Transcript Store Everything Online In A Database Jim Gray Microsoft Research [email protected] http://research.microsoft.com/~gray/talks http://research.microsoft.com/~gray/talks/cern_2001.ppt Outline • Store Everything • Online (Disk not Tape) • In a Database • A Federated DB • Two Examples http://research.microsoft.com/~gray/talks/cern_2001.ppt.

Store
Everything
Online
In A Database
Jim Gray
Microsoft Research
[email protected]
http://research.microsoft.com/~gray/talks
http://research.microsoft.com/~gray/talks/cern_2001.ppt
1
Outline
• Store Everything
• Online (Disk not Tape)
• In a Database
• A Federated DB
• Two Examples
http://research.microsoft.com/~gray/talks/cern_2001.ppt
2
How Much is Everything?
Yotta
Everything
• Soon everything can be
!
recorded and indexed
Recorded
All Books
• Most bytes will never be
MultiMedia
seen by humans.
• Data summarization, trend
All LoC books
detection anomaly
(words)
detection are key
technologies
.Movi
See Mike Lesk:
How much information is there:
http://www.lesk.com/mlesk/ksg97/ksg.html
See Lyman & Varian:
How much information
http://www.sims.berkeley.edu/research/projects/how-much-info/
24
Yecto, 21 zepto, 18 atto, 15 femto, 12 pico, 9 nano, 6 micro, 3 milli
http://research.microsoft.com/~gray/talks/cern_2001.ppt
e
A Photo
A Book
Zetta
Exa
Peta
Tera
Giga
Mega
Kilo3
Storage capacity Disk TB Shipped per Year
beating Moore’s law
1E+7
1998 Disk Trend (Jim Porter)
http://www.disktrend.com/pdf/portrpkg.pdf.
ExaByte
1E+6
1E+5
2.3 k$/TB today (raw disk)
1k$/TB by end of 2002
http://research.microsoft.com/~gray/talks/cern_2001.ppt
Moore's Law:
58.7%/y
1E+4
1E+3
1988
Moores law
Revenue
TB growth
Price decline
disk TB
growth:
112%/y
1991
1994
1997
58.70% /year
7.47%
112.30% (since 1993)
50.70% (since 1993)
2000
4
Outline
• Store Everything
• Online (Disk not Tape)
• In a Database
• A Federated DB
• Two Examples
http://research.microsoft.com/~gray/talks/cern_2001.ppt
5
Online Data
• Can build 1PB of NAS disk for 5M$ today
• Can SCAN (read or write) entire PB in 3 hours.
• Operate it as a data pump: continuous sequential
scan
• Can deliver 1PB for 1M$ over Internet
– Access charge is 300$/Mbps bulk rate
• Need to Geoplex data (store it in two places).
• Need to filter/process data near the source,
– To minimize network costs.
http://research.microsoft.com/~gray/talks/cern_2001.ppt
6
The “Absurd” Disk
• 2.5 hr scan time
(poor sequential access)
• 1 access per second / 5 GB
(VERY cold data)
• It’s a tape!
100 MB/s
200 Kaps
http://research.microsoft.com/~gray/talks/cern_2001.ppt
1 TB
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Disk vs Tape
Tape
Disk
–
–
–
–
–
–
–
–
–
–
–
40 GB
10 MBps
80 GB
10 sec pick time
35 MBps
30-120 second seek time
5 ms seek time
2$/GB for media
3 ms rotate latency
8$/GB for drive+library
2$/GB for drive
2$/GB for ctlrs/cabinet – 10 TB/rack
15 TB/rack
– 1 week scan
– 1 hour scan
Guestimates
Cern: 200 TB
3480 tapes
2 col = 50GB
Rack = 1 TB
=12 drives
The price advantage of disk is growing
the performance advantage of disk is huge!
At 10K$/TB, disk is competitive with nearline tape.
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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Building a Petabyte Disk Store
• Cadillac ~ 500k$/TB =
plus FC switches plus…
• TPC-C SANs (Brand PC 18GB/…)
• Brand PC local SCSI
500
• Do it yourself ATA
500M$/PB
800M$/PB
60M$/PB
15M$/PB
5M$/PB
400
300
200
100
0
http://research.microsoft.com/~gray/talks/cern_2001.ppt
Premium
SAN
Dell/3ware
DIY
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Cheap Storage and/or Balanced System
• Low cost storage
(2 x 3k$ servers) 5K$ TB
2x ( 800 Mhz, 256Mb + 8x80GB disks + 100MbE)
raid5 costs 6K$/TB
• Balanced server (5k$/.64 TB)
– 2x 1Ghz (2k$)
– 512 MB
– 8 x 80 GB drives (2K$)
– Gbps Ethernet + switch (300$/port)
– 9k$/TB 18K$/mirrored TB
http://research.microsoft.com/~gray/talks/cern_2001.ppt
2x1 Ghz
512 MB
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Next step in the Evolution
• Disks become supercomputers
– Controller will have 1bips, 1 GB ram, 1 GBps net
– And a disk arm.
• Disks will run full-blown app/web/db/os stack
• Distributed computing
• Processors migrate to transducers.
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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It’s Hard to Archive a Petabyte
It takes a LONG time to restore it.
• At 1GBps it takes 12 days!
• Store it in two (or more) places online (on disk?).
A geo-plex
• Scrub it continuously (look for errors)
• On failure,
– use other copy until failure repaired,
– refresh lost copy from safe copy.
• Can organize the two copies differently
(e.g.: one by time, one by space)
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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Outline
• Store Everything
• Online (Disk not Tape)
• In a Database
• A Federated DB
• Two Examples
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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Why Not: file = object + GREP?
• It works if you have thousands of objects
(and you know them all)
• But hard to search millions/billions/trillions
with GREP
• Hard to put all attributes in file name.
– Minimal metadata
• Hard to do chunking right.
• Hard to pivot on space/time/version/attributes.
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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The Reality:
it’s build vs buy
• If you use a file system
you will eventually build a database system:
–
–
–
–
–
–
–
–
metadata,
Query,
parallel ops,
security,….
reorganize,
recovery,
distributed,
replication,
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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OK: so I’ll put lots of objects in a file
Do It Yourself Database
• Good news:
– Your implementation will be 10x faster (at least!)
– easier to understand and use
• Bad news:
– It will cost 10x more to build and maintain
– Someday you will get bored maintaining/evolving it
– It will lack some killer features:
•
•
•
•
•
•
Parallel search
Self-describing via metadata
SQL, XML, …
Replication
Online update – reorganization
Chunking is problematic (what granularity, how to aggregate)
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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Top 10 reasons to put Everything in a DB
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
Someone else writes the million lines of code
Captures data and Metadata,
Standard interfaces give tools and quick learning
Allows Schema Evolution without breaking old apps
Index and Pivot on multiple attributes
space-time-attribute-version….
Parallel terabyte searches in seconds or minutes
Moves processing & search close to the disk arm
(moves fewer bytes (qestons return datons).
Chunking is easier (can aggregate chunks at server).
Automatic geo-replication
Online update and reorganization.
Security
If you pick the right vendor, ten years from now, there will
be software that can read the data.
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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DB Centric Examples
• TerraServer
– All images and all data in the database (chunked as small
tiles).
www.TerraServer.Microsoft.com/
– http://research.microsoft.com/~gray/Papers/MSR_TR_99_29_TerraServer.doc
• SkyServer & Virtual Sky
– Both image and semantic data in a relational store.
– Parallel search & NonProcedural access are important.
–
http://research.microsoft.com/~gray/Papers/MS_TR_99_30_Sloan_Digital_Sky_Survey.doc
– http://skyserver.sdss.org/getMosaic.asp?Z=1&A=1&T=4&H=1&S=10&M=30
– http://virtualsky.org/servlet/Page?F=3&RA=16h+10m+1.0s&DE=%2B0d+42m+
45s&T=4&P=12&S=10&X=5096&Y=4121&W=4&Z=1&tile.2.1.x=55&tile.2.1.y=20
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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OK… Why don’t they use our stuff?
• Wrong metaphor:
HDF with hyper-slab is better match.
• Impedance match:
Getting stuff in/out of DB is too hard
• We sold them OODBs and they did not work
(unreliable, poor performance, no tools).
• …
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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So, why will the future be different?
• They have MUCH more data (109 files?)
• Java / C# eases impedance mismatch:
rowsets == ragged arrays of objects
iterators, exceptions,.. built in language
• Tools are better
– Optimizers are better
– CPU and disk parallelism actually works now
– Statistical packages are better.
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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Outline
• Store Everything
• Online (Disk not Tape)
• In a Database
• A Federated DB
• Two Examples
http://research.microsoft.com/~gray/talks/cern_2001.ppt
21
But…
Distributed shared databases have failed
even on their home turf.
blocks, files, tables are wrong abstraction for networks.
(too low level)
“Objects are the right abstraction”
So, UDDI / WSDL / SOAP is the solution (not SQL)
Replays the NAS is better than SAN argument:
methods > sql > file > disk
XML is the wire format, XLANG is the workflow protocol,
Query will be in there somewhere.
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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DDB technology GREAT in a Cluster
Beowulf
• Uniform architecture
• Trust among nodes
• High bandwidth-low latency communication
• Programs have single system image
• Queries run in parallel
• Global optimizer does query decomposition
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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But in a Distributed System
(a Grid vs Beowulf)
• Higher level abstraction
give modularity
minimize round trips
• Change is constant: need modularity.
• Heterogeneous architecture makes query
planning much harder
• No trust
• Communication is slow and expensive
(minimize it).
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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Federate Databases
• Each Database exposes services
– Self describing
– Discoverable
– Easy for programs to use/understand
– Built on standards (W3C, IETF,..)
• Client-side or server-side apps
– Integrate these services
– Combine information to produce answers
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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Outline
• Store Everything
• Online (Disk not Tape)
• In a Database
• A Federated DB
• Two Examples
http://research.microsoft.com/~gray/talks/cern_2001.ppt
27
How do we find information today?
• Human searches web
(with an index)
• Human browses pages
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How do we find information tomorrow?
• Agents gather and
digest it for us.
• Q: How?
• AW3C:
– Discovery:
UDDI, DISCO,
WSDL
– Use:
• SOAP
http://research.microsoft.com/~gray/talks/cern_2001.ppt
Digital Dashboard
My Agents
SOAP
WSDL
Web Services
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How do we publish information?
• Get the data.
• Conceptualize the data schema
• Provide methods that return data subsets.
f, g, x,
y…
– Challenge: how much processing on your server?
• Publish the schema and methods.
• We (you and I) are exploring these issues.
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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TerraServer Example
• TerraServer
– 3TB Internet Map DB available since June 1998
– USGS photo and topo maps of the US
– Integrated with Home Advisor
– Shows off SQL Server availability & scalability
– Designed for thin clients and voice network
• TerraService
– A .NET web service
– Makes TerraServer data available to other apps
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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Demo http://terraserver.microsoft.com
Show
photo
topo
gazetteer
demographics
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TerraServer Experience
•
Successful Web Site
– Top 1000 Web Site – continues to be popular
– Met goals – interesting, big, real, public, fast,
easy, accessible, and free
– High Availability – Windows Data Center &
Compaq SAN Technology
•
New Feature Requests
– Programmable access to meta-data
– User selectable image sizes, i.e. “a map server”
– Permission to use TerraServer data within
server applications
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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What is a Web Service?
Open
Internet
Protocols

Web
Service
A programmable application component
accessible via standard Web protocols
Provide a Directory of Services on the
Internet

You can ask a site for a description of the
Web Services it offers

Web Services are defined in terms of the
formats and ordering of messages

Web Service consumers can send and
receive messages using XML

All these capabilities are built using open
Internet protocols
http://research.microsoft.com/~gray/talks/cern_2001.ppt
UDDI
Universal Description, Design, and Integration
DISCO
Discovery
WSDL
Contract Language
SOAP
XML & HTTP
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TerraService Architecture
Standard
Browsers
Map UI
Web Forms
Existing
DB Server
Map Server
Http Handler
705 m Rows
SQL 2000
TerraServer
Web Service
1.0 TB Db
Smart
Clients
SQL 2000
1.0 TB Db
ADO.NET
http://research.microsoft.com/~gray/talks/cern_2001.ppt
OLEDB
SQL 2000
1.0 TB Db
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Terra Services
Terra-Tile-Service
•
•
•
•
Query Gazetteer
Retrieve imagery
meta-data
Retrieve imagery
Simple Projection
conversions
Clients can present
TerraServer imagery
in new ways.
http://research.microsoft.com/~gray/talks/cern_2001.ppt
Landmark-Service
•
Geo-coded places,
e.g. Schools, Golf
Courses, Hospitals,
etc.
•
Place Polygons
e.g. Zip Codes, Cities,
etc.
allows “overlay”
information for
Terra-Tile-Service
applications
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Terra Services
Place Search
–
–
–
–
•
GetPlaceFacts
GetPlaceList
GetPlaceListInRect
CountPlacesInRect
Projection
Tile
–
–
–
–
–
–
GetAreaFromPt
GetAreaFromRect
GetAreaFromTileId
GetTileMetaFromLonLat
GetTileMetaFromTileId
GetTile (Image)
– ConvertLonLatToUtm
• Landmark
– ConvertUtmToLonLat
– GetLandmarkTypes
– ConvertLonLatTo
– CountOfLandmarkPointsByRect
NearestPlace
– GetLandmarkPointsByRect
– GetTheme
– CountOfLandmarkShapesByRec
– GetLatLonMetrics
– GetLandmarkShapesByRect
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http://terraservice.net
http://research.microsoft.com/~gray/talks/cern_2001.ppt
Soil Viewer Uses TerraService
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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Custom End Product
WebInterpretation
XML
Soil
Soil Report
Data Viewer
Map
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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What You Saw
• Converted a Web Server
–HTML get post
–Server returns pictures to people
• to a Web Service
–SOAP service
–returns XML self-describing data
–Application integrates data
(Agriculture and Geo data)
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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SkyServer
Collaborating with:
Alex Szalay, Peter Kunszt, Ani Thakar @ JHU
Robert Brunner, Roy Williams @ Caltech
George Djorgovski, Julian Bunn @ Caltech
FermiLab operates Sky Server
Compaq donated hardware
Microsoft donated software and money
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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Sky Server
– Like TerraServer pictures of the sky.
– But also LOTS of data on each object
So a
data mining web service
• Luminosity (multi-spectra), morphology, spectrum
• So, it is a data mining application
• Cross-correlation is challenging because
–Multi-resolution
–Data is dirty/fuzzy (error bars, cosmic rays, airplanes…)
•50 K Spectro Objects
–Time varying
•~ 100 attributes + 30 lines
+
http://research.microsoft.com/~gray/talks/cern_2001.ppt
•15M Photo Objects ~ 400 attributes
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Astronomy Data
•
•
•
•
•
In the “old days” astronomers took photos.
Starting in the 1960’s they began to digitize.
New instruments are digital (100s of GB/nite)
Detectors are following Moore’s law.
Data avalanche: double every year
1000
100
Courtesy
of
Alex
Szalay
10
1
0.1
1975
1970
http://research.microsoft.com/~gray/talks/cern_2001.ppt
1980
1985
1990
1995
2000
CCDs
Glass
Total area of 3m+
telescopes in the world in
m2, total number of CCD
pixels in megapixel, as a
function of time. Growth
over 25 years is a factor
of 30 in glass, 3000 in
pixels.
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Astronomy Data
• Astronomers have a few Petabytes now.
– 1 pixel (byte) / sq arc second ~ 4TB
– Multi-spectral, temporal, … → 1PB
• They mine it looking for
new (kinds of) objects or
more of interesting ones(quasars),
density variations in 400-D space
correlations in 400D space
•
•
•
•
•
Data doubles every year.
Data is public after a year.
So, 50% of the data is public.
Some have private access to 5% more data.
So: 50% vs 55% access for everyone
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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Astronomy Data
• But…..
• How do I get at that 50% of the data?
• Astronomers have culture of publishing.
– FITS files and many tools.
http://fits.gsfc.nasa.gov/fits_home.html
– Encouraged by NASA.
• Publishing data “details” is difficult.
Astronomers want to do it but it is VERY hard.
(What programs where used? what were the processing steps? How were errors treated?…)
• File is wrong abstraction.
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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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 (1 year 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.
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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Virtual Observatory
Golden Age of Mega-Surveys
• Many new surveys
•
•
MACHO
– multi-TB in size, 100 million objects or more
2MASS
– individual archives planned, or under way
DENIS
– Data publication an integral part of the survey SDSS
PRIME
– Software bill a major cost in the survey
DPOSS
GSC-II
Multi-wavelength view of the sky
COBE
– more than 13 wavelength coverage in 5 years
MAP
NVSS
Impressive early discoveries
FIRST
– finding exotic objects by unusual colors
GALEX
• L,T dwarfs, high-z quasars
ROSAT
– finding objects by time variability
OGLE ...
• gravitational micro-lensing
Slide courtesy of Alex Szalay, modified by jim
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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Virtual Observatory
Federating the Archives
• The next generation mega-surveys are different
–
–
–
–
top-down design
large sky coverage
sound statistical plans
well controlled/documented data processing
• Each survey has a publication plan
• Data mining will lead to stunning new discoveries
• Federating these archives
 Virtual Observatory
Slide courtesy of Alex Szalay
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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The Multiwavelength Crab Nebula
Crab star
1053 AD
Nova first sighted
1054 A.D. by
Chinese Astronomers
Now: Crab Nebula
X-ray,
optical,
infrared, and
radio
Slide courtesy of Robert Brunner @ CalTech.
http://research.microsoft.com/~gray/talks/cern_2001.ppt
50
Virtual Observatory and Education
• In the beginning science was empirical.
• Then theoretical branches evolved.
• Now, we have a computational branches.
– The computational branch has been simulation
– It is becoming data analysis/visualization
• The Virtual Observatory can be used to
– Teach astronomy:
make it interactive,
demonstrate ideas and phenomena
– Teach computational science skills
and the process of scientific discovery
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Sloan Digital Sky Survey
http://sdss.org/
• A group of astronomers has been building a telescope
(with 90M$ from Sloan Foundation, NSF, and a dozen universities).
for the last 12 years
• Now data is arriving:
– 250GB/nite (20 nights per year).
– 100 M stars, 100 M galaxies, 1 M spectra.
• Public data at http://sdss.org/
– 5% of the survey, 600 sq degrees, 15 M objects 60GB.
– This data includes most of the known high z quasars.
– It has a lot of science left in it but… that is just the start.
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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Demo
of
Sky
Server
Alex built SkyServer
(based on TerraServer design).
http://skyserver.sdss.org/
Demo:
famous places
navigator
data
shopping cart
spectrum
SQL?
?
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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Virtual Observatory Challenges
• Size : multi-Petabyte
40,000 square degrees is 2 Trillion pixels
– One band (at 1 sq arcsec)
– Multi-wavelength
– Time dimension
4 Terabytes
10-100 Terabytes
>> 10 Petabytes
– Need auto parallelism tools
• Unsolved Meta-Data problem
– Hard to publish data & programs
– Hard to find/understand data & programs
• Current tools inadequate
– new analysis & visualization tools
• Transition to the new astronomy
– Sociological issues
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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The Challenges
• How to federate the Archives to make a VO?
• The hope:
XML is the answer.
• The reality:
XML is syntax and tools:
FITS on XML will be good but…..
Explaining the data will still be very difficult.
• Define Astronomy Objects and Methods.
– Based on UDDI, WSDL, SOAP.
– Each archive is a service
• http://TerraService.net/ shows the idea.
– Working with Caltech (Brunner, Williams, Djorgovski, Bunn)
– But, how does data mining work?
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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Three Steps to a VO 0.01
• Get SDSS and Palomar online
– Alex Szalay, Jan Vandenberg, Ani Thakar….
– Roy Williams, Robert Brunner, Julian Bunn
• Do queries and crossID matches
with CalTech and SDSS to expose
– Schema, Units,…
– Dataset problems
– the typical use scenarios.
• Implement WebServices at CalTech and SDSS
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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Summary
• All information at your fingertips.
• How do we publish information
so that our agents can digest it?
• Example: TerraServer -> TerraService
• The Virtual Observatory Concept
– The Internet is worlds best telescope
• For astronomy
• For teaching astronomy and
• For teaching computational science
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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Outline
• Store Everything
• Online (Disk not Tape)
• In a Database
• A Federated DB
• Two Examples
http://research.microsoft.com/~gray/talks/cern_2001.ppt
59
No time for what follows
http://research.microsoft.com/~gray/talks/cern_2001.ppt
60
SDSS what I have been doing
• Work with Alex Szalay, Don Slutz, and
others to define 20 canonical queries
and
10 visualization tasks.
• Don Slutz did a first cut of the queries,
I’m continuing that work.
• Working with Alex Szalay on
building Sky Server and
making data it public
(send out 80GB SQL DBs)
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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Two kinds of SDSS data
• 15M Photo Objects ~ 400 attributes
20K
Spectra
with ~10
lines/
spectrum
http://research.microsoft.com/~gray/talks/cern_2001.ppt
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Spatial Data Access
(Szalay, Kunszt, Brunner)
http://www.sdss.jhu.edu/ look at the HTM link
• Implemented Hierarchical Triangular Mesh (HTM)
as table-valued function for spatial joins.
• Every object has a 20-deep Mesh ID.
• Given a spatial definition:
Routine returns up to 500 covering triangles.
• Spatial query is then up to 500 range queries.
• Very fast: 1,000s of triangles per second.
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The 20 Queries
Q11: Find all elliptical galaxies with spectra that have an
anomalous emission line.
Q12: Create a grided count of galaxies with u-g>1 and r<21.5
over 60<declination<70, and 200<right ascension<210,
on a grid of 2’, and create a map of masks over the
same grid.
Q13: Create a count of galaxies for each of the HTM triangles
which satisfy a certain color cut, like 0.7u-0.5g-0.2i<1.25
&& r<21.75, output it in a form adequate for
visualization.
Q14: Find stars with multiple measurements and have
magnitude variations >0.1. Scan for stars that have a
secondary object (observed at a different time) and
compare their magnitudes.
Q15: Provide a list of moving objects consistent with an
asteroid.
Q16: Find all objects similar to the colors of a quasar at
5.5<redshift<6.5.
Q17: Find binary stars where at least one of them has the
colors of a white dwarf.
Q18: Find all objects within 30 arcseconds of one another
that have very similar colors: that is where the color
ratios u-g, g-r, r-I are less than 0.05m.
Q19: Find quasars with a broad absorption line in their
spectra and at least one galaxy within 10 arcseconds.
Return both the quasars and the galaxies.
Q20: For each galaxy in the BCG data set (brightest color
galaxy), in 160<right ascension<170, -25<declination<35
Also some good queries at:
count of galaxies within 30"of it that have a photoz within
http://www.sdss.jhu.edu/ScienceArchive/sxqt/sxQT/Example_Queries.html
0.05 of that galaxy.
64
Q1: Find all galaxies without unsaturated pixels within 1' of a
given point of ra=75.327, dec=21.023
Q2: Find all galaxies with blue surface brightness between
and 23 and 25 mag per square arcseconds, and 10<super galactic latitude (sgb) <10, and declination
less than zero.
Q3: Find all galaxies brighter than magnitude 22, where the
local extinction is >0.75.
Q4: Find galaxies with an isophotal surface brightness (SB)
larger than 24 in the red band, with an ellipticity>0.5, and
with the major axis of the ellipse having a declination of
between 30” and 60”arc seconds.
Q5: Find all galaxies with a deVaucouleours profile (r¼ falloff
of intensity on disk) and the photometric colors
consistent with an elliptical galaxy. The deVaucouleours
profile
Q6: Find galaxies that are blended with a star, output the
deblended galaxy magnitudes.
Q7: Provide a list of star-like objects that are 1% rare.
Q8: Find all objects with unclassified spectra.
Q9: Find quasars with a line width >2000 km/s and
2.5<redshift<2.7.
Q10: Find galaxies with spectra that have an equivalent width
in Ha >40Å (Ha is the main hydrogen spectral line.)
http://research.microsoft.com/~gray/talks/cern_2001.ppt
An easy one
Q7: Provide a list of star-like objects that are 1% rare.
• Found 14,681 buckets,
first 140 buckets have 99%
time 104 seconds
• Disk bound, reads 3 disks at 68 MBps.
Select cast((u-g) as int) as ug,
cast((g-r) as int) as gr,
cast((r-i) as int) as ri,
cast((i-z) as int) as iz,
count(*)
as Population
from stars
where (u+g+r+i+z) < 150
group by
cast((u-g) as int), cast((g-r) as int),
cast((r-i) as int), cast((i-z) as int)
order by count(*)
http://research.microsoft.com/~gray/talks/cern_2001.ppt
65
Another easy one
Q15: Provide a list of moving objects consistent with an
asteroid.
• Looks hard but there are 5 pictures of the
object at 5 different times (colors) and so
can compute velocity.
• Image pipeline computes velocity.
• Computing it from the 5 color x,y would also
be fast
• Finds 2167 objects in 7 minutes, 70MBps.
select object_id,
-- return object ID
sqrt(power(rowv,2)+power(colv,2)) as velocity
from
sxPhotObj
-- check each object.
where (power(rowv,2) + power(colv, 2)) > 50
-- square of velocity
and rowv >= 0 and colv >=0
-- negative values indicate error
http://research.microsoft.com/~gray/talks/cern_2001.ppt
66
A Hard One
Q14: Find stars with multiple measurements
that have magnitude variations >0.1.
• This should work, but SQL Server does not
allow table values to be piped to tablevalued functions.
Returns a table of nearby objects
select S.object_ID, S1.object_ID
-- return stars that
from
Stars
S,
-- S is a star
getNearbyObjEq(s.ra, s.dec, 0.017) as N -- N within 1 arcsec (3 pixels)
of S.
Stars S1
-- N == S1 (S1 gets the colors)
where S.Object_ID < N.Object_ID
-- S1 different from S == N
and N.Type = dbo.PhotoType('Star')
-- S1 is a star (an optimization)
and N.object_ID = S1.Object_ID
-- N == S1
and ( abs(S.u-S1.u) > 0.1
-- one of the colors is different.
or abs(S.g-S1.g) > 0.1
or abs(S.r-S1.r) > 0.1
or abs(S.i-S1.i) > 0.1
or abs(S.z-S1.z) > 0.1
)
order by S.object_ID, S1.object_ID
-- group the answer by parent star.
http://research.microsoft.com/~gray/talks/cern_2001.ppt
67
A Hard one: Second Try
Q14: Find stars with multiple measurements that
have magnitude variations >0.1.
• Write a program with a cursor, ran for 2 days
-------------------------------------------------------------------------------- Table-valued function that returns the binary stars within a certain radius
-- of another (in arc-minutes) (typically 5 arc seconds).
-- Returns the ID pairs and the distance between them (in arcseconds).
create function BinaryStars(@MaxDistanceArcMins float)
returns @BinaryCandidatesTable table(
S1_object_ID bigint not null, -- Star #1
S2_object_ID bigint not null, -- Star #2
distance_arcSec float)
-- distance between them
as
begin
declare @star_ID bigint, @binary_ID bigint;-- Star's ID and binary ID
declare @ra float, @dec float;
-- Star's position
declare @u float, @g float, @r float, @i float,@z float; -- Star's colors
----------------Open a cursor over stars and get position and colors
declare star_cursor cursor
for select object_ID, ra, [dec], u, g, r, i, z from Stars;
open star_cursor;
while (1=1)
-- for each star
begin
-- get its attribues
fetch next from star_cursor into @star_ID, @ra, @dec, @u, @g, @r, @i, @z;
if (@@fetch_status = -1) break;
-- end if no more stars
insert into @BinaryCandidatesTable
-- insert its binaries
select @star_ID, S1.object_ID,
-- return stars pairs
sqrt(N.DotProd)/PI()*10800 -- and distance in arc-seconds
from getNearbyObjEq(@ra, @dec,
-- Find objects nearby S.
@MaxDistanceArcMins) as N, -- call them N.
Stars as S1
-- S1 gets N's color values
where @star_ID < N.Object_ID
-- S1 different from S
and N.objType = dbo.PhotoType('Star')
-- S1 is a star
and N.object_ID = S1.object_ID
-- join stars to get colors of S1==N
and (abs(@u-S1.u) > 0.1
-- one of the colors is different.
or abs(@g-S1.g) > 0.1
or abs(@r-S1.r) > 0.1
or abs(@i-S1.i) > 0.1
or abs(@z-S1.z) > 0.1
)
end;
-- end of loop over all stars
-------------- Looped over all stars, close cursor and exit.
close star_cursor;
-deallocate star_cursor;
return;
-- return table
end
-- end of BinaryStars
GO
select * from dbo.BinaryStars(.05)
http://research.microsoft.com/~gray/talks/cern_2001.ppt
68
A Hard one: Third
Try
Q14: Find stars with multiple measurements that
have magnitude variations >0.1.
• Use pre-computed neighbors table.
• Ran in 17 minutes, found 31k pairs.
==================================================================================
-- Plan 2: Use the precomputed neighbors table
select top 100 S.object_ID, S1.object_ID, -- return star pairs and distance
str(N.Distance_mins * 60,6,1) as DistArcSec
from
Stars
S,
-- S is a star
Neighbors N,
-- N within 3 arcsec (10 pixels) of S.
Stars
S1
-- S1 == N has the color attibutes
where S.Object_ID = N.Object_ID
-- connect S and N.
and S.Object_ID < N.Neighbor_Object_ID -- S1 different from S
and N.Neighbor_objType = dbo.PhotoType('Star')-- S1 is a star (an optimization)
and N.Distance_mins < .05
-- the 3 arcsecond test
and N.Neighbor_object_ID = S1.Object_ID -- N == S1
and (
abs(S.u-S1.u) > 0.1
-- one of the colors is different.
or abs(S.g-S1.g) > 0.1
or abs(S.r-S1.r) > 0.1
or abs(S.i-S1.i) > 0.1
or abs(S.z-S1.z) > 0.1
)
-- Found 31,355 pairs (out of 4.4 m stars) in 17 min 14 sec.
http://research.microsoft.com/~gray/talks/cern_2001.ppt
69
The Pain of Going Outside SQL
(its fortunate that all the queries are single statements)
• Count parent objects
• 503 seconds
for 14.7 M objects
in 33.3 GB
• 66 MBps
• IO bound (30% of one cpu)
• 100 k records/cpu sec
select count(*)
from
sxPhotoObj
where nChild > 0
http://research.microsoft.com/~gray/talks/cern_2001.ppt
•
•
•
•
•
Use a cursor
No cpu parallelism
CPU bound
6 MBps, 2.7 k rps
5,450 seconds (10x slower)
declare @count int;
declare @sum int;
set @sum = 0;
declare PhotoCursor cursor for select
nChild from sxPhotoObj;
open PhotoCursor;
while (1=1)
begin
fetch next from PhotoCursor into @count;
if (@@fetch_status = -1) break;
set @sum = @sum + @count;
end
close PhotoCursor;
deallocate PhotoCursor;
print 'Sum is: '+cast(@sum as varchar(12))70
Summary of Current Status
• All 20 queries run
(still checking science)
• Also 15 “user” queries
• Run times: on 3k$ PC
(2 cpu, 4 disk, 256MB)
cpu vs IO
1E+7
IO count
1E+6
1E+5
100 IOs/cpu sec
1E+4
1E+3
~100 IO/cpu sec
~5MB/cpu sec
1E+2
1E+1
1E+0
0.01
0.1
1.
10.
100.
1,000.
10,000.
CPU sec
10000
cpu
elapsed
1000
100
10
1
Q01
Q02:
Q03
Q04
Q05:
Q06
Q07
Q08
Q09
Q10
Q10A
Q11
Q12***
Q13
Q14
Q15
Q16
Q17
Q18
Q19*
Q20
QSX01
0.1
0.01
http://research.microsoft.com/~gray/talks/cern_2001.ppt
71
Summary of Current Status
• 16 of the queries are simple
• 2 are iterative, 2 complex
• Many are sequential
one-pass and two-pass over data
• Covering indices make scans run fast
• Table valued functions are wonderful
but limitations on parameters are a pain.
• Counting is VERY common.
• Binning (grouping by some set of attributes) is common
• Did not request cube, but that may be cultural.
http://research.microsoft.com/~gray/talks/cern_2001.ppt
72
Reflections on the 20 Queries
• Data loading/scrubbing is labor intensive & tedious
– AUTOMATE!!!
• This is 5% of the data, and some queries take an hour.
• But this is not tuned (disk bound).
• All queries benefit from parallelism (both disk and cpu)
(if you can state the query right, e.g. inside SQL).
• Parallel database machines will do great on this:
– Hash machines
– Data pumps
– See paper in word or pdf on my web site.
•
SQL looks good.
The answers, need visualization
http://research.microsoft.com/~gray/talks/cern_2001.ppt
73
Call to Action
• If you do data visualization: we need you
(and we know it).
• If you do databases:
here is some data you can practice on.
• If you do distributed systems:
here is a federation you can practice on.
• If you do astronomy educational outreach
here is a tool for you.
• The astronomy folks are very good, and very
smart, and a pleasure to work with, and
the questions are cosmic, so …
http://research.microsoft.com/~gray/talks/cern_2001.ppt
74