Data Algorithms with Spark

Spis treści ebooka

• Foreword
• Preface
  • Why I Wrote This Book
  • Who This Book Is For
  • How This Book Is Organized
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• I. Fundamentals
• 1. Introduction to Spark and PySpark
  • Why Spark for Data Analytics
    • The Spark Ecosystem
    • Spark Architecture
      • Key Terms
      • Spark architecture in a nutshell
  • The Power of PySpark
    • PySpark Architecture
  • Spark Data Abstractions
    • RDD Examples
    • Spark RDD Operations
      • Transformations
      • Actions
    • DataFrame Examples
  • Using the PySpark Shell
    • Launching the PySpark Shell
    • Creating an RDD from a Collection
    • Aggregating and Merging Values of Keys
    • Filtering an RDDs Elements
    • Grouping Similar Keys
    • Aggregating Values for Similar Keys
  • ETL Example with DataFrames
    • Extraction
    • Transformation
    • Loading
  • Summary
• 2. Transformations in Action
  • The DNA Base Count Example
    • The DNA Base Count Problem
    • FASTA Format
    • Sample Data
  • DNA Base Count Solution 1
    • Step 1: Create an RDD[String] from the Input
    • Step 2: Define a Mapper Function
    • Step 3: Find the Frequencies of DNA Letters
    • Pros and Cons of Solution 1
  • DNA Base Count Solution 2
    • Step 1: Create an RDD[String] from the Input
    • Step 2: Define a Mapper Function
    • Step 3: Find the Frequencies of DNA Letters
    • Pros and Cons of Solution 2
  • DNA Base Count Solution 3
    • The mapPartitions() Transformation
    • Step 1: Create an RDD[String] from the Input
    • Step 2: Define a Function to Handle a Partition
    • Step 3: Apply the Custom Function to Each Partition
    • Pros and Cons of Solution 3
  • Summary
• 3. Mapper Transformations
  • Data Abstractions and Mappers
  • What Are Transformations?
    • Lazy Transformations
    • The map() Transformation
      • RDD mapper
      • Custom mapper functions
    • DataFrame Mapper
      • Mapper to single DataFrame column
      • Mapper to multiple DataFrame columns
  • The flatMap() Transformation
    • map() Versus flatMap()
    • Apply flatMap() to a DataFrame
  • The mapValues() Transformation
  • The flatMapValues() Transformation
  • The mapPartitions() Transformation
    • Handling Empty Partitions
    • Benefits and Drawbacks
    • DataFrames and mapPartitions() Transformation
  • Summary
• 4. Reductions in Spark
  • Creating Pair RDDs
  • Reduction Transformations
  • Sparks Reductions
  • Simple Warmup Example
    • Solving with reduceByKey()
    • Solving with groupByKey()
    • Solving with aggregateByKey()
    • Solving with combineByKey()
  • What Is a Monoid?
    • Monoid and Non-Monoid Examples
  • The Movie Problem
    • Input Dataset to Analyze
    • The aggregateByKey() Transformation
    • First Solution Using aggregateByKey()
    • Second Solution Using aggregateByKey()
    • Complete PySpark Solution Using groupByKey()
    • Complete PySpark Solution Using reduceByKey()
      • Step 1: Read data and create pairs
      • Step 2: Use reduceByKey() to sum up ratings
      • Step 3: Find average rating
    • Complete PySpark Solution Using combineByKey()
      • Step 1: Read data and create pairs
      • Step 2: Use combineByKey() to sum up ratings
      • Step 3: Find average rating
  • The Shuffle Step in Reductions
    • Shuffle Step for groupByKey()
    • Shuffle Step for reduceByKey()
  • Summary
• II. Working with Data
• 5. Partitioning Data
  • Introduction to Partitions
    • Partitions in Spark
  • Managing Partitions
    • Default Partitioning
    • Explicit Partitioning
  • Physical Partitioning for SQL Queries
  • Physical Partitioning of Data in Spark
    • Partition as Text Format
    • Partition as Parquet Format
  • How to Query Partitioned Data
    • Amazon Athena Example
  • Summary
• 6. Graph Algorithms
  • Introduction to Graphs
  • The GraphFrames API
    • How to Use GraphFrames
    • GraphFrames Functions and Attributes
  • GraphFrames Algorithms
    • Finding Triangles
      • Step 1: Build a graph
      • Step 2: Count triangles
    • Motif Finding
      • Triangle counting with motifs
        • Trial 1
        • Trial 2
        • Trial 3
      • Finding unique triangles with motifs
        • Input
        • Output
        • Algorithm
      • Other motif finding examples
        • Finding bidirectional vertices
        • Finding subgraphs
        • Friend recommendation
        • Product recommendations
  • Real-World Applications
    • Gene Analysis
      • Motif finding for genes
    • Social Recommendations
    • Facebook Circles
      • Input
      • Building the graph
      • Motif finding
    • Connected Components
      • Connected components in Spark
    • Analyzing Flight Data
      • Input
        • Vertices
        • Edges
      • Building the graph
      • Flight analysis
  • Summary
• 7. Interacting with External Data Sources
  • Relational Databases
    • Reading from a Database
      • Step 1. Create a database table
      • Step 2: Read the database table into a DataFrame
      • Step 3: Query the DataFrame
    • Writing a DataFrame to a Database
  • Reading Text Files
  • Reading and Writing CSV Files
    • Reading CSV Files
    • Writing CSV Files
  • Reading and Writing JSON Files
    • Reading JSON Files
    • Writing JSON Files
  • Reading from and Writing to Amazon S3
    • Reading from Amazon S3
    • Writing to Amazon S3
  • Reading and Writing Hadoop Files
    • Reading Hadoop Text Files
    • Writing Hadoop Text Files
    • Reading and Writing HDFS SequenceFiles
      • Reading HDFS SequenceFiles
      • Writing HDFS SequenceFiles
  • Reading and Writing Parquet Files
    • Writing Parquet Files
    • Reading Parquet Files
  • Reading and Writing Avro Files
    • Reading Avro Files
    • Writing Avro Files
  • Reading from and Writing to MS SQL Server
    • Writing to MS SQL Server
    • Reading from MS SQL Server
  • Reading Image Files
    • Creating a DataFrame from Images
  • Summary
• 8. Ranking Algorithms
  • Rank Product
    • Calculation of the Rank Product
    • Formalizing Rank Product
    • Rank Product Example
    • PySpark Solution
      • Input data format
      • Output data format
      • Rank product solution using combineByKey()
        • Step 1: Compute the mean per gene per study
        • Step 2: Compute the rank of each gene per study
        • Step 3: Calculate the rank product for each gene
      • Rank product solution using groupByKey()
  • PageRank
    • PageRanks Iterative Computation
    • Custom PageRank in PySpark Using RDDs
      • Input data format
      • Output data format
      • PySpark Solution
      • Sample output
    • Custom PageRank in PySpark Using an Adjacency Matrix
      • Input data format
      • Output data format
      • PySpark solution
    • PageRank with GraphFrames
      • Tolerance
      • Maximum iterations
  • Summary
• III. Data Design Patterns
• 9. Classic Data Design Patterns
  • Input-Map-Output
    • RDD Solution
    • DataFrame Solution
    • Flat Mapper functionality
  • Input-Filter-Output
    • RDD Solution
    • DataFrame Solution
    • DataFrame Filter
  • Input-Map-Reduce-Output
    • RDD Solution
    • DataFrame Solution
  • Input-Multiple-Maps-Reduce-Output
    • RDD Solution
    • DataFrame Solution
  • Input-Map-Combiner-Reduce-Output
  • Input-MapPartitions-Reduce-Output
  • Inverted Index
    • Problem Statement
    • Input
    • Output
    • PySpark Solution
  • Summary
• 10. Practical Data Design Patterns
  • In-Mapper Combining
    • Basic MapReduce Algorithm
    • In-Mapper Combining per Record
    • In-Mapper Combining per Partition
  • Top-10
    • Top-N Formalized
    • PySpark Solution
    • Finding the Bottom 10
  • MinMax
    • Solution 1: Classic MapReduce
    • Solution 2: Sorting
    • Solution 3: Sparks mapPartitions()
  • The Composite Pattern and Monoids
    • Monoids
    • Monoidal and Non-Monoidal Examples
      • Maximum over a set of integers
      • Subtraction over a set of integers
      • Addition over a set of integers
      • Union and intersection over integers
      • Multiplication over a set of integers
      • Mean over a set of integers
      • Median over a set of integers
      • Concatenation over lists
      • Matrix example
    • Non-Monoid MapReduce Example
    • Monoid MapReduce Example
    • PySpark Implementation of Monoidal Mean
    • Functors and Monoids
    • Conclusion on Using Monoids
  • Binning
  • Sorting
  • Summary
• 11. Join Design Patterns
  • Introduction to the Join Operation
  • Join in MapReduce
    • Map Phase
    • Reducer Phase
    • Implementation in PySpark
  • Map-Side Join Using RDDs
  • Map-Side Join Using DataFrames
    • Step 1: Create Cache for Airports
    • Step 2: Create Cache for Airlines
    • Step 3: Create Facts Table
    • Step 4: Apply Map-Side Join
  • Efficient Joins Using Bloom Filters
    • Introduction to Bloom Filters
    • A Simple Bloom Filter Example
    • Bloom Filters in Python
    • Using Bloom Filters in PySpark
  • Summary
• 12. Feature Engineering in PySpark
  • Introduction to Feature Engineering
  • Adding New Features
  • Applying UDFs
  • Creating Pipelines
  • Binarizing Data
  • Imputation
  • Tokenization
    • Tokenizer
    • RegexTokenizer
    • Tokenization with a Pipeline
  • Standardization
  • Normalization
    • Scaling a Column Using a Pipeline
    • Using MinMaxScaler on Multiple Columns
    • Normalization Using Normalizer
  • String Indexing
    • Applying StringIndexer to a Single Column
    • Applying StringIndexer to Several Columns
  • Vector Assembly
  • Bucketing
    • Bucketizer
    • QuantileDiscretizer
  • Logarithm Transformation
  • One-Hot Encoding
  • TF-IDF
  • FeatureHasher
  • SQLTransformer
  • Summary
• Index

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