How many ways are there to run Spark? If I just declare dependencies in build.sbt, Spark is supposed to be downloaded and works?
But if I want to run Spark locally (download the Spark tar file, winutils...), how can I specify in scala code that I want to run my code against the local Spark and not against the dependencies downloaded in the IntelliJ?
In order to connect Spark to Cassandra, do I need a local installation of Spark? I read somewhere it's not possible to connect from a "programmatically" Spark to a local Cassandra database
1) Spark runs in a slightly strange way, there is your application (the Spark Driver and Executors) and there is the Resource Manager (Spark Master/Workers, Yarn, Mesos or Local).
In your code you can run against the in process manager (local) by specifying the master as local or local[n]. The Local mode requires no installation of Spark as it will be automatically setup in the process you are running. This would be using the dependencies you downloaded.
To run against a Spark Master which is running locally, you use a spark:// url that points at your particular local Spark Master instance. Note that this will cause executor JVMS to start separate from your application necessitating the distribution of application code and dependencies. (Other resource managers have there own identifying urls)
2) You do not need a "Resource Manager" to connect to C* from Spark but this ability is basically for debugging and testing. To do this you would use the local master url. Normal Spark usage should have an external Resource Manager because without an external resource manager the system cannot be distributed.
For some more Spark Cassandra examples see
https://github.com/datastax/SparkBuildExamples
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We have a third-party jar file on which our Spark application is dependent that. This jar file size is ~15MB. Since we want to deploy our Spark application on a large-scale cluster(~500 workers), we are concerned about distributing the third-party jar file. According to the Apache Spark documentation(https://spark.apache.org/docs/latest/submitting-applications.html#advanced-dependency-management), we have such options as using HDFS, HTTP server, driver HTTP server, and local-path for distributing the file.
We do not prefer to use local-path because it requires copying the jar file on all workers' spark libs directory. On the other hand, if we use HDFS or HTTP server when spark workers try to get the jar file, they may make a DoS attack against our Spark driver server. So, What is the best way to address this challenge?
On the other hand, if we use HDFS or HTTP server when spark workers try to get the jar file, they may make a DoS attack against our Spark driver server. So, What is the best way to address this challenge?
If you put the 3rd jar in HDFS why it effect the spark driver server?!
each node should take the addintiol jar directly from the hdfs not from the spark server.
After examining different proposed methods, we found out that the best way to distribute the jar files is to copy all nodes(as #egor mentioned, too.) based on our deployment tools, we released a new spark ansible role that supports external jars. At this moment, Apache Spark does not provide any optimized solution. If you give a remote URL (HTTP, HTTPS, HDFS, FTP) as an external jar path, spark workers fetch the jar file every time a new job submit. So, it is not an optimized solution from a network perspective.
I have a Spark app written in Scala that is writing and reading from Parquet files.
The app exposes an HTTP API, and when it receives requests, sends work to a Spark cluster through a long-lived context that is persisted through the app's life.
It then returns the results to the HTTP client.
This all works fine when I'm using a local mode, with local[*] as master.
However, as soon as I'm trying to connect to a Spark cluster, I'm running into serialization issues.
With Spark's default serializer, I get the following:
java.lang.ClassCastException: cannot assign instance of scala.collection.immutable.List$SerializationProxy to field org.apache.spark.sql.execution.FilterExec.otherPreds of type scala.collection.Seq in instance of org.apache.spark.sql.execution.FilterExec.
If I enable Kryo serializer, I get java.lang.IllegalStateException: unread block data.
This happens when trying to read from the Parquet files, however I don't believe it has anything to do with the Parquet files themselves, simply with the serialization of the code that's being sent over to the Spark cluster.
From a lot of internet searches, I've gathered that this could be caused by incompatibilities between Spark versions, or even Java versions.
But the versions being used are identical.
The app is written in Scala 2.12.8 and ships with Spark 2.4.3.
The Spark cluster is running Spark 2.4.3 (the version compiled with Scala 2.12).
And the machine on which both the Spark cluster and the app are running is using openJDK 1.8.0_212.
According to another internet search, the problem could have been because of a mismatch in the spark.master URL.
So I've set spark.master in spark-defaults.conf to the same value I'm using within the app to connect to it.
However, this hasn't solved the issue and I am now running out of ideas.
I am not entirely sure what the underlying explanation is, but I fixed it by copying my application's jar into Spark's jars directory. Then I was still encountering an error, but a different one: something about a Cats/kernel/Eq class missing. So I added cats-kernel's jar into Spark's jars directory.
And now everything works. Something I read in another Stack Overflow thread may explain it:
I think that whenever you do any kind of map operation using a lambda which is referring to methods/classes of your project, you need to supply them as an additional jar. Spark does serializes the lambda itself, but is not pulling together its dependencies. Not sure why the error message is not informative at all.
I run multiple spark jobs on a cluster via $SPARK_HOME/bin/spark-submit --master yarn --deploy-mode cluster.
When a new version of Spark goes live I'd like to somehow roll out a new distribution over the cluster alongside with the old one and then gradually migrate all my jobs one by one.
Unfortunately, Spark relies on $SPARK_HOME global variable so I can't figure out how to achieve it.
It would be especially useful when Spark for Scala 2.12 is out.
It is possible to run any number of Spark distributions on YARN cluster. I've done it a lot of times on my MapR cluster, mixing 1-3 different versions, as well as setting up official Apache Spark there.
All you need is to tweak conf/spark-env.sh (rename spark-env.sh.template) and just add a line:
export SPARK_HOME=/your/location/of/spark/spark-2.1.0
I am trying to install an apache spark cluster on a hadoop cluster.
I am looking for best pracises in this regard. I am assuming that the spark master needs to be installed on the same machine as the hadoop namenode and the spark slaves on the hadoop datanodes. Also, where all do I need to install scala? Please advise.
If your Hadoop cluster is running YARN just use yarn mode for submitting your applications. That's going to be the easiest method without requiring you to install anything beyond simply downloading the Apache Spark distribution to a client machine. One additional thing you can do is deploy the Spark assembly to HDFS so that you can use the spark.yarn.jar config when you call spark-submit so that the JAR is cached on the nodes.
See here for all the details: http://spark.apache.org/docs/latest/running-on-yarn.html
I've been developing a Spark Streaming application with Eclipse, and I'm using sbt to run it locally.
Now I want to deploy the application on AWS using a jar, but when I try to use the command package of sbt it creates a jar without all dependencies so when I upload it on AWS it won't work because of Scala being missing.
Is there a way to create a uber-jar with SBT? Am I doing something wrong with the deployment of Spark on AWS?
For creating uber-jar with sbt, use sbt plugin sbt-assembly. For more details about creating uber-jar using sbt-assembly refer the blog post
After creating you can run the assembly jar using java -jar command.
But from Spark-1.0.0 onwards the spark-submit script in Spark’s bin directory is used to launch applications on a cluster for more details refer here
You should really be following Running Spark on EC2 that reads:
The spark-ec2 script, located in Spark’s ec2 directory, allows you to
launch, manage and shut down Spark clusters on Amazon EC2. It
automatically sets up Spark, Shark and HDFS on the cluster for you.
This guide describes how to use spark-ec2 to launch clusters, how to
run jobs on them, and how to shut them down. It assumes you’ve already
signed up for an EC2 account on the Amazon Web Services site.
I've only partially followed the document so I can't comment on how well it's written.
Moreover, according to Shipping Code to the Cluster chapter in the other document:
The recommended way to ship your code to the cluster is to pass it
through SparkContext’s constructor, which takes a list of JAR files
(Java/Scala) or .egg and .zip libraries (Python) to disseminate to
worker nodes. You can also dynamically add new files to be sent to
executors with SparkContext.addJar and addFile.