Page History

...

The picture represents a simple map-reduce job with two parallel tasks. We have two TaskManagers, with two tasks each (one map task and one reduce task) running in two different nodes, and one JobManager running in a third node. We focus on the initiation of the transfer between tasks M1 and R2. Data transfers are fat arrowsrepresented using thick arrows, and messages are represented using thin arrows. First, M1 produces a ResultPartition (RP1) (arrow 1). When the RP becomes available for consumption (we discuss when this is later), it informs the JobManager (arrow 2). The JobManager notifies the intended receivers of this partition (tasks R1 and R2) that the partition is ready. If the receivers have not been scheduled yet, this will actually trigger the deployment of the tasks (arrows 3a, 3b). Then, the receivers will request data from the RP (arrows 4a and 4b). This will initiate the data transfer between the tasks (arrows 5a and 5b), either locally (case 5a), or passing through the network stack of the TaskManagers (5b). This process leaves as a degree of freedom the when a RP decides to inform the JobManager of its availability. For example, if RP1 fully produces itself (and is perhaps written to a file) before informing the JM, the data exchange corresponds roughly to a batch exchange as implemented in Hadoop. If the IRP informs the JM as soon as its first record is produced, we have a streaming data exchange.

...