Archive for March, 2009

How QMF Submission to Condor Could Work

March 23, 2009

I recently goofed and told someone that they could use the Qpid Management Framework (QMF) to submit jobs to Condor. What I meant to say is they can use AMQP. This is maybe understandable because QMF is a management framework built on top of AMQP, and MRG Grid already has many parts of Condor modeled in QMF, but submission via QMF could be very different than via AMQP.

QMF is a framework that allows for the modeling of objects that can publish information about themselves as well as respond to actions. All information and control is sent via AMQP messages.

Along with a quick correction to my comment, s/QMF/AMQP/, I went ahead and mocked up a QMF submission interface to make my comment almost true.

Existing Submission Interfaces

Condor already has a number of submission interfaces: the command-line tools, e.g. condor_submit; a GAHP interface, the condor_c-gahp; a SOAP interface, once termed Birdbath; the previously mentioned AMQP interface; and a few others. So, what’s one more? Or, why one more!?

Command-line Interface

The command-line interface is the default means for submitting jobs to Condor’s Scheduler, the condor_schedd. The condor_submit tool takes a job description file, performs some processing on it, and generates one or many ClassAds representing jobs, a.k.a job ads. The condor_schedd only cares about job ads, and is never exposed to the job description file. condor_submit’s processing is sometimes shallow, e.g. executable = /bin/true becomes Cmd = "/bin/true", and sometimes not, e.g. getenv = TRUE becomes Environment = "<contents of env for condor_submit>". Sometimes the processing is even iterative in nature, e.g. queue 1000000 generates one million copies of the job constructed since the last queue command. The job description file is really a script in the condor_submit language that generates jobs. This makes the condor_submit tool thick, and optimizations that it performs requires it to be tightly integrated with condor_schedd.

SOAP Interface

The SOAP interface (starts slide 15) is very different from condor_submit. It is implemented within the condor_schedd, and exposes a transactional interface that accepts job ads as input. This means no high level job description file processing. It also means the thick condor_submit tool could be implemented on top of the SOAP interface. A job ad that might be submitted via SOAP would look like:

    Arguments="Hello there!";

This is a job ad that may have been created from a job description file like:

   executable = /bin/echo
   arguments = Hello there!
   requirements = TRUE
   queue 1

Pass that to condor_submit -dump to have a look.

A QMF Interface

So, what about a QMF submission interface. A nice aspect of the condor_submit interface is the script nature of the input. Unfortunately, there are some things that cannot be cleanly captured on the remote side of a submission, e.g. the getenv command, transfer_input_files, platform specific requirements bits, or working directory information. To some extent these reasons, and the desire to keep script processing out of the condor_schedd, is why the SOAP interface only deals in job ads. It’s also a reason why a QMF interface should only handle job ads.

A benefit of the SOAP interface is, quite obviously, that it makes for a more natural programmatic interface. Unfortunately, it also exposes concepts and optimizations that are used by condor_submit and may not be needed by other submission programs, e.g. transactions and clusters.

A Submission

One thing that is an afterthought when using both interfaces is the notion of a submission, something that binds together jobs based on their overall purpose. Often a cluster is thought of as the means to group jobs. However, a single job description file can generate multiple clusters. Likewise, the SOAP interface can allow for group all jobs into a single cluster, but if one of the jobs is a DAGMan workflow then the point of the single cluster is violated. The use of clusters to associate jobs is broken.

Two things the QMF interface can do are: 1) simplify the operations required to perform a submission; and, 2) motivate its users to materialize the notion of a submission.

A QMF submission API

   submit, ClassAd -> Id : Submit a new job described by ClassAd


   create, void -> Id : Create a transaction to submit data and a job ad
   send, Id x Data -> void : Spool data for a forthcoming job ad

This interface would be a great simplification over the SOAP API. It eliminates the necessity of a transaction and chunked data transfers, and it does not expose the notion of a cluster. Without a cluster, job association must be done in some other way. The natural way is via an attribute on job ads, including DAGMan jobs. All jobs in a submission could have an attribute Submission = "Monday Parameter Sweep Run, features: A, B, D", a +Submission = "Monday..." in a job description file.

This interface does not have some of the high level niceties of a condor_submit submission. However, those niceties are not necessarily the ability to do many things with one line, e.g. queue 100, but to have a well defined description of a job. Understanding executable becomes the Cmd attribute is one thing, knowing universe = vanilla becomes JobUniverse = 5 is significantly different. Shortcomings in the high level interface can be addressed with improved specification for a job ad.

Submitting jobs with AMQP to a Condor based Grid

March 8, 2009

I recently took MRG Grid’s low-latency feature for a spin. It’s basically a way to use AMQP to submit jobs onto Condor managed nodes, but without going through normal scheduling, called matchmaking. The jobs are AMQP messages, and they don’t go to a Condor scheduler (a Schedd) to be matched with an execute node (a Startd). They go directly to the execute nodes, and they can go there really quickly.

This all works because Condor nodes can operate in a very autonomous fashion. Even when traditional jobs get matched with a node by the global scheduler (the Negotiator), the node always has the final say about if it will run the matched job or not. This has all sorts of benefits in a distributed system, but for low-latency it means that an execute node can take jobs from the Condor scheduler or from some other source. That other source here is an AMQP message queue.

To get going I setup the low-latency feature as described in MRG Grid User Guide: Low Latency Scheduling with the help of condor_configure_node. Since I was doing this using MRG I also used the Qpid implementation of AMQP. Qpid comes in the qpidc package, and in qpidc-devel I found some really helpful examples – the direct example and request-response client were all I needed.

The code’s flow is pretty natural…

Setup the AMQP queues

From the low-latency config (carod.conf) I know that jobs are read from a queue named grid. So the first thing I need to do is setup a way to send messages to the grid queue.


Next, I want to make sure there’s a way for result to get back to me, so I setup my own unique queue where I can receive results.

				 // Automatically delete the queue
				 // when finished
			 arg::exclusive=true, arg::autoDelete=true);

Construct the Condor job

With the queues all setup, I need to construct a job. In Condor all jobs are represented as ClassAds, which is basically a bunch of (attribute,value) pairs. The low-latency code needs a job in a similar form, and does so with the message’s application headers.

   message.getHeaders().setString("Cmd", "\"/bin/echo\"");
   message.getHeaders().setString("Arguments", "\"Hello Grid!\"");
   message.getHeaders().setString("Out", "\"job.out\""); // stdout
   message.getHeaders().setString("Err", "\"job.err\""); // stderr
   message.getHeaders().setString("Requirements", "TRUE");

Immediately, it looks like crazy quoting going on here, and there is. In ClassAds, the values of attributes are typed in expected ways, e.g. ints floats strings, but there is also an expression type that can be evaluated to a boolean. An expression is something like FALSE, ClusterId > 15, ((KeyboardIdle 2 * 60) && (CurrentTime - JobStart) > 90)) or regexp(".*mf.*", Name). So, to distinguish between a string and an expression, strings are quoted and expressions are not, e.g. "/bin/echo" is a string and TRUE is an expression.

By the way, at first I thought I’d need to specify the MyType, TargetType, Owner, User, In, JobUniverse, … attributes found on traditional jobs, but I discovered MyType, TargetType, and In were superfluous, and JobUniverse has a sane default that can run /bin/echo, or essentially anything. I also didn’t care about the identify of the job owner for accessing resources, so I skipped the Owner and User attributes. The job just ran as the nobody user on the execute node.

One thing that I did on purpose was set the Requirements to TRUE, which means that job is happy to run anywhere. I can do that because I know all the nodes I setup to run jobs have /bin/echo.

Send and receive messages

There are a few final steps to do to setup the job message before it can be sent. It needs a unique message id, required by the low-latency feature.


And, it needs to pass along information about where to send results.


Finally, the message can be sent.


This got the job out onto the network, but didn’t actually give me a way to get the results back. For that I setup a function to receive messages on the my_grid_results queue.

Listener::received(Message &message) {
   const MessageProperties properties = message.getMessageProperties();
   const FieldTable headers = properties.getApplicationHeaders();

   const string state = headers.getAsString("JobState");
   const int status = headers.getAsInt("JobStatus");
   const int exitCode = headers.getAsInt("ExitCode");
   const string exitBySignal = headers.getAsString("ExitBySigbal");

      << "Response: " << properties.getMessageId() << std::endl
      << "JobState: " << state << std::endl
      << "JobStatus: " << status << std::endl
      << "ExitCode: " << exitCode << std::endl
      << "ExitBySignal: " << exitBySignal << std::endl
      << "Is Body Empty? " << (message.getData().empty() ? "Yes" : "No") << std::endl;
//    << headers << std::endl;

   if ("\"Exited\"" == state && 4 == status) {
         // There were some results returned, they're stored in the
         // message body as a zip archive
      if (!message.getData().empty()) {
         std::ofstream out;"job.results");
         out << message.getData();

The function is pretty simple. It prints out information about the messages on the my_grid_results queue, and when it sees a message that represents the completion of my job it writes out the results and stops listening.

To get the receive function called when messages come in, it needs to be setup and run. I started it running after I sent the job message.

   SubscriptionManager subscriptions(session);
   Listener listener(subscriptions);
   subscriptions.subscribe(listener, "my_grid_results");;

See it all work

That was basically it. I compiled the program, ran it, and in short order found the job.results file created.

$ ./a.out
Response: 00000000-0000-0000-0000-000000000000
JobStatus: 2
ExitCode: 0
Is Body Empty? Yes
Response: 00000000-0000-0000-0000-000000000000
JobState: "Exited"
JobStatus: 4
ExitCode: 0
Is Body Empty? No
$ unzip -l job.results   
Archive:  job.results
  Length     Date   Time    Name
 --------    ----   ----    ----
        0  03-06-09 13:33   job.err
       12  03-06-09 13:33   job.out
 --------                   -------
       12                   2 files

What happens when my job completes is all the files in its working directory are wrapped up in a zip archive and sent back in the body of a message that has a JobState header with a value of "Exited" and a JobStatus of 4. JobStatus 4 means Completed in the Condor world.

That’s pretty much it. The full example is in LL.cpp.

Hello world!

March 8, 2009

Hello blog!

%d bloggers like this: