VIEWS: 10 PAGES: 27 POSTED ON: 8/4/2011
Astronomy Data Bases Jim Gray Microsoft Research The Evolution of Science • Observational Science – Scientist gathers data by direct observation – Scientist analyzes data • Analytical Science – Scientist builds analytical model – Makes predictions. • Computational Science – Simulate analytical model – Validate model and makes predictions • Data Exploration Science Data captured by instruments Or data generated by simulator – Processed by software – Placed in a database / files – Scientist analyzes database / files Computational Science Evolves • Historically, Computational Science = simulation. • New emphasis on informatics: – Capturing, – Organizing, – Summarizing, – Analyzing, – Visualizing • Largely driven by observational science, but also needed by simulations. BaBar, Stanford • Too soon to say if comp-X and X-info will unify or compete. P&E Gene Sequencer From http://www.genome.uci.edu/ Space Telescope Information Avalanche • Both – better observational instruments and – Better simulations are producing a data avalanche • Examples – Turbulence: 100 TB simulation then mine the Information – BaBar: Grows 1TB/day 2/3 simulation Information 1/3 observational Information – CERN: LHC will generate 1GB/s 10 PB/y – VLBA (NRAO) generates 1GB/s today – NCBI: “only ½ TB” but doubling each year, very rich dataset. – Pixar: 100 TB/Movie Images courtesy of Charles Meneveau & Alex Szalay @ JHU What’s X-info Needs from us (cs) (not drawn to scale) Scientists Miners Data Mining Science Data Algorithms & Questions Plumbers Database Tools Question & To store data Answer Execute Visualization Queries Next-Generation Data Analysis • Looking for – Needles in haystacks – the Higgs particle – Haystacks: Dark matter, Dark energy • Needles are easier than haystacks • Global statistics have poor scaling – Correlation functions are N2, likelihood techniques N3 • As data and computers grow at same rate, we can only keep up with N logN • A way out? – Discard notion of optimal (data is fuzzy, answers are approximate) – Don’t assume infinite computational resources or memory • Requires combination of statistics & computer science Analysis and Databases • Much statistical analysis deals with – Creating uniform samples – – data filtering – Assembling relevant subsets – Estimating completeness – censoring bad data – Counting and building histograms – Generating Monte-Carlo subsets – Likelihood calculations – Hypothesis testing • Traditionally these are performed on files • Most of these tasks are much better done inside a database • Move Mohamed to the mountain, not the mountain to Mohamed. Data Access is hitting a wall FTP and GREP are not adequate • You can GREP 1 MB in a second • You can FTP 1 MB in 1 sec • You can GREP 1 GB in a minute • You can FTP 1 GB / min (= 1 $/GB) • You can GREP 1 TB in 2 days • … 2 days and 1K$ • You can GREP 1 PB in 3 years. • … 3 years and 1M$ • Oh!, and 1PB ~5,000 disks • At some point you need indices to limit search parallel data search and analysis • This is where databases can help Data Federations of Web Services • Massive datasets live near their owners: – Near the instrument’s software pipeline – Near the applications – Near data knowledge and curation – Super Computer centers become Super Data Centers • Each Archive publishes a web service – Schema: documents the data – Methods on objects (queries) • Scientists get “personalized” extracts • Uniform access to multiple Archives Federation – A common global schema Web Services: The Key? • Web SERVER: Your – Given a url + parameters program Web – Returns a web page (often dynamic) Server • Web SERVICE: – Given a XML document (soap msg) – Returns an XML document – Tools make this look like an RPC. • F(x,y,z) returns (u, v, w) Your program Web – Distributed objects for the web. Service – + naming, discovery, security,.. Data • Internet-scale In your address distributed computing space Grid and Web Services Synergy • I believe the Grid will be many web services • IETF standards Provide – Naming – Authorization / Security / Privacy – Distributed Objects Discovery, Definition, Invocation, Object Model – Higher level services: workflow, transactions, DB,.. • Synergy: commercial Internet & Grid tools World Wide Telescope Virtual Observatory http://www.astro.caltech.edu/nvoconf/ http://www.voforum.org/ • Premise: Most data is (or could be online) • So, the Internet is the world’s best telescope: – It has data on every part of the sky – In every measured spectral band: optical, x-ray, radio.. – As deep as the best instruments (2 years ago). – It is up when you are up. The “seeing” is always great (no working at night, no clouds no moons no..). – It’s a smart telescope: links objects and data to literature on them. Why Astronomy Data? IRAS 25m •It has no commercial value –No privacy concerns –Can freely share results with others –Great for experimenting with algorithms 2MASS 2m •It is real and well documented – High-dimensional data (with confidence intervals) – Spatial data DSS Optical – Temporal data •Many different instruments from many different places and IRAS 100m many different times •Federation is a goal •There is a lot of it (petabytes) WENSS 92cm •Great sandbox for data mining algorithms –Can share cross company –University researchers NVSS 20cm •Great way to teach both Astronomy and Computational Science ROSAT ~keV GB 6cm Put Your Data In a File? + Simple - Metadata in program + Reliable not in database + Common Practice - Recovery is + Matches C/Java/… “old-master new-master” programming model rather than transaction (streams) - Procedural access for queries - No indices unless you do it yourself - No parallelism unless you do it yourself Put Your Data In a DB? + Schematized - Complicated Schema evolution - New programming model Data independence - Depend on a vendor + Reliable all give an “extended subset” transactions, of the “standard” online backup,.. - Expensive + Query tools parallelism non procedural + Scales to large datasets Product sql X + Web services tools My Conclusion • Despite the drawbacks • DB is the only choice for large datasets for “complex” datasets (schema) for “complex” query for shared access (read & write) • But try to present “standard” SQL • Power users need full power of SQL The SDSS Experience • It takes a village…. MANY different skills The SDSS Experience not all DBMSs are DBMSs • DB#1 ● Schema evolves. ● crash & reload on evolution. ● no easy way to evolve ● No query tools ● Poor indices ● Dismal sequential performance (.5MB/s) ● Had to build their own parallelism. • This “database system” had virtually none of the DB benefits and all of the DB pain. The SDSS Experience • DB#2 (a fairly pure relational system) ● Schema evolution was easy. ● Query tools, indices, parallelism works ● Many admin tools for loading ● Good sequential performance (1 GB/s, 5 M records/second/cpu) ● Reliable • Had good vendor support (me) - Seduced by vendor extensions - Some query optimizer bugs (bad plans) are a constant nuisance. Astronomy DBs • Data starts with Pixels (10s of TB today) – Optical is pixels (flux @ (ra,dec)) – Radio is cube (f(band)@ (ra,dec)) – Many things vary with time • Pixels converted to “objects” (Billions today) – @(ra,dec) hundreds of attributes, each with estimated error • Most queries on “object” space. • Drill down to pixel space or to cube. • Many queries are spatial: need HTM or .. Demo • Show pixel space and object space explorers. Photo A Simple Schema Spectro How to Design the Database? 1. Decide what it is for 20 questions approach has worked well 2. Design it to answer those 20 questions 3. Iterate (it is easy to change designs). BUT.. Be careful about names: reddening → extinction causes problems fuzzy definitions cause problems documenting what a value means is hard The Answer is 42 • But what is the accuracy and precision? • What is the derivation? • Needs a man page The SDSS Experience • DB has worked out well – Tools are very important (especially data loading) – Integration with web servers/services is very important • Need more than single-node parallelism • Need better query plans • But overall… a success. • Have been able to clone it for several other datasets (FIRST, 2MASS, SSS, INT) • Database replicated at many sites (25?) • Built an interesting data-ingest system. Traffic Analysis • SDSS DR1 has been online for a while. • Peak hour is 12M records/hour • Peak query is 500,000 rows (limit) 1000000 elapsed 100000 cpu rows 10000 1000 100 10 1 0 1 2 4 8 16 32 64 128 256 512 1024 2048 4096 8192 16384 32768 65536 262144 524288 The Future • Things will get better. • Code is moving into the DB: easier to add spatial and other functions better performance No Inside/Outside dichotomy • XML Schema (XSD) describes data on the wire. • I love DataSets (an schematized network of records ) – XSD described – collections of record sets – With foreign keys – With updategrams • XML and xQuery is coming This may help some things This may confuse things (more choices) Probably both.