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http://issues.apache.org/jira/browse/HADOOP-489?page=comments#action_12432715 ]
Michel Tourn commented on HADOOP-489:
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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 log.
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:
B+C:
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 ids.
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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