Tag Archives: cloud

Developing bots for Hangouts Chat

Posted by Wesley Chun (@wescpy), Developer Advocate, G Suite

We recently introduced Hangouts Chat to general availability. This next-generation messaging platform gives G Suite users a new place to communicate and to collaborate in teams. It features archive & search, tighter G Suite integration, and the ability to create separate, threaded chat rooms. The key new feature for developers is a bot framework and API. Whether it's to automate common tasks, query for information, or perform other heavy-lifting, bots can really transform the way we work.

In addition to plain text replies, Hangouts Chat can also display bot responses with richer user interfaces (UIs) called cards which can render header information, structured data, images, links, buttons, etc. Furthermore, users can interact with these components, potentially updating the displayed information. In this latest episode of the G Suite Dev Show, developers learn how to create a bot that features an updating interactive card.

As you can see in the video, the most important thing when bots receive a message is to determine the event type and take the appropriate action. For example, a bot will perform any desired "paperwork" when it is added to or removed from a room or direct message (DM), generically referred to as a "space" in the vernacular.

Receiving an ordinary message sent by users is the most likely scenario; most bots do "their thing" here in serving the request. The last event type occurs when a user clicks on an interactive card. Similar to receiving a standard message, a bot performs its requisite work, including possibly updating the card itself. Below is some pseudocode summarizing these four event types and represents what a bot would likely do depending on the event type:

function processEvent(req, rsp) {
var event = req.body; // event type received
var message; // JSON response message

if (event.type == 'REMOVED_FROM_SPACE') {
// no response as bot removed from room
return;

} else if (event.type == 'ADDED_TO_SPACE') {
// bot added to room; send welcome message
message = {text: 'Thanks for adding me!'};

} else if (event.type == 'MESSAGE') {
// message received during normal operation
message = responseForMsg(event.message.text);

} else if (event.type == 'CARD_CLICKED') {
// user-click on card UI
var action = event.action;
message = responseForClick(
action.actionMethodName, action.parameters);
}

rsp.send(message);
};

The bot pseudocode as well as the bot featured in the video respond synchronously. Bots performing more time-consuming operations or those issuing out-of-band notifications, can send messages to spaces in an asynchronous way. This includes messages such as job-completed notifications, alerts if a server goes down, and pings to the Sales team when a new lead is added to the CRM (Customer Relationship Management) system.

Hangouts Chat supports more than JavaScript or Python and Google Apps Script or Google App Engine. While using JavaScript running on Apps Script is one of the quickest and simplest ways to get a bot online within your organization, it can easily be ported to Node.js for a wider variety of hosting options. Similarly, App Engine allows for more scalability and supports additional languages (Java, PHP, Go, and more) beyond Python. The bot can also be ported to Flask for more hosting options. One key takeaway is the flexibility of the platform: developers can use any language, any stack, or any cloud to create and host their bot implementations. Bots only need to be able to accept HTTP POST requests coming from the Hangouts Chat service to function.

At Google I/O 2018 last week, the Hangouts Chat team leads and I delivered a longer, higher-level overview of the bot framework. This comprehensive tour of the framework includes numerous live demos of sample bots as well as in a variety of languages and platforms. Check out our ~40-minute session below.

To help you get started, check out the bot framework launch post. Also take a look at this post for a deeper dive into the Python App Engine version of the vote bot featured in the video. To learn more about developing bots for Hangouts Chat, review the concepts guides as well as the "how to" for creating bots. You can build bots for your organization, your customers, or for the world. We look forward to all the exciting bots you're going to build!

Cloudprober: open source black-box monitoring software

Ever wonder if users can actually access your microservices? Observe timeouts in your applications, and not sure if it's the network or if your servers are too busy? Curious about the 99%-ile network latency between your on-premise data center and services running in the cloud?

Cloudprober, which we open sourced last year, answers questions like these and more. It’s black-box monitoring software that "probes" your systems and services and generates metrics based on probe results. This kind of monitoring strategy doesn’t make assumptions about how your service is implemented and it works at the same layer as your service’s users. You can make changes to your service’s implementation with peace of mind, knowing you’ll notice if a change prevents users from accessing the service.

A probe can be anything: a ping, an HTTP request, or even a custom program that mimics how your services are consumed (for example, creating and accessing a blog post). Cloudprober builds and exports standard metrics, and provides a way to easily integrate them with your existing monitoring stack, such as Prometheus-Grafana, Stackdriver and soon InfluxDB. Cloudprober is written in Go and works on all major platforms: Linux, Mac OS, and Windows. It's released as a static binary as well as a Docker image.

Here’s an example probe config that runs an HTTP probe against your forwarding rules and exports data to Stackdriver and Prometheus:
probe {
name: "internal-web"
type: HTTP
# Probe all forwarding rules that contain web-fr in their name.
targets {
gce_targets {
forwarding_rules {}
}
regex: "web-fr-.*"
}
interval_msec: 5000
timeout_msec: 1000
http_probe {
port: 8080
}
}

// Export data to stackdriver
surfacer {
type: STACKDRIVER
}

// Prometheus exporter
surfacer {
type: PROMETHEUS
}

The probe config is run like this from the command-line:
./cloudprober --config_file $HOME/cloudprober/cloudprober.cfg

This example probe config highlights two major features of Cloudprober: automatic, continuous discovery of cloud targets, and data export over multiple channels (Stackdriver and Prometheus in this case). Cloud deployments are dynamic and are often changing constantly. Cloudprober's dynamic target discovery feature ensures you have one less thing to worry about when doing minor infrastructure changes. Data export in various formats helps it integrate well with your existing monitoring setup.

Other features include:
  • Go text templates based configuration which adds programming capability to configs, such as "for" loops and conditionals
  • Fast and efficient implementation of core probe types
  • Custom probes through the "external" probe type
  • The ability to read config through metadata
  • And cloud (Stackdriver) logging
Though most of the cloud support is specific to Google Cloud Platform (GCP), it’s easy to add support for other providers. Cloudprober has an extensible architecture so you can add new types of targets, probes and monitoring backends.

Cloudprober was built by the Cloud Networking Site Reliability Engineering (SRE) team at Google to monitor network availability and associated features. Today, it's used by several other Google Cloud SRE teams as well.

We’re excited to share Cloudprober with the wider devops community! You can find more examples in the GitHub repository and more information on the project website.

By Manu Garg, Cloud Networking Team

Introducing Cloud Firestore: Our New Document Database for Apps

Originally posted by Alex Dufetel on the Firebase Blog

Today we're excited to launch Cloud Firestore, a fully-managed NoSQL document database for mobile and web app development. It's designed to easily store and sync app data at global scale, and it's now available in beta.

Key features of Cloud Firestore include:

  • Documents and collections with powerful querying
  • iOS, Android, and Web SDKs with offline data access
  • Real-time data synchronization
  • Automatic, multi-region data replication with strong consistency
  • Node, Python, Go, and Java server SDKs

And of course, we've aimed for the simplicity and ease-of-use that is always top priority for Firebase, while still making sure that Cloud Firestore can scale to power even the largest apps.

Optimized for app development

Managing app data is still hard; you have to scale servers, handle intermittent connectivity, and deliver data with low latency.

We've optimized Cloud Firestore for app development, so you can focus on delivering value to your users and shipping better apps, faster. Cloud Firestore:

  • Synchronizes data between devices in real-time. Our Android, iOS, and Javascript SDKs sync your app data almost instantly. This makes it incredibly easy to build reactive apps, automatically sync data across devices, and build powerful collaborative features -- and if you don't need real-time sync, one-time reads are a first-class feature.
  • Uses collections and documents to structure and query data. This data model is familiar and intuitive for many developers. It also allows for expressive queries. Queries scale with the size of your result set, not the size of your data set, so you'll get the same performance fetching 1 result from a set of 100, or 100,000,000.
  • Enables offline data access via a powerful, on-device database. This local database means your app will function smoothly, even when your users lose connectivity. This offline mode is available on Web, iOS and Android.
  • Enables serverless development. Cloud Firestore's client-side SDKs take care of the complex authentication and networking code you'd normally need to write yourself. Then, on the backend, we provide a powerful set of security rules so you can control access to your data. Security rules let you control which users can access which documents, and let you apply complex validation logic to your data as well. Combined, these features allow your mobile app to connect directly to your database.
  • Integrates with the rest of the Firebase platform. You can easily configure Cloud Functions to run custom code whenever data is written, and our SDKs automatically integrate with Firebase Authentication, to help you get started quickly.

Putting the 'Cloud' in Cloud Firestore

As you may have guessed from the name, Cloud Firestore was built in close collaboration with the Google Cloud Platform team.

This means it's a fully managed product, built from the ground up to automatically scale. Cloud Firestore is a multi-region replicated database that ensures once data is committed, it's durable even in the face of unexpected disasters. Not only that, but despite being a distributed database, it's also strongly consistent, removing tricky edge cases to make building apps easier regardless of scale.

It also means that delivering a great server-side experience for backend developers is a top priority. We're launching SDKs for Java, Go, Python, and Node.js today, with more languages coming in the future.

Another database?

Over the last 3 years Firebase has grown to become Google's app development platform; it now has 16 products to build and grow your app. If you've used Firebase before, you know we already offer a database, the Firebase Realtime Database, which helps with some of the challenges listed above.

The Firebase Realtime Database, with its client SDKs and real-time capabilities, is all about making app development faster and easier. Since its launch, it has been adopted by hundred of thousands of developers, and as its adoption grew, so did usage patterns. Developers began using the Realtime Database for more complex data and to build bigger apps, pushing the limits of the JSON data model and the performance of the database at scale. Cloud Firestore is inspired by what developers love most about the Firebase Realtime Database while also addressing its key limitations like data structuring, querying, and scaling.

So, if you're a Firebase Realtime Database user today, we think you'll love Cloud Firestore. However, this does not mean that Cloud Firestore is a drop-in replacement for the Firebase Realtime Database. For some use cases, it may make sense to use the Realtime Database to optimize for cost and latency, and it's also easy to use both databases together. You can read a more in-depth comparison between the two databases here.

We're continuing development on both databases and they'll both be available in our console and documentation.

Get started!

Cloud Firestore enters public beta starting today. If you're comfortable using a beta product you should give it a spin on your next project! Here are some of the companies and startups who are already building with Cloud Firestore:

Get started by visiting the database tab in your Firebase console. For more details, see the documentation, pricing, code samples, performance limitations during beta, and view our open source iOS and JavaScript SDKs on GitHub.

We can't wait to see what you build and hear what you think of Cloud Firestore!

Talk to Google at Node.js Interactive

We’re headed to Vancouver this week, with about 25 Googlers who are incredibly excited to attend Node.js Interactive. With a mix of folks working on Cloud, Chrome, and V8, we’re going to be giving demos and answering questions at the Google booth. 

A few of us are also going to be giving talks. Here’s a list of the talks Googlers will be giving at the conference, ranging from serverless Slack bots to JavaScript performance tuning.

Wednesday, October 4th

Keynote: Franzi Hinkelmann
9:40am - 10:00am, West Ballroom A
Franzi Hinkelmann, Software Engineer @ Google

Franzi is located in Munich, Germany where she works at Google on Chrome V8. Franzi, like James and Anna, is a member of the Node.js Core Technical Committee. She speaks across the globe on the topic of JavaScript virtual machines. She has a PhD in mathematics, but left academia to follow her true passion: writing code.

Franzi will discuss her perspective on Chrome V8 in Node.js, and what the Chrome V8 team is doing to continue to support Node.js. Want to know what the future of browser development looks like? This is a must-attend keynote.

Functionality Abuse: The Forgotten Class of Attacks
12:20pm - 12:50pm, West Ballroom A
Nwokedi Idika, Software Engineer @ Google

If you were given a magic wand that would remove all implementation flaws from your web application, would it be free of security problems? If it took you more five seconds to say “No!” (or if, worse, you said “Yes!”), then you’re the target audience for this talk. If you’re in the target audience, don’t fret, much of the security community is there with you. After this talk, attendees will understand why the answer to the aforementioned question is an emphatic “No!” and they will learn an approach to decrease their chance of failing to consider an important vector of attack for their current and future web applications.

High Performance JS in V8
5:20pm - 5:50pm, West Ballroom A
Peter Marshall, Software Engineer @ Google

This year, V8 launched Ignition and Turbofan, the new compiler pipeline that handles all JavaScript code generation. Previously, achieving high performance in Node.js meant catering to the oddities of our now-deprecated Crankshaft compiler. This talk covers our new code generation architecture - what makes it special, a bit about how it works, and how to write high performance code for the new V8 pipeline.

Thursday, October 5th

New DevTools Features for JavaScript
11:40am - 12:10pm, West Meeting Room 122
Yang Guo, Software Engineer @ Google

Ever since v8-inspector was moved to V8's repository, we have been working on a number of new features for DevTools, usable for both Chrome and Node.js. The talk will demonstrate code coverage, type profiling, and give a deep dive into how evaluating a code snippet in DevTools console works in V8.

Understanding and Debugging Memory Leaks in Your Node.js Applications
12:20pm - 12:50pm, West Meeting Room 122
Ali Sheikh, Software Engineer @ Google

Memory leaks are hard. This talk with introduce developers to what memory leaks are, how they can exist in a garbage collected language, the available tooling that can help them understand and isolate memory leaks in their code. Specifically it will talk about heap snapshots, the new sampling heap profiler in V8, and other various other tools available in the ecosystem.

Workshop: Serverless Bots with Node.js
2:20pm - 4:10pm, West Meeting Room 117
Bret McGowen, Developer Advocate @ Google
Amir Shevat, DevRel @ Slack

This talk will show you how to build both voice and chat bots using serverless technologies. Amir Shevat, Head of Developer Relations at Slack, has overseen 17K+ bots deployed on the platform. He will present a maturing model, as best practices, for enterprise bots covering all sorts of use cases ranging for devops, HR, and marketing. Alan Ho from Google Cloud will then show you how to use various serverless technologies to build these bots. He’ll give you a demo of Slack and Google Assistant bots incorporating Google’s latest serverless technology including Edge (API Management), CloudFunctions (Serverless Compute), Cloud Datastore, and API.ai.

Modules Modules Modules
3:00pm - 3:30pm, West Meeting Room 120
Myles Borins, Developer Advocate @ Google

ES Modules and Common JS go together like old bay seasoning and vanilla ice cream. This talk will dig into the inconsistencies of the two patterns, and how the Node.js project is dealing with reconciling the problem. The talk will look at the history of modules in the JavaScript ecosystem and the subtle difference between them. It will also skim over how ECMA-262 is standardized by the TC39, and how ES Modules were developed.

Keynote: The case for Node.js
4:50pm - 5:05pm, West Ballroom A
Justin Beckwith, Product Manager @ Google

Node.js has had a transformational effect on the way we build software. However, convincing your organization to take a bet on Node.js can be difficult. My personal journey with Node.js has included convincing a few teams to take a bet on this technology, and this community. Let’s take a look at the case for Node.js we made at Google, and how you can make the case to bring it to your organization.


We can’t wait to see everyone and have some great conversations. Feel free to reach out to us on Twitter @googlecloud, or request an invite to the Google Cloud Slack community and join the #nodejs channel.

By Justin Beckwith, Languages and Runtimes Team

Using BigQuery and Firebase Analytics to understand your mobile app

Originally posted on Google Cloud Platform Blog

At Google I/O this May, Firebase announced a new suite of products to help developers build mobile apps. Firebase Analytics, a part of the new Firebase platform, is a tool that automatically captures data on how people are using your iOS and Android app, and lets you define your own custom app events. When the data's captured, it’s available through a dashboard in the Firebase console. One of my favorite cloud integrations with the new Firebase platform is the ability to export raw data from Firebase Analytics to Google BigQuery for custom analysis. This custom analysis is particularly useful for aggregating data from the iOS and Android versions of your app, and accessing custom parameters passed in your Firebase Analytics events. Let’s take a look at what you can do with this powerful combination.

How does the BigQuery export work?


After linking your Firebase project to BigQuery, Firebase automatically exports a new table to an associated BigQuery dataset every day. If you have both iOS and Android versions of your app, Firebase exports the data for each platform into a separate dataset. Each table contains the user activity and demographic data automatically captured by Firebase Analytics, along with any custom events you’re capturing in your app. Thus, after exporting one week’s worth of data for a cross-platform app, your BigQuery project would contain two datasets, each with seven tables:


Diving into the data


The schema for every Firebase Analytics export table is the same, and we’ve created two datasets (one for iOS and one for Android) with sample user data for you to run the example queries below. The datasets are for a sample cross-platform iOS and Android gaming app. Each dataset contains seven tables  one week’s worth of analytics data.

The following query will return some basic user demographic and device data for one day of usage on the iOS version of our app:

SELECT
user_dim.app_info.app_instance_id,
user_dim.device_info.device_category,
user_dim.device_info.user_default_language,
user_dim.device_info.platform_version,
user_dim.device_info.device_model,
user_dim.geo_info.country,
user_dim.geo_info.city,
user_dim.app_info.app_version,
user_dim.app_info.app_store,
user_dim.app_info.app_platform
FROM
[firebase-analytics-sample-data:ios_dataset.app_events_20160601]

Since the schema for every BigQuery table exported from Firebase Analytics is the same, you can run any of the queries in this post on your own Firebase Analytics data by replacing the dataset and table names with the ones for your project.

The schema has user data and event data. All user data is automatically captured by Firebase Analytics, and the event data is populated by any custom events you add to your app. Let’s take a look at the specific records for both user and event data.

User data


The user records contain a unique app instance ID for each user (user_dim.app_info.app_instance_id in the schema), along with data on their location, device and app version. In the Firebase console, there are separate dashboards for the app’s Android and iOS analytics. With BigQuery, we can run a query to find out where our users are accessing our app around the world across both platforms. The query below makes use of BigQuery’s union feature, which lets you use a comma as a UNION ALL operator. Since a row is created in our table for each bundle of events a user triggers, we use EXACT_COUNT_DISTINCT to make sure each user is only counted once:
SELECT
user_dim.geo_info.country as country,
EXACT_COUNT_DISTINCT( user_dim.app_info.app_instance_id ) as users
FROM
[firebase-analytics-sample-data:android_dataset.app_events_20160601],
[firebase-analytics-sample-data:ios_dataset.app_events_20160601]
GROUP BY
country
ORDER BY
users DESC

User data also includes a user_properties record, which includes attributes you define to describe different segments of your user base, like language preference or geographic location. Firebase Analytics captures some user properties by default, and you can create up to 25 of your own.

A user’s language preference is one of the default user properties. To see which languages our users speak across platforms, we can run the following query:

SELECT
user_dim.user_properties.value.value.string_value as language_code,
EXACT_COUNT_DISTINCT(user_dim.app_info.app_instance_id) as users,
FROM
[firebase-analytics-sample-data:android_dataset.app_events_20160601],
[firebase-analytics-sample-data:ios_dataset.app_events_20160601]
WHERE
user_dim.user_properties.key = "language"
GROUP BY
language_code
ORDER BY
users DESC

Event data


Firebase Analytics makes it easy to log custom events such as tracking item purchases or button clicks in your app. When you log an event, you pass an event name and up to 25 parameters to Firebase Analytics and it automatically tracks the number of times the event has occurred. The following query shows the number of times each event in our app has occurred on Android for a particular day:

SELECT 
event_dim.name,
COUNT(event_dim.name) as event_count
FROM
[firebase-analytics-sample-data:android_dataset.app_events_20160601]
GROUP BY
event_dim.name
ORDER BY
event_count DESC

If you have another type of value associated with an event (like item prices), you can pass it through as an optional value parameter and filter by this value in BigQuery. In our sample tables, there is a spend_virtual_currency event. We can write the following query to see how much virtual currency players spend at one time:

SELECT 
event_dim.params.value.int_value as virtual_currency_amt,
COUNT(*) as num_times_spent
FROM
[firebase-analytics-sample-data:android_dataset.app_events_20160601]
WHERE
event_dim.name = "spend_virtual_currency"
AND
event_dim.params.key = "value"
GROUP BY
1
ORDER BY
num_times_spent DESC

Building complex queries


What if we want to run a query across both platforms of our app over a specific date range? Since Firebase Analytics data is split into tables for each day, we can do this using BigQuery’s TABLE_DATE_RANGE function. This query returns a count of the cities users are coming from over a one week period:

SELECT
user_dim.geo_info.city,
COUNT(user_dim.geo_info.city) as city_count
FROM
TABLE_DATE_RANGE([firebase-analytics-sample-data:android_dataset.app_events_], DATE_ADD('2016-06-07', -7, 'DAY'), CURRENT_TIMESTAMP()),
TABLE_DATE_RANGE([firebase-analytics-sample-data:ios_dataset.app_events_], DATE_ADD('2016-06-07', -7, 'DAY'), CURRENT_TIMESTAMP())
GROUP BY
user_dim.geo_info.city
ORDER BY
city_count DESC

We can also write a query to compare mobile vs. tablet usage across platforms over a one week period:

SELECT
user_dim.app_info.app_platform as appPlatform,
user_dim.device_info.device_category as deviceType,
COUNT(user_dim.device_info.device_category) AS device_type_count FROM
TABLE_DATE_RANGE([firebase-analytics-sample-data:android_dataset.app_events_], DATE_ADD('2016-06-07', -7, 'DAY'), CURRENT_TIMESTAMP()),
TABLE_DATE_RANGE([firebase-analytics-sample-data:ios_dataset.app_events_], DATE_ADD('2016-06-07', -7, 'DAY'), CURRENT_TIMESTAMP())
GROUP BY
1,2
ORDER BY
device_type_count DESC

Getting a bit more complex, we can write a query to generate a report of unique user events across platforms over the past two weeks. Here we use PARTITION BY and EXACT_COUNT_DISTINCT to de-dupe our event report by users, making use of user properties and the user_dim.user_id field:

SELECT 
STRFTIME_UTC_USEC(eventTime,"%Y%m%d") as date,
appPlatform,
eventName,
COUNT(*) totalEvents,
EXACT_COUNT_DISTINCT(IF(userId IS NOT NULL, userId, fullVisitorid)) as users
FROM (
SELECT
fullVisitorid,
openTimestamp,
FORMAT_UTC_USEC(openTimestamp) firstOpenedTime,
userIdSet,
MAX(userIdSet) OVER(PARTITION BY fullVisitorid) userId,
appPlatform,
eventTimestamp,
FORMAT_UTC_USEC(eventTimestamp) as eventTime,
eventName
FROM FLATTEN(
(
SELECT
user_dim.app_info.app_instance_id as fullVisitorid,
user_dim.first_open_timestamp_micros as openTimestamp,
user_dim.user_properties.value.value.string_value,
IF(user_dim.user_properties.key = 'user_id',user_dim.user_properties.value.value.string_value, null) as userIdSet,
user_dim.app_info.app_platform as appPlatform,
event_dim.timestamp_micros as eventTimestamp,
event_dim.name AS eventName,
event_dim.params.key,
event_dim.params.value.string_value
FROM
TABLE_DATE_RANGE([firebase-analytics-sample-data:android_dataset.app_events_], DATE_ADD('2016-06-07', -7, 'DAY'), CURRENT_TIMESTAMP()),
TABLE_DATE_RANGE([firebase-analytics-sample-data:ios_dataset.app_events_], DATE_ADD('2016-06-07', -7, 'DAY'), CURRENT_TIMESTAMP())
), user_dim.user_properties)
)
GROUP BY
date, appPlatform, eventName

If you have data in Google Analytics for the same app, it’s also possible to export your Google Analytics data to BigQuery and do a JOIN with your Firebase Analytics BigQuery tables.


Visualizing analytics data


Now that we’ve gathered new insights from our mobile app data using the raw BigQuery export, let’s visualize it using Google Data Studio. Data Studio can read directly from BigQuery tables, and we can even pass it a custom query like the ones above. Data Studio can generate many different types of charts depending on the structure of your data, including time series, bar charts, pie charts and geo maps.

For our first visualization, let’s create a bar chart to compare the device types from which users are accessing our app on each platform. We can paste the mobile vs. tablet query above directly into Data Studio to generate the following chart:
From this chart, it’s easy to see that iOS users are much more likely to access our game from a tablet. Getting a bit more complex, we can use the above event report query to create a bar chart comparing the number of events across platforms:
Check out this post for detailed instructions on connecting your BigQuery project to Data Studio.

What’s next?

If you’re new to Firebase, get started here. If you’re already building a mobile app on Firebase, check out this detailed guide on linking your Firebase project to BigQuery. For questions, take a look at the BigQuery reference docs and use the firebase-analytics and google-bigquery tags on Stack Overflow. And let me know if there are any particular topics you’d like me to cover in an upcoming post.

Announcing turndown of the Google Feed API

Posted by Dan Ciruli, Product Manager, Google Cloud Platform Team

The Google Feed API was one of Google’s original AJAX APIs, first announced in 2007. It had a good run. However, interest and use of the API has waned over time, and it is running on API infrastructure that is now two generations old at Google.

Along with many of our other free APIs, in April 2012, we announced that we would be deprecating it in three years time. As of April 2015, the deprecation period has elapsed. While we have continued to operate the API in the interim, it is now time to announce the turndown.

As a final courtesy to developers, we plan to operate the API until September 29, 2016, when Google Feed API will cease operation. Please ensure that your application is no longer using this API by then.

Google appreciates how important APIs and developer trust are and we do not take decisions like this one lightly. We remain committed to providing great services and being open and communicative about their statuses.

For those developers who find RSS an essential part of their workflow, there are commercial alternatives that may well fit your use case very well.

Announcing Google Cloud Platform Education Grants for computer science



While university students are on their summer holidays, internships or jobs, their professors are already hard at work planning for fall courses. These course maps will be at the center of student learning, research and academic growth. Google was founded on the basis of the work that Larry and Sergey did as computer science students at Stanford, and we understand the critical role that teachers play in fostering and inspiring the innovation we see today and will see in the years to come. That’s why we’re excited to offer Google Cloud Platform Education Grants for computer science.

Starting today, university faculty in the United States who teach courses in computer science or related subjects can apply for free credits for their students to use across the full suite of Google Cloud Platform tools, like App Engine and the Cloud Machine Learning Platform. These credits can be used any time during the 2016-17 academic year and give students access to the same tools and infrastructure used by Google engineers.
Students like Duke University undergrad Brittany Wenger are already taking advantage of cloud computing. After watching several women in her family suffer from breast cancer, Brittany used her knowledge of artificial intelligence to create Cloud4Cancer, an artificial neural network built on top of Google App Engine. By analyzing uploaded scans of benign and malignant breast cancer tumors, Cloud4Cancer has learned to distinguish between healthy and unhealthy tissue. It’s providing health care professionals with a powerful diagnostic tool in the fight against cancer.

Google Cloud Platform offers a range of tools and services that are unique among cloud providers. The tool that Brittany used -- Google App Engine -- lets you simply build and run an application without having to configure custom infrastructure. Our Machine Learning platform allows you to build models for any type of data, at any size, and TensorFlow provides access to an open-source public software library (tinker with that extensive data here). Students will also be able to get their hands on one of Cloud Platform’s most popular new innovations: the Cloud Vision API, which allows you to incorporate Google’s state-of-the-art image recognition capabilities into the most basic web or mobile app.

We look forward to seeing the creative ways that computer science students will use their Google Cloud Platform Education Grants, and will share stories along the way on this blog.

Computer science faculty in the United States can apply here for Education Grants. Students and others interested in Cloud Platform for Higher Education, should complete this form to register and stay up to date with the latest from Cloud Platform. For more information on Cloud Platform and its uses for higher education, visit our Google Cloud Platform for Higher Education site.

Google Cloud Messaging: State of the Union

Posted by Laurence Moroney, Developer Advocate

Google Cloud Messaging (GCM) is an infrastructure that allows you to do simple and reliable messaging to distribute your messages to and across many devices.

Every day, GCM delivers over 150 Billion messages to devices on various platforms including Android Devices, iOS Devices and Web Browsers. It has a number of different techniques for sending messages:

Single devices. Each device has a unique registration token. If you want to reach that device -- for example using GCM to build a 1:1 chat application, you can do so, addressing it via that token.

Device Groups allow you to bundle devices together into a group. For example, one of your users might have multiple devices -- including the very common scenario of having both a phone and a tablet. Using Device Groups in GCM, you can send a message to all of her devices, and if you desire, you can implement your app so that dismissing on one dismisses on all.

Topics allow you to create interest groups for your users. Once they subscribe to a topic, you can send messages to that topic, and your users will receive them. There’s no subscription limit to these, so you don’t have to worry about how many users subscribe to your topics! Some great scenarios of topics being used to improve user experience can be found in this blog post.

When it comes to reliability of messages, an internal study at Google found that the majority of notification messages (95th percentile) are delivered within 250ms to connected devices. Connectivity is impacted by many factors -- including carriers, routers and local connectivity. Indeed, in some locales it is common for people to disable mobile data for large parts of the day in order to save on bandwidth costs. In this scenario, users will still receive their notifications once they re-activate their data connection.

We’ve provided a number of resources to help you to build apps using GCM. Check out this talk where you are taken step by step through building an Android app and an associated server in PHP. There’s also an open source ‘GCM Playground’ on GitHub here, which provides a sample server implementation that runs on the Google Cloud Platform!

If you want to reach iOS users, today we’re adding an API that will help you to migrate your existing infrastructure to send notifications to iOS device, with no client code changes required. With the new batch Import API you can import the APNs device tokens that you collected from your iOS audience into GCM, and immediately start sending notifications through GCM. After you import the APNs device tokens, you can also use the InstanceID API to transparently subscribe users to GCM topics, achieving efficient fan-out of notifications based on interest groups, once again with no changes required on client code.

We’re continuing to build and innovate on this platform -- stay tuned for lots of cool new features coming soon!

You can learn more about Google Cloud Messaging on the Developers site here, including quickstarts for Android and iOS!

Google Cloud Messaging – We’ve Come a Long Way

Posted by Laurence Moroney Developer Advocate

Google Cloud Messaging (GCM) is a technology that provides simple and reliable messaging to devices. In the last six months, the number of messages that GCM handles has more than doubled -- to 150 billion messages per day, and the number of applications has grown 25% to 750,000. With this growth in mind, we’re continuing to improve the service with some helpful updates for developers.

Google Cloud Messaging supports topic messaging - an easy way to segment your users’ devices into groups and send a message to the entire segment at once. We’re now happy to announce that we’re allowing unlimited free topics for your app. This means app developers can place an unlimited number of devices within each topic and create an unlimited number of topics.

Moovit uses topics to efficiently scale

Moovit, a community of 30 million+ users, helps improve transit routes in cities worldwide. Using GCM, Moovit has been able to create over 60,000 topics to help users in individual cities navigate the headache of daily transit.

"We started using GCM to power our push infrastructure in a more seamless, efficient way. Not only does GCM help us to send real-time updates to a high volume of tens of millions of users, keeping them informed of any transit information they need for a stress-free commute, but we don't have to spend extra time or energy developing an infrastructure for delivery on the backend. GCM Topics allows us to message users in hundreds of cities around the world with multi-platform support for both iOS and Android."

For example: Users of London’s Underground Service were impacted by recent strikes that disrupted the regular service. While Moovit has a global audience, only those impacted were notified, as Moovit used GCM topic messaging to send the message to only those that needed it.

National Public Radio (NPR) uses Topics for news personalization

NPR is a mission-driven multimedia news organization and radio program producer in the United States. To reach their users efficiently, NPR sends and schedules personalized notifications to their listeners

via their NPR One App. For example, if you listened to the Aziz Ansari interview on the show All Things Considered, and wanted to hear more, you could subscribe to the topic ‘Aziz Ansari’ and receive a notification of his appearance on the Hidden Brain podcast. Similarly, you could subscribe to other topics such as Election 2016, Women in Combat or Pop Culture Happy Hour.

Tejas Mistrly, Mobile Product Manager for NPR, described their use of topics: “With GCM topic messaging, NPR is able to send and schedule personalized notifications to our listeners on NPR One. Whether to catch them up on the latest news or to tell them a story from a recommended podcast across public radio, GCM topic messaging gives us the tool set to send the most effective notifications that ties into our personalized radio app.”

New APIs for GCM topic management

Complementing unlimited free topics and the existing client-side API, we’re launching a new suite of server APIs that allow you to manage message subscriptions. The new APIs allow you to subscribe/unsubscribe devices individually or in batches, as well as allow retrieval of info on current subscriptions per device. We think the server-side API is a great tool to help you reduce roll-out friction, and allow for easy management and migration of subscriptions as your app grows.

To learn more about Google Cloud Messaging, visit the Google Developers Site, where you can learn more about how to build for this technology, and download sample implementations. There’s also a full reference implementation available on GitHub and the GCM Diagnostics tool for when you need help to troubleshoot.