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Spis treści

Spark: The Definitive Guide. Big Data Processing Made Simple eBook -- spis treści

• Preface
  • About the Authors
  • Who This Book Is For
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Safari
  • How to Contact Us
  • Acknowledgments
• I. Gentle Overview of Big Data and Spark
• 1. What Is Apache Spark?
  • Apache Sparks Philosophy
  • Context: The Big Data Problem
  • History of Spark
  • The Present and Future of Spark
  • Running Spark
    • Downloading Spark Locally
      • Downloading Spark for a Hadoop cluster
      • Building Spark from source
    • Launching Sparks Interactive Consoles
      • Launching the Python console
      • Launching the Scala console
      • Launching the SQL console
    • Running Spark in the Cloud
    • Data Used in This Book
• 2. A Gentle Introduction to Spark
  • Sparks Basic Architecture
    • Spark Applications
  • Sparks Language APIs
  • Sparks APIs
  • Starting Spark
  • The SparkSession
  • DataFrames
    • Partitions
  • Transformations
    • Lazy Evaluation
  • Actions
  • Spark UI
  • An End-to-End Example
    • DataFrames and SQL
  • Conclusion
• 3. A Tour of Sparks Toolset
  • Running Production Applications
  • Datasets: Type-Safe Structured APIs
  • Structured Streaming
  • Machine Learning and Advanced Analytics
  • Lower-Level APIs
  • SparkR
  • Sparks Ecosystem and Packages
  • Conclusion
• II. Structured APIsDataFrames, SQL, and Datasets
• 4. Structured API Overview
  • DataFrames and Datasets
  • Schemas
  • Overview of Structured Spark Types
    • DataFrames Versus Datasets
    • Columns
    • Rows
    • Spark Types
  • Overview of Structured API Execution
    • Logical Planning
    • Physical Planning
    • Execution
  • Conclusion
• 5. Basic Structured Operations
  • Schemas
  • Columns and Expressions
    • Columns
      • Explicit column references
    • Expressions
      • Columns as expressions
      • Accessing a DataFrames columns
  • Records and Rows
    • Creating Rows
  • DataFrame Transformations
    • Creating DataFrames
    • select and selectExpr
    • Converting to Spark Types (Literals)
    • Adding Columns
    • Renaming Columns
    • Reserved Characters and Keywords
    • Case Sensitivity
    • Removing Columns
    • Changing a Columns Type (cast)
    • Filtering Rows
    • Getting Unique Rows
    • Random Samples
    • Random Splits
    • Concatenating and Appending Rows (Union)
    • Sorting Rows
    • Limit
    • Repartition and Coalesce
    • Collecting Rows to the Driver
  • Conclusion
• 6. Working with Different Types of Data
  • Where to Look for APIs
  • Converting to Spark Types
  • Working with Booleans
  • Working with Numbers
  • Working with Strings
    • Regular Expressions
  • Working with Dates and Timestamps
  • Working with Nulls in Data
    • Coalesce
    • ifnull, nullIf, nvl, and nvl2
    • drop
    • fill
    • replace
  • Ordering
  • Working with Complex Types
    • Structs
    • Arrays
    • split
    • Array Length
    • array_contains
    • explode
    • Maps
  • Working with JSON
  • User-Defined Functions
  • Conclusion
• 7. Aggregations
  • Aggregation Functions
    • count
    • countDistinct
    • approx_count_distinct
    • first and last
    • min and max
    • sum
    • sumDistinct
    • avg
    • Variance and Standard Deviation
    • skewness and kurtosis
    • Covariance and Correlation
    • Aggregating to Complex Types
  • Grouping
    • Grouping with Expressions
    • Grouping with Maps
  • Window Functions
  • Grouping Sets
    • Rollups
    • Cube
    • Grouping Metadata
    • Pivot
  • User-Defined Aggregation Functions
  • Conclusion
• 8. Joins
  • Join Expressions
  • Join Types
  • Inner Joins
  • Outer Joins
  • Left Outer Joins
  • Right Outer Joins
  • Left Semi Joins
  • Left Anti Joins
  • Natural Joins
  • Cross (Cartesian) Joins
  • Challenges When Using Joins
    • Joins on Complex Types
    • Handling Duplicate Column Names
      • Approach 1: Different join expression
      • Approach 2: Dropping the column after the join
      • Approach 3: Renaming a column before the join
  • How Spark Performs Joins
    • Communication Strategies
      • Big tabletobig table
      • Big tabletosmall table
      • Little tabletolittle table
  • Conclusion
• 9. Data Sources
  • The Structure of the Data Sources API
    • Read API Structure
    • Basics of Reading Data
      • Read modes
    • Write API Structure
    • Basics of Writing Data
      • Save modes
  • CSV Files
    • CSV Options
    • Reading CSV Files
    • Writing CSV Files
  • JSON Files
    • JSON Options
    • Reading JSON Files
    • Writing JSON Files
  • Parquet Files
    • Reading Parquet Files
      • Parquet options
    • Writing Parquet Files
  • ORC Files
    • Reading Orc Files
    • Writing Orc Files
  • SQL Databases
    • Reading from SQL Databases
    • Query Pushdown
      • Reading from databases in parallel
      • Partitioning based on a sliding window
    • Writing to SQL Databases
  • Text Files
    • Reading Text Files
    • Writing Text Files
  • Advanced I/O Concepts
    • Splittable File Types and Compression
    • Reading Data in Parallel
    • Writing Data in Parallel
      • Partitioning
      • Bucketing
    • Writing Complex Types
    • Managing File Size
  • Conclusion
• 10. Spark SQL
  • What Is SQL?
  • Big Data and SQL: Apache Hive
  • Big Data and SQL: Spark SQL
    • Sparks Relationship to Hive
      • The Hive metastore
  • How to Run Spark SQL Queries
    • Spark SQL CLI
    • Sparks Programmatic SQL Interface
    • SparkSQL Thrift JDBC/ODBC Server
  • Catalog
  • Tables
    • Spark-Managed Tables
    • Creating Tables
    • Creating External Tables
    • Inserting into Tables
    • Describing Table Metadata
    • Refreshing Table Metadata
    • Dropping Tables
      • Dropping unmanaged tables
    • Caching Tables
  • Views
    • Creating Views
    • Dropping Views
  • Databases
    • Creating Databases
    • Setting the Database
    • Dropping Databases
  • Select Statements
    • casewhenthen Statements
  • Advanced Topics
    • Complex Types
      • Structs
      • Lists
    • Functions
      • User-defined functions
    • Subqueries
      • Uncorrelated predicate subqueries
      • Correlated predicate subqueries
      • Uncorrelated scalar queries
  • Miscellaneous Features
    • Configurations
    • Setting Configuration Values in SQL
  • Conclusion
• 11. Datasets
  • When to Use Datasets
  • Creating Datasets
    • In Java: Encoders
    • In Scala: Case Classes
  • Actions
  • Transformations
    • Filtering
    • Mapping
  • Joins
  • Grouping and Aggregations
  • Conclusion
• III. Low-Level APIs
• 12. Resilient Distributed Datasets (RDDs)
  • What Are the Low-Level APIs?
    • When to Use the Low-Level APIs?
    • How to Use the Low-Level APIs?
  • About RDDs
    • Types of RDDs
    • When to Use RDDs?
    • Datasets and RDDs of Case Classes
  • Creating RDDs
    • Interoperating Between DataFrames, Datasets, and RDDs
    • From a Local Collection
    • From Data Sources
  • Manipulating RDDs
  • Transformations
    • distinct
    • filter
    • map
      • flatMap
    • sort
    • Random Splits
  • Actions
    • reduce
    • count
      • countApprox
      • countApproxDistinct
      • countByValue
      • countByValueApprox
    • first
    • max and min
    • take
  • Saving Files
    • saveAsTextFile
    • SequenceFiles
    • Hadoop Files
  • Caching
  • Checkpointing
  • Pipe RDDs to System Commands
    • mapPartitions
    • foreachPartition
    • glom
  • Conclusion
• 13. Advanced RDDs
  • Key-Value Basics (Key-Value RDDs)
    • keyBy
    • Mapping over Values
    • Extracting Keys and Values
    • lookup
    • sampleByKey
  • Aggregations
    • countByKey
    • Understanding Aggregation Implementations
      • groupByKey
      • reduceByKey
    • Other Aggregation Methods
      • aggregate
      • aggregateByKey
      • combineByKey
      • foldByKey
  • CoGroups
  • Joins
    • Inner Join
    • zips
  • Controlling Partitions
    • coalesce
    • repartition
    • repartitionAndSortWithinPartitions
    • Custom Partitioning
  • Custom Serialization
  • Conclusion
• 14. Distributed Shared Variables
  • Broadcast Variables
  • Accumulators
    • Basic Example
    • Custom Accumulators
  • Conclusion
• IV. Production Applications
• 15. How Spark Runs on a Cluster
  • The Architecture of a Spark Application
    • Execution Modes
      • Cluster mode
      • Client mode
      • Local mode
  • The Life Cycle of a Spark Application (Outside Spark)
    • Client Request
    • Launch
    • Execution
    • Completion
  • The Life Cycle of a Spark Application (Inside Spark)
    • The SparkSession
      • The SparkContext
    • Logical Instructions
      • Logical instructions to physical execution
    • A Spark Job
    • Stages
    • Tasks
  • Execution Details
    • Pipelining
    • Shuffle Persistence
  • Conclusion
• 16. Developing Spark Applications
  • Writing Spark Applications
    • A Simple Scala-Based App
      • Running the application
    • Writing Python Applications
      • Running the application
    • Writing Java Applications
      • Running the application
  • Testing Spark Applications
    • Strategic Principles
      • Input data resilience
      • Business logic resilience and evolution
      • Resilience in output and atomicity
    • Tactical Takeaways
      • Managing SparkSessions
      • Which Spark API to Use?
    • Connecting to Unit Testing Frameworks
    • Connecting to Data Sources
  • The Development Process
  • Launching Applications
    • Application Launch Examples
  • Configuring Applications
    • The SparkConf
    • Application Properties
    • Runtime Properties
    • Execution Properties
    • Configuring Memory Management
    • Configuring Shuffle Behavior
    • Environmental Variables
    • Job Scheduling Within an Application
  • Conclusion
• 17. Deploying Spark
  • Where to Deploy Your Cluster to Run Spark Applications
    • On-Premises Cluster Deployments
    • Spark in the Cloud
  • Cluster Managers
    • Standalone Mode
      • Starting a standalone cluster
      • Cluster launch scripts
      • Standalone cluster configurations
      • Submitting applications
    • Spark on YARN
      • Submitting applications
    • Configuring Spark on YARN Applications
      • Hadoop configurations
      • Application properties for YARN
    • Spark on Mesos
      • Submitting applications
      • Configuring Mesos
    • Secure Deployment Configurations
    • Cluster Networking Configurations
    • Application Scheduling
      • Dynamic allocation
  • Miscellaneous Considerations
  • Conclusion
• 18. Monitoring and Debugging
  • The Monitoring Landscape
  • What to Monitor
    • Driver and Executor Processes
    • Queries, Jobs, Stages, and Tasks
  • Spark Logs
  • The Spark UI
    • Other Spark UI tabs
    • Configuring the Spark user interface
    • Spark REST API
    • Spark UI History Server
  • Debugging and Spark First Aid
    • Spark Jobs Not Starting
      • Signs and symptoms
      • Potential treatments
    • Errors Before Execution
      • Signs and symptoms
      • Potential treatments
    • Errors During Execution
      • Signs and symptoms
      • Potential treatments
    • Slow Tasks or Stragglers
      • Signs and symptoms
      • Potential treatments
    • Slow Aggregations
      • Signs and symptoms
      • Potential treatments
    • Slow Joins
      • Signs and symptoms
      • Potential treatments
    • Slow Reads and Writes
      • Signs and symptoms
      • Potential treatments
    • Driver OutOfMemoryError or Driver Unresponsive
      • Signs and symptoms
      • Potential treatments
    • Executor OutOfMemoryError or Executor Unresponsive
      • Signs and symptoms
      • Potential treatments
    • Unexpected Nulls in Results
      • Signs and symptoms
      • Potential treatments
    • No Space Left on Disk Errors
      • Signs and symptoms
      • Potential treatments
    • Serialization Errors
      • Signs and symptoms
      • Potential treatments
  • Conclusion
• 19. Performance Tuning
  • Indirect Performance Enhancements
    • Design Choices
      • Scala versus Java versus Python versus R
      • DataFrames versus SQL versus Datasets versus RDDs
    • Object Serialization in RDDs
    • Cluster Configurations
      • Cluster/application sizing and sharing
      • Dynamic allocation
    • Scheduling
    • Data at Rest
      • File-based long-term data storage
      • Splittable file types and compression
      • Table partitioning
      • Bucketing
      • The number of files
      • Data locality
      • Statistics collection
    • Shuffle Configurations
    • Memory Pressure and Garbage Collection
      • Measuring the impact of garbage collection
      • Garbage collection tuning
  • Direct Performance Enhancements
    • Parallelism
    • Improved Filtering
    • Repartitioning and Coalescing
      • Custom partitioning
    • User-Defined Functions (UDFs)
    • Temporary Data Storage (Caching)
    • Joins
    • Aggregations
    • Broadcast Variables
  • Conclusion
• V. Streaming
• 20. Stream Processing Fundamentals
  • What Is Stream Processing?
    • Stream Processing Use Cases
      • Notifications and alerting
      • Real-time reporting
      • Incremental ETL
      • Update data to serve in real time
      • Real-time decision making
      • Online machine learning
    • Advantages of Stream Processing
    • Challenges of Stream Processing
  • Stream Processing Design Points
    • Record-at-a-Time Versus Declarative APIs
    • Event Time Versus Processing Time
    • Continuous Versus Micro-Batch Execution
  • Sparks Streaming APIs
    • The DStream API
    • Structured Streaming
  • Conclusion
• 21. Structured Streaming Basics
  • Structured Streaming Basics
  • Core Concepts
    • Transformations and Actions
    • Input Sources
    • Sinks
    • Output Modes
    • Triggers
    • Event-Time Processing
      • Event-time data
      • Watermarks
  • Structured Streaming in Action
  • Transformations on Streams
    • Selections and Filtering
    • Aggregations
    • Joins
  • Input and Output
    • Where Data Is Read and Written (Sources and Sinks)
      • File source and sink
      • Kafka source and sink
    • Reading from the Kafka Source
    • Writing to the Kafka Sink
      • Foreach sink
      • Sources and sinks for testing
    • How Data Is Output (Output Modes)
      • Append mode
      • Complete mode
      • Update mode
      • When can you use each mode?
    • When Data Is Output (Triggers)
      • Processing time trigger
      • Once trigger
  • Streaming Dataset API
  • Conclusion
• 22. Event-Time and Stateful Processing
  • Event Time
  • Stateful Processing
  • Arbitrary Stateful Processing
  • Event-Time Basics
  • Windows on Event Time
    • Tumbling Windows
      • Sliding windows
    • Handling Late Data with Watermarks
  • Dropping Duplicates in a Stream
  • Arbitrary Stateful Processing
    • Time-Outs
    • Output Modes
    • mapGroupsWithState
    • flatMapGroupsWithState
  • Conclusion
• 23. Structured Streaming in Production
  • Fault Tolerance and Checkpointing
  • Updating Your Application
    • Updating Your Streaming Application Code
    • Updating Your Spark Version
    • Sizing and Rescaling Your Application
  • Metrics and Monitoring
    • Query Status
    • Recent Progress
      • Input rate and processing rate
      • Batch duration
    • Spark UI
  • Alerting
  • Advanced Monitoring with the Streaming Listener
  • Conclusion
• VI. Advanced Analytics and Machine Learning
• 24. Advanced Analytics and Machine Learning Overview
  • A Short Primer on Advanced Analytics
    • Supervised Learning
      • Classification
      • Regression
    • Recommendation
    • Unsupervised Learning
    • Graph Analytics
    • The Advanced Analytics Process
      • Data collection
      • Data cleaning
      • Feature engineering
      • Training models
      • Model tuning and evaluation
      • Leveraging the model and/or insights
  • Sparks Advanced Analytics Toolkit
    • What Is MLlib?
      • When and why should you use MLlib (versus scikit-learn, TensorFlow, or foo package)
  • High-Level MLlib Concepts
    • Low-level data types
  • MLlib in Action
    • Feature Engineering with Transformers
    • Estimators
    • Pipelining Our Workflow
    • Training and Evaluation
    • Persisting and Applying Models
  • Deployment Patterns
  • Conclusion
• 25. Preprocessing and Feature Engineering
  • Formatting Models According to Your Use Case
  • Transformers
  • Estimators for Preprocessing
    • Transformer Properties
  • High-Level Transformers
    • RFormula
    • SQL Transformers
    • VectorAssembler
  • Working with Continuous Features
    • Bucketing
      • Advanced bucketing techniques
    • Scaling and Normalization
    • StandardScaler
      • MinMaxScaler
      • MaxAbsScaler
      • ElementwiseProduct
      • Normalizer
  • Working with Categorical Features
    • StringIndexer
    • Converting Indexed Values Back to Text
    • Indexing in Vectors
    • One-Hot Encoding
  • Text Data Transformers
    • Tokenizing Text
    • Removing Common Words
    • Creating Word Combinations
    • Converting Words into Numerical Representations
      • Term frequencyinverse document frequency
    • Word2Vec
  • Feature Manipulation
    • PCA
    • Interaction
    • Polynomial Expansion
  • Feature Selection
    • ChiSqSelector
  • Advanced Topics
    • Persisting Transformers
  • Writing a Custom Transformer
  • Conclusion
• 26. Classification
  • Use Cases
  • Types of Classification
    • Binary Classification
    • Multiclass Classification
    • Multilabel Classification
  • Classification Models in MLlib
    • Model Scalability
  • Logistic Regression
    • Model Hyperparameters
    • Training Parameters
    • Prediction Parameters
    • Example
    • Model Summary
  • Decision Trees
    • Model Hyperparameters
    • Training Parameters
    • Prediction Parameters
  • Random Forest and Gradient-Boosted Trees
    • Model Hyperparameters
      • Random forest only
      • Gradient-boosted trees (GBT) only
    • Training Parameters
    • Prediction Parameters
  • Naive Bayes
    • Model Hyperparameters
    • Training Parameters
    • Prediction Parameters
  • Evaluators for Classification and Automating Model Tuning
  • Detailed Evaluation Metrics
  • One-vs-Rest Classifier
  • Multilayer Perceptron
  • Conclusion
• 27. Regression
  • Use Cases
  • Regression Models in MLlib
    • Model Scalability
  • Linear Regression
    • Model Hyperparameters
    • Training Parameters
    • Example
    • Training Summary
  • Generalized Linear Regression
    • Model Hyperparameters
    • Training Parameters
    • Prediction Parameters
    • Example
    • Training Summary
  • Decision Trees
    • Model Hyperparameters
    • Training Parameters
    • Example
  • Random Forests and Gradient-Boosted Trees
    • Model Hyperparameters
    • Training Parameters
    • Example
  • Advanced Methods
    • Survival Regression (Accelerated Failure Time)
    • Isotonic Regression
  • Evaluators and Automating Model Tuning
  • Metrics
  • Conclusion
• 28. Recommendation
  • Use Cases
  • Collaborative Filtering with Alternating Least Squares
    • Model Hyperparameters
    • Training Parameters
    • Prediction Parameters
    • Example
  • Evaluators for Recommendation
  • Metrics
    • Regression Metrics
    • Ranking Metrics
  • Frequent Pattern Mining
  • Conclusion
• 29. Unsupervised Learning
  • Use Cases
  • Model Scalability
  • k-means
    • Model Hyperparameters
    • Training Parameters
    • Example
    • k-means Metrics Summary
  • Bisecting k-means
    • Model Hyperparameters
    • Training Parameters
    • Example
    • Bisecting k-means Summary
  • Gaussian Mixture Models
    • Model Hyperparameters
    • Training Parameters
    • Example
    • Gaussian Mixture Model Summary
  • Latent Dirichlet Allocation
    • Model Hyperparameters
    • Training Parameters
    • Prediction Parameters
    • Example
  • Conclusion
• 30. Graph Analytics
  • Building a Graph
  • Querying the Graph
    • Subgraphs
  • Motif Finding
  • Graph Algorithms
    • PageRank
    • In-Degree and Out-Degree Metrics
    • Breadth-First Search
    • Connected Components
    • Strongly Connected Components
    • Advanced Tasks
  • Conclusion
• 31. Deep Learning
  • What Is Deep Learning?
  • Ways of Using Deep Learning in Spark
  • Deep Learning Libraries
    • MLlib Neural Network Support
    • TensorFrames
    • BigDL
    • TensorFlowOnSpark
    • DeepLearning4J
    • Deep Learning Pipelines
  • A Simple Example with Deep Learning Pipelines
    • Setup
    • Images and DataFrames
    • Transfer Learning
      • Applying deep learning models at scale
    • Applying Popular Models
      • Applying custom Keras models
      • Applying TensorFlow models
      • Deploying models as SQL functions
  • Conclusion
• VII. Ecosystem
• 32. Language Specifics: Python (PySpark) and R (SparkR and sparklyr)
  • PySpark
    • Fundamental PySpark Differences
    • Pandas Integration
  • R on Spark
    • SparkR
      • Pros and cons of using SparkR instead of other languages
      • Setup
      • Key Concepts
      • Function masking
      • SparkR functions only apply to SparkDataFrames
      • Data manipulation
      • Data sources
      • Machine learning
      • User-defined functions
    • sparklyr
      • Key concepts
      • No DataFrames
      • Data manipulation
      • Executing SQL
      • Data sources
      • Machine learning
  • Conclusion
• 33. Ecosystem and Community
  • Spark Packages
    • An Abridged List of Popular Packages
    • Using Spark Packages
      • In Scala
      • In Python
      • At runtime
    • External Packages
  • Community
    • Spark Summit
    • Local Meetups
  • Conclusion
• Index