Paper Review: Dryad: Distributed Data-Parallel Programs from Sequential Building Blocks

Paper Review:


Dryad is Microsoft version of distributed/parallel computing model. It aims to free the developers from the rigid settings and process of MapReduce and design your own concurrent computation model. It doesn’t care about the data flow connection method or the computation process as well (as long as it’s not cycle) so that developers can pretty much configure Dryad the way they want.

Strong points:

  1. Dryad strikes me as a generic version of MapReduce at first. It has all the nodes and looks like map/reduce model but the process is much more configurable. As long as the process forms a acyclic graph, Dryad can go with it. Unlike MapReduce, which is only good for certain kind of massive parallel processing model, Dryad seems able to fit into anything.
  2. One of the biggest constraint of MapReduce is that the developer doesn’t have the freedom to choose how intermediate data is transferred during the process. And Spark beats MapReduce partial due to it transfer data via memory. In Dryad, developers can choose different ways to make the transfer, like TCP, memory or disk. Of course the memory transfer would be the fast way but it’s always nice to have some other options around in case the memory is not enough or some other reason.
  3. The computation is modeled with acyclic graphs. Dryad offers ways to monitors the vertices as well as edges (state manager for each vertex and connection manager, which is not in the paper). It could make dynamic changes to the computation graph according to the monitored results to handle special cases like slow machines.

Weak points:

  1. While Dryad aims to “make it easier for developers to write efficient parallel and distributed applications”, it doesn’t hide all the execution details from developers. Instead it’s doing the exact opposite by exposing more internal structures and leave the decision to developers. The computation, connection, input/output and the monitoring looks intimidating. And the insufficient language support (from what I’ve seen so far it uses C++ and query languages only) makes things even harder.
  2. The execution stops as the job manager fails is a definitely a disadvantage in the system. It could be fixed with 1) distributed coordination with Paxos and some other consensus (slow but effective) 2) shadow master (faster recovery). Either way it’s not hard to implement, which makes me wonder why is this still an issue in Dryad.
  3. There are two reasons why the graph should be acyclic: 1) scheduling is easy because there are no deadlocks and the process is sequential and 2) without cycles, recovering is straightforward. However, there might be some cases where the developers might need to run one method on a piece of data for multiple times in order to meet the requirement. And this is not allowed in current Dryad system.

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