This week, we’re returning to Google I/O for the 4th year in a row to share how we’re making Firebase better for all app developers, from the smallest one-person startup to the largest enterprise businesses. No matter how many times we take the stage, our mission remains the same: to help mobile and web developers succeed by making it easier to build, improve, and grow your apps. Since launching Firebase as Google’s mobile development platform at I/O 2016, we’ve been continuously amazed at what you’ve built with our tools. It is an honor to help you on your journey to change the world!
For example, in Uganda, a start-up called Teheca is using Firebase to reduce the mortality rate of infants and new mothers by connecting parents with nurses for post-natal care. Over in India where smartphones are quickly replacing TVs as the primary entertainment source, Hotstar, India’s largest video streaming app, is using Firebase with BigQuery to transform the viewing experience by making it more social and interactive. Here’s how they’re doing it, in their own words:
Stories like these inspire us to keep making Firebase better. In fact, we’ve released over 100 new features and improvements over the last 6 months! Read on to learn about our biggest announcements at Google I/O 2019.
New translation, object detection and tracking, and AutoML capabilities in ML Kit
Last year, we launched ML Kit, bringing Google's machine learning expertise to mobile developers in a powerful, yet easy-to-use package. It came with a set of ready-to-use on-device and cloud-based APIs with support for custom models, so you could apply the power of machine learning to your app, regardless of your familiarity with ML. Over the past few months, we’ve expanded on these by adding solutions for Natural Language Processing, such as Language Identification and Smart Reply APIs. Now, we’re launching three more capabilities in beta: On-device Translation API, Object Detection & Tracking API, and AutoML Vision Edge.
The On-device Translation API allows you to use the same offline models that support Google Translate to provide fast, dynamic translation of text in your app into 58 languages. The Object Detection & Tracking API lets your app locate and track, in real-time, the most prominent object in a live camera feed. With AutoML Vision Edge, you can easily create custom image classification models tailored to your needs. For example, you may want your app to be able to identify different types of food, or distinguish between species of animals. Whatever your need, just upload your training data to the Firebase console and you can use Google’s AutoML technology to build a custom TensorFlow Lite model for you to run locally on your user's device. And if you find that collecting training datasets is hard, you can use our open source app which makes the process simpler and more collaborative.
Customers like IKEA, Fishbrain, and Lose It! are already using ML Kit’s capabilities to enhance their app experiences. Here’s what they had to say:
"We’re working with Google Cloud to create a new mobile experience that enables customers, wherever they are, to take photos of home furnishing and household items and quickly find that product or similar in our online catalogue. The Cloud Vision Product Search API provided IKEA a fast and easy way to index our catalogue, while ML Kit’s Object Detection and Tracking API let us seamlessly implement the feature on a live viewfinder on our app. Google Cloud helps us make use of Vision Product Search and we are very excited to explore how this can help us create a better and more convenient experience for our customers.” - Susan Standiford, Chief Technology Officer of Ingka Group, a strategic partner in the IKEA franchise system and operating IKEA in 30 markets.
“Our users are passionate about fishing, so capturing and having access to images of catches and species information is central to their experience. Through AutoML Vision Edge, we’ve increased the number of catches logged with species information by 30%, and increased our species recognition model accuracy from 78% to 88%..”- Dimitris Lachanas, Android Engineering Manager at Fishbrain
“Through AutoML Vision Edge, we were able to create a highly predictive, on-device model from scratch. With this improvement to our state-of-the-art food recognition algorithm, Snap It, we’ve increased the number of food categories our customers can classify in images by 21% while reducing our error rate by 36%, which is huge for our customers.” - Will Lowe Ph.D., Director of Data Science & AI, Lose It!
Performance Monitoring now supports web apps
Native mobile developers have loved using Firebase Performance Monitoring to find out what parts of their app are running slower than they expect, and for which app users. Today, we’re excited to announce that Performance Monitoring is available for web apps too, in beta, so web developers can understand how real users are experiencing their app in the wild.
By pasting a few lines of code to their site, the Performance Monitoring dashboard will track and visualize high level web metrics (like page load and network stats) as well as more granular metrics (like time to first paint and first input delay) across user segments. The Performance Monitoring dashboard will also give you the ability to drill down into these different user segments by country, browser, and more. Now, you can get deep insight into the speed and performance of your web apps and fix issues fast to ensure your end users have a consistently great experience. By adding web support to one of our most popular tools, we’re reaffirming our commitment to make app development easier for both mobile and web developers.
Brand new audience builder in Google Analytics for Firebase
Google Analytics for Firebase provides free, unlimited, and robust analytics so you can measure the things that matter in your app and understand your users. A few weeks ago, we announced advanced filtering in Google Analytics for Firebase, which allows you to filter your Analytics event reports by any number of different user properties or audiences at the same time.
Today, we’re thrilled to share that we’ve completely rebuilt our audience system from scratch with a new interface. This new audience builder includes new features like sequences, scoping, time windows, membership duration, and more to enable you to create dynamic, precise, and fresh audiences for personalization (through Remote Config) or re-engagement (through Cloud Messaging and/or the new App campaigns).
For example, if you wanted to create a "Coupon users" audience based on people who redeem a coupon code within your app, and then complete an in-app purchase within 20 minutes, this is now possible with the new audience builder.
In addition to the three big announcements above, we’ve also made the following improvements to other parts of Firebase.
Support for collection group queries in Cloud Firestore
In January, we graduated Cloud Firestore - our fully-managed NoSQL database - out of beta into general availability with lower pricing tiers and new locations. Now, we’ve added support for Collection Group queries. This allows you to search for fields across all collections of the same name, no matter where they are in the database. For example, imagine you had a music app which stored its data like so:
This data structure makes it easy to query the songs by a given artist. But until today, it was impossible to query across artists — such as finding the longest songs regardless of who wrote them. With collection group queries, Cloud Firestore now can perform these searches across all song documents, even though they're in different collections. This means it’s easier to organize your data hierarchically, while still being able to search for the documents you want.
Cloud Functions emulator
We’ve also been steadily improving our tools and emulator suite to increase your productivity for local app development and testing. In particular, we’re releasing a brand new Cloud Functions emulator that can also communicate with the Cloud Firestore emulator. So if you want to build a function that triggers upon a Firestore document update and writes data back to the database you can code and test that entire flow locally on your laptop, for much faster development.
Configurable velocity alerts in Crashlytics
Firebase Crashlytics helps you track, prioritize, and solve stability issues that erode app quality, in real time. One of the most important alerts within Crashlytics is the velocity alert, which notifies you when an issue suddenly increases in severity and impacts a significant percentage of your users. However, we recognize that every app is unique and the one-size-fits-all alerting threshold might not be what’s best for you and your business. That’s why you can now customize velocity alerts and determine how often and when you want to be alerted about changes to your app’s stability. We’re also happy to announce that we’ve expanded Crashlytics to include Unity and NDK support.
Improvements to Test Lab
Firebase Test Lab makes it easy for you to test your app on real, physical devices, straight from your CLI or the Firebase console. Over the past few months, we’ve released a number of improvements to Test Lab. We’ve expanded the types of apps you can run tests on by adding support for Wear OS by Google and Android App Bundles. We’ve also added ML vision to Test Lab’s monkey action feature so we can more intelligently simulate where users will tap in your app or game. Lastly, we’ve made your tests more reliable with test partitioning, flaky test detection, and the robo action timeline, which tells you exactly what the crawler was doing while the test was running.
Greater control over Firebase project permissions
Security and data privacy remain part of our top priorities. We want to make sure you have control over who can access your Firebase projects, which is why we’ve leveraged Google Cloud Platform’s Identity & Access Management controls to give you finer grained permission controls. Right from the Firebase console, you can control who has access to which parts of your Firebase project. For example, you can grant access to a subset of tools so team members who run notification campaigns aren’t able to change your Firebase database’s security rules. You can go even further and use the GCP console to create custom roles permitting access to only the actions your team members are required to take.
More open-sourced SDKs
To make Firebase more usable and extensible, we’re continuing to open source our SDKs and accepting contributions from the community. We are committed to giving you transparency and flexibility with the code you integrate into your mobile and web apps. Most recently, we open sourced our C++ SDK.
In case you missed the news at Cloud Next 2019, here’s a quick recap of the updates we unveiled back in April:
In addition to making Firebase more powerful, we’ve also been hard at work bringing the best of Fabric into Firebase. We know many of you have been waiting for more information on this front, so we have outlined our journey in more detail here.
We’re continuing to invest in Firebase and as always, we welcome your feedback! With every improvement to Firebase, we aim to simplify your app development workflows and infrastructure needs, so you can stay focused on building amazing user experiences. To get a sneak peek at what’s next, join our Alpha program and help us shape the future
The Cloud Functions for Firebase team is pleased to provide a turnkey solution for secure, scheduled functions using Firebase tools and SDKs.
One of the most common questions about Google Cloud Functions is how to schedule a function for regular execution, similar to a cron job. In the past, the Firebase team recommended solutions involving App Engine or third party schedulers as a scheduling mechanism to invoke a function, which works fine. However, solutions that involve the invocation of an HTTP function aren’t fully secure, because the HTTP endpoint URL is effectively public and accessible to anyone who knows that URL. And, solutions that involve creating and sending messages to a Cloud Pub/Sub endpoint can be difficult to manage.
Recently, Google Cloud released Cloud Scheduler, which allows you to schedule HTTP requests or Cloud Pub/Sub messages to functions that you deploy. You can follow a tutorial for getting that set up in your project, if you like. But today, it gets even easier than that, and it doesn’t require working with the Google Cloud console.
Firebase now supports a new type of Pub/Sub function, built on top of Cloud Scheduler, that automatically configures Cloud Scheduler, along with a Pub/Sub topic, that invokes a function that you define using the Cloud Functions for Firebase SDK.
If you’re using version 2.3.0 of the firebase-functions NodeJS module, along with 6.7.0 of the Firebase CLI, you can now define functions that use Google Scheduler’s standard cron syntax like this:
export scheduledFunctionCrontab = functions.pubsub.schedule('5 11 * * *').onRun((context) => { console.log('This will be run every day at 11:05 AM UTC!'); });
You can schedule invocations using an English description as well:
export scheduledFunctionPlainEnglish = functions.pubsub.schedule('every 5 minutes').onRun((context) => { console.log('This will be run every 5 minutes!'); });
To get started with scheduled functions using Firebase tools, navigate to the documentation, and check out this sample available in GitHub. Your project must be on the Blaze payment plan, as Cloud Scheduler and Pub/Sub require billing information. Each Cloud Scheduler job costs $0.10 (USD) per month, and there is an allowance of three free jobs per Google account. Besides the normal costs for Cloud Functions, the costs for this feature are the same as if you set up the underlying infrastructure manually - for more information, check out the full pricing information for Cloud Scheduler and Cloud PubSub.
Today we're excited to announce Firebase Hosting integration for Google Cloud’s new Cloud Run service. Cloud Run is a fully managed compute platform that enables developers to run stateless containers that are invocable via HTTP requests in a language and framework of their choosing. Firebase Hosting integration lets you use this architecture as a backend for a web app or microservice in your Firebase project.
Firebase Hosting is already a convenient and secure way to host sites and microservices. It can serve static pages you upload directly and, with the proper configuration in the firebase.json file, direct incoming requests to Cloud Functions for Firebase to serve dynamic content. This workflow is a one stop shop if you don’t mind working in the NodeJS environment. You can already build a fast site with dynamic content that automatically scales horizontally to meet user demand.
Not every developer wants to work with NodeJS though. Many already have large teams with existing knowledge in other languages and frameworks. Languages such as Go, Ruby, and Java have a huge presence in the server market but are currently absent in Firebase’s existing cloud backend solutions.
Leveraging the power of Google’s own experience building infrastructure for Kubernetes and the efforts of the Knative open source project, Google Cloud Platform now lets you deploy stateless servers. The only requirements are that you can generate a docker image able to interact to HTTP requests on the port specified in the $PORT environment variable for Kubernetes and that you respond within 60 seconds for Firebase Hosting. How does this tie into Firebase Hosting though?
If you’re new to Hosting, you may only be aware of static hosting or the free SSL certificates. To facilitate serving dynamic content, rewrites allow you to hit your cloud functions, which we’ve extended to support Cloud Run as well. With a few minor changes to your firebase.json file, you can now point a specific path to your container:
{ "hosting": { "public": "public", "rewrites": [ { "source": "/cloudrun", "run": { "serviceId": "my-awesome-api", // Optional (default is us-central1) "region": "us-central1", } } ] } }
or use wildcards to expose an entire API
{ "hosting": { "public": "public", "rewrites": [ { "source": "/api/**", "run": { "serviceId": "my-awesome-api", // Optional (default is us-central1) "region": "us-central1", } } ] } }
If you have a dynamic site that doesn’t update very frequently, take advantage of Firebase Hosting’s global CDN (content delivery network) to improve your site’s response time. For example, if you’re using ExpressJS and NodeJS, configure the caching behavior using the Cache-Control header like so:
Cache-Control
res.set('Cache-Control', 'public, max-age=300, s-maxage=600');
which caches the results of a request in the browser (max-age) for 5 minutes and in the CDN (s-maxage) for 10 minutes. With properly tuned cache settings, you can have a fast, flexible, dynamically rendered site that doesn’t need to run your server logic every time the user opens the page.
max-age
s-maxage
Unlike Cloud Functions for Firebase, when you use Cloud Run, you can build an image with any combination of languages and frameworks to handle these requests. Ruby developers can easily pull in Sinatra, you can fire up the Spring framework for Java teams, or check out server side Dart using Shelf to serve content. You don’t have to wait for any official language support -- if you can create a docker container, you can make and deploy backend code. Even if you’re working in high performance computing and your engineering team is trained up in Fortran, you can just leverage that existing knowledge to create a web dashboard with Fortran.io without having to wait for any official support from Google or Firebase.
Similar to Cloud Functions, Cloud Run automatically scales your containers horizontally to meet the demands of your users. There’s no need to manage clusters or node pools; you simply use the resources needed at the time to accomplish the task at hand. One tradeoff is that, also like Cloud Functions, Cloud Run images are stateless. However, unlike Cloud Functions, each container can handle up to 80 concurrent users, which can help reduce the frequency of cold starts.
Using Firebase Hosting with Cloud Run, we hope to empower you to build better web apps faster than ever before. Now your frontend and backend developers can truly use a single language, even share a code base. To get started right away, follow our step-by-step guide. Note that Cloud Run exists in the Google Cloud console rather than the Firebase console, but if you have a Firebase project then you already have a Google Cloud Platform project as well.
Hey there, Firebase developers!
Well, Cloud Next 2019 is upon us, and if you happen to be one of the several thousand people descending upon Moscone Center this year and want to get your fill of Firebase knowledge, you're in luck! There are a bunch of great sessions the Firebase team is putting on throughout the conference. And if you want to talk to any of us in person, swing on by the App Dev zone in the expo area. We'll be at the Firebase booth from now until Thursday the 11th.
But if you're not able to make it to beautiful downtown San Francisco this year, never fear! You can still find out everything that's new with Firebase in this blog post, so read on!
For those of you who are Google Cloud Platform customers, we are pleased to announce that the GCP support plan now includes support for Firebase products. This means that if you are using any of the paid GCP support packages, you can get the same high-quality support that you've come to expect from GCP for Firebase products as well. This includes target response times as quick as 15 minutes, technical account management (for enterprise customers), phone support, and much more.
Now if you're not a paying GCP customer, don't worry -- free community support isn't going anywhere. But for many of our larger customers who were interested in a more robust paid support experience, this new option is welcome news. To find out more, you can check out the support pages on the GCP site as well as the Firebase Support Guide.
One of the new GCP products that we announced at this year's Cloud Next is Cloud Run, a fully managed compute platform that lets you run stateless containers which you can invoke via HTTP requests. And we're happy to announce that you can use Cloud Run in conjunction with Firebase Hosting.
Why do you care? Because Firebase Hosting isn't just good for hosting static sites. You can run microservices on top of Hosting as well. In the past, you did this by connecting your Hosting site with Cloud Functions for Firebase, which meant that you had to write all of your code in Node.js. But now that you can deploy stateless servers through Cloud Run and have Hosting talk to them, you can build your microservices in anything from Python to Ruby to Swift.
This is a pretty deep topic which deserves its own blog post, so keep an eye out for that in the next couple of days. Or check out the documentation if you want to get started today.
In the past, you could filter your event reports in Google Analytics for Firebase by a single user property (or audience). So you could quickly answer questions like how many iOS 12 users were signing up for your newsletter. But up until now, you couldn't filter by more than one different user property at once. So if you wanted to find out how many iOS 12 users on iPad Pros were signing up for your newsletter, that wasn't really possible.
Well, we're happy to announce that you'll be able to filter your Analytics event reports by any number of different user properties or audiences -- both ones defined by Firebase as well as custom user properties -- at the same time. So if you want to find out how many iOS 12 users with iPad Pros who prefer dogs over cats signed up for your newsletter, that's now something you can see directly within the Firebase console.
This change is currently rolled out to a small number of users, and will be available to everybody over the next few weeks. This will apply automatically to all of your data going back to December of 2018 when it becomes available, so hop on over to the Firebase console and give it a try!
About 9 months ago ago, we gave developers the ability to create nicer looking domains for their Dynamic Links. So instead of having Dynamic Links with domains that looked like a8bc7w.app.goo.gl, you could set them to something much nicer, like example.page.link.
a8bc7w.app.goo.gl
example.page.link
We improved upon this feature to give you the ability to create dynamic links with any custom domain you own. So if you want to create a link with a domain like www.example.com, this is now something you can do with Dynamic Links.
www.example.com
The one caveat here is that your site needs to be hosted using Firebase Hosting. If migrating your primary domain over to Firebase Hosting isn't feasible, you can easily setup a subdomain of your site instead. For instance, maybe you can't move all of www.example.com to Firebase Hosting, but you could pretty easily set up links.example.com on Firebase Hosting, and use that for your Dynamic Links moving forward.
links.example.com
To find out more about custom domains in Dynamic Links and to get started, make sure to check out the documentation.
Of course, we're always rolling out new features and improvements to the Firebase platform, and with I/O happening just next month, maybe we'll have something more to talk about in May 😉. There's only one way to find out: Attend I/O in person, or keep reading the Firebase blog! (Okay, that's two ways. Counting was never a strong suit of mine.)
Today we are announcing the release of two new features to ML Kit: Language Identification and Smart Reply.
You might notice that both of these features are different from our existing APIs that were all focused on image/video processing. Our goal with ML Kit is to offer powerful but simple-to-use APIs to leverage the power of ML, independent of the domain. As such, we are excited to expand ML Kit with solutions for Natural Language Processing (NLP)!
NLP is a category of ML that deals with analyzing and generating text, speech, and other kinds of natural language data. We're excited to start out with two APIs: one that helps you identify the language of text, and one that generates reply suggestions in chat applications. Both of these features work fully on-device and are available on the latest version of the ML Kit SDK, on iOS (9.0 and higher) and Android (4.1 and higher).
Generate reply suggestions based on previous messages
A new feature popping up in messaging apps is to provide the user with a selection of suggested responses, either as actions on a notification or inside the app itself. This can really help a user to quickly respond when they are busy or a handy way to initiate a longer message.
With the new Smart Reply API you can now quickly achieve the same in your own apps. The API provides suggestions based on the last 10 messages in a conversation, although it still works if only one previous message is available. It is a stateless API that fully runs on-device, so we don't keep message history in memory nor send it to a server.
textPlus app providing response suggestions using Smart Reply
We have worked closely with partners like textPlus to ensure Smart Reply is ready for prime time and they have now implemented in-app response suggestions with the latest version of their app (screenshot above).
Adding Smart Reply to your own app is done with a simple function call (using Kotlin in this example):
val smartReply = FirebaseNaturalLanguage.getInstance().smartReply smartReply.suggestReplies(conversation) .addOnSuccessListener { result -> if (result.status == SmartReplySuggestionResult.STATUS_NOT_SUPPORTED_LANGUAGE) { // The conversation's language isn't supported, so the // the result doesn't contain any suggestions. } else if (result.status == SmartReplySuggestionResult.STATUS_SUCCESS) { // Task completed successfully // ... } } .addOnFailureListener { // Task failed with an exception // ... }
After you initialize a Smart Reply instance, call suggestReplies with a list of recent messages. The callback provides the result which contains a list of suggestions.
suggestReplies
result
For details on how to use the Smart Reply API, check out the documentation.
Tell me more ...
Although as a developer, you can just pick up this new API and easily get it integrated in your app, it may be interesting to reveal a bit on how it works under the hood. At the core of Smart Reply is a machine-learned model that is executed using TensorFlow Lite and has a state-of-the-art modern architecture based on SentencePiece text encoding[1] and Transformer[2].
However, as we realized when we started development of the API, the core suggestion model is not all that’s needed to provide a solution that developers can use in their apps. For example, we added a model to detect sensitive topics, so that we avoid making suggestions in response to profanity or in cases of personal tragedy/hardship. Also, we included language identification, to ensure we do not provide suggestions for languages the core model is not trained on. The Smart Reply feature is launching with English support first.
Identify the language of a piece of text
The language of a given text string is a subtle but helpful piece of information. A lot of apps have functionality with a dependency on the language: you can think of features like spell checking, text translation or Smart Reply. Rather than asking a user to specify the language they use, you can use our new Language Identification API.
ML Kit recognizes text in 110 different languages and typically only requires a few words to make an accurate determination. It is fast as well, typically providing a response within 1 to 2 ms across iOS and Android phones.
Similar to the Smart Reply API, you can identify the language with a function call (using Kotlin in this example):
val languageIdentification = FirebaseNaturalLanguage.getInstance().languageIdentification languageIdentification .identifyLanguage("¿Cómo estás?") .addOnSuccessListener { identifiedLanguage -> Log.i(TAG, "Identified language: $identifiedLanguage") } .addOnFailureListener { e -> Log.e(TAG, "Language identification error", e) }
The identifyLanguage functions takes a piece of a text and its callback provides a BCP-47 language code. If no language can be confidently recognized, ML Kit returns a code of und for undetermined. The Language Identification API can also provide a list of possible languages and their confidence values.
identifyLanguage
und
For details on how to use the Language Identification API, check out the documentation.
Get started today
We're really excited to expand ML Kit to include Natural Language APIs. Give the two new NLP APIs a spin today and let us know what you think! You can always reach us in our Firebase Talk Google Group.
As ML Kit grows we look forward to adding more APIs and categories that enables you to provide smarter experiences for your users. With that, please keep an eye out for some exciting ML Kit announcements at Google I/O.
