Category: Jupyter scala add jar

Jupyter scala add jar

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Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I need to use a third party jar mysql in my Scala script, if I use spark shell, I can specify the jar in the starting command like below:. However, how can I do this in Jupyter notebook? I remember there is a magic way to do it in pyspark, I am using Scala, and I can't change the environment setting of the kernel I am using.

Specify Dependencies in the Almond Scala Kernel in JupyterLab

Learn more. Asked 1 year ago. Active 5 months ago. Viewed times. I need to use a third party jar mysql in my Scala script, if I use spark shell, I can specify the jar in the starting command like below: spark2-shell --driver-class-path mysql-connector-java Active Oldest Votes. I have the solution now, and it is very simple indeed as below: Use a toree based Scala kernel which is what I am using Use AddJar: in the notebook and run it, the jar will be downloaded and voila!

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Downloading and Importing Libraries in Jupyter notebook- Python

Technical site integration observational experiment live on Stack Overflow.Jupyter Scala is a Scala kernel for Jupyter. It aims at being a versatile and easily extensible alternative to other Scala kernels or notebook UIs, building on both Jupyter and Ammonite.

The current version is available for Scala 2. Support for Scala 2. First ensure you have Jupyter installed. See Jupyter installation if it's not the case. Simply run the jupyter-scala script of this repository to install the kernel. Launch it with --help to list available non mandatory options.

Compared to them, jupyter-scala aims at being versatile, allowing to add support for big data frameworks on-the-fly. It aims at building on the nice features of both Jupyter alternative UIs, Most of what can be done via notebooks can also be done in the console via ammonium slightly modified Ammonite.

The specific features of jupyter-scala support for big data frameworks in particular should be relied on with caution - some are just POC for now support for Flink, Scioothers are a bit more used Status: some specific uses Spark on YARN well tested in particular contexts especially the previous version, the current one less so for nowothers Mesos, standalone clusters unknown with the current code base.

Important: SparkSession s should not be manually created. Only the ones from the org. Note that no Spark distribution is required to have the kernel work. In particular, on YARN, the call to.

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Being based on a slightly modified version of Ammonitejupyter-scala allows to. Like for big data frameworks, support for plotting libraries can be added on-the-fly during a notebook session.

Vegas is a Scala wrapper for Vega-Lite. Additional Vegas samples with jupyter-scala notebook are here. Check that you have Jupyter installed by running jupyter --version.

If it's not the case, a quick way of setting it up consists in installing the Anaconda Python distribution or its lightweight counterpart, Minicondaand then running.

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In a way, jupyter-scala is just a bridge between these two projects. The API as seen from a jupyter-scala session is defined in the scala-api module, that itself depends on the api module of jupyter-kernel. It also has a third module, scala-cliwhich deals with command-line argument parsing, and launches the kernel itself. The launcher script just runs this third module.

Install it:. If one wants to make changes to jupyter-kernel or ammonium, and test them via jupyter-scala, just clone their sources. Then adjust the ammoniumVersion or jupyterKernelVersion in the build. That will make the locally published artifacts of jupyter-scala depend on the locally published ones of ammonium or jupyter-kernel.

Once installed, the kernel should be listed by jupyter kernelspec list. The kernel ID scala can be changed with --id custom allows to install the kernel alongside already installed Scala kernels. If a kernel with the same ID is already installed and should be erased, the --force option should be specified. Documentation Scaladoc.

Mill build tool ivy"org. Try online with Scastie. License ApacheGitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Already on GitHub? Sign in to your account.

Is there something simliar with jupyter-scala. You're fast, I just pushed things this morning! Then it's like in Ammonite, you should call load. Maven ones - but in maybe half an hour once Travis had built and published new ammonite-shell and jupyter-scala packages :. That's great. Especially if you can point it at a local repo Haven't tested, but load. If that's the case, note that in the next version, an addition of modules that made the resolution fail will be ignored, and not kept around like here.

How to package a Scala project to a Jar file with SBT

It should be pushed any time soon. Here the errors:. That's become classpath. I have an ivy repository where the xml file is 1. I ran classpath. How can I work around this? Rand ; object LogisticFunction. Per the documentation this should be correct syntax to add local jar folders but this does not seem to work as I can't import any spark classes.

If you have only one scala installed on your machine, than classpath. If you happen to installed scala 2. Closing this. The upcoming version of the kernel on the develop branch for now just wraps Ammonite. See the Ammonite doc for that. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

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How does one add libraries to the classpath? Labels doc question.Toree is a kernel for the Jupyter Notebook platform providing interactive access to Apache Spark.

Apache Toree is a kernel for the Jupyter Notebook platform providing interactive access to Apache Spark. Toree supports a number of interaction scenarios. In one common case, applications send snippets of code which are then executed by Spark, and the results are returned directly to the application.

This style of interaction is what users of Notebooks experience when they evaluate code in cells. Toree provides a well-defined mechanism to associate functionality with magics, and this is a useful point of extensibility of the system.

Apache Toree

Applications wanting to work with Spark can be located remotely from a Spark cluster and use a Apache Toree Client or Jupyter Client to communicate with a Apache Toree Server running on the cluster, or they can communicate directly with the Apache Toree Server.

Apache Toree provides a set of magics that enhances the user experience manipulating data coming from Spark tables or data. Toggle navigation Apache Toree. Get Toree 0. Use Cases Toree supports a number of interaction scenarios.In How To. On October 6, Tagspythonscalaspark. JupyterLab is an awesome piece of technology for prototyping and self-documenting research. But can you use it for projects that have a big codebase?

The notebook workflow was a big improvement for all data scientist around the globe.

jupyter scala add jar

The ability to directly see the result of each step and not running over an over the same program was a huge productivity boost. Moreover, the self-documenting capacity make it so easy to share to coworkers. That said, there is a limit to what you can achieve in a notebook. Being developed outside of your current project, a good library should be generic enough to help you, and your coworker, on a wide range of projects. See it as an investment that will pay back many times in the future.

Suppose you want to add new functionalities to Spark objects, for instance a doSomething method on Spark and a predict method on a DataFrame. You create a module a file in Python, so for instance mylib. All you have to do in your notebook is to create a code block with the two line below block 4. The minor issue is that if you change the mylib.

I found out about the autoreload plugin in this post. This will allow Jupyter to check every 2 seconds for a new version of the.

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So that was the easy case when you create the Spark driver inside your Jupyter instance using the pyspark library for instance by using the code below. Sparkmagic will send your code chunk as web request to a Livy server.

Then, Livy will translate it to the Spark Driver and return results. As both the driver and the executors have access to HDFS, it will work. For Scala, we want to be able to add one or more JAR file to the classpath. JAR are libraries in the Java ecosystem. Sadly, while there is a addJar on SparkContext, this way will not work. Luckily, there is a way to inform Livy which JAR to add to the classpath when it creates the SparkContext before any code is sent to be compiled.

Notice that this block will restart a context if there is already one, so it should likely be the first block of your notebook. As we have seen, whichever your situation, there is a way to leverage an existing codebase and keep only high-level meaningful code in the Jupyter Notebook. I strongly feel that the key to a high performance data science team is to always invest time in productivity increasing tools. A shared library of high-level primitives is paramount to productivity.

Possible related posts: Data science: software engineering or business intelligence?GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Already on GitHub? Sign in to your account. Hi, I would like to run a spark streaming application in the all-spark notebookconsuming from Kafka.

This requires spark-submit with custom parameters -jars and the kafka-consumer jar. I do not completely understand how I could do this from the jupyter notebook. Has any of you tried this? The alternative is to add it with --packages. Is this easier? The techniques about Using spark packages on the docker-stacks recipes page might work.

Can you give that approach a shot?. Basically I have to run the notebook with some custom flags to. In that case adding jars or a mvn ref won't work. I tried the following in my notebook: os. I've been doing some elimination on the possible problems. The spark csv example you provided was actually working but that was present in the spark packages repository while the kafka consumer wasn't.

This seemed to imply that I had to add the kafka consumer jar to the environment via the --jars flag. As far as I can see I have something working right now: note that the pyspark-shell is also very important! And this seems to be working. It would be great to get this on the recipes page if you did not hit any further problems with the approach you took above. For now no issues, but since then I did not work further on this.When you develop a Spark project using Scala language, you have to package your project into a jar file.

This tutorial describes how to use SBT to compile and run a Scala project, and package the project as a Jar file.

jupyter scala add jar

This will be helpful for you to create a spark project and package it to a jar file. The build. You define most settings that SBT needs in this file, including specifying library dependencies, repositories, and any other basic settings your project requires.

To start a spark project, you need to add the dependency jar files such as spark-core or spark mllib to the project. We can use the build. Here is an example to show the contents of my build. It includes the necessary libraries such as spark core library, Spark Ml lib library and scalatest library for a typical Spark SBT project. If you want to build a standalone executable jar with dependencies, you may use the sbt-assembly plugin.

And then build it by. The first time you run SBT, it may take a while to download all the dependencies it needs, but after that first run, it will download new dependencies only as needed. You can list its contents with the usual jar tvf command:. Table shows a list of the most common commands.

There are many other SBT commands available, and when you use plug-ins, they can also make their own commands available. For instance, Recipe See the SBT documentation for more information.

When you issue this command, SBT watches your source code files, and automatically recompiles them whenever it sees the code change.

jupyter scala add jar

Now, any time you change and save a source code file, SBT automatically recompiles it. From time to time when working in the SBT shell you may have a problem, such as with incremental compiling. For instance, you may issue a compile command, and then see something wrong in the output. The last command prints logging information for the last command that was executed.

Typing help last in the SBT interpreter shows a few additional details, including a note about the last-grep command, which can be useful when you need to filter a large amount of output. Learn for Master. It's never too late to learn to be a master. An example of the build. If you want it to be executable you need to add the following to your. Table Descriptions of the most common SBT commands Command Description clean Removes all generated files from the target directory.

When given a command, help provides a description of that command. For instance, inspect library-dependencies displays information about the libraryDependencies setting. Variables in build. See Recipe Related posts: Scala read file examples A Spark program using Scopt to Parse Arguments Scala vs Java examples Run spark on oozie with command line arguments Scala for loop check the size of directory or file on linux pySpark check if file exists Test whether a file or directory exist in shell How to setup ipython notebook server to run spark in local or yarn model spark submit multiple jars run pyspark on oozie untar decompress a tgz or tbz file in linux.

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