VM: why perform maintenance operations

This application is designed to be highly resilient and does not require many maintenance operations. However, in some situations, you may need to restart Docker to update your application version and benefit from the latest patch releases.

In some cases, for example if an error occurs during data processing, some files created for the operation may not be deleted. They can accumulate and fill up the allocated volume. This issue can usually be fixed by stopping and restarting the app.

When to operate

Data processes failed

The first signal that your application may need maintenance is when some data processes launched from Salesforce end with a Failed status. Before checking your VM, make sure the error description does not indicate an issue elsewhere. For example, if you built your own deduplication rules, some errors may occur and stop the process.

For example, if you check your Salesforce Lightning application DQE One, open the Runs tab, and find a failed run as shown below, you may need to restart your VM.

Data processes pending

Sometimes, the job queue can get stuck on the same message and block data processing. In this situation, clear the RabbitMQ Docker volume and reload the application. This should fix the issue in most cases.

1. Connect to your VM as an administrator.

sudo bash

2. Go to the repository where the DQE docker-compose.yml file is located and stop Docker Compose.

docker-compose down

3. Find the name of the message queue volume.

docker volume ls

4. The volume name must contain rabbitmq and data, for example dqe_rabbitmq_data. Delete it.

docker volume rm <name of the volume>

5. Start Docker Compose.

docker-compose up

Backend server engine update

Check the image version defined in the docker-compose.yml file.

If necessary, update it. Then:

1. Connect to your VM as an administrator.

sudo bash

2. Go to the repository where the DQE docker-compose.yml file is located and stop Docker Compose.

docker-compose down

3. Run the following pull command.

docker-compose pull

4. Start Docker Compose.

docker-compose up