Category Archives: Google Cloud Platform Blog

Product updates, customer stories, and tips and tricks on Google Cloud Platform

How to build your own recommendation engine using machine learning on Google Compute Engine

You might like this blog post . . . if you like recommendation engines. If that sentence has a familiar ring, you've probably browsed many websites that use a recommendation engine.

Recommendation engines are the technology behind content discovery networks and the suggestion features of most ecommerce websites. They improve a visitor's experience by offering relevant items at the right time and on the right page. Adding that intelligence makes your application more attractive, enhances the customer experience and increases their satisfaction. Digital Trends studies show that 73% of customers prefer to get a personalized experience during their shopping experience.

There are various components to a recommendation engine, ranging from data ingestion and analytics to machine learning algorithms. In order to provide relevant recommendations, the system must be scalable and able to handle the demands that come with processing Big Data and must provide an easy way to improve the algorithms.

Recommendation engines, particularly the scalable ones that produce great suggestions, are highly compute-intensive workloads. The following features of Google Cloud Platform are well-suited to support this kind of workload:


Customers building recommendation engines are jumping on board. Antvoice uses Google Cloud Platform to deploy their self-learning, multi-channel, predictive recommendation platform.

This new solution article provides an introduction to implementing product recommendations. It shows you how you can use open source technologies to setup a basic recommendation engine on Cloud Platform. It uses the example of a house-renting website that suggests houses that the user might be interested in based on their previous behavior through a technique known as collaborative filtering.

To provide recommendations, whether in real time while customers browse your site, or through email later on, several things need to happen. At first, while you know little about your users' tastes and preferences, you might base recommendations on item attributes alone. But your system needs to be able to learn from your users, collecting data about their tastes and preferences.

Over time and with enough data, you can use machine learning algorithms to perform useful analysis and deliver meaningful recommendations. Other users’ inputs can also improve the results, effectively periodically retraining the system. This solution deals with a recommendations system that already has enough data to benefit from machine learning algorithms.

A recommendation engine typically processes data through the following four phases:


The following diagram represents the architecture of such a system:

Each component of this architecture can be deployed using various easy-to-implement technologies to get you started:

  • Front-End: By deploying a simple application on Google App Engine, a user can see a page with top recommendations. You can take it from there, easily building a strong and scalable web platform that can manage one to several millions of users, with minimum operations.
  • Storage: The solution uses Google Cloud SQL, our managed MySQL option. A commonly used database in the ecommerce domain, this database integrates well with MLlib, a machine learning library.
  • Machine learning: Using Google Cloud Dataproc or bdutil, two options that simplify deployment and management of Hadoop/Spark clusters, you'll deploy and run MLlib-based scripts.

The solution also discusses considerations for how to analyze the data, including:

  • Timeliness concerns, such as real-time, near-real-time, and batch data analysis. This information can help you understand your options for how quickly you can present recommendations to the user and what it takes to implement each option. The sample solution focuses mainly on a near-real-time approach.
  • Filtering methods, such as content-based, cluster and collaborative filtering. You'll need to decide exactly what information goes into making a recommendation, and these filtering methods are the common ones in use today. The sample solution focuses mainly on collaborative filtering, but a helpful appendix provides more information about the other options.

We hope that this solution will give you the nuts and bolts you need to build an intelligent and ever-improving application that makes the most of the information that your users give you. Happy reading!

If you liked this blog post . . . you can get started today following these steps:
  1. Sign up for a free trial
  2. Download and follow the instructions on the Google Cloud Platform Github page
  3. “Recommend” this solution to your friends.

- Posted by Matthieu Mayran, Cloud Solutions Architect

Want to know why your application is slow? Cloud Trace’s new features can help.

Since releasing Cloud Trace for Google Cloud Platform beta last year, thousands of developers have been using the service to improve their applications’ performance. Today, we’re adding more features and functionality, based on feedback from our beta users.

Here’s a shortlist of the latest updates:

1. Automatic tracing and performance analysis for all App Engine projects

Cloud Trace now automatically instruments Google App Engine applications. It continuously evaluates all App Engine requests and periodically analyzes each endpoint-level traces to identify performance bottlenecks and insights. It looks for suboptimal patterns in RPC calls and provides recommendations to fix them.

Here's how it works:

Cloud Trace currently analyzes billions of traces daily and generates millions of reports. It continuously inspects your application requests for a number of things — such as memcache size, datastore batch size, cursors usage and other data — and it looks for opportunities to optimize your application performance.

For example, using a cursor to make datastore queries may be more optimal than using an offset when the offset is large. When Cloud Trace observes a call pattern with an offset that slows down the application, it surfaces an insight with a recommendation to use cursors. We're continuously refining existing insights and adding new insights to Cloud Trace to provide accurate and actionable suggestions.

2. Latency shift detection

Cloud Trace builds analysis reports for the most frequently used endpoints, and now we can use these reports to determine changes in your application’s latency. Using our latency shift detection algorithms, we can surface significant and minor changes in your application latencies when there's a noticeable change. You can access this feature directly from the Analysis Reports tab within Cloud Trace.

3. Use Trace API to trace custom workloads

If you have your own custom workloads that you wish to trace and analyze, the Cloud Trace API and Trace SDK can now be used to optimize the performance of those workloads. The Cloud Trace API can be used to add custom spans to a trace. A span represents a unit of work within a trace, such as an RPC request or a section of code. For custom workloads, you can define your custom start and end of a span using the Cloud Trace SDK. This data is uploaded to Cloud Trace, where you can leverage all the Trace Insights and Analytics features mentioned above. Currently Cloud Trace SDK is available for Java and Node.js. A REST API is available for all other languages.

4. Intuitive UI with a focus on developer workflows

The new Trace Overview page brings together all the Cloud Trace goodness into a simple unified view. The page summarizes the various performance insights gleaned from your application traces. It also summarizes the latencies for each application endpoint and the latencies associated with your most frequently called RPCs. The latest analysis report is readily available on the overview page.


Many users have often expressed a desire to analyze Traces when viewing logs or when viewing the health of App Engine projects. Trace is now accessible from the Logs Viewer in Cloud Console and from the App Engine dashboard. If you're using Google Cloud Monitoring, then you'll now be able to navigate to a relevant and filtered set of Traces from your monitoring dashboard.
Stay tuned for more Cloud Trace improvements over the coming weeks, including expanded support for Google Compute Engine and Google Container Engine as well as more detailed auto analysis and insights. As always, we love direct feedback and will be monitoring Stack Overflow for issues and suggestions.

If you haven’t tried Cloud Trace to optimize your application performance, go ahead and give it a try! If you're attending GCP NEXT 2016, check out the talk “Diagnostics - Spend less time diagnosing and more time developing” to learn more about Cloud Trace and all the other development tools available on Google Cloud Platform to manage your applications.

- Posted by Sharat Shroff, Product Manager, Google Cloud Platform

Announcing the Google Cloud Platform Podcast

The Google Cloud Platform team is constantly updating and releasing new products, and sometimes there isn’t enough time in the day to sit down in front of a screen and read through everything.

We’re fixing that with the Google Cloud Platform Podcast — where you can listen to all the new and exciting things that are happening on Google Cloud during your commute, at the gym, mowing the lawn, cooking dinner, or whenever you feel the desire to learn something new about our platform.
Google Cloud Platform Developer Advocates (left to right) Mark Mandel and Francesc Campoy








This weekly production is hosted by myself, Mark Mandel and my partner in crime Francesc Campoy. We are two members of the Google Cloud Platform Developer Advocacy team.

The show includes a weekly news roundup and community contributed questions, deep dives into interesting technical topics, such as Big Data, Kubernetes and HTTP/2, interviews with Google Cloud product managers — most recently Ram Ramanathan for the Cloud Vision API and Chris Sells for Developer Experience — and conversations with customers such as Shine Technologies to see how they're using Cloud Platform in the wild.

You can subscribe to the podcast to listen to all the episodes already published as well as those to come via RSS and iTunes.

We’ve got big plans for more upcoming episodes, so stay tuned!

- Posted by Mark Mandel, Developer Advocate and Francesc Campoy Flores, Developer Advocate

Spotify chooses Google Cloud Platform to power data infrastructure

It’s not every day you move a 75 million+ user company from a home-grown infrastructure to the cloud. But if you use Spotify, more and more of your musical experience will be delivered by Google Cloud Platform over the coming weeks and months — we’re partnering on an ambitious project to move Spotify’s backend into GCP.
Spotify aims to make music special for everyone. Today, the company hosts more than 2 billion playlists and gives consumers access to more than 30 million songs. Users can search for music across any device by artist, album, genre, playlist or record label, while features like Discover Weekly suggest personalized playlists for millions of people around the world.

While Spotify had engineers running its core infrastructure and buying or leasing data-center space, PC hardware and networking gear to provide a seamless experience for users — time and again it asked whether the tradeoff of resources that could otherwise focus on innovative features and software, was worth it.

Recently Spotify decided it didn’t want to be in the data center business, and chose Cloud Platform over the public cloud competition after careful review and testing. The company split their migration to Cloud Platform into two streams: a services track and a data track. Spotify runs their products on a multitude of tiny microservices, several of which are now being moved from on premise data centers into Google’s cloud using our Cloud Storage, Compute Engine and other products.

With Compute Engine, teams can rely on consistent performance from ultra high IOPS SSD and local SSD storage capabilities. And with autoscaling, they can build resilient and cost-efficient applications that use just the right amount of resources necessary at any given time. For storage, Spotify is now implementing Google Cloud Datastore and Google Cloud Bigtable. This rich fabric of storage services lets engineers work on complex back end logic, instead of focusing on how to store the data and maintain databases. Spotify is also deploying Google’s Cloud Networking services, such as Direct Peering, Cloud VPN and Cloud Router, to transfer petabytes of data. This results in a fast, reliable and secure experience for users around the globe.

On the data side of things, the company is adopting an entirely new technology stack. This includes moving from Hadoop, MapReduce, Hive and a series of home-grown dashboarding tools, to adopting the latest in data processing tools, including Google Cloud Pub/Sub, Google Cloud Dataflow, Google BigQuery, and Google Cloud Dataproc.

With BigQuery and Cloud Dataproc, data teams can run complex queries and get answers in a minute or two, rather than hours. This lets Spotify perform more frequent in-depth, interactive analysis, guiding product development, feature testing and more intelligent user-facing features. To gather and forward all events to its ecosystem, Spotify is using Cloud Pub/Sub, Google’s global service for messaging and streaming data. This gives teams the ability to process hundreds of thousands of messages per second, in a reliable no-ops manner. And to power its ETL workloads, Spotify is deploying Cloud Dataflow, Google’s data processing service. This lets the company rely on a single cloud-based managed service for both batch and stream processing.

What makes us most excited to work with Spotify is their company-wide focus on forward-looking user experiences. Now that they’ve begun using Google Cloud Platform, we can’t wait to see what Spotify builds next.

Join us for the GCP NEXT 2016 opening keynote, where we’ll feature a talk from Nicholas Harteau, VP of Engineering and Infrastructure at Spotify. You can also attend Spotify-led technical sessions where you can learn more about how they’re deploying Google Cloud BigQuery and Dataflow.

- Posted by Guillaume Leygues, Lead Sales Engineer, Google Cloud Platform

Google seeks new disks for data centers

Today, during my keynote at the 2016 USENIX conference on File and Storage Technologies (FAST 2016), I’ll be talking about our goal to work with industry and academia to develop new lines of disks that are a better fit for data centers supporting cloud-based storage services. We're also releasing a white paper on the evolution of disk drives that we hope will help continue the decades of remarkable innovation achieved by the industry to date.

But why now? It's a fun but apocryphal story that the width of Roman chariots drove the spacing of modern train tracks. However, it is true that the modern disk drive owes its dimensions to the 3½” floppy disk used in PCs. It's very unlikely that's the optimal design, and now that we're firmly in the era of cloud-based storage, it's time to reevaluate broadly the design of modern disk drives.

The rise of cloud-based storage means that most (spinning) hard disks will be deployed primarily as part of large storage services housed in data centers. Such services are already the fastest growing market for disks and will be the majority market in the near future. For example, for YouTube alone, users upload over 400 hours of video every minute, which at one gigabyte per hour requires more than one petabyte (1M GB) of new storage every day or about 100x the Library of Congress. As shown in the graph, this continues to grow exponentially, with a 10x increase every five years.


At the heart of the paper is the idea that we need to optimize the collection of disks, rather than a single disk in a server. This shift has a range of interesting consequences including the counter-intuitive goal of having disks that are actually a little more likely to lose data, as we already have to have that data somewhere else anyway. It’s not that we want the disk to lose data, but rather that we can better focus the cost and effort spent trying to avoid data loss for other gains such as capacity or system performance.

We explore physical changes, such as taller drives and grouping of disks, as well as a range of shorter-term firmware-only changes. Our goals include higher capacity and more I/O operations per second, in addition to a better overall total cost of ownership. We hope this is the beginning of both a new chapter for disks and a broad and healthy discussion, including vendors, academia and other customers, about what “data center” disks should be in the era of cloud.

- Posted by Eric Brewer, VP Infrastructure, Google

Google Cloud Dataproc managed Spark and Hadoop service now GA

Today Google Cloud Dataproc, our managed Apache Hadoop and Apache Spark service, says goodbye to its beta label and is now generally available.

When analyzing data, your attention should be focused on insights, not your tools. Often, popular tools to process data, such as Apache Hadoop and Apache Spark, require a careful balancing act between cost, complexity, scale, and utilization. Unfortunately, this means you focus less on what is important — your data — and more on what should require little or no attention — the cluster processing it.

We created our managed Spark and Hadoop cloud service, Google Cloud Dataproc, to rectify the balance, so that using these powerful data tools is as easy as 1-2-3.

Since Cloud Dataproc entered beta last year, customers have taken advantage of its speed, scalability, and simplicity. We’ve seen them create clusters from three to thousands of virtual CPUs, using our Developers Console and Google Cloud SDK, without wasting time waiting for their cluster to be ready.

With integrations to Google BigQuery, Google Cloud Bigtable, and Google Cloud Storage, which provide reliable storage independent from Dataproc clusters, customers have created clusters only when they need them, saving time and money, without losing data. Cloud Dataproc can also be used in conjunction with Google Cloud Dataflow for real-time batch and stream processing.

While in beta, Cloud Dataproc added several important features including property tuning, VM metadata and tagging, and cluster versioning. In general availability, just like in beta, new versions of Cloud Dataproc, with new features, functionalities and software components, will be frequently released. One example is support for custom machine types, available today.
Cloud Dataproc tips the scale of running Spark and Hadoop in your favor by lowering cost and complexity while increasing scalability and productivity





Cloud Dataproc minimizes two common and major distractions in data processing — cost and complexity by providing:

  • Low-cost. We believe two things — using Spark and Hadoop should not break the bank and that you should pay for what you actually use. As a result, Cloud Dataproc is priced at only 1 cent per virtual CPU in your cluster per hour, on top of the other Cloud Platform resources you use. Moreover, with per-minute billing and a low 10-minute minimum, you pay for what you actually use, not a rounded (up) approximation.
  • Speed. With Cloud Dataproc, clusters do not take 10, 15, or more minutes to start or stop. On average, Cloud Dataproc start and stop operations take 90 seconds or less. This can be a 2-10x improvement over other on-premises and IaaS solutions. As a result, you spend less time waiting on clusters and more time hands-on with data.
  • Management. Cloud Dataproc clusters don't require specialized administrators or software products. Cloud Dataproc clusters are built on proven Cloud Platform services, such as Google Compute Engine, Google Coud Networking, and Google Cloud Logging to increase availability while eliminating the need for complicated hands-on cluster administration. Moreover, Cloud Dataproc supports cluster versioning, giving you access to modern, tested, and stable versions of Spark and Hadoop.

Cloud Dataproc makes two often problematic needs in data processing easy — scale and productivity by being:

  • Modern. Cloud Dataproc is frequently updated with new image versions to support new software releases from the Spark and Hadoop ecosystem. This provides access to the latest stable releases while also ensuring backward compatibility. For general availability we're releasing image version 1.0.0 with support for Hadoop 2.7.2, Spark 1.6.0, Hive 1.2.1, and Pig 0.15.0. Support for other components, such as Apache Zeppelin (incubating) are provided in our GitHub repository for initialization actions.
  • Integrated. Cloud Dataproc has built-in integrations with other Cloud Platform services, such as BigQuery, Cloud Storage, Cloud Bigtable, and Google Cloud Logging so you have more than just a Spark or Hadoop cluster — you have a complete data platform. You can also use Cloud Dataproc initialization actions to extend the functionality of your clusters.

Our growing partner ecosystem offers certified support from several third-party tools and service partners. We're excited to collaborate with technology partners including Arimo, Attunity, Looker, WANdisco, and Zoomdata to make working in Cloud Dataproc even easier. Service providers like Moser, Pythian, and Tectonic are on standby to provide expert support during your Cloud Dataproc implementations. Reach out to any of our partners if you need help getting up and running.

To learn more about Cloud Dataproc, visit the Cloud Dataproc site, follow our getting started guide, take a look at a code example of how you can predict keno outcomes with Cloud Dataproc, or submit your questions and feedback on Stack Overflow.

- Posted by James Malone, Product Manager

Sports Authority handles 2,000 transactions per second with Google Cloud Platform

Athletic gear, much like all apparel categories, is quickly shifting to an online sales business. Sports Authority, seeing the benefits that cloud could offer around agility and speed, turned to Google Cloud Platform to help it respond to its customers faster.

In 2014, Sports Authority’s technical team was asked to build a solution that would expose all in-store product inventory to its ecommerce site, sportsauthority.com, allowing customers to see local store availability of products as they were shopping online. That’s nearly half a million products to choose from in over 460 stores across the U.S. and Puerto Rico.

This use case posed a major challenge for the company. Its in-store inventory data was “locked” deep inside a mainframe. Exposing millions of products to thousands of customers, 24 hours a day, seven days a week would not be possible using this system.

The requirements for a new solution included finding the customer’s location, searching the 90 million record inventory system and returning product availability in just the handful of stores nearest in location to that particular customer. On top of that, the API would need to serve at least 50 customers per second, while returning results in less than 200 milliseconds.

Choosing the right cloud provider

At the time this project began, Sports Authority had already been a Google Apps for Work (Gmail, Google Sites, Docs) customer since 2011. However, it had never built any custom applications on Google Cloud Platform.

After a period of due diligence checking out competing cloud provider options, Sports Authority decided that Google App Engine and Google Cloud Datastore had the right combination of attributes — elastic scaling, resiliency and simplicity of deployment — to support this new solution.

Through the combined efforts of a dedicated project team, business partners and three or four talented developers, it was able to build a comprehensive solution on Cloud Platform in about five months. It consisted of multiple modules: 1) batch processes, using Informatica to push millions of product changes from its IBM mainframe to Google Cloud Storage each night, 2) load processes — python code running on App Engine, which spawn task queue jobs to load Cloud Datastore, and 3) a series of SOAP and REST APIs to expose the search functionality to its ecommerce website.

Sports Authority used tools including SOAPUI and LOADUI to simulate thousands of virtual users to measure the scalability of SOAP and REST APIs. It found that as the number of transactions grew past 2,000 per second, App Engine and Cloud Datastore continued to scale seamlessly, easily meeting its target response times.

The company implemented the inventory locator solution just in time for the 2014 holiday season. It performed admirably during that peak selling period and continues to do so today.
This screenshot shows what customers see when they shop for products on the website — a list of local stores, showing the availability of any given product in each store



When a customer finds a product she's interested in buying, the website requests inventory availability from Sports Authority’s cloud API, which provides a list of stores and product availability to the customer, as exhibited in the running shoe example above.

In-store kiosk

As Sports Authority became comfortable building solutions on Cloud Platform, it opened its eyes to other possibilities for creating new solutions to better serve its customers.

For example, it recently developed an in-store kiosk, which allows customers to search for products that may not be available in that particular store. It also lets them enroll in the loyalty program and purchase gift cards. This kiosk is implemented on a Google Chromebox, connected to a web application running on App Engine.
This image shows the in-store kiosk that customers use to locate products available in other stores. 




Internal store portal

Additionally, it built a store portal and task management system, which facilitates communication between the corporate office and its stores. This helps the store team members plan and execute their work more efficiently, allowing them to serve customers better when needs arise. This solution utilizes App Engine, Cloud Datastore and Google Custom Search, and was built with the help of a local Google partner, Tempus Nova.
This screenshot shows the internal store portal that employees use to monitor daily tasks.




Learning how to build software in any new environment such as Cloud Platform takes time, dedication and a willingness to learn. Once up to speed, the productivity and power of Google Cloud Platform allowed the Sports Authority team to work like a software company and build quickly while wielding great power.

- Posted by Jon Byrum, Product Marketing Manager, Google Cloud Platform

Google Cloud Vision API enters Beta, open to all to try!

Today, we're announcing the beta release of Google Cloud Vision API. Now anyone can submit their images to the Cloud Vision API to understand the contents of those images — from detecting everyday objects (for example, “sports car,” “sushi,” or “eagle”) to reading text within the image or identifying product logos.

With the beta release of Cloud Vision API, you can access the API with location of images stored in Google Cloud Storage, along with existing support of embedding an image as part of the API request. We’re also announcing pricing for Cloud Vision API and added additional capabilities to identify the dominant color of an image. For example, you can now apply Label Detection on an image for as little as $2 per 1,000 images or Optical Character Recognition (OCR) for $0.60 for 1,000 images. Pricing will be effective, starting March 1st.


Cloud Vision API supports a broad set of scenarios from:

  • Insights from your images: Powered by the same technologies behind Google Photos, Cloud Vision API detects broad sets of objects in your images — from flowers to popular landmarks
  • Inappropriate content detection: Powered by Google SafeSearch, Cloud Vision API moderates content from your crowd sourced images by detecting different types of inappropriate content.
  • Image sentiment analysis: Cloud Vision API can analyze emotional attributes of people in your images, like joy, sorrow and anger, along with detecting popular product logos.
  • Text extraction: Optical Character Recognition (OCR) enables you to detect text within your images, along with automatic language identification across a broad set of languages.

Since we announced the limited preview of Google Cloud Vision API in early December, thousands of companies have used the API, generating millions of requests for image annotations. We're grateful for your feedback and comments and have been amazed by the breadth of applications using Cloud Vision API.

PhotoFy, a social photo editing and branding app, moderates over 150,000 photos a day created by a wide audience. Before the Cloud Vision API was available, CTO Chris Keenan said that protecting these branded photos from abuse was almost impossible. With the Cloud Vision API, PhotoFy can flag potentially violent and adult content on user created photos in line with their abuse policies

Marsal Gavaldà, Director of Engineering for machine intelligence over at Yik Yak, a location-based social network, ran over a million images through the Cloud Vision API. The company was impressed with the accuracy of its feature detectors and content analyzers and the precision and recall of the text extraction in multiple languages. The number of objects that can be identified with the Cloud Vision API is an order of magnitude greater than comparable services from other cloud providers.

During the beta timeframe, each user will have a quota of 20 million images/month. As such, Cloud Vision API is not intended for real-time mission critical applications. You can access the documentation, with samples and tutorials showing usage of the API in Python and Java1, along with mobile app samples for Android and iOS.

Google Cloud Vision API is our first step on the journey to enable applications to see, hear and make information in the world more useful. We welcome customers to join us on the journey and start using the API today. You can reach us with questions or feedback here.

We couldn't resist showing you our favorite robot again.
- Posted by Ram Ramanathan, Product Manager, Google Cloud Platform

1 Java is the registered trademark of Oracle and/or its affiliates.

Google and Red Hat announce cloud-based scalable file servers

Google Cloud Platform brings you lots of ways to store, access and archive your data, including Google Cloud Storage, DataStore and BigQuery. In some cases, there's a need to be able to access a POSIX compatible shared file system across a fleet of your cloud instances. To support these use cases with a robust scale out and scale up solution, Google Cloud Platform and Red Hat are proud to announce the availability of Red Hat Gluster Storage on Google Compute Engine.

Red Hat Gluster Storage offers a highly available and fault tolerant shared file system that can scale vertically and horizontally. Red Hat Gluster Storage makes use of compute instances with disks attached in order to provide a distributed, scale-out file system. Users of Red Hat Gluster Storage on GCE can take advantage of the performance, scalability and flexibility of our Persistent Disks.

Disks used for your Red Hat Gluster Storage installation can be chosen based on various performance and cost tradeoffs. For example, you can choose to use standard Persistent Disks for data that does not require high I/O throughput or use the more performant SSD Persistent Disks for your IOPS hungry workloads, such as media rendering. Each node in your cluster can leverage disks of up to 64TB in size and with up to 15,000 IOPS.

In order to protect mission critical data, Red Hat Gluster Storage enables users to synchronously replicate their files across multiple zones in the same region while at the same time asynchronously replicating them to a separate region for disaster recovery. In the example architecture below, we're using us-east1-b as our primary zone with a hot standby in us-east1-c:




- Posted by Vic Iglesias, Cloud Solutions Architect

Diagnose problems in your production apps faster with Google Cloud Debugger

Google Cloud Debugger, which lets you inspect the state of an application at any code location without stopping or slowing it down, now has an enhanced UI, expanded language support and debugging from more places.

You can view the application state without adding logging statements and we’ve made several important improvements to Cloud Debugger that’ll make production debugging more accessible, more intuitive, and more fun.


1. Language and runtimes and platforms

Cloud Debugger can help you quickly find and fix bugs in production applications. We started with support for Java 1 applications running in App Engine Managed VMs and have rapidly expanded support for more languages across Google Cloud Platform. With this release, Cloud Debugger is now available for the following languages and platforms:


2. UI enhanced for debugging

Cloud Debugger can be turned on without getting in your way. For example, debugger agents capture runtime information in a few milliseconds without user-perceptible delay to incoming requests. In production, when time is of the essence, Cloud Debugger is there when you need it and invisible when you don’t.

Additionally, Cloud Debugger is intuitive and easy to use. If you're familiar with setting breakpoints and inspecting applications when using local debuggers you'll be able to quickly transition to debugging in the cloud using a familiar UI for taking snapshots, setting conditions and specifying watch expressions.

With this release, we've completely overhauled the Cloud Debugger section of the Cloud Console to make it easier to get started and simpler to navigate. For example, now you can quickly complete each of the following actions using the new debugger web UI:

  1. Take snapshots. Cloud Debugger is integrated into common workflows such as deployment.
  2. Setup and select the source code that matches the deployed application by choosing among a variety of source code repositories. Both local and cloud repositories are now supported. You can use it without source code as well.
  3. Traverse a complex source hierarchy using the familiar treeview layout.
  4. Share snapshots and collaborate with other project members, as easily as sharing a URL.



This is just the beginning of the UI enhancements to make Cloud Debugger easier to use and to make diagnosing production errors more productive.

3. Debug using your source code, or none at all

You can inspect your application state and link it back to source code regardless of where your source code is stored or how you access it.
  1. Debug with no access to source at all

    We recognize that in many cases, developers may not be able to provide access to their source code. Cloud Debugger now lets you enter just the filename and line number to take a snapshot at that location.
  2. Debug with a source capture

    Upload a capture of your source code to help debug your application over multiple sessions without having to connect to a source repository.
  3. Debug with a local source

    You can simply point Cloud Debugger to any local source file to take a snapshot. When debugging with local files, the source code is used for that debug session only. No source code is uploaded to Google servers.
  4. Debug with a cloud source repository

    Like before, developers can use Cloud Debugger by providing access to the source code for their application using the source code storage and management features provided by Cloud Source Repositories. A source repository provides version control via git and can be managed using the Cloud Console and the new gcloud command-line tool. When a source control system is available, displaying accurate source information is simply a matter of pointing to the correct version of the source code in the source control repository using the developer console.

4. Debug on your terms in your tools

Developers working with IntelliJ IDEA can debug live production applications without leaving the IDE using the familiar IDEA debugger interface.
In case you’re unable to share your Java source code with us, that’s no problem, as the Cloud Tools for IntelliJ plugin uses the code from your local machine during your Cloud Debugging sessions. 

Stay tuned for more Cloud Debugger improvements over the coming weeks. As always, we love direct feedback and will be monitoring Stack Overflow for issues and suggestions. If you haven’t tried Cloud Debugger to diagnose problems in your production applications, now is a perfect time to start!

  - Posted by Sharat Shroff, Product Manager, Google Cloud Platform

1 Java is the registered trademark of Oracle and/or its affiliates.