Jun 01, 2017. When I try to use Spark in the notebook I get this: val sparkHome = '/opt/spark-2.3.0-bin-hadoop2.7' val scalaVersion = scala.util.Properties.versionNumberString import org.apache.spark.ml.Pipeline Compilation Failed Main.scala:57: object apache is not a member of package org; import org.apache.spark.ml.Pipeline ^ I tried. I wrote this article for Linux users but I am sure Mac OS users can benefit from it too. Why use PySpark in a Jupyter Notebook? While using Spark, most data engineers recommends to develop either in Scala (which is the “native” Spark language) or in Python through complete PySpark API. Python for Spark is obviously slower than Scala.
Whether it’s for social science, marketing, business intelligence or something else, the number of times data analysis benefits from heavy duty parallelization is growing all the time.
Apache Spark is an awesome platform for big data analysis, so getting to know how it works and how to use it is probably a good idea. Setting up your own cluster, administering it etc. etc. is a bit of a hassle to just learn the basics though (although Amazon EMR or Databricks make that quite easy, and you can even build your own Raspberry Pi cluster if you want…), so getting Spark and Pyspark running on your local machine seems like a better idea. https://treememory.weebly.com/mcafee-download-mac-os-x.html. You can also use Spark with
R
and Scala, among others, but I have no experience with how to set that up. So, we’ll stick to Pyspark in this guide.While dipping my toes into the water I noticed that all the guides I could find online weren’t entirely transparent, so I’ve tried to compile the steps I actually did to get this up and running here. The original guides I’m working from are here, here and here.
Pre-requesites
Before we can actually install Spark and Pyspark, there are a few things that need to be present on your machine.
You need:
You need:
- The XCode Developer Tools
homebrew
pipenv
- Java (v8)
Installing the XCode Developer Tools
- Open Terminal
- Type
xcode-select --install
- Confirm, proceed with install
Installing homebrew
- Open Terminal
- Paste the command listed on the brew homepage: brew.sh
At the time of this writing, that’s/usr/bin/ruby -e '$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)'
Installing Pipenv
- Open Terminal
- Type
brew install pipenv
If that doesn’t work for some reason, you can do the following:
- Open Terminal
- Type
pip install --user pipenv
This does a pip user install, which puts pipenv in your home directory. If pipenv isn’t available in your shell after installation, you need to add stuff to you
PATH
. Here’s pipenv’s guide on how to do that.Installing Java
To run, Spark needs Java installed on your system. It’s important that you do not install Java with
Just go here to download Java for your Mac and follow the instructions.
brew
for uninteresting reasons.Just go here to download Java for your Mac and follow the instructions.
You can confirm Java is installed by typing
$ java --showversion
in Terminal.Installing Spark
With the pre-requisites in place, you can now install Apache Spark on your Mac.
- Go to the Apache Spark Download Page
- Download the newest version, a file ending in
.tgz
- Move the file to your
/opt
folder$ sudo mv spark-2.4.3-bin-hadoop2.7 /opt/spark-2.4.3
- Create a symbolic link (symlink) to your Spark version
$ sudo ln -s /opt/spark-2.4.3 /opt/spark
What’s happening here? By creating a symbolic link to our specific version (2.4.3) we can have multiple versions installed in parallel and only need to adjust the symlink to work with them. - Tell your shell where to find Spark
Until macOS 10.14 the default shell used in the Terminal app wasbash
, but from 10.15 on it is Zshell (zsh
). So depending on your version of macOS, you need to do one of the following:$ nano ~/.bashrc
(10.14)$ nano ~/.zshrc
(10.15)
- Set Spark variables in your
~/.bashrc
/~/.zshrc
file https://treememory.weebly.com/sniper-elite-2-mac-download.html.
Install Jupyter On Mac
Installing Pyspark
I recommend that you install Pyspark in your own virtual environment using
pipenv
to keep things clean and separated.- Open Terminal
- Make yourself a new folder somewhere, like
~/coding/pyspark-project
and move into it$ cd ~/coding/pyspark-project
- Create a new environment
$ pipenv --three
if you want to use Python 3$ pipenv --two
if you want to use Python 2
- Install pyspark
$ pipenv install pyspark
- Install Jupyter
$ pipenv install jupyter
- Now tell Pyspark to use Jupyter: in your
~/.bashrc
/~/.zshrc
file, add - If you want to use Python 3 with Pyspark (see step 3 above), you also need to add:
Your
~/.bashrc
or ~/.zshrc
should now have a section that looks kinda like this:Now you save the file, and source your Terminal:
$ source ~/.bashrc
or$ source ~/.zshrc
To start Pyspark and open up Jupyter, you can simply run
$ pyspark
. You only need to make sure you’re inside your pipenv environment. That means:- Go to your pyspark folder (
$ cd ~/coding/pyspark project
) - Type
$ pipenv shell
- Type
$ pyspark
Using Pyspark inside your Jupyter Notebooks
To test whether Pyspark is running as it is supposed to, put the following code into a new notebook and run it:
(You might need to install
numpy
inside your pipenv
environment if you haven’t already done so without my instruction ?)If you get an error along the lines of
Still no luck? Send me an email if you want (I most definitely can’t guarantee that I know how to fix your problem), particularly if you find a bug and figure out how to make it work!
sc is not defined
, you need to add sc = SparkContext.getOrCreate()
at the top of the cell. If things are still not working, make sure you followed the installation instructions closely.Still no luck? Send me an email if you want (I most definitely can’t guarantee that I know how to fix your problem), particularly if you find a bug and figure out how to make it work!
Related
Apache Spark is an analytics engine and parallelcomputation framework with Scala, Python and R interfaces. Spark can load datadirectly from disk, memory and other data storage technologies such as AmazonS3, Hadoop Distributed File System (HDFS), HBase, Cassandra and others.
Anaconda Scale can be used with a cluster that already has a managedSpark/Hadoop stack. Anaconda Scale can be installed alongside existingenterprise Hadoop distributions such asCloudera CDH orHortonworks HDP and canbe used to manage Python and R conda packages and environments across a cluster.
Vst guitar metal free download. To run a script on the head node, simply execute
pythonexample.py
on thecluster. Alternatively, you can install Jupyter Notebook on the cluster usingAnaconda Scale. See the Installation documentation for moreinformation.Different ways to use Spark with Anaconda¶
You can develop Spark scripts interactively, and you can write them as Python scripts or in a Jupyter Notebook. Alfa awuso36h driver for mac.
You can submit a PySpark script to a Spark cluster using various methods:
- Run the script directly on the head node by executing python example.py on the cluster.
- Use the spark-submitcommand either in Standalone mode or with the YARN resource manager.
- Submit the script interactively in an IPython shell or Jupyter Notebook on the cluster. For information on using Anaconda Scale to install Jupyter Notebook on the cluster, see Installation.
You can also use Anaconda Scale with enterprise Hadoop distributions such asCloudera CDH or Hortonworks HDP.
Jupyter Spark Kernel
Using Anaconda Scale with Spark¶
Harman kardon aura studio user manual. The topics listed below describe how to:
- Use Anaconda and Anaconda Scale with Apache Spark and PySpark
- Interact with data stored within the Hadoop Distributed File System (HDFS) on the cluster
Spark Jupyter Notebook
While these tasks are independent and can be performed in any order, we recommend that you begin with Configuring Anaconda with Spark.