Wes McKinney @wesmckinn
SHARED INFRASTRUCTURE FOR
DATA SCIENCE
WES MCKINNEY @WESMCKINN
Rice Data Science Conference | October 2017

ME
2

I M P O R TA N T L E G A L I N F O R M AT I O N
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• Copyright © 2017 TWO SIGMA INVESTMENTS, LP. All rights reserved
Wes McKinney @wesmckinn 3

THINKING ON THE LAST 10 YEARS
4
2007 2017

CLOSED SOURCE OPEN SOURCE
5

Shared front-ends
for data science

THE NEXT 10 YEARS AND BEYOND
7
2017 2027 …

THE AI ARMS RACE
Wes McKinney @wesmckinn 8

CHANGING HARDWARE LANDSCAPE
DISK
PROCESSIN
G
MEMORY
9

T
DATA SCIENCE “LANGUAGE “SILOS”
FRONT-END
PYTHON R JVM JULIA …
10

WHAT’S IN A
SILO?
STORAGE /
DATA ACCESS
DATA
STRUCTURES /
IN-MEMORY
FORMATS
GENERAL
COMPUTE
ENGINE(S)
ADVANCED
ANALYTICS
11

WHAT’S IN A
SILO?
STORAGE /
DATA ACCESS
DATA
STRUCTURES /
IN-MEMORY
FORMATS
GENERAL
COMPUTE
ENGINE(S)
ADVANCED
ANALYTICS
pandas NumPy
pandas
NumPy
pandas
scikit-learn
12

RENOVATING PANDAS
Wes McKinney @wesmckinn 13

27

T
MAKING THE SILOS “SMALLER”
FRONT-END
PYTHON R JVM JULIA
?
…
14

PROGRAMMING LANGUAGES
AS USER INTERFACES
15

GRAPHIC: Iceberg under sea (only top
part visible to naked eye)

T
df <- read_csv(…)
df % group_by(…) % summarise(…)
df = read_csv(…)
df.groupby(…).aggregate(…)
PYTHON
R
SAME ANALYSIS, DIFFERENT
IMPLEMENTATION
17

T
A SHARED RUNTIME FOR DATA SCIENCE
FRONT-END
PYTHON R JVM JULIA
SHARED DATA SCIENCE RUNTIME
…
18

FROM IDEA TO ACTION
19

T
PART 1: STANDARD IN-MEMORY FORMAT
R
PYTHON
JVM
PORTABLE DATA
FRAME
Non-Portable Data Frames
20…

T
PART 2: ZERO COPY INTERCHANGE
RPYTHON JVM
SHARED MEMORY + STANDARD MEMORY FORMATS
…
21

T
PART 3: HIGH PERFORMANCE DATA
ACCESS
BINARY
COLUMNAR
CSV
SQL
PORTABLE
DATA FRAME
Storage Formats/ Databases
… 22

T
PART 4: FLEXIBLE COMPUTATION ENGINE
• Zero-overhead User-defined Functions
• Portable Operator “Graphs”
• “Embeddable” in Larger Systems
23

APACHE ARROW
Language-agnostic Data Frame Format
Zero-Copy Interchange
24

24
Without Arrow With Arrow
Simple, fast data interchange

24
• Cache-efficient columnar memory: optimized for CPU affinity and
SIMD / parallel processing, O(1) random value access
• Zero-copy messaging / IPC: Language-agnostic metadata,
batch/file-based and streaming binary formats
• Complex schema support: Flat and nested data types
• Main implementations in C++ and Java: with integration tests
• Bindings / implementations for C, Python, Ruby, Javascript in
various stages of development
Big picture Arrow goals

T
BUILDING THE ARROW FORMAT
• “Superset” of representations supported by
R, pandas, SQL engines
• Optimized for CPU cache affinity
• ASF Governance: Open + Transparent
Community Project
25

FEATHER: MINIMALIST ARROW ON DISK

Some Arrow OSS Users
Feather Format
Ray Project
27

FROM ARROW TO PANDAS2
28

Logical Operator Graphs
27
(a + b).log()
Log Add
a
b

Terminology
27
• Kernel functions: atomic units of
computation
• Operator nodes: input/output types,
operator parallelism properties

Parallel Execution of Operator Graphs
27
a b
ADD LOG
tmp out

Some Optimization strategies
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• Multicore scheduling
• Elimination of temporaries
• Operator fusion / pipelinng

A
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Arrow-optimized data connectors
Arrow in-memory format
Logical Data Frame Expression Graphs
Parallel Dataflow Execution Engine
Python user API, DataFrame semantics,
User-defined functions
pandas2
Apache Arrow

BUILDING THE FUTURE
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Wes McKinney @wesmckinn
THANK YOU
WES MCKINNEY @WESMCKINN
Apache Arrow: http://arrow.apache.org