Databases Meet Astronomy
a db view of astronomy data
Jim Gray
Microsoft Research
Collaborating with:
Alex Szalay, Peter Kunszt, Ani Thakar @ JHU
Robert Brunner, Roy Williams @ Caltech
George Djorgovski, Julian Bunn @ Caltech
1
Outline
• Astronomy data
• The Virtual Observatory Concept
• The Sloan Digital Sky Survey
2
Computational
Science
• Traditional Empirical Science
– Scientist gathers data by direct
observation
– Scientist analyzes data
• Computational Science
– Data captured by instruments
Or data generated by simulator
– Processed by software
– Placed in a database
– Scientist analyzes database
3
Astronomy Data Growth
•
•
•
•
•
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 2 years
1000
100
Courtesy
of
Alex
Szalay
10
1
0.1
1970
1975
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, 30004 in
pixels.
Universal Access to 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 400-D space
•
•
•
•
•
Data doubles every 2 years.
Data is public after 2 years.
So, 50% of the data is public.
Some have private access to 5% more data.
So: 50% vs 55% access for everyone
5
Astronomy Data Publishing and Access
• 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.
• But, data “details” are hard to document.
Astronomers want to do it but it is VERY hard.
(What programs where used? What were the processing steps? How were errors treated?…)
• The answer is 42.
6
Astronomy Data Access
• And by the way, few astronomers
have a spare petabyte of storage in their pocket.
• But that is getting better:
- Public SDSS is 5% of total
- Public SDSS is ~50GB + 500GB of images (5TB raw)
- “data” fits on a 200$ disk, 2000$ computer.
- (more on that later).
- THESIS:
Challenging problems are
publishing data
providing good query & visualization tools
7
Time and Spectral Dimensions
The Multiwavelength Crab Nebulae
Crab star
1053 AD
X-ray,
optical,
infrared, and
radio
views of the nearby
Crab Nebula, which is
now in a state of
chaotic expansion after
a supernova explosion
first sighted in 1054
A.D. by Chinese
Astronomers.
9
Slide courtesy of Robert Brunner @ CalTech.
Even in “optical” images are very different
Optical Near-Infrared Galaxy Image Mosaics
BJ
RF
IN
J
H
K
BJ
RF
IN
J
H
K
11
Slide courtesy of Robert Brunner @ CalTech.
Exploring Parameter Space
Manual or Automatic Data Mining
• There is LOTS of data
– people cannot examine most of it.
– Need computers to do analysis.
• Manual or Automatic Exploration
– Manual: person suggests hypothesis,
computer checks hypothesis
– Automatic: Computer suggests hypothesis
person evaluates significance
• Given an arbitrary parameter space:
–
–
–
–
–
–
Data Clusters
Points between Data Clusters
Isolated Data Clusters
Isolated Data Groups
Holes in Data Clusters
Isolated Points
Nichol et al. 2001
Slide courtesy of and adapted from
12
Robert Brunner @ CalTech.
Outline
• Astronomy data
• The Virtual Observatory Concept
• The Sloan Digital Sky Survey
15
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:
16
links objects and data to literature on them.
The Age of Mega-Surveys
• Large number of new surveys
– multi-TB in size, 100 million objects or more
– Data publication an integral part of the survey
– Software bill a major cost in the survey
• 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
MACHO
2MASS
DENIS
SDSS
PRIME
DPOSS
GSC-II
COBE
MAP
NVSS
FIRST
GALEX
ROSAT
OGLE ...
• Federating these archives
Slide courtesy of Alex17Szalay,
 Virtual Observatory
modified by Jim
Virtual Observatory Challenges
• Size : multi-Petabyte
40,000 square degrees is 2 Trillion pixels
– One band (at 1 sq arcsec)
4 Terabytes
– Multi-wavelength
10-100 Terabytes
– Time dimension
>> 10 Petabytes
– Need auto parallelism tools
• Unsolved MetaData problem
– Hard to publish data & programs
– How to federate Archives
– Hard to find/understand data & programs
• Current tools inadequate
– new analysis & visualization tools
– Data Federation is problematic
• Transition to the new astronomy
– Sociological issues
18
3-steps to Virtual Observatory
• Get SDSS and Palomar online
– Alex Szalay, Jan Vandenberg, Ani Thacker….
– Roy Williams, Robert Brunner, Julian Bunn
• Do some local queries and crossID matches
with CalTech and SDSS to expose
– Schema, Units,…
– Dataset problems
– the typical use scenarios.
• Implement Web Service that lets you get data from
both CalTech and SDSS (WSDL/SOAP/…) 19
Demo of Sky Server
Alex Szalay of Johns Hopkins built SkyServer (based on TerraServer design).
http://skyserver.fnal.gov/
21
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
22
What Next?
(after the data online, after the web servers)
• How to federate the Archives to make a VO?
• Send XML: a non-answer
equivalent to “send Unicode”
• “Bytes” is the wrong abstraction
Publish Methods on Objects.
• Define a set of Astronomy Objects and methods.
– Based on UDDI, WSDL, SOAP.
– Each archive is a web service
• We have started this with TerraService
– http://TerraService.net/ shows the idea.
– Working with Caltech (Brunner, Williams, Djorgovski, Bunn)
and JHU (Szalay et al) on this
23
SkyServer as a WebServer
WSDL+SOAP
just add details 
Archive ss = new VOService(SkyServer);
Attributes A[] = ss.GetObjects(ra,dec,radius)
…
?? What are the objects (attributes…)?
?? What are the methods (GetObjects()...)?
?? Is the query language SQL or Xquery or what?
24
Outline
• Astronomy data
• The Virtual Observatory Concept
• The Sloan Digital Sky Survey
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Sloan Digital Sky Survey
http://www.sdss.org/
• For the last 12 years a group of astronomers has
been building a telescope (with funding from
Sloan Foundation, NSF, and a dozen universities).
90M$.
• Last year was engineer, calibrate, commission
They are making the calibration data public.
– 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.
• Now the data is arriving:
– 250GB/nite (20 nights per year).
– 100 M stars, 100 M galaxies, 1 M spectra.
• http://www.sdss.org/ and http://www.sdss.jhu.edu/
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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)
27
Two kinds of SDSS data
• 15M Photo Objects ~
400 attributes
50K
Spectra with
~10 lines/
spectrum
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Data Loading
• Load is JavaScript
of DTS steps
• Web ops interface
• A workflow system
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Data Loading
• Data ingest and scrubbing is where I spend most
of my time.
• It is 99% perspiration
– Test data quality
– Chase down
• Other major task is data documentation
– Explain the data
– Explain the schema and functions.
• If we supported users, …
– Support would be the major ongoing task.
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The 20 Queries
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.)
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 count of
galaxies within 30"of it that have a photoz within 0.05 of that
galaxy.
Also some good queries at:
http://www.sdss.jhu.edu/ScienceArchive/sxqt/sxQT/Example_Queries.html
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Spatial Data Access
(Szalay, Kunszt, Brunner) http://www.sdss.jhu.edu/
• Made Hierarchical Triangular Mesh (HTM) a
table-valued function for spatial joins.
• Every object has a 20-deep Mesh ID.
2
2,3,0
2,0
2,3,1
2,3,2
2,1
2,3,3
2,2
2,3
• Given a spatial definition:
Routine returns up to ~10 covering triangles.
• Spatial query is then up to ~10 range queries.
• Very fast: 10,000 triangles / second / cpu.32
HTM and SQL
• Spatial spec in http://www.sdss.jhu.edu/htm/
• List of triangles out (about 10-20 range queries)
• Table valued function, then geometry rejects false positives
Use SkyServerV3
GO
-- show an HTM ID
select dbo.fHTM_To_String(dbo.fHTM_Lookup('J2000 20 185 0'))
Go
-- show triangles covering a circle
select dbo.fHTM_To_String(HTMIDstart) as start,
dbo.fHTM_To_String(HTMIDend) as stop
from dbo.fHTM_Cover('CIRCLE J2000 12 185 0 5 ')
GO
-- Show the spatial join
declare @shift real
set @shift = CONVERT(int,POWER(4.,20-12)) -- 4 = 2^2 and 2 bits per htm level
select ObjID
from PhotoObj as P,
dbo.fHTM_Cover('CIRCLE J2000 12 185 0 1 ') as C
where P.htmID between C.HTMIDstart*@shift and C.HTMIDend*@shift
GO
-- show a user-level function.
select ObjID
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from dbo.fGetNearbyObjEq(185,0,1)
A Simple Query Using HTM tables
34
An easy query
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
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(*)
35
Another easy one
Q15: Provide a list of moving objects consistent with an asteroid.
• Sounds 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
36
Q15: Fast Moving Objects
• Find near earth asteroids:
SELECT r.objID as rId, g.objId as gId, r.run, r.camcol, r.field as field, g.field as gField,
r.ra as ra_r, r.dec as dec_r, g.ra as ra_g, g.dec as dec_g,
sqrt( power(r.cx -g.cx,2)+ power(r.cy-g.cy,2)+power(r.cz-g.cz,2) )*(10800/PI()) as distance
FROM PhotoObj r, PhotoObj g
WHERE
r.run = g.run and r.camcol=g.camcol and abs(g.field-r.field)<2 -- the match criteria
-- the red selection criteria
and ((power(r.q_r,2) + power(r.u_r,2)) > 0.111111 )
and r.fiberMag_r between 6 and 22 and r.fiberMag_r < r.fiberMag_g and r.fiberMag_r < r.fiberMag_i
and r.parentID=0 and r.fiberMag_r < r.fiberMag_u and r.fiberMag_r < r.fiberMag_z
and r.isoA_r/r.isoB_r > 1.5 and r.isoA_r>2.0
-- the green selection criteria
and ((power(g.q_g,2) + power(g.u_g,2)) > 0.111111 )
and g.fiberMag_g between 6 and 22 and g.fiberMag_g < g.fiberMag_r and g.fiberMag_g < g.fiberMag_i
and g.fiberMag_g < g.fiberMag_u and g.fiberMag_g < g.fiberMag_z
and g.parentID=0 and g.isoA_g/g.isoB_g > 1.5 and g.isoA_g > 2.0
-- the matchup of the pair
and sqrt(power(r.cx -g.cx,2)+ power(r.cy-g.cy,2)+power(r.cz-g.cz,2))*(10800/PI())< 4.0
and abs(r.fiberMag_r-g.fiberMag_g)< 2.0
• Finds 3 objects in 11 minutes
• Ugly,
but consider the alternatives
(c programs an files and…)
–
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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 table-valued
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.
41
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)
42
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.
43
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
•
•
•
•
•
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;
44
print 'Sum is: '+cast(@sum as varchar(12))
Performance
• Run times: on 3k$ PC
(2 cpu, 4 disk, 256MB)
• Some take 10 minutes
• Some take 1 minute
• Most take 15 seconds.
• Ghz processors are fast!
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.
100.
1,000.
10,000.
CPU sec
– (10 mips/IO, 200 ins/IO)
10000
10.
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*
0.1
45
0.01
Q20
QSX01
Summary of Queries
• 16 of the queries are simple
• 2 are iterative, 2 are unknown
• 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.
46
Reflections on the 20 Queries
• Data loading/scrubbing is labor intensive & tedious
– AUTOMATE!!!
• This is 5% of the data, and some queries take 1/2 hour.
• But this is not tuned (disk bound).
• All queries benefit from parallelism (both disk and cpu)
(if you can state the query inside SQL).
• Parallel database machines will do well on this:
– Hash machines
– Data pumps
– See paper in word or pdf on my web site.
• Conclusion: SQL looks good.
Once you get the answers,
you need visualization
47
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 …
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