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From Michael Segel <michael_se...@hotmail.com>
Subject Re: Hadoop counter
Date Sun, 21 Oct 2012 12:15:54 GMT

On Oct 21, 2012, at 1:45 AM, Lin Ma <linlma@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?

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_segel@hotmail.com> wrote:
> On Oct 19, 2012, at 10:27 PM, Lin Ma <linlma@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_segel@hotmail.com>
>> 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 <linlma@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_segel@hotmail.com>
>>> On Oct 19, 2012, at 11:27 AM, Lin Ma <linlma@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
>>> 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_segel@hotmail.com>
>>>> 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
>>>> 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 <linlma@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

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