Use cases for alerts

Explore use cases for alerts on the Digibee Integration Platform

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 Pipeline Metrics documentation.

Pipeline executions and Pipeline Executions per instance

Fixing a pipeline execution or a Pipeline Executions per instance issue

If the number of executions per second of a pipeline is outside the expected range, it could be due to the following:

Your pipeline is not being triggered as expected

For example, if your pipeline has a REST trigger that is activated by a third-party application, a failure in that application will cause that pipeline not to activate. Consequently, the execution rates of your pipeline would take a value lower than the expected one. If this is the case, fixing the issue in the third-party application will return your pipeline execution rate to the expected level.

If, on the other hand, the execution rate of a pipeline is higher than expected, this may be due to an incorrect trigger configuration, such as a cron expression in the settings of a scheduler trigger that was entered incorrectly.

Your pipeline architecture is inappropriate

An improper pipeline architecture can lead to unexpected execution rates. For example, if you build a pipeline that reprocesses failed executions and forget to set a limit on reprocessing, that pipeline might try to execute an infinite number of times.

An inefficient architecture may also cause pipelines to take longer than expected to execute, decreasing their execution rate. If this is the case, review your pipeline architecture. Consider using an event-driven architecture and/or pagination.

Read the articles Event-driven architecture and Pagination to learn more about these concepts.

Your pipeline deployment size is too small

When the deployment size of a pipeline is too small to support the frequency in which it's triggered, execution requests stack up instead of executing immediately. This can cause the execution rate to take on a value that is lower than expected. If this is the case, consider increasing the deployment size of your pipeline. Read the article about deployment sizes to learn more.

For more information, read our Pipeline Metrics documentation.

Pipeline Inflight Executions

Fixing a pipeline inflight execution issue

If the period that the pipeline inflight execution is outside the expected range, it could be due to the following:

Increase the number of replicas of a pipeline in Run

When many messages are executed at the same time, we can check if it is necessary to increase the number of replicas of a given pipeline in order to distribute the execution load among all of them.

By increasing the number of replicas, we increase the ability to process more executions simultaneously. Read the article about deployment sizes to learn more.

Pipeline Memory Usage

Fixing a pipeline memory usage issue

If the memory usage of a pipeline is outside the expected range, it could be due to the following:

Your pipeline deployment size is too small

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. Learn more in the deployment sizes documentation.

Solve the “Out of memory” errors in deployment

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. Learn more on how to solve the “Out of memory” errors in deployment documentation.

Pipeline Message Size

Fixing a pipeline message size issue

If the pipeline message size is outside the expected range, it could be due to the following:

Your pipeline deployment size is too small

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. Learn more in the deployment sizes documentation.

Pipeline Messages on Queue

Fixing an MOQ issue

If the number of messages on queue for a pipeline is out of the expected range, it could be due to the following factors:

Redeploy your pipeline

Increase the number of replicas on the Run tab and check the number of concurrent executions.

Move your pipeline to a larger size

If you have available licenses, consider scaling your pipelines to a larger number to allow for more concurrent executions.

Pipeline Response Time

Fixing a pipeline response time issue

If the period that the pipeline response time is outside the expected range, it could be due to the following:

Increase the number of replicas of a pipeline in Run

By increasing the number of replicas, the number of messages on queue decreases because there is a higher throughput and processing is accelerated. Learn more in deployment sizes documentation.