Transient Detection and SQL

Gijs Molenaar

Transcript

1. Transient Detection
   and SQL
   LOFAR Transients Key Science Project meeting
   January 9 2014
   Gijs Molenaar
   @gijzelaerr
   gijs@pythonic.nl
   

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2. TRAP
   • TRAnsient Detection Pipeline
   • Detect Transients in
   AARTFAAC data
   • 24/7 full sky monitor
   

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3. Images
   10 bands (image cube)
   • 1 image cube per second
   • In future 10 images per second
   • 20 to 50 sources per image
   

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4. Data
   • Source (unique id)
   • Position (ra & decl)
   • Error (ra & decl)
   • Flux
   • Shape (2d gaussian)
   • Timestamp
   • Frequency (band)
   

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5. Data rate
   • 500 - 5000 inserts per second
   • May get more in future
   

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6. Calculations
   • Associate in frequency
   • Associate in time
   • Transients detection
   

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7. SQL
   Storing, manipulating and retrieving data
   De facto standard
   Structured data
   Relational data
   Old language
   

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8. The data
   Unique id, ra, decl, errors, flux, shape, timestamp, frequency
   Unique id, ra, decl, errors, flux, shape, timestamp, frequency
   Unique id, ra, decl, errors, flux, shape, timestamp, frequency
   Unique id, ra, decl, errors, flux, shape, timestamp, frequency
   Unique id, ra, decl, errors, flux, shape, timestamp, frequency
   Unique id, ra, decl, errors, flux, shape, timestamp, frequency
   Unique id, ra, decl, errors, flux, shape, timestamp, frequency
   Unique id, ra, decl, errors, flux, shape, timestamp, frequency
   Unique id, ra, decl, errors, flux, shape, timestamp, frequency
   Unique id, ra, decl, errors, flux, shape, timestamp, frequency
   Unique id, ra, decl, errors, flux, shape, timestamp, frequency
   Unique id, ra, decl, errors, flux, shape, timestamp, frequency
   Unique id, ra, decl, errors, flux, shape, timestamp, frequency
   Unique id, ra, decl, errors, flux, shape, timestamp, frequency
   Unique id, ra, decl, errors, flux, shape, timestamp, frequency
   Unique id, ra, decl, errors, flux, shape, timestamp, frequency
   Unique id, ra, decl, errors, flux, shape, timestamp, frequency
   table
   

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15. Quite a lot of data
    • So quite a lot of calculations
    • move calculations to data (base)
    • Minimal communication
    

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16. Problems with SQL
    ●
    Mix of language domains (python, SQL)
    ●
    No SQL standard (various dialects)
    ●
    Manual:
    ●
    type checking / conversion
    ●
    Error handling
    ●
    schema migrations / instantiation
    

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17. Problems with
    monolithic queries
    • hard to read
    • hard to reason about
    • Hard to atomize
    • hard to test
    • Code reuse difficult
    • hard to maintain
    

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18. Example
    

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19. Solution?
    • Server side functions?
    

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20. NO!
    • DB revisions, versioning hell
    • For every code change new version
    • Schema migrations
    • Init functions on startup?
    • DB specific
    

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21. SOLUTION
    

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22. Why?
    • Object Relational Mapper
    • Support multiple database dialect
    • Client side error checking (before query execution)
    • Less code
    • Better Query Builder Language
    SQLAlchemy -> SQL assembler -> C
    

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23. But also
    ●
    ●
    3rd party applications!
    ●
    Schema management
    ●
    Creation and Up/down grading
    ●
    Tools for creating migration paths
    

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24. And last but not least
    ●
    Backwards compatible!
    ●
    Can still do raw queries
    

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25. example
    

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28. Why not earlier?
    • Unforeseen problem at
    project start
    • No MonetDB dialect for
    SQLAlchemy
    • (I made one)
    

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29. sqlalchemy-monetdb
    • pip install sqlalchemy-monetdb
    • https://github.com/gijzelaerr/sqlalchemy-monetdb
    • https://github.com/gijzelaerr/djonet
    • (banana)
    

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30. Possible Disadvantage
    A bit harder to see the actual SQL
    BUT SQLALchemy very similar to SQL
    

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31. Conclusion
    ●
    SQLAlchemy is SQL powertool
    ●
    I see no reason NOT to use SQLAlchemy
    ●
    Will result in improved code readability and
    maintainability
    ●
    (if properly used of course)
    

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32. Questions?
    

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