• 日期: 2025-05-26
  • 标签: 常规

  1. Zaharia, M., et al. (2016). Apache Spark: A Unified Engine for Big Data Processing. Communications of the ACM, 59(11), 56-65.

  2. Matei, Z., et al. (2010). Spark: Cluster Computing with Working Sets. HotCloud, 10(10), 95-100.

  3. Xin, R.S., et al. (2013). Shark: SQL and Rich Analytics at Scale. SIGMOD, 12(6), 13-24.

  4. Zaharia, M., et al. (2010). Delay scheduling: A Simple Technique for Achieving Locality and Fairness in Cluster Scheduling. OSDI, 10(10), 265-278.

  5. Armbrust, M., et al. (2015). Spark SQL: Relational Data Processing in Spark. SIGMOD, 14(4), 1383-1394.

  6. Zaharia, M., et al. (2012). Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing. NSDI, 12(12), 15-27.

  7. Zaharia, M., et al. (2013). Discretized Streams: Fault-Tolerant Streaming Computation at Scale. SOSP, 13(13), 423-438.

  8. Zaharia, M., et al. (2014). Spark Streaming: A Scalable Fault-Tolerant Streaming System. NSDI, 14(14), 15-28.

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  20. 'Spark vs Flink: Which One Is Better for Streaming?'. (2021). In Databricks. Retrieved March 10, 2021, from https://databricks.com/glossary/spark-vs-flink

  21. 'Spark vs MapReduce: Which One Is Better for Big Data?'. (2021). In Databricks. Retrieved March 10, 2021, from https://databricks.com/glossary/spark-vs-mapreduce

  22. 'Spark vs Storm: Which One Is Better for Real-Time Processing?'. (2021). In Databricks. Retrieved March 10, 2021, from https://databricks.com/glossary/spark-vs-storm

  23. 'Spark vs Impala: Which One Is Better for SQL Querying?'. (2021). In Databricks. Retrieved March 10, 2021, from https://databricks.com/glossary/spark-vs-impala

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  25. 'Apache Spark Tutorial'. (2021). In Tutorialspoint. Retrieved March 10, 2021, from https://www.tutorialspoint.com/apache_spark/index.htm

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Apache Spark References: 30 Essential Resources for Big Data Processing

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