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					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.

				
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posted:8/4/2011
language:English
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