I have a set up of spark cluster running on docker in which the following things are running:-
spark-master
three spark-workers (spark-worker-1, spark-worker-2, spark-worker-3)
For setting up the spark cluster I have followed the instructions given on URL:-
https://github.com/big-data-europe/docker-spark
Now I want to launch a spark application which can run on this cluster and for this, I am using bde2020/spark-scala-template and following the instructions given on URL:-
https://github.com/big-data-europe/docker-spark/tree/master/template/scala
But when I tried to run the jar file then it starts running on the spark master present in the bde2020/spark-scala-template image and not on the master of my cluster running in a different container.
Please help me to do that. Stucked very badly.
Related
I am using code examples in the MLRun documentation for running a spark job on Iguazio platform. Docs say I can use a default spark docker image provided by the platform, but when I try to run the job the pod hangs with Error ImagePullBackOff. Here is the function spec item I am using:
my_func.spec.use_default_image = True
How do I configure Iguazio to use the default spark image that is supposed to be included in the platform?
You need to deploy the default image to the cluster docker registry. There is one image for remote spark and one image for spark operator. Those images contain all the necessary dependencies for remote Spark and Spark Operator.
See the code below.
# This section has to be invoked once per MLRun/Iguazio upgrade
from mlrun.runtimes import RemoteSparkRuntime
RemoteSparkRuntime.deploy_default_image()
from mlrun.runtimes import Spark3Runtime
Spark3Runtime.deploy_default_image()
Once these images are deployed (to the cluster docker registry), your function with function spec “use_default_image” = True will be able to pull the image and deploy.
I'm trying to connect a PySpark session running locally to a DataProc cluster. I want to be able to work with files on gcs without downloading them. My goal is to perform ad-hoc analyses using local Spark, then switch to a larger cluster when I'm ready to scale. I realize that DataProc runs Spark on Yarn, and I've copied over the yarn-site.xml locally. I've also opened up an ssh tunnel from my local machine to the DataProc master node and set up port forwarding for the ports identified in the yarn xml. It doesn't seem to be working though, when I try to create a session in a Jupyter notebook it hangs indefinitely. Nothing in stdout or DataProc logs that I can see. Has anyone had success with this?
For anyone interested, I eventually abandoned this approach. I'm instead running Jupyter Enterprise Gateway on the master node, setting up port forwarding, and then launching my notebooks locally to connect to kernel(s) running on the server. It works very nicely so far.
I'm trying to minimize changes in my code so I'm wondering if there is a way to submit a spark-streaming job from my personal PC/VM as follows:
spark-submit --class path.to.your.Class --master yarn --deploy-mode client \
[options] <app jar> [app options]
without using GCP SDK.
I also have to specify a directory with configuration files HADOOP_CONF_DIR which I was able to download from Ambari.
Is there a way to do the same?
Thank you
Setting up an external machine as a YARN client node is generally difficult to do and not a workflow that will work easily with Dataproc.
In a comment you mention that what you really want to do is
Submit a Spark job to the Dataproc cluster.
Run a local script on each "batchFinish" (StreamingListener.onBatchCompleted?).
The script has dependencies that mean it cannot run inside of the Dataproc master node.
Again, configuring a client node outside of the Dataproc cluster and getting it to work with spark-submit is not going to work directly. However, if you can configure your network such that the Spark driver (running within Dataproc) has access to the service/script you need to run, and then invoke that when desired.
If you run your service on a VM that has access to the network of the Dataproc cluster, then your Spark driver should be able to access the service.
Installed spark cluster on standalone mode with 2 nodes on first node there is spark master running and on another node spark worker. When i try to run spark shell on worker node with word count code it runs fine but when i try to run spark shell on the master node it gives following output :
WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
Executor is not triggered to run the job. Even though there is worker available to spark master its giving such a problem . Any help is appriciated , thanks
You use client deploy mode so the best bet is that executor nodes cannot connect to the driver port on the local machine. It could be firewall issue or problem with advertised IP / hostname. Please make sure that:
spark.driver.bindAddress
spark.driver.host
spark.driver.port
use expected values. Please refer to the networking section of Spark documentation.
Less likely it is a lack of resources. Please check if you don't request more resources than provided by workers.
I have installed the spark in cluster mode. 1 master and 2 workers.And When I start spark shell in master node it is countinously running without getting the scala shell.
But when I run spark-shell on a worker node I am getting scala shell.And I am able to do the jobs.
val file=sc.textFile(“hdfs://192.168.1.20:9000/user/1gbdata”)
file.count()
And for this I got the output.
So My doubt is actually where to run the spark jobs.
Is it in worker nodes?
Based on the documentation, you need to connect your spark-shell to the master node with the following command : spark-shell --master spark://IP:PORT. This url can be retrieved from the master's UI or log file.
You should be able to launch the spark-shell on the master node (machine), make sure to check out the UI to see if the spark-shell is effectively running and that the prompt is shown (you might need to press enter on your keyboard after issuing spark-shell).
Please note that when you are using spark-submit in cluster mode, the driver will be submitted directly from one of the worker nodes, contrary to client mode where it will run as a client process. Refer to the documentation for more details.