Have you ever come across Hashicorp’s Vault? It started life as a place to store application “secrets” (e.g. database passwords) securely, without hard-coding them in configuration files. Over time, it has grown into something much more powerful.
There are a number of ways in which users can authenticate themselves to Vault. This can be used for access control to Vault itself, such as granting the user access to specific secrets, or management access to modify data and access policies.
However, once a user has authenticated, Vault can now in turn vouch for their identity — by issuing X509 certificates, JWT…
In a previous article, I introduced Linstor. Now I’m going to explain some of the concepts needed to use it.
If you’ve read the Linstor user’s guide but are still confused about the relationship between resource-groups, resource-definitions, resources and volumes, then this is for you.
I’m going to leave storage pools for a moment, and go straight to the most important part: resources and volumes
Resources and Volumes
There are six key entities:
Ultimately, the storage that appears as /dev/drbdXXXX is a volume.
The relationship between resources and volumes is described here:
Volumes are a subset of a Resource. A Resource could have…
Prometheus is a metrics collection system, and its node_exporter exposes a rich range of system metrics.
In this article I’m going to break down the individual metrics for disk I/O. They provide critical information about how your disks are performing, how busy they are and the I/O latency that your applications are experiencing.
There are a number of Grafana dashboards for node_exporter, but not all of them label the stats correctly. Hence it’s well worth understanding exactly what you’re looking at.
Raw diskstats
On a Linux system, node_exporter reads disk metrics from /proc/diskstats. The format of this file is given in the…
With step-by-step guide to deployment under Ubuntu 18.04
When running your own infrastructure, persistent storage has always been a problem for virtual machines and stateful containers.
On the one hand, you have local storage — which means your VMs and containers cannot move without copying their entire disk image.
On the other hand, you have network-attached storage (SANs and NASes), which are expensive and a critical point of failure. And on the third hand, you have distributed storage systems like Ceph and Longhorn, which can be complex to deploy and expand, and very hard to diagnose when they don’t perform as expected.
The IO Monad — laid bare
From the Functional Programming illustrated in Python series
But I don’t like Monads!
Monads, Monads, Monads… have you got anything without Monads?
Well, there’s Direct Function Application. That doesn’t have much Monad in it.
The problem is, functions don’t do anything. Sooner or later you’re going to want to write a program which interacts with the real world. It reads and writes to the terminal. It writes to the filesystem. It updates a SQL database. It turns a little red LED on and off. All of these things are decidedly stateful and side-effect-ful, and they are implemented in dirty, impure languages like C and ultimately…
Monads
From the Functional Programming illustrated in Python series
If you’re reading about functional programming, sooner or later you’re going to come across Monads. People seem to find Monads a very difficult topic to understand, so strap yourself in for a bumpy ride.
Err, no. I already covered Monads back in part 2 of this series. Move along please — nothing to see here. Go back and read part 2 again if you need to.
Actually, all I did there was write some code involving indirect application of functions to wrapped values, and I avoided using the M-word. Maybe the code…
Assignment as parameter binding
From the Functional Programming illustrated in Python series
Consider the following sequence of computations, using assignment to temporary variables:
v0 = 4
v1 = v0*v0
v2 = 2*v1
v3 = 1+v2
print(v3)Can it be done in a purely functional form, without var = expr assignment? It certainly can.
Binding to function parameters
When we invoke a function, its parameters are set (“bound”) to the arguments passed. For example:
def myfunc(x):
... more stuffmyfunc(5)
During the execution of myfunc x is bound to the value 5. Eliminating the named function using a lambda, and split over multiple lines for clarity, this becomes:
(lambda…Function composition with wrapped values
From the Functional Programming illustrated in Python series
In the previous article, I showed three functions f, g and h, applied in that order to a value. There were several ways to achieve that. One was to nest them, giving “inside out” evaluation:
result = h(g(f(v0)))Another was to assign to intermediate variables, so that the program flows top-to-bottom:
v1 = f(v0)
v2 = g(v1)
v3 = h(v2)And a third was to wrap the value, where the wrapper class has an operator which applies the function on the right to the value on the left.
result = Value(v0) >>…Introduction
Welcome to a short series of articles which introduce functional programming concepts in a simple, easy-to-digest and non-mathematical way.
I have chosen Python because it’s familiar to many people. Whilst not generally considered a “functional” language, it has the tools needed to show the concepts clearly. It’s also comforting — it doesn’t have lots of brackets like Lisp, or almost no brackets at all like Haskell, and it has a good old print() function!
Road map
Simple function composition
From the Functional Programming illustrated in Python series
Functions calculate values from values. In the simplest case, to combine functions you would take the output from one function and use it as the input to another function.
I am going to take a very simple example:
- The first function f() calculates the square of its input
- The second function g() doubles its input
- The third function h() adds one to its input
By applying the functions in this order, we can calculate 2x²+1.
This is very simple. We start with an input value of 4, square it (16), double…
