Spark: the definitive guide
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Beijing
O'Reilly
2018
|
| Ausgabe: | First edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xxvi, 576 Seiten Illustrationen |
| ISBN: | 9781491912218 |
Internformat
MARC
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Datensatz im Suchindex
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| adam_text | Table of Contents Preface...................................................................................................... xxi Part 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 Launching Spark’s Interactive Consoles Running Spark in the Cloud Data Used in This Book 3 4 6 7 8 9 9 10 11 11 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 13 13 14 15 16 16 17 17 18 19 20 20 21 22
DataFrames and SQL Conclusion 25 ЗО 3. A Tour of Spark s Toolset.......................................................................... 31 Running Production Applications Datasets: Type-Safe Structured APIs Structured Streaming Machine Learning and Advanced Analytics Lower-Level APIs SparkR Sparks Ecosystem and Packages Conclusion Part II. 32 33 34 38 43 44 45 45 Structured APIs—DataFrames, SQL, and Datasets Structured API Overview................................................ ......................... 49 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 50 50 51 51 52 52 52 56 56 57 58 58 Basic Structured Operations............................................ ......................... 59 Schemas Columns and Expressions Columns Expressions Records and Rows Creating Rows DataFrame Transformations Creating DataFrames select and selectExpr Converting to Spark Types (Literals) Adding Columns 60 61 62 63 64 65 66 66 68 71 72
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 72 73 74 74 74 74 76 76 77 77 78 79 80 81 81 Working with Different Types of Data...................................... .................... 83 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, nulllf, 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 83 85 85 88 92 93 97 102 103 103 103 104 105 105 105 105 106 106 107 107 107 108 109 111 115
Aggregations............................................................. .......................... 117 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 119 119 120 120 121 121 121 122 122 123 124 124 125 125 126 127 127 130 132 133 134 134 135 137 Joins......................................................................... ........................ 139 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 How Spark Performs Joins Communication Strategies 139 140 141 142 143 143 144 144 145 145 146 146 147 148 149
Conclusion 9. Data Sources............................................................... ...................... The Structure of the Data Sources API Read API Structure Basics of Reading Data Write API Structure Basics of Writing Data 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 Writing Parquet Files ORC Files Reading Ore Files Writing Ore Files SQL Databases Reading from SQL Databases Query Pushdown 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 Writing Complex Types Managing File Size Conclusion 151 153 154 154 154 155 15 6 156 157 158 160 160 161 162 162 163 163 164 164 164 165 165 167 169 172 173 173 173 174 174 174 174 176 176 177 10. Spark SQL.................................................................... ...................... 179 What Is SQL? Big Data and SQL: Apache Hive Big Data and SQL: Spark SQL Spark’s Relationship to Hive 179 180 180 181
How to Run Spark SQL Queries Spark SQL CLI Spark’s 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 Caching Tables Views Creating Views Dropping Views Databases Creating Databases Setting the Database Dropping Databases Select Statements case.. .when.. .then Statements Advanced Topics Complex Types Functions Subqueries Miscellaneous Features Configurations Setting Configuration Values in SQL Conclusion 181 181 181 182 183 184 184 184 186 186 186 187 187 188 188 188 189 189 190 190 190 190 191 191 191 193 194 195 195 196 196 Datasets........................................................................................................................ 197 When to Use Datasets Creating Datasets In Java: Encoders In Scala: Case Classes Actions Transformations Filtering Mapping Joins 198 199 199 199 200 200 201 202 202
Grouping and Aggregations Conclusion Part III. 203 205 Low-Level APIs Resilient Distributed Datasets (RDDs).............................................. ......... 209 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 sort Random Splits Actions reduce count first max and min take Saving Files saveAsTextFile SequenceFiles Hadoop Files Caching Checkpointing Pipe RDDs to System Commands mapPartitions foreachPartition glom Conclusion 209 209 210 210 211 212 212 213 213 214 214 215 215 215 215 216 217 217 217 217 218 219 219 220 220 220 221 221 221 221 222 222 223 224 224
13. Advanced RDDs.................................................................................... 225 Key-Value Basics (Key-Value RDDs) keyBy Mapping over Values Extracting Keys and Values lookup sampleByKey Aggregations countByKey Understanding Aggregation Implementations Other Aggregation Methods CoGroups Joins Inner Join zips Controlling Partitions coalesce repartition repartitionAndSortWithinPartitions Custom Partitioning Custom Serialization Conclusion 226 226 226 227 227 227 228 229 229 230 232 233 233 233 234 234 234 235 235 237 238 14. Distributed Shared Variables..................................................................... 239 Broadcast Variables Accumulators Basic Example Custom Accumulators Conclusion Part IV. 239 241 242 245 246 Production Applications 15. How Spark Runs on a Cluster................................................................... 249 The Architecture of a Spark Application Execution Modes The Life Cycle of a Spark Application (Outside Spark) Client Request Launch Execution Completion The Life Cycle of a Spark Application (Inside Spark) 249 251 252 253 254 254 255 255
The SparkSession Logical Instructions A Spark Job Stages Tasks Execution Details Pipelining Shuffle Persistence Conclusion 256 257 258 259 260 260 260 261 261 16. Developing Spark Applications................................................................. 263 Writing Spark Applications A Simple Scala-Based App Writing Python Applications Writing Java Applications Testing Spark Applications Strategic Principles Tactical Takeaways 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 Spark on YARN Configuring Spark on YARN Applications 263 263 266 267 268 268 269 271 271 271 272 274 275 276 276 277 278 278 278 278 279 280 281 282 282 283 284 284 286 287
Spark on Mesos Secure Deployment Configurations Cluster Networking Configurations Application Scheduling Miscellaneous Considerations Conclusion 288 289 289 290 291 292 18. Monitoring and Debugging...................................................................... 293 The Monitoring Landscape What to Monitor Driver and Executor Processes Queries, Jobs, Stages, and Tasks Spark Logs The Spark UI Spark REST API Spark UI History Server Debugging and Spark First Aid Spark Jobs Not Starting Errors Before Execution Errors During Execution Slow Tasks or Stragglers Slow Aggregations Slow Joins Slow Reads and Writes Driver OutOfMemoryError or Driver Unresponsive Executor OutOfMemoryError or Executor Unresponsive Unexpected Nulls in Results No Space Left on Disk Errors Serialization Errors Conclusion 293 294 295 295 295 296 303 303 304 304 305 305 306 308 309 310 310 311 312 313 313 314 19. Performance Tuning................................................................................ 315 Indirect Performance Enhancements Design Choices Object Serialization in RDDs Cluster Configurations Scheduling Data at Rest Shuffle Configurations Memory Pressure and Garbage Collection Direct Performance Enhancements Parallelism 316 316 317 318 318 319 322 322 324 324
Improved Filtering Repartitioning and Coalescing User-Defined Functions (UDFs) Temporary Data Storage (Caching) Joins Aggregations Broadcast Variables Conclusion PartV. 325 325 325 326 328 329 329 329 Streaming 20. Stream Processing Fundamentals............................................................ What Is Stream Processing? Stream Processing Use Cases 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 B33 334 334 336 337 338 338 339 339 341 341 342 342 21. Structured Streaming Basics.................................................................... 345 Structured Streaming Basics Core Concepts Transformations and Actions Input Sources Sinks Output Modes Triggers Event-Time Processing Structured Streaming in Action Transformations on Streams Selections and Filtering Aggregations Joins Input and Output Where Data Is Read and Written (Sources and Sinks) 345 346 347 347 347 348 348 348 349 353 353 354 355 355 356
Reading from the Kafka Source Writing to the Kafka Sink How Data Is Output (Output Modes) When Data Is Output (Triggers) Streaming Dataset API Conclusion 357 359 362 363 364 365 Event-Time and Stateful Processing......................................... ................. 367 Event Time Stateful Processing Arbitrary Stateful Processing Event-Time Basics Windows on Event Time Tumbling Windows Handling Late Data with Watermarks Dropping Duplicates in a Stream Arbitrary Stateful Processing Time-Outs Output Modes mapGroupsWithState flatMapGroupsWithState Conclusion 3 67 369 369 370 371 371 374 377 378 379 380 381 385 388 Structured Streaming in Production......................................... ................ 389 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 Spark UI Alerting Advanced Monitoring with the Streaming Listener Conclusion Part VI. 389 391 391 391 392 392 392 393 395 395 395 397 Advanced Analytics and Machine Learning 24. Advanced Analytics and Machine Learning Overview..................................... 401 A Short Primer on Advanced Analytics 402
Supervised Learning Recommendation Unsupervised Learning Graph Analytics The Advanced Analytics Process Spark’s Advanced Analytics Toolkit What Is MLlib? High-Level MLlib Concepts MLlib in Action Feature Engineering with Transformers Estimators Pipelining Our Workflow Training and Evaluation Persisting and Applying Models Deployment Patterns Conclusion 403 405 405 406 407 410 410 411 414 415 417 419 421 423 424 425 Preprocessing and Feature Engineering...................................... ............. 427 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 Scaling and Normalization StandardScaler Working with Categorical Features Stringlndexer 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 Word2Vec Feature Manipulation 427 429 430 431 431 432 433 434 435 435 437 438 441 441 443 443 444 445 445 447 448 449 452 454
PCA Interaction Polynomial Expansion Feature Selection ChiSqSelector Advanced Topics Persisting Transformers Writing a Custom Transformer Conclusion 454 455 455 456 456 457 457 458 459 26. Classification....................................................................................... 461 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 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 461 462 462 462 462 462 463 464 464 465 466 466 467 468 468 469 469 470 47O 471 47I 472 472 472 473 473 474 475 475 475
27. Regression........................................................................... ............. 477 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 477 478 478 479 479 479 479 480 480 481 482 482 482 483 483 483 484 484 484 484 485 485 485 485 486 487 488 488 28. Recommendation.................................................................. ............. 489 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 489 490 490 491 492 492 494 494 494 495 496
Conclusion 497 29. Unsupervised Learning................................................. ......................... 499 Use Cases Model Scalability k-means Model Hyperparameters Training Parameters Example к-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 499 500 501 501 501 502 502 503 503 503 503 504 504 505 505 505 505 506 506 507 508 508 509 30. Graph Analytics......................................................... ......................... 511 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 513 515 516 516 518 518 519 521 522 524 524 524
31. Deep Learning..................................................................................... 527 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 Popular Models Conclusion Part VII. 527 529 530 530 530 531 531 531 531 532 533 533 534 535 537 Ecosystem 32. Language Specifics: Python (PySpark) and R (SparkR and sparklyr)................... 541 PySpark Fundamental PySpark Differences Pandas Integration R on Spark SparkR sparklyr Conclusion 542 542 542 543 543 550 553 33. Ecosystem and Community..................................................................... 555 Spark Packages An Abridged List of Popular Packages Using Spark Packages External Packages Community Spark Summit Local Meetups Conclusion Index. 555 556 556 557 558 558 558 559 561
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| indexdate | 2024-07-10T08:03:03Z |
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| isbn | 9781491912218 |
| language | English |
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| spelling | Chambers, Bill Verfasser (DE-588)136166016 aut Spark the definitive guide Bill Chambers and Matei Zahari First edition Beijing O'Reilly 2018 xxvi, 576 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier Big Data (DE-588)4802620-7 gnd rswk-swf Apache Spark (DE-588)1129113116 gnd rswk-swf SPARK 2.0 (DE-588)4338029-3 gnd rswk-swf Apache Spark (DE-588)1129113116 s Big Data (DE-588)4802620-7 s DE-604 SPARK 2.0 (DE-588)4338029-3 s Zahari, Matei Verfasser aut Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030253080&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Chambers, Bill Zahari, Matei Spark the definitive guide Big Data (DE-588)4802620-7 gnd Apache Spark (DE-588)1129113116 gnd SPARK 2.0 (DE-588)4338029-3 gnd |
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| title | Spark the definitive guide |
| title_auth | Spark the definitive guide |
| title_exact_search | Spark the definitive guide |
| title_full | Spark the definitive guide Bill Chambers and Matei Zahari |
| title_fullStr | Spark the definitive guide Bill Chambers and Matei Zahari |
| title_full_unstemmed | Spark the definitive guide Bill Chambers and Matei Zahari |
| title_short | Spark |
| title_sort | spark the definitive guide |
| title_sub | the definitive guide |
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| topic_facet | Big Data Apache Spark SPARK 2.0 |
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