Thanks for the long discussion Mile. Learned a lot from you. regards, Lin
On Tue, Oct 23, 2012 at 11:57 AM, Michael Segel <michael_se...@hotmail.com>wrote: > Yup. > The counters at the end of the job are the most accurate. > > On Oct 22, 2012, at 3:00 AM, Lin Ma <lin...@gmail.com> wrote: > > Thanks for the help so much, Mike. I learned a lot from this discussion. > > So, the conclusion I learned from the discussion should be, since how/when > JT merge counter in the middle of the process of a job is undefined and > internal behavior, it is more reliable to read counter after the whole job > completes? Agree? > > regards, > Lin > > On Sun, Oct 21, 2012 at 8:15 PM, Michael Segel > <michael_se...@hotmail.com>wrote: > >> >> On Oct 21, 2012, at 1:45 AM, Lin Ma <lin...@gmail.com> wrote: >> >> Thanks for the detailed reply, Mike. Yes, my most confusion is resolved >> by you. The last two questions (or comments) are used to confirm my >> understanding is correct, >> >> - is it normal use case or best practices for a job to consume/read the >> counters from previous completed job in an automatic way? I ask this >> because I am not sure whether the most use case of counter is human read >> and manual analysis, other then using another job to automatic consume the >> counters? >> >> >> Lin, >> Every job has a set of counters to maintain job statistics. >> This is specifically for human analysis and to help understand what >> happened with your job. >> It allows you to see how much data is read in by the job, how many >> records processed to be measured against how long the job took to complete. >> It also showed you how much data is written back out. >> >> In addition to this, a set of use cases for counters in Hadoop center on >> quality control. Its normal to chain jobs together to form a job flow. >> A typical use case for Hadoop is to pull data from various sources, >> combine them and do some process on them, resulting in a data set that gets >> sent to another system for visualization. >> >> In this use case, there are usually data cleansing and validation jobs. >> As they run, its possible to track a number of defective records. At the >> end of that specific job, from the ToolRunner, or whichever job class you >> used to launch your job, you can then get these aggregated counters for the >> job and determine if the process passed or failed. Based on this, you can >> exit your program with either a success or failed flag. Job Flow control >> tools like Oozie can capture this and then decide to continue or to stop >> and alert an operator of an error. >> >> - I want to confirm my understanding is correct, when each task >> completes, JT will aggregate/update the global counter values from the >> specific counter values updated by the complete task, but never expose >> global counters values until job completes? If it is correct, I am >> wondering why JT doing aggregation each time when a task completes, other >> than doing a one time aggregation when the job completes? Is there any >> design choice reasons? thanks. >> >> >> That's a good question. I haven't looked at the code, so I can't say >> definitively when the JT performs its aggregation. However, as the job runs >> and in process, we can look at the job tracker web page(s) and see the >> counter summary. This would imply that there has to be some aggregation >> occurring mid-flight. (It would be trivial to sum the list of counters >> periodically to update the job statistics.) Note too that if the JT web >> pages can show a counter, its possible to then write a monitoring tool that >> can monitor the job while running and then kill the job mid flight if a >> certain threshold of a counter is met. >> >> That is to say you could in theory write a monitoring process and watch >> the counters. If lets say an error counter hits a predetermined threshold, >> you could then issue a 'hadoop job -kill <job-id>' command. >> >> >> regards, >> Lin >> >> On Sat, Oct 20, 2012 at 3:12 PM, Michael Segel <michael_se...@hotmail.com >> > wrote: >> >>> >>> On Oct 19, 2012, at 10:27 PM, Lin Ma <lin...@gmail.com> wrote: >>> >>> Thanks for the detailed reply Mike, I learned a lot from the discussion. >>> >>> - I just want to confirm with you that, supposing in the same job, when >>> a specific task completed (and counter is aggregated in JT after the task >>> completed from our discussion?), the other running task in the same job >>> cannot get the updated counter value from the previous completed task? I am >>> asking this because I am thinking whether I can use counter to share a >>> global value between tasks. >>> >>> >>> Yes that is correct. >>> While I haven't looked at YARN (M/R 2.0) , M/R 1.x doesn't have an easy >>> way for a task to query the job tracker. This might have changed in YARN >>> >>> - If so, what is the traditional use case of counter, only use counter >>> values after the whole job completes? >>> >>> Yes the counters are used to provide data at the end of the job... >>> >>> BTW: appreciate if you could share me a few use cases from your >>> experience about how counters are used. >>> >>> Well you have your typical job data like the number of records >>> processed, total number of bytes read, bytes written... >>> >>> But suppose you wanted to do some quality control on your input. >>> So you need to keep a track on the count of bad records. If this job is >>> part of a process, you may want to include business logic in your job to >>> halt the job flow if X% of the records contain bad data. >>> >>> Or your process takes input records and in processing them, they sort >>> the records based on some characteristic and you want to count those sorted >>> records as you processed them. >>> >>> For a more concrete example, the Illinois Tollway has these 'fast pass' >>> lanes where cars equipped with RFID tags can have the tolls automatically >>> deducted from their accounts rather than pay the toll manually each time. >>> >>> Suppose we wanted to determine how many cars in the 'Fast Pass' lanes >>> are cheaters where they drive through the sensor and the sensor doesn't >>> capture the RFID tag. (Note its possible that you have a false positive >>> where the car has an RFID chip but doesn't trip the sensor.) Pushing the >>> data in a map/reduce job would require the use of counters. >>> >>> Does that help? >>> >>> -Mike >>> >>> regards, >>> Lin >>> >>> On Sat, Oct 20, 2012 at 5:05 AM, Michael Segel < >>> michael_se...@hotmail.com> wrote: >>> >>>> Yeah, sorry... >>>> >>>> I meant that if you were dynamically creating a counter foo in the >>>> Mapper task, then each mapper would be creating their own counter foo. >>>> As the job runs, these counters will eventually be sent up to the JT. >>>> The job tracker would keep a separate counter for each task. >>>> >>>> At the end, the final count is aggregated from the list of counters for >>>> foo. >>>> >>>> >>>> I don't know how you can get a task to ask information from the Job >>>> Tracker on how things are going in other tasks. That is what I meant that >>>> you couldn't get information about the other counters or even the status of >>>> the other tasks running in the same job. >>>> >>>> I didn't see anything in the APIs that allowed for that type of flow... >>>> Of course having said that... someone pops up with a way to do just that. >>>> ;-) >>>> >>>> >>>> Does that clarify things? >>>> >>>> -Mike >>>> >>>> >>>> On Oct 19, 2012, at 11:56 AM, Lin Ma <lin...@gmail.com> wrote: >>>> >>>> Hi Mike, >>>> >>>> Sorry I am a bit lost... As you are thinking faster than me. :-P >>>> >>>> From your this statement "It would make sense that the JT maintains a >>>> unique counter for each task until the tasks complete." -- it seems each >>>> task cannot see counters from each other, since JT maintains a unique >>>> counter for each tasks; >>>> >>>> From your this comment "I meant that if a Task created and updated a >>>> counter, a different Task has access to that counter. " -- it seems >>>> different tasks could share/access the same counter. >>>> >>>> Appreciate if you could help to clarify a bit. >>>> >>>> regards, >>>> Lin >>>> >>>> On Sat, Oct 20, 2012 at 12:42 AM, Michael Segel < >>>> michael_se...@hotmail.com> wrote: >>>> >>>>> >>>>> On Oct 19, 2012, at 11:27 AM, Lin Ma <lin...@gmail.com> wrote: >>>>> >>>>> Hi Mike, >>>>> >>>>> Thanks for the detailed reply. Two quick questions/comments, >>>>> >>>>> 1. For "task", you mean a specific mapper instance, or a specific >>>>> reducer instance? >>>>> >>>>> >>>>> Either. >>>>> >>>>> 2. "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." -- do you mean if a >>>>> mapper is updating custom counter ABC, and another mapper is updating the >>>>> same customer counter ABC, their counter values are updated independently >>>>> by different mappers, and will not published (aggregated) externally until >>>>> job completed successfully? >>>>> >>>>> I meant that if a Task created and updated a counter, a different Task >>>>> has access to that counter. >>>>> >>>>> To give you an example, if I want to count the number of quality >>>>> errors and then fail after X number of errors, I can't use Global counters >>>>> to do this. >>>>> >>>>> regards, >>>>> Lin >>>>> >>>>> On Fri, Oct 19, 2012 at 10:35 PM, Michael Segel < >>>>> michael_se...@hotmail.com> wrote: >>>>> >>>>>> 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 <lin...@gmail.com> 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 >>>>>> >>>>>> >>>>>> >>>>> >>>>> >>>> >>>> >>> >>> >> >> > >
