Hi, I am new to the list, I am working with hadoop in the context of my MSc graduation project (has nothing to do with task scheduling per se). I came across task scheduling because I ran into the fifo starvation bug (MAPREDUCE-4613). Now, I am running 2.1.0 branch where the fifo starvation issue is solved. The behavior of task scheduling I observe in this branch is as follows. It begins with all containers allocated to mappers. Pretty quickly reducers are starting to be scheduled. In a linear way more containers are given to reducers, until about 50% (does anybody know why 50%?) of available containers are reducers (this point is reached when ~ 50% of the mappers are finished). It stays ~50-50 for until all mappers are scheduled. Only then the proportion of containers allocated to reducers is increased to > 50%.
I don't think this is in general quite the optimal (in terms of total job completion time) scheduling behavior. The reason being that the last reducer can only be scheduled when a free container becomes available after all mappers are scheduled. Thus, in order to shorten total job completion time the last reducer must be scheduled as early as possible. For the following gedankenexperiment, assume # reducer is set to 99% capacity, as suggested somewhere in the hadoop docs, and that each reducer will process roughly the same amount of work. I am going to schedule as in 2.1.0, but instead of allocating reducers slowly up to 50 % of capacity, I am just going to take away containers. Thus, the amount of map work is the same as in 2.1.0, only no reduce work will be done. At the point that the proportion of reducers would increased to more than 50% of the containers (i.e., near the end of the map phase), I schedule all reducers in the containers I took away, making sure that the last reducer is scheduled at the same moment as it would be in 2.1.0. My claim is that the job completion time of this hypothetical scheduling is about the same as the scheduling in 2.1.0 (as the last reducer is scheduled at the same time), even though I took away 50% of the available resources for a large part of the job! The conclusion is that it would be better to allocate all available containers to mappers, and that reducers are starting to be scheduled when the map phase is nearing its end, instead of right at the beginning of the job. Scheduling reducers early seems to me the way to go only when: 1) the output from mappers is very skewed, i.e., some reducers are expected to need much more time than others, 2) the network connection between nodes is (expected to be) a big bottleneck, i.e., schedule reducers early to smear out data transfer over the lifetime of a job, or 3) there is no contention for resource containers. with regard to point 1: skewedness can be determined by looking at relative sizes of partitioned mapper output. with regard to point 2: I think the network is only a bottleneck if it feeds tuples slower than the reducer can merge sort the tuples (am I right?). Also, it might be a nice optimization to transfer the intermediate data to the machine that is going/likely to run a specific reducer before the reducer is actually ran there (e.g., something like a per machine prefetch manager?). A per machine task scheduling queue would be needed for this, to determine where a reducer is going/likely to be scheduled. Just my two cents. I'm interested in hearing opinions on this matter. Regards, Vasco
