• SIGMOD 2009: Vertica 100x < Hadoop (Grep, Aggregation, Join)

• VLDB 2010: HaLoop ~100x < Hadoop (PageRank, ShortestPath)

• SIGMOD 2010: Pregel (no comparisons)

• HotCloud 2010: Spark ~100x < Hadoop (logistic regression, ALS)

• ICDE 2011: SystemML ~100x < Hadoop

• ICDE 2011: Asterix ~100x < Hadoop (K-Means)

• VLDB 2012: Graphlab ~100x < Hadoop, GraphLab 5x > Pregel, Graphlab ~

MPI (Recommendation/ALS, CoSeq/GMM, NER)

• NSDI 2012: Spark 20x < Hadoop (logistic regression, PageRank)

• VLDB 2012: Asterix (no comparisons)

• SIGMOD 2013: Cumulon 5x < SystemML

• VLDB 2013: Giraph ~ GraphLab (Connected Components)

• SIGMOD 2014: SimSQL vs. Spark vs. GraphLab vs. Giraph (GMM,

bayesian regression, HMM, LDA, imputation)

• VLDB 2014: epiC ~ Impala, epiC ~ Shark, epiC 2x < Hadoop (Grep, Sort,

TPC-H Q3, PageRank)

A quick and dirty meta-analysis of

Big Data systems literature

Pregel (Malewicz)

Hadoop 2008

2009

2010

2011

2012

2013

2014

HaLoop (Bu)

Spark (Zakaria)

Vertica (Pavlo)

~100x faster

SystemML (Ghoting)

Hyracks (Borkar)

GraphLab (Low)

faster

Cumulon (Huang)

comparable or

inconclusive

Giraph (Tian)

Dremel (Melnik)

SimSQL (Cai)

epiC (Jiang)

Impala (Cloudera)

Shark (Xin)

HIVE (Thusoo)

“The good old days”

“WTF?”

• …to facilitate experiments to determine who performs well at what

• …to orchestrate multiple systems in production (assuming there is no dominant winner)

3

We need big data middleware

• Relational stuff

• but also

– linear algebra?

– graph algorithms?

– signal/image processing?

• Appears to imply at least tables and arrays

So what does this middleware need to afford?

Big Data Middleware

Syntaxes

Common IntermediateRepresentation

Clients

Data andAnalyticsPlatforms

Goals:• Comparability• Portability• Reasoning• Multi-Server

Applications

MS LINQ Works This Way

Surface Syntaxes

Standard Query Operator API

.Net Clients

LINQ Providers

SQLServer RDF

Files

MySQL

Streams

LDAP

C#

JScriptVB

LINQPad

Select(), Where(), Aggregate(), Join(), Reverse(), Union(),

Take(), Any(), …

XML

+ 25 more

Translate Everything to Operator Exprs

Surface Syntax

Standard Query Operator API

.Net Clients

LINQ Providers

SQLServer RDF

Files

MySQL

Streams

LDAP

C#

JScriptVB

LINQPad

XML

from b in DNA

where b.call = ‘G’

select b.prob

DNA

.Where(b.call = ‘G’)

.Select(b.prob)

Select

Where

DNA

Why Wasn’t Previous Slide the Last One?

• LINQ model is ordered collections

We need multi-D arrays and appropriate operations

• LINQ doesn’t handle multi-provider queries

Want a single query that maps to SciDB + ScaLAPACK, say

LINQ client would have to move intermediates

• LINQ doesn’t support (control) iteration

Common in graph algs and machine learning (e.g., K-means)

LINQ client has to orchestrate computation

Advocating a Similar Approach

• Standard set of operators

• But a broader data model

• Spans at least tables and arrays

• Includes iteration

Top Dog API (TDA)

TDA

MyriaX

SciDBRDBMS

ScaLAPACKBLAS

SQL

BQL

AQLMyriaQ

Translators

Compilers

Basis for TDA: Tarrays

• Table model with 0 or more DIMENSION attributes

• Operators act appropriately on dimensions

• Trying to capture enough semantics to find correct translations

• Dimensions + key declaration = arrays

Example Tarray Declarations

Create TARRAY DNA

Integer Dimension pos Range 1 to 300000;

Char call;

Float probability;

Primary Key(I)

Create TARRAY myMatrix

Integer Dimension J Range 1 to 100;

Integer Dimension K;

Float Val;

Primary Key (J, K);

Example Tarray Operator: Filter

Must respect DIMENSION attributesFilter(call = ‘G’) Filter(pos >

3)

DNA(ps cl prb)

1 A .8

2 G .9

3 T .7

4 G .8

5 G .5

6 C .6

7 A .9

…

DNA(ps cl prb)

1 - -

2 G .9

3 - -

4 G .8

5 G .5

6 - -

7 - -

…

DNA(ps cl prb)

4 G .8

5 G .5

6 C .6

7 A .9

…

Dimension Info Guides Translation

Filter(T, A > 2)

SELECT *

FROM T

WHERE A > 2

SELECT I,

(if A>2 then A else -) AS A

FROM T

AA is DIMENSION(or no dims)

AA is not DIMENSION

1/31/2015 Bill Howe, UW 15

Stop the madness

At scale, arrays are sparse

A sparse array is just a relation

select A.i, B.k, sum(A.val*B.val)

from A, B

where A.j = B.j

group by A.i, B.k

Matrix multiply in RA

Matrix multiply

That doesn’t seem so bad so far.

sparsity exponent (r s.t. m=nr)

Complexity

exponent

n2.38

mn

m0.7n1.2+n2

slide adapted from ZwickR. Yuster and U. Zwick, Fast Sparse Matrix Multiplication

n = number of rows

m = number of non-zerosComplexity of matrix multiply

naïve sparse algorithm

best known sparse algorithm

best known dense algorithmlots of room

here

select AB.i, C.m, sum(AB.val*C.val)

from

(select A.i, B.k, sum(A.val*B.val)

from A, B

where A.j = B.j

group by A.i, B.k

) AB,

C

where AB.k = C.k

group by AB.i, C.m

A x B x C

select A.i, C.m, sum(A.val*B.val*C.val)

from A, B, C

where A.j = B.j

and B.k = C.k

group by A.i, C.m

A(i, j, val)B(j, k, val)C(k, m, val)

take three sparse matrices

Now compute

multiway hypercube join:O (|A|/p + |B|/p^2 + |C|/p)

Group by:~O (N)

But wait, there’s more…..

2 seconds, balanced

Hypercube shuffle

Partitioned hash join

43 seconds, tons of skew

Task: self-multiply with 1M non-zeros

Maybe just forget databases – let’s compile RA to bare metal PGAS programs and let the HPC folks work their magic RADISH

[B. Myers, PLDI 15]

Does it work?Beats up on Shark/Spark. Competitive with hand-coded PR, Naïve Bayes

Last Slide

• We need middleware to bring order out of chaos

– …to expose and exploit common idioms and capabilities, to

afford reasoning, to make experimental comparisons tractable

• The right abstraction seems to be some form of

“extended RA” (iteration, dimension attributes, etc.)

• But new optimization ideas are warranted (arithmetic-

aware rewrites, compiler tricks)

http://myria.cs.washington.edu