Spark 2.0 takes an all-in-one approach to big data

Spark 2.0 takes an all-in-one approach to big data

Spark 2.0 takes an all-in-one approach to big data
Apache Spark, the in-memory processing system that’s fast become a centerpiece of modern big data frameworks, has officially released its long-awaited version 2.0.

Aside from some major usability and performance improvements, Spark 2.0’s mission is to become a total solution for streaming and real-time data. This comes as a number of other projects — including others from the Apache Foundation — provide their own ways to boost real-time and in-memory processing.

Most of Spark 2.0’s big changes have been known well in advance, which has made them even more hotly anticipated.

One of the largest and most technologically ambitious additions is Project Tungsten, a reworking of Spark’s treatment for memory and code generation. Pieces of Project Tungsten have showed up in earlier releases, but 2.0 adds more, such as applying Tungsten’s memory management to both caching and runtime execution.

For users, these changes, plus a great many other under-the-hood improvements, provide across-the-board performance gains. Spark’s developers claim a two-to-tenfold increase in speed for common DataFrames and SQL operations, thanks to a new code generation system. Window functions, used for tasks like moving averages in data, have been reimplemented natively for further speed-ups.

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Spark 2.0 also brings a major shift in programming APIs. DataFrames and Datasets, previously two different ways of accessing structured data, are now the same under the hood; DataFrames are now “just a type alias for Dataset of Row,” per Spark’s release notes.

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