I'm working on integration between Mesos & Spark. For now, I can start SlaveMesosDispatcher in a docker; and I like to also run Spark executor in Mesos docker. I do the following configuration for it, but I got an error; any suggestion?
Configuration:
Spark: conf/spark-defaults.conf
spark.mesos.executor.docker.image ubuntu
spark.mesos.executor.docker.volumes /usr/bin:/usr/bin,/usr/local/lib:/usr/local/lib,/usr/lib:/usr/lib,/lib:/lib,/home/test/workshop/spark:/root/spark
spark.mesos.executor.home /root/spark
#spark.executorEnv.SPARK_HOME /root/spark
spark.executorEnv.MESOS_NATIVE_LIBRARY /usr/local/lib
NOTE: The spark are installed in /home/test/workshop/spark, and all dependencies are installed.
After submit SparkPi to the dispatcher, the driver job is started but failed. The error messes is:
I1015 11:10:29.488456 18697 exec.cpp:134] Version: 0.26.0
I1015 11:10:29.506619 18699 exec.cpp:208] Executor registered on slave b7e24114-7585-40bc-879b-6a1188cb65b6-S1
WARNING: Your kernel does not support swap limit capabilities, memory limited without swap.
/bin/sh: 1: ./bin/spark-submit: not found
Does any know how to map/set spark home in docker for this case?
I think the issue you're seeing here is a result of the current working directory of the container isn't where Spark is installed. When you specify a docker image for Spark to use with Mesos, it expects the default working directory of the container to be inside $SPARK_HOME where it can find ./bin/spark-submit.
You can see that logic here.
It doesn't look like you're able to configure the working directory through Spark configuration itself, which means you'll need to build a custom image on top of ubuntu that simply does a WORKDIR /root/spark.
Related
I am trying to run the spark-submit command on my Hadoop cluster Here is a summary of my Hadoop Cluster:
The cluster is built using 5 VirtualBox VM's connected on an internal network
There is 1 namenode and 4 datanodes created.
All the VM's were built from the Bitnami Hadoop Stack VirtualBox image
I am trying to run one of the spark examples using the following spark-submit command
spark-submit --class org.apache.spark.examples.SparkPi $SPARK_HOME/examples/jars/spark-examples_2.12-3.0.3.jar 10
I get the following error:
[2022-07-25 13:32:39.253]Container exited with a non-zero exit code 1. Error file: prelaunch.err.
Last 4096 bytes of prelaunch.err :
Last 4096 bytes of stderr :
Error: Could not find or load main class org.apache.spark.deploy.yarn.ExecutorLauncher
I get the same error when trying to run a script with PySpark.
I have tried/verified the following:
environment variables: HADOOP_HOME, SPARK_HOME and HADOOP_CONF_DIR have been set in my .bashrc file
SPARK_DIST_CLASSPATH and HADOOP_CONF_DIR have been defined in spark-env.sh
Added spark.master yarn, spark.yarn.stagingDir hdfs://hadoop-namenode:8020/user/bitnami/sparkStaging and spark.yarn.jars hdfs://hadoop-namenode:8020/user/bitnami/spark/jars/ in spark-defaults.conf
I have uploaded the jars into hdfs (i.e. hadoop fs -put $SPARK_HOME/jars/* hdfs://hadoop-namenode:8020/user/bitnami/spark/jars/ )
The logs accessible via the web interface (i.e. http://hadoop-namenode:8042 ) do not provide any further details about the error.
This section of the Spark documentation seems relevant to the error since the YARN libraries should be included, by default, but only if you've installed the appropriate Spark version
For with-hadoop Spark distribution, since it contains a built-in Hadoop runtime already, by default, when a job is submitted to Hadoop Yarn cluster, to prevent jar conflict, it will not populate Yarn’s classpath into Spark. To override this behavior, you can set spark.yarn.populateHadoopClasspath=true. For no-hadoop Spark distribution, Spark will populate Yarn’s classpath by default in order to get Hadoop runtime. For with-hadoop Spark distribution, if your application depends on certain library that is only available in the cluster, you can try to populate the Yarn classpath by setting the property mentioned above. If you run into jar conflict issue by doing so, you will need to turn it off and include this library in your application jar.
https://spark.apache.org/docs/latest/running-on-yarn.html#preparations
Otherwise, yarn.application.classpath in yarn-site.xml refers to local filesystem paths in each of ResourceManager servers where JARs are available for all YARN applications (spark.yarn.jars or extra packages should get layered onto this)
Another problem could be file permissions. You probably shouldn't put Spark jars into an HDFS user folder if they're meant to be used by all users. Typically, I'd put it under hdfs:///apps/spark/<version>, then give that 744 HDFS permissions
In the Spark / YARN UI, it should show the complete classpath of the application for further debugging
I figured out why I was getting this error. It turns out that I made an error while specifying spark.yarn.jars in spark-defaults.conf
The value of this property must be
hdfs://hadoop-namenode:8020/user/bitnami/spark/jars/*
instead of
hdfs://hadoop-namenode:8020/user/bitnami/spark/jars/
i.e. Basically, we need to specify the jar files as the value to this property and not the folder containing the jar files.
I use Pyspark with Spark 2.4 in the standalone mode on Linux for processing a lot of incoming data via Kafka using a Jupyter notebook (currently for testing). I want to add these options to this notebook in order to prevent the /tmp/ directory to be filled with dozens of gigabytes after few hours:
spark.worker.cleanup.enabled=true
spark.worker.cleanup.appDataTtl=120
But these conf locations do not work:
Spark’s default configuration (spark/conf/spark-env.sh) seems not be used by Juypter notebooks at all:
SPARK_WORKER_OPTS="spark.worker.cleanup.enabled=true
spark.worker.cleanup.appDataTtl=120"
So, I created a sperate kernel configuration in ~/.local/share/jupyter/kernels/python3-spark1/kernel.json that I can select in Jupyterhub and that is really used for the RAM adjustments what I can see in htop:
"env": {
"PYSPARK_SUBMIT_ARGS": "--master local[*]
--conf spark.worker.cleanup.enabled=true --conf=spark.worker.cleanup.appDataTtl=120 driver-memory 145g --executor-memory 50g pyspark-shell"
but the /tmp still fills with dozens of gigs.
I also tried the “magic” in a jupyter cell but it also did not work.
Do you know how to configure the Jupyter notebooks for this Spark adjustments properly?
Configuration properties that apply only to the worker in the form "-Dx=y"
export SPARK_WORKER_OPTS="$SPARK_WORKER_OPTS -Dspark.worker.cleanup.enabled=true -Dspark.worker.cleanup.interval=60 -Dspark.worker.cleanup.appDataTtl=120"
If that not work you can try any of the below options.
Option-1: Updating default.conf
In Worker node set the following configuration option in the /spark/conf/spark-defaults.conf file:
spark.worker.cleanup.enabled: Enables periodic cleanup of worker and application directories. Disabled by default. Set to true to enable it. Note: that this only affects standalone mode, as YARN works differently.
spark.worker.cleanup.interval: The frequency, in seconds, that the worker cleans up old application work directories. The default is 30 minutes.
spark.worker.cleanup.appDataTtl: The number of seconds to retain application work directories on each worker. The default is 7 days.
Then stop and start the workers.
sbin/stop-worker.sh - Stops all worker instances on the machine the script is executed on.
sbin/start-worker.sh - Starts a worker instance on the machine the script is executed on.
Option-2: If you setup a spark cluster using docker-compose then set environment in Docker compose file
spark-worker-x:
image: spark-worker
container_name: spark-worker-x
environment:
- SPARK_WORKER_OPTS="-Dspark.worker.cleanup.enabled=true -Dspark.worker.cleanup.interval=60 -Dspark.worker.cleanup.appDataTtl=120"
While analyzing the yarn launch_container.sh logs for a spark job, I got confused by some part of log.
I will point out those asks step by step here
When you will submit a spark job with spark-submit having --pyfiles and --files on cluster mode on YARN:
The config files passed in --files , executable python files passed in --pyfiles are getting uploaded into .sparkStaging directory created under user hadoop home directory.
Along with these files pyspark.zip and py4j-version_number.zip from $SPARK_HOME/python/lib is also getting copied
into .sparkStaging directory created under user hadoop home directory
After this launch_container.sh is getting triggered by yarn and this will export all env variables required.
If we have exported anything explicitly such as PYSPARK_PYTHON in .bash_profile or at the time of building the spark-submit job in a shell script or in spark_env.sh , the default value will be replaced by the value which we
are providing
This PYSPARK_PYTHON is a path in my edge node.
Then how a container launched in another node will be able to use this python version ?
The default python version in data nodes of my cluster is 2.7.5.
So without setting this pyspark_python , containers are using 2.7.5.
But when I will set pyspark_python to 3.5.x , they are using what I have given.
It is defining PWD='/data/complete-path'
Where this PWD directory resides ?
This directory is getting cleaned up after job completion.
I have even tried to run the job in one session of putty
and kept the /data folder opened in another session of putty to see
if any directories are getting created on run time. but couldn't find any?
It is also setting the PYTHONPATH to $PWD/pyspark.zip:$PWD/py4j-version.zip
When ever I am doing a python specific operation
in spark code , its using PYSPARK_PYTHON. So for what purpose this PYTHONPATH is being used?
3.After this yarn is creating softlinks using ln -sf for all the files in step 1
soft links are created for for pyspark.zip , py4j-<version>.zip,
all python files mentioned in step 1.
Now these links are again pointing to '/data/different_directories'
directory (which I am not sure where they are present).
I know soft links can be used for accessing remote nodes ,
but here why the soft links are created ?
Last but not the least , whether this launch_container.sh will run for each container launch ?
Then how a container launched in another node will be able to use this python version ?
First of all, when we submit a Spark application, there are several ways to set the configurations for the Spark application.
Such as:
Setting spark-defaults.conf
Setting environment variables
Setting spark-submit options (spark-submit —help and —conf)
Setting a custom properties file (—properties-file)
Setting values in code (exposed in both SparkConf and SparkContext APIs)
Setting Hadoop configurations (HADOOP_CONF_DIR and spark.hadoop.*)
In my environment, the Hadoop configurations are placed in /etc/spark/conf/yarn-conf/, and the spark-defaults.conf and spark-env.sh is in /etc/spark/conf/.
As the order of precedence for configurations, this is the order that Spark will use:
Properties set on SparkConf or SparkContext in code
Arguments passed to spark-submit, spark-shell, or pyspark at run time
Properties set in /etc/spark/conf/spark-defaults.conf, a specified properties file
Environment variables exported or set in scripts
So broadly speaking:
For properties that apply to all jobs, use spark-defaults.conf,
for properties that are constant and specific to a single or a few applications use SparkConf or --properties-file,
for properties that change between runs use command line arguments.
Now, regarding the question:
In Cluster mode of Spark, the Spark driver is running in container in YARN, the Spark executors are running in container in YARN.
In Client mode of Spark, the Spark driver is running outside of the Hadoop cluster(out of YARN), and the executors are always in YARN.
So for your question, it is mostly relative with YARN.
When an application is submitted to YARN, first there will be an ApplicationMaster container, which nigotiates with NodeManager, and is responsible to control the application containers(in your case, they are Spark executors).
NodeManager will then create a local temporary directory for each of the Spark executors, to prepare to launch the containers(that's why the launch_container.sh has such a name).
We can find the location of the local temporary directory is set by NodeManager's ${yarn.nodemanager.local-dirs} defined in yarn-site.xml.
And we can set yarn.nodemanager.delete.debug-delay-sec to 10 minutes and review the launch_container.sh script.
In my environment, the ${yarn.nodemanager.local-dirs} is /yarn/nm, so in this directory, I can find the tempory directories of Spark executor containers, they looks like:
/yarn/nm/nm-local-dir/container_1603853670569_0001_01_000001.
And in this directory, I can find the launch_container.sh for this specific container and other stuffs for running this container.
Where this PWD directory resides ?
I think this is a special Environment Variable in Linux OS, so better not to modify it unless you know how it works percisely in your application.
As per above, if you export this PWD environment at the runtime, I think it is passed to Spark as same as any other Environment Variables.
I'm not sure how the PYSPARK_PYTHON Environment Variable is used in Spark's launch scripts chain, but here you can find the instruction in the official documentation, showing how to set Python binary executable while you are using spark-submit:
spark-submit --conf spark.pyspark.python=/<PATH>/<TO>/<FILE>
As for the last question, yes, YARN will create a temp dir for each of the containers, and the launch_container.sh is included in the dir.
I am trying to setup a spark cluster on k8s. I've managed to create and setup a cluster with three nodes by following this article:
https://kubernetes.io/docs/setup/independent/create-cluster-kubeadm/
After that when I tried to deploy spark on the cluster it failed at spark submit setup.
I used this command:
~/opt/spark/spark-2.3.0-bin-hadoop2.7/bin/spark-submit \
--master k8s://https://206.189.126.172:6443 \
--deploy-mode cluster \
--name word-count \
--class org.apache.spark.examples.SparkPi \
--conf spark.executor.instances=5 \
--conf spark.kubernetes.container.image=docker.io/garfiny/spark:v2.3.0 \
—-conf spark.kubernetes.driver.pod.name=word-count \
local:///opt/spark/examples/jars/spark-examples_2.11-2.3.0.jar
And it gives me this error:
Exception in thread "main" org.apache.spark.SparkException: The Kubernetes mode does not yet support referencing application dependencies in the local file system.
at org.apache.spark.deploy.k8s.submit.DriverConfigOrchestrator.getAllConfigurationSteps(DriverConfigOrchestrator.scala:122)
at org.apache.spark.deploy.k8s.submit.KubernetesClientApplication$$anonfun$run$5.apply(KubernetesClientApplication.scala:229)
at org.apache.spark.deploy.k8s.submit.KubernetesClientApplication$$anonfun$run$5.apply(KubernetesClientApplication.scala:227)
at org.apache.spark.util.Utils$.tryWithResource(Utils.scala:2585)
at org.apache.spark.deploy.k8s.submit.KubernetesClientApplication.run(KubernetesClientApplication.scala:227)
at org.apache.spark.deploy.k8s.submit.KubernetesClientApplication.start(KubernetesClientApplication.scala:192)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:879)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:197)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:227)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:136)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
2018-06-04 10:58:24 INFO ShutdownHookManager:54 - Shutdown hook called
2018-06-04 10:58:24 INFO ShutdownHookManager:54 - Deleting directory /private/var/folders/lz/0bb8xlyd247cwc3kvh6pmrz00000gn/T/spark-3967f4ae-e8b3-428d-ba22-580fc9c840cd
Note: I followed this article for installing spark on k8s.
https://spark.apache.org/docs/latest/running-on-kubernetes.html
The error message comes from commit 5d7c4ba4d73a72f26d591108db3c20b4a6c84f3f and include the page you mention: "Running Spark on Kubernetes" with the mention that you indicate:
// TODO(SPARK-23153): remove once submission client local dependencies are supported.
if (existSubmissionLocalFiles(sparkJars) || existSubmissionLocalFiles(sparkFiles)) {
throw new SparkException("The Kubernetes mode does not yet support referencing application " +
"dependencies in the local file system.")
}
This is described in SPARK-18278:
it wouldn't accept running a local: jar file, e.g. local:///opt/spark/examples/jars/spark-examples_2.11-2.2.0-k8s-0.5.0.jar, on my spark docker image (allowsMixedArguments and isAppResourceReq booleans in SparkSubmitCommandBuilder.java get in the way).
And this is linked to kubernetes issue 34377
The issue SPARK-22962 "Kubernetes app fails if local files are used" mentions:
This is the resource staging server use-case. We'll upstream this in the 2.4.0 timeframe.
In the meantime, that error message was introduced in PR 20320.
It includes the comment:
The manual tests I did actually use a main app jar located on gcs and http.
To be specific and for record, I did the following tests:
Using a gs:// main application jar and a http:// dependency jar. Succeeded.
Using a https:// main application jar and a http:// dependency jar. Succeeded.
Using a local:// main application jar. Succeeded.
Using a file:// main application jar. Failed.
Using a file:// dependency jar. Failed.
That issue should been fixed by now, and the OP garfiny confirms in the comments:
I used the newest spark-kubernetes jar to replace the one in spark-2.3.0-bin-hadoop2.7 package. The exception is gone.
According to the mentioned documentation:
Dependency Management
If your application’s dependencies are all
hosted in remote locations like HDFS or HTTP servers, they may be
referred to by their appropriate remote URIs. Also, application
dependencies can be pre-mounted into custom-built Docker images. Those
dependencies can be added to the classpath by referencing them with
local:// URIs and/or setting the SPARK_EXTRA_CLASSPATH environment
variable in your Dockerfiles. The local:// scheme is also required
when referring to dependencies in custom-built Docker images in
spark-submit.
Note that using application dependencies from the
submission client’s local file system is currently not yet supported.
I am running spark-submit to run on Kubernetes (Spark 2.3). My problem is that the InitContainer does not download my jar file if it's specified as an s3a:// path but does work if I put my jar on an HTTP server and use http://. The spark driver fails, of course, because it can't find my Class (and the jar file in fact is not in the image).
I have tried two approaches:
specifying the s3a path to jar as the argument to spark-submit and
using --jars to specify the jar file's location on s3a, but both fail in the same way.
edit: also, using local:///home/myuser/app.jar does not work with the same symptoms.
On a failed run (dependency on s3a), I logged into the container and found the directory /var/spark-data/spark-jars/ to be empty. The init-container logs don't indicate any type of error.
Questions:
What is the correct way to specify remote dependencies on S3A?
Is S3A not supported yet? Only http(s)?
Any suggestions on how to further debug the InitContainer to determine why the download doesn't happen?