![]() View all monitor policies on the Configuration > Monitor Policies page. To group the conditions, use the following parentheses and Boolean operators from their corresponding lists:Īdd other configurations in any or all of the following tabs by clicking them:.To add more than one condition, click to remove an existing condition, click.You can enable it later from the Monitor Policies page.Ĭreate the PATROL Agent selection criteria based on which the policy must be applied to the Agents. Add the agent selection criteria. If you want to enable the policy immediately, select Enable Policy. You can add a custom value in this field, or use the arrows to increase or decrease the value. If you want to share the policy with the user group that you selected, select the Share with User Group checkbox.Īdd a unique precedence number to the policy. If the user belongs to multiple user groups, select the appropriate user group for the policy. For example, you can easily find all Windows policies if the policies are named as follows:Īdd the associated user group for the policy.Īn associated user group is the user group that the logged-on user belongs to. Option 2: To enable easy searching of policies, include policy-specific information in the policy naming.Option 1: To enable easy sorting of policies, include the precedence number of a policy as a prefix in the policy naming as per the following format: _.Our test class does not use a blocking queue and thread pool so as to avoid a point of contention. 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. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |