The document summarizes the journey of HAWQ and MADlib from being proprietary Pivotal technologies to becoming Apache open source projects. It provides an overview of HAWQ, including its key features like SQL compliance, performance advantages over other SQL-on-Hadoop systems, and flexible deployment options. It also summarizes MADlib, describing its machine learning functions and advantages of scalable in-database machine learning. Both projects are now available on open source platforms like Hadoop and aim to advance SQL and machine learning on big data through open collaboration.

1. 11Pivotal Confidential–Internal Use Only
Apache HAWQ and
Apache MADlib
Journey to Apache
Pivotal San Francisco
Dec 3, 2015

2. 2
Topics
• Journey to Apache
• HAWQ overview
• MADlib overview

3. 33Pivotal Confidential–Internal Use Only
Journey to Apache

4. 1986 … 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014
1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015
Journey to Apache
Michael Stonebraker develops Postgres at UCB
Postgres adds support for SQL
Open Source PostgreSQL
PostgreSQL 7.0 released
PostgreSQL 8.0 released
Greenplum forks
PostgreSQL
Hadoop 1.0 Released
HAWQ & MADlib
go Apache
HAWQ
launched
Hadoop 2.0 Released
MADlib
launched
Greenplum
open sourced

5. 5
Pivotal is Committed to
Open Source
Pivotal GemFire Apache Geode (April 2015)
Pivotal HDB Apache HAWQ (Sept 2015)
Pivotal Query OptimizerComing
Soon
MADlib OSS
(BSD License)
Apache MADlib (Sept 2015)
Pivotal Greenplum Greenplum Database (Oct 2015)
(Apache 2 License)

6. 6
 Collaborate on software in open and productive ways
 Need strong community for innovation
 MADlib and HAWQ are complementary technologies
Why Apache?

7. 77Pivotal Confidential–Internal Use Only
Apache HAWQ Overview

8. 8
What is Apache HAWQ?

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Key Features
of
HAWQ
5

10. 10
5 • Up to 30x SQL-on-Hadoop performance
advantage
• Faster time to insight
• Massive MPP scalability to petabytes
Benefits: Near real-time latency, complex
queries and advanced analytics
at scale
1. Advanced Analytics Performance
Key Features
of
HAWQ

11. 11
5 • ANSI SQL-92, -99, -2003
• All 99 TPC-DS queries tested, no
modifications
• Plus, OLAP extensions
• Complete ACID integrity and reliability
Benefits: 100% SQL compliant
No risk to SQL applications
All native on HDP via HAWQ
2. 100% ANSI SQL Compliant
Key Features
of
HAWQ

12. 12
HAWQ Performance vs Impala
HAWQ
Faster
Impala
Faster
2 28 46 66 73 76 79 80 88 90
96
HAWQ
• Faster on 46 of 62
TPC-DS queries
completed*
• 4.55x mean avg.
• 12 hrs faster total
* Impala supported 74 of 99
queries, 12 crashed mid-run

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HAWQ vs Apache Hive w/Tez
HAWQ
Faster
Hive
Faster
3 7 15 25 27 34 46 48 76 79 89 90
96
HAWQ
• Faster on 45 of 60
TPC-DS queries
completed*
• 3.44x mean avg.
• 9 hrs faster total
* Hive supported 65 of 99 queries,
5 crashed mid-run

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5 • Advanced machine learning for big data
• Local, in-database operation
• Exceptional MPP/parallel performance
• Open source, Postgres-based
Benefits: Advanced, highly scalable,
machine learning, directly on
data in Hadoop
3. Integrated Machine Learning
Key Features
of
HAWQ

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5 • HDP, PHD, other ODPi-derived distros
• Easily managed via Ambari
• On premises, in cloud, or PaaS
• HBase, Avro, Parquet and more
• Connectors to make HAWQ data
available to other SQL query tools
Benefits: Flexibility
Accessibility
Portability
4. Flexible Deployment
Key Features
of
HAWQ

16. 16
Open Data Platform
A shared industry effort to advance the state of Apache Hadoop® and Big Data
technologies for the enterprise

17. 17
The open ecosystem of big data
September 25, 2015
http://odpi.org

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5 • Cost-based query optimization
• Robust query plan optimization
• Complex big data management
Benefits: Optimize performance and costs
Maximize Hadoop cluster resources
Offload EDW w/o compromise
5. Query Optimization Options
Key Features
of
HAWQ

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Apache HAWQ
● Discover New Relationships
● Enable Data Science
● Analyze External Sources
● Query All Data Types!
Multi-level
Fault Tolerance
Granular
Authorization
Resource Mgmt
(+ YARN)
high multi-tenancy
ANSI SQL
Standard
OLAP
Extensions
JDBC ODBC
Connectivity
Parallel
Processing
Online
Expansion
HDFS
Petabyte Scale
Cost Based Optimizer
Dynamic
Pipelining
ACID +
Transactional
Multi-Language
UDF Support
Built-in Data
Science Library
Extensible
(PXF)
Query External
Sources
Hardened, 10+ Years Investment, Production
Proven
Accessibility + Usability
HDFS Native
File Formats
● Manage Multiple Workloads
● Petabyte Scale Analytics
● Security controls
● Leverage Existing
SQL Skills & BI Tools
● Easily Integrate with
Other Tools
● Sub-second
Performance Compression
+ Partitioning
core
compliance
● Hadoop-Native
● Supports Pivotal HD
and Hortonworks
Data Platform
● Ambari-Integrated

20. 20
Apache HAWQ 2.0 (in beta)
Areas of Enhancement New Features
Elastic & Scalable Architecture
Hadoop-Native Integrations
Simplified External Data Access/Queries
Performance & Optimizations
On-Demand Virtual Segments
Flexible Query Dispatch on subset nodes
3 Tier RM: YARN level>User>Query-Operator
Dynamic Cluster Expansion (no redistribute)
New Fault Tolerance Service
HCatalog integration - Read Access
HDFS Catalog Cache
Per Table Directory storage (user friendly)
Single physical segment per node
Easier Administration/Usage
Cloud-Ready
Simpler Management Commands

21. 21
HAWQ
Segments
HAWQ
Masters
Yarn
Physical Segment
Client
Parser/
Analyzer
Optimizer
Dispatcher
DataNode
NodeManager
NameNodeNameNode
External Data Stores via Xtension Framework (Hive/HBase/etc)
Resource
Manager
Fault Tolerance
Service
Catalog
Service
Virtual
Segment
Virtual
Segment
Physical Segment
DataNode
NodeManager
Virtual
Segment
Virtual
Segment
Physical Segment
DataNode
NodeManager
Virtual
Segment
Virtual
Segment
Resource
Broker
libYARN
HDFS Catalog Cache
Interconnect Interconnect
Apache HAWQ 2.0
Architecture

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Example Use Cases
Smart/connected car
• PHD, HAWQ
• Ability to have numerous data
in Hadoop
• Generate new business models
• Predictive analytics
Network & Call Center Analysis
• PHD, HAWQ
• Store and maintain 2B records/day
• Analyze drop and completed calls
• Analyze networks, care-center
responsiveness
• 5X capacity of EDW at half the cost
Revenue Prediction
• PHD, HAWQ, GPDB
• Predict ad revenue
to within 1%
• Transform into data-driven
company that builds
close relationships with
customers
Archive Analytics, Customer
Behavior Analytics
• PHD, HAWQ
• Mainframe alternative
• Archive analytics
• Customer behavior
profiling and analytics

23. 2323Pivotal Confidential–Internal Use Only
Apache MADlib Overview

24. 24
Scalable, In-Database
Machine Learning
• Open Source https://github.com/apache/incubator-madlib
• Supports Greenplum DB, Apache HAWQ/HDB and PostgreSQL
• Downloads and Docs: http://madlib.incubator.apache.org/
Apache (incubating)

25. 25
History
MADlib project was initiated in 2011 by EMC/Greenplum architects and
Joe Hellerstein from Univ. of California, Berkeley.
• MAD stands for:
• lib stands for SQL library of:
• advanced (mathematical, statistical, machine learning)
• parallel & scalable in-database functions
UrbanDictionary.com:
mad (adj.): an adjective used to enhance a noun.
1- dude, you got skills.
2- dude, you got mad skills.

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Functions
Predictive Modeling Library
Linear Systems
• Sparse and Dense Solvers
• Linear Algebra
Matrix Factorization
• Singular Value Decomposition (SVD)
• Low Rank
Generalized Linear Models
• Linear Regression
• Logistic Regression
• Multinomial Logistic Regression
• Cox Proportional Hazards Regression
• Elastic Net Regularization
• Robust Variance (Huber-White), Clustered
Variance, Marginal Effects
Other Machine Learning Algorithms
• Principal Component Analysis (PCA)
• Association Rules (Apriori)
• Topic Modeling (Parallel LDA)
• Decision Trees
• Random Forest
• Support Vector Machines
• Conditional Random Field (CRF)
• Clustering (K-means)
• Cross Validation
• Naïve Bayes
• Support Vector Machines (SVM)
Descriptive Statistics
Sketch-Based Estimators
• CountMin (Cormode-Muth.)
• FM (Flajolet-Martin)
• MFV (Most Frequent Values)
Correlation
Summary
Support Modules
Array Operations
Sparse Vectors
Random Sampling
Probability Functions
Data Preparation
PMML Export
Conjugate Gradient
Inferential Statistics
Hypothesis Tests
Time Series
• ARIMA
Oct 2014

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MADlib Advantages
 Better parallelism
– Algorithms designed to leverage MPP and
Hadoop architecture
 Better scalability
– Algorithms scale as your data set scales
 Better predictive accuracy
– Can use all data, not a sample
 ASF open source (incubating)
– Available for customization and optimization

28. 28
Supported Platforms
GPDB PostgreSQL
PHD
HDP
Other ODPi distros
Scale-out machine learning
now available on open
source, MPP execution
engines.

29. 29
Example Usage
Train a model
Predict for new data

30. 30
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.

31. 31
Pivotal is very proud to deepen
our relationship with the ASF to
advance SQL-on-Hadoop and
machine learning technologies.
Please join us!

32. 32
Contributors Welcome!
• Web sites
– http://hawq.incubator.apache.org/
– http://madlib.incubator.apache.org/
– https://cran.r-project.org/web/packages/PivotalR/index.html
• Github
– https://github.com/apache/incubator-hawq
– https://github.com/apache/incubator-madlib
– https://github.com/pivotalsoftware/PivotalR