# Spark Monitor - An extension for Jupyter Lab
This project was originally written by krishnan-r as a Google Summer of Code project for Jupyter Notebook. [Check his website out here.](https://krishnan-r.github.io/sparkmonitor/)
As a part of my internship as a Software Engineer at Yelp, I created this fork to update the extension to be compatible with JupyterLab - Yelp's choice for sharing and collaborating on notebooks.
## About
SparkMonitor is an extension for Jupyter Lab that enables the live monitoring of Apache Spark Jobs spawned from a notebook. The extension provides several features to monitor and debug a Spark job from within the notebook interface itself.
---
### Requirements
- At least JupyterLab 2.0.0 (necessary to get cell execution metadata)
- pyspark 2.X.X or older (pyspark 3.X is currently not supported)
## Features
- Automatically displays a live monitoring tool below cells that run Spark jobs in a Jupyter notebook
- A table of jobs and stages with progressbars
- A timeline which shows jobs, stages, and tasks
- A graph showing number of active tasks & executor cores vs time
- A notebook server extension that proxies the Spark UI and displays it in an iframe popup for more details
- For a detailed list of features see the use case [notebooks](https://krishnan-r.github.io/sparkmonitor/#common-use-cases-and-tests)
- Support for multiple SparkSessions (default port is 4040)
- [How it Works](https://krishnan-r.github.io/sparkmonitor/how.html)
## Quick Start
### To do a quick test of the extension
This docker image has pyspark and several other related packages installed alongside the sparkmonitor extension.
```bash
docker run -it -p 8888:8888 itsjafer/sparkmonitor
```
### Setting up the extension
```bash
jupyter labextension install jupyterlab_sparkmonitor # install the jupyterlab extension
pip install jupyterlab-sparkmonitor # install the server/kernel extension
jupyter serverextension enable --py sparkmonitor
# set up ipython profile and add our kernel extension to it
ipython profile create --ipython-dir=.ipython
echo "c.InteractiveShellApp.extensions.append('sparkmonitor.kernelextension')" >> .ipython/profile_default/ipython_config.py
# run jupyter lab
IPYTHONDIR=.ipython jupyter lab --watch
```
With the extension installed, a SparkConf object called `conf` will be usable from your notebooks. You can use it as follows:
```python
from pyspark import SparkContext
# start the spark context using the SparkConf the extension inserted
sc=SparkContext.getOrCreate(conf=conf) #Start the spark context
# Monitor should spawn under the cell with 4 jobs
sc.parallelize(range(0,100)).count()
sc.parallelize(range(0,100)).count()
sc.parallelize(range(0,100)).count()
sc.parallelize(range(0,100)).count()
```
If you already have your own spark configuration, you will need to set `spark.extraListeners` to `sparkmonitor.listener.JupyterSparkMonitorListener` and `spark.driver.extraClassPath` to the path to the sparkmonitor python package `path/to/package/sparkmonitor/listener.jar`
```python
from pyspark.sql import SparkSession
spark = SparkSession.builder\
.config('spark.extraListeners', 'sparkmonitor.listener.JupyterSparkMonitorListener')\
.config('spark.driver.extraClassPath', 'venv/lib/python3.7/site-packages/sparkmonitor/listener.jar')\
.getOrCreate()
# should spawn 4 jobs in a monitor bnelow the cell
spark.sparkContext.parallelize(range(0,100)).count()
spark.sparkContext.parallelize(range(0,100)).count()
spark.sparkContext.parallelize(range(0,100)).count()
spark.sparkContext.parallelize(range(0,100)).count()
```
## Development
If you'd like to develop the extension:
```bash
make venv # Creates a virtual environment using tox
source venv/bin/activate # Make sure we're using the virtual environment
make build # Build the extension
make develop # Run a local jupyterlab with the extension installed
```
