I'm submitting a spark job from a shell script that has a bunch of env vars and parameters to pass to spark. Strangely, the driver host is not one of these parameters (there are driver cores and memory however). So if I have 3 machines in the cluster, a driver will be chosen randomly. I don't want this behaviour since 1) the jar I'm submitting is only on one of the machines and 2) the driver machine should often be smaller than the other machines which is not the case if it's random choice.
So far, I found no way to specify this param on the command line to spark-submit. I've tried --conf SPARK_DRIVER_HOST="172.30.1.123, --conf spark.driver.host="172.30.1.123 and many other things but nothing has any effect. I'm using spark 2.1.0. Thanks.
I assume you are running with Yarn cluster. In brief yarn uses containers to launch and implement tasks. And resource manager decides where to run which container based on availability of resources. In spark case drivers and executors also launched as containers with separate jvms. Driver dedicated to splitting tasks among executors and collect the results from them. If your node from where you launch your application included in cluster then it will be also used as shared resource for launching driver/executor.
From the documentation: http://spark.apache.org/docs/latest/running-on-yarn.html
When running the cluster in standalone or in Mesos the driver host (this is the master) can be launched with:
--master <master-url> #e.g. spark://23.195.26.187:7077
When using YARN it works a little different. Here the parameter is yarn
--master yarn
The yarn is specified in Hadoop its configuration for the ResourceManager. For how to do this see this interesting guide https://dqydj.com/raspberry-pi-hadoop-cluster-apache-spark-yarn/ . Basically in the hdfs the hdfs-site.xml and in yarn the yarn-site.xml
Related
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.
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.
I noticed that when I start a job in spark submit using yarn, the driver and executor nodes get set randomly. Is it possible to set this manually, so that when I collect the data and write it to file, it can be written on the same node every single time?
As of right now, the parameter I tried playing around with are:
spark.yarn.am.port <driver-ip-address>
and
spark.driver.hostname <driver-ip-address>
Thanks!
If you submit to Yarn with --master yarn --deploy-mode client, the driver will be located on the node you are submitting from.
Also you can configure node labels for executors using property: spark.yarn.executor.nodeLabelExpression
A YARN node label expression that restricts the set of nodes executors will be scheduled on. Only versions of YARN greater than or equal to 2.6 support node label expressions, so when running against earlier versions, this property will be ignored.
Docs - Running Spark on YARN - Latest Documentation
A spark cluster can run in either yarncluster or yarn-client mode.
In yarn-cluster mode, the Spark driver runs inside an application master process which is managed by YARN on the cluster, and the client machine can go away after initiating the application.
In yarn-client mode, the driver runs in the client
process, and the application master is only used for requesting resources from YARN.
So as you see, depending upon the mode, the spark picks up the Application Master. Its not happened randomly until this stage. However, the worker nodes which the application master requests the resource manager to perform tasks will be randomly picked based on the availability of the worker nodes.
Can any one please let me know how to submit spark Job from locally and connect to Cassandra cluster.
Currently I am submitting the Spark job after I login to Cassandra node through putty and submit the below dse-spark-submit Job command.
Command:
dse spark-submit --class ***** --total-executor-cores 6 --executor-memory 2G **/**/**.jar --config-file build/job.conf --args
With the above command, my spark Job able to connect to cluster and its executing, but sometimes facing issues.
So I want to submit spark job from my local machine. Can any one please guide me how to do this.
There are several things you could mean by "run my job locally"
Here are a few of my interpretations
Run the Spark Driver on a Local Machine but access a remote Cluster's resources
I would not recommend this for a few reasons, the biggest being that all of your job management will still be handled between your remote machine and the executors in the cluster. This would be equivalent of having a Hadoop Job Tracker running in a different cluster than the rest of the Hadoop distribution.
To accomplish this though you need to run a spark submit with a specific master uri. Additionally you would need to specify a Cassandra node via spark.cassandra.connection.host
dse spark-submit --master spark://sparkmasterip:7077 --conf spark.cassandra.connection.host aCassandraNode --flags jar
It is important that you keep the jar LAST. All arguments after the jar are interpreted as arguments for the application and not spark-submit parameters.
Run Spark Submit on a local Machine but have the Driver run in the Cluster (Cluster Mode)
Cluster mode means your local machine sends the jar and environment string over to the Spark Master. The Spark Master then chooses a worker to actually run the driver and the driver is started as a separate JVM by the worker. This is triggered using the --deploy-mode cluster flag. In addition to specifying the Master and Cassandra Connection Host.
dse spark-submit --master spark://sparkmasterip:7077 --deploy-mode cluster --conf spark.cassandra.connection.host aCassandraNode --flags jar
Run the Spark Driver in Local Mode
Finally there exists a Local mode for Spark which starts the entire Spark Framework in a single JVM. This is mainly used for testing. Local mode is activated by passing `--master local``
For more information check out the Spark Documentation on submitting applications
http://spark.apache.org/docs/latest/submitting-applications.html
I'm trying to submit a Spark app from local machine Terminal to my Cluster.
I'm using --master yarn-cluster. I need to run the driver program on my Cluster too, not on the machine I do submit the application i.e my local machine
When I provide the path to application jar which is in my local machine, would spark-submit automatically upload it to my Cluster?
I'm using
bin/spark-submit
--class com.my.application.XApp
--master yarn-cluster --executor-memory 100m
--num-executors 50 /Users/nish1013/proj1/target/x-service-1.0.0-201512141101-assembly.jar
1000
and getting error
Diagnostics: java.io.FileNotFoundException: File file:/Users/nish1013/proj1/target/x-service-1.0.0-201512141101- does not exist
In Documentation ,http://spark.apache.org/docs/latest/submitting-applications.html#launching-applications-with-spark-submit
Advanced Dependency Management When using spark-submit, the
application jar along with any jars included with the --jars option
will be automatically transferred to the cluster.
But seems like it does not !
I see you are quoting the spark-submit page from Spark Docs but I would spend a lot more time on the Running Spark on YARN page. Bottom-line, look at:
There are two deploy modes that can be used to launch Spark
applications on YARN. In yarn-cluster mode, the Spark driver runs
inside an application master process which is managed by YARN on the
cluster, and the client can go away after initiating the application.
In yarn-client mode, the driver runs in the client process, and the
application master is only used for requesting resources from YARN.
Further you note, "I need to run the driver program on my Cluster too, not on the machine I do submit the application i.e my local machine"
So I agree with you that you are right to run --master yarn-cluster instead of --master yarn-client
(and one comment notes what might just be a syntax error where you dropped "assembly.jar" but I think this will apply as well...)
Some of the basic assumptions about non-YARN implementations change a lot when YARN is introduced, mostly related to Classpaths and the need to push jars to the workers.
From an email on the Apache Spark User list:
YARN cluster mode. Spark submit does upload your jars to the cluster.
In particular, it puts the jars in HDFS so your driver can just read
from there. As in other deployments, the executors pull the jars from
the driver.
So finally, from the Apache Spark YARN doc:
Ensure that HADOOP_CONF_DIR or YARN_CONF_DIR points to the directory
which contains the (client side) configuration files for the Hadoop
cluster. These configs are used to write to HDFS and connect to the
YARN ResourceManager.
NOTE: I only see you adding a single JAR, if there's a need to add other JARs there's a special note about doing that with YARN:
In yarn-cluster mode, the driver runs on a different machine than the
client, so SparkContext.addJar won’t work out of the box with files
that are local to the client. To make files on the client available to
SparkContext.addJar, include them with the --jars option in the launch
command.
That page in the link has some examples.
And of course you downloaded or built the YARN-specific version of Spark.
Background, in a standalone cluster deployment using spark-submit and the option --deploy-mode cluster, yes you do need to make sure every worker node has access to all the dependencies, Spark will not push them to the cluster. This is because in "standalone cluster" mode with Spark as the job manager, you don't know which node the driver will run on! But that doesn't apply to your case.
But if I could, depending on the size of the jars you are uploading, I would still explicitly put the jars on each node, or "globally available" via HDFS, for another reason from the docs:
From Advanced Dependency Management, it seems to present the best of both worlds, but also a great reason for manually pushing your jars out to all nodes:
local: - a URI starting with local:/ is expected to exist as a local
file on each worker node. This means that no network IO will be
incurred, and works well for large files/JARs that are pushed to each
worker, or shared via NFS, GlusterFS, etc.
But I assume that local:/... would change to hdfs:/ ... not sure on that one.
Yes and no. It depends on what you mean. Spark deploys the .jar to the nodes in the cluster. However, it won't upload your .jar file from your local machine to the cluster.
You can find more info in the Submitting Applications page. As you can see, in the arguments you pass to spark-submit, there is one that needs to be globally visible: the application-jar.
application-jar: Path to a bundled jar including your application and
all dependencies. The URL must be globally visible inside of your
cluster, for instance, an hdfs:// path or a file:// path that is
present on all nodes.
As far as I understand, what you want is to use yarn-client, not yarn-cluster. This will run the driver in the client (e.g., the machine which you are trying to call spark-submit on, for example your laptop), without the need of copying the .jar file on the cluster. More about this here.
Try adding --jars option before your /path/to/jar/file
spark-submit --jars /tmp/test.jar