the Use Case 253

Using Hadoop for Clickstream Analysis 255

Design Overview 256

Storage 257

Ingestion 260

The Client Tier 264

The Collector Tier 266

Processing 268

Data Deduplication 270

Sessionization 272

Analyzing 275

Orchestration 276

Conclusion 279

9．Fraud Detection 281

Continuous Improvement 281

Taking Action 282

Architectural Requirements of Fraud Detection Systems 283

Introducing Our Use Case 283

High-Level Design 284

Client Architecture 286

Profile Storage and Retrieval 287

Caching 288

HBase Data Definition 289

Delivering Transaction Status：Approved or Denied？ 294

Ingest 295

Path Between the Client and Flume 296

Near-Real-Time and Exploratory Analytics 302

Near-Real-Time Processing 302

Exploratory Analytics 304

What About Other Architectures？ 305

Flume Interceptors 305

Kafka to Storm or Spark Streaming 306

External Business Rules Engine 306

Conclusion 307

10．Data Warehouse 309

Using Hadoop for Data Warehousing 312

Defining the Use Case 314

OLTP Schema 316

Data Warehouse：Introduction and Terminology 317

Data Warehousing with Hadoop 319

High-Level Design 319

Data Modeling and Storage 320

Ingestion 332

Data Processing and Access 337

Aggregations 341

Data Export 343

Orchestration 344

Conclusion 345

A．Joins in Impala 347

Index 353