Where The Rubber Meets the Sky Giving Access to Science Data

1. Where The Rubber Meets the SkyGiving Access to Science Data Talk at National Institute of Informatics, Tokyo, Japan October 2005 Jim Gray Microsoft Research Gray@Microsoft.com Http://research.Microsoft.com/~Gray Alex SzalayJohns Hopkins University Szalay@JHU.edu

2. Abstract: I have been working with some astronomers for the last 6 years trying to apply DB technology to science problems. These are some lessons I learned Paper at: Where the Rubber Meets the Sky: Bridging the Gap between Databases and Science,”Jim Gray; Alexander S. Szalay; MSR-TR-2004-110, October 2004

3. New Science Paradigms • Thousand years ago: science was empirical describing natural phenomena • Last few hundred years: theoretical branch using models, generalizations • Last few decades:a computational branch simulating complex phenomena • Today: data exploration (eScience) unify theory, experiment, and simulation using data management and statistics • Data captured by instrumentsOr generated by simulator • Processed by software • Scientist analyzes database / files

4. Experiments & Instruments facts questions facts ? Other Archives facts answers Literature facts Simulations The Big Picture • Data ingest • Managing a petabyte • Common schema • How to organize it? • How to reorganize it? • How to coexist with others? The Big Problems • Data Query and Visualization tools • Support/training • Performance • Execute queries in a minute • Batch (big) query scheduling

5. Software for Instrument scheduling Instrument control Data gathering Data reduction Database Analysis Visualization Millions of lines of code Repeated for experiment after experiment Not much sharing or learning Let’s work to change this Identify generic tools Workflow schedulers Databases and libraries Analysis packages Visualizers … Experiment Budgets ¼…½ Software

6. Level 1 calibrated Level 0 raw Level 2 derived instrument or simulator pipeline pipeline other data other data Data Lifecycle • Raw data → primary data → derived data • Data has bugs: • Instrument bugs • Pipeline bugs • Data comes in versions • later versions fix known bugs • Just like software (indeed data is software) • Can’t “un-publish” bad data.

7. Level 1AGrows X TB/year ~ .4X TB/y compressed (level 1A in NASA terms) Level 2Derived data products ~10x smaller But there are many. L2≈L1 Publish new edition each year Fixes bugs in data. Must preserve old editions Creates data pyramid Store each edition 1, 2, 3, 4… N ~ N2 bytes Net: Data Inflation: L2 ≥ L1 Level 1A 4 editions of 4 Level 2 products E4 E3 E2 time E1 4 editions oflevel 1A data(source data) 4 editions of level 2 derived data products. Note that each derived product is small, but they are numerous. This proliferation combined with the data pyramid implies that level2 data more than doubles the total storage volume. Data Inflation – Data Pyramid

8. The Year 5 Problem • Data arrives at R bytes/year • New Storage & Processing • Need to buy R units in year N • Data inflation means ~N2R • Need to buy NR units • Depreciate over 3 years • After year 3 need to buy N2R + (N-3)2R • Moore’s law:60%/year price decline • Capital expense peaks at year 5 • See 6x Over-Power slide next

9. 3.6TB PubDB 1.8TB DR2C 1.8TB DR2M 1.8TB DR2P 2.4TB DR3C 2.4TB DR3M 2.4TB DR3P 6x Over-Power Ratio • If you think you need X raw capacity, then you probably need 6X • Reprocessing • Backup copies • Versions • … • Hardware is cheap, Your time is precious.

10. Data Loading • Data from outside • Is full of bugs • Is not in your format • Advice • Get it in a “Universal Format” (e.g. Unicode CSV) • Create Blood-Brain barrier Quarantine in a “load database” • Scrub the data • Cross check everything you can • Check data statistics for sanity • Reject or repair bad data • Generate detailed bug reports(needed to send rejection upstream) • Expect to reload many times Automate everything!

11. Performance Prediction & Regression • Database grows exponentially • Set up response-time requirements • For load • For access • Define a workload to measure each • Run it regularly to detect anomalies • SDSS uses • one-week to reload • 20 queries with response of 10 sec to 10 min.

12. Data Subsets For Science and Development • Offer 1GB, 10GB, …, Full subsets • Wonderful tool for you Design & Debug • Good tool for scientists • Experiment on subset • Not for needle in haystack, but good for global stats • Challenge: How make statistically valid subsets? • Seems domain specific • Seems problem specific • But, must be some general concepts.

13. Data Curation Problem Statement • Once published, scientific data needs to be available forever,so that the science can be reproduced/extended. • What does that mean? • Data can be characterized as • Primary Data: could not be reproduced • Derived data: could be derived from primary data. • Meta-data: how the data was collected/derivedis primary • Must be preserved • Includes design docs, software, email, pubs, personal notes, teleconferences, NASA “level 0”

14. Schema (aka metadata) • Everyone starts with the same schema <stuff/>Then the start arguing about semantics. • Virtual Observatory:http://www.ivoa.net/ • Metadata based on Dublin Core:http://www.ivoa.net/Documents/latest/RM.html • Universal Content Descriptors (UCD): http://vizier.u-strasbg.fr/doc/UCD.htxCaptures quantitative concepts and their unitsReduced from ~100,000 tables in literature to ~1,000 terms • VOtable – a schema for answers to questionshttp://www.us-vo.org/VOTable/ • Common Queries:Cone Search and Simple Image Access Protocol, SQL • Registry: http://www.ivoa.net/Documents/latest/RMExp.htmlstill a work in progress.

15. Archive Challenges • Cost of administering storage: • Presently 10x to 100x the hardware cost. • Resist attack: geographic diversity • At 1GBps it takes 12 days to move a PB • Store it in two (or more) places online (on disk).A geo-plex • Scrub it continuously (look for errors) • On failure, • use other copy until failure repaired, • refresh lost copy from safe copy. • Can organize the copies differently (e.g.: one by time, one by space)

16. References http://SkyServer.SDSS.org/http://research.microsoft.com/pubs/http://research.microsoft.com/Gray/SDSS/ (download personal SkyServer) Extending the SDSS Batch Query System to the National Virtual Observatory Grid, M. A. Nieto-Santisteban, W. O'Mullane, J. Gray, N. Li, T. Budavari, A. S. Szalay, A. R. Thakar, MSR-TR-2004-12, Feb. 2004 Scientific Data Federation,J. Gray, A. S. Szalay, The Grid 2: Blueprint for a New Computing Infrastructure, I. Foster, C. Kesselman, eds, Morgan Kauffman, 2003, pp 95-108. Data Mining the SDSS SkyServer Database,J. Gray, A.S. Szalay, A. Thakar, P. Kunszt, C. Stoughton, D. Slutz, J. vandenBerg, Distributed Data & Structures 4: Records of the 4th International Meeting, pp 189-210, W. Litwin, G. Levy (eds),, Carleton Scientific 2003, ISBN 1-894145-13-5, also MSR-TR-2002-01, Jan. 2002 Petabyte Scale Data Mining: Dream or Reality?,Alexander S. Szalay; Jim Gray; Jan vandenBerg, SIPE Astronomy Telescopes and Instruments, 22-28 August 2002, Waikoloa, Hawaii, MSR-TR-2002-84 Online Scientific Data Curation, Publication, and Archiving,J. Gray; A. S. Szalay; A.R. Thakar; C. Stoughton; J. vandenBerg, SPIE Astronomy Telescopes and Instruments, 22-28 August 2002, Waikoloa, Hawaii, MSR-TR-2002-74 The World Wide Telescope: An Archetype for Online Science,J. Gray; A. Szalay,, CACM, Vol. 45, No. 11, pp 50-54, Nov. 2002, MSR TR 2002-75, The SDSS SkyServer: Public Access To The Sloan Digital Sky Server Data,A. S. Szalay, J. Gray, A. Thakar, P. Z. Kunszt, T. Malik, J. Raddick, C. Stoughton, J. vandenBerg:, ACM SIGMOD 2002: 570-581 MSR TR 2001 104. The World Wide Telescope,A.S., Szalay, J., Gray, Science, V.293 pp. 2037-2038. 14 Sept 2001. MS-TR-2001-77 Designing & Mining Multi-Terabyte Astronomy Archives: Sloan Digital Sky Survey,A. Szalay, P. Kunszt, A. Thakar, J. Gray, D. Slutz, P. Kuntz, June 1999, ACM SIGMOD 2000, MS-TR-99-30,