Jupyter - SparkMagic

Notebook Components


Sparkmagic is a kernel that provides Ipython magic for working with Spark clusters through Livy in Jupyter notebooks.

Installation Steps

Package Installation


  • Start a shell with admin right (The anaconda shell if you have installed Jupyter with Anaconda)
pip install sparkmagic
  • Show
pip show sparkmagic
Name: sparkmagic
Version: 0.12.5
Summary: SparkMagic: Spark execution via Livy
Home-page: https://github.com/jupyter-incubator/sparkmagic
Author: Jupyter Development Team
Author-email: [email protected]
License: BSD 3-clause
Location: c:\anaconda\lib\site-packages
Requires: autovizwidget, pandas, nose, requests, tornado, hdijupyterutils, numpy, ipython, ipykernel, notebook, ipywidgets, requests-kerberos, mock

Enable Extensions

jupyter nbextension enable --py --sys-prefix widgetsnbextension
Enabling notebook extension jupyter-js-widgets/extension...
      - Validating: ok

Install the wrapper kernels.

# Location from sparkmagic package info ''pip show sparkmagic'' 
cd c:\anaconda\lib\site-packages
jupyter-kernelspec install sparkmagic/kernels/sparkkernel
jupyter-kernelspec install sparkmagic/kernels/pysparkkernel
jupyter-kernelspec install sparkmagic/kernels/pyspark3kernel
jupyter-kernelspec install sparkmagic/kernels/sparkrkernel
[InstallKernelSpec] Installed kernelspec sparkkernel in C:\ProgramData\jupyter\kernels\sparkkernel
[InstallKernelSpec] Installed kernelspec pysparkkernel in C:\ProgramData\jupyter\kernels\pysparkkernel
[InstallKernelSpec] Installed kernelspec pyspark3kernel in C:\ProgramData\jupyter\kernels\pyspark3kernel
[InstallKernelSpec] Installed kernelspec sparkrkernel in C:\ProgramData\jupyter\kernels\sparkrkernel

Enable the sparkmagic extension

  • Enable the server extension so that clusters can be programatically changed
jupyter serverextension enable --py sparkmagic
Enabling: sparkmagic
- Writing config: C:\Users\gerardn\.jupyter
    - Validating...
      sparkmagic  ok

Configure (config.json)

If you are creating/modifying this file, you need to restart the server

  • Create the config home
mkdir %USERPROFILE%/.sparkmagic
mkdir ~/.sparkmagic
  • Create in it the config.json configuration file.

Example on a not secured cluster (from config.json)


  "kernel_python_credentials" : {
    "username": "nico",
    "password": "pwd",
    "url": "",
    "auth": "Basic_Access"

  "kernel_scala_credentials" : {
    "username": "nico",
    "password": "",
    "url": "",
    "auth": "None"
  "kernel_r_credentials": {
    "username": "nico",
    "password": "",
    "url": ""

  "logging_config": {
    "version": 1,
    "formatters": {
      "magicsFormatter": { 
        "format": "%(asctime)s\t%(levelname)s\t%(message)s",
        "datefmt": ""
    "handlers": {
      "magicsHandler": { 
        "class": "hdijupyterutils.filehandler.MagicsFileHandler",
        "formatter": "magicsFormatter",
        "home_path": "~/.sparkmagic"
    "loggers": {
      "magicsLogger": { 
        "handlers": ["magicsHandler"],
        "level": "DEBUG",
        "propagate": 0

  "wait_for_idle_timeout_seconds": 15,
  "livy_session_startup_timeout_seconds": 60,

  "fatal_error_suggestion": "The code failed because of a fatal error:\n\t{}.\n\nSome things to try:\na) Make sure Spark has enough available resources for Jupyter to create a Spark context.\nb) Contact your Jupyter administrator to make sure the Spark magics library is configured correctly.\nc) Restart the kernel.",

  "ignore_ssl_errors": false,

  "session_configs": {
    "driverMemory": "1000M",
    "executorCores": 2

  "use_auto_viz": true,
  "coerce_dataframe": true,
  "max_results_sql": 2500,
  "pyspark_dataframe_encoding": "utf-8",
  "heartbeat_refresh_seconds": 30,
  "livy_server_heartbeat_timeout_seconds": 0,
  "heartbeat_retry_seconds": 10,

  "server_extension_default_kernel_name": "pysparkkernel",
  "custom_headers": {
      "X-Requested-By": "admin"
  "retry_policy": "configurable",
  "retry_seconds_to_sleep_list": [0.2, 0.5, 1, 3, 5],
  "configurable_retry_policy_max_retries": 8


  • You can see in the log that Sparkmagic is enabled when starting the notebook server
jupyter notebook
[I 17:39:43.691 NotebookApp] [nb_conda_kernels] enabled, 4 kernels found
[I 17:39:43.696 NotebookApp] Writing notebook server cookie secret to C:\Users\gerardn\AppData\Roaming\jupyter\runtime\notebook_cookie_secret
[I 17:39:47.055 NotebookApp] [nb_anacondacloud] enabled
[I 17:39:47.091 NotebookApp] [nb_conda] enabled
[I 17:39:47.605 NotebookApp] ✓ nbpresent HTML export ENABLED
[W 17:39:47.606 NotebookApp] ✗ nbpresent PDF export DISABLED: No module named 'nbbrowserpdf'
[I 17:39:48.112 NotebookApp] sparkmagic extension enabled!

print "Hello";

Sparkmagic Hello

Magics By Kernel

iPython - Magic Function by kernel

magics are special commands that you can call with %%

%%MAGIC <args>


From a ipython kernel

Example from magics in IPython Kernel.ipynb

  • Load the Sparkmagic
%load_ext sparkmagic.magics
  • The %%manage_spark line magic lets you manage Livy endpoints and Spark sessions.
%spark logs -s testsession
  • sql
%%spark -c sql
%%spark -c sql -o df_hvac --maxrows 10
SELECT * FROM hivesampletable
  • Use the Pandas dataframe df_hvac created above via the -o option




The contexts are automatically created. There is no need to create them. ie

sc = SparkContext('yarn-client')
sqlContext = HiveContext(sc)
spark = SparkSession \
    .builder.appName("yarn-client") \

Dependent of the version, you may have the following variable names:


Session configuration

%%configure -f 
{"name":"remotesparkmagics-sample", "executorMemory": "4G", "executorCores":4 }


  • -f change the running session
  • name is the application name and should start with remotesparkmagics to allow sessions to get automatically cleaned up if an error happened.


use myDatabase


select * from hivesampletable


Spark Magic Info



Sparkmagic Help



See HOME/.sparkmagic/log

You need to have at least 1 client created to execute commands.

Your kernel has crashed. Restart it ?

Add a jar

With the configure magic:

  • Set the jars parameters
  • or set the conf spark parameters with the maven coordinates
%% configure
{ "conf": {"spark.jars.packages": "com.databricks:spark-csv_2.10:1.4.0" }}

For HdInisght, see apache-spark-jupyter-notebook-use-external-packages

Documentation / Reference

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