Great question! Let's break this down based on Google's official guidance for Dataflow pipeline updates and template usage:

Can you update an existing pipeline deployed via a template? #

Yes, you absolutely can—but there are important eligibility rules to follow (from the official pipeline update docs):

• Template-deployed pipelines are just regular Dataflow jobs under the hood. As long as your running job meets the update criteria, you can modify it.
• Key eligibility notes:
  • Streaming jobs have the broadest support for updates. Batch jobs are more restricted—you can't update a batch job that's already started processing data, for example.
  • The updated code/configuration must be topologically compatible with the existing job. That means you can't make changes that alter the fundamental structure of the pipeline, like adding/removing core transforms that change data flow, modifying windowing strategies for in-flight data, or switching input/output sources in a way that breaks compatibility.
• How to do it: Use the gcloud dataflow jobs update command (or the Dataflow API) to push updates. For example, you can adjust worker machine types, tweak runtime parameters, or point to a revised code artifact (like an updated JAR or container image) as long as it’s compatible with the existing job topology.

Can you update an existing pipeline using a newer version of the template? #

Templates are designed primarily for creating new jobs, not directly updating existing ones—but you can leverage a newer template’s code and configuration to update your running job, as long as you follow these steps (aligned with the template overview docs):

1. Extract the updated assets: Templates store their job configuration and code artifacts (like JARs or container images) in Google Cloud Storage. You’ll need to access these assets from your newer template.
2. Verify compatibility: Ensure the new code from the template is topologically compatible with your running job (per the update rules mentioned earlier). Minor changes like bug fixes, parameter tweaks, or compatible transform updates should work here.
3. Run the update: Use the standard pipeline update mechanism (gcloud dataflow jobs update or API) to point your existing job to the new code artifact from the updated template. You can also adjust any exposed template parameters (like worker counts or output paths) during this process.

• Important caveat: If the newer template introduces breaking topological changes (e.g., adding a new input source, changing how data is processed in an incompatible way), you won’t be able to update the existing job. In that case, you’ll need to deploy a new job from the updated template and migrate your workload to it.

Quick Recap #

• Template-deployed jobs are fully eligible for updates when they meet Dataflow’s compatibility rules.
• You can reuse code from a newer template to update an existing job, but only if the code doesn’t break the existing job’s topology.
• Always test updates in a staging environment first to avoid disrupting production workflows!

内容的提问来源于stack exchange，提问作者Shakir Mukkath