Shared Infrastructure for Data Science
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Wes McKinney discussed the evolution of data science tools and infrastructure over the past 10 years and a vision for the next 10 years. He argued that current data science languages like Python, R, and Julia operate in "silos" with separate implementations for data storage, processing, and analytics. However, new projects like Apache Arrow aim to break down these silos by establishing shared standards for in-memory data formats and interchange that can unite the implementations across languages. Arrow provides a portable data frame format, zero-copy interchange capabilities, and potential for high performance data access and flexible computation engines. This would allow data science work to be more portable across programming languages while improving performance.
Shared Infrastructure for Data Science
- 1. Wes McKinney @wesmckinn SHARED INFRASTRUCTURE FOR DATA SCIENCE WES MCKINNEY @WESMCKINN Rice Data Science Conference | October 2017
- 2. ME 2
- 3. I M P O R TA N T L E G A L I N F O R M AT I O N • The information presented here is offered for informational purposes only and should not be used for any other purpose (including, without limitation, the making of investment decisions). Examples provided herein are for illustrative purposes only and are not necessarily based on actual data. Nothing herein constitutes: an offer to sell or the solicitation of any offer to buy any security or other interest; tax advice; or investment advice. This presentation shall remain the property of Two Sigma Investments, LP (“Two Sigma”) and Two Sigma reserves the right to require the return of this presentation at any time. • Some of the images, logos or other material used herein may be protected by copyright and/or trademark. If so, such copyrights and/or trademarks are most likely owned by the entity that created the material and are used purely for identification and comment as fair use under international copyright and/or trademark laws. Use of such image, copyright or trademark does not imply any association with such organization (or endorsement of such organization) by Two Sigma, nor vice versa. • Copyright © 2017 TWO SIGMA INVESTMENTS, LP. All rights reserved Wes McKinney @wesmckinn 3
- 4. THINKING ON THE LAST 10 YEARS 4 2007 2017
- 5. CLOSED SOURCE OPEN SOURCE 5
- 6. Shared front-ends for data science
- 7. THE NEXT 10 YEARS AND BEYOND 7 2017 2027 …
- 8. THE AI ARMS RACE Wes McKinney @wesmckinn 8
- 10. T DATA SCIENCE “LANGUAGE “SILOS” FRONT-END PYTHON R JVM JULIA … 10
- 11. WHAT’S IN A SILO? STORAGE / DATA ACCESS DATA STRUCTURES / IN-MEMORY FORMATS GENERAL COMPUTE ENGINE(S) ADVANCED ANALYTICS 11
- 12. 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
- 13. RENOVATING PANDAS Wes McKinney @wesmckinn 13
- 14. 27
- 15. T MAKING THE SILOS “SMALLER” FRONT-END PYTHON R JVM JULIA ? … 14
- 16. PROGRAMMING LANGUAGES AS USER INTERFACES 15
- 17. GRAPHIC: Iceberg under sea (only top part visible to naked eye)
- 18. T df <- read_csv(…) df % group_by(…) % summarise(…) df = read_csv(…) df.groupby(…).aggregate(…) PYTHON R SAME ANALYSIS, DIFFERENT IMPLEMENTATION 17
- 19. T A SHARED RUNTIME FOR DATA SCIENCE FRONT-END PYTHON R JVM JULIA SHARED DATA SCIENCE RUNTIME … 18
- 20. FROM IDEA TO ACTION 19
- 21. T PART 1: STANDARD IN-MEMORY FORMAT R PYTHON JVM PORTABLE DATA FRAME Non-Portable Data Frames 20…
- 22. T PART 2: ZERO COPY INTERCHANGE RPYTHON JVM SHARED MEMORY + STANDARD MEMORY FORMATS … 21
- 23. T PART 3: HIGH PERFORMANCE DATA ACCESS BINARY COLUMNAR CSV SQL PORTABLE DATA FRAME Storage Formats/ Databases … 22
- 24. T PART 4: FLEXIBLE COMPUTATION ENGINE • Zero-overhead User-defined Functions • Portable Operator “Graphs” • “Embeddable” in Larger Systems 23
- 25. APACHE ARROW Language-agnostic Data Frame Format Zero-Copy Interchange 24
- 26. 24 Without Arrow With Arrow Simple, fast data interchange
- 27. 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
- 28. 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
- 29. FEATHER: MINIMALIST ARROW ON DISK
- 30. Some Arrow OSS Users Feather Format Ray Project 27
- 31. FROM ARROW TO PANDAS2 28
- 32. Logical Operator Graphs 27 (a + b).log() Log Add a b
- 33. Terminology 27 • Kernel functions: atomic units of computation • Operator nodes: input/output types, operator parallelism properties
- 34. Parallel Execution of Operator Graphs 27 a b ADD LOG tmp out
- 35. Some Optimization strategies 27 • Multicore scheduling • Elimination of temporaries • Operator fusion / pipelinng
- 36. A 28 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
- 39. Wes McKinney @wesmckinn THANK YOU WES MCKINNEY @WESMCKINN Apache Arrow: http://arrow.apache.org