1. Data-Intensive Science at Johns Hopkins University Institute for Data-Intensive Engineering and Science (IDIES) Johns Hopkins University Jordan Raddick

2. Case Study: Astronomy In the “old days” astronomers took photos. New instruments are digital (< 100 GB/night) Detectors are following Moore’s law. Data avalanche: double every 2 years Total area of 3m+ telescopes in the world in m 2, total number of CCD pixels in Megapix, as a function of time. Growth over 25 years is a factor of 30 in glass, 3000 in pixels.

3. Why astronomy? Because “it’s worthless” (Jim Gray) –No privacy restrictions –No intellectual property –No one wants to sell you data Because it’s intrinsically interesting And because there’s a lot of it

4. Astronomy Data Astronomers have a few Petabytes now Data doubles every 2 years Data is public after 2 years So, 50% of the data is public But… how do I get at that 50% of the data?

5. Science is breaking You can… –search 1 MB in 1 second, transfer for < 1¢ –search 1 GB in 1 minute, transfer for $1 –search 1 TB in 2 days, transfer for $1,000 –search 1 PB in 3 years, transfer for $1,000,000 –…and 1 PB is 5,000 disks “Data avalanche” in all fields of science –New approach needed

6. Data Intensive Science Tools Databases instead of files (DBMSs) –Data integrity ensured –Optimized data access (DB indices) –Ability to define methods on data –Do the science inside the database! –Bring the analysis to the data, not vice-versa Scalable (parallel) data access High-speed transport protocols –Move the data rapidly when necessary Asynchronous Web services –Web browsers cannot handle data volumes

7. Gray’s Laws of Data Engineering Jim Gray –Scientific computing is revolving around data –Need scale-out solution for analysis –Take the analysis to the data! –Start with “20 queries” use cases

8. Virtual Observatory 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 observing conditions are always great (no working at night, no clouds no moons no..). –It’s a smart telescope: links objects and data to literature on them.

9. Sloan Digital Sky Survey A map of the universe Telescope at Apache Point Observatory (Sunspot, NM) –2.5 meter reflector –~3 degree field of view –Drift scanning (telescope stationary, sky appears to move)

10. An Ambitious Survey A thousand-fold increase in the amount of data! Info content > US Library of CongressInfo content > US Library of Congress Before SDSS, total number of galaxies with measured parameters ~ 200kBefore SDSS, total number of galaxies with measured parameters ~ 200k With SDSS, we already have detailed parameters for over 200 Million galaxies!!With SDSS, we already have detailed parameters for over 200 Million galaxies!!

11. SDSS Data Overview Data Archive Server (DAS) FITS files (raw data) Images, spectra, corrected frames, atlas images, binned images, masks Online form-based access Rsync and wget file retrieval Data Archive Server (DAS) FITS files (raw data) Images, spectra, corrected frames, atlas images, binned images, masks Online form-based access Rsync and wget file retrieval Catalog Archive Server (CAS) Science parameters extracted to catalogs Stuffed into relational DBMS (SQL Server) Heavily indexed, optimized Online access via SkyServer Several levels of access, query tools Catalog Archive Server (CAS) Science parameters extracted to catalogs Stuffed into relational DBMS (SQL Server) Heavily indexed, optimized Online access via SkyServer Several levels of access, query tools SDSS Data Release www.sdss.org das.sdss.org/DRx-cgi-bin/DAS www.sdss.org das.sdss.org/DRx-cgi-bin/DAS cas.sdss.org skyserver.sdss.org cas.sdss.org skyserver.sdss.org

12. The CAS Databases Processed data is stuffed into a commercial relational DBMS –Microsoft SQL Server 2000 Allows fast exploration and analysis - Data Mining Two versions of the sky: Best and Target –Target is version of sky on which spectroscopic targets were chosen –Best is latest, greatest processing of the data –2 DBs for each release, e.g. BestDR3 and TargDR3 Heavily indexed to speed up access – HTM + DB Indices Short queries can run interactively. Long queries (> 1 hr) require a custom Batch Query System. SDSS Pipelines

13. SkyServer Web Access Supports several levels of SQL access –Novice/casual users Radial (cone) and Rectangular search –Intermediate/astro users Imaging and Spectro Form Query –Expert Free-form SQL, Object Crossid (upload RA/dec list) CasJobs workbench environment (MyDB) Visual tools –ImageCutout service Finding Chart, Navigate/browse images, image lists –Explore tool Detailed info for each image object, including spectrum Downloadable interfaces –Emacs, Java tool (sdssQA), sqlcl (command-line) http://cas.sdss.orghttp://cas.sdss.org/, http://skyserver.sdss.org/http://skyserver.sdss.org

14. CAS Workload Management Query execution time follows power law Vast majority of queries under a minute Separate short and long queries Execute long queries in batch mode Short and long queues (short=1min, long=8hrs) Strictly limit time of query in a queue Provide user workspace DB – MyDB –Reduce network traffic from repeat and intermediate results –Allow sharing of query results between user groups

15. CasJobs and MyDB Batch Query Workbench for SDSS CASBatch Query Workbench for SDSS CAS Queries are queued for batch execution –Load balancing – queues on multiple servers –Limit of 2 simultaneous queries per server Short (1 minute) queue for immediate mode –Query aborted after 1 minute MyDB personal database –1 GB (more on demand) SQL DB for each user –Long queries write to MyDB table by default –User can extract output (download) when ready –Ability to share MyDB tables with other users via group visibility

16. SkyServer Entire database of the Sloan Digital Sky Survey –Free to anyone –4 TB of data –287 million sky objects –1.2 million spectra

17. Why data to the public? All SDSS data available to general public –Commitment to data sharing –People can look at any star or galaxy –Inquiry learning known to be effective (learners answer a question themselves, with their own design)

18. Browse Data

19. Explore Data

20. Search for Data

21. From Access to Learning Not enough just to give public access to data How are they using it? Are they learning anything from it? –Do they understand what they’re doing and why?* *Understanding = long-term memory, transfer to new situations (How People Learn)

22. Formal Education: Projects

23. SkyServer projects Three levels –Kids (K-8) –Basic (high school or Astro 101) –Advanced (skilled and motivated students) Research challenges –Independent research (open inquiry) with data Activities created by users

24. SkyServer projects

25. Use of educational projects 2 of top 20 IPs to hit entire site are K-12 districts –Orlando, FL & Los Angeles

26. Citizen science: Galaxy Zoo Background: galaxies come in different shapes Classifying is hard for computers, easy for people So get the public to help! spiral elliptical

27. Galaxy Zoo

28. Galaxy Zoo Discovery But if you mirror galaxies, it’s still 48/52! Counter-clockwise excess due to perception Unintentional psychology discovery! clockwise (48%) counter-clockwise (52%)

29. Galaxy Zoo Discovery “Hanny’s Voorwerp” (“Voorwerp” = object) Strange blue thing found by Dutch teacher Hanny van Arkel Most likely “light echo” from a long-dead quasar Follow-up time on HST Galaxy Zoo great for finding the unexpected!

30. Galaxy Zoo and learning Even people who love science often don’t understand peer review, proposals, etc. Want to use Galaxy Zoo to show process of science As we write science papers, we share results with volunteers

31. Galaxy Zoo blog

32. Other Science: Sensors Small, self-contained devices to measure soil temperature, etc. Deployed around Maryland Study carbon cycle, nesting of turtles, etc. Wide range of applications

33. Other science: Turbulence Large-scale simulation of isotropic turbulence in fluid –Imagine shaking a box of water FORTRAN code 1024 x 1024 x 1024 mesh 10,240 timesteps Every 10 th timestep written to SQL database

34. Other science: Turbulence Database accessible online through web services Can call database from FORTRAN or C code Like having a supercomputer simulation on your laptop!

35. Graywulf Data-intensive computing architecture –40 worker nodes, 1440 MB/s each –6 head nodes, 2100 MB/s each –DDR InfiniBand interconnect Deployed mid-2008 Architecture for future projects

36. Graywulf at SC08

37. Contact information Contact Information Jordan Raddick 410-516-8889 raddick@jhu.edu