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.
Related
I know there is information worth 10 google pages on this but, all of them tell me to just put --master yarn in the spark-submit command. But, in cluster mode, how can my local laptop even know what that means? Let us say I have my laptop and a running dataproc cluster. How can I use spark-submit from my laptop to submit a job to this cluster?
Most of the documentation on running a Spark application in cluster mode assumes that you are already on the same cluster where YARN/Hadoop are configured (e.g. you are ssh'ed in), in which case most of the time Spark will pick up the appropriate local configs and "just work".
This is same for Dataproc: if you ssh onto the Dataproc master node, you can just run spark-submit --master yarn. More detailed instructions can be found in the documentation.
If you are trying to run applications locally on your laptop, this is more difficult. You will need to set up an ssh tunnel to the cluster, and then locally create configuration files that tell Spark how to reach the master via the tunnel.
Alternatively, you can use the Dataproc jobs API to submit jobs to the cluster without having to directly connect. The one caveat is that you will have to use properties to tell Spark to run in cluster mode instead of client mode (--properties spark.submit.deployMode=cluster). Note that when submitting jobs via the Dataproc API, the difference between client and cluster mode is much less pressing because in either case the Spark driver will actually run on the cluster (on the master or a worker respectively), not on your local laptop.
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 :)
I just created a Google Cloud cluster (1 master and 6 workers) and by default Spark is configured.
I have a pure python code that uses NLTK to build the dependency tree for each line from a text file. When I run this code on the master spark-submit run.py I get the same execution time when I run it using my machine.
How to make sure that the master is using the workers in order to reduce the execution time ?
You can check the spark UI. If its running on top of yarn, please open the yarn UI and click on your application id which will open the spark UI. Check under the executors tab it will have the node ip address also.
could you please share your spark submit config.
Your command 'spark-submit run.py' doesn't seem to send your job to YARN. To do such thing, you need to add the --master parameter. For example, a valid command to execute a job in YARN is:
./bin/spark-submit --master yarn python/pi.py 1000
If you execute your job from the master, this execution will be straightforward. Anyway, check this link for another parameter that spark-submit accept.
For a Dataproc cluster (Hadoop Google cluster) you have two options to check the job history including the ones that are running:
By command line from the master: yarn application -list, this option sometimes needs additional configuration. If you have troubles, this link will be useful.
By UI. Dataproc enables you to access the Spark Web UI, it improves monitoring tasks. Check this link to learn how to access the Spark UI and other Dataproc UIs. In summary, you have to create a tunnel and configure your browser to use socks proxy.
Hope the information above help you.
If I have a Spark job (2.2.0) compiled with setMaster("local") what will happen if I send that job with spark-submit --master yarn --deploy-mode cluster ?
I tried this and it looked like the job did get packaged up and executed on the YARN cluster rather than locally.
What I'm not clear on:
why does this work? According to the docs, things that you set in SparkConf explicitly have precedence over things passed in from the command line or via spark-submit (see: https://spark.apache.org/docs/latest/configuration.html). Is this different because I'm using SparkSession.getBuilder?
is there any less obvious impact of leaving setMaster("local") in code vs. removing it? I'm wondering if what I'm seeing is something like the job running in local mode, within the cluster, rather than properly using cluster resources.
It's because submitting your application to Yarn happens before SparkConf.setMaster.
When you use --master yarn --deploy-mode cluster, Spark will run its main method in your local machine and upload the jar to run on Yarn. Yarn will allocate a container as the application master to run the Spark driver, a.k.a, your codes. SparkConf.setMaster("local") runs inside a Yarn container, and then it creates SparkContext running in the local mode, and doesn't use the Yarn cluster resources.
I recommend that not setting master in your codes. Just use the command line --master or the MASTER env to specify the Spark master.
If I have a Spark job (2.2.0) compiled with setMaster("local") what will happen if I send that job with spark-submit --master yarn --deploy-mode cluster
setMaster has the highest priority and as such excludes other options.
My recommendation: Don't use this (unless you convince me I'm wrong - feel challenged :))
That's why I'm a strong advocate of using spark-submit early and often. It defaults to local[*] and does its job very well. It even got improved in the recent versions of Spark where it adds a nice-looking application name (aka appName) so you don't have to set it (or even...please don't...hardcore it).
Given we are in Spark 2.2 days with Spark SQL being the entry point to all the goodies in Spark, you should always start with SparkSession (and forget about SparkConf or SparkContext as too low-level).
The only reason I'm aware of when you could have setMaster in a Spark application is when you want to run the application inside your IDE (e.g. IntelliJ IDEA). Without setMaster you won't be able to run the application.
A workaround is to use src/test/scala for the sources (in sbt) and use a launcher with setMaster that will execute the main application.
My team is deploying a new Big Data architecture on Amazon Cloud. We have Mesos up and running Spark jobs.
We are submitting Spark jobs (i.e.: jars) from a bastion host inside the same cluster. Doing so, however, the bastion host is the driver program and this is called the client mode (if I understood correctly).
We would like to try the cluster mode, but we don't understand where to start the dispatcher process.
The documentation says to start it in the cluster, but I'm confused since our masters don't have Spark installed and we use Zookeeper for master election. Starting it on a slave node is not a vailable option, since slave can fail and we don't want to expose a slave ip or public DNS to the bastion host.
Is it correct to start the dispatcher on the bastion host?
Thank you very much
Documentation is not very detailed.
However, we are quite happy with what we discovered:
according to the documentation, cluster mode is not supported for Mesos clusters (and for Python applications).
However, we started the dispatcher using --master mesos://zk://...
For submitting applications, you need the following:
spark-submit --deploy-mode cluster <other options> --master mesos://<dispatcher_ip>:7077 <ClassName> <jar>
If you run this command from a bastion machine, it won't work, because the Mesos master will look for the submitable jar in the same path as the bastion. We ended exposing the file as a downloadable URL.
Hope this helps
I haven't used cluster mode in Mesos and the cluster mode description is not very detailed. There isn't even a --help option on the script, like there should be, IMHO. However, if you don't pass the --master argument, it errors out with a help message and it turns out there is a --zk option for specifying the Zookeeper URL.
What might work is to launch this script on the bastion itself with the appropriate --master and --zk options. Would that work for you?
You could use a docker image with spark and your application.jar instead of uploading the jar to s3. I didn't try yet, but I think it should work. The environment variable is SPARK_DIST_CLASSPATH in spark-env.sh. I use spark distribution compiled without hadoop with apache hadoop 2.7.1
export SPARK_DIST_CLASSPATH=$(/opt/hadoop/bin/hadoop classpath):/opt/hadoop/share/hadoop/tools/lib/*:/opt/application.jar