Migrating from App Engine Memcache to Cloud Memorystore (Module 13)

Posted by Wesley Chun (@wescpy), Developer Advocate, Google Cloud

Introduction and background

The previous Module 12 episode of the Serverless Migration Station video series demonstrated how to add App Engine Memcache usage to an existing app that has transitioned from the webapp2 framework to Flask. Today’s Module 13 episode continues its modernization by demonstrating how to migrate that app from Memcache to Cloud Memorystore. Moving from legacy APIs to standalone Cloud services makes apps more portable and provides an easier transition from Python 2 to 3. It also makes it possible to shift to other Cloud compute platforms should that be desired or advantageous. Developers benefit from upgrading to modern language releases and gain added flexibility in application-hosting options.

While App Engine Memcache provides a basic, low-overhead, serverless caching service, Cloud Memorystore “takes it to the next level” as a standalone product. Rather than a proprietary caching engine, Cloud Memorystore gives users the option to select from a pair of open source engines, Memcached or Redis, each of which provides additional features unavailable from App Engine Memcache. Cloud Memorystore is typically more cost efficient at-scale, offers high availability, provides automatic backups, etc. On top of this, one Memorystore instance can be used across many applications as well as incorporates improvements to memory handling, configuration tuning, etc., gained from experience managing a huge fleet of Redis and Memcached instances.

While Memcached is more similar to Memcache in usage/features, Redis has a much richer set of data structures that enable powerful application functionality if utilized. Redis has also been recognized as the most loved database by developers in StackOverflow’s annual developers survey, and it’s a great skill to pick up. For these reasons, we chose Redis as the caching engine for our sample app. However, if your apps’ usage of App Engine Memcache is deeper or more complex, a migration to Cloud Memorystore for Memcached may be a better option as a closer analog to Memcache.

Migrating to Cloud Memorystore for Redis featured video


Performing the migration

The sample application registers individual web page “visits,” storing visitor information such as IP address and user agent. In the original app, the most recent visits are cached into Memcache for an hour and used for display if the same user continuously refreshes their browser during this period; caching is a one way to counter this abuse. New visitors or cache expiration results new visits as well as updating the cache with the most recent visits. Such functionality must be preserved when migrating to Cloud Memorystore for Redis.

Below is pseudocode representing the core part of the app that saves new visits and queries for the most recent visits. Before, you can see how the most recent visits are cached into Memcache. After completing the migration, the underlying caching infrastructure has been swapped out in favor of Memorystore (via language-specific Redis client libraries). In this migration, we chose Redis version 5.0, and we recommend the latest versions, 5.0 and 6.x at the time of this writing, as the newest releases feature additional performance benefits, fixes to improve availability, and so on. In the code snippets below, notice how the calls between both caching systems are nearly identical. The bolded lines represent the migration-affected code managing the cached data.

Switching from App Engine Memcache to Cloud Memorystore for Redis

Wrap-up

The migration covered begins with the Module 12 sample app (“START”). Migrating the caching system to Cloud Memorystore and other requisite updates results in the Module 13 sample app (“FINISH”) along with an optional port to Python 3. To practice this migration on your own to help prepare for your own migrations, follow the codelab to do it by-hand while following along in the video.

While the code migration demonstrated seems straightforward, the most critical change is that Cloud Memorystore requires dedicated server instances. For this reason, a Serverless VPC connector is also needed to connect your App Engine app to those Memorystore instances, requiring more dedicated servers. Furthermore, neither Cloud Memorystore nor Cloud VPC are free services, and neither has an “Always free” tier quota. Before moving forward this migration, check the pricing documentation for Cloud Memorystore for Redis and Serverless VPC access to determine cost considerations before making a commitment.

One key development that may affect your decision: In Fall 2021, the App Engine team extended support of many of the legacy bundled services like Memcache to next-generation runtimes, meaning you are no longer required to migrate to Cloud Memorystore when porting your app to Python 3. You can continue using Memcache even when upgrading to 3.x so long as you retrofit your code to access bundled services from next-generation runtimes.

A move to Cloud Memorystore and today’s migration techniques will be here if and when you decide this is the direction you want to take for your App Engine apps. All Serverless Migration Station content (codelabs, videos, source code [when available]) can be accessed at its open source repo. While our content initially focuses on Python users, we plan to cover other language runtimes, so stay tuned. For additional video content, check out our broader Serverless Expeditions series.

How can App Engine users take advantage of Cloud Functions?

Posted by Wesley Chun (@wescpy), Developer Advocate, Google Cloud

Introduction

Recently, we discussed containerizing App Engine apps for Cloud Run, with or without Docker. But what about Cloud Functions… can App Engine users take advantage of that platform somehow? Back in the day, App Engine was always the right decision, because it was the only option. With Cloud Functions and Cloud Run joining in the serverless product suite, that’s no longer the case.

Back when App Engine was the only choice, it was selected to host small, single-function apps. Yes, when it was the only option. Other developers have created huge monolithic apps for App Engine as well… because it was also the only option. Fast forward to today where code follows more service-oriented or event-driven architectures. Small apps can be moved to Cloud Functions to simplify the code and deployments while large apps could be split into smaller components, each running on Cloud Functions.

Refactoring App Engine apps for Cloud Functions

Small, single-function apps can be seen as a microservice, an API endpoint “that does something,” or serve some utility likely called as a result of some event in a larger multi-tiered application, say to update a database row or send a customer email message. App Engine apps require some kind web framework and routing mechanism while Cloud Function equivalents can be freed from much of those requirements. Refactoring these types of App Engine apps for Cloud Functions will like require less overhead, helps ease maintenance, and allow for common components to be shared across applications.

Large, monolithic applications are often made up of multiple pieces of functionality bundled together in one big package, such as requisitioning a new piece of equipment, opening a customer order, authenticating users, processing payments, performing administrative tasks, and so on. By breaking this monolith up into multiple microservices into individual functions, each component can then be reused in other apps, maintenance is eased because software bugs will identify code closer to their root origins, and developers won’t step on each others’ toes.

Migration to Cloud Functions

In this latest episode of Serverless Migration Station, a Serverless Expeditions mini-series focused on modernizing serverless apps, we take a closer look at this product crossover, covering how to migrate App Engine code to Cloud Functions. There are several steps you need to take to prepare your code for Cloud Functions:

  • Divest from legacy App Engine “bundled services,” e.g., Datastore, Taskqueue, Memcache, Blobstore, etc.
  • Cloud Functions supports modern runtimes; upgrade to Python 3, Java 11, or PHP 7
  • If your app is a monolith, break it up into multiple independent functions. (You can also keep a monolith together and containerize it for Cloud Run as an alternative.)
  • Make appropriate application updates to support Cloud Functions

    The first three bullets are outside the scope of this video and its codelab, so we’ll focus on the last one. The changes needed for your app include the following:

    1. Remove unneeded and/or unsupported configuration
    2. Remove use of the web framework and supporting routing code
    3. For each of your functions, assign an appropriate name and install the request object it will receive when it is called.

    Regarding the last point, note that you can have multiple “endpoints” coming into a single function which processes the request path, calling other functions to handle those routes. If you have many functions in your app, separate functions for every endpoint becomes unwieldy; if large enough, your app may be more suited for Cloud Run. The sample app in this video and corresponding code sample only has one function, so having a single endpoint for that function works perfectly fine here.

    This migration series focuses on our earliest users, starting with Python 2. Regarding the first point, the app.yaml file is deleted. Next, almost all Flask resources are removed except for the template renderer (the app still needs to output the same HTML as the original App Engine app). All app routes are removed, and there’s no instantiation of the Flask app object. Finally for the last step, the main function is renamed more appropriately to visitme() along with a request object parameter.

    This “migration module” starts with the (Python 3 version of the) Module 2 sample app, applies the steps above, and arrives at the migrated Module 11 app. Implementing those required changes is illustrated by this code “diff:”

    Migration of sample app to Cloud Functions

    Next steps

    If you’re interested in trying this migration on your own, feel free to try the corresponding codelab which leads you step-by-step through this exercise and use the video for additional guidance.

    All migration modules, their videos (when published), codelab tutorials, START and FINISH code, etc., can be found in the migration repo. We hope to also one day cover other legacy runtimes like Java 8 as well as content for the next-generation Cloud Functions service, so stay tuned. If you’re curious whether it’s possible to write apps that can run on App Engine, Cloud Functions, or Cloud Run with no code changes at all, the answer is yes. Hope this content is useful for your consideration when modernizing your own serverless applications!

Google Cloud projects: Tips and best practices

By Peter Jacobsen, Google Technical Writer

Least privilege

Always apply the principle of least privilege when you provide access to Google Cloud resources. The best practice is to grant only the most limited predefined roles or custom roles that meet your needs.

For more information, see Least privilege.

Google Cloud billing alerts

Set up Google Cloud billing alerts for your projects at specified intervals for early warning of usage patterns, and to help reduce costs.

For more information, see Create, edit, or delete budgets and budget alerts.

API quotas

API quotas protect the Google infrastructure from excessive API requests. Traffic is blocked when the level of requests reaches the daily API quota level or a per-user rate limit.

To avoid disruptions due to an API quota level that’s too low, set the quota for your app or API appropriately. Note that the lead time for the increase of quotas is one month.

For more information, see API Quotas.

Checklist for production-ready enterprise workloads

Use this checklist to set up scalable, production-ready enterprise workloads. Note that the checklist assumes that you’re an administrator with control over your company’s Google Cloud resources.

For more information, see Google Cloud setup checklist.

Google Workspace domain ownership of projects

Google Workspace domain ownership of your group’s project lets you tie it into a Google Workspace account, rather than have it tied to a personal account.

For more information, see Best practices for planning accounts and organizations.

Identity-Aware Proxy (IAP)

IAP lets you hide your website until you’re ready for people to see it. IAP establishes a central authorization layer for apps accessed by HTTPS, so you can adopt an app-level access-control model rather than use network-level firewalls. When IAP protects an app or resource, only users who have the correct Identity and Access Management (IAM) role can access it through the proxy.

For more information, see Identity-Aware Proxy overview.

Cloud Build

Cloud Build can import source code from a variety of repositories or cloud storage spaces, execute a build to your specifications, and produce artifacts, such as Docker containers or Java archives. You can configure builds to fetch dependencies and run unit tests, static analyzes, and integration tests.

For more information, see Cloud Build.

Useful Google Cloud tools and services

Google Cloud has many tools and services that can help you create and keep your projects in sync, such as:

  • Cloud Build: executes your builds on Google Cloud infrastructure.
  • Google Cloud Deploy: deploys releases continuously to Google Kubernetes Engine.
  • Container Registry: provides a single place for your team to manage Docker images and control access.
  • Artifact Registry: provides a single place for your organization to manage container images and language packages, such as Maven and npm.
  • Cloud Source Repositories: provides a single place for your team to store, manage, and track code.
  • Cloud Deployment Manager: automates the creation and management of Google Cloud resources.

Google Groups for management across projects

Google Groups can help you manage teams across projects, which includes the setup of the group access through IAM. Groups such as project teams, departments, or classmates can communicate and collaborate with Google Groups. If you want to invite a group to an event or share documents with a group, you can send a single email to everyone in the group.

For more information about how to set up a group, see Google Groups.

Watch for Google suggestions

Google provides many useful tips and suggestions for best practices within the context of your work. For example, if you go to a project that you haven’t used in a while, you may get a warning like this one:

If you click the link, you see a page that tells you how to apply role recommendations to help you enforce the principle of least privilege to ensure that principals have only the permissions that they actually need. Google offers many suggestions for best practices such as this one, so watch for them as you work.

Here’s an example of a useful in-console recommendation that you might see from your billing page:

If you click Learn more, you arrive at a Cloud billing checklist, which is part of a longer billing-specific checklist that you might find useful.

Here’s another example found on the API & Services page:

If you click Edit settings, you arrive on a page where you can change the settings.

Prediction Framework, a time saver for Data Science prediction projects

Posted by Álvaro Lamas, Héctor Parra, Jaime Martínez, Julia Hernández, Miguel Fernandes, Pablo Gil

Acquiring high value customers using predicted Lifetime Value, taking specific actions on high propensity of churn users, generating and activating audiences based on machine learning processed signals…All of those marketing scenarios require of analyzing first party data, performing predictions on the data and activating the results into the different marketing platforms like Google Ads as frequently as possible to keep the data fresh.

Feeding marketing platforms like Google Ads on a regular and frequent basis, requires a robust, report oriented and cost reduced ETL & prediction pipeline. These pipelines are very similar regardless of the use case and it’s very easy to fall into reinventing the wheel every time or manually copy & paste structural code increasing the risk of introducing errors.

Wouldn’t it be great to have a common reusable structure and just add the specific code for each of the stages?

Here is where Prediction Framework plays a key role in helping you implement and accelerate your first-party data prediction projects by providing the backbone elements of the predictive process.

Prediction Framework is a fully customizable pipeline that allows you to simplify the implementation of prediction projects. You only need to have the input data source, the logic to extract and process the data and a Vertex AutoML model ready to use along with the right feature list, and the framework will be in charge of creating and deploying the required artifacts. With a simple configuration, all the common artifacts of the different stages of this type of projects will be created and deployed for you: data extraction, data preparation (aka feature engineering), filtering, prediction and post-processing, in addition to some other operational functionality including backfilling, throttling (for API limits), synchronization, storage and reporting.

The Prediction Framework was built to be hosted in the Google Cloud Platform and it makes use of Cloud Functions to do all the data processing (extraction, preparation, filtering and post-prediction processing), Firestore, Pub/Sub and Schedulers for the throttling system and to coordinate the different phases of the predictive process, Vertex AutoML to host your machine learning model and BigQuery as the final storage of your predictions.

Prediction Framework Architecture

To get involved and start using the Prediction Framework, a configuration file needs to be prepared with some environment variables about the Google Cloud Project to be used, the data sources, the ML model to make the predictions and the scheduler for the throttling system. In addition, custom queries for the data extraction, preparation, filtering and post-processing need to be added in the deploy files customization. Then, the deployment is done automatically using a deployment script provided by the tool.

Once deployed, all the stages will be executed one after the other, storing the intermediate and final data in the BigQuery tables:

  • Extract: this step will, on a timely basis, query the transactions from the data source, corresponding to the run date (scheduler or backfill run date) and will store them in a new table into the local project BigQuery.
  • Prepare: immediately after the extract of the transactions for one specific date is available, the data will be picked up from the local BigQuery and processed according to the specs of the model. Once the data is processed, it will be stored in a new table into the local project BigQuery.
  • Filter: this step will query the data stored by the prepare process and will filter the required data and store it into the local project BigQuery. (i.e only taking into consideration new customers transactionsWhat a new customer is up to the instantiation of the framework for the specific use case. Will be covered later).
  • Predict: once the new customers are stored, this step will read them from BigQuery and call the prediction using Vertex API. A formula based on the result of the prediction could be applied to tune the value or to apply thresholds. Once the data is ready, it will be stored into the BigQuery within the target project.
  • Post_process: A formula could be applied to the AutoML batch results to tune the value or to apply thresholds. Once the data is ready, it will be stored into the BigQuery within the target project.

One of the powerful features of the prediction framework is that it allows backfilling directly from the BigQuery user interface, so in case you’d need to reprocess a whole period of time, it could be done in literally 4 clicks.

In summary: Prediction Framework simplifies the implementation of first-party data prediction projects, saving time and minimizing errors of manual deployments of recurrent architectures.

For additional information and to start experimenting, you can visit the Prediction Framework repository on Github.

Migrating App Engine push queues to Cloud Tasks

Posted by Wesley Chun (@wescpy), Developer Advocate, Google Cloud

Banner image that shows the Cloud Task logo

Introduction

The previous Module 7 episode of Serverless Migration Station gave developers an idea of how App Engine push tasks work and how to implement their use in an existing App Engine ndb Flask app. In this Module 8 episode, we migrate this app from the App Engine Datastore (ndb) and Task Queue (taskqueue) APIs to Cloud NDB and Cloud Tasks. This makes your app more portable and provides a smoother transition from Python 2 to 3. The same principle applies to upgrading other legacy App Engine apps from Java 8 to 11, PHP 5 to 7, and up to Go 1.12 or newer.

Over the years, many of the original App Engine services such as Datastore, Memcache, and Blobstore, have matured to become their own standalone products, for example, Cloud Datastore, Cloud Memorystore, and Cloud Storage, respectively. The same is true for App Engine Task Queues, whose functionality has been split out to Cloud Tasks (push queues) and Cloud Pub/Sub (pull queues), now accessible to developers and applications outside of App Engine.

Migrating App Engine push queues to Cloud Tasks video

Migrating to Cloud NDB and Cloud Tasks

The key updates being made to the application:

  1. Add support for Google Cloud client libraries in the app’s configuration
  2. Switch from App Engine APIs to their standalone Cloud equivalents
  3. Make required library adjustments, e.g., add use of Cloud NDB context manager
  4. Complete additional setup for Cloud Tasks
  5. Make minor updates to the task handler itself

The bulk of the updates are in #3 and #4 above, and those are reflected in the following “diff”s for the main application file:

Screenshot shows primary differences in code when switching to Cloud NDB & Cloud Tasks

Primary differences switching to Cloud NDB & Cloud Tasks

With these changes implemented, the web app works identically to that of the Module 7 sample, but both the database and task queue functionality have been completely swapped to using the standalone/unbundled Cloud NDB and Cloud Tasks libraries… congratulations!

Next steps

To do this exercise yourself, check out our corresponding codelab which leads you step-by-step through the process. You can use this in addition to the video, which can provide guidance. You can also review the push tasks migration guide for more information. Arriving at a fully-functioning Module 8 app featuring Cloud Tasks sets the stage for a larger migration ahead in Module 9. We’ve accomplished the most important step here, that is, getting off of the original App Engine legacy bundled services/APIs. The Module 9 migration from Python 2 to 3 and Cloud NDB to Cloud Firestore, plus the upgrade to the latest version of the Cloud Tasks client library are all fairly optional, but they represent a good opportunity to perform a medium-sized migration.

All migration modules, their videos (when available), codelab tutorials, and source code, can be found in the migration repo. While the content focuses initially on Python users, we will cover other legacy runtimes soon so stay tuned.

How to use App Engine push queues in Flask apps

Posted by Wesley Chun (@wescpy), Developer Advocate, Google Cloud

Banner image that shows the Cloud Task logo

Introduction

Since its original launch in 2008, many of the core Google App Engine services such as Datastore, Memcache, and Blobstore, have matured to become their own standalone products: for example, Cloud Datastore, Cloud Memorystore, and Cloud Storage, respectively. The same is true for App Engine Task Queues with Cloud Tasks. Today’s Module 7 episode of Serverless Migration Station reviews how App Engine push tasks work, by adding this feature to an existing App Engine ndb Flask app.

App Engine push queues in Flask apps video

That app is where we left off at the end of Module 1, migrating its web framework from App Engine webapp2 to Flask. The app registers web page visits, creating a Datastore Entity for each. After a new record is created, the ten most recent visits are displayed to the end-user. If the app only shows the latest visits, there is no reason to keep older visits, so the Module 7 exercise adds a push task that deletes all visits older than the oldest one shown. Tasks execute asynchronously outside the normal application flow.

Key updates

The following are the changes being made to the application:

  1. Add use of App Engine Task Queues (taskqueue) API
  2. Determine oldest visit displayed, logging and saving that timestamp
  3. Create task to delete old visits
  4. Update web page template to display timestamp threshold
  5. Log how many and which visits (by Entity ID) are deleted

Except for #4 which occurs in the HTML template file, these updates are reflected in the “diff”s for the main application file:

Screenshot of App Engine push tasks application source code differences

Adding App Engine push tasks application source code differences

With these changes implemented, the web app now shows the end-user which visits will be deleted by the new push task:

Screenshot of VisitMe example showing last ten site visits. A red circle around older visits being deleted

Sample application output

Next steps

To do this exercise yourself, check out our corresponding codelab which leads you step-by-step through the process. You can use this in addition to the video, which can provide guidance. You can also review the push queue documentation for more information. Arriving at a fully-functioning Module 7 app featuring App Engine push tasks sets the stage for migrating it to Cloud Tasks (and Cloud NDB) ahead in Module 8.

All migration modules, their videos (when available), codelab tutorials, and source code, can be found in the migration repo. While the content focuses initially on Python users, we will cover other legacy runtimes soon so stay tuned.

Exploring serverless with a nebulous app: Deploy the same app to App Engine, Cloud Functions, or Cloud Run

Posted by Wesley Chun (@wescpy), Developer Advocate, Google Cloud

Banner image that shows the App Engine, Cloud Functions, and Cloud Run logos

Introduction

Google Cloud offers three distinct ways of running your code or application in a serverless way, each serving different use cases. Google App Engine, our first Cloud product, was created to give users the ability to deploy source-based web applications or mobile backends directly to the cloud without the need of thinking about servers or scaling. Cloud Functions came later for scenarios where you may not have an entire app, great for one-off utility functions or event-driven microservices. Cloud Run is our latest fully-managed serverless product that gives developers the flexibility of containers along with the convenience of serverless.

As all are serverless compute platforms, users recognize they share some similarities along with clear differences, and often, they ask:

  1. How different is deploying code to App Engine, Cloud Functions, or Cloud Run?
  2. Is it challenging to move from one to another if I feel the other may better fit my needs?

We’re going to answer these questions today by sharing a unique application with you, one that can be deployed to all three platforms without changing any application code. All of the necessary changes are done in configuration.

More motivation

Another challenge for developers can be trying to learn how to use another Cloud product, such as this request, paraphrased from a user:

  1. I have a Google App Engine app
  2. I want to call the Cloud Translation API from that app

Sounds simple enough. This user went straight to the App Engine and Translation API documentation where they were able to get started with the App Engine Quickstart to get their app up and going, then found the Translation API setup page and started looking into permissions needed to access the API. However, they got stuck at the Identity and Access Management (IAM) page on roles, being overwhelmed at all the options but no clear path forward. In light of this, let’s add a third question to preceding pair outlined earlier:

  1. How do you access Cloud APIs from a Cloud serverless platform?

Without knowing what that user was going to build, let’s just implement a barebones translator, an “MVP” (minimally viable product) version of a simple “My Google Translate” Python Flask app using the Translation API, one of Google Cloud’s AI/ML “building block” APIs. These APIs are backed by pre-trained machine learning models, giving developers with little or no background in AI/ML the ability to leverage the benefits of machine learning with only API calls.

The application

The app consists of a simple web page prompting the user for a phrase to translate from English to Spanish. The translated results along with the original phrase are presented along with an empty form for a follow-up translation if desired. While the majority of this app’s deployments are in Python 3, there are still many users working on upgrading from Python 2, so some of those deployments are available to help with migration planning. Taking this into account, this app can be deployed (at least) eight different ways:

  1. Local (or hosted) Flask server (Python 2)
  2. Local (or hosted) Flask server (Python 3)
  3. Google App Engine (Python 2)
  4. Google App Engine (Python 3)
  5. Google Cloud Functions (Python 3)
  6. Google Cloud Run (Python 2 via Docker)
  7. Google Cloud Run (Python 3 via Docker)
  8. Google Cloud Run (Python 3 via Cloud Buildpacks)

The following is a brief glance at the files and which configurations they’re for: Screenshot of Nebulous serverless sample app files

Nebulous serverless sample app files

Diving straight into the application, let’s look at its primary function, translate():

@app.route('/', methods=['GET', 'POST'])
def translate(gcf_request=None):
local_request = gcf_request if gcf_request else request
text = translated = None
if local_request.method == 'POST':
text = local_request.form['text'].strip()
if text:
data = {
'contents': [text],
'parent': PARENT,
'target_language_code': TARGET[0],
}
rsp = TRANSLATE.translate_text(request=data)
translated = rsp.translations[0].translated_text
context = {
'orig': {'text': text, 'lc': SOURCE},
'trans': {'text': translated, 'lc': TARGET},
}
return render_template('index.html', **context)

Core component (translate()) of sample application

Some key app components:

  • Upon an initial request (GET), an HTML template is rendered featuring a simple form with an empty text field for the text to translate.
  • The form POSTs back to the app, and in this case, grabs the text to translate, sends the request to the Translation API, receives and displays the results to the user along with an empty form for another translation.
  • There is a special “ifdef” for Cloud Functions near the top to receive a request object because a web framework isn’t used like you’d have with App Engine or Cloud Run, so Cloud Functions provides one for this reason.

The app runs identically whether running locally or deployed to App Engine, Cloud Functions, or Cloud Run. The magic is all in the configuration. The requirements.txt file* is used in all configurations, whether to install third-party packages locally, or to direct the Cloud Build system to automatically install those libraries during deployment. Beyond requirements.txt, things start to differ:

  1. App Engine has an app.yaml file and possibly an appengine_config.py file.
  2. Cloud Run has either a Dockerfile (Docker) or Procfile (Cloud Buildpacks), and possibly a service.yaml file.
  3. Cloud Functions, the “simplest” of the three, has no configuration outside of a package requirements file (requirements.txt, package.json, etc.).

The following is what you should expect to see after completing one translation request: Screenshot of My Google Translate (1990s Edition) in Incognito Window

“My Google Translate” MVP app (Cloud Run edition)

Next steps

The sample app can be run locally or on your own hosting server, but now you also know how to deploy it to each of Cloud’s serverless platforms and what those subtle differences are. You also have a sense of the differences between each platform as well as what it takes to switch from one to another. Lastly, you now know how to access Cloud APIs from these platforms.

The user described earlier was overwhelmed at all the IAM roles and options available because this type of detail is required to provide the most security options for accessing Cloud services, but when prototyping, the fastest on-ramp is to use the default service account that comes with Cloud serverless platforms. These help you get that prototype working while allowing you to learn more about IAM roles and required permissions. Once you’ve progressed far enough to consider deploying to production, you can then follow the best practice of “least privileges” and create your own (user-managed) service accounts with the minimal permissions required so your application functions properly.

To dive in, the code and codelabs (free, self-paced, hands-on tutorials) for each deployment are available in its open source repository. An active Google Cloud billing account is required to deploy this application to each of our serverless platforms even though you can do all of them without incurring charges. More information can be found in the “Cost” section of the repo’s README. We hope this sample app teaches you more about the similarities and differences between our plaforms, shows you how you can “shift” applications comfortably between them, and provides a light introduction to another Cloud API. Also check out my colleague’s post featuring similar content for Node.js.

An easier way to move your App Engine apps to Cloud Run

Posted by Wesley Chun (@wescpy), Developer Advocate, Google Cloud

Blue header

An easier yet still optional migration

In the previous episode of the Serverless Migration Station video series, developers learned how to containerize their App Engine code for Cloud Run using Docker. While Docker has gained popularity over the past decade, not everyone has containers integrated into their daily development workflow, and some prefer “containerless” solutions but know that containers can be beneficial. Well today’s video is just for you, showing how you can still get your apps onto Cloud Run, even If you don’t have much experience with Docker, containers, nor Dockerfiles.

App Engine isn’t going away as Google has expressed long-term support for legacy runtimes on the platform, so those who prefer source-based deployments can stay where they are so this is an optional migration. Moving to Cloud Run is for those who want to explicitly move to containerization.

Migrating to Cloud Run with Cloud Buildpacks video

So how can apps be containerized without Docker? The answer is buildpacks, an open-source technology that makes it fast and easy for you to create secure, production-ready container images from source code, without a Dockerfile. Google Cloud Buildpacks adheres to the buildpacks open specification and allows users to create images that run on all GCP container platforms: Cloud Run (fully-managed), Anthos, and Google Kubernetes Engine (GKE). If you want to containerize your apps while staying focused on building your solutions and not how to create or maintain Dockerfiles, Cloud Buildpacks is for you.

In the last video, we showed developers how to containerize a Python 2 Cloud NDB app as well as a Python 3 Cloud Datastore app. We targeted those specific implementations because Python 2 users are more likely to be using App Engine’s ndb or Cloud NDB to connect with their app’s Datastore while Python 3 developers are most likely using Cloud Datastore. Cloud Buildpacks do not support Python 2, so today we’re targeting a slightly different audience: Python 2 developers who have migrated from App Engine ndb to Cloud NDB and who have ported their apps to modern Python 3 but now want to containerize them for Cloud Run.

Developers familiar with App Engine know that a default HTTP server is provided by default and started automatically, however if special launch instructions are needed, users can add an entrypoint directive in their app.yaml files, as illustrated below. When those App Engine apps are containerized for Cloud Run, developers must bundle their own server and provide startup instructions, the purpose of the ENTRYPOINT directive in the Dockerfile, also shown below.

Starting your web server with App Engine (app.yaml) and Cloud Run with Docker (Dockerfile) or Buildpacks (Procfile)

Starting your web server with App Engine (app.yaml) and Cloud Run with Docker (Dockerfile) or Buildpacks (Procfile)

In this migration, there is no Dockerfile. While Cloud Buildpacks does the heavy-lifting, determining how to package your app into a container, it still needs to be told how to start your service. This is exactly what a Procfile is for, represented by the last file in the image above. As specified, your web server will be launched in the same way as in app.yaml and the Dockerfile above; these config files are deliberately juxtaposed to expose their similarities.

Other than this swapping of configuration files and the expected lack of a .dockerignore file, the Python 3 Cloud NDB app containerized for Cloud Run is nearly identical to the Python 3 Cloud NDB App Engine app we started with. Cloud Run’s build-and-deploy command (gcloud run deploy) will use a Dockerfile if present but otherwise selects Cloud Buildpacks to build and deploy the container image. The user experience is the same, only without the time and challenges required to maintain and debug a Dockerfile.

Get started now

If you’re considering containerizing your App Engine apps without having to know much about containers or Docker, we recommend you try this migration on a sample app like ours before considering it for yours. A corresponding codelab leading you step-by-step through this exercise is provided in addition to the video which you can use for guidance.

All migration modules, their videos (when available), codelab tutorials, and source code, can be found in the migration repo. While our content initially focuses on Python users, we hope to one day also cover other legacy runtimes so stay tuned. Containerization may seem foreboding, but the goal is for Cloud Buildpacks and migration resources like this to aid you in your quest to modernize your serverless apps!

Containerizing Google App Engine apps for Cloud Run

Posted by Wesley Chun (@wescpy), Developer Advocate, Google Cloud

Google App Engine header

An optional migration

Serverless Migration Station is a video mini-series from Serverless Expeditions focused on helping developers modernize their applications running on a serverless compute platform from Google Cloud. Previous episodes demonstrated how to migrate away from the older, legacy App Engine (standard environment) services to newer Google Cloud standalone equivalents like Cloud Datastore. Today’s product crossover episode differs slightly from that by migrating away from App Engine altogether, containerizing those apps for Cloud Run.

There’s little question the industry has been moving towards containerization as an application deployment mechanism over the past decade. However, Docker and use of containers weren’t available to early App Engine developers until its flexible environment became available years later. Fast forward to today where developers have many more options to choose from, from an increasingly open Google Cloud. Google has expressed long-term support for App Engine, and users do not need to containerize their apps, so this is an optional migration. It is primarily for those who have decided to add containerization to their application deployment strategy and want to explicitly migrate to Cloud Run.

If you’re thinking about app containerization, the video covers some of the key reasons why you would consider it: you’re not subject to traditional serverless restrictions like development language or use of binaries (flexibility); if your code, dependencies, and container build & deploy steps haven’t changed, you can recreate the same image with confidence (reproducibility); your application can be deployed elsewhere or be rolled back to a previous working image if necessary (reusable); and you have plenty more options on where to host your app (portability).

Migration and containerization

Legacy App Engine services are available through a set of proprietary, bundled APIs. As you can surmise, those services are not available on Cloud Run. So if you want to containerize your app for Cloud Run, it must be “ready to go,” meaning it has migrated to either Google Cloud standalone equivalents or other third-party alternatives. For example, in a recent episode, we demonstrated how to migrate from App Engine ndb to Cloud NDB for Datastore access.

While we’ve recently begun to produce videos for such migrations, developers can already access code samples and codelab tutorials leading them through a variety of migrations. In today’s video, we have both Python 2 and 3 sample apps that have divested from legacy services, thus ready to containerize for Cloud Run. Python 2 App Engine apps accessing Datastore are most likely to be using Cloud NDB whereas it would be Cloud Datastore for Python 3 users, so this is the starting point for this migration.

Because we’re “only” switching execution platforms, there are no changes at all to the application code itself. This entire migration is completely based on changing the apps’ configurations from App Engine to Cloud Run. In particular, App Engine artifacts such as app.yaml, appengine_config.py, and the lib folder are not used in Cloud Run and will be removed. A Dockerfile will be implemented to build your container. Apps with more complex configurations in their app.yaml files will likely need an equivalent service.yaml file for Cloud Run — if so, you’ll find this app.yaml to service.yaml conversion tool handy. Following best practices means there’ll also be a .dockerignore file.

App Engine and Cloud Functions are sourced-based where Google Cloud automatically provides a default HTTP server like gunicorn. Cloud Run is a bit more “DIY” because users have to provide a container image, meaning bundling our own server. In this case, we’ll pick gunicorn explicitly, adding it to the top of the existing requirements.txt required packages file(s), as you can see in the screenshot below. Also illustrated is the Dockerfile where gunicorn is started to serve your app as the final step. The only differences for the Python 2 equivalent Dockerfile are: a) require the Cloud NDB package (google-cloud-ndb) instead of Cloud Datastore, and b) start with a Python 2 base image.

Image of The Python 3 requirements.txt and Dockerfile

The Python 3 requirements.txt and Dockerfile

Next steps

To walk developers through migrations, we always “START” with a working app then make the necessary updates that culminate in a working “FINISH” app. For this migration, the Python 2 sample app STARTs with the Module 2a code and FINISHes with the Module 4a code. Similarly, the Python 3 app STARTs with the Module 3b code and FINISHes with the Module 4b code. This way, if something goes wrong during your migration, you can always rollback to START, or compare your solution with our FINISH. If you are considering this migration for your own applications, we recommend you try it on a sample app like ours before considering it for yours. A corresponding codelab leading you step-by-step through this exercise is provided in addition to the video which you can use for guidance.

All migration modules, their videos (when published), codelab tutorials, START and FINISH code, etc., can be found in the migration repo. We hope to also one day cover other legacy runtimes like Java 8 so stay tuned. We’ll continue with our journey from App Engine to Cloud Run ahead in Module 5 but will do so without explicit knowledge of containers, Docker, or Dockerfiles. Modernizing your development workflow to using containers and best practices like crafting a CI/CD pipeline isn’t always straightforward; we hope content like this helps you progress in that direction!

#IamaGDE: Diana Rodríguez Manrique

#IamaGDE series presents: Google Maps Platform

Welcome to #IamaGDE – a series of spotlights presenting Google Developer Experts (GDEs) from across the globe. Discover their stories, passions, and highlights of their community work.

Today, meet Diana Rodríguez— Maps, Web, Cloud, and Firebase GDE.

Google Developer Expert, Diana Rodríguez

Diana Rodríguez’s 20 years in the tech industry have been focused on community and making accessible content. She is a full-stack developer with experience in backend infrastructure, automation, and a passion for Python. A self-taught programmer, Diana also learned programming skills from attending meetups and being an active member of her local developer community. She is the first female Venezuelan GDE.

“I put a lot of myself into public speaking, workshops, and articles,” says Diana. “I want to make everything I do as open and transparent as possible.”

Diana’s first foray into working with Google Maps was in 2016, when she built an app that helped record institutional violence against women in Argentina. As a freelance developer, she uses the Google Maps Platform for her delivery services clients.

“I have plenty of clients who need not only location tracking for their delivery fleet, but also to provide specific routes,” says Diana.

“The level of interaction that’s been added to Maps has made it easier for me as a developer to work with direct clients,” says Diana, who uses the Plus Codes feature to help delivery drivers find precise locations on a map. “I’m a heavy user of plus codes. They give people in remote areas and underserved communities the chance to have location services, including emergency and delivery services.”

Getting involved in the developer community

Diana first became involved in the developer community 20 years ago, in 1999, beginning with a university user group. She attended her first Devfest in Bangkok in 2010 and has worked in multiple developer communities since then. She was a co-organizer of GDG Triangle and is now an organizer of GDG Durham in North Carolina. In 2020, she gave virtual talks to global audiences.

“It’s been great to get to know other communities and reach the far corners of the Earth,” she says.

Image of Diana Rodriguez

Favorite Google Maps Platform features and current projects

Diana is excited about the Places API and the Maps team’s continuous improvements. She says the Maps team keeps the GDEs up to date on all the latest news and takes their feedback very seriously.

“Shoutout to Claire, Alex, and Angela, who are in direct contact with us, and everyone who works with them; they have been amazing,” she says. “I look forward to showcasing more upcoming changes. What comes next will be mind-blowing, immersing people into location in a different way that is more interactive.”

Of the new features released in June 2020, which include Cloud-based maps styling and Local Context, Diana says, “Having the freedom to customize the experience a lot more is amazing.”

As a Maps GDE in 2021, Diana plans to continue working on open source tech projects that benefit the greater good, like her recently completed app for Diabetes users, ScoutX, which notifies emergency contacts when a Diabetic person’s blood glucose values are too high or too low, in case they need immediate help.

She envisions an app that expands connectivity and geolocation tracking for hikers in remote areas, using LoRaWan technologies that can withstand harsh temperatures and conditions.

“Imagine you go to Yellowstone and get lost, with no GPS signal or phone signal, but there’s a tracking device connected to a LoRaWan network sending your location,” Diana says. “It’s much easier for rescue services to find you. Rack Wireless is working on providing satellite access, as well, and having precise latitude and longitude makes mapping simple.”

In the future, Diana sees herself managing a team that makes groundbreaking discoveries and puts technologies to use to help other people.

Follow Diana on Twitter at @cotufa82

Check out Diana’s projects on GitHub

For more information on Google Maps Platform, visit our website.

For more information on Google Developer Experts, visit our website.

Cloud NDB to Cloud Datastore migration

Posted by Wesley Chun (@wescpy), Developer Advocate, Google Cloud

An optional migration

Serverless Migration Station is a mini-series from Serverless Expeditions focused on helping users on one of Google Cloud’s serverless compute platforms modernize their applications. The video today demonstrates how to migrate a sample app from Cloud NDB (or App Engine ndb) to Cloud Datastore. While Cloud NDB suffices as a current solution for today’s App Engine developers, this optional migration is for those who want to consolidate their app code to using a single client library to talk to Datastore.

Cloud Datastore started as Google App Engine’s original database but matured to becoming its own standalone product in 2013. At that time, native client libraries were created for the new product so non-App Engine apps as well as App Engine second generation apps could access the service. Long-time developers have been using the original App Engine service APIs to access Datastore; for Python, this would be App Engine ndb. While the legacy ndb service is still available, its limitations and lack of availability in Python 3 are why we recommend users switch to standalone libraries like Cloud NDB in the preceding video in this series.

While Cloud NDB lets users break free from proprietary App Engine services and upgrade their applications to Python 3, it also gives non-App Engine apps access to Datastore. However, Cloud NDB’s primary role is a transition tool for Python 2 App Engine developers. Non-App Engine developers and new Python 3 App Engine developers are directed to the Cloud Datastore native client library, not Cloud NDB.

As a result, those with a collection of Python 2 or Python 3 App Engine apps as well as non-App Engine apps may be using completely different libraries (ndb, Cloud NDB, Cloud Datastore) to connect to the same Datastore product. Following the best practices of code reuse, developers should consider consolidating to a single client library to access Datastore. Shared libraries provide stability and robustness with code that’s constantly tested, debugged, and battle-proven. Module 2 showed users how to migrate from App Engine ndb to Cloud NDB, and today’s Module 3 content focuses on migrating from Cloud NDB to Cloud Datastore. Users can also go straight from ndb directly to Cloud Datastore, skipping Cloud NDB entirely.

Migration sample and next steps

Cloud NDB follows an object model identical to App Engine ndb and is deliberately meant to be familiar to long-time Python App Engine developers while use of the Cloud Datastore client library is more like accessing a JSON document store. Their querying styles are also similar. You can compare and contrast them in the “diffs” screenshot below and in the video.

The diffs between the Cloud NDB and Cloud Datastore versions of the sample app

The “diffs” between the Cloud NDB and Cloud Datastore versions of the sample app

All that said, this migration is optional and only useful if you wish to consolidate to using a single client library. If your Python App Engine apps are stable with ndb or Cloud NDB, and you don’t have any code using Cloud Datastore, there’s no real reason to move unless Cloud Datastore has a compelling feature inaccessible from your current client library. If you are considering this migration and want to try it on a sample app before considering for yours, see the corresponding codelab and use the video for guidance.

It begins with the Module 2 code completed in the previous codelab/video; use your solution or ours as the “START”. Both Python 2 (Module 2a folder) and Python 3 (Module 2b folder) versions are available. The goal is to arrive at the “FINISH” with an identical, working app but using a completely different Datastore client library. Our Python 2 FINISH can be found in the Module 3a folder while Python 3’s FINISH is in the Module 3b folder. If something goes wrong during your migration, you can always rollback to START, or compare your solution with our FINISH. We will continue our Datastore discussion ahead in Module 6 as Cloud Firestore represents the next generation of the Datastore service.

All of these learning modules, corresponding videos (when published), codelab tutorials, START and FINISH code, etc., can be found in the migration repo. We hope to also one day cover other legacy runtimes like Java 8 and others, so stay tuned. Up next in Module 4, we’ll take a different turn and showcase a product crossover, showing App Engine developers how to containerize their apps and migrate them to Cloud Run, our scalable container-hosting service in the cloud. If you can’t wait for either Modules 4 or 6, try out their respective codelabs or access the code samples in the table at the repo above. Migrations aren’t always easy, and we hope content like this helps you modernize your apps.

Migrating from App Engine ndb to Cloud NDB

Posted by Wesley Chun (@wescpy), Developer Advocate, Google Cloud

Migrating to standalone services

Today we’re introducing the first video showing long-time App Engine developers how to migrate from the App Engine ndb client library that connects to Datastore. While the legacy App Engine ndb service is still available for Datastore access, new features and continuing innovation are going into Cloud Datastore, so we recommend Python 2 users switch to standalone product client libraries like Cloud NDB.

This video and its corresponding codelab show developers how to migrate the sample app introduced in a previous video and gives them hands-on experience performing the migration on a simple app before tackling their own applications. In the immediately preceding “migration module” video, we transitioned that app from App Engine’s original webapp2 framework to Flask, a popular framework in the Python community. Today’s Module 2 content picks up where that Module 1 leaves off, migrating Datastore access from App Engine ndb to Cloud NDB.

Migrating to Cloud NDB opens the doors to other modernizations, such as moving to other standalone services that succeed the original App Engine legacy services, (finally) porting to Python 3, breaking up large apps into microservices for Cloud Functions, or containerizing App Engine apps for Cloud Run.

Moving to Cloud NDB

App Engine’s Datastore matured to becoming its own standalone product in 2013, Cloud Datastore. Cloud NDB is the replacement client library designed for App Engine ndb users to preserve much of their existing code and user experience. Cloud NDB is available in both Python 2 and 3, meaning it can help expedite a Python 3 upgrade to the second generation App Engine platform. Furthermore, Cloud NDB gives non-App Engine apps access to Cloud Datastore.

As you can see from the screenshot below, one key difference between both libraries is that Cloud NDB provides a context manager, meaning you would use the Python with statement in a similar way as opening files but for Datastore access. However, aside from moving code inside with blocks, no other changes are required of the original App Engine ndb app code that accesses Datastore. Of course your “YMMV” (your mileage may vary) depending on the complexity of your code, but the goal of the team is to provide as seamless of a transition as possible as well as to preserve “ndb“-style access.

The difference between the App Engine ndb and Cloud NDB versions of the sample app

The “diffs” between the App Engine ndb and Cloud NDB versions of the sample app

Next steps

To try this migration yourself, hit up the corresponding codelab and use the video for guidance. This Module 2 migration sample “STARTs” with the Module 1 code completed in the previous codelab (and video). Users can use their solution or grab ours in the Module 1 repo folder. The goal is to arrive at the end with an identical, working app that operates just like the Module 1 app but uses a completely different Datastore client library. You can find this “FINISH” code sample in the Module 2a folder. If something goes wrong during your migration, you can always rollback to START, or compare your solution with our FINISH. Bonus content migrating to Python 3 App Engine can also be found in the video and codelab, resulting in a second FINISH, the Module 2b folder.

All of these learning modules, corresponding videos (when published), codelab tutorials, START and FINISH code, etc., can be found in the migration repo. We hope to also one day cover other legacy runtimes like Java 8 and others, so stay tuned! Developers should also check out the official Cloud NDB migration guide which provides more migration details, including key differences between both client libraries.

Ahead in Module 3, we will continue the Cloud NDB discussion and present our first optional migration, helping users move from Cloud NDB to the native Cloud Datastore client library. If you can’t wait, try out its codelab found in the table at the repo above. Migrations aren’t always easy; we hope this content helps you modernize your apps and shows we’re focused on helping existing users as much as new ones.