Lets say I am resource constrained in my Hadoop environment and I don't want to schedule really long running jobs (ie takes days to complete). I am analyzing vast amount of past time series data. I want to schedule mapreduce jobs that take a day's worth of data at a time (which takes an hour to crunch).
So how do I schedule such that new job is submitted as soon as previous job is completed?
If you want a quick and simple approach you could just write a shell script that calls hadoop jar in sequence for each job you want to run.
If you want a more robust approach you could use Apache Oozie to define a workflow of jobs that will run your jobs in sequence. If you are new to Hadoop you may find it easiest to define and run your Oozie workflow using the Hue GUI.
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I want to create a Scheduled Job in Oracle11g Express.
I am new to Job Scheduling and my search so far points to chains but as I want to create a Job out of a function that runs irregulary at a yet unknown date I believe that Chains aren't working for my case.
The Job will be created when a Procedure finishes, which determines its Scheduled Date X.
The Job will perform some critical changes which is why I don't want it to start while other regular scheduled jobs are running.
I want it to wait until the other jobs finish.
I only want to have it run once and than drop the job.
Is there some good practice for this case or some Option I have missed?
i am using hadoop to process bigdata, i first load data to hdfs and then execute jobs, but it is sequential. Is it possible to do it in parallel. For example,
running 3 jobs and 2 process of load data from others jobs at same time on my cluster.
Cheers
It is possible to run the all job's in parallel in hadoop if your cluster and jobs satisfies the below criteria:
1) Hadoop Cluster should have capability to run reasonable number of map/reduce task(depends on jobs) in parallel(i.e. should have enough map/reduce slots).
2) If jobs that is currently being run , depends on the data which is loaded through another process, we cannot run data load and job in parallel.
If you process satisfies the above condition, you can all the jobs in parallel.
Using Oozie you can schedule all the process to run in parallel. Fork and Join properties in Oozie allows you to accomplish the task to run in parallel.
If your cluster has enough resources to run the jobs in parallel, then yes. But be sure that the work of each job, doesn't interfere with the others. Like load the data at the same time that another job in execution should be using it, that won't work as you expected.
If there is not enough resources, then hadoop will enqueue the jobs until the resources are available, depending on the Scheduler configured.
I need to process a lot of files with some specifically date. I find only one solution, which is to launch a job N times with each time a different dataset. The partitions used is based on yyyy, mm, dd. I have a java action which generate the good partition to use for each data.
My question is, how can I create a loop to launch my script N times ? I work today with oozie workflow.
Thanks
This sounds like a use case for coordinators.
You can declare Datasets and let oozie automatically start a workflow when a specfic dataset instance is available.
I ran a pig work-flow using oozie. The job completed successfully but now I want to know on which slave or master the job ran. My input file is a 1.4GB file which is distributed on the nodes (1 master and 2 slaves).
And I also want to figure out how much time did the pig executed on each node.
Thank you in advance
Point your web browser to "JobTracker_Machine:50030" and it will presetn you the MapReduce webUI. Here you'll find all the jobs you have run(Running, Completed and Retired). Click on the job which you want to analyze and it will give you all the information you need including the node where a particular task has run and the time taken to finish the task.
HTH
Go to the Oozie Web console and click on the workflow (which contains the pig node).Clicking on the worklfow job will lead to a dialog box (for your workflow) containing details of all the action nodes in the workflow. Select the pig node (which you want to analyse) and a detailed dialog box will appear containing the Job Tracker's URL of that pig job.
There you will find all the details you are looking for.
Customers able to upload urls in any time to database and application should processes urls as soon as possible. So i need periodic hadoop jobs running or run hadoop job automatically from other application(any script identifies new links were added, generates data for hadoop job and runs job). For PHP or Python script, i could set up cronjob, but what is best practice for periodic hadoop jobs running (prepare data for hadoop, upload data, run hadoop job and move data back to database?
Take a look at Oozie, the new workflow system from Y!, which can run jobs based on different triggers. A good overflow is presented by Alejandro here: http://www.slideshare.net/ydn/5-oozie-hadoopsummit2010
If you want urls to be processed as soon as possible, you'll have them processed each at a time. My recommendation is to wait for some number of links (or MB of links, or for example 10 min, every day).
And batch process them (I do my processing daily, but that jobs takes few hours)