I am looking for help in running hop pipelines on Spark cluster, running on kubernetes.
I have spark master deployed with 3 worker nodes on kubernetes
I am using hop-run.sh command to run pipeline on spark running on kubernetes.
Facing Below exception
-java.lang.NoClassDefFoundError: Could not initialize class com.amazonaws.services.s3.AmazonS3ClientBuilder
Looks like fat.jar is not getting associated with the spark when running hop-run.sh command.
I tried running same with spark-submit command too but not sure how to pass references of pipelines and workflows to Spark running on kubernetes, though I am able to add fat jar to the classpath (can be seen in logs)
Any kind of help is appreciated.
Thanks
like
Could it be that you are using version 1.0?
We had a missing jar for S3 VFS which has been resolved in 1.1
https://issues.apache.org/jira/browse/HOP-3327
For more information on how to use spark-submit you can take a look at the following documentation:
https://hop.apache.org/manual/latest/pipeline/pipeline-run-configurations/beam-spark-pipeline-engine.html#_running_with_spark_submit
The location to the fat-jar the pipeline and the required metadata-export can all be VFS locations so no need to place those on the cluster itself.
Related
We have a requirement to schedule spark jobs, since we are familiar with apache-airflow we want to go ahead with it to create different workflows. I searched web but did not find a step by step guide to schedule spark job on airflow and option to run them on different server running master.
Answer to this will be highly appreciated.
Thanks in advance.
There are 3 ways you can submit Spark jobs using Apache Airflow remotely:
(1) Using SparkSubmitOperator: This operator expects you have a spark-submit binary and YARN client config setup on our Airflow server. It invokes the spark-submit command with given options, blocks until the job finishes and returns the final status. The good thing is, it also streams the logs from the spark-submit command stdout and stderr.
You really only need to configure a yarn-site.xml file, I believe, in order for spark-submit --master yarn --deploy-mode client to work.
Once an Application Master is deployed within YARN, then Spark is running locally to the Hadoop cluster.
If you really want, you could add a hdfs-site.xml and hive-site.xml to be submitted as well from Airflow (if that's possible), but otherwise at least hdfs-site.xml files should be picked up from the YARN container classpath
(2) Using SSHOperator: Use this operator to run bash commands on a remote server (using SSH protocol via paramiko library) like spark-submit. The benefit of this approach is you don't need to copy the hdfs-site.xml or maintain any file.
(3) Using SimpleHTTPOperator with Livy: Livy is an open source REST interface for interacting with Apache Spark from anywhere. You just need to have REST calls.
I personally prefer SSHOperator :)
Like some article that I previously read. It said that in new Kubernetes version, already include Spark capabilities. But with some different ways such as using KubernetesPodOperator instead of using BashOperator / PythonOperator to do SparkSubmit.
Is that the best practice to Combine Airflow + Kubernetes is to remove Spark and using KubernetesPodOperator to execute the task?
Which is have a better performance since Kubernetes have AutoScaling that Spark doesn’t have.
Need someone expert in Kubernetes to help me explain this. I’m still newbie with this Kubernetes, Spark, and Airflow things. :slight_smile:
Thank You.
in new Kubernetes version, already include Spark capabilities
I think you got that backwards. New versions of Spark can run tasks in a Kubernetes cluster.
using KubernetesPodOperator instead of using BashOperator / PythonOperator to do SparkSubmit
Using Kubernetes would allow you to run containers with whatever isolated dependencies you wanted.
Meaning
With BashOperator, you must distribute the files to some shared filesystem or to all the nodes that ran the Airflow tasks. For example, spark-submit must be available on all Airflow nodes.
Similarly with Python, you ship out some zip or egg files that include your pip/conda dependency environment
remove Spark and using KubernetesPodOperator to execute the task
There is still good reasons to run Spark with Airflow, but instead you would be packaging a Spark driver container to execute spark-submit inside a container against the Kubernetes cluster. This way, you only need docker installed, not Spark (and all dependencies)
Kubernetes have AutoScaling that Spark doesn’t have
Spark does have Dynamic Resource Allocation...
One more solution which may help you is to use Apache Livy on Kubernetes (PR: https://github.com/apache/incubator-livy/pull/167) with Airflow HttpOperator.
I'm trying to figure out which is the best way to work with Airflow and Spark/Hadoop.
I already have a Spark/Hadoop cluster and I'm thinking about creating another cluster for Airflow that will submit jobs remotely to Spark/Hadoop cluster.
Any advice about it? Looks like it's a little complicated to deploy spark remotely from another cluster and that will create some file configuration duplication.
You really only need to configure a yarn-site.xml file, I believe, in order for spark-submit --master yarn --deploy-mode client to work. (You could try cluster deploy mode, but I think having the driver being managed by Airflow isn't a bad idea)
Once an Application Master is deployed within YARN, then Spark is running locally to the Hadoop cluster.
If you really want, you could add a hdfs-site.xml and hive-site.xml to be submitted as well from Airflow (if that's possible), but otherwise at least hdfs-site.xml files should be picked up from the YARN container classpath (not all NodeManagers could have a Hive client installed on them)
I prefer submitting Spark Jobs using SSHOperator and running spark-submit command which would save you from copy/pasting yarn-site.xml. Also, I would not create a cluster for Airflow if the only task that I perform is running Spark jobs, a single VM with LocalExecutor should be fine.
There are a variety of options for remotely performing spark-submit via Airflow.
Emr-Step
Apache-Livy (see this for hint)
SSH
Do note that none of these are plug-and-play ready and you'll have to write your own operators to get things done.
Im using kubernetes cluster on AWS to run spark jobs ,im using spark 2.3 ,now i want to run spark-submit from AWS lambda function to k8s master,would like to know if there is any REST interface to run Spark submit on k8s Master?
Unfortunately, it is not possible for Spark 2.3, in case you are using native Kubernetes support.
Based on description from deployment instruction, submission process contains several steps:
Spark creates a Spark driver running within a Kubernetes pod.
The driver creates executors which are also running within Kubernetes pods
The driver connects to them, and executes application code
When the application completes, executor pods terminate and are cleaned up, but the driver pod persists its logs and remains in “completed” state in the Kubernetes API until it’s eventually garbage collected or manually cleaned up.
So, in fact, you have no place to submit a job until you start a submission process, which will launch the first Spark's pod (driver) for you. Only once application completes, everything is terminated.
Please also see similar answer for this question under the link
I'm using Cloudera Quickstart VM 5.3.0 (running in Virtual Box 4.3 on Windows 7) and I wanted to learn Spark (on YARN).
I started Cloudera Manager. In the sidebar I can see all the services, there is Spark but in standalone mode. So I click on "Add a new service", select "Spark". Then I have to select the set of dependencies for this service, I have no choices I must pick HDFS/YARN/zookeeper.
Next step I have to choose a History Server and a Gateway, I run the VM in local mode so I can only choose localhost.
I click on "Continue" and this error occures (+ 69 traces) :
A server error as occurred. Send the following information to
Cloudera.
Path : http://localhost:7180/cmf/clusters/1/add-service/reviewConfig
Version: Cloudera Express 5.3.0 (#155 built by jenkins on
20141216-1458 git: e9aae1d1d1ce2982d812b22bd1c29ff7af355226)
org.springframework.web.bind.MissingServletRequestParameterException:Required
long parameter 'serviceId' is not present at
AnnotationMethodHandlerAdapter.java line 738 in
org.springframework.web.servlet.mvc.annotation.AnnotationMethodHandlerAdapter$ServletHandlerMethodInvoker
raiseMissingParameterException()
I don't know if an internet connection is needed but I precise that I can't connect to the internet with the VM. (EDIT : Even with an internet connection I get the same error)
I have no ideas how to add this service, I tried with or without gateway, many network options but it never worked. I checked the known issues; nothing...
Someone knows how I can solve this error or how I can work around ? Thanks for any help.
Julien,
Before I answer your question I'd like to make some general notes about Spark in Cloudera Distribution of Hadoop 5 (CDH5):
Spark runs in three different formats: (1) local, (2) Spark's own stand-alone manager, and (3) other cluster resource managers like Hadoop YARN, Apache Mesos, and Amazon EC2.
Spark works out-of-the-box with CHD 5 for (1) and (2). You can initiate a local
interactive spark session in Scala using the spark-shell command
or pyspark for Python without passing any arguments. I find the interactive Scala and Python
interpreters help learning to program with Resilient Distributed
Datasets (RDDs).
I was able to recreate your error on my CDH 5.3.x distribution. I didn't mean to take credit for the bug you discovered, but I posted to the Cloudera developer community for feedback.
In order to use Spark in the QuickStart pseudo-distributed environment, see if all of the Spark daemons are running using the following command (you can do this inside the Cloudera Manager (CM) UI):
[cloudera#quickstart simplesparkapp]$ sudo service --status-all | grep -i spark
Spark history-server is not running [FAILED]
Spark master is not running [FAILED]
Spark worker is not running [FAILED]
I've manually stopped all of the stand-alone Spark services so we can try to submit the Spark job within Yarn.
In order to run Spark inside a Yarn container on the quick start cluster, we have to do the following:
Set the HADOOP_CONF_DIR to the root of the directory containing the yarn-site.xml configuration file. This is typically /etc/hadoop/conf in CHD5. You can set this variable using the command export HADOOP_CONF_DIR="/etc/hadoop/conf".
Submit the job using spark-submit and specify you are using Hadoop YARN.
spark-submit --class CLASS_PATH --master yarn JAR_DIR ARGS
Check the job status in Hue and compare to the Spark History server. Hue should show the job placed in a generic Yarn container and Spark History should not have a record of the submitted job.
References used:
Learning Spark, Chapter 7
Sandy Ryza's Blog Post on Spark and CDH5
Spark Documentation for Running on Yarn