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Astronomy Data Bases Jim Gray Microsoft Research.

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Presentation on theme: "Astronomy Data Bases Jim Gray Microsoft Research."— Presentation transcript:

1 Astronomy Data Bases Jim Gray Microsoft Research

2 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

3 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. Too soon to say if comp-X and X-info will unify or compete. BaBar, Stanford Space Telescope P&E Gene Sequencer From

4 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

5 Whats X-info Needs from us (cs) (not drawn to scale) Science Data & Questions Scientists Database To store data Execute Queries Plumbers Data Mining Algorithms Miners Question & Answer Visualization Tools

6 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 N 2, likelihood techniques N 3 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) –Dont assume infinite computational resources or memory Requires combination of statistics & computer science

7 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.

8 Data Access is hitting a wall FTP and GREP are not adequate You can GREP 1 MB in a second You can GREP 1 GB in a minute You can GREP 1 TB in 2 days You can GREP 1 PB in 3 years. 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 You can FTP 1 MB in 1 sec You can FTP 1 GB / min (= 1 $/GB) … 2 days and 1K$ … 3 years and 1M$

9 Federation Data Federations of Web Services Massive datasets live near their owners: –Near the instruments 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 –A common global schema

10 Web Services: The Key? Web SERVER: –Given a url + parameters –Returns a web page (often dynamic) 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) –Distributed objects for the web. –+ naming, discovery, security,.. Internet-scale distributed computing Your program Data In your address space Web Service soap object in xml Your program Web Server http Web page

11 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

12 World Wide Telescope Virtual Observatory Premise: Most data is (or could be online) So, the Internet is the worlds 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..). –Its a smart telescope: links objects and data to literature on them.

13 Why Astronomy Data? It has no commercial value –No privacy concerns –Can freely share results with others –Great for experimenting with algorithms It is real and well documented – High-dimensional data (with confidence intervals) – Spatial data – Temporal data Many different instruments from many different places and many different times Federation is a goal There is a lot of it (petabytes) Great sandbox for data mining algorithms –Can share cross company –University researchers Great way to teach both Astronomy and Computational Science IRAS 100 ROSAT ~keV DSS Optical 2MASS 2 IRAS 25 NVSS 20cm WENSS 92cm GB 6cm

14 Put Your Data In a File? + Simple + Reliable + Common Practice + Matches C/Java/… programming model (streams) -Metadata in program not in database -Recovery is old-master new-master rather than transaction -Procedural access for queries -No indices unless you do it yourself -No parallelism unless you do it yourself

15 Put Your Data In a DB? + Schematized Schema evolution Data independence + Reliable transactions, online backup,.. + Query tools parallelism non procedural + Scales to large datasets + Web services tools -Complicated -New programming model -Depend on a vendor all give an extended subset of the standard -Expensive Product X sql

16 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

17 The SDSS Experience It takes a village…. MANY different skills

18 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.

19 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.

20 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..

21 Demo Show pixel space and object space explorers.

22 A Simple Schema PhotoSpectro

23 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

24 The Answer is 42 But what is the accuracy and precision? What is the derivation? Needs a man page

25 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.

26 Traffic Analysis SDSS DR1 has been online for a while. Peak hour is 12M records/hour Peak query is 500,000 rows (limit)

27 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.

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