Spark: big data cluster computing in production
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Indianapolis, IN
Wiley
2016
|
| Schlagworte: | |
| Online-Zugang: | FHI01 FRO01 UBG01 Volltext |
| Beschreibung: | Description based upon print version of record 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 |
| Beschreibung: | 1 Online-Ressource (219 Seiten) |
| ISBN: | 1119254043 1119254051 1119254809 9781119254041 9781119254058 9781119254805 |
Internformat
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| 500 | |a 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 | ||
| 500 | |a 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 | ||
| 500 | |a 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 | ||
| 500 | |a 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 | ||
| 500 | |a Collecting User Activity Log with Spark and KafkaReal-Time Recommendation with Spark; Real-Time Categorization of Twitter Bots; Summary; Index; EULA | ||
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Datensatz im Suchindex
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| any_adam_object | |
| author | Ganelin, Ilya |
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| author_facet | Ganelin, Ilya |
| author_role | aut |
| author_sort | Ganelin, Ilya |
| author_variant | i g ig |
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| dewey-ones | 006 - Special computer methods |
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| dewey-search | 006.3/12 |
| dewey-sort | 16.3 212 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
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| spelling | Ganelin, Ilya Verfasser (DE-588)1106273389 aut Spark big data cluster computing in production Ilya Ganelin ... [et al.] Indianapolis, IN Wiley 2016 1 Online-Ressource (219 Seiten) txt rdacontent c rdamedia cr rdacarrier Description based upon print version of record 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 SPARK (Electronic resource) SPARK (Electronic resource) fast COMPUTERS / General bisacsh Big data fast Big data Big Data (DE-588)4802620-7 gnd rswk-swf SPARK 2.0 (DE-588)4338029-3 gnd rswk-swf SPARK 2.0 (DE-588)4338029-3 s Big Data (DE-588)4802620-7 s DE-604 Orhian, Ema Sonstige (DE-588)1106273788 oth Sasaki, Kai Sonstige (DE-588)1106274229 oth York, Brennon Sonstige (DE-588)1106275314 oth Erscheint auch als Druck-Ausgabe 9781119254010 Erscheint auch als Druckausgabe 978-1-119-25480-5 https://onlinelibrary.wiley.com/doi/book/10.1002/9781119254805 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Ganelin, Ilya Spark big data cluster computing in production SPARK (Electronic resource) SPARK (Electronic resource) fast COMPUTERS / General bisacsh Big data fast Big data Big Data (DE-588)4802620-7 gnd SPARK 2.0 (DE-588)4338029-3 gnd |
| subject_GND | (DE-588)4802620-7 (DE-588)4338029-3 |
| title | Spark big data cluster computing in production |
| title_auth | Spark big data cluster computing in production |
| title_exact_search | Spark big data cluster computing in production |
| title_full | Spark big data cluster computing in production Ilya Ganelin ... [et al.] |
| title_fullStr | Spark big data cluster computing in production Ilya Ganelin ... [et al.] |
| title_full_unstemmed | Spark big data cluster computing in production Ilya Ganelin ... [et al.] |
| title_short | Spark |
| title_sort | spark big data cluster computing in production |
| title_sub | big data cluster computing in production |
| topic | SPARK (Electronic resource) SPARK (Electronic resource) fast COMPUTERS / General bisacsh Big data fast Big data Big Data (DE-588)4802620-7 gnd SPARK 2.0 (DE-588)4338029-3 gnd |
| topic_facet | SPARK (Electronic resource) COMPUTERS / General Big data Big Data SPARK 2.0 |
| url | https://onlinelibrary.wiley.com/doi/book/10.1002/9781119254805 |
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