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High-Level APIs and Languages

Over the years since MapReduce first became popular, the execution engines for distributed batch processing have matured. By now, the infrastructure has become robust enough to store and process many petabytes of data on clusters of over 10,000 machines. As the problem of physically operating batch processes at such scale has been considered more or less solved, attention has turned to other areas: improving the programming model, improving the efficiency of processing, and broadening the set of problems that these technologies can solve.

As discussed previously, higher-level languages and APIs such as Hive, Pig, Cascading, and Crunch became popular because programming MapReduce jobs by hand is quite laborious. As Tez emerged, these high-level languages had the additional benefit of being able to move to the new dataflow execution engine without the need to rewrite job code. Spark and Flink also include their own high-level dataflow APIs, often taking inspiration from FlumeJava [34].

These dataflow APIs generally use relational-style building blocks to express a computation: joining datasets on the value of some field; grouping tuples by key; filtering by some condition; and aggregating tuples by counting, summing, or other functions. Internally, these operations are implemented using the various join and grouping algorithms that we discussed earlier in this chapter.

Besides the obvious advantage of requiring less code, these high-level interfaces also allow interactive use, in which you write analysis code incrementally in a shell and run it frequently to observe what it is doing. This style of development is very helpful when exploring a dataset and experimenting with approaches for processing it. It is also reminiscent of the Unix philosophy, which we discussed in “The Unix Philosophy” on page 394.

Moreover, these high-level interfaces not only make the humans using the system more productive, but they also improve the job execution efficiency at a machine level.

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