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http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 1
Building Petabyte Databases SQL+.NetBuilding Petabyte Databases SQL+.Net
Jim GrayJim GrayMicrosoft research
http://research.microsoft.com/~gray/talks
VSlive! SQL To The Max 15 February 2002 @ San Francisco
Objects are closer than they appear in the mirror
Objects are closer than they appear in the mirror
PhotoServer:Tom BarclayYa Feng Sung
TerraServer Tom Barclay USGSSkyServer Alex Szalay Ani Thakar Peter Kunszt Tanu Malik Jordan Raddick Don Slutz Jan vandenBergSome Slides
Robert Brunner
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 2
SQLserver™: Past and Future HistorySQLserver™: Past and Future History
SQL 2000– SQL– XML– Replication x, y, z,
…– Auto Admin– Data
Transformation– OLAP – Data Mining– Text Indexing– English Query– Partitioning– Clusters
SQL 200x– Beta late this year
– Trustworthy:AvailabilityPrivacySecurity
– CLR (objects)
– XML (xQuery,….)
– Unify Files & Records
– Manageability,
– Scalability
.Net – XML schema support
– updategrams
– More xPath support
– SPs and templates as web services
WebReference.soap proxy = new WebReference.soap(); object[] results1 = proxy.StoredProcedure
(inParam, ref inoutParam, out returnValue); object[] results2 = proxy.Template(inParam);
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 3
OutlineOutline
We will be able to store everything,– How do we represent it? (objects)– How will we find it (aka: who cares?)
PhotoServer: Objects vs records vs files,– XML++ gives us portable objects.– Similarity search: better than nothing!
Scalability: a solved problem,– but… Trustworthy & Manageable is not.
TerraServer and TerraService– Why put everything in the database?– A prototypical Web Service.
SkyServer and the World Wide Telescope – Data Mining science data– Serving Windows/Macintosh/Unix clients with .Net– Federating Archives with .Net
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 4
Record Everything? What’s that?Record Everything? What’s that?
Disks will get 100x to 1,000x more capacity– 10x to 30x more bandwidth.
Other technologies in the wings:– mram,mems, …
The 20TB … 200TB disk drive!– Library of Congress (books)
– A billion photos
– 2…20 years of video (continuous)
Yotta
Zetta
Exa
Peta
Tera
Giga
Mega
KiloA BookA Book
.Movie
All Books MultiMedia
Everything!
Recorded
A Photo
All LoC books(words)
See Mike Lesk: How much information is there: http://www.lesk.com/mlesk/ksg97/ksg.htmlSee Lyman & Varian: How much informationhttp://www.sims.berkeley.edu/research/projects/how-much-info/
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 5
Why Put Everything in Cyberspace?Why Put Everything in Cyberspace?
Low rentmin $/byte
Shrinks timenow or later
Shrinks spacehere or there
Automate processingknowbots
Point-to-Point OR Broadcast
Imm
edia
te O
R T
ime
Del
ayed
LocateProcessAnalyzeSummarize
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 6
Gordon Bell’s shoebox: Scans 20 k “pages” tiff@ 300 dpi 1 GB Music: 2 k “tacks” 7 GB Photos: 13 k images 2 GB Video: 10 hrs 3 GB Docs: 3 k ppt, word,.. 2 GB Mail: 50 k messages 2 GB
16 GB
Most storage is personalMost storage is personal
90% of disks are IDE/ATA85% of bytes are
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 7
How will we find it?How will we find it?Put everything in the DB (and index it)Put everything in the DB (and index it)
SQLSQL
More than a file system Unifies data and meta-
dataSimpler to manage Easier to subset and reorganize Set-oriented access Allows online updates Automatic indexing Automatic replication
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 8
How do we represent it How do we represent it to the outside world?to the outside world?
File metaphor too primitive: just a blob Table metaphor too primitive: just
records Need Metadata describing data context
– Format– Providence (author/publisher/ citations/…)– Rights– History– Related documents
In a standard format XML and XML schema DataSet is great example of this World is now defining standard schemas
schema
Data ordifgram
<?xml version="1.0" encoding="utf-8" ?>
- <DataSet xmlns="http://WWT.sdss.org/">
- <xs:schema id="radec" xmlns="" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:msdata="urn:schemas-microsoft-com:xml-msdata">
<xs:element name="radec" msdata:IsDataSet="true">
<xs:element name="Table">
<xs:element name="ra" type="xs:double" minOccurs="0" />
<xs:element name="dec" type="xs:double" minOccurs="0" /> …
- <diffgr:diffgram xmlns:msdata="urn:schemas-microsoft-com:xml-msdata" xmlns:diffgr="urn:schemas-microsoft-com:xml-diffgram-v1">
- <radec xmlns="">
- <Table diffgr:id="Table1" msdata:rowOrder="0">
<ra>184.028935351008</ra>
<dec>-1.12590950121524</dec>
</Table>
…
- <Table diffgr:id="Table10" msdata:rowOrder="9">
<ra>184.025719033547</ra>
<dec>-1.21795827920186</dec>
</Table>
</radec>
</diffgr:diffgram>
</DataSet>
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 9
There is a problem: There is a problem: Need Standard Data AND MethodsNeed Standard Data AND Methods
XML data is GREAT!!!!– XML documents are portable objects– XML documents are complex objects– WSDL defines the methods on objects (the class)
But will all the implementations match?– Think of UNIX or SQL or C or…
We need conformance tests. That’s why Web Services Interoperability
is so important. http://www.ws-i.org/
Niklaus Wirth: Algorithms + Data Structures = Programs
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 10
OutlineOutline
We will be able to store everything,– How do we represent it? (objects)– How will we find it (aka: who cares?)
PhotoServer: Objects vs records vs files,– XML++ gives us portable objects.– Similarity search: better than nothing!
Scalability: a solved problem,– but… Trustworthy & Manageable is not.
TerraServer and TerraService– Why put everything in the database?– A prototypical Web Service.
SkyServer and the World Wide Telescope – Data Mining science data– Serving Windows/Macintosh/Unix clients with .Net– Federating Archives with .Net
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 11
SQL(for xml)
TemplatesSchema
PhotoServer: Managing PhotosPhotoServer: Managing Photos
Load all photos into the database Annotate the photos View by various attributes Do similarity Search Use XML for interchange Use dbObject, Template for access
DOM
SQL, Templates, XML data
XML datasets & mime data
IISjScript
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 12
How Similarity Search WorksHow Similarity Search Works
For each picture Loader– Inserts thumbnails
– Extracts 270 Features into a blob
When looking for similar picture– Scan all photos comparing features
(dot product of vectors)
– Sort by similarity
Feature blob is an array– Today I fake the array with functions and cast
cast(substring(feature,72,8) as float)
– When SQL Server gets C#, we won’t have to fake it.
– And… it will run 100x faster (compiled managed code).
Idea pioneered by IBM Research,we use a variant by MS Beijing Research.
No black squares20% orange
…etc
many black squares10% orange
…etc
72% match 27% match
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 13
Things I LearnedThings I Learnedfrom PhotoServerfrom PhotoServer
Data:– XML data sets are a universal way to represent
answers
– XML data sets minimize round trips: 1 request/response
Search– It is BEST to index
– You can put objects and attributes in a row (SQL puts big blobs off-page)
– If you can’t index, You can extract attributes and quickly compare
– SQL can scan at 2M records/cpu/second
– Sequential scans are embarrassingly parallel.
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 14
OutlineOutline
We will be able to store everything,– How do we represent it? (objects)– How will we find it (aka: who cares?)
PhotoServer: Objects vs records vs files,– XML++ gives us portable objects.– Similarity search: better than nothing!
Scalability: a solved problem,– but… Trustworthy & Manageable is not.
TerraServer and TerraService– Why put everything in the database?– A prototypical Web Service.
SkyServer and the World Wide Telescope – Data Mining science data– Serving Windows/Macintosh/Unix clients with .Net– Federating Archives with .Net
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 15
Big!Big! Servers Servers
ScaleUP: a BIG box
– SMP (32 cpus)
– 64 bit
ScaleOut: computing by the slice– 6 years ago: 8ktpmC, today 750ktpmC– SQL Server is #1, #2, #3 (Windows is best DB2 platform
too)
VLDB Management Availability:
– Clusters, remote logging, replication
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 16
TPC measures peak performance and TPC measures peak performance and Price/PerformancePrice/Performance
Rank Company System tpmC price/tpmC Database OS TP Mon Date
1 ProLiant DL760-900-256P
709,220 14.96US$
Microsoft SQL Server 2000 Enterprise
Microsoft Windows 2000
Advanced
Microsoft COM+
09/19/01
2 IBM
eSeries370 c/s
688,220 22.58US$ Microsoft SQL Server 2000
Microsoft Windows 2000
Datacenter
Microsoft COM+
04/10/01
3 ProLiant DL760-900-192P
567,882 14.04US$
Microsoft SQL Server 2000 Enterprise
Microsoft Windows 2000
Advanced
Microsoft COM+
09/19/01
7 HP HP 9000 Superdome
389,435 21.24US$Oracle 9i Enterprise
HP UX 11.i 64-bit BEA
Tuxedo6.4 12/21/01
14 Unisys
e-@ction Enterprise
Server ES7000
165,219 21.33US$
Microsoft SQL Server 2000 Enterprise
Microsoft Windows 2000 Datacenter LE
Microsoft COM+
09/19/01
32x8 900Mhz Xenon256GB ram59 TB disk
32 900Mhz Xeon 64GB ram 15TB disk
SQL Server always had best price Performance Now best of both (using scaleout) SMP performance also impressive
Source: http://www.tpc.org/tpcc/results/tpcc_perf_results.asp
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 17
Scale OutScale Out: Buy Computing by the Slice: Buy Computing by the Slice709,202 tpmC! == 709,202 tpmC! == 1 Billion transactions/day1 Billion transactions/day
Slice: 8cpu, 8GB, 100 disks (=1.8TB) 20ktpmC per slice, ~300k$/slice
clients and 4 DTC nodes not shown
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 18
ScaleUp: A Very Big System! ScaleUp: A Very Big System!
UNISYS Windows 2000 Data Center Limited Edition
32 cpus on 32 GB of RAM and 1,061 disks (15.5 TB) Will be helped by 64bit addressing
24fiber
channel
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 19
OutlineOutline
We will be able to store everything,– How do we represent it? (objects)– How will we find it (aka: who cares?)
PhotoServer: Objects vs records vs files,– XML++ gives us portable objects.– Similarity search: better than nothing!
Scalability: a solved problem,– but… Trustworthy & Manageable is not.
TerraServer and TerraService– Why put everything in the database?– A prototypical Web Service.
SkyServer and the World Wide Telescope – Data Mining science data– Serving Windows/Macintosh/Unix clients with .Net– Federating Archives with .Net
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 20
TerraServer – A SQL poster childTerraServer – A SQL poster child http://TerraServer.HomeAdvisor.Microsoft.comhttp://TerraServer.HomeAdvisor.Microsoft.com//
3 x 2 TB databases 18TB disk
tri-plexed (=6TB)
3 + 1 Cluster 99.96% uptime 1B page views
5B DB queries Now a .NET
web service
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 21
Image DataImage Data
USGS Aerial photos “DOQ”USGS Topo Maps
EncartaVirtualGlobe
1 Km resolution
100 % WorldCoverage
All in the database 200x200 pixel tiles compressed
Spatial access z-Tranform Btree
12 TB95 % U.S. Coverage
1 m resolution
1 TB100% U.S. Coverage
2 m resolution
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 22
TerraServer Traffic & Database GrowthTerraServer Traffic & Database Growth
Jan 2002Jan 2002
SQL 7.0SQL 7.0
1.0 TB Db1.0 TB Db
SQL 2000SQL 2000
1.0 TB Db1.0 TB Db
SQL 2000SQL 2000
1.0 TB Db1.0 TB Db
SQL 2000SQL 2000
1.0 TB Db1.0 TB Db
SQL 2000SQL 2000
2.0 TB Db2.0 TB Db
SQL 2000SQL 2000
2.0 TB Db2.0 TB Db
SQL 2000SQL 2000
2.0 TB Db2.0 TB Db
1 Server / Win NT 4.0 EE1 Server / Win NT 4.0 EE 22ndnd Server / Win 2k DataCenter Server / Win 2k DataCenter 4 Node / Win2k Datacenter 4 Node / Win2k Datacenter Failover ClusterFailover Cluster
SQL 7.0SQL 7.0
1.0 TB Db1.0 TB Db
217 m Rows217 m Rows
SQL 7.0SQL 7.01 Server1 Server1.5 TB Db1.5 TB Db
SQL 2000SQL 20001 Server1 Server.8 TB Db.8 TB Db
298 m Rows298 m Rows
SQL 7.0SQL 7.0.75 TB Db.75 TB Db
173 m Rows173 m Rows
678 m Rows678 m Rows
SQL 2000SQL 2000
.8 TB Db.8 TB Db
231 m Rows231 m Rows
900 m Rows900 m Rows
Sessions Page Views Image Tiles Db Queries Bytes Xfered
Average Day
44,320879,720
3,786,5514,566,024
59 GB
Peak Day
277,292
12,388,10410,475,674
163 GB
2,401,209
1998 -2001
44,851,547890,277087
3,831,989,8874,620,815,913
59 TB
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 23
HardwareHardware
SQL\Inst1SQL\Inst1
SQL\Inst2SQL\Inst2
SQL\Inst3SQL\Inst3
SpareSpare
F GLKP Q
E EJ JO O
IHM NR S
22002200 22002200 22002200
220022002200220022002200
22002200 22002200 22002200
One SQL database per rackOne SQL database per rackEach rack contains 4.5 tbEach rack contains 4.5 tb261 total drives / 13.7 TB total261 total drives / 13.7 TB total
Meta DataMeta DataStored on 101 GBStored on 101 GB““Fast, Small Disks”Fast, Small Disks”(18 x 18.2 GB)(18 x 18.2 GB)
Imagery DataImagery DataStored on 4 339 GBStored on 4 339 GB““Slow, Big Disks”Slow, Big Disks”(15 x 73.8 GB)(15 x 73.8 GB)
To Add 90 72.8 GBTo Add 90 72.8 GBDisks in Feb 2001Disks in Feb 2001to create 18 TB SANto create 18 TB SAN
8 Compaq DL360 “Photon” Web Servers8 Compaq DL360 “Photon” Web Servers
Fiber SANFiber SANSwitchesSwitches
4 Compaq ProLiant 8500 Db Servers4 Compaq ProLiant 8500 Db Servers
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 24
TerraServer Lessons LearnedTerraServer Lessons Learned Hardware is 5 9’s (with clustering) Software is 5 9’s (with clustering) Admin is 4 9’s (offline maintenance) Network is 3 9’s (mistakes, environment)
Simple designs are best 10 TB DB is management limit
1 PB = 100 x 10 TB DBthis is 100x better than 5 years ago.
Minimize use of tape– Backup to disk (snapshots)– Portable disk TBs
9 9 9 999 9 9 999 9 999 9 9
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 25
TerraServiceTerraServicehttp://TerraService.Net/http://TerraService.Net/
Added .NET web services to TerraServer– A great way to learn what Web
Services are
– And what .Net is.
Image server– Gives arbitrary rectangle/zoom
of US
– Overlays features (hospitals, schools,..)
Census service You can use it in your app. USDA is using it today.
Demo Tour API Demo map maker Mention location and census services
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 26
OutlineOutline
We will be able to store everything,– How do we represent it? (objects)– How will we find it (aka: who cares?)
PhotoServer: Objects vs records vs files,– XML++ gives us portable objects.– Similarity search: better than nothing!
Scalability: a solved problem,– but… Trustworthy & Manageable is not.
TerraServer and TerraService– Why put everything in the database?– A prototypical Web Service.
SkyServer and the World Wide Telescope – Data Mining science data– Serving Windows/Macintosh/Unix clients with .Net– Federating Archives with .Net
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 27
Computational Science Computational Science The Third Science Branch is EvolvingThe Third Science Branch is Evolving
In the beginning science was empirical. Then theoretical branches evolved. Now, we have computational branches.
– Has primarily been simulation– Growth area data analysis/visualization
of peta-scale instrument data.
Computational Science– Data captured by instruments
Or data generated by simulator– Processed by software– Placed in a database / files– Scientist analyzes database / files
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 28
Exploring Parameter SpaceExploring Parameter SpaceManual or Automatic Data MiningManual 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. 2001Slide courtesy of and adapted from Robert Brunner @ CalTech.
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 29
What’s needed?What’s needed?(not drawn to scale)(not drawn to scale)
Scientists Miners
ToolsPlumbersDatabases to
Store DataAnd
Execute Queries
Science Data & Questions
Question & AnswerVisualization
Data Mining Algorithms
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 30
Some science is hitting a wallSome science is hitting a wallFTPFTP and and GREPGREP are not adequate 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 ~10,000 disks
At some point you need indices to limit searchparallel data search and analysis
This is where databases can help Goal Make it easy to
– Publish: Record structured data– Find: Find data anywhere in the network
Get the subset you need– Explore datasets interactively
You can FTP 1 MB in 1 sec You can FTP 1 GB / min (= 1 $/GB)
… 2 days and 1K$ … 3 years and 1M$
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 31
Web Services are The KeyWeb Services are 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
Yourprogram
DataIn your address
space
Web Service
soap
object
in xml
Yourprogram Web
Server
http
Web
page
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 32
Federation
Data Federations of Data Federations of Web ServicesWeb 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
– A common global schema
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 33
Why Astronomy Data?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 timesFederation is a goalThe questions are interesting
–How did the universe form?
There is a lot of it (petabytes)
IRAS 100
ROSAT ~keV
DSS Optical
2MASS 2
IRAS 25
NVSS 20cm
WENSS 92cm
GB 6cm
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 34
Web Services & Grid Enable Virtual ObservatoryWeb Services & Grid Enable Virtual Observatoryhttp://www.astro.caltech.edu/nvoconf/http://www.astro.caltech.edu/nvoconf/
http://www.voforum.org/http://www.voforum.org/
The Internet will be 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.
W3C & IETF standards Provide – Naming
– Authorization / Security / Privacy
– Distributed ObjectsDiscovery, Definition, Invocation, Object Model
– Higher level services: workflow, transactions, DB,..
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 35
Steps to Virtual Observatory PrototypeSteps to Virtual Observatory Prototype
Define a set of Astronomy Objects and methods.– Based on UDDI, WSDL, XSL, SOAP, dataSet
Use them locally to debug ideas – Schema, Units,…– Dataset problems– Typical use scenarios.
Federate different archives – Each archive is a web service– Global query tool accesses them
Working on this plan with– Sloan Digital Sky Survey and CalTech/Palomar.
Especially Alex Szalay et. al. at JHU
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 36
Sloan Digital Sky Survey Sloan Digital Sky Survey http://www.sdss.org/ http://www.sdss.org/
For the last 12 years astronomers have been building a telescope (with funding from Sloan Foundation, NSF, and a dozen universities). 90M$.
Y2000: engineer, calibrate, commission: now public data.– 5% of the survey, 600 sq degrees, 15 M objects
60GB, ½ TB raw.– This data includes most of the known high z quasars.– It has a lot of science left in it but….
New the data is arriving: – 250GB/nite (20 nights per year) = 5TB/y.– 100 M stars, 100 M galaxies, 1 M spectra.
http://www.sdss.org/
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 37
Demo of Sky ServerDemo of Sky Server
http://skyserver.sdss.org/
Demo sky serverDemo ExplorerExplain need for Unix/Mac clientsDemo Java SQLQA?
Talk about federation plan.
Work is product of Alex Szalay @ Johns HopkinsTanu Malik did SQLQA.
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 38
Two kinds of SDSS data in an SQL DBTwo kinds of SDSS data in an SQL DB(objects and images all in DB)(objects and images all in DB)
15M Photo Objects ~ 400 attributes
50K Spectra with ~30 lines/spectrum
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 39
Spatial Data Access – SQL extensionSpatial Data Access – SQL extension(Szalay, Kunszt, Brunner) http://www.sdss.jhu.edu/htm(Szalay, Kunszt, Brunner) http://www.sdss.jhu.edu/htm
Added Hierarchical Triangular Mesh (HTM) table-valued function for spatial joins.
Every object has a 20-deep Mesh ID.
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. Based onSQL Server Extended Stored
Procedure
2
2,2
2,1
2,0
2,3
2,3,0
2,3,12,3,2 2,3,3
2
2,2
2,1
2,0
2,32,2
2,1
2,0
2,3
2,3,0
2,3,12,3,2 2,3,3
2,3,0
2,3,12,3,2 2,3,3
2
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 40
Data LoadingData Loading
JavaScript of DB loader (DTS) Web ops interface & workflow system Data ingest and scrubbing is major effort
– Test data quality
– Chase down bugs / inconsistencies
Other major task is data documentation– Explain the data
– Explain the schema and functions.
If we supported users, …
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 41
Scenario Design Scenario Design Astronomers proposed 20 questions
– Typical of things they want to do– Each would require a week of programming in tcl / C++/
FTP
Goal, make it easy to answer questions DB and tools design motivated by this goal
– Implemented utility procedures– JHU Built GUI for Linux clients
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.
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://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 43
An Easy OneAn Easy OneQ15: Find asteroids Q15: Find asteroids
Sounds hard but there are 5 pictures of the object at 5 different times (color filters) and so can “see” velocity.
Image pipeline computes velocity. Computing it from the 5 color x,y would also be fast Finds 1,303 objects in 3 minutes, 140MBps.
(could go 2x faster with more disks)
select objId, dbo.fGetUrlEq(ra,dec) as url --return object ID & url sqrt(power(rowv,2)+power(colv,2)) as velocity from photoObj -- check each object.where (power(rowv,2) + power(colv, 2)) -- square of velocity
between 50 and 1000 -- huge values =error
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 44
Find near earth asteroids:
Finds 3 objects in 11 minutes– (or 52 seconds with an index)
Ugly, but consider the alternatives (c programs an files and…)
–
Q15: Fast Moving ObjectsQ15: Fast Moving Objects
SELECT r.objID as rId, g.objId as gId, dbo.fGetUrlEq(g.ra, g.dec) as url
FROM PhotoObj r, PhotoObj gWHERE r.run = g.run and r.camcol=g.camcol
and abs(g.field-r.field)<2 -- nearby-- the red selection criteriaand ((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_iand r.parentID=0 and r.fiberMag_r < r.fiberMag_u and r.fiberMag_r < r.fiberMag_zand r.isoA_r/r.isoB_r > 1.5 and r.isoA_r>2.0-- the green selection criteriaand ((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_iand g.fiberMag_g < g.fiberMag_u and g.fiberMag_g < g.fiberMag_zand g.parentID=0 and g.isoA_g/g.isoB_g > 1.5 and g.isoA_g > 2.0-- the matchup of the pairand sqrt(power(r.cx -g.cx,2)+ power(r.cy-g.cy,2)+power(r.cz-g.cz,2))*(10800/PI())< 4.0and abs(r.fiberMag_r-g.fiberMag_g)< 2.0
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 45
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 46
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 47
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 48
Performance (on current SDSS data)Performance (on current SDSS data)
time vs queryID
1
10
100
1000
Q08 Q01 Q09 Q10A Q19 Q12 Q10 Q20 Q16 Q02 Q13 Q04 Q06 Q11 Q15B Q17 Q07 Q14 Q15A Q05 Q03 Q18
seco
nd
s
cpu
elapsedae
Run times: on 15k$ COMPAQ Server (2 cpu, 1 GB , 8 disk)
Some take 10 minutes Some take 1 minute Median ~ 22 sec. Ghz processors are fast!
– (10 mips/IO, 200 ins/byte)
– 2.5 m rec/s/cpu
cpu vs IO
1E+0
1E+1
1E+2
1E+3
1E+4
1E+5
1E+6
1E+7
0.01 0.1 1. 10. 100. 1,000.CPU sec
IO c
ount 1,000 IOs/cpu sec
~1,000 IO/cpu sec ~ 64 MB IO/cpu sec
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 49
Sequential Scan Speed is ImportantSequential Scan Speed is Important
In high-dimension data, best way is to search. Sequential scan covering index is 10x faster
– Seconds vs minutes
SQL scans at 2M records/s/cpu (!)MBps vs Disk Config
0
50
100
150
200
250
300
350
400
450
500
1disk 2disk 3disk 4disk 5disk 6disk 7disk 8disk 9disk 10disk 11disk 12disk 12disk2vol
MB
ps
memspeed avg.
mssql
linear quantum
64bit/33MHz pci bus
1 disk controler saturates
1 PCI bus saturates
SQL saturates CPU
added 2nd ctlr
added 4th ctlr
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 50
What we learned from the 20 QueriesWhat we learned from the 20 Queries
All have fairly short SQL programs -- a substantial advance over (tcl, C++)
Many are sequential one-pass and two-pass over data
Covering indices make scans run fast Table valued functions are wonderful
but limitations are painful. Counting, Binning, Histograms VERY common Spatial indices helpful, Materialized view (Neighbors) helpful.
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 51
Memory in GB
1.0
10.0
100.0
1000.0
10000.0
100000.0
0 10 20 30 40 50 60 70 80 90 100
No of galaxies in Millions
CP
U t
ime
(hrs
)
1
4
32
256
year
decade
week
day
month
Cosmo: Cosmo: Computing the Cosmological ConstantComputing the Cosmological Constant
Compares simulated galaxy distribution to observed distribution
Measure distance between each pair of galaxiesA lot of work (108 x 108 = 1016 steps)
Good algorithms make this ~Nlog2N Needs LARGE main memory Using Itanium
donated by Compaqand SQL server for data store
(this is Alex Szalay, Adrian Pope,… of JHU).
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 52
SummarySummary We will be able to store everything,
– The challenge is organizing and finding answers.
PhotoServer: Objects vs records vs files,– XML++ gives us portable objects.
– Similarity search: better than nothing!
Scalability: a solved problem,– but… Trustworthy & Manageable is not.
TerraServer and TerraService– Why put everything in the database?
– A prototypical Web Service.
SkyServer and the World Wide Telescope – Data Mining science data
– Serving Windows/Macintosh/Unix clients with .Net
– Federating Archives with .Net
http://gray.microsoft.com/~gray/talks/PetabyteDatabasesSql+.Net1.ppt 53
ReferencesReferences
These Slides– http://research.Microsoft.com/~Gray/talks/
TerraServer & TerraService– http://terraService.Net/
Virtual Observatory (aka World Wide Telescope)– http://www.voforum.org/
SkyServer – http://SkyServer.SDSS.org/
– See documents at http://SkyServer.SDSS.org/en/help/download/
Download “personal SkyServer” (1GB) – http://research.Microsoft.com/~Gray/sdss/
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