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Your application receives output events from Esper statements through the UpdateListener interface or via the strongly-typed subscriber POJO object.
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This section describes performance best practices and explains how to assess Esper performance by using our It is also possible to use Esper on a soft-real-time or hard-real-time JVM to maximize predictability even How we use the performance kitĮsper has been highly optimized to handle very high throughput streams with very little latency between event receipt and output result posting. Measure throughput of non-matches as well as matches 21.3. Incremental Versus Recomputed Aggregation for Named Window Events 21.2.40. Comparing Single-Threaded and Multi-Threaded Performance 21.2.39. Do not create the same EPL Statement X times 21.2.38. Query Planning Expression Analysis Hints 21.2.35. Prefer Constant Variables over Non-Constant Variables 21.2.31. Context Partition Related Information 21.2.30. Expression Evaluation Order and Early Exit 21.2.27.
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Statement and Engine Metric Reporting 21.2.26. Optimizing Stream Filter Expressions 21.2.25.
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Performance, JVM, OS and hardware 21.2.23. Statement Design for Reduced Memory Consumption - Diagnosing OutOfMemoryError 21.2.22. Pattern Sub-Expression Instance Versus Data Window Use 21.2.20. Patterns and Pattern Sub-Expression Instances 21.2.19. Subqueries versus Joins And Where-clause And Data Windows 21.2.18. High-Arrival-Rate Streams and Single Statements 21.2.17. Use a Subscriber Object to Receive Events 21.2.15. Tune or disable delivery order guarantees 21.2.14. Consider casting the underlying event 21.2.11. Consider using EventPropertyGetter for fast access to event properties 21.2.10. Reduce the use of arithmetic in expressions 21.2.7. Prefer stream-level filtering over where-clause filtering 21.2.6. Select the underlying event rather than individual fields 21.2.5. Understand how to tune your Java virtual machine 21.2.2.