I am trying to configure Spark interpreter on a local machine installed Zeppelin version 0.10.0 so that I can run scripts on a Spark cluster created also local on Docker. I am using docker-compose.yml from https://github.com/big-data-europe/docker-spark and Spark version 3.1.2. After docker compose-up, I can see in the browser spark-master on localhost:8080 and History Server on localhost:18081. After reading the ID of the spark-master container, I can also run shell and spark-shell on it (docker exec -it xxxxxxxxxxxx /bin/bash). As host OS I am using Ubuntu 20.04, the spark.master in Zeppelin is set now to spark://localhost:7077, zeppelin.server.port in zeppelin-site.xml to 8070.
There is a lot of information about connecting a container running Zeppelin or running both Spark and Zeppelin in the same container but unfortunately I also use that Zeppelin to connect to the Hive via jdbc on VirtualBox Hortonworks cluster like in one of my previous posts and I wouldn't want to change that configuration now due to hardware resources. In one of the posts (Running zeppelin on spark cluster mode) I saw that such a connection is possible, unfortunately all attempts end with the "Fail to open SparkInterpreter" message.
I would be grateful for any tips.
You need to change the spark.master in Zeppelin to point to the spark master in the docker container not the local machine. Hence spark://localhost:7077 won't work.
The port 7077 is fine because that is the port specified in the docker-compose file you are using. To get the IP address of the docker container you can follow this answer. Since I suppose your container is named spark-master you can try the following:
docker inspect -f '{{range.NetworkSettings.Networks}}{{.IPAddress}}{{end}}' spark-master
Then specify this as the spark.master in Zeppelin: spark://docker-ip:7077
Related
any suggestion in which library/tool should I use for plotting over time RAM,CPU and (optionally) GPU usage of a spark-app submitted to a Docker containerized Spark cluster through spark-submit?
In the documentation Apache suggests to use memory_profiler with commands like:
python -m memory_profiler profile_memory.py
but after accessing to my master node through a remote shell:
docker exec -it spark-master bash
I can't launch locally my spark apps because I need to use the spark-submit command in order to submit it to the cluster.
Any suggestion? I launch the apps w/o YARN but in cluster mode through
/opt/spark/spark-submit --master spark://spark-master:7077 appname.py
I would like also to know if I can use memory_profiler even if I need to use spark-submit
I've been playing around with HDFS and Spark. I've set up a five node cluster on my network running HDFS, Spark, and managed by Yarn. Workers are running in client mode.
From the master node, I can launch the PySpark shell just fine. Running example jars, the job is split up to the worker nodes and executes nicely.
I have a few questions on whether and how to run python/Pyspark files against this cluster.
If I have a python file with a PySpark calls elsewhere else, like on my local dev laptop or a docker container somewhere, is there a way to run or submit this file locally and have it executed on the remote Spark cluster? Methods that I'm wondering about involve running spark-submit in the local/docker environment and but the file has SparkSession.builder.master() configured to the remote cluster.
Related, I see a configuration for --master in spark-submit, but the only yarn option is to pass "yarn" which seems to only queue locally? Is there a way to specify remote yarn?
If I can set up and run the file remotely, how do I set up SparkSession.builder.master()? Is the url just to the hdfs:// url to port 9000, or do I submit it to one of the Yarn ports?
TIA!
way to run or submit this file locally and have it executed on the remote Spark cluster
Yes, well "YARN", not "remote Spark cluster". You set --master=yarn when running with spark-submit, and this will run against the configured yarn-site.xml in HADOOP_CONF_DIR environment variable. You can define this at the OS level, or in spark-env.sh.
You can also use SparkSession.builder.master('yarn') in code. If both options are supplied, one will get overridden.
To run fully "in the cluster", also set --deploy-mode=cluster
Is there a way to specify remote yarn?
As mentioned, this is configured from yarn-site.xml for providing resourcemanager location(s).
how do I set up SparkSession.builder.master()? Is the url just to the hdfs:// url to port 9000
No - The YARN resource manager has its own RPC protocol, not hdfs:// ... You can use spark.read("hdfs://namenode:port/path") to read HDFS files, though. As mentioned, .master('yarn') or --master yarn are the only configs you need that are specific for Spark.
If you want to use Docker containers, YARN does support this, but Spark's Kubernetes master will be easier to setup, and you can use Hadoop Ozone or MinIO rather than HDFS in Kubernetes.
I have :
Apache Spark : 2.4.4
JupyterHub : 1.1.0
Helm chart version : 0.9.0
K8S : 1.15
I build Jupyterhub on k8s with the official doc : https://zero-to-jupyterhub.readthedocs.io/
I use official Spark image to do some local jobs : jupyter/all-spark-notebook:latest
Spark works well in local mode.
But I want to use JupyterHub notebook to do some jobs on remote (homemade) Apache Spark cluster (with K8s as orchestrator).
I already tried Apache Zeppelin, it's works well ! but I want to do the same thing with Jupyterhub.
How can I do this ?
I understand your pain.
I burn a lot of time to create spark cluster + jupyter server work.
Try use my docker-compose.yaml.
docker-compose up -d
For get jupyter token run:
docker-compose logs jupyter
Copy url starting 127.0.0.1 include token and put into your browser. Change port to 7777.
You will see empty jupyter page.
Create new notebook and run cell as you see on picture
Enjoy using jupyter with spark...
Hope it's help you.
Using a Spark 1.6.1 standalone cluster. After a system restart (and only minor config changes to /etc/hosts per worker) Spark executors suddenly started throwing errors that they couldn't connect to spark_master.
When I echo $SPARK_MASTER_IP on the same shell used to start the master, it correctly identifies the host as master.cluster. And when I open the GUI at port 8080 it also identifies the master as Spark Master at spark://master.cluster:7077.
I've also set in spark-env.sh the SPARK_MASTER_IP as well. Why are my executors trying to connect to spark_master?
I am running a AWS EMR cluster with Spark (1.3.1) installed via the EMR console dropdown. Spark is current and processing data but I am trying to find which port has been assigned to the WebUI. I've tried port forwarding both 4040 and 8080 with no connection. I'm forwarding like so
ssh -i ~/KEY.pem -L 8080:localhost:8080 hadoop#EMR_DNS
1) How do I find out what the Spark WebUI's assigned port is?
2) How do I verify the Spark WebUI is running?
Spark on EMR is configured for YARN, thus the Spark UI is available by the application url provided by the YARN Resource Manager (http://spark.apache.org/docs/latest/monitoring.html). So the easiest way to get to it is to setup your browser with SOCKS using a port opened by SSH then from the EMR console open Resource Manager and click the Application Master URL provided to the right of the running application. Spark History server is available at the default port 18080.
Example of socks with EMR at http://docs.aws.amazon.com/ElasticMapReduce/latest/DeveloperGuide/emr-web-interfaces.html
Here is an alternative if you don't want to deal with the browser setup with SOCKS as suggested on the EMR docs.
Open a ssh tunnel to the master node with port forwarding to the machine running spark ui
ssh -i path/to/aws.pem -L 4040:SPARK_UI_NODE_URL:4040 hadoop#MASTER_URL
MASTER_URL (EMR_DNS in the question) is the URL of the master node that you can get from EMR Management Console page for the cluster
SPARK_UI_NODE_URL can be seen near the top of the stderr log. The log line will look something like:
16/04/28 21:24:46 INFO SparkUI: Started SparkUI at http://10.2.5.197:4040
Point your browser to localhost:4040
Tried this on EMR 4.6 running Spark 2.6.1
Glad to announce that this feature is finally available on AWS. You won't need to run any special commands (or to configure a SSH tunnel) :
By clicking on the link to the spark history server ui, you'll be able to see the old applications logs, or to access the running spark job's ui :
For more details: https://docs.aws.amazon.com/emr/latest/ManagementGuide/app-history-spark-UI.html
I hope it helps !
Just run the following command:
ssh -i /your-path/aws.pem -N -L 20888:ip-172-31-42-70.your-region.compute.internal:20888 hadoop#ec2-xxx.compute.amazonaws.com.cn
There are 3 places you need to change:
your .pem file
your internal master node IP
your public DNS domain.
Finally, on the Yarn UI you can click your Spark Application Tracking URL, then just replace the url:
"http://your-internal-ip:20888/proxy/application_1558059200084_0002/"
->
"http://localhost:20888/proxy/application_1558059200084_0002/"
It worked for EMR 5.x
Simply use SSH tunnel
On your local machine do:
ssh -i /path/to/pem -L 3000:ec2-xxxxcompute-1.amazonaws.com:8088 hadoop#ec2-xxxxcompute-1.amazonaws.com
On your local machine browser hit:
localhost:3000