Spark in Action
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Dig in and get your hands dirty with one of the hottest data processing engines today. A great guide.
Spark in Action teaches you the theory and skills you need to effectively handle batch and streaming data using Spark. Fully updated for Spark 2.0.
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Part 1: First Steps
1. Introduction to Apache Spark
1.1. What is Spark?
1.1.1. The Spark revolution
1.1.2. MapReduce's shortcomings
1.1.3. What Spark brings to the table
1.2. Spark components
1.2.1. Spark Core
1.2.2. Spark SQL
1.2.3. Spark Streaming
1.2.4. Spark MLlib
1.2.5. Spark GraphX
1.3. Spark program flow
1.4. Spark ecosystem
1.5. Setting up the spark-in-action virtual machine
1.5.1. Downloading and starting the virtual machine
1.5.2. Stopping the virtual machine
1.6. Summary
2. Spark fundamentals
2.1. Using the spark-in-action virtual machine
2.1.1. Cloning the Spark in Action GitHub repository
2.1.2. Finding Java
2.1.3. Using the virtual machine’s Hadoop installation
2.1.4. Examining virtual machine’s Spark installation
2.2. Using Spark shell and writing your first Spark program
2.2.1. Starting Spark shell
2.2.2. The first Spark code example
2.2.3. The notion of Resilient Distributed Dataset
2.3. Basic RDD actions and transformations
2.3.1. Using the map transformation
2.3.2. Using distinct and flatMap transformations
2.3.3. Obtaining RDD’s elements with sample, take and takeSample operations
2.4. Double RDD functions
2.4.1. Basic statistics with double RDD functions
2.4.2. Visualizing data distribution with histogram
2.4.3. Approximate sum and mean
2.5. Summary
3. Writing Spark applications
3.1. Generating a new Spark project in Eclipse
3.2. Developing the application
3.2.1. Preparing the GitHub Archive data set
3.2.2. Loading JSON
3.2.3. Running the application from Eclipse
3.2.4. Aggregating the data
3.2.5. Excluding non-employees
3.2.6. Broadcast variables
3.2.7. Using the whole data set
3.3. Submitting the application
3.3.1. Building the "uberjar"
3.3.2. Adapting the application
3.3.3. Using spark-submit
3.4. Summary
4. The Spark API in depth
4.1. Working with pair RDDs
4.1.1. Creating pair RDDs
4.1.2. Basic pair RDD functions
4.2. Understanding data partitioning and reducing data shuffling
4.2.1. Using Spark's data partitioners
4.2.2. Understanding and avoiding unnecessary shuffling
4.2.3. Repartitioning RDDs
4.2.4. Mapping data inside partitions
4.3. Joining, sorting, and grouping data
4.3.1. Joining data
4.3.2. Sorting data
4.3.3. Grouping data
4.4. Understanding RDD dependencies
4.4.1. RDD dependencies and Spark execution
4.4.2. Spark stages and tasks
4.4.3. Saving RDD lineage with checkpointing
4.5. Using accumulators and broadcast variables for communicating with Spark executors
4.5.1. Obtaining data from executors with accumulators
4.5.2. Sending data to executors using broadcast variables
4.6. Summary
Part 2: Meet the Spark Family
5. Sparkling queries with Spark SQL
5.1. Working with DataFrames
5.1.1. Creating DataFrames from RDDs
5.1.2. DataFrame API basics
5.1.3. Using SQL functions to perform calculations on the data
5.1.4. Working with missing values
5.1.5. Converting DataFrames to RDDs
5.1.6. Grouping and joining data
5.1.7. Performing joins
5.2. Beyond DataFrames: introducing DataSets
5.3. Using SQL commands
5.3.1. Table catalog and Hive metastore
5.3.2. Executing SQL queries
5.3.3. Connecting to Spark SQL through Thrift server
5.4. Saving and loading DataFrame data
5.4.1. Built-in data sources
5.4.2. Saving data
5.4.3. Loading data
5.5. Catalyst optimizer
5.6. Performance improvements with Tungsten
5.7. Summary
6. Ingesting data with Spark Streaming
6.1. Writing Spark streaming applications
6.1.1. Introducing the example application
6.1.2. Creating a streaming context
6.1.3. Creating a discretized stream
6.1.4. Using discretized streams
6.1.5. Saving the results to a file
6.1.6. Starting and stopping the streaming computation
6.1.7. Saving computation state over time
6.1.8. Using window operations for time-limited calculations
6.1.9. Examining the other built-in input streams
6.2. Using external data sources
6.2.1. Setting up Kafka
6.2.2. Changing the streaming application to use Kafka
6.3. Performance of Spark Streaming jobs
6.3.1. Obtaining good performance
6.3.2. Achieving fault-tolerance
6.4. Structured Streaming
6.4.1. Creating a streamin DataFrame
6.4.2. Outputting streaming data
6.4.3. Examining streaming executions
6.4.4. Future direction of structured streaming
6.5. Summary
7. Getting smart with MLlib
7.1. Introduction to machine learning
7.1.1. Definition of machine learning
7.1.2. Classification of machine learning algorithms
7.1.3. Machine learning with Spark
7.2. Linear algebra in Spark
7.2.1. Local vector and matrix implementations
7.2.2. Distributed matrices
7.3. Linear regression
7.3.1. About linear regression
7.3.2. Simple linear regression
7.3.3. Expanding the model to multiple linear regression
7.4. Analyzing and preparing the data
7.4.1. Analyzing data distribution
7.4.2. Analyzing column cosine similarities
7.4.3. Computing the covariance matrix
7.4.4. Transforming to labeled points
7.4.5. Splitting the data
7.4.6. Feature scaling and mean normalization
7.5. Fitting and using a linear regression model
7.5.1. Predicting the target values
7.5.2. Evaluating the model's performance
7.5.3. Interpreting the model parameters
7.5.4. Loading and saving the model
7.6. Tweaking the algorithm
7.6.1. Finding the right step size and the number of iterations
7.6.2. Adding higher order polynomials
7.6.3. Bias-variance tradeoff and model complexity
7.6.4. Plotting residual plots
7.6.5. Avoiding overfitting by using regularization
7.6.6. K-fold cross-validation
7.7. Optimizing linear regression
7.7.1. Mini-batch stochastic gradient descent
7.7.2. LBFGS optimizer
7.8. Summary
8. ML: classification and clustering
8.1. Spark ML library
8.1.1. Estimators, transformers and evaluators
8.1.2. ML parameters
8.1.3. ML pipelines
8.2. Logistic regression
8.2.1. Binary logistic regression model
8.2.2. Preparing data for using logistic regression in Spark
8.2.3. Training the model
8.2.4. Evaluating classification models
8.2.5. Performing k-fold cross-validation
8.2.6. Multiclass logistic regression
8.3. Decision trees and random forest
8.3.1. Decision Tree
8.3.2. Random forest
8.4. Using k-means clustering
8.4.1. K-means clustering
8.5. Summary
9. Connecting the dots with GraphX
9.1. Graph processing with Spark
9.1.1. Constructing graphs using GraphX API
9.1.2. Transforming graphs
9.2. Graph algorithms
9.2.1. Presentation of the data set
9.2.2. Shortest paths algorithm
9.2.3. Page rank
9.2.4. Connected components
9.2.5. Strongly connected components
9.3. Implementing A* search algorithm
9.3.1. Understanding the A* algorithm
9.3.2. Implementing the A* algorithm
9.3.3. Testing the implementation
9.4. Summary
Part 3: Spark Ops
10. Running Spark
10.1. An overview of Spark’s runtime architecture
10.1.1. Spark runtime components
10.1.2. Spark cluster types
10.2. Job and resource scheduling
10.2.1. Cluster resource scheduling
10.2.2. Spark job scheduling
10.2.3. Data locality considerations
10.2.4. Spark memory scheduling
10.3. Configuring Spark
10.3.1. Spark configuration file
10.3.2. Command-line parameters
10.3.3. System environment variables
10.3.4. Setting configuration programmatically
10.3.5. The master parameter
10.3.6. Viewing all configured parameters
10.4. Spark Web UI
10.4.1. Jobs page
10.4.2. Stages page
10.4.3. Storage page
10.4.4. Environment page
10.4.5. Executors page
10.5. Running Spark on the local machine
10.5.1. Local mode
10.5.2. Local cluster mode
10.6. Summary
11. Running on a Spark standalone cluster
11.1. Spark standalone cluster components
11.2. Starting the standalone cluster
11.2.1. Starting the cluster with shell scripts
11.2.2. Starting the cluster manually
11.2.3. Viewing Spark processes
11.2.4. Standalone master high availability and recovery
11.3. Standalone cluster Web UI
11.4. Running applications in a standalone cluster
11.4.1. Location of the driver
11.4.2. Specifying the number of executors
11.4.3. Specifying extra classpath entries and files
11.4.4. Killing applications
11.4.5. Application automatic restart
11.5. Spark History Server and event logging
11.6. Running on Amazon EC2
11.6.1. Prerequisites
11.6.2. Creating an EC2 standalone cluster
11.6.3. Using the EC2 cluster
11.6.4. Destroying the cluster
11.7. Summary
12. Running on YARN and Mesos
12.1. Running Spark on YARN
12.1.1. YARN architecture
12.1.2. Installing, configuring, and starting YARN
12.1.3. Resource scheduling inside YARN
12.1.4. Submitting Spark applications to YARN
12.1.5. Configuring Spark on YARN
12.1.6. Configuring resources for Spark jobs
12.1.7. YARN UI
12.1.8. Finding logs on YARN
12.1.9. Security considerations
12.1.10. Dynamic resource allocation
12.2. Running Spark on Mesos
12.2.1. Mesos architecture
12.2.2. Installing and configuring Mesos
12.2.3. Mesos Web UI
12.2.4. Mesos resource scheduling
12.2.5. Submitting Spark applications to Mesos
12.2.6. Running Spark with Docker
12.3. Summary
Part 4: Bringing it Together
13. Case study: Real-time dashboard
13.1. Understanding the use case
13.1.1. The overall picture
13.1.2. Understanding the application's components
13.2. Running the application
13.2.1. Starting the application inside the spark-in-action virtual machine
13.2.2. Starting the application manually
13.3. Understanding the source code
13.3.1. The KafkaLogsSimulator project
13.3.2. The StreamingLogAnalyzer project
13.3.3. The WebStatsDashboard project
13.3.4. Building the projects
13.4. Summary
14. Deep learning on Spark with H2O
14.1. What is deep learning?
14.2. Using H2O with Spark
14.2.1. What is H2O?
14.2.2. Starting Sparkling Water on Spark
14.2.3. Starting the H2O cluster
14.2.4. Accessing Flow UI
14.3. Performing regression with H2O’s deep learning
14.3.1. Loading data into an H2O frame
14.3.2. Building and evaluating a deep learning model using Flow UI
14.3.3. Building and evaluating a deep learning model using Sparkling Water API
14.4. Performing classification with H2O’s deep learning
14.4.1. Loading and splitting the data
14.4.2. Building the model through Flow UI
14.4.3. Building the model with Sparkling Water API
14.4.4. Stopping the H2O cluster
14.5. Summary
Appendixes
Appendix A: Installing Apache Spark
A.1. Prerequisites: Installing JDK
A.1.1. Setting the JAVA_HOME environment variable
A.1.2. Downloading and configuring Spark
A.1.3. Spark shell
Appendix B: Understanding MapReduce
Appendix C: A primer on linear algebra
About the Technology
Big data systems distribute datasets across clusters of machines, making it a challenge to efficiently query, stream, and interpret them. Spark can help. It is a processing system designed specifically for distributed data. It provides easy-to-use interfaces, along with the performance you need for production-quality analytics and machine learning. Spark 2 also adds improved programming APIs, better performance, and countless other upgrades.
About the book
Spark in Action teaches you the theory and skills you need to effectively handle batch and streaming data using Spark. You'll get comfortable with the Spark CLI as you work through a few introductory examples. Then, you'll start programming Spark using its core APIs. Along the way, you'll work with structured data using Spark SQL, process near-real-time streaming data, apply machine learning algorithms, and munge graph data using Spark GraphX. For a zero-effort startup, you can download the preconfigured virtual machine ready for you to try the book's code.
What's inside
- Updated for Spark 2.0
- Real-life case studies
- Spark DevOps with Docker
- Examples in Scala, and online in Java and Python
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