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Transforming Big Data with D4M

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Transforming Big Data with D4M. Jeremy Kepner MIT Lincoln Laboratory 3 October 2012.

Transforming Big Data with D4M

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Transforming Big Data with D4M

Jeremy Kepner

MIT Lincoln Laboratory

3 October 2012

This work is sponsored by the Department of the Air Force under Air Force Contract #FA8721-05-C-0002. Opinions, interpretations, recommendations and conclusions are those of the authors and are not necessarily endorsed by the United States Government.

- Nicholas Arcolano
- Michelle Beard
- Bob Bond
- Josh Haines
- Matthew Schmidt
- Ben Miller
- Benjamin O’Gwynn
- Tamara Yu
- Bill Arcand
- Bill Bergeron

- David Bestor
- ChansupByun
- Matt Hubbell
- Pete Michaleas
- Julie Mullen
- AndyProut
- Albert Reuther
- Tony Rosa
- Charles Yee
- Dylan Hutchinson

- Introduction
- Theory
- Results
- Summary

ISR

Social

Cyber

- Graphs represent entities and relationships detected through multi-INT sources
- 1,000s – 1,000,000s tracks and locations
- GOAL: Identify anomalous patterns of life

- Graphs represent relationships between individuals or documents
- 10,000s – 10,000,000s individual and interactions
- GOAL: Identify hidden social networks

- Graphs represent communication patterns of computers on a network
- 1,000,000s – 1,000,000,000s network events
- GOAL: Detect cyber attacks or malicious software

- Cross-Mission Challenge: Detection of subtle patterns in massivemulti-source noisy datasets

Enterprise

Big Compute

- Interactive

- On-demand

- Elastic

- High performance

- Parallel Languages

- Scientific computing

- Java

- Map/Reduce

- Easy admin

- Indexing

- Search

- Security

Big Data

DBMS

- Each ecosystem is at the center of a multi-$B market
- Pros/cons of each are numerous; diverging hardware/software
- Some missions can exist wholly in one ecosystem; some can’t

LLGrid

Enterprise

Big Compute

- Interactive

- On-demand

- Elastic

- High performance

- Parallel Languages

- Scientific computing

MapReduce

- Java

- Map/Reduce

- Easy admin

- Indexing

- Search

- Security

Big Data

DBMS

- LLGridMapReduce provides map/reduce interface in a big compute environment
- D4M provides an interactive parallel scientific computing environment to databases

Database Worldview

“It’s the data!”

Delivering data is the end

Supercomputing Worldview

“It’s the computer!”

Delivering data is the start

Shared Compute

Shared Data

Separate Compute

Separate Data

- Database and supercomputing views are fundamentally different
- Have never coexisted; do not know how to coexist
- Big Data “Analytics” are forcing them together
- Current standard practice duplicates hardware and data

Novel Analytics for:

Text, Cyber, Bio

Weak Signatures,

Noisy Data,

Dynamics

B

High Level Composable API: D4M (“Databases for Matlab”)

A

Array Algebra

C

E

Distributed Database/ Distributed File System

Distributed Database: Accumulo (triple store)

High Performance Computing: LLGrid + Hadoop

Interactive Super-computing

- Combining Big Compute and Big Data enables entirely new domains

D4M

Dynamic

Distributed

Dimensional

Data

Model

- Associative Arrays
- Numerical Computing Environment

Distributed Database

B

A

C

Query:

Alice

Bob

Cathy

David

Earl

E

D

A D4M query returns a sparse matrix or a graph…

…for statistical signal processing or graph analysis in MATLAB

D4M binds associative arrays to databases, enabling rapid prototyping of data-intensive cloud analytics and visualization

- Introduction
- Theory
- Associate Arrays
- Incidence Matrix

- Results
- Summary

Big Tables

Spreadsheets

- Spreadsheets are the most commonly used analytical structure on Earth (100M users/day?)
- Big Tables (Google, Amazon, …) store most of the analyzed data in the world (Exabytes?)
- Simultaneous diverse data: strings, dates, integers, reals, …
- Simultaneous diverse uses: matrices, functions, hash tables, databases, …
- No formal mathematical basis; Zero papers in AMA or SIAM

- Extends associative arrays to 2D and mixed data types
A('alice ','bob ') = 'cited '

orA('alice ','bob ') = 47.0

- Key innovation: 2D is 1-to-1 with triple store('alice ','bob ','cited ')
or('alice ','bob ',47.0)

ATx

x

AT

bob

bob

cited

carl

alice

cited

carl

alice

- Key innovation: mathematical closure
- All associative array operations return associative arrays

- Enables composable mathematical operations
A + B A - B A & B A|B A*B

- Enables composable query operations via array indexing
A('alice bob ',:) A('alice',:) A('al* ',:)

A('alice : bob ',:) A(1:2,:) A == 47.0

- Simple to implement in a library (~2000 lines) in programming environments with: 1st class support of 2D arrays, operator overloading, sparse linear algebra

- Complex queries with ~50x less effort than Java/SQL
- Naturally leads to high performance parallel implementation

- Keys and values are from the infinite strict totally ordered set S
- Associative arrayA(k) : SdS, k=(k1,…,kd), is a partial function from d keys (typically 2) to 1 value, where
A(ki) = vi and otherwise

- Binary operations on associative arrays A3 = A1 A2,
where = f()orf(), have the properties

- If A1(ki) = v1 and A2(ki) = v2, then A3(ki)is
v1f() v2 = f(v1,v2)orv1f() v2 = f(v1,v2)

- IfA1(ki) = v orand A2(ki) = orv, then A3(ki)is
v f() = v orv f() =

- If A1(ki) = v1 and A2(ki) = v2, then A3(ki)is

- High level usage dictated by these definitions
- Deeper algebraic properties set by the collision function f()
- Frequent switching between “algebras” (how spreadsheets are used)

- Associative arrays can be constructed from a few definitions
- Similar to linear algebra, but applicable to a wider range of data
- Key questions
- Which linear algebra properties do apply to associative arrays (intuitive)
- Which linear algebra properties do not apply to associative arrays (watch out)
- Which associative array properties do not apply to linear algebra (new)

Associative

Arrays

Linear

Algebra

new

watch out

intuitive

- Book: “Graph Algorithms in the Language of Linear Algebra”
- Editors: Kepner (MIT-LL) and Gilbert (UCSB)
- Contributors:
- Bader (Ga Tech)
- Bliss (MIT-LL)
- Bond (MIT-LL)
- Dunlavy (Sandia)
- Faloutsos (CMU)
- Fineman (CMU)
- Gilbert (USCB)
- Heitsch (Ga Tech)
- Hendrickson (Sandia)
- Kegelmeyer (Sandia)
- Kepner (MIT-LL)
- Kolda (Sandia)
- Leskovec (CMU)
- Madduri (Ga Tech)
- Mohindra (MIT-LL)
- Nguyen (MIT)
- Radar (MIT-LL)
- Reinhardt (Microsoft)
- Robinson (MIT-LL)
- Shah (USCB)

- Introduction
- Theory
- Associate Arrays
- Incidence Matrix

- Results
- Summary

Artist: Ann Pibal; Painting: “XCRS”

Blue

Silver

Green

Orange

Pink

Artist: Ann Pibal; Painting: “XCRS”

V12

V14

V3

V17

V8

V19

V13

V7

V20

V9

V11

V2

V6

V16

V5

V10

V1

V15

V4

V18

Artist: Ann Pibal; Painting: “XCRS”

P4

Artist: Ann Pibal; Painting: “XCRS”

B1,S1,G1,O1,O2,P1

B2,S2,G2,O3,O4,P2

Artist: Ann Pibal; Painting: “XCRS”

P5

B1,S1,G1,O1,O2,P1

P8

B1,S1,G1,O1,O2,P1

B2,S2,G2,O3,O4,P2

B2,S2,G2,O3,O4,P2

O5

P7

P3

P6

Artist: Ann Pibal; Painting: “XCRS”

O5 < P3,P6,P7,P8

O5 < B1,S1,G1,O1,O2,P1

O5 < B2,S2,G2,O3,O4,P2 < P7,P8

P5

B1,S1,G1,O1,O2,P1

P8

B2,S2,G2,O3,O4,P2

O5

P7

P3

P6

Artist: Ann Pibal; Painting: “XCRS”

P5x2

(B1,S1,G1,O1,O2,P1)x2

P8x2

(B2,S2,G2,O3,O4,P2)x4

O5x5

P7x2

P3x3

P6x2

Artist: Ann Pibal; Painting: “XCRS”

- Standard edge representation fragments hyper edges
- Information is lost

- Digraph representation compresses multi-edges
- Information is lost

- Matrix representation drops edge labels
- Information is lost

- Standard graph representation drops edge order
- Information is lost

- Need edge representation that preserves information

Artist: Ann Pibal; Painting: “XCRS”

Artist: Ann Pibal; Painting: “XCRS”

- Introduction
- Theory
- Results
- Network monitoring example
- Bioinformatics example

- Summary

Raw Data

CSV Files

Distributed Database

Assoc.Arrays

Create columns for each unique type/value pair

Dense Table

Use as row indices

Exploded Table

Raw Data

CSV Files

Distributed Database

Assoc.Arrays

Exploded Table

D4M stores the triple data representing both the exploded table and its transpose

Table Triples

Table Transpose Triples

Raw Data

CSV Files

Distributed Database

Assoc.Arrays

D4M Query #1

keys = T(:,’time_stamp|10/May/2011:00:00:00’,:, ... ’time_stamp|13/May/2011:23:59:59’,);

(‘log_id|001’,‘time_stamp|11/May/2011:09:52:53’,1)

(‘log_id|002’,‘time_stamp|12/May/2011:13:24:11’,1)

(‘log_id|003’,‘time_stamp|13/May/2011:11:05:12’,1)

...

Raw Data

CSV Files

Distributed Database

Assoc.Arrays

D4M Query #1

keys = T(:,’time_stamp|10/May/2011:00:00:00’,:, ... ’time_stamp|13/May/2011:23:59:59’,);

D4M Query #2

data = T(Row(keys), :);

(‘log_id|001’,‘server_ip|208.29.69.138’,1)

(‘log_id|001’,‘src_ip|128.0.0.1’,1)

(‘log_id|001’,‘time_stamp|11/May/2011:09:52:53’,1)

...

(‘log_id|002’,‘server_ip|157.166.255.18’,1)

(‘log_id|002’,‘src_ip|192.168.1.2’,1)

(‘log_id|002’,‘time_stamp|12/May/2011:13:24:11’,1)

...

(‘log_id|003’,‘server_ip|74.125.224.72’,1)

(‘log_id|003’,‘src_ip|128.0.0.1’,1)

(‘log_id|003’,‘time_stamp|13/May/2011:11:05:12’,1)

...

Raw Data

Raw Data

CSV Files

CSV Files

Distributed Database

Distributed Database

Assoc.Arrays

Assoc.Arrays

D4M Query #1

keys = T(:,’time_stamp|10/May/2011:00:00:00’,:, ... ’time_stamp|13/May/2011:23:59:59’,);

D4M Query #2

data = T(Row(keys), :);

Associative Array Algebra

G = data(:,’src_ip|*’).’ * data(:,’server_ip|*’);

(‘src_ip|128.0.0.1’,‘server_ip|208.29.69.138’,1)

(‘src_ip|128.0.0.1’,‘server_ip|74.125.224.72’,1)

(‘src_ip|192.168.1.2’,‘server_ip|157.166.255.18’,1)

...

Raw Data

CSV Files

Distributed Database

Assoc.Arrays

D4M Query #1

keys = T(:,’time_stamp|10/May/2011:00:00:00’,:, ... ’time_stamp|13/May/2011:23:59:59’,);

D4M Query #2

data = T(Row(keys), :);

Associative Array Algebra

G = data(:,’src_ip|*’).’ * data(:,’server_ip|*’);

Adj(G);

- Graphs can be constructed with minimal effort using D4M queries and associative array algebra

Accumulo Database: 1 Master + 7 Tablet servers

4 Mil e/s

Data #1:

5 GB of 200 files

Data #2:

30 GB of 1000 files

- Introduction
- Theory
- Results
- Network monitoring example
- Bioinformatics example

- Summary

Big Data

Energy Efficient

Portable

Sequencer

High Volume Sequencer

Wetterstrand KA. DNA Sequencing Costs: Data from the NHGRI Large-Scale Genome Sequencing Program Available at: www.genome.gov/sequencingcosts. Accessed 03/08/2012

May-July 2011 - Virulent E. Coli Outbreak Germany

Outbreak

identified

Spanish

Cucumbers

implicated

diarrhea

kidney

DNA

Sequence

released

Sprouts

Identified

Deaths

www.rki.de EHEC final report

Conclusions: Identification of E. Coli source too late to have substantial impact on illnesses

Publishing sequence data allowed for broad community to fully characterize pathogen

Sequencing and crowd source analysis showed promising potential -> Still too slow

Collected Sample

RNA Reference Set

reference

bacteria

unknown

bacteria

A1

A2

unknown

sequence ID

reference

sequence ID

A1 A2'

sequence word (10mer)

sequence word (10mer)

reference

sequence ID

unknown sequence ID

- Associative arrays provide a natural framework for sequence matching

5%

0.5%

50%

- Using 10mer distribution can quickly select reference 10mers that maximally differentiate sample sequences and eliminate most 10mers

100x smaller

D4M

- High performance triple store database trades computations for lookups
- Used Apache Accumulo database to accelerate comparison by 100x
- Used Lincoln D4M software to reduce code size by 100x

BLAST

100x faster

D4M +

Triple Store

- Big data is found across a wide range of areas
- Document analysis
- Computer network analysis
- DNA Sequencing

- Currently there is a gap in big data analysis tools for algorithm developers
- D4M fills this gap by providing algorithm developers composable associative arrays that admit linear algebraic manipulation