As I understand it... each Task has its own counters and are independently updated. As they report back to the JT, they update the counter(s)' status. The JT then will aggregate them.
In terms of performance, Counters take up some memory in the JT so while its OK to use them, if you abuse them, you can run in to issues. As to limits... I guess that will depend on the amount of memory on the JT machine, the size of the cluster (Number of TT) and the number of counters. In terms of global accessibility... Maybe. The reason I say maybe is that I'm not sure by what you mean by globally accessible. If a task creates and implements a dynamic counter... I know that it will eventually be reflected in the JT. However, I do not believe that a separate Task could connect with the JT and see if the counter exists or if it could get a value or even an accurate value since the updates are asynchronous. Not to mention that I don't believe that the counters are aggregated until the job ends. It would make sense that the JT maintains a unique counter for each task until the tasks complete. (If a task fails, it would have to delete the counters so that when the task is restarted the correct count is maintained. ) Note, I haven't looked at the source code so I am probably wrong. HTH Mike On Oct 19, 2012, at 5:50 AM, Lin Ma <[email protected]> wrote: > Hi guys, > > I have some quick questions regarding to Hadoop counter, > > Hadoop counter (customer defined) is global accessible (for both read and > write) for all Mappers and Reducers in a job? > What is the performance and best practices of using Hadoop counters? I am not > sure if using Hadoop counters too heavy, there will be performance downgrade > to the whole job? > regards, > Lin
