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:
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–
–
–
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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
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)
Miners
Scientists
Science Data
& Questions
Data Mining
Algorithms
Plumbers
Database
To store data
Execute
Queries
Question &
Answer
Visualization
Tools
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
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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
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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.
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You can FTP 1 MB in 1 sec
You can FTP 1 GB / min (= 1 $/GB)
… 2 days and 1K$
… 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:
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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
– A common global schema
Federation
Web Services: The Key?
• Web SERVER:
– Given a url + parameters
– Returns a web page (often dynamic)
Your
program
Web
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)
– Distributed objects for the web.
– + naming, discovery, security,..
• Internet-scale
distributed computing
Your
program
Data
In your
address
space
Web
Service
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:
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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
– Temporal data
DSS Optical
•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
IRAS 100m
WENSS 92cm
–Can share cross company
–University researchers
•Great way to teach both
Astronomy and
Computational Science
NVSS 20cm
ROSAT ~keV
GB 6cm
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
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
sql
X
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
cpu
rows
100000
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 )
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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.