High Performance Spark. Best Practices for Scaling and Optimizing Apache Spark

Spis treści książki

• Preface
  • First Edition Notes
  • Supporting Books and Materials
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Safari
  • How to Contact the Authors
  • How to Contact Us
  • Acknowledgments
• 1. Introduction to High Performance Spark
  • What Is Spark and Why Performance Matters
  • What You Can Expect to Get from This Book
  • Spark Versions
  • Why Scala?
    • To Be a Spark Expert You Have to Learn a Little Scala Anyway
    • The Spark Scala API Is Easier to Use Than the Java API
    • Scala Is More Performant Than Python
    • Why Not Scala?
    • Learning Scala
  • Conclusion
• 2. How Spark Works
  • How Spark Fits into the Big Data Ecosystem
    • Spark Components
  • Spark Model of Parallel Computing: RDDs
    • Lazy Evaluation
      • Performance and usability advantages of lazy evaluation
      • Lazy evaluation and fault tolerance
      • Lazy evaluation and debugging
    • In-Memory Persistence and Memory Management
    • Immutability and the RDD Interface
    • Types of RDDs
    • Functions on RDDs: Transformations Versus Actions
    • Wide Versus Narrow Dependencies
  • Spark Job Scheduling
    • Resource Allocation Across Applications
    • The Spark Application
      • Default Spark Scheduler
  • The Anatomy of a Spark Job
    • The DAG
    • Jobs
    • Stages
    • Tasks
  • Conclusion
• 3. DataFrames, Datasets, and Spark SQL
  • Getting Started with the SparkSession (or HiveContext or SQLContext)
  • Spark SQL Dependencies
    • Managing Spark Dependencies
    • Avoiding Hive JARs
  • Basics of Schemas
  • DataFrame API
    • Transformations
      • Simple DataFrame transformations and SQL expressions
      • Specialized DataFrame transformations for missing and noisy data
      • Beyond row-by-row transformations
      • Aggregates and groupBy
      • Windowing
      • Sorting
    • Multi-DataFrame Transformations
      • Set-like operations
    • Plain Old SQL Queries and Interacting with Hive Data
  • Data Representation in DataFrames and Datasets
    • Tungsten
  • Data Loading and Saving Functions
    • DataFrameWriter and DataFrameReader
    • Formats
      • JSON
      • JDBC
      • Parquet
      • Hive tables
      • RDDs
      • Local collections
      • Additional formats
    • Save Modes
    • Partitions (Discovery and Writing)
  • Datasets
    • Interoperability with RDDs, DataFrames, and Local Collections
    • Compile-Time Strong Typing
    • Easier Functional (RDD like) Transformations
    • Relational Transformations
    • Multi-Dataset Relational Transformations
    • Grouped Operations on Datasets
  • Extending with User-Defined Functions and Aggregate Functions (UDFs, UDAFs)
  • Query Optimizer
    • Logical and Physical Plans
    • Code Generation
    • Large Query Plans and Iterative Algorithms
  • Debugging Spark SQL Queries
  • JDBC/ODBC Server
  • Conclusion
• 4. Joins (SQL and Core)
  • Core Spark Joins
    • Choosing a Join Type
    • Choosing an Execution Plan
      • Speeding up joins by assigning a known partitioner
      • Speeding up joins using a broadcast hash join
      • Partial manual broadcast hash join
  • Spark SQL Joins
    • DataFrame Joins
      • Self joins
      • Broadcast hash joins
    • Dataset Joins
  • Conclusion
• 5. Effective Transformations
  • Narrow Versus Wide Transformations
    • Implications for Performance
    • Implications for Fault Tolerance
    • The Special Case of coalesce
  • What Type of RDD Does Your Transformation Return?
  • Minimizing Object Creation
    • Reusing Existing Objects
    • Using Smaller Data Structures
  • Iterator-to-Iterator Transformations with mapPartitions
    • What Is an Iterator-to-Iterator Transformation?
    • Space and Time Advantages
    • An Example
  • Set Operations
  • Reducing Setup Overhead
    • Shared Variables
    • Broadcast Variables
    • Accumulators
  • Reusing RDDs
    • Cases for Reuse
      • Iterative computations
      • Multiple actions on the same RDD
      • If the cost to compute each partition is very high
    • Deciding if Recompute Is Inexpensive Enough
    • Types of Reuse: Cache, Persist, Checkpoint, Shuffle Files
      • Persist and cache
      • Checkpointing
      • Checkpointing example
    • Alluxio (nee Tachyon)
    • LRU Caching
      • Shuffle files
    • Noisy Cluster Considerations
    • Interaction with Accumulators
  • Conclusion
• 6. Working with Key/Value Data
  • The Goldilocks Example
    • Goldilocks Version 0: Iterative Solution
    • How to Use PairRDDFunctions and OrderedRDDFunctions
  • Actions on Key/Value Pairs
  • Whats So Dangerous About the groupByKey Function
    • Goldilocks Version 1: groupByKey Solution
      • Why GroupByKey fails
  • Choosing an Aggregation Operation
    • Dictionary of Aggregation Operations with Performance Considerations
      • Preventing out-of-memory errors with aggregation operations
  • Multiple RDD Operations
    • Co-Grouping
  • Partitioners and Key/Value Data
    • Using the Spark Partitioner Object
    • Hash Partitioning
    • Range Partitioning
    • Custom Partitioning
    • Preserving Partitioning Information Across Transformations
      • Using narrow transformations that preserve partitioning
    • Leveraging Co-Located and Co-Partitioned RDDs
    • Dictionary of Mapping and Partitioning Functions PairRDDFunctions
  • Dictionary of OrderedRDDOperations
    • Sorting by Two Keys with SortByKey
  • Secondary Sort and repartitionAndSortWithinPartitions
    • Leveraging repartitionAndSortWithinPartitions for a Group by Key and Sort Values Function
    • How Not to Sort by Two Orderings
    • Goldilocks Version 2: Secondary Sort
      • Defining the custom partitioner
      • Filtering on each partition
      • Combine the elements associated with one key
      • Performance
    • A Different Approach to Goldilocks
      • Map to (cell value, column index) pairs
      • Sort and count values on each partition
      • Determine location of rank statistics on each partition
      • Filter for rank statistics
    • Goldilocks Version 3: Sort on Cell Values
  • Straggler Detection and Unbalanced Data
    • Back to Goldilocks (Again)
    • Goldilocks Version 4: Reduce to Distinct on Each Partition
      • Aggregate to ((cell value, column index), count) on each partition
      • Sort and find rank statistics
      • Goldilocks postmortem
  • Conclusion
• 7. Going Beyond Scala
  • Beyond Scala within the JVM
  • Beyond Scala, and Beyond the JVM
    • How PySpark Works
      • PySpark RDDs
      • PySpark DataFrames and Datasets
      • Accessing the backing Java objects and mixing Scala code
      • PySpark dependency management
      • Installing PySpark
    • How SparkR Works
    • Spark.jl (Julia Spark)
    • How Eclair JS Works
    • Spark on the Common Language Runtime (CLR)C# and Friends
  • Calling Other Languages from Spark
    • Using Pipe and Friends
    • JNI
    • Java Native Access (JNA)
    • Underneath Everything Is FORTRAN
    • Getting to the GPU
  • The Future
  • Conclusion
• 8. Testing and Validation
  • Unit Testing
    • General Spark Unit Testing
      • Factoring your code for testability
      • Regular Spark jobs (testing with RDDs)
      • Streaming
    • Mocking RDDs
      • Testing DataFrames
  • Getting Test Data
    • Generating Large Datasets
    • Sampling
  • Property Checking with ScalaCheck
    • Computing RDD Difference
  • Integration Testing
    • Choosing Your Integration Testing Environment
      • Local mode
      • Docker-based
      • Yarn MiniCluster
  • Verifying Performance
    • Spark Counters for Verifying Performance
    • Projects for Verifying Performance
  • Job Validation
  • Conclusion
• 9. Spark MLlib and ML
  • Choosing Between Spark MLlib and Spark ML
  • Working with MLlib
    • Getting Started with MLlib (Organization and Imports)
    • MLlib Feature Encoding and Data Preparation
      • Working with Spark vectors
      • Preparing textual data
      • Preparing data for supervised learning
    • Feature Scaling and Selection
    • MLlib Model Training
    • Predicting
    • Serving and Persistence
      • Saveable (internal format)
      • PMML
      • Custom
    • Model Evaluation
  • Working with Spark ML
    • Spark ML Organization and Imports
    • Pipeline Stages
    • Explain Params
    • Data Encoding
    • Data Cleaning
    • Spark ML Models
    • Putting It All Together in a Pipeline
    • Training a Pipeline
    • Accessing Individual Stages
    • Data Persistence and Spark ML
      • Automated model selection (parameter search)
    • Extending Spark ML Pipelines with Your Own Algorithms
      • Custom transformers
      • Custom estimators
    • Model and Pipeline Persistence and Serving with Spark ML
  • General Serving Considerations
  • Conclusion
• 10. Spark Components and Packages
  • Stream Processing with Spark
    • Sources and Sinks
      • Receivers
      • Repartitioning
    • Batch Intervals
    • Data Checkpoint Intervals
    • Considerations for DStreams
      • Output operations
    • Considerations for Structured Streaming
      • Data sources
      • Output operations
      • Custom sinks
      • Machine learning with Structured Streaming
      • Stream status and debugging
    • High Availability Mode (or Handling Driver Failure or Checkpointing)
  • GraphX
  • Using Community Packages and Libraries
    • Creating a Spark Package
  • Conclusion
• A. Tuning, Debugging, and Other Things Developers Like to Pretend Dont Exist
  • Spark Tuning and Cluster Sizing
    • How to Adjust Spark Settings
    • How to Determine the Relevant Information About Your Cluster
  • Basic Spark Core Settings: How Many Resources to Allocate to the Spark Application?
    • Calculating Executor and Driver Memory Overhead
    • How Large to Make the Spark Driver
    • A Few Large Executors or Many Small Executors?
      • Many small executors
      • Many large executors
    • Allocating Cluster Resources and Dynamic Allocation
      • Restrictions on dynamic allocation
    • Dividing the Space Within One Executor
    • Number and Size of Partitions
  • Serialization Options
    • Kryo
      • Spark settings conclusion
  • Some Additional Debugging Techniques
    • Out of Disk Space Errors
    • Logging
    • Configuring logging
    • Accessing logs
    • Attaching debuggers
    • Debugging in notebooks
    • Python debugging
    • Debugging conclusion
• Index

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