Why Docker is Not Yet Succeeding Widely in Production

“Docker’s momentum has been increasing by the week, and from that it’s clearly touching on real problems. However, for many production users today, the pros do not outweigh the cons. Docker has done fantastically well at making containers appeal to developers for development, testing and CI environments—however, it has yet to disrupt production. In light of DockerCon 2015’s “Docker in Production” theme I’d like to discuss publicly the challenges Docker has yet to overcome to see wide adoption for the production use case. None of the issues mentioned here are new; they all exist on GitHub in some form. Most I’ve already discussed in conference talks or with the Docker team. This post is explicitly not to point out what is no longer an issue: For instance the new registry overcomes many shortcomings of the old. Many areas that remain problematic are not mentioned here, but I believe that what follows are the most important issues to address in the short term to enable more organizations to take the leap to running containers in production. The list is heavily biased from my experience of running Docker at Shopify, where we’ve been running the core platform on containers for more than a year at scale. With a technology moving as fast as Docker, it’s impossible to keep everything current. Please reach out if you spot inaccuracies…”


Ceryx – A dynamic NGINX

“Reverse proxying hundreds, or even thousands of contained micro-services is an interesting problem and one that we face daily at Sourcelair. That’s why, today, we’re glad to announce Ceryx, a dynamic reverse proxy using OpenResty, Lua and Flask that can be used to proxy hosts to any number of services, with it’s configuration being available instantly. Ceryx is a project we’ve been working on the last couple of months and we’re open sourcing now…”


Installing OpenCV 3.0 for both Python 2.7 and Python 3+ on your Raspberry Pi 2

“So if you’re interested in building awesome computer vision based projects like this, then follow along with me and we’ll have OpenCV 3.0 with Python bindings installed on your Raspberry Pi 2 in no time…”

Installing OpenCV 3.0 for both Python 2.7 and Python 3+ on your Raspberry Pi 2

more: https://www.pyimagesearch.com/practical-python-opencv/?src=resource-guide-conf

Neural Networks and Deep Learning

Neural Networks and Deep Learning is a free online book. The book will teach you about:

  • Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data
  • Deep learning, a powerful set of techniques for learning in neural networks

Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you many of the core concepts behind neural networks and deep learning.

For more details about the approach taken in the book, see here. Or you can jump directly to Chapter 1 and get started…”


Understanding the Impact of Cassandra Compact Storage

“At Librato, our primary data store for time-series metrics is Apache Cassandra built using a custom schema we’ve developed over time. We’ve written and presented on it several times in the past. We store both real-time metrics and historical rollup time-series in Cassandra. Cassandra storage nodes have the largest footprint in our infrastructure and hence drive our costs, so we are always looking for ways to improve the efficiency of our data model.

As part of our ongoing efficiency improvements and development of new backend functionality, we recently took the time to reevaluate our storage schema. Coming from the early days of Cassandra 0.8.x, our schema has always been built atop the legacy Thrift APIs, and whenever we stood up a new ring, we migrated it using the `nodetool` command. We’ve been closely following the development of CQL and had already moved parts of our read-path to the new native interface in 2.0.x. However, we wanted to take a closer look at fully constructing our schema migrations (creating the CQL tables, or “column families” as they were called) using the native CQL interface…”