Siddhi CEP 2nd sideshow presentation
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Siddhi CEP 2nd sideshow presentation

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The sideshow used at the 2nd presentation of the project Siddhi CEP

The sideshow used at the 2nd presentation of the project Siddhi CEP

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  • 1. S IDDHI -CEP A H IGH P ERFORMANCE C OMPLEX E VENT P ROCESSING E NGINE
  • 2. Q UICK R ECAP
      • What is CEP?
      • Why Siddhi?
  • 3. W HAT IS CEP
    • In abstract, the tasks of the CEP is to identify meaningful patterns, relationships and data abstractions among unrelated events and fire an immediate response such as an Alert message.
  • 4. W HY S IDDHI ?
      • Cons in current CEP solutions
      • Proprietary
      • Not enough support for complex queries
      • Less efficient - High latency and memory consumption
      • Advantages in Siddhi
  • 5. P ROGRESS SO FAR ...
  • 6. P ROGRESS SO FAR ...
      • Initial research 
      • System Design
      • 1 st iteration
      • Web site
      • Improved Siddhi API
      • 2 nd iteration 
        • All major functionalities are implemented  
      • Profiling and Performance Testing
  • 7. S IDDHI F UNCTIONALITIES
    • Filter
    • State Machine (Sequence Query)
    • Join Event Streams
    • Time Window
    • Length Window
  • 8. API TO W RITE Q UERIES
    • Sample query for Filter
    • Query query = qf.createQuery(
    •                     "StockQuote" ,
    •                     qf.output("price=CSEStream.price") ,
    •                     qf.inputStream(cseEventStream),
    •                     qf.condition("CSEStream.symbol", EQUAL, "IBM")
    •             );
  • 9. API TO W RITE Q UERIES
    • Sample query for Sequence Query
    • Query query = qf.createQuery(
    •                     "StockQuote" ,
    •                     qf.output("action=$0.action", "priceA=$1.price", "priceA=$2.price"), 
    •                     qf.inputStreams(cseEventStream, infoStock),
    •                     qf.sequence(
    •                             qf.condition("infoStock.action", EQUAL, "buy"),
    •                             qf.every(
    •                                     qf.condition("CSEStream.price", GREATERTHAN, "75"),
    •                                     qf.condition("CSEStream.price", GREATERTHAN, "$1.price")
    •                             )
    •                     )
    •             );
  • 10. API TO W RITE Q UERIES
    • Aggregators
    • avg
    • count
    • max
    • min
    • sum
    • Query query = qf.createQuery(
    •                     "StockQuote",
    •                     qf.output( "symbol=CSEStream.symbol“, "avgPrice= avg(CSEStream.price) ",
    • "count=count(CSEStream.symbol)"),
    •                     qf.inputStream(cseEventStream),
    •                     qf.condition("CSEStream.symbol", EQUAL, "IBM")
    •             );
  • 11. P ERFORMANCE T ESTING
    •  
    Details of the machine we used Hardware : Intel- Core 2 Duo 2.10 GHz Memory 1.9GB OS : Ubuntu Release 9.10 Kernel Linux 2.6.31-14-generic GNOME - 2.28.1
  • 12. P ERFORMANCE C OMPARISON WITH E SPER
    •  
    Performance Comparison for a simple filter without a window Events Siddhi (ms) Esper (ms) 10 4 5 100 5.6 9.8 1000 34.5 55.2 10,000 252.6 491 100,000 961.4 1669.2 1000,000 3730.6 5546.8
  • 13. P ERFORMANCE C OMPARISON WITH E SPER
    •  
    Performance Comparison for a timed window query for average calculation for a given symbol Events Siddhi (ms) Esper (ms) 10 5 9 100 10.6 16.2 1000 52 109.2 10,000 419.2 1159.2 100,000 1302.8 2724 1000,000 7883.2 11589.8
  • 14. P ERFORMANCE C OMPARISON WITH E SPER
    •  
    Performance Comparison for a State machine query Events Siddhi (ms) Esper (ms) 10 5.8 30 100 11 58.4 1000 98.6 333 10,000 550.4 2285.6 100,000 1606 9663.2 1000,000 12563 77641
  • 15. Questions?
  • 16. Thank You 