Jeff naughton april 25 2006
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Jeff Naughton April 25, 2006. Leveraging Database Technologies in Condor. Overview. Introducing ourselves What we have done since last year Obtained funding (Yay! thank you NSF!) Quill: deployed DB-centric data tool

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Leveraging Database Technologies in Condor

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Jeff naughton april 25 2006

Jeff Naughton

April 25, 2006

Leveraging Database Technologies in Condor


Overview

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


Who we are

Who we are

  • Faculty: David DeWitt, Jeff Naughton

  • Students: Jiansheng Huang, Ameet Kini, Christine Reilly, Eric Robinson, Srinath Shankar, Lakshmikant Shrinivas


How do we fit in

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.


Why condor and dbms

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.


Quill

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?


Quill architecture

Quill Architecture

Master

Startd

Schedd

Quill

Store

events

Write

events

Get new

events

RDBMS

Queue

+

History

Tables

Job

Queue

log


Quill1

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


Data capture in quill

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


Quill architecture1

Master

Startd

Schedd

Quill++

Store

events

Write

events

Get new

events

RDBMS

Queue,

History,

Machine,

Match etc.

Job

Queue

log

Event

logs

Quill++ Architecture


Implementation details

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


Web interface

Web Interface

  • Useful for:

    • User job monitoring

    • Administrative monitoring over jobs and resources

    • Debugging


Condordb admin screen

Jobs in queue

History jobs

Machine Status

Recency summary

Condordb Admin Screen


Job history by owner

Job history by owner


Machine report

Machine Report


Status about a job

Classad Info

Run Info

Event Info

Match Info

Rejects Info

Status about a job


Recency info for exceptional data sources

Recency info for exceptional data sources


Present status

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.


Caveats

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)


Condorj2

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.)


Motivation

Motivation

  • Scalability

  • Flexibility

  • Administratibility


Java application servers

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


Leveraging database technologies in condor

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


What can do in condorj2 via browsers and web services

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


Virtuous cycle

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.


Other ongoing work

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


Visit us and see demos

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.


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