Google Cloud Run vs. Percona Operator

Microservices and RestFul API application as it is fast and reliant. Seamless integration with event triggers such as pubsub or event arc, so you can easily integrate that with usecases with file uploads, database changes, etc. Basically great with short-lived tasks, if however, you have long-running processses, Cloud Run might not be idle for this. For example if you have a long running data processing task, other solutions such as kubeflow pipelines or dataflow are more suited for this kind of tasks. Cloud Run is also stateless, so if you need memory, you will have to connect an external database.

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Production ready, robust DB solution built for Kubernetes envs, we was able, for the first time, to ship production db workloads inside k8s. Be sure to check differences with "traditional" MySQL and other clustering solutions. Also be sure to match it with proper k8s storage solutions.

• Auto scaling is the best one
• provide direct VPC connectivity and rigid network
• Cloud SQL and Pub/Sub services
• Handling latency issues

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• Easy and fast deployment.
• A reliable, fully automated, high-available db lifecycle management solution.
• Exaustive documentation.
• Production ready.

• The UI can be made simpler. Currently the UI is bloated and it takes time to find out what you want
• More integrations with container registry providers (ECR, dockerhub)
• Better permissions UX. Currently GCP requires service accounts to be used with cloud products, the experience adding/removing permissions is difficult to navigate

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• Some differences compared to "traditional" MySQL solution.
• Had some hard times trying to use it combined with pre-existing techs like WordPress, Magento, etc.
• The specific k8s environment features can have an impact on it, like the storage solutions offered by the cloud vendor.

We definitely need to renew it because we dont own our own infrastructure and storage and we are happy with Cloud Run features

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The UI/console is great... the documentation is top-notch for developers, but the CLI itself when you have to script around it is very complex and easy to forget some options... the downside of a generic command line client.

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Easy and fast deployment. A reliable, fully automated, high-available db lifecycle management solution. It requires a bit of learning time for people new to operators ecosystem.

Not seen any major issues when we run applications its good

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Initially we felt slow but slowly it picked up and easy to manage

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Well documented, there was no need to contact support.

I was involved in the initial implementation setup, Its easy with the given documentaiton we can do ourself. Not that critical

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AWS Lambda supports code zip package, enabling lower cold start time. Also, AWS Lambda pricing is much simpler, easier to understand.

Other than that, the 2 products are very similar, including the Docker image support: the image must be built based on proprietary base image.

Obviously, if your other services are running in GCP, then Google Cloud Run is your only choice for tight integration, & private networking.

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For a long time we struggled finding a viable solution to migrate our existing db workloads inside Kubernetes. Before "operators era" proper db workloads required manual management, of course that easily raised administrative overhead. Then the future started to be brighter with the introduction of operators and the "official" Oracle's MySQL Operator, then Presslab's one, finally Percona's operator. Compared to other operators, the last one allowed us to ship production db workloads inside k8s.

Not part of purchase.

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It has good auto scale feature and reliable also

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We have very good support when needed

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• Built in support for auto scaling helps reduce operational overhead
• Any application performance issues can be addressed quickly by allocating more resources while a proper fix can be planned and rolled out later
• Using Google Cloud Run enables development of microservices which provides granular control for scaling critical services in the platform

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• It allowed us to use k8s on our entire infrastructure.
• Decreased administrative overhead around our db services.
• As it is an open source technology, it cut down our db pricings.