Copyright
Brief Table of Contents
Table of Contents
Preface
Acknowledgments
About this Book
About the Cover Illustration
1. Spark and graphs
Chapter 1. Two important technologies: Spark and graphs
1.1. Spark: the step beyond Hadoop MapReduce
1.1.1. The elusive definition of Big Data
1.1.2. Hadoop: the world before Spark
1.1.3. Spark: in-memory MapReduce processing
1.2. Graphs: finding meaning from relationships
1.2.1. Uses of graphs
1.2.2. Types of graph data
1.2.3. Plain RDBMS inadequate for graphs
1.3. Putting them together for lightning fast graph processing: Spark GraphX
1.3.1. Property graph: adding richness
1.3.2. Graph partitioning: graphs meet Big Data
1.3.3. GraphX lets you choose: graph parallel or data parallel
1.3.4. Various ways GraphX fits into a processing flow
1.3.5. GraphX vs. other systems
1.3.6. Storing the graphs: distributed file storage vs. graph database
1.4. Summary
Chapter 2. GraphX quick start
2.1. Getting set up and getting data
2.2. Interactive GraphX querying using the Spark Shell
2.3. PageRank example
2.4. Summary
Chapter 3. Some fundamentals
3.1. Scala, the native language of Spark
3.1.1. Scala���s philosophy: conciseness and expressiveness
3.1.2. Functional programming
3.1.3. Inferred typing
3.1.4. Class declaration
3.1.5. Map and reduce
3.1.6. Everything is a function
3.1.7. Java interoperability
3.2. Spark
3.2.1. Distributed in-memory data: RDDs
3.2.2. Laziness
3.2.3. Cluster requirements and terminology
3.2.4. Serialization
(try again in a couple of minutes)