SPARKLY NOTEBOOK: INTERACTIVE ANALYSIS AND VISUALIZATION WITH SPARK

FELIX CHEUNG

APRIL 2015 HTTP://WWW.MEETUP.COM/SEATTLE-SPARK-MEETUP/EVENTS/208711962/

SETUP

• Spark on CDH cluster

• Vagrant - 2-nodes - custom provisioning

AGENDA

• IPython + PySpark cluster

• Zeppelin

• Spark’s Streaming k-means

• Lightning

SPARK - 10 SEC INTRODUCTION

• Spark

• Spark SQL + Data Frame + data source

• Spark Streaming

• MLlib

• GraphX

It’s a lot of time looking at data..

REPL

• Read-Eval-Print-Loop

Set of REPL related to Spark…

$  spark-­‐shell  

Welcome  to  

           ____                            __  

         /  __/__    ___  _____/  /__  

       _\  \/  _  \/  _  `/  __/    '_/  

     /___/  .__/\_,_/_/  /_/\_\      version  1.2.0-­‐SNAPSHOT  

           /_/  

Using  Scala  version  2.10.4  (Java  HotSpot(TM)  64-­‐Bit  Server  VM,  Java  1.7.0_67)  

Type  in  expressions  to  have  them  evaluated.  

Type  :help  for  more  information.  

15/04/15  11:31:28  INFO  SparkILoop:  Created  spark  context..  

Spark  context  available  as  sc.  

scala>  val  a  =  sc.parallelize(1  to  100)  

a:  org.apache.spark.rdd.RDD[Int]  =  ParallelCollectionRDD[0]  at  parallelize  at  <console>:12  

scala>  a.collect.foreach(x  =>  println(x))  

1  

2  

3  

4

GOOD

• See results instantly

NOT SO GOOD

• Ok as an IDE

• No Save / Repeat

• No visualization

NOTEBOOK

Jupyter IPython will continue to exist as a Python kernel for Jupyter, but the notebook and other language-agnostic parts of IPython will move to new projects under the Jupyter name. IPython 3.0 will be the last monolithic release of IPython. !“IPython” http://ipython.org/ • interactive shell • browser-based notebook • 'Kernel' • great support for visualization library (eg. matplotlib) • built on pyzmq, tornado

IPYTHON/JUPYTER

IPYTHON NOTEBOOK NOTEBOOK == BROWSER-BASED REPL

IPython Notebook is a web-based interactive computational environment for creating IPython notebooks. An IPython notebook is a JSON document containing an ordered list of input/output cells which can contain code, text, mathematics, plots and rich media.

MATPLOTLIBmatplotlib tries to make easy things easy and hard things possible. You can generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc, with just a few lines of code, with familiar MATLAB APIs.

plt.barh(y_pos,  performance,  xerr=error,  align='center',  alpha=0.4)  

plt.yticks(y_pos,  people)  

plt.xlabel('Performance')  

plt.title('How  fast  do  you  want  to  go  today?')  

plt.show()

PYSPARK

• Spark on Python, this serves as the Kernel, integrating with IPython

• Each notebook spins up a new instance of the Kernel (ie. PySpark running as the Spark Driver, in different deploy mode Spark/PySpark supports)

(All notebook examples are a subset of those in the Meetup reconstructed here)

Markdown

Spark in Python

Source: http://nbviewer.ipython.org/github/ResearchComputing/scientific_computing_tutorials/blob/master/spark/02_word_count.ipynb

WORD2VEC EXAMPLE

Word2Vec computes distributed vector representation of words. Distributed vector representation is showed to be useful in many natural language processing applications such as named entity recognition, disambiguation, parsing, tagging and machine translation.https://code.google.com/p/word2vec/

Spark MLlib implements the Skip-gram approach. With Skip-gram we want to predict a window of words given a single word.

WORD2VEC DATASET

Wikipedia dump http://mattmahoney.net/dc/textdata

grep  -­‐o  -­‐E  '\w+(\W+\w+){0,15}'  text8  >  text8_lines  

then randomly sampled to ~200k lines

matplotlib: http://matplotlib.org Seaborn: http://stanford.edu/~mwaskom/software/seaborn/ Bokeh: http://bokeh.pydata.org/en/latest/

MORE VISUALIZATIONS Seaborn

Bokehmatplotlib

SETUPTo setup IPython

• Python 2.7.9 (separate from CentOS default 2.6.6), on all nodes

• matplotlib, on the host running IPython

To run IPython with the PySpark Kernel, set these in the environment(Please check out my handy script on github)

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PYSPARK_PYTHON command to run python, eg. “python2.7”

PYSPARK_DRIVER_PYTHON command to run ipython

PYSPARK_DRIVER_PYTHON_OPTS “notebook —profile”

PYSPARK_SUBMIT_ARGS pyspark commandline, eg. --master --deploy_mode

YARN_CONF_DIR if YARN mode

LD_LIBRARY_PATH for matplotlib

IPYTHON/JUPYTER KERNELS • IPython

• IGo

• Bash

• IR

• IHaskell

• IMatlab

• ICSharp

• IScala

• IRuby

• IJulia

.. and more https://github.com/ipython/ipython/wiki/IPython-kernels-for-other-languages

ZEPPELIN

Apache Zeppelin (incubating) is interactive data analytics environment for distributed data processing system. It provides beautiful interactive web-based interface, data visualization, collaborative work environment and many other nice features to make your data analytics more fun and enjoyable.

Zeppelin has been incubating since Dec 2014.https://zeppelin.incubator.apache.org/

shell script & calling library package

Load and process data with Spark

SQL query powered by Spark SQL - progress &

parameterization via dynamic form

Python & data passing across

languages (interpreters)

ZEPPELIN ARCHITECTURE

Realtime collaboration - enabled by websocket communications

Frontend: AngularJS Backend server: Java Interpreters: JavaVisualization: NVD3

INTERPRETERS• Spark group

• Spark (Scala)

• PySpark

• Spark SQL

• Dependency

• Markdownjs

• Shell

• Hive

• Coming: jdbc, Tajo, etc.

CLUSTERING

• Clustering tries to find natural groupings in data. It puts objects into groups in which those within a group are more similar to each other than to those in other groups.

• Unsupervised learning

K-MEANS

• First, given an initial set of k cluster centers, we find which cluster each data point is closest to

• Then, we compute the average of each of the new clusters and use the result to update our cluster centers

K-MEANS|| IN MLLIB• a parallelized variant of the k-means++

http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf

Parameters:

• k is the number of desired clusters.

• maxIterations is the maximum number of iterations to run.

• initializationMode specifies either random initialization or initialization via k-means||.

• runs is the number of times to run the k-means algorithm (k-means is not guaranteed to find a globally optimal solution, and when run multiple times on a given dataset, the algorithm returns the best clustering result).

• initializationSteps determines the number of steps in the k-means|| algorithm.

• epsilon determines the distance threshold within which we consider k-means to have converged.

CASE STUDY: K-MEANS - ZEPPELIN

Details on github at: http://bit.ly/1JWOPh8

ANOMALY DETECTION WITH K-MEANS Using Spark DataFrame, csv data source, to process KDDCup’99 dataScoring with different k values

COMING SOON (NOW!)

Realtime updates

Dashboard

Spark-notebook: https://github.com/andypetrella/spark-notebook ISpark: https://github.com/tribbloid/ISpark Spark Kernel: https://github.com/ibm-et/spark-kernel Jove: https://github.com/jove-sh/jove-notebook Beaker: https://github.com/twosigma/beaker-notebook

OTHER NOTEBOOKS

• Spark-notebook

• ISpark

• Spark Kernel

• Jove Notebook

• Beaker

• Databricks Cloud notebook

PART 2STREAMING K-MEANS

WHY STREAMING?

• Train - model - predict works well on static data

• What if data is

• Coming in streams

• Changing over time?

STREAMING K-MEANS DESIGN

• Proposed by Dr Jeremy Freeman (here)

STREAMING K-MEANS

• key concept: forgetfulness

• balances the relative importance of new data versus past history

• half-life

• time it takes before past data contributes to only one half of the current model

STREAMING K-MEANS

• time unit

• batches (which have a fixed duration in time), or points

• eliminate dying clusters

VISUALIZING STREAMING K-MEANS - LIGHTNING

LIGHTNING

• Lightning - data visualization serverhttp://lightning-viz.org

• provides API-based access to reproducible, web-based, interactive visualizations. It includes a core set of visualization types, but is built for extendability and customization. Lightning supports modern libraries like d3.js and three.js, and is designed for interactivity over large data sets and continuously updating data streams.

VISUALIZING STREAMING K-MEANS ON IPYTHON + LIGHTNING

RUNNING LIGHTNING

• API: node.js, Python, Scala

• Extension support for custom chart (eg. d3.js)

• Requirements:

• Postgres recommended (SQLlite ok)

• node.js (npm , gulp)

The Freeman Lab at Janelia Research Campus uses Lightning to visualize large-scale neural recordings from zebrafish, in collaboration with the Ahrens Lab

SPARK STREAMING K-MEANS DEMOEnvironment

• requires: numpy, scipy, scikit-learn

• IPython/Python requires: lightning-python package

Demo consists of 3 parts: https://github.com/felixcheung/spark-ml-streaming

• Python driver script, data generator

• Scala job - Spark Streaming & Streaming k-means

• IPython notebook to process result, visualize with Lightning Originally this was part of the Python driver script - it has been modified for this talk to run within IPython

CHALLENGES

• Package management

• Version/build conflicts!

YOU CAN RUN THIS TOO!

• Notebooks available at http://bit.ly/1JWOPh8

• Everything is heavily scripted and automatedVagrant config for local, virtual environment available at http://bit.ly/1DB3OLw

QUESTION?!

https://github.com/felixcheung linkedin: http://linkd.in/1OeZDb7

blog: http://bit.ly/1E2z6OI !