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After we edited something YAML or scrpts again do we need to stop the server and restrt ?

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After we edited something YAML or scrpts again do we need to stop the server and restrt ?

asked a year ago158 views

1 Answer
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The need to restart services after making changes depends on the specific AWS service you're working with:

For AWS AppConfig, when you make configuration changes, you don't need to manually restart servers. Instead, you deploy the configuration changes using the AppConfig deployment process. If there are issues with a deployment, you can use the StopDeployment API operation to roll back an in-progress deployment or revert a completed deployment (within 72 hours) using the AllowRevert parameter.

For Amazon EMR services like Zeppelin, you typically do need to restart the service after making configuration changes for them to take effect. This would involve stopping and then starting the service.

For AWS CodePipeline, when you edit a pipeline, the changes take effect immediately without requiring a restart. However, if you want your latest changes to run through the edited pipeline, you'll need to manually run the pipeline again, as ongoing executions won't automatically incorporate your changes.

For Amazon EMR HBase clusters, when making changes to configurations, you may need to restart specific services depending on what was modified. In EMR versions earlier than 5.30.0, this might involve stopping the service nanny before stopping the instance controller.

For Amazon API Gateway, certain changes to REST APIs require redeployment rather than a restart for the changes to take effect.

The specific restart requirements depend on which AWS service you're working with and what type of changes you've made.
Sources
Reverting a configuration - AWS AppConfig
Step 3 — Restarting Zeppelin service for changes to take effect - Teaching Big Data Skills with Amazon EMR
Updates to REST APIs that require redeployment - Amazon API Gateway
Edit a pipeline in CodePipeline - AWS CodePipeline
Implement Amazon EMR HBase Graceful Scaling | AWS Big Data Blog

answered a year ago

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