Apache Spark is implemented in Scala, and its user-facing Scala API is very similar to Scala’s own Collections API. The power and concision of this API have already brought many developers to Scala. The core abstractions in Spark have created a flexible, extensible platform for applications like streaming, SQL queries, machine learning, and more. Scala offers many advantages over Java:
Spark, like almost all open source, big data tools, leverages the JVM, which is an excellent general-purpose platform for scalable computing. However, its management of objects is suboptimal for high-performance data crunching. Dean Wampler gives an overview of Spark, explaining ongoing improvements and what we should do to improve Scala and the JVM for big data to make them better tools for our needs. For example, the way objects are organized in memory and the subsequent impact that has on garbage collection can be improved for the special case of big data. Hence, the Spark project has recently started Project Tungsten to build internal optimizations using the following techniques:
Dean Wampler is an expert in streaming data systems, focusing on applications of machine learning and artificial intelligence (ML/AI). He’s head of developer relations at Anyscale, which is developing Ray for distributed Python, primarily for ML/AI. Previously, he was an engineering VP at Lightbend, where he led the development of Lightbend CloudFlow, an integrated system for building and running streaming data applications with Akka Streams, Apache Spark, Apache Flink, and Apache Kafka. Dean is the author of Fast Data Architectures for Streaming Applications, Programming Scala, and Functional Programming for Java Developers, and he’s the coauthor of Programming Hive, all from O’Reilly. He’s a contributor to several open source projects. A frequent conference speaker and tutorial teacher, he’s also the co-organizer of several conferences around the world and several user groups in Chicago. He earned his PhD in physics from the University of Washington.
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