Building BIG Data Servers on the Web Talk at Flash Mob Supercomputer 3 April 2004 Jim Gray Microsoft Research [email protected] http://research.microsoft.com/~Gray.
Download ReportTranscript Building BIG Data Servers on the Web Talk at Flash Mob Supercomputer 3 April 2004 Jim Gray Microsoft Research [email protected] http://research.microsoft.com/~Gray.
Building BIG Data Servers on the Web Talk at Flash Mob Supercomputer 3 April 2004 Jim Gray Microsoft Research [email protected] http://research.microsoft.com/~Gray Numbers TeraBytes and Gigabytes are BIG! • • • • Mega – a house in san francisco Giga – a very rich person Tera – ~ The Bush national debt Peta – more than all the money in the world • A Gigabyte: the Human Genome • A Terabyte: 150 mile long shelf of books. How much information is there? Yotta • Soon everything can be recorded and indexed • Most bytes will never be seen by humans. • Data summarization, trend detection anomaly detection are key technologies See Mike Lesk: How much information is there: Everything ! Recorded All Books MultiMedia Zetta Exa Peta All books (words) .Movi e Tera Giga 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 A Photo A Book Mega Kilo e-Science • Data captured by instruments Or data generated by simulator • Processed by software • Placed in a files or database • Scientist analyzes files / database • Virtual laboratories – Networks connecting e-Scientists – Strong support from funding agencies • Better use of resources – Primitive today The Big Picture Experiments & Instruments Other Archives Literature questions facts facts ? answers Simulations The Big Problems • • • • • • Data ingest Managing a petabyte Common schema How to organize it? How to reorganize it How to coexist with others • Query and Vis tools • Support/training • Performance – Execute queries in a minute – Batch query scheduling e-Science is Data Mining • There are 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 Robert Brunner @ CalTech. 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 TerraServer/TerraService http://terraService.Net/ • US Geological Survey Photo (DOQ) & Topo (DRG) images online. • On Internet since June 1998 • Operated by Microsoft Corporation • Cross Indexed with – Home sales, – Demographics, – Encyclopedia • A web service • 20 TB data source • 10 M web hits/day USGS Image Data • Digital OrthoQuads – 18 TB, 260,000 files uncompressed – Digitized aerial imagery – 88% coverage conterminous US – 1 meter resolution – < 10 years old • Digital Raster Graphics – 1 TB compressed TIFF, 65,000 files – Scanned topographic maps – 100% U.S. coverage – 1:24,000, 1:100,000 and 1:250,000 scale maps – Maps vary in age User Interface Concept Display Imagery: 316 m 200 x 200 pixel images 7 level image pyramid Resolution 1 meter/pixel to 64 meter/pixel Concept: User navigates an ‘almost seamless’ image of earth Navigation Tools: 1.5 m place names “Click-on” Coverage map Longitude and Latitude search U.S. Address Search External Geo-Spatial Links to: USGS On-line Stream Gauges Home Advisor Demographics Home Advisor Real Estate Encarta Articles Steam flow gauges Click on image to zoom in Buttons to pan NW, N, NE, W, E, SW, S, SE Links to switch between Topo, Imagery, and Relief data Links to Print, Download and view meta-data information Terra Service New Things • A popular web service – Exactly the map you want. • Dynamic Map Re-projection – UTM to Geographic projection – Dynamic texture mapping? • New Data – 1 foot resolution natural color imagery – Census Tiger data • Lights Out Management – MOM – Auto-backup / restore on drive failure New “Urban Area” Data Microsoft Campus at 4 meter resolution “Redundant Bunch 1” Ball field at .25 meter resolution TerraServer Becomes a Web Service TerraServer.net -> TerraService.Net • Web server is for people. • Web Service is for programs – The end of screen scraping – No faking a URL: pass real parameters. – No parsing the answer: data formatted into your address space. • Hundreds of users but a specific example: – US Department of Agriculture TerraServer Web Services Terra-Tile-Service • Get image meta-data • Query TS Gazetteer • Retrieve TS ImageTiles • Projection conversions Landmark-Service • Geo-coded data of wellknown objects (points), e.g. Schools, Golf Courses, Hospitals, etc. • Polygons of well-known objects (shapes), e.g. Zip Codes, Cities, etc Sample Apps • Web Map Client – OpenGIS “like” – Landmarks layered on TerraServer imagery • Fat Map Client – Visual Basic / C# Windows Form – Access Web Services for all data http://terraservice.net Web Services • 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 Terraserver Architecture Standard Browsers HTML Smart Clients Image/jpeg Windows Forms .NET Framework Map UI Web Forms DB Server Map Server Http Handler 668 m Rows SQL 2000 2.0 TB Db TerraServer Web Service SQL 2000 2.0 TB Db ADO.NET OLEDB SQL 2000 2.0 TB Db TerraServer Schema External Group Image Source Search Job Search Dest AltCountry Country Name External Link SourceMeta Scale Job Search Job Log AltState State Name External Geo ImageMeta Load Job JobQueue AltPlace Place Name Image Search Imagery JobSystem Media Feature Type Small PlaceName Famous Category Image Type TerraServer MediaFile Pyramid Famous Place NoImage Terra Database Search Imagery Gazetteer Admin LoadMgmt Remote Management Internet Data Center Load Process Terminal Server Active Server Pages Loading Scheduling System Terra Scale 2 TB Database 2 TB Database 2 TB Database SQL Server SQL Server SQL Server Stored Procs Stored Procs Stored Procs Corporate Network Bricks Fire Wire disks 6 TB Staging Area Read Image Files Terra Cutter TerraServer Hardware • Storage Bricks – “White-box commodity servers” – 4tb raw / 2TB Raid1 SATA storage – Dual Hyper-threaded Xeon 2.4ghz, 4GB RAM • Partitioned Databases (PACS – partitioned array) – 3 Storage Bricks = 1 TerraServer data – Data partitioned across 20 databases – More data & partitions coming • Low Cost Availability – 4 copies of the data • RAID1 SATA Mirroring • 2 redundant “Bunches” – Spare brick to repair failed brick 2N+1 design – Web Application “bunch aware” • Load balances between redundant databases • Fails over to surviving database on failure • ~100K$ capital expense. KVM / IP Research Objectives User/App Goals • Public: Access to remote sensing data with no GIS expertise required • Ubiquitous: No special hw/sw required by client • Delivery: All OnLine/Internet Based, no tape or CD distribution • Simple: Designed to be used by a “6th grade geography student” Technology Goals • Test/show scalability • Test/show availability: • Test/show lights out: – all operations & maintenance occurs remotely – Minimal ops and dev staff • “web service” poster child Virtual Observatory http://www.astro.caltech.edu/nvoconf/ http://www.voforum.org/ • Premise: Most data is (or could be online) • So, the Internet is the world’s best telescope: – – – – It has data on every part of the sky In every measured spectral band: optical, x-ray, radio.. As deep as the best instruments (2 years ago). It is up when you are up. The “seeing” is always great (no working at night, no clouds no moons no..). – It’s a smart telescope: links objects and data to literature on them. Why Astronomy Data? IRAS 25m •It has no commercial value –No privacy concerns –Can freely share results with others –Great for experimenting with algorithms 2MASS 2m •It is real and well documented –High-dimensional data (with confidence intervals) –Spatial data –Temporal data •Many different instruments from many different places and many different times •Federation is a goal •The questions are interesting DSS Optical IRAS 100m WENSS 92cm NVSS 20cm –How did the universe form? •There is a lot of it (petabytes) ROSAT ~keV GB 6cm 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. Slide courtesy of Robert Brunner @ CalTech. SkyServer.SDSS.org • A modern archive – Raw Pixel data lives in file servers – Catalog data (derived objects) lives in Database – Online query to any and all • Also used for education – 150 hours of online Astronomy – Implicitly teaches data analysis • Interesting things – – – – – – Spatial data search Client query interface via Java Applet Query interface via Emacs Popular -- 1% of Terraserver Cloned by other surveys (a template design) Web services are core of it. Demo of SkyServer • • • • • Shows standard web server Pixel/image data Point and click Explore one object Explore sets of objects (data mining) Data Federations of Web Services • Massive datasets live near their owners: – – – – Near the instrument’s software pipeline Near the applications Near data knowledge and curation Super Computer centers become Super Data Centers • Each Archive publishes a web service – Schema: documents the data – Methods on objects (queries) • Scientists get “personalized” extracts • Uniform access to multiple ArchivesFederation – A common global schema Federation: SkyQuery.Net • Combine 4 archives initially • Just added 10 more • Send query to portal, portal joins data from archives. • Problem: want to do multi-step data analysis (not just single query). • Solution: Allow personal databases on portal • Problem: some queries are monsters • Solution: “batch schedule” on portal server, Deposits answer in personal database. SkyQuery Structure • Each SkyNode publishes – Schema Web Service – Database Web Service • Portal is – Plans Query (2 phase) – Integrates answers – Is itself a web service Image Cutout SDSS SkyQuery Portal FIRST 2MASS INT SkyQuery: http://skyquery.net/ • Distributed Query tool using a set of web services • Four astronomy archives from Pasadena, Chicago, Baltimore, Cambridge (England). • Feasibility study, built in 6 weeks – Tanu Malik (JHU CS grad student) – Tamas Budavari (JHU astro postdoc) – With help from Szalay, Thakar, Gray • Implemented in C# and .NET • Allows queries like: SELECT o.objId, o.r, o.type, t.objId FROM SDSS:PhotoPrimary o, TWOMASS:PhotoPrimary t WHERE XMATCH(o,t)<3.5 AND AREA(181.3,-0.76,6.5) AND o.type=3 and (o.I - t.m_j)>2 SkyNode Basic Web Services • Metadata information about resources – Waveband – Sky coverage – Translation of names to universal dictionary (UCD) • Simple search patterns on the resources – Cone Search – Image mosaic – Unit conversions • Simple filtering, counting, histogramming • On-the-fly recalibrations Portals: Higher Level Services • Built on Atomic Services • Perform more complex tasks • Examples – – – – – Automated resource discovery Cross-identifications Photometric redshifts Outlier detections Visualization facilities • Goal: – Build custom portals in days from existing building blocks (like today in IRAF or IDL) MyDB added to SkyQuery • Moves analysis to the data • Users can cooperate (share MyDB) • Still exploring this • Let users add personal DB 1GB for now. • Use it as a workbook. • Online and batch queries. INT Image Cutout SDSS SkyQuery Portal MyDB FIRST 2MASS The Big Picture Experiments & Instruments Other Archives Literature questions facts facts ? answers Simulations The Big Problems • • • • • • Data ingest Managing a petabyte Common schema How to organize it? How to reorganize it How to coexist with others • Query and Vis tools • Support/training • Performance – Execute queries in a minute – Batch query scheduling Grid and Web Services Synergy • I believe the Grid will be many web services share data (computrons are free) • 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