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Dataflow engines

In order to fix these problems with MapReduce, several new execution engines for distributed batch computations were developed, the most well known of which are Spark [61, 62], Tez [63, 64], and Flink [65, 66]. There are various differences in the way they are designed, but they have one thing in common: they handle an entire workflow as one job, rather than breaking it up into independent subjobs.

Since they explicitly model the flow of data through several processing stages, these systems are known as dataflow engines. Like MapReduce, they work by repeatedly calling a user-defined function to process one record at a time on a single thread. They parallelize work by partitioning inputs, and they copy the output of one function over the network to become the input to another function.

Unlike in MapReduce, these functions need not take the strict roles of alternating map and reduce, but instead can be assembled in more flexible ways. We call these functions operators, and the dataflow engine provides several different options for connecting one operator’s output to another’s input:

  • • One option is to repartition and sort records by key, like in the shuffle stage of MapReduce (see “Distributed execution of MapReduce” on page 400). This feature enables sort-merge joins and grouping in the same way as in MapReduce.
  • • Another possibility is to take several inputs and to partition them in the same way, but skip the sorting. This saves effort on partitioned hash joins, where the partitioning of records is important but the order is irrelevant because building the hash table randomizes the order anyway.
  • • For broadcast hash joins, the same output from one operator can be sent to all partitions of the join operator.

This style of processing engine is based on research systems like Dryad [67] and Nephele [68], and it offers several advantages compared to the MapReduce model:

  • • Expensive work such as sorting need only be performed in places where it is actually required, rather than always happening by default between every map and reduce stage.
  • • There are no unnecessary map tasks, since the work done by a mapper can often be incorporated into the preceding reduce operator (because a mapper does not change the partitioning of a dataset).
  • • Because all joins and data dependencies in a workflow are explicitly declared, the scheduler has an overview of what data is required where, so it can make locality optimizations. For example, it can try to place the task that consumes some data on the same machine as the task that produces it, so that the data can be exchanged through a shared memory buffer rather than having to copy it over the network.
  • • It is usually sufficient for intermediate state between operators to be kept in memory or written to local disk, which requires less I/O than writing it to HDFS (where it must be replicated to several machines and written to disk on each replica). MapReduce already uses this optimization for mapper output, but dataflow engines generalize the idea to all intermediate state.
  • • Operators can start executing as soon as their input is ready; there is no need to wait for the entire preceding stage to finish before the next one starts.
  • • Existing Java Virtual Machine (JVM) processes can be reused to run new operators, reducing startup overheads compared to MapReduce (which launches a new JVM for each task).

You can use dataflow engines to implement the same computations as MapReduce workflows, and they usually execute significantly faster due to the optimizations described here. Since operators are a generalization of map and reduce, the same processing code can run on either execution engine: workflows implemented in Pig, Hive, or Cascading can be switched from MapReduce to Tez or Spark with a simple configuration change, without modifying code [64].

Tez is a fairly thin library that relies on the YARN shuffle service for the actual copying of data between nodes [58], whereas Spark and Flink are big frameworks that include their own network communication layer, scheduler, and user-facing APIs. We will discuss those high-level APIs shortly.

 
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