I have cluster of 3 Mesos slaves, where I have two applications: “redis” and “memcached”. Where redis depends on memcached and the requirement is both of the applications/services should start on same node instead of different slave nodes.
So I have created the application group and added the dependency properly in the JSON file. After launching the JSON file via “v2/groups” REST API, I observe that sometime both application group will start on same node but sometimes it will start on different slaves which breaks our requirement.
So intent/requirement is; if any application fails to start on a slave both the application should failover to other slave node. Also can I configure the JSON file to tell Marathon to start the application group on slave-1 (specific slave first) if it is available else start it on other slave in a cluster. Due to some reason if this application group will start on other slave can Marathon relaunch the application group to slave-1 if it is available to serve the request.
Thanks in advance for help.
Edit/Update (2):
Mesos, Marathon, and DC/OS support for PODs is available now:
DC/OS: https://dcos.io/docs/1.9/usage/pods/using-pods/
Mesos: https://github.com/apache/mesos/blob/master/docs/nested-container-and-task-group.md
Marathon: https://github.com/mesosphere/marathon/blob/master/docs/docs/pods.md
I assume you are talking about marathon apps.
Marathon application groups don't have any semantics concerning co-location on the same node and the same is the case for dependencies.
You seem to be looking for a Kubernetes like Pod abstraction in marathon, which is on the roadmap but not yet available (see update above :-)).
Hope this helps!
I think this should be possible (as a workaround) if you specify the correct app contraints within the group's JSON.
Have a look at the example request at
https://mesosphere.github.io/marathon/docs/generated/api.html#v2_groups_post
and the constraints syntax at
https://mesosphere.github.io/marathon/docs/constraints.html
e.g.
"constraints": [["hostname", "CLUSTER", "slave-1"]]
should do. Downside is that there will be no automatic failover to another slave that way. Still, I'd be curious why both apps need to specifically run on the same slave node...
Related
I have two servers in a HA mode. I'd like to know if is it possible to deploy an application on the slave server? If yes, how to configure it in jgroups? I need to run a specific program that access the master database, but I would not like to run on master serve to avoid overhead on it.
JGroups itself does not know much about WildFly and the deployments, it only creates a communication channel between nodes. I don't know where you get the notion of master/slave, but JGroups always has single* node marked as coordinator. You can check the membership through Channel.getView().
However, you still need to deploy the app on both nodes and just make it inactive if this is not its target node.
*) If there's no split-brain partition, or similar rare/temporal issues
How to deploy apache airflow (formally known as airbnb's airflow) scheduler in high availability?
I am not asking about the backend DB or RabbitMQ that should obviously be deployed in high availability configuration.
My main focus is the scheduler - is there something special needs to be done?
After a bit digging I found that it is not safe to run multiple schedulers simoultanously, this means that out of the box - airflow schedulers are not safe to use in high availablity environments.
The airflow team are planning to solve this issue by adding a lock mechanism on the DAG data structure, but this is not implemented yet (I checked by running 2 schedulers and saw that they schedule the same dag instances which is not good).
This is described here:
https://groups.google.com/forum/#!topic/airbnb_airflow/-1wKa3OcwME
I did found a way to workaround this high availalbilty issue by wrapping the schedulers with my own code and use cluster tools for leader election (I personanlly use consul for this purpose). This way only the elected master is running the scheduler and when the master is down the slave replaces him.
Please consider this when u use airflow in high availabilty environments since out of the box, airflow scheduler is currently not suitable for this (unless you solve this issue yourself).
Edit - an alternative approach to the master slave solution is to use a cluster manager/scheduler to make sure that only one airflow scheduler instance is always available. This approach relies on the self healing abilities of the cluster manager u have. For example both mesos and nomad supports this kind of configuration (I presonally chose nomad for its simplicity).
My personal experience was to follow the instructions I found for some best practices; that is to restart the scheduler every 10 runs ( -N 10 ) and use this software when possible:
https://github.com/teamclairvoyant/airflow-scheduler-failover-controller
I also use a DAG which pings a monitoring system to be sure that the scheduler has not gone away.
In my scenario, I have 2 schedulers (on 2 separate docker swarms), with the standby cluster scheduler turned off (using docker swarm service scale=0). I needed to make sure the primary scheduler had stopped fully before I started up the standby scheduler. What I found was that having 2 running schedulers (even for a brief time period) resulted in an occasional DAG scheduled to run on both clusters leading to duplicate reports generated from two different cluster zone.
I'm using Mesos and Marathon. I created an application on Marathon.
When applications failover to other node in cluster, can we control where they should invoke?
I tried with LIKE "Constrains" in Marathon but it doesn't work as my expectation.
Thanks in advance
You can use a LIKE or UNLIKE constraint (or set of contraints) to restrict where marathon can place any given app instance; however, you can't choose a specific one upon failure.
I recently came across Apache Mesos and successfully deployed my Storm topology over Mesos.
I want to try running Storm topology/Hadoop jobs over Apache Marathon (had issues running Storm directly on Apache Mesos using mesos-storm framework).
I couldn't find any tutorial/article that could list steps how to launch a Hadoop/Spark tasks from Apache Marathon.
It would be great if anyone could provide any help or information on this topic (possibly a Json job definition for Marathon for launching storm/hadoop job).
Thanks a lot
Thanks for your reply, I went ahead and deployed a Storm-Docker cluster on Apache Mesos with Marathon. For service discovery I used HAProxy. This setup allows services (nimbus or zookeeper etc) to talk to each other with the help of ports, so for example adding multiple instances for a service is not a problem since the cluster will find them using the ports and loadbalance the requests between all the instances of a service. Following is the GitHub project which has the Marathon recipes and Docker images: https://github.com/obaidsalikeen/storm-marathon
Marathon is intended for long-running services, so you could use it to start your JobTracker or Spark scheduler, but you're better off launching the actual batch jobs like Hadoop/Spark tasks on a batch framework like Chronos (https://github.com/airbnb/chronos). Marathon will restart tasks when the complete/fail, whereas Chronos (a distributed cron with dependencies) lets you set up scheduled jobs and complex workflows.
While a little outdated, the following tutorial gives a good example.
http://mesosphere.com/docs/tutorials/etl-pipelines-with-chronos-and-hadoop/
Can a Hadoop Yarn instance manage nodes from different places on Earth, networks? Can it manage nodes that use different platforms?
Every note about Yarn I found tells that Yarn manages clusters, but if the app I deploy is written in Java then it should probably work on the nodes regardless of the nodes' hardware.
Similarly, Yarn seems general enough to support more than just a LAN.
YARN is not platform aware. It is also not aware about how application processes on different hosts communicate with each other to perform the work.
In the same time for YARN application master should be run as a command line - and thereof any node on the cluster with enough resources should be able to run it.
If not every platform is capable to run specific app master- then YARN should be aware on it. Today it can not, but I can imegine platform to be special kind of resource - and then YARN will select appropriate node
Regarding LAN if you have application master which knows how to manage job over several LAN - it is should be fine with YARN.