I am completely new to the DDS world. I understand basic concepts like publish and subscribe, and the stuff that can be gained from the documentation. I am attempting to use OpenSlice DDS, and am able to get through the tutorial without much difficulty. However, I want to get two different computers on the same network to talk to each other, which seems like a relatively simple task, but i can find no documentation on it.
For example, the message chat room tutorial... how would i get the message board running on one machine, and the chatter on another machine?
Thanks!
Found it! http://opensplice.org/pipermail/developer/2009-July/000094.html.
To summarize from the link:
Setup your environment on node 1 by running the release file in the OSPL_HOME directory (release.bat)
start the opensplice daemon on node 1 (ospl start)
run the messageboard application on node 1
Setup your environment on node 1 by running the release file in the OSPL_HOME directory (release.bat)
start the opensplice daemon on node 2 (ospl start)
run the chatter application on node 2
Related
I'm using a open-source chef server managing about 150 nodes.
Analytics/Reporting module is not activated in the chef server due to resource constraints.
"chef-client" is running on all the nodes every 30 minutes
How can I find, how much time each chef-client run is taking to complete?
I'm trying to find the nodes that are slowest in completing their chef-client runs
Chef Server doesn't store this information. You'll need to manage it yourself, possibly using a handler as linked above in the comments. A simple option would be to make a handler which stores the duration of the last run as a node attribute, but the sky is the limit. If you want something to help debug long runs once you find them, check out my poise-profiler cookbook.
I have two clusters, one in local virtual machine another in remote cloud. Both clusters in Standalone mode.
My Environment:
Scala: 2.10.4
Spark: 1.5.1
JDK: 1.8.40
OS: CentOS Linux release 7.1.1503 (Core)
The local cluster:
Spark Master: spark://local1:7077
The remote cluster:
Spark Master: spark://remote1:7077
I want to finish this:
Write codes(just simple word-count) in IntelliJ IDEA locally(on my laptp), and set the Spark Master URL to spark://local1:7077 and spark://remote1:7077, then run my codes in IntelliJ IDEA. That is, I don't want to use spark-submit to submit a job.
But I got some problem:
When I use the local cluster, everything goes well. Run codes in IntelliJ IDEA or use spark-submit can submit job to cluster and can finish the job.
But When I use the remote cluster, I got a warning log:
TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
It is sufficient resources not sufficient memory!
And this log keep printing, no further actions. Both spark-submit and run codes in IntelliJ IDEA result the same.
I want to know:
Is it possible to submit codes from IntelliJ IDEA to remote cluster?
If it's OK, does it need configuration?
What are the possible reasons that can cause my problem?
How can I handle this problem?
Thanks a lot!
Update
There is a similar question here, but I think my scene is different. When I run my codes in IntelliJ IDEA, and set Spark Master to local virtual machine cluster, it works. But I got Initial job has not accepted any resources;... warning instead.
I want to know whether the security policy or fireworks can cause this?
Submitting code programatically (e.g. via SparkSubmit) is quite tricky. At the least there is a variety of environment settings and considerations -handled by the spark-submit script - that are quite difficult to replicate within a scala program. I am still uncertain of how to achieve it: and there have been a number of long running threads within the spark developer community on the topic.
My answer here is about a portion of your post: specifically the
TaskSchedulerImpl: Initial job has not accepted any resources; check
your cluster UI to ensure that workers are registered and have
sufficient resources
The reason is typically there were a mismatch on the requested memory and/or number of cores from your job versus what were available on the cluster. Possibly when submitting from IJ the
$SPARK_HOME/conf/spark-defaults.conf
were not properly matching the parameters required for your task on the existing cluster. You may need to update:
spark.driver.memory 4g
spark.executor.memory 8g
spark.executor.cores 8
You can check the spark ui on port 8080 to verify that the parameters you requested are actually available on the cluster.
I'm experimenting with the Distributed Shell example in YARN 2.2 and am hoping that someone can clarify what the difference between a managed and and an un-managed application manager is?
For example the following lines appear in the client code
// unmanaged AM
appContext.setUnmanagedAM(true);
but I am unable to find documentation explaining the difference this line makes to the execution behaviour.
Many thanks.
The setUnmanagedAM(true) is used for debugging purposes i.e. it runs an application manager in local mode and does not submit it to a cluster so it is easier to step into code and debug.
You can see it in use in the hadoop-yarn-applications-unmanaged-am-launcher.jar that ships with yarn
Check the respective JIRA tickets: JIRA-420 and JIRA-419 (client side)
Currently, the RM itself manages the AM by allocating a container for it and negotiating the launch on the NodeManager and manages the AM lifecycle. Thereafter, the AM negotiates resources with the RM and launches tasks to do the real work.
It would be a useful improvement to enhance this model by allowing the AM to be launched independently by the client without requiring the RM. These AM's would be launched on a gateway machine that can talk to the cluster. This would open up new use cases such as the following
1) Easy debugging of AM, specially during initial development. Having the AM launched on an arbitrary cluster node makes it hard to looks at logs or attach a debugger to the AM. If it can be launched locally then these tasks would be easier.
2) Running AM's that need special privileges that may not be available on machines managed by the NodeManager
Blog post with more implementation details on unmanaged AM: click-me
Example of how Impala manages its resources with the help of unmanaged applications: Llama
Preface
I'm writing an web-server that gives to users an access to some program written on C (I'm using an Python wrapper over this C-program, it is PyCLIPS). To serve a lot of users, the web-server has to start a lot of copies of this C-program, because one copy can serve very few users in the same time, about 1-3 users. In addition, each user should work only with his own copy, therefore should be a lot of copies of the C-program.
This C-program is a CLIPS engine if it'll help to understand.
So, to solve this design problem, I want write a Twisted TCP server that will be like a pool of long-running-processes. Each of long-running-processes is a small Twisted TCP server that gives an access to one copy of the C-program.
In example, a user ask the pool-server to reserve a long-running-process for him, then the pool-server create and run a long-running-process that starts listening on some port, then the pool-server return the host and port of this long-running-process to user. Now, user can communicate with this long-running-process directly.
Questions
How start these long-running-process from the pool-server? The pool-server and each of long-running-processes are should be separate Twisted servers.
Is Twisted a good choice for these aims?
Maybe there are another ways how solve this design problem?
Thanks a lot.
Using Twisted for this sounds like it makes sense. The low-level API that supports running processes in Twisted is reactor.spawnProcess. Some examples of its usage are given in the process howto document.
This API is well suited for dealing with many long running processes, and as with all I/O APIs in Twisted, it works well when combined with another event source, such as a web server users can use to request new processes be started up.
reactor.spawnProcess is more like the standard library subprocess module than like the multiprocessing package. It gives you a way to launch a child process running a particular executable, with particular arguments, etc. It does not provide a high-level API for running a particular Python function in another process. However, it's not too hard to build such a thing (at least for a particular case). Consider this approach:
from sys import executable
from os import environ
from twisted.internet import reactor
implementation = """\
from yourapp import somefunction
somefunction()
"""
reactor.spawnProcess(executable, [executable, "-c", implementation], env=environ)
reactor.run()
This just launches a new Python interpreter (whichever one you happen to be running) and uses the -c option to specify a program on the command line for it to run.
Is there any reason that the rabbitmq-management plugin wouldn't work when I'm using 'rabbitmq-multi' to spin up a cluster of nodes on my desktop? Or, more precisely, that the management plugin would cause that spinup to fail?
I get Error: {node_start_failed,normal} when rabbitmq-multi starts rabbit_1#localhost
The first node, rabbit#localhost seems to start okay though.
If I take out the management plugins, all the nodes start up (and then cluster) fine. I think I'm using a recent enough Erlang version (5.8/OTP R14A according to the README in my erl5.8.2 folder). I'm using all the plugins that are listed as required on the plugins page, including mochiweb, webmachine, amqp_client, rabbitmq-mochiweb, rabbitmq-management-agent, and rabbitmq-management. Those plugins, and only those plugins.
The problem is that rabbitmq-multi only assigns sequential ports for AMQP, not HTTP (or STOMP or AMQPS or anything else the broker may open). Therefore each node tries to listen on the same port for the management plugin and only the first succeeds. rabbitmq-multi will be going away in the next release; this is one reason why.
I think you'll want to start the nodes without using rabbitmq-multi, just with multiple invocations of rabbitmq-server, using environment variables to configure each node differently. I use a script like:
start-node.sh:
#!/bin/sh
RABBITMQ_NODE_PORT=$1 RABBITMQ_NODENAME=$2 \
RABBITMQ_MNESIA_DIR=/tmp/rabbitmq-$2-mnesia \
RABBITMQ_PLUGINS_EXPAND_DIR=/tmp/rabbitmq-$2-plugins-scratch \
RABBITMQ_LOG_BASE=/tmp \
RABBITMQ_SERVER_START_ARGS="-rabbit_mochiweb port 5$1" \
/path/to/rabbitmq-server -detached
and then invoke it as
start-node.sh 5672 rabbit
start-node.sh 5673 hare