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From "Michel Tourn (JIRA)" <j...@apache.org>
Subject [jira] Commented: (HADOOP-489) Seperating user logs from system logs in map reduce
Date Wed, 06 Sep 2006 02:15:23 GMT
    [ http://issues.apache.org/jira/browse/HADOOP-489?page=comments#action_12432715 ] 
Michel Tourn commented on HADOOP-489:

1) One log file per job per task tracker: sounds good.
Avoiding tasks that write simultaneously to the shared job log:
you could write to a per-Task temp file, then atomically catenate to the job file at the end.

Ways to enforce Atomicity:
a) TaskTracker rather than TaskRunner is responsible for catenate. That way you can assume
there is only one such server running 
(one per machine, one per config set or per HADOOP_IDENT_STRING)

b) Use an interprocess locking mechanism. 
The standard way in Java is to use java.nio.channels.FileLock. 
Just like with pid files, you can encode the pid in the FileLock name to help detect orphan
lock files.
HadoopStreaming used to have code to do b)

Aside: did you mention that there is a need for an index into the per-machine job log?
When the servlet API serves a task log content: it needs to retrieve a *range* of the job
Associated to a joblog file, there is a list of {taskid, begin offset, length}. 

2) says "all errors" 3) says "just one". Which is it? 
I would propose: either one, configurable.
Note there is a large variety of jobclient applications: 
each pure-java user application and HadoopStreaming.
So the canonical client-side log retrieval code should:

A. be customizable: hooks that let you grab 1 or k or ALL task logs.

B. should not assume it has complete control over the Java application's stdout/stderr: user
code normally has control over this. 

C. should fit as extension to the normal job submitter pattern.

For example:
     new TaskLogsSubscriber(System.err); // NEW
      while (! running_.isComplete()) {
        sleep a bit;
        running_ = jc_.getJob(jobId_);
        if (!report.equals(lastReport)) println(report);        

A: TaskLogsSubscriber API ideas.
This API would be used in the sample JobSubmitter examples.
a goal should be: provide useful out-of-the box behaviour. But also allow the user to customize
everything, starting from the servlet request.

boolean showLog(String full_task_id, boolean failed); 
// default implem: remember first failed id, first non-failed id. Return false for all other

void printTaskLog(String full_task_id)
// default implem: open TaskTracker servlet URL, pass the URLInputStream to printLogStream.

void printLogStream(String full_task_id , InputStream in) 
// default implem: consume and write all to System.err (of the JobSubmitter process)

caveat: "taskid" should be abstracted enough to address :
both map and reduce tasks and both cluster and local-maprunner tasks.

>3) iii) This would entail running a servlet on each of the tasktrackers..
Yes. And since these processes already run a Jetty instance, the incremental overhead is minimal.

>4) Yes, this gives the best of both worlds (real-time access and HDFS access)
Seems that the JobConf alternative is simpler and good enough.
I suppose you mean something like
JobConf.setJobLogDirectory(Path dfsPath)
If you don't call it, the logs are not moved to dfs. 
This also resolves the issue of how the log data may later become a mapreduce job input 

>5) job-log files could be deleted on schedule
Yes, same as what is done to delete the global TaskTracker logs.
But with its own configurable deletion delay.

> Seperating user logs from system logs in map reduce
> ---------------------------------------------------
>                 Key: HADOOP-489
>                 URL: http://issues.apache.org/jira/browse/HADOOP-489
>             Project: Hadoop
>          Issue Type: Improvement
>          Components: mapred
>            Reporter: Mahadev konar
>         Assigned To: Mahadev konar
>            Priority: Minor
> Currently the user logs are a part of system logs in mapreduce. Anything logged by the
user is logged into the tasktracker log files. This create two issues-
> 1) The system log files get cluttered with user output. If the user outputs a large amount
of logs, the system logs need to be cleaned up pretty often.
> 2) For the user, it is difficult to get to each of the machines and look for the logs
his/her job might have generated.
> I am proposing three solutions to the problem. All of them have issues with it -
> Solution 1.
> Output the user logs on the user screen as part of the job submission process. 
> Merits- 
> This will prevent users from printing large amount of logs and the user can get runtime
feedback on what is wrong with his/her job.
> Issues - 
> This proposal will use the framework bandwidth while running jobs for the user. The user
logs will need to pass from the tasks to the tasktrackers, from the tasktrackers to the jobtrackers
and then from the jobtrackers to the jobclient using a lot of framework bandwidth if the user
is printing out too much data.
> Solution 2.
> Output the user logs onto a dfs directory and then concatenate these files. Each task
can create a file for the output in the log direcotyr for a given user and jobid.
> Issues -
> This will create a huge amount of small files in DFS which later can be concatenated
into a single file. Also there is this issue that who would concatenate these files into a
single file? This could be done by the framework (jobtracker) as part of the cleanup for the
jobs - might stress the jobtracker.
> Solution 3.
> Put the user logs into a seperate user log file in the log directory on each tasktrackers.
We can provide some tools to query these local log files. We could have commands like for
jobid j and for taskid t get me the user log output. These tools could run as a seperate map
reduce program with each map grepping the user log files and a single recude aggregating these
logs in to a single dfs file.
> Issues-
> This does sound like more work for the user. Also, the output might not be complete since
a tasktracker might have went down after it ran the job. 
> Any thoughts?

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