Posted by Swathi Dharshna Subbaraj – Project Coordinator, Google Dev Library
Android offers developers a rich set of tools and SDKs/APIs for building innovative and engaging mobile apps. Developers can create applications for a large and growing user base of over 2.5 billion devices worldwide.
Google Dev Library curates open-source Android libraries created and contributed by developers from around the world. Developers can easily leverage the vast array of useful code samples, GitHub repos, and libraries featuring Compose, networking, data storage to user interface design and image processing to build your own Android apps !
In this blog, we are sharing 7 popular projects by android contributors. These projects are some of the highest viewed projects on the platform and we hope these will give you a sneak peak into the type of interesting and innovative projects found on the platform. Let’s dive into the list:
Coil by Colin White Image loading for Android backed by Kotlin Coroutines
Coil is designed to be lightweight, efficient, and easy to use, and it offers a number of features such as automatic image caching, support for various image formats, and integration with popular image loading libraries like Glide and Picasso. If you are working on an Android app and need a reliable way to load and display images, this repository is definitely worth checking out !
LitePal by Lin Guo An Android library that makes developers use SQLite database extremely easy
If you’re looking to streamline your database management processes,LitePal is an open source library for Android that helps developers with database management in your app development.
Tivi is a TV show tracking app that uses some of the latest Android libraries
Tivi showcases modern development practices, including the use of Android Jetpack and other libraries. This TV show tracking Android project is helpful for developers to learn more about interesting and fun practices for Android development.
Showkase is an annotation-processor based Android library
Showkase helps you organize, discover, search and visualize Jetpack Compose UI elements. With minimal configuration it generates a UI browser that helps you easily find your components, colors & typography.
Pokedex follows Google’s official android architecture guidance
Pokedex demonstrates modern Android development with Hilt, Coroutines, Flow, Jetpack (Room, ViewModel), and Material Design based on MVVM architecture. The repository includes the app’s layout, features, and functionality, as well as documentation on how to implement and get resourceful.
Resource for learning about the Android Jetpack Compose framework.
If you are looking to learn or improve your knowledge of Jetpack Compose,Learn-Jetpack-Compose-By-Example contains a collection of example code and accompanying explanations for various components and features of Jetpack Compose. This repository aims to show the Jetpack Compose way of building common Android UI that we are accustomed to building.
MaterialDialog library is built upon Google’s Material Design library
The author, Shreyas Patil, goes into detail about how to use the MaterialDialog library and provides code examples to demonstrate its capabilities. The library allows developers to easily create dialogs with a variety of customization options, such as adding buttons, selecting the theme, and setting the title and content. Overall, the MaterialDialog library is a useful tool for Android developers looking to implement Material Design in your apps.
We hope these projects will inspire and help guide your own development efforts. Join our global community of Android developers to showcase your projects and access tools and resources. To contribute, submit your content.
Posted by Stephen McDonald, Developer Programs Engineer, and Nick Alteen, Technical Writer, Engineering, Wallet
Each of the mobile wallet apps implement their own technical specification for passes that can be saved to the wallet. Pass structure and configuration varies by both the wallet application and the specific type of pass, meaning developers have to build and maintain code bases for each platform.
As part of Developer Relations for Google Wallet, our goal is to make life easier for those who want to integrate passes into their mobile or web applications. Today, we’re excited to release the open-source Pass Converter project. The Pass Converter lets you take existing passes for one wallet application, convert them, and make them available in your mobile or web application for another wallet platform.
The Pass Converter launches with support for Google Wallet and Apple Wallet apps, with plans to add support for others in the future. For example, if you build an event ticket pass for one wallet, you can use the converter to automatically create a pass for another wallet. The following list of pass types are supported for their respective platforms:
Other transit passes
We designed the Pass Converter with flexibility in mind. The following features provide additional customization to your needs.
A hints.json file can be provided to the Pass Converter to map Google Wallet pass properties to custom properties in other passes.
For pass types that require certificate signatures, you can simply generate the pass structure and hand it off to your existing signing process
Since images in Google Wallet passes are referenced by URLs, the Pass Converter can host the images itself, store them in Google Cloud Storage, or send them to another image host you manage.
If you want to quickly test converting different passes, the Pass Converter includes a demo mode where you can load a simple webpage to test converting passes. Later, you can run the tool via the command line to convert existing passes you manage. When you’re ready to automate pass conversion, the tool can be run as a web service within your environment.
The following command provides a demo web page on http://localhost:3000 to test converting passes.
The next command converts passes locally. If the output path is omitted, the Pass Converter will output JSON to the terminal (for PKPass files, this will be the contents of pass.json).
nodeapp.js <pass input path> <pass output path>
Lastly, the following command runs the Pass Converter as a web service. This service accepts POST requests to the root URL (e.g. https://localhost:3000/) with multipart/form-data encoding. The request body should include a single pass file.
Ready to get started? Check out the GitHub repository where you can try converting your own passes. We welcome contributions back to the project as well!
Introducing the Dev Library Contributor Spotlights – a blog series highlighting developers that are supporting the thriving development ecosystem by contributing their resources and tools to Google Dev Library.
We met with Doug Duhaime, Full Stack Developer in Yale University’s Digital Humanities Lab, to discuss his passion for Machine Learning, his processes and what inspired him to release his PixPlot project as an Open Source.
What led you to explore the field of machine learning?
I was an English major in undergrad and in graduate school. I have a PhD in English literature. My dissertation was exploring copyright history and the ways that changes in copyright law affected the book market. How does the institution of fixed duration copyright influence the book market? To answer this question, I had to mine an enormous collection of data – half a million books, published before 1800 – to look at different patterns. That was one of the key projects that got me inspired to further explore the world of Machine Learning.
In fact, one of my projects – the PixPlot library – uses computer vision to analyze image collections, which was also partially used in my research. Part of my research looked at plagiarism detection and how readily people are inclined to copy images once it becomes legal to copy them from other texts. Computer vision helps us to answer these questions and identify key patterns.
I’ve seen machine learning and programming as a way to ask new questions in historical contexts. And there’s a whole field of us – we’re called digital humanists. Yale University, where I’ve been for the last five years, has a fantastic digital humanities program where researchers are asking questions like this and using fun machine learning platforms like TensorFlow to answer those questions.
Can you tell us more about the evolution of your PixPlot library project?
We started in Yale’s digital humanities lab with a project called neural neighbors. And the idea here was to find patterns in the Meserve-Kunhardt Collection of images.
Meserve-Kunhardt is a collection of photographs largely from the 19th century that Yale recently acquired. After being acquired by the university, some curators were preparing to identify all this really rich metadata to describe these images. However, they had a backlog, and they needed help to try to make sense of what’s in this collection. And so, Neural Neighbors was our initial attempt to answer this question.
As this project went on, we started running up against limitations and asking bigger questions. For example, instead of just looking at the pictures, what would it be like to look at the entire collection all at once? In order to answer this question, we needed a more performant rendering layer.
So we decided to utilize TensorFlow, which allowed us to extract vector representation of each image. We then compressed the dimensionality of those vectors down to 2D. But for PixPlot, we decided to use a different dimensionality reduction technique called umap. And that brought us to the first release of PixPlot.
The idea here was to take the whole collection, shoot it down into 2D, and then let you move through it and look at the images in the collection wherein we expect images with similar content to be placed close by one another.
And so it’s just evolved from that early genesis and Neural Neighbors through to where it is today.
What inspired you to release PixPlot as an open source project?
In the case of PixPlot, I was working for Yale University, and we had a goal to make as much of our contributions to the software world as possible open and publicly accessible without any commercial terms.
It was a huge privilege to spend time with the lab and build software that others found useful. I would say even more generally, in my personal life, I really like building things that people find useful and, when possible, contributing back to the open source world because, I think, so many of us learn from open source.
Posted by Ankita Tripathi, Community Manager (Dev Library)
Witnessing a plethora of open-source enthusiasts in the developer ecosystem in recent years gave birth to the idea of Google’s Dev Library. The inception of the platform happened in June 2021 with the only objective of giving visibility to developers who have been creating and building projects relentlessly using Google technologies. But why the Dev Library?
Why Dev Library?
Open-source communities are currently at a boom. The past 3 years have seen a surge of folks constantly building in public, talking about open-source contributions, digging into opportunities, and carving out a valuable portfolio for themselves. The idea behind the Dev Library as a whole was also to capture these open-source projects and leverage them for the benefit of other developers.
This platform acted as a gold mine for projects created using Google technologies (Android, Angular, Flutter, Firebase, Machine Learning, Google Assistant, Google Cloud).
With the platform, we also catered to the burning issue – creating a central place for the huge number of projects and articles scattered across various platforms. Therefore, the Dev Library became a one-source platform for all the open source projects and articles for Google technologies.
How can you use the Dev Library?
“It is a library full of quality projects and articles.”
External developers cannot construe Dev Library as the first platform for blog posts or projects, but the vision is bigger than being a mere platform for the display of content. It envisages the growth of developers along with tech content creation. The uniqueness of the platform lies in the curation of its submissions. Unlike other platforms, you don’t get your submitted work on the site by just clicking ‘Submit’. Behind the scenes, Dev Library has internal Google engineers for each product area who:
thoroughly assess each submission,
check for relevancy, freshness, and quality,
approve the ones that pass the check, and reject the others with a note.
It is a painstaking process, and Dev Library requires a 4-6 week turnaround time to complete the entire curation procedure and get your work on the site.
What we aim to do with the platform:
Provide visibility: Developers create open-source projects and write articles on platforms to bring visibility to their work and attract more contributions. Dev Library’s intention is to continue to provide this amplification for the efforts and time spent by external contributors.
Kickstart a beginner’s open-source contribution journey: The biggest challenge for a beginner to start applying their learnings to build Android or Flutter applications is ‘Where do I start my contributions from’? While we see an open-source placard unfurled everywhere, beginners still struggle to find their right place. With the Dev Library, you get a stack of quality projects hand-picked for you keeping the freshness of the tech and content quality intact. For example, Tomas Trajan, a Dev Library contributor created an Angular material starter project where they have ‘good first issues’ to start your contributions with.
Recognition: Your selection of the content on the Dev Library acts as recognition to the tiring hours you’ve put in to build a running open-source project and explain it well. Dev Library also delivers hero content in their monthly newsletter, features top contributors, and is in the process to gamify the developer efforts. As an example, one of our contributors created a Weather application using Android and added a badge ‘Part of Dev Library’.
Keeping developers in mind, we’ve updated features on the platform as follows:
Added a new product category; Google Assistant – All Google Assistant and Smart home projects now have a designated category on the Dev Library.
Integrated a new way to make submissions across product areas via the Advocu form.
Introduced a special section to submit Cloud Champion articles on Google Cloud.
Included displays on each Author page indicating the expertise of individual contributors
Upcoming: An expertise filter to help you segment out content based on Beginner, Intermediate, or Expert levels.
To submit your idea or suggestion, refer to this form, and put down your suggestions.
Dev Library as a platform is more about the contributors who lie on the cusp of creation and consumption of the available content. Here are some contributors who have utilized the platform their way. Here’s how the Dev Library has helped along their journey:
Roaa Khaddam: Roaa is a Senior Flutter Mobile Developer and Co-Founder at MultiCaret Inc.
How has the Dev Library helped you?
“It gave me the opportunity to share what I created with an incredible community and look at the projects my fellow Flutter mates have created. It acts as a great learning resource.”
“I used to discover new open source libraries and helpful articles for Android development in many places and it took me longer than necessary. But the Dev Library allows me to explore these useful resources in one place.”
Kevin Kreuzer: Kevin is an Angular developer and contributes to the community in various ways.
How has the Dev Library helped you?
“Dev Library is a great tool to find excellent Angular articles or open source projects. Dev Library offers a great filtering function and therefore makes it much easier to find the right open source library for your use case.”
What started as a platform to highlight and showcase some open-source projects has grown into a product where developers can share their learnings, inspire others, and contribute to the ecosystem at large.
Do you have an Open Source learning or project in the form of a blog or GitHub repo you’d like to share? Please submit it to the Dev Library platform. We’d love to add you to our ever growing list of developer contributors!
Posted by Nir Kalush, Dvir Kalev, Chen Yoveg, Elad Ben-David
Ads Review Tool
This tool flags (and optionally deletes) policy violating ads across your accounts. Advertisers can learn from the output to ensure their ads are compliant with Google Ads Policies at all times.
Advertisers operating at scale need a solution to holistically review policy violating ads across their accounts so they can ensure compliance with Google’s Ad Policies. As Google introduces more policies and enforcement mechanisms, advertisers need to continue checking their accounts to ensure they comply with Google’s Ads Policies.
“Disapproved Ads Auditor” is a tool that enables advertisers to review at scale all disapproved ads across their Google Ads accounts. This view allows advertisers to proactively audit their accounts , analyze the ad disapprovals holistically and identify learnings to minimize and reduce submission of potentially policy violating ads.
The tool is based on a Python script, which can be run in either of the following modes:
“Audit Mode”– export an output of disapproved ads across your accounts
“Remove Mode” – delete currently disapproved ads and log details.
There are a few output files (see here) which are saved locally under the “output” folder and there is an optional feature to export on BigQuery for further data analysis (“Disapproved Ads Auditor” dataset).
“Disapproved Ads Auditor” tool automates auditing ads across your accounts to provide you insights into non compliant ads. You can take the learnings from the output to ensure ads are compliant with Google Ads Policies and avoid creating non compliant ads. Moreover, you can optionally remove disapproved ads.
Posted by Hector Parra, Jaime Martínez, Miguel Fernandes, Julia Hernández
Merchant Center lets merchants manage how their in-store and online product inventory appears on Google. It allows them to reach hundreds of millions of people looking to buy products like yours each day.
To upload their products, merchants can make use of feeds, that is, files with a list of products in a specific format. These can be shared with Merchant Center in different ways: using Google Sheets, SFTP or FTP shares, Google Cloud Storage or manually through the user interface. These methods work great for the majority of cases. But, if a merchant’s product list grows over time, they might reach the usage limits of the feeds. Depending on the case, quota extensions could be granted, but if the list continues to grow, it might reach a point where feeds no longer support that scale, and the Content API for Shopping would become the recommended way to go forward.
The main issue is, if a merchant is recommended to stop using feeds and start using the Content API due to scale problems, it means that the number of products is massive, and trying to use the Content API directly will give them usage and quota errors, as the QPS and products per call limits will be exceeded.
For this specific use case, Centimani becomes critical in helping merchants handle the upload process through the Content API in a controlled manner, avoiding any overload of the API.
Centimani is a configurable massive file processor able to split text files in chunks, process them following a strategic pattern and store the results in BigQuery for reporting. It provides configurable options for chunk size and number of retries, and takes care of exponential backoff to ensure all requests have enough retries to overcome potential temporary issues or errors. Centimani comes with two operators: Google Ads Offline Conversions Uploader, and Merchant Center Products Uploader, but it can be extended to other uses easily.
Centimani uses Google Cloud as its platform, and makes use of Cloud Storage for storing the data, Cloud Functions to do the data processing and the API calls, Cloud Tasks to coordinate the execution of each call, and BigQuery to store the audit information for reporting.
To start using Centimani, a couple of configuration files need to be prepared with information about the Google Cloud Project to be used (including the element names), the credentials to access the Merchant Center accounts and how the load will be distributed (e.g., parallel executions, number of products per call). Then, the deployment is done automatically using a deployment script provided by the tool.
After the tool is deployed, a cloud function will be monitoring the input bucket in Cloud Storage, and every time a file is uploaded there, it will be processed. The tool uses the name of the file to select the operator that is going to be used (“MC” indicates Merchant Center Products Uploader), and the particular configuration to use (multiple configurations can be used to connect to Merchant Center accounts with different access credentials).
Whenever a file is uploaded, it will be sliced in parts if it is greater than the number of products allowed per call, they will be stored in the output bucket in Cloud Storage, and Cloud Tasks will start launching the API calls until all files are processed. Any file with errors will be stored in a folder called “slices_failed” to help troubleshoot any issues found in the process. Also, all the information about the executions will be stored temporarily in Datastore and then moved to BigQuery, where it can be used for monitoring the whole process from a centralized place.
Centimani Status Dashboard Architecture
Centimani provides an easy way for merchants to start using the Content API for Shopping to manage their products, without having to deal with the complexity of keeping the system under the limits.
In light of the new policy that might cause accounts suspension, Bowling is a mitigation tool allowing clients to act and remove disapproved ads before risking account suspension. The tool audits (and offers the option to delete) disapproved ads that may lead eventually to account suspension in perpetuity.
Starting Oct 2021 Google is introducing a new strike-based system to enforce against advertisers who repeatedly violate Google Ads policies (read more about the change here).
An advertiser’s first policy violation will result in a warning. If we detect continued violation of our policies advertisers will receive a notice they got a strike on the account, with a maximum of three strikes. The penalties applied with each strike will progressively rise. Temporary account holds will be applied for the first and second strikes, while the third strike will cause an account suspension.
Advertisers with hundreds of accounts and billions of search keywords lack the bandwidth to monitor each violation, thus might receive repeated strikes and get suspended.
“3 Strike Bowling” is an automated solution which identifies and gathers all relevant disapproved apps and includes the option to remove violating ads, in order to ensure compliance and avoid account suspension. *The user can define an exclusion list of policy topics to ignore and not remove.
It’s a simple Python script, which can be run in either of the following modes:
“Audit Mode”– export all the disapproved ads without deleting them;
“Remove Mode” – delete all the disapproved ads and log the disapproved ads’ details.
There are a few output files (see here) which are saved locally under the “output” folder and optionally on BigQuery as well ( “google_3_strikes” dataset).
Posted by Sebastian Trzcinski-Clément, Program Manager, Developer Relations
Developers around the world are constantly creating open source tools and tutorials but have a hard time getting them discovered. The content published often spanned many different sites – from GitHub to Medium. Therefore we decided to create a space where we can highlight the best projects related to Google technologies in one place – introducing the Developer Library.
The platform showcases blog posts and open source tools with easy-to-use navigation. Content is categorized by product areas; Machine Learning, Flutter, Firebase, Angular, Cloud, Android, with more to come.
What makes the Developer Library unique is that each piece featured on the site is reviewed, in detail, by a team of Google experts for accuracy and relevancy, so you know when you view the content on the site it has the stamp of approval from Google.
To demonstrate the breadth of content on the site here are some examples of published content pieces and video interviews with the developers who authored these posts:
A blood cell detection code sample using TensorFlow’s Object Detection API and interview with author Sayak Paul
The Google Pay API enables fast, simple checkout for your website.
Introducing the Google Pay button for React
React is one of the most widely-used tools for building web UI’s, so we are launching the Google Pay Button for React to provide a streamlined integration experience. This component will make it easier to incorporate Google Pay into your React website whether you are new to React or a seasoned pro, and similarly, if this is your first Google Pay integration or if you’ve done this before.
We’re making this component available as an open source project on GitHub and publishing it to npm. We’ve authored the React component with TypeScript to bring code completion to supported editors, and if your website is built with TypeScript you can also take advantage of type validation to identify common issues as you type.
Get real time code completion and validation as you integrate with supported editors.
The first step is to install the Google Pay button module from npm:
npm install @google-pay/button-react
Adding and configuring the button
The Google Pay button can be added to your React component by first importing it:
import GooglePayButton from '@google-pay/button-react';
And then rendering it with the necessary configuration values:
Like the React component, the Google Pay button custom element is hosted on GitHub and published to npm. In fact, the React component and the custom element share the same repository and large portion of code. This ensures that both versions maintain feature parity and receive the same level of care and attention.
This is the first time that we (the Google Pay team) have released a framework specific library. We would love to hear your feedback.
Aside from React, most frameworks can use the Web Component version of the Google Pay Button. We may consider adding support for other frameworks based on interest and demand.
If you encounter any problems with the React component or custom element, please raise a GitHub issue. Alternatively, if you know what the problem is and have a solution in mind, feel free to raise a pull request. For other Google Pay related requests and questions, use the Contact Support option in the Google Pay Business Console.
What do you think?
Do you have any questions? Let us know in the comments below or tweet using #AskGooglePayDev.
Istio, the open-source service mesh that we created with IBM and Lyft, is now at version 1.4, and we’re very excited by how quickly the project is evolving and being adopted by end users.
When we released Istio 1.1 in March, we announced that we would move to quarterly releases to get functionality out faster, and with this fourth release of the year, we’re happy to be fulfilling that promise.
Much of the work we are doing in open source Istio comes from what we’ve learned working with users of Google’s Anthos and Anthos Service Mesh, the hybrid application deployment platform and Istio-based service mesh that we released earlier this year to help enterprises monitor, secure and manage traffic in complex deployments.
Working with Anthos users, we saw that we needed to focus on Istio usability and performance. In Istio 1.4 we are particularly excited about the advances in “mixerless telemetry”—a simplified architecture that allows full fidelity and pluggability of L7 telemetry, with a much smaller CPU footprint. Istio’s Envoy proxies can now send telemetry to Prometheus or Stackdriver without first having to install, run and scale Mixer instances.
“Many of the customers I talk to love the observability that they get with Istio but didn’t love the amount of resources that Mixer consumed,” said Mandar Jog, lead for the Istio Policies and Telemetry working group. “Istio’s goal is to be both feature-rich and performant, and we’re well on the way with this release.”
We also noticed that Anthos Service Mesh users often use it to enforce access policies among their services. To help with that, we redesigned Istio’s authorization APIs, simplifying them and making them easier to use.
It’s also getting easier for operators to install and upgrade Istio, thanks to simpler configuration options via the Kubernetes Operator mechanism. This will help not only Anthos customers but all open source Istio users—that includes Google Kubernetes Engine (GKE) customers who use the Istio on GKE add-on to install open-source Istio in their GKE clusters.
Accelerating Istio for all
As we increased our contributions to Istio, the whole community grew as well. In fact, GitHub recently noted that Istio is in the top five projects in contributor growth over the last year—across all projects in GitHub! Of course, the success of an open source project is as much about building an ecosystem as it is about building a community, and that’s been happening too, with the arrival of Istio-based service mesh products from companies small and large—from Aspen Mesh and Banzai Cloud to Mulesoft to VMware.
Finally, we’re happy to see people talking about their own journey to service mesh with Istio. AutoTrader UK announced recently that Istio and GKE have let them migrate 300 services from VMs in a data center to the cloud. And at KubeCon this week, we heard from the likes of ING Bank, Freddie Mac and Yahoo! about how they’re using Istio.
The Go gopher was created by renowned illustrator Renee French. This image is adapted from a drawing by Egon Elbre.
November 10 marked Go’s 10th anniversary—a milestone that we are lucky enough to celebrate with our global developer community.
The Gopher community will be celebrating Go’s 10th anniversary at conferences such as Gopherpalooza in Mountain View and KubeCon in San Diego, and dozens of meetups around the world.
In recognition of this milestone, we’re taking a moment to reflect on the tremendous growth and progress Go (also known as golang) has made: from its creation at Google and open sourcing, to many early adopters and enthusiasts, to the global enterprises that now rely on Go everyday for critical workloads.
New to Go?
Go is an open-source programming language designed to help developers build fast, reliable, and efficient software at scale. It was created at Google and is now supported by over 2100 contributors, primarily from the open-source community. Go is syntactically similar to C, but with the added benefits of memory safety, garbage collection, structural typing, and CSP-style concurrency.
Most importantly, Go was purposefully designed to improve productivity for multicore, networked machines and large codebases—allowing programmers to rapidly scale both software development and deployment.
Millions of Gophers!
Today, Go has more than a million users worldwide, ranging across industries, experience, and engineering disciplines. Go’s simple and expressive syntax, ease-of-use, formatting, and speed have helped it become one of the fastest growing languages—with a thriving open source community.
As Go’s use has grown, more and more foundational services have been built with it. Popular open source applications built on Go include Docker, Hugo, Kubernetes. Google’s hybrid cloud platform, Anthos, is also built with Go.
One exciting example of Go in action is at MercadoLibre, which uses Go to scale and modernize its ecommerce ecosystem, improve cost-efficiencies, and system response times.
MercadoLibre’s core API team builds and maintains the largest APIs at the center of the company’s microservices solutions. Historically, much of the company’s stack was based on Grails and Groovy backed by relational databases. However this big framework with multiple layers was soon found encountering scalability issues.
Converting that legacy architecture to Go as a new, very thin framework for building APIs streamlined those intermediate layers and yielded great performance benefits. For example, one large Go service is now able to run 70,000 requests per machine with just 20 MB of RAM.
“Go was just marvelous for us,” explains Eric Kohan, Software Engineering Manager at MercadoLibre. “It’s very powerful and very easy to learn, and with backend infrastructure has been great for us in terms of scalability.”
Using Go allowed MercadoLibre to cut the number of servers they use for this service to one-eighth the original number (from 32 servers down to four), plus each server can operate with less power (originally four CPU cores, now down to two CPU cores). With Go, the company obviated 88 percent of their servers and cut CPU on the remaining ones in half—producing a tremendous cost-savings.
With Go, MercadoLibre’s build times are three times (3x) faster and their test suite runs an amazing 24 times faster. This means the company’s developers can make a change, then build and test that change much faster than they could before.
Today, roughly half of Mercadolibre’s traffic is handled by Go applications.
“We really see eye-to-eye with the larger philosophy of the language,” Kohan explains. “We love Go’s simplicity, and we find that having its very explicit error handling has been a gain for developers because it results in safer, more stable code in production.”
Visit go.dev to Learn More
We’re thrilled by how the Go community continues to grow, through developer usage, enterprise adoption, package contribution, and in many other ways.
Building off of that growth, we’re excited to announce go.dev, a new hub for Go developers.
There you’ll find centralized information for Go packages and modules, a wealth of learning resources to get started with the language, and examples of critical use cases and case studies of companies using Go.
MercadoLibre’s recent experience is just one example of how Go is being used to build fast, reliable, and efficient software at scale.
You can read more about MercadoLibre’s success with Go in the full case study.