Leveraging Database Technologies in Condor

1. Jeff Naughton April 25, 2006 Leveraging Database Technologies in Condor

2. Overview • Introducing ourselves • What we have done since last year • Obtained funding (Yay! thank you NSF!) • Quill: deployed DB-centric data tool • Quill++: more comprehensive, deployed in test-cluster, running (guinea pig) user jobs • Condor J2EE: radical departure experimental system, deployed last week in test cluster • Published some research papers… • BOF 1:30 on Thursday

3. Who we are • Faculty: David DeWitt, Jeff Naughton • Students: Jiansheng Huang, Ameet Kini, Christine Reilly, Eric Robinson, Srinath Shankar, Lakshmikant Shrinivas

4. How do we fit in? • Advanced Development/Research group focused on data management. • Goal: • Interact frequently with Condor Dev. Team and users • Design and prototype new technology; • transfer to Condor team for deployment. • What we don’t do: • Determine roadmap and schedules for deployment within Condor.

5. Why Condor and DBMS? • Premise: A running Condor system is awash in data: • Operational data, Historical data, User data • DBMS technology can help capture, organize, manage, archive, and query this data. • This can make Condor even more powerful, usable, and useful.

6. Quill • Non-invasive approach to capturing job related information • Works by sniffing updates to the job queue log • Serves condor_q and condor_history queries • Independent, reliable, and efficient querying of job related information, with underlying SQL interface So how does it work?

7. Quill Architecture Master Startd … Schedd Quill Store events Write events Get new events RDBMS Queue + History Tables Job Queue log

8. Quill++ • More comprehensive than Quill (data from all daemons, not just SchedD) • Built on Quill code base • Condor daemons write to SQL logs, Quill daemon reads and inserts in DBMS • Central database serves entire pool • Web-based query GUI

9. Schedd Schedd Shadow Startd Database Starter Negotiator A Machine Data Capture in Quill++ • Condor daemons augmented to record important events in a database • Database is in addition to standard daemon logs • Pool will run unaffected even in the absence of a database

10. Master … Startd Schedd Quill++ Store events Write events Get new events RDBMS Queue, History, Machine, Match etc. Job Queue log Event logs Quill++ Architecture

11. Implementation Details • Quill++: First class condor daemon • Managed by Condor Master • Native PostgreSQL API • Can be ported to any platform for which PostgreSQL drivers are available (AIX, BSD, IRIX, HP-UX, Linux, Solaris, Windows etc.) • Porting Quill++ to other databases involves implementing a database virtual class

12. Web Interface • Useful for: • User job monitoring • Administrative monitoring over jobs and resources • Debugging

13. Jobs in queue History jobs Machine Status Recency summary Condordb Admin Screen

14. Job history by owner

15. Machine Report

16. Classad Info Run Info Event Info Match Info Rejects Info Status about a job

17. Recency info for exceptional data sources

18. Present Status • Deployed in testbed • dbc cluster (93 machines) • Has successfully run almost 100,000 jobs. • Working with Condor team planning future distribution with Condor.

19. Caveats • Web interface to DB • Basic prototype implemented • Needs to be made more robust, user friendly (!) • Gathers incomplete information in multiple pool scenarios (flocking, glide-in, condor-c)

20. CondorJ2 • To boldly go where no one has gone before • Quill/Quill++: Database reflects state of Condor pool • Condor J2: Database is the state of Condor pool • Overview of CondorJ2 • Use database to maintain operational data (workflow state, machine state, config policies, etc.) • Implement workflow management, resource management and resource allocation in Application Server environment • Modify master, startd and starter to be thin web service clients • Provide web interface for all system services (workflow submission, machine reconfiguration etc.)

21. Motivation • Scalability • Flexibility • Administratibility

22. Java Application Servers • Industrial strength middleware for high performance & scalable web applications • Widely deployed systems • Oracle AS 10g, IBM WebSphere, BEA WebLogic, JBoss (open source) • Key features • Support for transactions • Web service interfaces • Support for clustering (for scalability) • Configurable security • Backend database independence

23. Condor Database JDBC Application Server Machine Modules Matchmaking Modules Workflow Modules Condor Pool Web Site Condor Web Services HTTP SOAP over HTTP User’s Web Browser User’s Custom Tools master startd starter Web Service Clients Execute Machines

24. What can do in CondorJ2 via browsers and web services? • Add and configure new machines • Reconfigure machines on the fly • Specify, submit, monitor and manage workflows • Monitor global system state

25. Virtuous Cycle • As we learn where Condor can use DBMS technology, we learn where DBMS technology can be (must be?) improved. • Support for sparse data sets [ICDE 2006]. • Pushing match-making style operations into DBMS [SIGMOD 2006]. • Data provenance as byproduct of Quill++ data capture. [IPAW 2006] • Improving DBMS technology will lead to more places that it can be installed.

26. Other ongoing work… • File caching in Condor pools • Techniques for explaining data consistency rather than dictating consistency • Automatic monitoring of system “health” by mining captured data

27. Visit us and see demos! • Come see demos of Quill, Quill++, and CondorJ2 in Rm. 216/218 Fluno Center on Thurs. afternoon 1:30 – 2:30pm.