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By doing a practice run before the real migration, you can accurately estimate the amount of time needed. However, because the production user doesn't have rights to install new extensions, you might get errors similar to the following when importing:. The plpgsql extension is activated by default in Cloud SQL, so no action is required.

Heroku is a platform as a service PaaS environment, so there are predefined buildpacks for each language, which are used to compile app slugs. In Kubernetes, building a container equates to compiling a slug, which you can then deploy on GKE. This means the Dockerfile used to build your container must set up a complete build environment for your app. In the terminal window of your bastion host VM, go to the top-level directory of your repository. This Dockerfile starts with a standard Ruby 2.

It then installs necessary system packages, installs the gems specified in your app's Gemfile, and precompiles its assets. This key is random, but might not be cryptographically secure. For production use, generate a key with rake secret in a Ruby development environment.

Build the container image. This might take several minutes while all necessary packages are downloaded. Your app is now running in Docker on the VM. The -p flag tells Docker to publish port from the container externally as port Leave the first Cloud Shell session open, because it's needed later. If your app doesn't load properly or returns errors, inspect the running container for errors. Check the container's logs. Use docker exec to run commands in the container. For example, to open a Bash shell in the container so you can inspect its contents and verify that it is built correctly:.

If you need to make any changes to the app's configuration, rebuild your image and start a new container with the previous instructions, starting from Build the container image. When the container image can run the app, it's time to push it from your VM to Container Registry. Kubernetes has a secret object for storing sensitive information, such as passwords.

This lets you check configuration files into source control without exposing sensitive information. In the terminal window of the bastion VM, create a secret called ruby-credentials to store the database username, password, and the previously generated Rails secret key base :. Create the deployment. Each pod also has environment variables either populated directly or from the ruby-credentials secret you created in the previous section. Each pod is labeled as ruby-app and available on port To test the pod's readiness and liveness checks, end the Rails process on one pod.

Deploy an App and Provision a Heroku Postgres Database

In the output, check that the pod enters an error state and restarts, indicating that it's ready again:. For more information, see Readiness and liveness probes. The final step is to create a Service to direct requests from the internet to your app. In the terminal window of the bastion VM, Create the service configuration service. It can take a few minutes to create the load balancer.

This section covers scaling your app on GKE and how this differs from Heroku. In Heroku, you can add more capacity by either upgrading to larger dynos vertical scaling or running more dynos horizontal scaling. In either case, the additional capacity is tied to a single app, so if app A deploys a third dyno, that dyno cannot be used by app B.

GKE provides an efficient scaling model by letting you deploy pods for more than one app deployment onto each underlying node. In this example, both app A and app B have pods running in node 1 and node 2, and app A can make use of space capacity in node 1 by running multiple pods.

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This means there are two ways to scale a GKE app: you can add more capacity by adding nodes, or you can make better use of existing capacity by adding pods. In the terminal shell of your bastion VM, to determine whether more nodes are necessary, check how much free capacity your nodes currently have. It might take a few minutes to create the new nodes.

Resizing to 2 adds a new node in each zone, so this doubles capacity from 3 1 node per zone to 6 2 nodes per zone. This command doesn't add more resources to the cluster, so the new pods compete with pods already running in the pool. By default, the new pods are automatically allocated to the least-loaded nodes. In the NODE column, the pods are evenly distributed across all 6 nodes, including those you created in the previous section. In addition to adding pods or nodes, you can set resource requests and limits to control how many resources your pods are allowed to consume.

Alternatively, you can group your pods in dedicated node pools , only used for a defined app. Finally, you can automate both types of scaling in response to metrics, such as CPU load. A full treatment of this approach is beyond the scope of this tutorial, but Kubernetes lets you autoscale pods with the Horizontal Pod Autoscaler , while GKE lets you autoscale nodes with cluster autoscaling.

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Using pg:pull to migrate a database requires app downtime. If the downtime is too long, there are two alternatives that can reduce or eliminate downtime:. You can only use pg:pull if you are migrating a database where your Heroku and Cloud SQL Postgres versions are the same. If you need to migrate across versions an alternative is to export and import text. However, exporting and importing text is slower and more prone to errors than using pg:pull due to potential version incompatibilities. During the migration, you might still receive extension-related warnings because the text dump tries to recreate the extensions.

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If you are doing a text format migration, comment out the relevant SQL commands in the terminal window of your bastion host VM. In this tutorial, the app container is built and deployed by hand, but this is cumbersome and error-prone for a frequently released app. Stateless apps in Kubernetes, such as the one used in this tutorial, are usually deployed as Deployments , which define a configuration for your app, including restarting pods as necessary. If your app is stateful, meaning the containers need local resources, consider using a StatefulSet instead.

If you run into any problems while deploying your app, the GKE troubleshooting guide is a good place to start. To examine the current state and recent events of Kubernetes resources such as pods, use kubectl to inspect the resource in the terminal window of your bastion host VM. For example, to inspect a pod, first get its pod ID, then describe the pod.

The same Docker debugging commands you used earlier also work with kubectl , so you can quickly fetch logs and interactively explore a running container. When your app displays an error, it's often not clear which pod it originates from. Instead of inspecting pods one by one, open the GCP console and search all pod logs with Stackdriver Logging.

To avoid incurring charges to your GCP and Heroku accounts for the resources used in this tutorial:. The easiest way to clean up resources you created for this tutorial is to delete the GCP project. Go to the Manage resources page.

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    Send feedback. Objectives Create and scale a GKE cluster. Build a Docker image for a Ruby on Rails app. Deploy the app to GKE. Scale the app on GKE. Map Heroku dynos to Kubernetes nodes In Heroku, dynos are tied to single apps. Heroku dyno type GKE machine type Free or hobby f1-micro standard-1x g1-small standard-2x n1-standard-1 performance-m n1-standard-4 performance-l n1-standard-8 For this tutorial, you use machine type g1-small to minimize cost.

    In Cloud Shell, create a regional cluster with one node per zone. Set up a private IP address range With Cloud SQL, you can set up a database that is accessible only by using a private IP address, so traffic isn't exposed to the public internet. Create the production database. In Cloud Shell, create a VM. Configure host utilities In the terminal window of your bastion host VM, install git , kubectl , and psql on the bastion host. Resources Resources. Case Studies Customer success stories. Content Library Learn about data management, science and our latest tech.

    Panoply Blog. Analytics Stack Guide. Community Forum. Contact Us. Try Free Login Demo. Try Panoply for Free. Integrations Databases Heroku Postgres. Data is stored in the cloud, where it can instantly be combined and analyzed with data saved from other tools and databases. Panoply works seamlessly with the data from your Heroku Postgres database. Access, transform, and explore your Heroku Postgres data — more efficiently with Panoply. Other integrations with Heroku Postgres. Heroku Postgres is a Partner Data Source.

    Integrations Databases MongoDB.

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    MongoDB is known for its automatic, easily scaled data clusters. With Panoply, importing MongoDB data is as simple as a few clicks, and once there it is easily accessible from within the cloud database.

    Heroku Postgres: SQL Database-as-a-Service

    It can be combined with other SQL and NoSQL data sources, and your data scientists and analysts can immediately query the data and drive actionable conclusions. The Panoply platform makes it easy to consolidate all of your data, including MongoDB, and begin drawing conclusions in minutes without any custom scripts or programming. Secure, scalable, and powerful, Panoply is an invaluable tool for making use of your stored MongoDB data and replaces the need for a large data infrastructure. Create your own MongoDB data warehouse with Panoply. Other integrations with MongoDB.

    Panoply in 30 seconds. Request a Demo. Automate your data management Loved by founders, analysts, and engineers, Panoply automatically models the data as its uploaded so you can immediately start querying multiple tables together.