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Data Management Best Practices at Lorentz Center

Learn how to handle large data, work collaboratively, and manage projects effectively at Lorentz Center. Keep data private or make it public, while organizing projects with specific source code requirements.

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Data Management Best Practices at Lorentz Center

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  1. Context in practice … and what’s new Lorentz center, Wednesday, 21 November 2006

  2. Context • Handling large amounts of data • Work together • Work in a consistent way • Experiment with source code • Know what data and code was used • Keep data private • Make data public • Throw away data

  3. Projects • Organize in Projects, that have • A name and description • Users • Default privileges • Responsible - privileged – users • New objects are automatically part of the currently active context

  4. Privileges • User – Project – Astro-Wise … • MyDB • Project with same name as user • User-only objects within a project, i.e.user can see all data within a projectbut creates data that is invisible to others within a projectcontext.set_privileges_user_only()context.set_privileges_project_only()context.set_default_privileges()

  5. Migrating data • Migrating everything from MyDBawe dbmigrateall.py AWAUSER KIDS • Migrating a single object including its dependenciesfor sl in SourceList.name.like('*TEST*'): context.migrate(sl, 'VESUVIO')

  6. Deleting data • Deleting everything in MyDBawe dbdeleteall.py AWAUSER • Deleting a single object • Requires careful checking for ALL dependent objects • Is not yet possible • DBA’s run dbvacuum to delete files from dataservers that belong to deleted objects

  7. Project specific source code • Put project source code in separate CVS repositories • awe will use code that is specific to the currently selected project • Only code that is different needs to be kept in project specific CVS • Beware of unnecessary divergence!

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