Posted by Wesley Chun (@wescpy), Developer Advocate, Google Cloud
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.
Dockerfile
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.
ndb
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.
entrypoint
app.yaml
ENTRYPOINT
Starting your web server with App Engine (app.yaml) and Cloud Run with Docker (Dockerfile) or Buildpacks (Procfile)
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.
.dockerignore
gcloud run deploy
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!
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).
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.
appengine_config.py
lib
service.yaml
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.
gunicorn
requirements.txt
google-cloud-ndb
The Python 3 requirements.txt and Dockerfile
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!
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.
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.
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.
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
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.
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.
webapp2
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.
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.
with
The "diffs" between the App Engine ndb and Cloud NDB versions of the sample app
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.
#IamaGDE series presents: Google Maps
Homing Tam is a product manager at Lalamove, an on-demand logistics company. He started at the company as a product manager focusing on location-based systems, talking with developers and business users to enhance the company’s mapping solutions, before moving into product management. Now, Homing handles corporate solutions; takes care of people who want to integrate with his company’s systems; handles the API side of things to help make integration easier; and provides recommendations for developers and other technical teammates.
Homing studied geomatics and computing at university, and his 2009 thesis was based on Google’s API backend. His dissertation focused on using the Google Maps API to perform mapping and overlay. His first full-time job was as a GIS analyst at Esri, the largest private software company in the world. A year and a half later, he became a solutions consultant for a different company, helping customers interested in integrating Google Maps with their software.
After Homing got involved in the Google Technology User Group (now known as Google Developer Groups), his boss at the time told him about the Google Developer Experts program. For his interview, Homing presented a product using the Google Maps APIs. When he became a GDE, he gave presentations and talks in the greater China region as a surrogate for the Google Maps Platform team. Homing is currently one of the organizers for GDG Hong Kong, organizing and giving community talks.
Homing says the Maps Styling Wizard, the precursor to the newer Cloud-based Maps Styling features, is one of his favorite features.
“Cartography, which I studied in college, matters a lot, especially to a simple black and white schematic map, or when matching the theme of a map to a site,” he says. “I like that feature a lot.”
In 2020, Homing gave one talk on Android in the Android 11 Meetup and another talk on Maps at the first-ever virtual Hong Kong Devfest, and he’s ready to do more speaking.
“It had been a while since I gave a talk on maps, and the launch of Cloud-based Maps Styling is so exciting that I feel like it’s time to do some presentations and let the community know more about it. Beyond knowing how to use the API, you need to know how you can make the most of the API.”
Homing notes that this year, in particular, more small business owners need to know how to collect customer addresses, allow customers to place on-demand delivery orders, and update customers.
In 2021, in addition to giving more talks, Homing hopes to work with the GDG organizers in Hong Kong to plan a hackathon or otherwise teach community members more about the new Maps features.
“Can we make an MVP or a really initial stage cycling app to use as a base to explore the new features and use different Google components?’
As his career continues, Homing says he has two priorities: progressing as a product manager and leveraging technology, including maps, to improve lives.
“This year was a year for everyone to become digitally literate,” he says. “With the extra time we spend on technology, we should make good use of technology to make life better.”
The Google Cloud team recently introduced a series of codelabs (free, self-paced, hands-on tutorials) and corresponding videos designed to help users on one of our serverless compute platforms modernize their apps, with an initial focus on our earliest users running their apps on Google App Engine. We kick off this content by showing users how to migrate from App Engine's webapp2 web framework to Flask, a popular framework in the Python community.
While users have always been able to use other frameworks with App Engine, webapp2 comes bundled with App Engine, making it the default choice for many developers. One new requirement in App Engine's next generation platform (which launched in 2018) is that web frameworks must do their own routing, which unfortunately, means that webapp2 is no longer supported, so here we are. The good news is that as a result, modern App Engine is more flexible, lets users to develop in a more idiomatic fashion, and makes their apps more portable.
For example, while webapp2 apps can run on App Engine, Flask apps can run on App Engine, your servers, your data centers, or even on other clouds! Furthermore, Flask has more users, more published resources, and is better supported. If Flask isn't right for you, you can select from other WSGI-compliant frameworks such as Django, Pyramid, and others.
In this "Module 1" episode of Serverless Migration Station (part of the Serverless Expeditions series) Google engineer Martin Omander and I explore this migration and walk developers through it step-by-step.
In the previous video, we introduced developers to the baseline Python 2 App Engine NDB webapp2 sample app that we're taking through each of the migrations. In the video above, users see that the majority of the changes are in the main application handler, MainHandler:
MainHandler
Upon (re)deploying the app, users should see no visible changes to the output from the original version:
Today's video picks up from where we left off: the Python 2 baseline app in its Module 0 repo folder. We call this the "START". By the time the migration has completed, the resulting source code, called "FINISH", can be found in the Module 1 repo folder. If you mess up partway through, you can rewind back to the START, or compare your solution with ours, FINISH. We also hope to one day provide a Python 3 version as well as cover other legacy runtimes like Java 8, PHP 5, and Go 1.11 and earlier, so stay tuned!
All of the migration learning modules, corresponding videos (when published), codelab tutorials, START and FINISH code, etc., can all be found in the migration repo. The next video (Module 2) will cover migrating from App Engine's ndb library for Datastore to Cloud NDB. We hope you find all these resources helpful in your quest to modernize your serverless apps!
Earlier this year, the Google Cloud team introduced a series of codelabs (free, online, self-paced, hands-on tutorials) designed for technical practitioners modernizing their serverless applications. Today, we're excited to announce companion videos, forming a set of "learning modules" made up of these videos and their corresponding codelab tutorials. Modernizing your applications allows you to access continuing product innovation and experience a more open Google Cloud. The initial content is designed with App Engine developers in mind, our earliest users, to help you take advantage of the latest features in Google Cloud. Here are some of the key migrations and why they benefit you:
taskqueue
The "Serverless Migration Station" videos are part of the long-running Serverless Expeditions series you may already be familiar with. In each video, Google engineer Martin Omander and I explore a variety of different modernization techniques. Viewers will be given an overview of the task at hand, a deeper-dive screencast takes a closer look at the code or configuration files, and most importantly, illustrates to developers the migration steps necessary to transform the same sample app across each migration.
The baseline sample app is a simple Python 2 App Engine NDB and webapp2 application. It registers every web page visit (saving visiting IP address and browser/client type) and displays the most recent queries. The entire application is shown below, featuring Visit as the data Kind, the store_visit() and fetch_visits() functions, and the main application handler, MainHandler.
Visit
store_visit()
fetch_visits()
import os import webapp2 from google.appengine.ext import ndb from google.appengine.ext.webapp import template class Visit(ndb.Model): 'Visit entity registers visitor IP address & timestamp' visitor = ndb.StringProperty() timestamp = ndb.DateTimeProperty(auto_now_add=True) def store_visit(remote_addr, user_agent): 'create new Visit entity in Datastore' Visit(visitor='{}: {}'.format(remote_addr, user_agent)).put() def fetch_visits(limit): 'get most recent visits' return (v.to_dict() for v in Visit.query().order( -Visit.timestamp).fetch(limit)) class MainHandler(webapp2.RequestHandler): 'main application (GET) handler' def get(self): store_visit(self.request.remote_addr, self.request.user_agent) visits = fetch_visits(10) tmpl = os.path.join(os.path.dirname(__file__), 'index.html') self.response.out.write(template.render(tmpl, {'visits': visits})) app = webapp2.WSGIApplication([ ('/', MainHandler), ], debug=True)
Upon deploying this application to App Engine, users will get output similar to the following:
This application is the subject of today's launch video, and the main.py file above along with other application and configuration files can be found in the Module 0 repo folder.
main.py
Each migration learning module covers one modernization technique. A video outlines the migration while the codelab leads developers through it. Developers will always get a starting codebase ("START") and learn how to do a specific migration, resulting in a completed codebase ("FINISH"). Developers can hit the reset button (back to START) if something goes wrong or compare their solutions to ours (FINISH). The hands-on experience helps users build muscle-memory for when they're ready to do their own migrations.
All of the migration learning modules, corresponding Serverless Migration Station videos (when published), codelab tutorials, START and FINISH code, etc., can all be found in the migration repo. While there's an initial focus on Python 2 and App Engine, you'll also find content for Python 3 users as well as non-App Engine users. We're looking into similar content for other legacy languages as well so stay tuned. We hope you find all these resources helpful in your quest to modernize your serverless apps!
Posted by Wesley Chun, Developer Advocate, Google Cloud
Since its initial launch in 2008 as the first product from Google Cloud, Google App Engine, our fully-managed serverless app-hosting platform, has been used by many developers worldwide. Since then, the product team has continued to innovate on the platform: introducing new services, extending quotas, supporting new languages, and adding a Flexible environment to support more runtimes, including the ability to serve containerized applications.
With many original App Engine services maturing to become their own standalone Cloud products along with users' desire for a more open cloud, the next generation App Engine launched in 2018 without those bundled proprietary services, but coupled with desired language support such as Python 3 and PHP 7 as well as introducing Node.js 8. As a result, users have more options, and their apps are more portable.
With the sunset of Python 2, Java 8, PHP 5, and Go 1.11, by their respective communities, Google Cloud has assured users by expressing continued long-term support of these legacy runtimes, including maintaining the Python 2 runtime. So while there is no requirement for users to migrate, developers themselves are expressing interest in updating their applications to the latest language releases.
Google Cloud has created a set of migration guides for users modernizing from Python 2 to 3, Java 8 to 11, PHP 5 to 7, and Go 1.11 to 1.12+ as well as a summary of what is available in both first and second generation runtimes. However, moving from bundled to unbundled services may not be intuitive to developers, so today we're introducing additional resources to help users in this endeavor: App Engine "migration modules" with hands-on "codelab" tutorials and code examples, starting with Python.
Each module represents a single modernization technique. Some are strongly recommended, others less so, and, at the other end of the spectrum, some are quite optional. We will guide you as far as which ones are more important. Similarly, there's no real order of modules to look at since it depends on which bundled services your apps use. Yes, some modules must be completed before others, but again, you'll be guided as far as "what's next."
More specifically, modules focus on the code changes that need to be implemented, not changes in new programming language releases as those are not within the domain of Google products. The purpose of these modules is to help reduce the friction developers may encounter when adapting their apps for the next-generation platform.
Central to the migration modules are the codelabs: free, online, self-paced, hands-on tutorials. The purpose of Google codelabs is to teach developers one new skill while giving them hands-on experience, and there are codelabs just for Google Cloud users. The migration codelabs are no exception, teaching developers one specific migration technique.
Developers following the tutorials will make the appropriate updates on a sample app, giving them the "muscle memory" needed to do the same (or similar) with their applications. Each codelab begins with an initial baseline app ("START"), leads users through the necessary steps, then concludes with an ending code repo ("FINISH") they can compare against their completed effort. Here are some of the initial modules being announced today:
What should you expect from the migration codelabs? Let's preview a pair, starting with the web framework: below is the main driver for a simple webapp2-based "guestbook" app registering website visits as Datastore entities:
class MainHandler(webapp2.RequestHandler): 'main application (GET) handler' def get(self): store_visit(self.request.remote_addr, self.request.user_agent) visits = fetch_visits(LIMIT) tmpl = os.path.join(os.path.dirname(__file__), 'index.html') self.response.out.write(template.render(tmpl, {'visits': visits}))
A "visit" consists of a request's IP address and user agent. After visit registration, the app queries for the latest LIMIT visits to display to the end-user via the app's HTML template. The tutorial leads developers a migration to Flask, a web framework with broader support in the Python community. An Flask equivalent app will use decorated functions rather than webapp2's object model:
LIMIT
@app.route('/') def root(): 'main application (GET) handler' store_visit(request.remote_addr, request.user_agent) visits = fetch_visits(LIMIT) return render_template('index.html', visits=visits)
The framework codelab walks users through this and other required code changes in its sample app. Since Flask is more broadly used, this makes your apps more portable.
The second example pertains to Datastore access. Whether you're using App Engine's ndb or the Cloud NDB client libraries, the code to query the Datastore for the most recent limit visits may look like this:
limit
def fetch_visits(limit): 'get most recent visits' query = Visit.query() visits = query.order(-Visit.timestamp).fetch(limit) return (v.to_dict() for v in visits)
If you decide to switch to the Cloud Datastore client library, that code would be converted to:
def fetch_visits(limit): 'get most recent visits' query = DS_CLIENT.query(kind='Visit') query.order = ['-timestamp'] return query.fetch(limit=limit)
The query styles are similar but different. While the sample apps are just that, samples, giving you this kind of hands-on experience is useful when planning your own application upgrades. The goal of the migration modules is to help you separate moving to the next-generation service and making programming language updates so as to avoid doing both sets of changes simultaneously.
As mentioned above, some migrations are more optional than others. For example, moving away from the App Engine bundled ndb library to Cloud NDB is strongly recommended, but because Cloud NDB is available for both Python 2 and 3, it's not necessary for users to migrate further to Cloud Datastore nor Cloud Firestore unless they have specific reasons to do so. Moving to unbundled services is the primary step to giving users more flexibility, choices, and ultimately, makes their apps more portable.
For those who are interested in modernizing their apps, a complete table describing each module and links to corresponding codelabs and expected START and FINISH code samples can be found in the migration module repository. We are also working on video content based on these migration modules as well as producing similar content for Java, so stay tuned.
In addition to the migration modules, our team has also setup a separate repo to support community-sourced migration samples. We hope you find all these resources helpful in your quest to modernize your App Engine apps!
Posted by Jon Harmer, Product Manager, Google Cloud
We recently introduced Google Workspace, which seamlessly brings together messaging, meetings, docs, and tasks and is a great way for teams to create, communicate, and collaborate. Google Workspace has what you need to get anything done, all in one place. This includes giving developers the ability to extend Google Workspace’s standard functionality like with Google Workspace Add-ons, launched earlier this year.
Google Workspace Add-ons, at launch, allowed a developer to build a single integration for Google Workspace that surfaces across Gmail, Google Drive, and Google Calendar. We recently announced that we added to the functionality of Google Workspace Add-ons by enabling more of the Google Workspace applications with the newer add-on framework, Google Docs, Google Sheets, and Google Slides. With Google Workspace Add-ons, developers can scale their presence across multiple touchpoints in which users can engage, and simplifies processes for building and managing add-ons.
One of our early developers for Google Workspace Add-ons has been Adobe. Adobe has been working to integrate Creative Cloud Libraries into Google Workspace. Using Google Workspace Add-ons, Adobe was able to quickly design a Creative Cloud Libraries experience that felt native to Google Workspace. “With the new add-ons framework, we were able to improve the overall performance and unify our Google Workspace and Gmail Add-ons.” said Ryan Stewart, Director of Product Management at Adobe. “This means a much better experience for our customers and much higher productivity for our developers. We were able to quickly iterate with the updated framework controls and easily connect it to the Creative Cloud services.”
One of the big differences between the Gmail integration and the Google Workspace integration is how it lets users work with Libraries. With Gmail, they’re sharing links to Libraries, but with Docs and Slides, they can add Library elements to their document or presentation. So by offering all of this in a single integration, we are able to provide a more complete Libraries experience. Being able to offer that breadth of experiences in a consistent way for users is exciting for our team.
Adobe’s Creative Cloud Libraries API announced at Adobe MAX, was also integral to integrating Creative Cloud with Google Workspace, letting developers retrieve, browse, create, and get renditions of the creative elements in libraries.
Adobe’s new Add-on for Google Workspace lets you add brand colors, character styles and graphics from Creative Cloud Libraries to Google Workspace apps like Docs and Slides. You can also save styles and assets back to Creative Cloud.
With Google Workspace Add-ons, we understand that teams require many applications to get work done, and we believe that process should be simple, and those productivity applications should connect all of a company’s workstreams. With Google Workspace Add-ons, teams can bring their favorite workplace apps like Adobe Creative Cloud into Google Workspace, enabling a more productive day-to-day experience for design and marketing teams. With quick access to Creative Cloud Libraries, the Adobe Creative Cloud Add-on for Google Workspace lets eveyone easily access and share assets in Gmail and apply brand colors, character styles, and graphics to Google Docs and Slides to keep deliverables consistent and on-brand. There’s a phased rollout to users, first with Google Docs, then Slides, so if you don’t see it in the Add-on yet, stay tuned as it is coming soon.
For developers, Google Workspace Add-ons lets you build experiences that not only let your customers manage their work, but also simplify how they work.
To learn more about Google Workspace Add-ons, please visit our Google Workspace developer documentation.
Posted by Guillaume Laforge, Developer Advocate for Google Cloud
With the beta of the new Java 11 runtime for Google Cloud Functions, Java developers can now write their functions using the Java programming language (a language often used in enterprises) in addition to Node.js, Go, or Python. Cloud Functions allow you to run bits of code locally or in the cloud, without provisioning or managing servers: Deploy your code, and let the platform handle scaling up and down for you. Just focus on your code: handle incoming HTTP requests or respond to some cloud events, like messages coming from Cloud Pub/Sub or new files uploaded in Cloud Storage buckets.
In this article, let’s focus on what functions look like, how you can write portable functions, how to run and debug them locally or deploy them in the cloud or on-premises, thanks to the Functions Framework, an open source library that runs your functions. But you will also learn about third-party frameworks that you might be familiar with, that also let you create functions using common programming paradigms.
There are two types of functions: HTTP functions, and background functions. HTTP functions respond to incoming HTTP requests, whereas background functions react to cloud-related events.
The Java Functions Framework provides an API that you can use to author your functions, as well as an invoker which can be called to run your functions locally on your machine, or anywhere with a Java 11 environment.
To get started with this API, you will need to add a dependency in your build files. If you use Maven, add the following dependency tag in pom.xml:
pom.xml
<dependency> <groupId>com.google.cloud.functions</groupId> <artifactId>functions-framework-api</artifactId> <version>1.0.1</version> <scope>provided</scope> </dependency>
If you are using Gradle, add this dependency declaration in build.gradle:
build.gradle
compileOnly("com.google.cloud.functions:functions-framework-api")
A Java function that receives an incoming HTTP request implements the HttpFunction interface:
HttpFunction
import com.google.cloud.functions.*; import java.io.*; public class Example implements HttpFunction { @Override public void service(HttpRequest request, HttpResponse response) throws IOException { var writer = response.getWriter(); writer.write("Hello developers!"); } }
The service() method provides an HttpRequest and an HttpResponse object. From the request, you can get information about the HTTP headers, the payload body, or the request parameters. It’s also possible to handle multipart requests. With the response, you can set a status code or headers, define a body payload and a content-type.
service()
HttpRequest
HttpResponse
Background functions respond to events coming from the cloud, like new Pub/Sub messages, Cloud Storage file updates, or new or updated data in Cloud Firestore. There are actually two ways to implement such functions, either by dealing with the JSON payloads representing those events, or by taking advantage of object marshalling thanks to the Gson library, which takes care of the parsing transparently for the developer.
With a RawBackgroundFunction, the responsibility is on you to handle the incoming cloud event JSON-encoded payload. You receive a JSON string, so you are free to parse it however you like, with your JSON parser of your choice:
import com.google.cloud.functions.Context; import com.google.cloud.functions.RawBackgroundFunction; public class RawFunction implements RawBackgroundFunction { @Override public void accept(String json, Context context) { ... } }
But you also have the option to write a BackgroundFunction which uses Gson for unmarshalling a JSON representation into a Java class (a POJO, Plain-Old-Java-Object) representing that payload. To that end, you have to provide the POJO as a generic argument:
import com.google.cloud.functions.Context; import com.google.cloud.functions.BackgroundFunction; public class PubSubFunction implements BackgroundFunction<PubSubMsg> { @Override public void accept(PubSubMsg msg, Context context) { System.out.println("Received message ID: " + msg.messageId); } } public class PubSubMsg { String data; Map<String, String> attributes; String messageId; String publishTime; }
The Context parameter contains various metadata fields like timestamps, the type of events, and other attributes.
Which type of background function should you use? It depends on the control you need to have on the incoming payload, or if the Gson unmarshalling doesn’t fully fit your needs. But having the unmarshalling covered by the framework definitely streamlines the writing of your function.
Coding is always great, but seeing your code actually running is even more rewarding. The Functions Framework comes with the API we used above, but also with an invoker tool that you can use to run functions locally. For improving developer productivity, having a direct and local feedback loop on your own computer makes it much more comfortable than deploying in the cloud for each change you make to your code.
If you’re building your functions with Maven, you can install the Function Maven plugin in your pom.xml:
<plugin> <groupId>com.google.cloud.functions</groupId> <artifactId>function-maven-plugin</artifactId> <version>0.9.2</version> <configuration> <functionTarget>com.example.Example</functionTarget> </configuration> </plugin>
On the command-line, you can then run:
$ mvn function:run
You can pass extra parameters like --target to define a different function to run (in case your project contains several functions), --port to specify the port to listen to, or --classpath to explicitly set the classpath needed by the function to run. These are the parameters of the underlying Invoker class. However, to set these parameters via the Maven plugin, you’ll have to pass properties with -Drun.functionTarget=com.example.Example and -Drun.port.
--target
--port
--classpath
Invoker
-Drun.functionTarget=com.example.Example
-Drun.port
With Gradle, there is no dedicated plugin, but it’s easy to configure build.gradle to let you run functions.
First, define a dedicated configuration for the invoker:
configurations { invoker }
In the dependencies, add the Invoker library:
dependencies { invoker 'com.google.cloud.functions.invoker:java-function-invoker:1.0.0-beta1' }
And then, create a new task to run the Invoker:
tasks.register("runFunction", JavaExec) { main = 'com.google.cloud.functions.invoker.runner.Invoker' classpath(configurations.invoker) inputs.files(configurations.runtimeClasspath, sourceSets.main.output) args('--target', project.findProperty('runFunction.target') ?: 'com.example.Example', '--port', project.findProperty('runFunction.port') ?: 8080 ) doFirst { args('--classpath', files(configurations.runtimeClasspath, sourceSets.main.output).asPath) } }
By default, the above launches the function com.example.Example on port 8080, but you can override those on the command-line, when running gradle or the gradle wrapper:
com.example.Example
$ gradle runFunction -PrunFunction.target=com.example.HelloWorld \ -PrunFunction.port=8080
What’s interesting about the Functions Framework is that you are not tied to the Cloud Functions platform for deploying your functions. As long as, in your target environment, you can run your functions with the Invoker class, you can run your functions on Cloud Run, on Google Kubernetes Engine, on Knative environments, on other clouds when you can run Java, or more generally on any servers on-premises. It makes your functions highly portable between environments. But let’s have a closer look at deployment now.
You can deploy functions with the Maven plugin as well, with various parameters to tweak for defining regions, memory size, etc. But here, we’ll focus on using the cloud SDK, with its gcloud command-line, to deploy our functions.
gcloud
For example, to deploy an HTTP function, you would type:
$ gcloud functions deploy exampleFn \ --region europe-west1 \ --trigger-http \ --allow-unauthenticated \ --runtime java11 \ --entry-point com.example.Example \ --memory 512MB
For a background function that would be notified of new messages on a Pub/Sub topic, you would launch:
$ gcloud functions deploy exampleFn \ --region europe-west1 \ --trigger-topic msg-topic \ --runtime java11 \ --entry-point com.example.PubSubFunction \ --memory 512MB
Note that deployments come in two flavors as well, although the above commands are the same: functions are deployed from source with a pom.xml and built in Google Cloud, but when using a build tool other than Maven, you can also use the same command to deploy a pre-compiled JAR that contains your function implementation. Of course, you’ll have to create that JAR first.
So far, we looked at Java and the plain Functions Framework, but you can definitely use alternative JVM languages such as Apache Groovy, Kotlin, or Scala, and third-party frameworks that integrate with Cloud Functions like Micronaut and Spring Boot!
Without covering all those combinations, let’s have a look at two examples. What would an HTTP function look like in Groovy?
The first step will be to add Apache Groovy as a dependency in your pom.xml:
<dependency> <groupId>org.codehaus.groovy</groupId> <artifactId>groovy-all</artifactId> <version>3.0.4</version> <type>pom</type> </dependency>
You will also need the GMaven compiler plugin to compile the Groovy code:
<plugin> <groupId>org.codehaus.gmavenplus</groupId> <artifactId>gmavenplus-plugin</artifactId> <version>1.9.0</version> <executions> <execution> <goals> <goal>addSources</goal> <goal>addTestSources</goal> <goal>compile</goal> <goal>compileTests</goal> </goals> </execution> </executions> </plugin>
When writing the function code, just use Groovy instead of Java:
import com.google.cloud.functions.* class HelloWorldFunction implements HttpFunction { void service(HttpRequest request, HttpResponse response) { response.writer.write "Hello Groovy World!" } }
The same explanations regarding running your function locally or deploying it still applies: the Java platform is pretty open to alternative languages too! And the Cloud Functions builder will happily build your Groovy code in the cloud, since Maven lets you compile this code thanks to the Groovy library.
Third-party frameworks also offer a dedicated Cloud Functions integration. Let’s have a look at Micronaut.
Micronaut is a “modern, JVM-based, full-stack framework for building modular, easily testable microservice and serverless applications”, as explained on its website. It supports the notion of serverless functions, web apps and microservices, and has a dedicated integration for Google Cloud Functions.
In addition to being a very efficient framework with super fast startup times (which is important, to avoid long cold starts on serverless services), what’s interesting about using Micronaut is that you can use Micronaut’s own programming model, including Dependency Injection, annotation-driven bean declaration, etc.
For HTTP functions, you can use the framework’s own @Controller / @Get annotations, instead of the Functions Framework’s own interfaces. So for example, a Micronaut HTTP function would look like:
@Controller
@Get
import io.micronaut.http.annotation.*; @Controller("/hello") public class HelloController { @Get(uri="/", produces="text/plain") public String index() { return "Example Response"; } }
This is the standard way in Micronaut to define a Web microservice, but it transparently builds upon the Functions Framework to run this service as a Cloud Function. Furthermore, this programming model offered by Micronaut is portable across other environments, since Micronaut runs in many different contexts.
Last but not least, if you are using the Micronaut Launch project (hosted on Cloud Run) which allows you to scaffold new projects easily (from the command-line or from a nice UI), you can opt for adding the google-cloud-function support module, and even choose your favorite language, build tool, or testing framework:
google-cloud-function
Be sure to check out the documentation for the Micronaut Cloud Functions support, and Spring Cloud Function support.
Now it’s your turn to try Cloud Functions for Java 11 today, with your favorite JVM language or third-party frameworks. Read the getting started guide, and try this for free with Google Cloud Platform free trial. Explore Cloud Functions’ features and use cases, take a look at the quickstarts, perhaps even contribute to the open source Functions Framework. And we’re looking forward to seeing what functions you’re going to build on this platform!
Posted by Yuri Grinshteyn, Site Reliability Engineer
We know many of you are looking for ways to keep learning and connecting with other developers virtually right now, and we want to help. Below you can check out our top on-demand Google Cloud training webinars and resources where you can take hands-on labs and learn, at no charge, more about everything from the basics of Google Cloud to more advanced topics like building robust cloud architecture.
You can tune in from May 19-20 to watch instructors in Cloud OnBoard break down what it takes to migrate to Google Cloud and explain the basics of the Google Kubernetes Engine, a managed, production-ready environment for running containerized applications. After the sessions, you’ll have a chance to test what you’ve learned by participating in hands-on labs and challenges with the Cloud Hero Online Challenge. Missed the live recording on May 19-20? No worries! You can view it on-demand starting May 21 and still participate in hands-on labs.
Ready to gain more hands-on cloud experience and deeper product knowledge? We have webinars where Googlers will walk you through more hands-on labs on Qwiklabs and share product tips and tricks.
If you’re interested in big data and machine learning, you can do a lab I recorded in the Baseline: Data, ML, AI webinar to get more experience using tools like Big Query, Cloud Speech API, and Cloud ML Engine. You can also learn how to use BigQuery and other Google tools to draw insights and visualize data from the public health data sets Google released to support the COVID-19 research process in our Data science for public health: Working with public COVID-19 datasets webinar.
For those of you who are already cloud professionals, our top webinars this year so far are Professional Cloud DevOps and Professional Cloud Architect.
You can learn how to improve the way you build software delivery pipelines, deploy and monitor services, and manage incidents in the DevOps webinar. The Cloud Architect webinar will discuss how to ensure you’re designing, developing, and managing effective solutions.
Both webinars will also help prepare you to earn Google Cloud certifications. If you’d like to learn more about the certification program, you can attend our on-demand webinar Why Certify? Everything to know about Google Cloud Certification.
We’re also offering our extensive catalog of Google Cloud on-demand training courses on Pluralsight and Qwiklabs at no cost when you sign up by May 31, 20201. You can learn how to prototype an app, build prediction models, and more—at your own pace by registering here.
We hope these webinars and resources help you continue learning new skills and stay connected with the broader Google developer community.
1. Your 30-days access to these Google Cloud training courses at no cost starts when you enroll for your courses. These offers are valid until May 31, 2020. After your 30-days, you will incur charges on Pluralsight; for Qwiklabs, you will need to purchase credits to continue taking labs.
