PERFORMANCE MONITORING WITH OPENTRACING, OPENCENSUS, AND OPENMETRICS
If you are familiar with instrumenting applications, you may have heard of OpenMetrics, OpenTracing, and OpenCensus. These projects aim to create standards for application performance monitoring and collecting metric data. Although the projects do overlap in terms of their goals, they each take a different approach to observability and instrumentation. In this post, we’ll provide an introduction to all three projects, along with some key differentiators of each, and how they best support application monitoring. OpenMetrics aims to create a standard format for exposing metric data, while OpenTracing and OpenCensus focus on creating a standard for distributed tracing. Because the OpenCensus and OpenTracing projects share similar goals, there is a lot of overlap with their tracing APIs.
Read moreMACHINE LEARNING-POWERED SEARCH RANKING OF AIRBNB EXPERIENCES
How we built and iterated on a machine learning Search Ranking platform for a new two-sided marketplace and how we helped itgrow. Airbnb Experiences are handcrafted activities designed and led by expert hosts that offer a unique taste of local scene and culture. Each experience is vetted for quality by a team of editors before it makes its way onto the platform. We launched Airbnb Experiences in November 2016 with 500 Experiences in 12 cities worldwide. During 2017, we grew the business to 5,000 Experiences in 60 cities. In 2018, the rapid growth continued, and we managed to bring Experiences to more than 1,000 destinations, including unique places like Easter Island, Tasmania, and Iceland.
Read moreHOW UBER MONITORS 4,000 MICROSERVICES
With 4,000 proprietary microservices and a growing number of open source systems that needed to be monitored, by late 2014 Uber was outgrowing its usage of Graphite and Nagios for metrics. They evaluated several technologies, including Atlas and OpenTSDB, but the fact that a growing number of open source systems were adding native support for the Prometheus Metrics Exporter format tipped the scales in that direction. Uber found with its use of Prometheus and M3, Uber’s storage costs for ingesting metrics became 8.53x more cost effective per metric per replica.
Read moreTHE STATE OF KUBERNETES CONFIGURATION MANAGEMENT
Configuration management is a hard, unsolved problem. We share some unique insights about the strengths and weaknesses of several popular K8s config management tools. Of all the problems we have confronted, the ones over which the most brainpower, ink, and code have been spilled are related to managing configurations. — Brendan Burns, Brian Grant, David Oppenheimer, Eric Brewer, and John Wilkes, Google Inc. Configuration management is a hard, unsolved problem.
Read moreTEACHING AI TO LEARN SPEECH THE WAY CHILDREN DO
A collaboration between the Facebook AI Research (FAIR) group and the Paris Sciences & Lettres University, with additional sponsorship from Microsoft Research, to challenge other researchers to teach AI systems to learn speech in a way that more closely resembles how young children learn. The ZeroSpeech 2019 challenge (which builds on previous efforts in 2015 and 2017) asks participants to build a speech synthesizer using only audio input, without any text or phonetic labels. The challenge’s central task is to build an AI system that can discover, in an unknown language, the machine equivalent of text of phonetic labels and use them to re-synthesize a sentence in a given voice.
Read moreAMBASSADOR 0.50 GA RELEASE NOTES: SNI, NEW AUTHSERVICE AND ENVOY V2 SUPPORT
We are pleased to announce the GA release of Ambassador 0.50, with the headline features of Server Name Indication (SNI) support, more powerful rate limiting semantics, and a new AuthService. This release includes a major re-architecture under the hood that adds support for the Envoy Proxy v2 API and also uses the Aggregate Discovery Service (ADS) functionality, which removes the need for hot restarts. We are extremely grateful for everyone who contributed to this release, and also those who offered feedback via GitHub and our Slack.
Read moreUPDATING NEURAL NETWORKS TO RECOGNIZE NEW CATEGORIES, WITH MINIMAL RETRAINING
Many of today’s most popular AI systems are, at their core, classifiers. They classify inputs into different categories: this image is a picture of a dog, not a cat; this audio signal is an instance of the word “Boston”, not the word “Seattle”; this sentence is a request to play a video, not a song. But what happens if you need to add a new class to your classifier — if, say, someone releases a new type of automated household appliance that your smart-home system needs to be able to control?
Read moreUSING PERFORCE IN A COMPLEX JENKINS PIPELINE
Hi, I’m Guy ‘RiotSomeOtherGuy’ Kisel, a software engineer at Riot. You might remember me from Running an Automated Test Pipeline for the League Client Update. I work on the Riot Developer Experience team – our responsibilities include providing Jenkins servers and related infrastructure for engineers to use for building, testing, and shipping their software to the millions of players that play League of Legends.
Read moreINTRODUCING ARESDB: UBER’S GPU-POWERED OPEN SOURCE, REAL-TIME ANALYTICS ENGINE
At Uber, real-time analytics allow us to attain business insights and operational efficiency, enabling us to make data-driven decisions to improve experiences on the Uber platform. For example, our operations team relies on data to monitor the market health and spot potential issues on our platform; software powered by machine learning models leverages data to predict rider supply and driver demand; and data scientists use data to improve machine learning models for better forecasting. In the past, we have utilized many third-party database solutions for real-time analytics, but none were able to simultaneously address all of our functional, scalability, performance, cost, and operational requirements.
Read moreMAYHEM, THE MACHINE THAT FINDS SOFTWARE VULNERABILITIES, THEN PATCHES THEM
Back in 2011, when the venture capitalist Marc Andreessen said that “software is eating the world,” it was still a fresh idea. Now it’s obvious that software permeates our lives. From complex electronics like medical devices and autonomous vehicles to simple objects like Internet-connected lightbulbs and thermometers, we’re surrounded by software. And that means we’re all more exposed to attacks on that software than everbefore. Every year, 111 billion lines are added to the mass of software code in existence, and every line presents a potential new target. Steve Morgan, founder and editor in chief at the research firm Cybersecurity Ventures, predicts that system break-ins made through a previously unknown weakness—what the industry calls “zero-day exploits”—will average one per day in the United States by 2021, up from one per week in 2015.
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