Oracle Integration Cloud (OIC) offers industry-leading SaaS integration capabilities. It provides extensive monitoring, tracking and reporting features out-of-the-box. Occasionally, enterprises do have reporting and analysis needs those are better met by additional reporting and analytics products. This article discusses couple of such use cases and describes how to implement one of them using Elastic stack. Information in this article is applicable to release 18.1.3 of integration cloud.
Let’s consider these scenarios:
Use cases represented by these scenarios can be met by externalizing integration metrics from OIC into another platform specializing on analytics. Let’s look at some recommended ways to extract metrics from OIC and importing them into ELK (Elastic-LogStash-Kibana). Elastic stack is a widely-used opensource platform for analytics and dashboards. Jump to one of the sections by click the link.
Elastic is among products that allow infinite scaling and support map-reduce for efficient distributed queries. Note that other products such as Oracle big-data analytics cloud service or Oracle log analytics can also meet aforementioned requirements. Elastic is used in this blog for its simplicity for demonstration purposes.
For sake of simplicity, the post does not address deployment of ELK stack. Refer to Elastic web site for instructions. A simple installation could run on a laptop. More complex, distributed deployments will require careful planning of compute, storage resources and indexes.
Consolidated Reporting is achieved by collecting monitoring metrics from multiple OIC instances and feeding them into one analytics application instance.
With ELK stack, LogStash is the agent/aggregator, Elastic is the indexer and Kibana is the analytics and reporting client. This pattern could help building reports for billing and historic analytics or correlate traffic patterns from multiple integration platforms.
End-to-end transaction monitoring can be achieved by collecting start, end times, tracking id and completion status for parts of an end-to-end transaction from each participating application, feeding them into an analytics application and running map-reduce queries that correlate parts of a transaction using tracking id. Read the complete article here.
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