System monitor ii 23.29/8/2023 ![]() ![]() ![]() CE 23.2 brings combined strengths to help our customers succeed in a multi-cloud world. ![]() CE is part of Project Titanium, OpenText’s next-generation cloud strategy and roadmap to help our customers speed up cloud-based digital transformation. From global connected supply chains to seven-star service experience management, OpenText is building the most secure and elastic information cloud for evolving content types that will increase productivity for IT, operations, developers, climate innovators, and more.Ī critical route to success is adoption of the cloud, and the latest OpenText Cloud Editions (CE) 23.2 innovations accelerates this journey.ĭriven by 90-day release cycles, CE 23.2 has delivered more than 100 innovations designed to drive key modernizations. Since the acquisition of Micro Focus 90 days ago, OpenText World EMEA was the first time we showcased our expanded mission and the new innovations underway. To remain competitive, organizations must explore new ways of harnessing information – to not only power and protect it – but to innovate, ramp up growth, and increase speed to market. Our test class does not use a blocking queue and thread pool so as to avoid a point of contention.As information expands exponentially, it is becoming more complex and extensive than ever before. We recommend using multiple threads to send events into Esper. Also consider decoupling your read operation from the event processing operation (sendEvent method) by having multiple readers or by pre-fetching your data from the store. In such case you may want to consider an in-memory driver for use in performance testing. For optimal throughput, consider performing such blocking operations in a separate thread.Īdditionally, when reading input events from a store or network in a performance test, you may find that Esper processes events faster then you are able to feed events into Esper. ![]() It can therefore be beneficial for your application to process output events asynchronously and not block the Esper engine while an output event is being processed by your listener, especially if your listener code performs blocking IO operations.įor example, your application may want to send output events to a JMS destination or write output event data to a relational database. The processing of output events that your listener or subscriber performs temporarily blocks the thread until the processing completes, and may thus reduce throughput. Such output events are delivered by the application or timer thread(s) that sends an input event into the engine instance. Your application receives output events from Esper statements through the UpdateListener interface or via the strongly-typed subscriber POJO object. 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 to 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 23.3. Incremental Versus Recomputed Aggregation for Named Window Events 23.2.39. Comparing Single-Threaded and Multi-Threaded Performance 23.2.38. Do Not Create the Same or Similar EPL Statement X Times 23.2.37. Query Planning Expression Analysis Hints 23.2.34. Prefer Constant Variables Over Non-Constant Variables 23.2.30. Context Partition Related Information 23.2.29. Expression Evaluation Order and Early Exit 23.2.26. Statement and Engine Metric Reporting 23.2.25. Optimizing Stream Filter Expressions 23.2.24. Performance, JVM, OS and Hardware 23.2.22. Statement Design for Reduced Memory Consumption - Diagnosing OutOfMemoryError 23.2.21. Pattern Sub-Expression Instance Versus Data Window Use 23.2.19. Patterns and Pattern Sub-Expression Instances 23.2.18. Subqueries Versus Joins and Where-Clause and Data Windows 23.2.17. High-Arrival-Rate Streams and Single Statements 23.2.16. Use a Subscriber Object to Receive Events 23.2.14. Tune or Disable Delivery Order Guarantees 23.2.13. Consider Casting the Underlying Event 23.2.11. Consider Using EventPropertyGetter for Fast Access to Event Properties 23.2.10. Reduce the Use of Arithmetic in Expressions 23.2.7. Prefer Stream-Level Filtering Over Where-Clause Filtering 23.2.6. Select the Underlying Event Rather Than Individual Fields 23.2.5. Understand How to Tune Your Java Virtual Machine 23.2.2. ![]()
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