I wanted to understand the limitations of LocalExecutionEnvironment and if it can be used to run in production ?
Appreciate any help/insight. Thanks
LocalExecutionEnvironment spins up a Flink MiniCluster, which runs the entire Flink system (JobManager, TaskManager) in a single JVM. So you're limited to CPU cores and memory available on that one machine. You also don't have HA from multiple JobManagers. I haven't looked at other limitations of the MiniCluster environment, but I'm sure more exist.
A LocalExecutionEnvironment doesn't load a config file on startup, so you have to do all of the configuration in the application. By default it also doesn't offer a REST endpoint. You can solve both these issues by doing something like this:
String cwd = Paths.get(".").toAbsolutePath().normalize().toString();
Configuration conf = GlobalConfiguration.loadConfiguration(cwd);
env = StreamExecutionEnvironment.createLocalEnvironmentWithWebUI(conf);
Logging may be another issue that will require a workaround.
I don't believe you'll be able to use the Flink CLI to control the job, but if you create the Web UI (as shown above) you can at least use the REST API to do things like triggering savepoints (after first using the REST API to get the job ID).
Related
I am using apache camel as integration framework in my microservice. I am deploying it in a Kubernetes cloud as multiple pods. I had written a route for reading file from a directory and write to another. But I am facing an issue as the different pods are picking same file. I need to avoid that. I only want any of the pod to pick the file and process but currently all the pods are picking and processing the file. Can someone help with this. Please suggest some examples available in GitHub or any other.
Thanks in advance.
Camel recently introduced some interesting clustering capabilities - see here.
In your particular case, you could model a route which is taking the leadership when starting the directory polling, preventing thereby other nodes from picking the (same or other) files.
Set it up is very easy and all you need is to prefix singleton
endpoints according to the master component syntax:
master:namespace:delegateUri
This would result in something like this:
from("master:mycluster:file://...")
.routeId("clustered-route")
.log("Clustered file polling !");
Here is the deal;
I'm dealing with adding new worker (embbeded) to on running the cluster (flink statefun 2.2.1).
As you see the new task manager can be registered to the cluster;
Screenshot of new deployed taskmanager
But it doesn't initialize (it doesn't deploying sources);
What am I missing here?? (master and workers has to same jar files too? or it should be enough deploying taskmanager with jar file)
Any help would be appreciated,
Thx.
Flink supports two different approaches to rescaling: active and reactive.
Reactive mode is new in Flink 1.13 (released just this week), and works as you expected: add (or remove) a task manager, and your application will adjust to the new parallelism. You can read about elastic scaling and reactive mode in the docs.
Reactive mode is currently a work in progress, but might need your needs.
In broad strokes, for active mode rescaling you need to:
Do a stop with savepoint to bring down your current job while taking a snapshot of its state.
Relaunch with the new parallelism, using the savepoint as the starting point.
The exact details depend on how your cluster is deployed.
For a step-by-step tutorial, see Upgrading & Rescaling a Job in the Flink Operations Playground.
The above applies to rescaling statefun embedded functions. Being stateless, remote functions can be rescaled more straightforwardly.
My team are trying set-up Apache Flink (v1.4) cluster on Mesos/Marathon. We are using the docker image provided by mesosphere. It works really well!
Because of a new requirement, the task managers have to launched with extend runtime privileges. We can easily enable this runtime privileges for the app manager via the Marathon web UI. However, we cannot find a way to enable the privileges for task managers.
In Apache Spark, we can set spark.mesos.executor.docker.parameters privileged=true in Spark's configuration file. Therefore, Spark can pass this parameter to docker run command. I am wondering if Apache Flink allow us to pass a custom parameter to docker run when launching task managers. If not, how can we start task managers with extended runtime privileges?
Thanks
There is a new parameter mesos.resourcemanager.tasks.container.docker.parameters introduced in this commit which will allow passing arbitrary parameters to Docker.
Unfortunately, this is not possible as of right now (or only for the framework scheduler as Tobi pointed out).
I went ahead and created a Jira for this feature so you can keep track/add details/contribute it yourself: https://issues.apache.org/jira/browse/FLINK-8490
You should be able to tweak the setting for the parameters in the ContainerInfo of https://github.com/mesoshq/flink-framework/blob/master/index.js to support this. I’ll eventually update the Flink version in the Docker image...
I am using Flink 1.2.1 running on Docker, with Task Managers distributed across different VMs as part of a Docker Swarm.
Uploading an Apache Beam application using the Flink Web UI and trying to set the parallelism at job submission point doesn't work. Neither does submit a job using the Flink CLI.
It seems like the parallelism doesn't get picked up at client level, it ends up defaulting to 1.
When I set the parallelism programmatically within the Apache Beam code, it works: flinkPipelineOptions.setParallelism(4);
I suspect the root of the problem may be in the org.apache.beam.runners.flink.DefaultParallelismFactory class, as it checks for Flink's GlobalConfiguration, which may not pick up runtime values passed to Flink.
Any ideas on how this could be fixed or worked around? I need to be able to change the parallelism dynamically, so the programmatic approach won't work, nor will setting the Flink configuration at system level.
I am using the following documentation:
https://ci.apache.org/projects/flink/flink-docs-release-1.2/dev/parallel.html
https://beam.apache.org/documentation/sdks/javadoc/2.0.0/org/apache/beam/runners/flink/DefaultParallelismFactory.html
This should probably be fixed in the Beam Flink Runner but as a workaround you can try setting the parallelism to -1 programatically. This should make the translation pick up the parallelism that is specified when submitting the job.
I have a couple of questions about the App Engine Map Reduce API. First of all there's a mapreduce package in the SDK, and there's a separate mapreduce bundle here:
https://developers.google.com/appengine/downloads
Which one should I be using? Should I be using the bundle, or is the documentation out of date and I should actually use the SDK version?
Second I'd like to be able to run mapreduce's on a non-default version to make sure that the requests from the mapreduce don't interfere with user requests.
What's the best way to do this? Can I start the pipeline with a task queue, and set the target version of that queue to be my non-default version?
We recommend using the open source version of Map Reduce for GAE at http://code.google.com/p/appengine-mapreduce/
The stale bundle link in the docs is a bug. That'll get cleaned up soon.
A few of our SDKs have bits of MapReduce (for historic reasons), but the open source version is the way to go for now.
As for using a separate version, this is kind of "it depends". If you're thinking of interference in terms of competition for the processor, that's not likely to be a noticeable issue. Depending on queue processing rates you've set up, more instances of your app will be spun up to handle mapping tasks as needed. I'd try some experiments first. Make sure you have a problem before you invest time and effort solving it.
mapreduce can be start on a not default version. And after it starts, it will continue run on that version automatically.
In my case I just deploy the code on a non default version and trigger the mapreduce with version_id.app_id.appspot.com/path_to_start_a_job.
cron job can also trigger the mapreduce on non default version without problem.