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Sigmod 2002, Madison Sky. Server: Public Access to the Sloan Digital Sky Survey Alex Sigmod 2002, Madison Sky. Server: Public Access to the Sloan Digital Sky Survey Alex Szalay, Jim Gray, Ani Thakar, Peter Kunszt, Tanu Malik, Tamas Budavari, Jordan Raddick, Chris Stoughton, Jan vanden. Berg

Outline • The Sloan Digital Sky Survey • The SDSS database design – HTM Outline • The Sloan Digital Sky Survey • The SDSS database design – HTM – spatial queries – 20 queries • • Demo of the Sky. Server The next steps The World-Wide Telescope Web Services – Sky Query/Image Cutout

Features of the SDSS Goal Create the most detailed map of the Northern sky Features of the SDSS Goal Create the most detailed map of the Northern sky in 5 years 2. 5 m telescope, Apache Point, NM 3 degree field of view ¼ of the whole sky Two surveys in one Photometric survey in 5 bands Spectroscopic redshift survey Automated data reduction 150 man-years of development Very high data volume 40 TB of raw data 5 TB processed catalogs Data is public The University of Chicago Princeton University The Johns Hopkins University The University of Washington New Mexico State University Fermi National Accelerator Laboratory US Naval Observatory The Japanese Participation Group The Institute for Advanced Study Max Planck Inst, Heidelberg Sloan Foundation, NSF, DOE, NASA

The Imaging Survey Continuous data rate of 8 Mbytes/sec Northern Galactic Cap drift scan The Imaging Survey Continuous data rate of 8 Mbytes/sec Northern Galactic Cap drift scan of 10, 000 square degrees 24 k x 1 M pixel “panoramic” images in 5 colors – broad-band filters (u, g, r, i, z) exposure time: 55 sec pixel size: 0. 4 arcsec astrometry: 60 mas calibration: 2% done only in best seeing (20 nights/year) Southern Galactic Cap multiple scans (> 30 times) of the same stripe

Elliptical galaxy The Spectroscopic Survey Expanding universe redshift = distance SDSS Redshift Survey 1 Elliptical galaxy The Spectroscopic Survey Expanding universe redshift = distance SDSS Redshift Survey 1 million galaxies 100, 000 quasars 100, 000 stars Two high throughput spectrographs spectral range 3900 -9200 Å 640 spectra simultaneously R=2000 resolution, 1. 3 Å Features Automated reduction of spectra Very high sampling density and completeness

Data Flow Pixel data collected by telescope Sent to Fermilab for processing Beowulf Cluster Data Flow Pixel data collected by telescope Sent to Fermilab for processing Beowulf Cluster produces catalog Loaded in a SQL database

SDSS Data Products Object catalog 6000 GB parameters of >108 objects Redshift Catalog 1 SDSS Data Products Object catalog 6000 GB parameters of >108 objects Redshift Catalog 1 GB parameters of 106 objects Atlas Images 1500 GB 5 color cutouts of >108 objects Spectra 60 GB in a one-dimensional form Derived Catalogs 20 GB clusters QSO absorption lines 4 x 4 Pixel All-Sky Map 60 GB heavily compressed Corrected Frames 15 TB

Spatial Data Access – SQL extension Szalay, Kunszt, Brunner http: //www. sdss. jhu. edu/htm Spatial Data Access – SQL extension Szalay, Kunszt, Brunner http: //www. sdss. jhu. edu/htm • Added Hierarchical Triangular Mesh (HTM) table-valued function for spatial joins • Every object has a 20 -deep Mesh ID • Given a spatial definition, 2, 3, 0 routine returns up to 10 2, 3, 1 2, 3, 2 2, 3, 3 covering triangles 2, 1 • Spatial query is then up 2, 2 2, 3 to 10 range queries • Very fast: 10, 000 triangles / second / cpu

20 Queries • DB design started with “ 20 queries” in English • These 20 Queries • DB design started with “ 20 queries” in English • These then dictated DB design – Spatial extensions, neighbors • Then implemented in SQL – Heavy use of SP, UDF – All run in 10 mins, most under 1 min • Tag tables – replaced by covering indices • Sequential IO – The worst case, a full scan – reached 400 MB/sec on Wintel

Q 15: Fast Moving Objects • Find near earth asteroids: SELECT r. obj. ID Q 15: Fast Moving Objects • Find near earth asteroids: SELECT r. obj. ID as r. Id, g. obj. Id as g. Id, dbo. f. Get. Url. Eq(g. ra, g. dec) as url FROM Photo. Obj r, Photo. Obj g WHERE r. run = g. run and r. camcol=g. camcol and abs(g. field-r. field)<2 -- nearby -- the red selection criteria and ((power(r. q_r, 2) + power(r. u_r, 2)) > 0. 111111 ) and r. fiber. Mag_r between 6 and 22 and r. fiber. Mag_r < r. fiber. Mag_g and r. fiber. Mag_r < r. fiber. Mag_i and r. parent. ID=0 and r. fiber. Mag_r < r. fiber. Mag_u and r. fiber. Mag_r < r. fiber. Mag_z and r. iso. A_r/r. iso. B_r > 1. 5 and r. iso. A_r>2. 0 -- the green selection criteria and ((power(g. q_g, 2) + power(g. u_g, 2)) > 0. 111111 ) and g. fiber. Mag_g between 6 and 22 and g. fiber. Mag_g < g. fiber. Mag_r and g. fiber. Mag_g < g. fiber. Mag_i and g. fiber. Mag_g < g. fiber. Mag_u and g. fiber. Mag_g < g. fiber. Mag_z and g. parent. ID=0 and g. iso. A_g/g. iso. B_g > 1. 5 and g. iso. A_g > 2. 0 -- the matchup of the pair and sqrt(power(r. cx -g. cx, 2)+ power(r. cy-g. cy, 2)+power(r. cz-g. cz, 2))*(10800/PI())< 4. 0 and abs(r. fiber. Mag_r-g. fiber. Mag_g)< 2. 0 • Finds 3 objects in 11 minutes – (or 52 seconds with an index) • Ugly, but consider the alternatives (c programs and files and time…) –

Demo of Sky. Server • Based on the Terra. Server design • Designed for Demo of Sky. Server • Based on the Terra. Server design • Designed for high school students – Contains 150 hours of interactive courses • Experiment for easy visual interfaces • Opened June 5, 2001 • After a year: http: //skyserver. sdss. org/ – 1. 6 M page views – 60 K visitors – 4. 7 M page hits • Added Web Services – Cutout – Sky. Query

Public Data Release • June 2002: EDR – Early Data Release EDR • January Public Data Release • June 2002: EDR – Early Data Release EDR • January 2003: DR 1 – Contains 30% of final data – 100 million photo objects • 4 versions of the data DR 1 DR 2 – Target, best, runs, spectro • Total catalog volume 1. 7 TB DR 3 DR 2 DR 3 – See Terascale sneakernet paper… • Published releases served forever – EDR, DR 1, DR 2, …. • O(N 2) – only possible because of Moore’s Law! DR 3

Why Is Astronomy Data Special? • It has no commercial value IRAS 25 m Why Is Astronomy Data Special? • It has no commercial value IRAS 25 m 2 MASS 2 m –No privacy concerns –Can freely share results with others –Great for experimenting with algorithms DSS Optica • It is real and well documented –High-dimensional (with confidence intervals) –Spatial –Temporal IRAS 100 m • Diverse and distributed –Many different instruments from many different places and many different times WENSS 92 cm • The questions are interesting • There is a lot of it (petabytes) NVSS 20 cm ROSAT ~ke. V GB 6 cm

Living in an Exponential World • Astronomers have a few hundred TB now – Living in an Exponential World • Astronomers have a few hundred TB now – 1 pixel (byte) / sq arc second ~ 4 TB – Multi-spectral, temporal, … → 1 PB • They mine it looking for new (kinds of) objects or more of interesting ones (quasars), density variations in 400 -D space correlations in 400 -D space • • • Data doubles every year Data is public after 1 year So, 50% of the data is public Some have private access to 5% more data So: 50% vs 55% access for everyone

Virtual Observatory • Many new surveys are coming – SDSS is a dry run Virtual Observatory • Many new surveys are coming – SDSS is a dry run for the next ones – LSST will be 1 TB/night • All the data will be on the Internet – But how? ftp, webservice… • Data and apps will be associated with the instruments – Distributed world wide – Cross-indexed – Federation is a must, but how? • Will be the best telescope in the world – World Wide Telescope

Sky. Query: Experimental Federation • Federated 5 Web Services – – Portal unifies 3 Sky. Query: Experimental Federation • Federated 5 Web Services – – Portal unifies 3 archives and a cutout service to visualize results Fermilab/SDSS, JHU/FIRST, Caltech/2 MASS Archives Multi-survey spatial join and SQL select Distributed query optimization (T. Malik, T. Budavari) in 6 weeks http: //www. skyquery. net/ SELECT o. obj. Id, o. ra, o. r, o. type, t. obj. Id FROM SDSS: Photo. Primary o, TWOMASS: Photo. Primary t WHERE XMATCH(o, t)<3. 5 AND AREA(181. 3, -0. 76, 6. 5) AND o. type=3 AND o. I – t. m_j > 2 • Cutout web service: annotated SDSS images – http: //Sky. Service. jhu. pha. edu/Sdss. Cutout

Relevant Papers • • • Data Mining the SDSS Sky. Server Database Jim Gray; Relevant Papers • • • Data Mining the SDSS Sky. Server Database Jim Gray; Peter Kunszt; Donald Slutz; Alex Szalay; Ani Thakar; Jan Vandenberg; Chris Stoughton Jan. 2002 40 p. An earlier paper described the Sloan Digital Sky Survey’s (SDSS) data management needs [Szalay 1] by defining twenty database queries and twelve data visualization tasks that a good data management system should support. We built a database and interfaces to support both the query load and also a website for ad-hoc access. This paper reports on the database design, describes the data loading pipeline, and reports on the query implementation and performance. The queries typically translated to a single SQL statement. Most queries run in less than 20 seconds, allowing scientists to interactively explore the database. This paper is an in-depth tour of those queries. Readers should first have studied the companion overview paper “The SDSS Sky. Server – Public Access to the Sloan Digital Sky Server Data” [Szalay 2]. SDSS Sky. Server–Public Access to Sloan Digital Sky Server Data • Jim Gray; Alexander Szalay; Ani Thakar; Peter Z. Zunszt; Tanu Malik; Jordan Raddick; Christopher Stoughton; Jan Vandenberg November 2001 11 p. : Word 1. 46 Mbytes PDF 456 Kbytes The Sky. Server provides Internet access to the public Sloan Digital Sky Survey (SDSS) data for both astronomers and for science education. This paper describes the Sky. Server goals and architecture. It also describes our experience operating the Sky. Server on the Internet. The SDSS data is public and welldocumented so it makes a good test platform for research on database algorithms and performance. • The World-Wide Telescope • • Jim Gray; Alexander Szalay August 2001 6 p. : Word 684 Kbytes PDF 84 Kbytes All astronomy data and literature will soon be online and accessible via the Internet. The community is building the Virtual Observatory, an organization of this worldwide data into a coherent whole that can be accessed by anyone, in any form, from anywhere. The resulting system will dramatically improve our ability to do multi-spectral and temporal studies that integrate data from multiple instruments. The virtual observatory data also provides a wonderful base for teaching astronomy, scientific discovery, and computational science. Designing and Mining Multi-Terabyte Astronomy Archives Robert J. Brunner; Jim Gray; Peter Kunszt; Donald Slutz; Alexander S. Szalay; Ani Thakar June 1999 8 p. : Word (448 Kybtes) PDF (391 Kbytes) The next-generation astronomy digital archives will cover most of the sky at fine resolution in many wavelengths, from X-rays, through ultraviolet, optical, and infrared. The archives will be stored at diverse geographical locations. One of the first of these projects, the Sloan Digital Sky Survey (SDSS) is creating a 5 wavelength catalog over 10, 000 square degrees of the sky (see http: //www. sdss. org/). The 200 million objects in the multi-terabyte database will have mostly numerical attributes in a 100+ dimensional space. Points in this space have highly correlated distributions. The archive will enable astronomers to explore the data interactively. Data access will be aided by multidimensional spatial and attribute indices. The data will be partitioned in many ways. Small tag objects consisting of the most popular attributes will accelerate frequent searches. Splitting the data among multiple servers will allow parallel, scalable I/O and parallel data analysis. Hashing techniques will allow efficient clustering, and pair-wise comparison algorithms that should parallelize nicely. Randomly sampled subsets will allow de-bugging otherwise large queries at the desktop. Central servers will operate a data pump to support sweep searches touching most of the data. The anticipated queries will re-quire special operators related to angular distances and complex similarity tests of object properties, like shapes, colors, velocity vectors, or temporal behaviors. These issues pose interesting data management challenges.

References and Links • Sky. Server – http: //skyserver. sdss. org/ – http: //research. References and Links • Sky. Server – http: //skyserver. sdss. org/ – http: //research. microsoft. com/pubs/ • Virtual Observatory – http: //www. us-vo. org/ – http: //www. voforum. org/ • World-Wide Telescope – paper in Science V. 293 pp. 2037 -2038. 14 Sept 2001. (MS-TR-2001 -77 word or pdf. ) • SDSS DB is a data mining challenge: – Get your personal copy at http: //research. microsoft. com/~gray/sdss