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Spark : big data cluster computing in production /
Spark: Big Data Cluster Computing in Production goes beyond general Spark overviews to provide targeted guidance toward using lightning-fast big-data clustering in production. Written by an expert team well-known in the big data community, this book walks you through the challenges in moving from pr...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Indianapolis, IN :
John Wiley & Sons, Inc.,
[2016]
©2016 |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Spark"!Big Data Cluster Computing in Production; About the Authors; About the Technical Editors; Credits; Acknowledgments; Contents at a glance; Contents; Introduction; Chapter 1 Finishing Your Spark Job; Installation of the Necessary Components; Native Installation Using a Spark Standalone Cluster; The History of Distributed Computing That Led to Spark; Enter the Cloud; Understanding Resource Management; Using Various Formats for Storage; Text Files; Sequence Files; Avro Files; Parquet Files; Making Sense of Monitoring and Instrumentation; Spark UI; Spark Standalone UI; Metrics REST API.
- Metrics SystemExternal Monitoring Tools; Summary; Chapter 2 Cluster Management; Background; Spark Components; Driver; Workers and Executors; Configuration; Spark Standalone; Architecture; Single-Node Setup Scenario; Multi-Node Setup; YARN; Architecture; Dynamic Resource Allocation; Scenario; Mesos; Setup; Architecture; Dynamic Resource Allocation; Basic Setup Scenario; Comparison; Summary; Chapter 3 Performance Tuning; Spark Execution Model; Partitioning; Controlling Parallelism; Partitioners; Shuffling Data; Shuffling and Data Partitioning; Operators and Shuffling.
- Shuffling Is Not That Bad After AllSerialization; Kryo Registrators; Spark Cache; Spark SQL Cache; Memory Management; Garbage Collection; Shared Variables; Broadcast Variables; Accumulators; Data Locality; Summary; Chapter 4 Security; Architecture; Security Manager; Setup Configurations; ACL; Configuration; Job Submission; Web UI; Network Security; Encryption; Event logging; Kerberos; Apache Sentry; Summary; Chapter 5 Fault Tolerance or Job Execution; Lifecycle of a Spark Job; Spark Master; Spark Driver; Spark Worker; Job Lifecycle; Job Scheduling; Scheduling within an Application.
- Scheduling with External UtilitiesFault Tolerance; Internal and External Fault Tolerance; Service Level Agreements (SLAs); Resilient Distributed Datasets (RDDs); Batch versus Streaming; Testing Strategies; Recommended Configurations; Summary; Chapter 6 Beyond Spark; Data Warehousing; Spark SQL CLI; Thrift JDBC/ODBC Server; Hive on Spark; Machine Learning; DataFrame; MLlib and ML; Mahout on Spark; Hivemall on Spark; External Frameworks; Spark Package; XGBoost; spark-jobserver; Future Works; Integration with the Parameter Server; Deep Learning; Enterprise Usage.
- Collecting User Activity Log with Spark and KafkaReal-Time Recommendation with Spark; Real-Time Categorization of Twitter Bots; Summary; Index; EULA.