pivotal data labs - technology and tools in our data scientist's arsenal

1 © Copyright 2013 Pivotal. All rights reserved. 1 © Copyright 2013 Pivotal. All rights reserved.

Pivotal Data Labs – Technology and Tools in our Data Scientist’s Arsenal

Srivatsan Ramanujam Senior Data Scientist Pivotal Data Labs 15 Oct 2014

2 © Copyright 2013 Pivotal. All rights reserved.

Agenda �  Pivotal: Technology and Tools Introduction

–  Greenplum MPP Database and Pivotal Hadoop with HAWQ

�  Data Parallelism –  PL/Python, PL/R, PL/Java, PL/C

�  Complete Parallelism –  MADlib

�  Python and R Wrappers –  PyMADlib and PivotalR

�  Open Source Integration –  Spark and PySpark examples

�  Live Demos – Pivotal Data Science Tools in Action –  Topic and Sentiment Analysis –  Content Based Image Search


Technology and Tools


MPP Architectural Overview Think of it as multiple PostGreSQL servers

Segments/Workers

Master

Rows are distributed across segments by a particular field (or randomly)


Implicit Parallelism – Procedural Languages


Data Parallelism – Embarrassingly Parallel Tasks

� Little or no effort is required to break up the problem into a number of parallel tasks, and there exists no dependency (or communication) between those parallel tasks.

� Examples: –  map() function in Python:

>>> x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] >>> map(lambda e: e*e, x) >>> [1, 4, 9, 16, 25, 36, 49, 64, 81, 100]

www.slideshare.net/SrivatsanRamanujam/python-powered-data-science-at-pivotal-pydata-2013


�  The interpreter/VM of the language ‘X’ is installed on each node of the Greenplum Database Cluster

•  Data Parallelism: -  PL/X piggybacks on

Greenplum/HAWQ’s MPP architecture

•  Allows users to write Greenplum/PostgreSQL functions in the R/Python/Java, Perl, pgsql or C languages Standby

Master

…

Master Host

SQL

Interconnect

Segment Host Segment Segment




PL/X : X in {pgsql, R, Python, Java, Perl, C etc.}


User Defined Functions – PL/Python Example �  Procedural languages need to be installed on each database used.

�  Syntax is like normal Python function with function definition line replaced by SQL wrapper. Alternatively like a SQL User Defined Function with Python inside.

CREATE FUNCTION pymax (a integer, b integer) RETURNS integer AS $$ if a > b: return a return b $$ LANGUAGE plpythonu;

SQL wrapper

SQL wrapper

Normal Python


Returning Results �  Postgres primitive types (int, bigint, text, float8, double precision, date, NULL etc.) �  Composite types can be returned by creating a composite type in the database:

CREATE TYPE named_value AS ( name text, value integer );

�  Then you can return a list, tuple or dict (not sets) which reference the same structure as the table:

CREATE FUNCTION make_pair (name text, value integer) RETURNS named_value AS $$ return [ name, value ] # or alternatively, as tuple: return ( name, value ) # or as dict: return { "name": name, "value": value } # or as an object with attributes .name and .value $$ LANGUAGE plpythonu;

�  For functions which return multiple rows, prefix “setof” before the return type

http://www.slideshare.net/PyData/massively-parallel-process-with-prodedural-python-ian-huston


Returning more results You can return multiple results by wrapping them in a sequence (tuple, list or set), an iterator or a generator:

CREATE FUNCTION make_pair (name text) RETURNS SETOF named_value AS $$ return ([ name, 1 ], [ name, 2 ], [ name, 3]) $$ LANGUAGE plpythonu;

Sequence

Generator

CREATE FUNCTION make_pair (name text) RETURNS SETOF named_value AS $$ for i in range(3): yield (name, i) $$ LANGUAGE plpythonu;


Accessing Packages �  On Greenplum DB: To be available packages must be installed on the

individual segment nodes. –  Can use “parallel ssh” tool gpssh to conda/pip install –  Currently Greenplum DB ships with Python 2.6 (!)

�  Then just import as usual inside function:

CREATE FUNCTION make_pair (name text) RETURNS named_value AS $$ import numpy as np return ((name,i) for i in np.arange(3)) $$ LANGUAGE plpythonu;


UCI Auto MPG Dataset – A toy problem Sample Data

�  Sample Task: Aero-dynamics aside (attributable to body style), what is the effect of engine parameters (bore, stroke, compression_ratio, horsepower, peak_rpm) on the highway mpg of cars?

�  Solution: Build a Linear Regression model for each body style (hatchback, sedan) using the features bore, stroke, compression ration, horsepower and peak_rpm with highway_mpg as the target label.

�  This is a data parallel task which can be executed in parallel by simply piggybacking on the MPP architecture. One segment can build a model for Hatchbacks another for Sedan

http://archive.ics.uci.edu/ml/datasets/Auto+MPG


Ridge Regression with scikit-learn on PL/Python

Python

SQL wrapper

SQL wrapper

User Defined Function

User Defined Type User Defined Aggregate


PL/Python + scikit-learn : Model Coefficients

Physical machine on the cluster in which the regression model was built

Invoke UDF

Build Feature Vector

Choose Features

One model per body style


Parallelized R in Pivotal via PL/R: An Example � With placeholders in SQL, write functions in the native R language

�  Accessible, powerful modeling framework

http://pivotalsoftware.github.io/gp-r/


Parallelized R in Pivotal via PL/R: An Example �  Execute PL/R function

�  Plain and simple table is returned



Aggregate and obtain final prediction

Each tree makes a prediction

Parallelized R in Pivotal via PL/R: Parallel Bagged Decision Trees



Complete Parallelism


Complete Parallelism – Beyond Data Parallel Tasks

� Data Parallel computation via PL/X libraries only allow us to run ‘n’ models in parallel.

� This works great when we are building one model for each value of the group by column, but we need parallelized algorithms to be able to build a single model on all the available data

� For this, we use MADlib – an open source library of parallel in-database machine learning algorithms.


MADlib: Scalable, in-database Machine Learning

http://madlib.net


MADlib In-Database Functions

Predictive Modeling Library

Linear Systems •  Sparse and Dense Solvers

Matrix Factorization •  Single Value Decomposition (SVD) •  Low-Rank

Generalized Linear Models •  Linear Regression •  Logistic Regression •  Multinomial Logistic Regression •  Cox Proportional Hazards •  Regression •  Elastic Net Regularization •  Sandwich Estimators (Huber white,

clustered, marginal effects)

Machine Learning Algorithms •  Principal Component Analysis (PCA) •  Association Rules (Affinity Analysis, Market

Basket) •  Topic Modeling (Parallel LDA) •  Decision Trees •  Ensemble Learners (Random Forests) •  Support Vector Machines •  Conditional Random Field (CRF) •  Clustering (K-means) •  Cross Validation

Descriptive Statistics

Sketch-based Estimators •  CountMin (Cormode-

Muthukrishnan) •  FM (Flajolet-Martin) •  MFV (Most Frequent

Values) Correlation Summary

Support Modules

Array Operations Sparse Vectors Random Sampling Probability Functions


Linear Regression: Streaming Algorithm

� Finding linear dependencies between variables

� How to compute with a single scan?


Linear Regression: Parallel Computation

XT

y

XT y = xiT yi

i∑



y

XT

Master

XT y

Segment 1 Segment 2

X1T y1 X2

T y2+ =



y

XT

Master Segment 1 Segment 2

XT yX1T y1 X2

T y2+ =


Performing a linear regression on 10 million rows in seconds

Hellerstein, Joseph M., et al. "The MADlib analytics library: or MAD skills, the SQL." Proceedings of the VLDB Endowment 5.12 (2012): 1700-1711.


Calling MADlib Functions: Fast Training, Scoring

�  MADlib allows users to easily and create models without moving data out of the systems

–  Model generation –  Model validation –  Scoring (evaluation of) new data

�  All the data can be used in one model

�  Built-in functionality to create of multiple smaller models (e.g. classification grouped by feature)

�  Open-source lets you tweak and extend methods, or build your own

SELECT madlib.linregr_train( 'houses’,!'houses_linregr’,!

'price’,!'ARRAY[1, tax, bath, size]’);!

MADlib model function Table containing

training data

Table in which to save results

Column containing dependent variable Features included in the

model

https://www.youtube.com/watch?v=Gur4FS9gpAg




'price’,!'ARRAY[1, tax, bath, size]’,!

‘bedroom’);!

MADlib model function Table containing

training data

Table in which to save results

Column containing dependent variable

Create multiple output models (one for each value of bedroom)






Features included in the model

https://www.youtube.com/watch?v=Gur4FS9gpAg




'price’,!'ARRAY[1, tax, bath, size]’);!

SELECT houses.*, madlib.linregr_predict(ARRAY[1,tax,bath,size],

m.coef!)as predict !

FROM houses, houses_linregr m;!

MADlib model scoring function

Table with data to be scored Table containing model







Python and R wrappers to MADlib


PivotalR: Bringing MADlib and HAWQ to a familiar R interface � Challenge

Want to harness the familiarity of R’s interface and the performance & scalability benefits of in-DB analytics

� Simple solution: Translate R code into SQL

d <- db.data.frame(”houses")!houses_linregr <- madlib.lm(price ~ tax!

! ! !+ bath!! ! !+ size!! ! !, data=d)!

Pivotal R SELECT madlib.linregr_train( 'houses’,!

'houses_linregr’,!'price’,!

'ARRAY[1, tax, bath, size]’);!

SQL Code

https://github.com/pivotalsoftware/PivotalR


PivotalR: Bringing MADlib and HAWQ to a familiar R interface � Challenge

Want to harness the familiarity of R’s interface and the performance & scalability benefits of in-DB analytics

� Simple solution: Translate R code into SQL

d <- db.data.frame(”houses")!houses_linregr <- madlib.lm(price ~ as.factor(state)!

! ! ! !+ tax!! ! ! !+ bath!! ! ! !+ size!! ! ! !, data=d)!

Pivotal R

# Build a regression model with a different!# intercept term for each state!# (state=1 as baseline).!# Note that PivotalR supports automated!# indicator coding a la as.factor()!!



PivotalR Design Overview

2. SQL to execute

3. Computation results 1. R à SQL

RPostgreSQL

PivotalR

Data lives here No data here

Database/Hadoop w/ MADlib

•  Call MADlib’s in-DB machine learning functions directly from R

•  Syntax is analogous to native R function

•  Data doesn’t need to leave the database •  All heavy lifting, including model estimation

& computation, are done in the database



http://pivotalsoftware.github.io/pymadlib/

PyMADlib : Power of MADlib + Flexibility of Python Linear Regression

Logistic Regression

Extras –  Support for Categorical variables –  Pivoting

Current PyMADlib Algorithms –  Linear Regression –  Logistic Regression –  K-Means –  LDA


Visualization


Visualization

Commercial Open Source


Hack one when needed – Pandas_via_psql

http://vatsan.github.io/pandas_via_psql/

SQL Client DB


Integration with Open Source – (Py)Spark Example


Apache Spark Project – Quick Overview

http://spark-summit.org/wp-content/uploads/2013/10/Zaharia-spark-summit-2013-matei.pdf

•  Apache Project, originated in AMPLab Berkeley •  Supported on Pivotal Hadoop 2.0!


MapReduce vs. Spark

http://spark-summit.org/wp-content/uploads/2013/10/Zaharia-spark-summit-2013-matei.pdf


Data Parallelism in PySpark – A Simple Example

•  Next we’ll take the UCI automobile dataset example from PL/Python and demonstrate how to run in PySpark


Scikit-Learn on PySpark – UCI Auto Dataset Example

•  This is in essence similar to the PL/Python example from the earlier slide, except we’re using data store on HDFS (Pivotal HD) with Spark as the platform in place of HAWQ/Greenplum


Large Scale Topic and Sentiment Analysis of Tweets

Social Media Demo


Pivotal GNIP Decahose Pipeline

Parallel Parsing of JSON

PXF

Twitter Decahose (~55 million tweets/day)

Source: http Sink: hdfs

HDFS

External Tables

PXF

Nightly Cron Jobs

Topic Analysis through MADlib pLDA

Unsupervised Sentiment Analysis

(PL/Python)

D3.js

http://www.slideshare.net/SrivatsanRamanujam/a-pipeline-for-distributed-topic-and-sentiment-analysis-of-tweets-on-pivotal-greenplum-database


Data Science + Agile = Quick Wins

� The Team –  1 Data Scientist –  2 Agile Developers –  1 Designer (part-time) –  1 Project Manager (part-time)

� Duration –  3 weeks!


Live Demo – Topic and Sentiment Analysis

47 Pivotal Confidential–Internal Use Only

Content Based Image Search

CBIR Live Demo


Content Based Information Retrieval - Task

http://blog.pivotal.io/pivotal/features/content-based-image-retrieval-using-pivotal-hd-or-pivotal-greenplum-database


CBIR - Components



Live Demo – Content Based Image Search



Appendix


Acknowledgements •  Ian Huston, Woo Jung, Sarah Aerni, Gautam Muralidhar, Regunathan

Radhakrishnan, Ronert Obst, Hai Qian, MADlib Engineering Team, Sumedh Mungee, Girish Lingappa

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