December 6, 2017
SF Video Tech: Glenn Sweeney’s Color Chalk Talk
In this fascinating talk, Glenn Sweeney visits the earliest color scientists’ labs to show how our modern understanding of color is a dire...
Heavybit member company Librato recently hosted the SF Metrics meetup in our San Francisco Clubhouse, where speakers Ben Sigelman from LightStep, and Gian Merlino, from Imply gave two great talks on OpenTracing and Druid. Sign up here to attend the next SF Metrics meetup, and check out the Heavybit Events calendar for all of our upcoming developer focused events.
This talk describes why distributed tracing is important, why its instrumentation presents uncommon standardization problems, and the way that OpenTracing addresses these problems. It’s been 12 years since Google started using Dapper internally. Zipkin was open-sourced over 4 years ago. This stuff is not new! Yet if you operate a complex services architecture, deploying a distributed tracing system today requires person-years of engineer effort, monkey-patched communication packages, and countless inconsistencies across platforms.
If distributed tracing is so valuable, why doesn’t everyone do it already?
Because tracing instrumentation has been broken until now, which brings us to the OpenTracing project.
OpenTracing is a new, open distributed tracing standard for applications and OSS packages. Ben describes how OpenTracing integrates with application code and OSS libraries, how it interoperates with Zipkin, Appdash, LightStep, and other tracing backends, and where the project is headed. We will end with a deep dive of some OpenTracing libraries and show a few demos.
Druid is an open source, distributed data store designed to analyze event data. Druid powers user-facing data applications, provides fast queries on data in Hadoop, and helps you glean insights from streaming data. The architecture unifies historical and real-time data and enables fast, flexible OLAP analytics at scale. Gian covers Druid’s design and architecture, and how Druid can be utilized to monitor metrics data.