Jesús García Crespo sevein

How to set up stress-free SSL on an OS X development machine

One of the best ways to reduce complexity (read: stress) in web development is to minimize the differences between your development and production environments. After being frustrated by attempts to unify the approach to SSL on my local machine and in production, I searched for a workflow that would make the protocol invisible to me between all environments.

Most workflows make the following compromises:

Use HTTPS in production but HTTP locally. This is annoying because it makes the environments inconsistent, and the protocol choices leak up into the stack. For example, your web application needs to understand the underlying protocol when using the secure flag for cookies. If you don't get this right, your HTTP development server won't be able to read the cookies it writes, or worse, your HTTPS production server could pass sensitive cookies over an insecure connection.
Use production SSL certificates locally. This is annoying

Turning PostgreSQL into a queue serving 10,000 jobs per second

RDBMS-based job queues have been criticized recently for being unable to handle heavy loads. And they deserve it, to some extent, because the queries used to safely lock a job have been pretty hairy. SELECT FOR UPDATE followed by an UPDATE works fine at first, but then you add more workers, and each is trying to SELECT FOR UPDATE the same row (and maybe throwing NOWAIT in there, then catching the errors and retrying), and things slow down.

On top of that, they have to actually update the row to mark it as locked, so the rest of your workers are sitting there waiting while one of them propagates its lock to disk (and the disks of however many servers you're replicating to). QueueClassic got some mileage out of the novel idea of randomly picking a row near the front of the queue to lock, but I can't still seem to get more than an an extra few hundred jobs per second out of it under heavy load.

So, many developers have started going straight t

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Workflows (States) in Django

I'm going to cover a simple, but effective, utility for managing state and transitions (aka workflow). We often need to store the state (status) of a model and it should only be in one state at a time.

Common Software Uses

Publishing (Draft->Approved->Published->Expired->Deleted)

A state machine is defined as follows:

Input - a set of inputs
Output - a set of outputs
State - a set of states
S0 ∈ S - an initial state
T : Input * State -> Output * State - a transition function

If you model your services (aggregates, projections, process managers, sagas, whatever) as state machines, one issue to address is management of State. There must be a mechanism to provide State to the state machine, and to persist resulting State for subsequent retrieval. One way to address this is by storing State is a key-value store. Another way is to use a SQL database. Yet another way is event sourcing. The benefit of even sourcing is that you never need to store State itself. Instead, you rely on the Output of a service to reconstitute state. In order to do that, the state machine transition function needs to be factored into two functions as follows:

Terraform, CoreOS, and Digital Ocean

Let's use Terraform to easily get a CoreOS cluster up on Digital Ocean. In this example we will get a 5 node CoreOS cluster up and running on the Digital Ocean 8GB size.

Install Terraform

Grab a copy of Terraform for your platform from http://www.terraform.io/downloads.html. Follow the instructions at http://www.terraform.io/intro/getting-started/install.html by getting Terraform in your PATH and testing that it works.

Digital Ocean API Key

Channels Are Not Enough

... or Why Pipelining Is Not That Easy

Golang Concurrency Patterns for brave and smart.

By @kachayev

Intro

Rust developer tools - status and strategy

Availability and quality of developer tools are an important factor in the success of a programming language. C/C++ has remained dominant in the systems space in part because of the huge number of tools tailored to these lanaguages. Succesful modern languages have had excellent tool support (Java in particular, Scala, Javascript, etc.). Finally, LLVM has been successful in part because it is much easier to extend than GCC. So far, Rust has done pretty well with developer tools, we have a compiler which produces good quality code in reasonable time, good support for debug symbols which lets us leverage C++/lanaguge agnostic tools such as debuggers, profilers, etc., there are also syntax highlighting, cross-reference, code completion, and documentation tools.

In this document I want to layout what Rust tools exist and where to find them, highlight opportunities for tool developement in the short and long term, and start a discussion about where to focus our time an

	package main

	import (
	"net/http"
	"database/sql"
	"fmt"
	"log"
	"os"
	)

	{
	"atom": ["https://www.accesstomemory.org/docs/latest/", null],
	"archivematica": ["https://www.archivematica.org/docs/archivematica-latest/", null],
	"storageservice": ["https://www.archivematica.org/docs/storage-service-latest/", null]
	}