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Google Summer of Code 2018 statistics part 2

Wednesday, June 20, 2018

Now that Google Summer of Code (GSoC) 2018 is underway and students are wrapping up their first month of coding, we wanted to bring you some more statistics on the 2018 program. Lots and lots of numbers follow:

Organizations

Students are working with 206 organizations (the most we’ve ever had!), 41 of which are participating in GSoC for the first time.

Student Registrations

25,873 students from 147 countries registered for the program, which is a 25.3% increase over the previous high for the program back in 2017. There are 9 new countries with students registering for the first time: Angola, Bahamas, Burundi, Cape Verde, Chad, Equatorial Guinea, Kosovo, Maldives, and Mali.

Project Proposals

5,199 students from 101 countries submitted a total of 7,209 project proposals. 70.5% of the students submitted 1 proposal, 18.1% submitted 2 proposals, and 11.4% submitted 3 proposals (the max allowed).

Gender Breakdown

11.63% of accepted students are women, a 0.25% increase from last year. We are always working toward making our programs and open source more inclusive, and we collaborate with organizations and communities that help us improve every year.

Universities

The 1,268 students accepted into the GSoC 2018 program hailed from 613 universities, of which 216 have students participating for the first time in GSoC.

Schools with the most accepted students for GSoC 2018:
University Country Students
Indian Institute of Technology, Roorkee India 35
International Institute of Information Technology - Hyderabad India 32
Birla Institute of Technology and Science, Pilani (BITS Pilani) India 23
Indian Institute of Technology, Kharagpur India 22
Birla Institute of Technology and Science Pilani, Goa campus / BITS-Pilani - K.K.Birla Goa Campus India 18
Indian Institute of Technology, Kanpur India 16
University of Moratuwa Sri Lanka 16
Indian Institute of Technology, Patna India 14
Amrita University India 13
Indian Institute of Technology, Mandi India 11
Indraprastha Institute of Information and Technology, New Dehli India 11
University of Buea Cameroon 11
BITS Pilani, Hyderabad Campus India 11
Another post with stats on our awesome GSoC mentors will be coming soon!

By Stephanie Taylor, Google Open Source

31st anniversary of the GIF: give your terminal some personality with Tenor GIF for CLI

Thursday, June 14, 2018

Creating ASCII art for your terminal isn’t new. Displaying animating ASCII GIFs in the CLI (command line interface), however, hasn’t been possible, or at least, not easy to do -- until now.

Shortly after Tenor was acquired by Google, I had an idea.

Many developers configure static ASCII art to appear when opening a terminal, but I imagined that ASCII art could animate like a GIF, and could easily be created from any GIF on Tenor.

After some tinkering, GIF for CLI was born.

Just in time for the 31st anniversary of the GIF, GIF for CLI is available today on GitHub. GIF for CLI takes in a GIF, short video, or a query to the Tenor GIF API and converts it to animated ASCII art. This means each time you log on to your programming workstation, your GIF is there to greet you in ASCII form. Animation and color support are performed using ANSI escape sequences.

Rob Delaney as “Peter” from Deadpool 2, in ASCII GIF form. See the original GIF on Tenor here.
For the more technically-minded, here are the details:

When the command line program is run, it takes the chosen .gif file (file, url, or Tenor query) and uses ffmpeg to split the animated gif or short video into static jpg frames. Those jpg frames are then converted to ASCII art (these ASCII art frames are cached in $HOME/.cache/gif-for-cli). The program then prints one frame at a time to the console, clearing the console using ANSI escape sequences between each frame.

You could also use GIF for CLI to run gif-for-cli in your .bashrc or .profile to get an animated ASCII art image as your MOTD, or with Git hooks.

GIF for CLI integrates with the Tenor GIF API to source the GIFs. The Tenor API powers GIF search within many of today’s most popular messaging apps and social platforms on iOS and Android. 

Share screen captures of your ASCII GIFs with us on Twitter with #GIFforCLI. 

By Sean Hayes, Tenor

OpenCensus’s journey ahead: enhanced feature set

Wednesday, May 30, 2018

This is the second half of a series of blog posts about what’s coming next for OpenCensus. The OpenCensus Roadmap is composed of two pillars: increased language, framework, and platform coverage, and the addition of more powerful features.

In this blog post we’re going to discuss the second pillar: new functionality that makes OpenCensus more powerful. This includes dramatically improved sampling capabilities and new types of telemetry that we’re looking to capture.

More Power

Intelligent Sampling

In addition to expanding the list of languages and frameworks that OpenCensus supports out of the box, we’ll also be increasing the usefulness of existing functionality.

Services instrumented with OpenCensus currently randomly (at a configurable rate) sample new requests (without context, usually received directly from clients). While this does provide an effective view into application latency, developers are mostly interested in traces of particularly slow requests or requests that also capture a useful event, such as an exception.

We’re adding support for OpenCensus to make deferred sampling decisions - that is, to sample requests after they’ve propagated through several systems, while still preserving the full critical path of the trace. Though the feature is just starting development, we’re focusing on making sampling more intelligent - for example, by triggering traces based on accumulated latency, errors, and debugging events. Expect to hear more about this soon.

New Telemetry, Including Logs and Errors

As we mentioned in our last blog post, our ambition is for OpenCensus to become a ubiquitous observability framework, meaning that collecting traces and stats alone won’t be enough. Correlating traces and tags with logs and errors represents an obvious next step, and we’re currently working through what this might look like. Longer term, this list could grow to include profiles and other signals.

The topic of what signals will come next is worth of its own blog post, and you can expect us to start talking about this more in the coming months.

Server-provided Traces and Metrics

Distributed applications can obtain observability into their own performance by instrumenting themselves with OpenCensus, however visibility into the performance of external services or APIs that they call into is still limited. For example, imagine a web service that calls into Google Cloud Platform’s Cloud Bigtable service: the application developer would have visibility into their client side traces but would not be able to tell how much time Cloud BigTable took to respond vs time taken by network. We’re working on adding server side traces and metrics - essentially a way for service providers to summarize server side traces and metrics.

Cluster wide Z pages

Today, OpenCensus provides a stand-alone application called z-pages that includes an embedded web server and displays configuration parameters and trace information in real-time, as captured from any OpenCensus libraries running on the same host. By accessing a z-page, developers can configure sampling rate for the local instance, or view traces, tags, and stats as they’re being processed in real-time.

Longer-term, we wish to extend this functionality to enable cluster wide z-pages, which could provide the same functionality as the current z-pages experience, aggregated over all of the instances of a particular service. We’re still discussing different implementation options, and if we can tie this into other aggregation-related workstreams that we’re already pursuing.

Wrapping up

Does the strategy and roadmap above resonate with what you’d want to get from OpenCensus library? We’d love to hear your ideas and what you’d like to see prioritized.

As we mentioned in our last post, none of this is possible without the support and participation from the community. Check out our repo and start contributing. No contribution or idea is too small. Join other developers and users on the OpenCensus Gitter channel. We’d love to hear from you.

By Pritam Shah and Morgan McLean, Census team
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