1 Where The Rubber Meets the Sky Giving Access to Science Data Talk at National Institute of Informatics, Tokyo, Japan October 2005 Jim Gray Microsoft Research Alex Szalay Johns Hopkins University

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,” Where the Rubber Meets the Sky: Bridging the Gap between Databases and Science,” Jim Gray; Alexander S. Szalay; MSR-TR , 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 instruments Or generated by simulator –Processed by software –Scientist analyzes database / files

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

5 Experiment Budgets ¼…½ Software 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 …

6 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. instrument or simulator pipeline other data other data pipeline Level 0 raw Level 1 calibrated Level 2 derived

7 Data Inflation – Data Pyramid Level 1A Grows X TB/year ~.4X TB/y compressed (level 1A in NASA terms) Level 2 Derived 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 ~ N 2 bytes Net: Data Inflation: L2 ≥ L1 E1 E2 E3 E4 4 editions of level 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. time Level 1A4 editions of 4 Level 2 products

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 ~N 2 R –Need to buy NR units Depreciate over 3 years –After year 3 need to buy N 2 R + (N-3) 2 R Moore’s law: 60%/year price decline Capital expense peaks at year 5 See 6x Over-Power slide next

9 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. PubDB 3.6TB DR3C 2.4TB DR2C 1.8TB DR2M 1.8TB DR2P 1.8TB DR3M 2.4TB DR3P 2.4TB

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/derived is primary Must be preserved Includes design docs, software, , pubs, personal notes, teleconferences, NASA “level 0”

14 Schema (aka metadata) Everyone starts with the same schema Then the start arguing about semantics. Virtual Observatory: Metadata based on Dublin Core: Universal Content Descriptors (UCD): Captures quantitative concepts and their units Reduced from ~100,000 tables in literature to ~1,000 terms VOtable – a schema for answers to questions Common Queries: Cone Search and Simple Image Access Protocol, SQL Registry: still 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)

References / (download personal SkyServer) / Extending the SDSS Batch Query System to the National Virtual Observatory Grid, 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 , Feb Scientific Data Federation, 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 Data Mining the SDSS SkyServer Database, 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 , W. Litwin, G. Levy (eds),, Carleton Scientific 2003, ISBN , also MSR-TR , Jan Petabyte Scale Data Mining: Dream or Reality?, Alexander S. Szalay; Jim Gray; Jan vandenBerg, SIPE Astronomy Telescopes and Instruments, August 2002, Waikoloa, Hawaii, MSR-TR Petabyte Scale Data Mining: Dream or Reality?, Online Scientific Data Curation, Publication, and Archiving, Online Scientific Data Curation, Publication, and Archiving, J. Gray; A. S. Szalay; A.R. Thakar; C. Stoughton; J. vandenBerg, SPIE Astronomy Telescopes and Instruments, August 2002, Waikoloa, Hawaii, MSR-TR The World Wide Telescope: An Archetype for Online ScienceThe World Wide Telescope: An Archetype for Online Science, J. Gray; A. Szalay,, CACM, Vol. 45, No. 11, pp 50-54, Nov. 2002, MSR TR , The SDSS SkyServer: Public Access To The Sloan Digital Sky Server DataThe 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: MSR TR The World Wide TelescopeThe World Wide Telescope, A.S., Szalay, J., Gray, Science, V.293 pp Sept MS-TR Designing & Mining Multi-Terabyte Astronomy Archives: Sloan Digital Sky SurveyDesigning & 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,