Use cases for alerts
Explore use cases for alerts on the Digibee Integration Platform
Last updated
Was this helpful?
Explore use cases for alerts on the Digibee Integration Platform
Last updated
Was this helpful?
This documentation describes use cases that show how the metrics available in the Digibee Integration Platform can be applied in real-world scenarios. These use cases will help you understand how to interpret the data and make decisions to improve your operational results.
For more information, read our .
If the number of executions per second of a pipeline is outside the expected range, it could be due to the following:
If the period that the pipeline inflight execution is outside the expected range, it could be due to the following:
If the memory usage of a pipeline is outside the expected range, it could be due to the following:
If the pipeline message size is outside the expected range, it could be due to the following:
If the number of messages on queue for a pipeline is out of the expected range, it could be due to the following factors:
If the period that the pipeline response time is outside the expected range, it could be due to the following:
By increasing the number of replicas, we increase the ability to process more executions simultaneously.
When the deployment size of a pipeline is too small to support the size of the request and the memory usage may become larger than is available to the pipeline, there is not enough memory to execute the given request. This can cause the pipeline to display an “out of memory” error. If this is the case, you should increase the size of the pipeline deployment size..
When deploying a pipeline, some of the most common errors that can occur include an out of memory. Then it is necessary to correctly identify and fix the error..
When the deployment size of a pipeline is too small to support the size of the request and the memory usage may become larger than is available to the pipeline, there is not enough memory to execute the given request. This can cause the pipeline to display an “out of memory” error. If this is the case, you should increase the size of the pipeline deployment size..
By increasing the number of replicas, the number of messages on queue decreases because there is a higher throughput and processing is accelerated..