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1 Big Data Principles and best practices of scalable real-time data systems NATHAN MARZ JAMES WARREN II MANNING Shelter Island

2 contents preface xiii acknowledgments xv about this book xviii ~1 Anew paradigm for Big Data How this book is structured Scaling with a traditional database 3 Scaling with a queue 3 Scaling by sharding the database 4 Fault-tolerance issues begin 5 Corruption issues 5 What went wrong? 5m How will Big Data techniques help? NoSQL is not a panacea First principles Desired properties of a Big Data system 7 Robustness and fault tolerance 7 Low latency reads and updates 8 Scalability 8 Generalization 8 Extensibility 8 Ad hoc queries 8 Minimal maintenance 9 Debuggability The problems with fully incremental architectures 9 Operational complexity 10 Extreme complexity of achieving eventual consistency 11 Lack of human-fault tolerance 12 Fully incremental solution vs. Lambda Architecture solution 13 v

3 vi CONTENTS 1.7 Lambda Architecture 14 Batch layer 16 Serving layer 17 * Batch and serving layers satisfy almost all properties 17* Speed layer Recent trends in technology 20 CPUs aren't getting faster 20 Elastic clouds 21 Vibrant open source ecosystemfor BigData Example application: SuperWebAnalytics.com Summary 23 Part 1 Batch layer 25 2 Data model for Big Data The properties of data 29 Data is raw 31 * Data is immutable 34 Data is eternally true The fact-based model for representing data 37 Example facts and their properties 37 Benefits of the fact-based model Graph schemas 43 Elements of a graph schema 43 * The need for an enforceable schema A complete data model for SuperWebAnalytics.com Summary 46 3 Data modelfor Big Data: Illustration Why a serialization framework? Apache Thrift 48 Nodes 49 Edges 49 Properties 50 Tying everything together into data objects 51 Evolving your schema Limitations of serialization frameworks 52 ^ 3.4 Summary 53 Data storage on the batch layer Storage requirements for the master dataset Choosing a storage solution for the batch layer 56 Using a key/value store for the master dataset jilesystems 57 56* Distributed

4 4.3 How distributed filesystems work Storing a master dataset with a distributed filesystem Vertical partitioning Low-level nature of distributed filesystems Storing the SuperWebAnalytics.com distributed filesystem Summary 64 master dataset on a Data storage on the batch layer: Illustration Using the Hadoop Distributed File System 66 The small-files problem 67 Towards a higher-level abstraction Data storage in the batch layer with Pail 68 Basic Pail operations 69 Serializing objects into pails 70 Batch operations using Pail 72 Vertical partitioning with Pail 73 Pail fileformats and compression 74 Summarizing the benefits of Pail Storing the master dataset for SuperWebAnalytics.com 76 A structured pail for Thrift objects 77 A basic pail for SuperWebAnalytics.com 78 A split pail to vertically partition the dataset Summary 82 Batch layer Motivating examples 84 Number of pageviews over time 84 Gender inference 85 Influence score Computing on the batch layer Recomputation algorithms vs. incremental algorithms 88 Performance 89 Human-fault tolerance 90 Generality ofthe algorithms 91 Choosing a style of algorithm Scalability in the batch layer MapReduce: a paradigm for Big Data computing 93 Scalability 94 Fault-tolerance 96 Generality ofmapreduce Low-level nature of MapReduce 99 Multistep computations Joins complicated to implement manually execution tightly coupled 101 are unnatural 99 are very 99 Logical and physical

5 6.7 Pipe diagrams: a higher-level way of thinking about batch computation 102 Concepts ofpipe diagrams 102 Executing pipe diagrams via MapReduce 106 Combiner aggregators 107 Pipe diagram examples Summary 109 Batch layer: Illustration An illustrative example Common pitfalls of data-processing Custom languages 114 Poorly composable 7.3 An introduction to JCascalog 115 tools 114 abstractions 115 TheJCascalog data model 116 The structure of a JCascalog query 117 Querying multiple datasets 119 Grouping and aggregators 121 Stepping though an example query 122 Custom predicate operations Composition 130 Combining subqueries 130 Dynamically created subqueries 131 Predicate macros 134 Dynamically created predicate macros Summary 138 An example batch layer: Architecture and algorithms Design of the SuperWebAnalytics.com batch layer 140 Supported queries 140 Batch views Workflow overview Ingesting new data URL normalization User-identifier normalization Deduplicate pageviews Computing batch views 151 Pageviews Bounce-rate analysis 152 over time 151 Unique visitors over time Summary 154

6 ix An example batch layer: Implementation Starting point Preparing the workflow Ingesting new data URL normalization User-identifier normalization Deduplicate pageviews Computing batch views 169 Pageviews Uniques over time 171 Bouncerate analysis 172 over time Summary 175 Part 2 Serving layer 177 Serving layer Performance metrics for the serving layer The serving layer solution to the normalization/ denormalization problem Requirements for a serving layer database DesigningaservinglayerforSuperWebAnalytics.com 186 Pageviews over time 186 Uniques over time 187 Bouncerate analysis Contrasting with a fully incremental solution 188 Fully incremental solution to uniques over time 188 Comparing to the Lambda Architecture solution Summary 195 Serving layer: Illustration Basics of ElephantDB 197 View creation in ElephantDB 197 View serving in ElephantDB 197 Using ElephantDB BuildingtheservinglayerforSuperWebAnalytics.com 200 Pageviews over time 200 Uniques over time 202 Bouncerate analysis Summary 204

7 3 Speed layer Realtime views Computing 12.2 Storing realtime views 209 realtime views 210 Eventual accuracy 211 layer 211 Amount of state stored in the speed 12.3 Challenges of incremental computation 212 Validity of the CAP theorem 213 The complex interaction between the CAP theorem and incremental algorithms Asynchronous versus synchronous updates Expiring 12.6 Summary 219 realtime views 217 Realtime views: Illustration Cassandra's data model Using Cassandra 222 Advanced Cassandra Summary 224 Queuing and stream processing Queuing 226 Single-consumer queue servers 226 Multi-consumer queues Stream processing 229 Queues and workers 230 Queues-and-workers pitfalls Higher-level, one-at-a-time stream processing 231 Storm model 232 Guaranteeing message processing SuperWebAnalytics.com speed layer 238 Topology 14.5 Summary 241 structure 240 Queuing and stream processing: 15.1 Defining topologies with Apache Illustration 242 Storm Apache Storm clusters and deployment Guaranteeing message processing 247

8 xi 15.4 Implementing the SuperWebAnalytics.com uniques-over-time speed layer Summary Micro-batch stream processing Achieving exactly-once semantics 255 Strongly ordered processing 255 Micro-batch stream processing 256 Micro-batch processing topologies Core concepts of micro-batch stream processing Extending pipe diagrams for micro-batch processing FinishingthespeedlayerforSuperWebAnalytics.com 262 Pageviews over time 262 Bounce-rate analysis Another look at the bounce-rate-analysis example Summary 268 ~1 Micro-batch stream processing: Illustration 269 * 17.1 Using Trident Finishing the SuperWebAnalytics.com speed layer 273 Pageviews over time 273 Bounce-rate analysis Fully fault-tolerant, in-memory, micro-batch processing Summary 283 Lambda Architecture in depth Defining data systems Batch and serving layers 286 Incremental batch processing 286 Measuring and optimizing batch layer resource usage Speed layer Query layer Summary 299 index 301

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