Google runs millions of lines of Python code. The front-end server that drives youtube.com and YouTube’s APIs is primarily written in Python, and it serves millions of requests per second! YouTube’s front-end runs on CPython 2.7,
.. but we always run up against the same issue: it’s very difficult to make concurrent workloads perform well on CPython.
.. Grumpy is an experimental Python runtime for Go. It translates Python code into Go programs, and those transpiled programs run seamlessly within the Go runtime.
.. The goal is for Grumpy to be a drop-in replacement runtime for any pure-Python project.
.. In particular, Grumpy has no global interpreter lock, and it leverages Go’s garbage collection for object lifetime management instead of counting references. We think Grumpy has the potential to scale more gracefully than CPython for many real world workloads.
.. Grumpy programs can import Go packages just like Python modules! For example, the Python snippet below uses Go’s standard net/http package to start a simple server
The idea was that people building commercial plugins and themes should be able to use the automated update system that WordPress provides. There were a few self-managed solutions out there for this, but I thought building a SaaS product would be a good way to learn some new tech.
Many websites and places that talk about integrating Amazon Cloudfront with WordPress are simply talking about static assets. Using some caching plugin that rewrites the URLs in the page’s source to point at your Cloudfront distribution, and that’s about it. They don’t talk about how your server can be (as WP SuperCache puts it) Stephen Fry-proof. No one actually says anything about how to put YOUR ENTIRE WEBSITE behind Amazon Cloudfront (and by this, I mean pointing your www.* domain at Cloudfront and letting it take care of everything).
Almost all our software is written in Node with the exception of the Kraken.io frontend which is PHP-based. We make heavy use of Node Streams as our optimization pipeline is capable of consuming a stream of binary data.
.. Image optimization and recompression has enormous processing requirements. Cloud was never an option for us as we are continuously trying to lower our total cost of ownership. By signing a pretty long contract with our datacenter we were able to reduce colocation bills by 30%.
.. All single-socket machines (API, Web, Load Balancers, Webhook Delivery) are currently running Xeon E3-1280 v5 (Skylake). For Optimization Cluster where all the hard work is done we use 2 x Xeon E5-2697 v3 per machine with 128 GB RAM and four SSD hard drives in RAID-1 setup for mirroring. With HT enabled the above setup gives us access to 28 physical cores and 56 threads per Cluster machine.
.. Kraken.io’s platform is both CPU and I/O intensive
.. two independent 10 Gbps uplinks