Tuesday, June 9, 2015

Developing command-line utilities

Some people around me have noticed that I frequently use the command-line for development and system administration tasks, in particular when I'm working on Linux and other UNIX-like operating systems.

Typically, when you see me working on something behind my computer, you will most likely observe a screen that looks like this:

The above screenshot shows a KDE plasma desktop session in which I have several terminal screens opened running a command-line shell session.

For example, I do most of my file management tasks on the command-line. Moreover, I'm also a happy Midnight Commander user and I use the editor that comes with it (mcedit) quite a lot as well.

As a matter of fact, mcedit is my favorite editor for the majority of my tasks, so you will never see me picking any side in the well-known Emacs vs vi discussion (or maybe I upset people to say that I also happen to know Vim and never got used to Emacs at all). :-).

Using the command-line

For Linux users who happen to do development, this way of working makes (sort of) sense. To most outsiders, however, the above screenshot looks quite menacing and they often question me why this is a good (or appealing) way of working. Some people even advised me to change my working style, because they consider it to be confusing and inefficient.

Although I must admit that it takes a bit of practice to learn a relevant set of commands and get used to it, the reasons for me to stick to such a working style are the following:

  • It's a habit. This is obviously not a very strong argument, but I'm quite used to typing commands when executing system administration and development tasks, and they are quite common in a Linux/Unix world -- much of the documentation that you will find online explain how to do things on the command-line.

    Moreover, I have been using a command-line interface since the very first moment I was introduced to a computer. For example, my first computer's operating system shell was a BASIC programming language interpreter, which you even had to use to perform simple tasks, such as loading a program from disk/tape and running it.

  • Speed. This argument may sound counter-intuitive to some people, but I can accomplish many tasks quite quickly on the command-line and probably even faster than using a graphical user interface.

    For example, when I need to do a file management task, such as removing all backup files (files having a ~ suffix), I can simply run the following shell command:

    $ rm *~

    If I would avoid the shell and use a graphical file manager (e.g. Dolphin, GNOME Files, Windows Explorer, Apple Finder), picking these files and moving them to the trashcan would certainly take much more time and effort.

    Moreover, shells (such as bash) have many more useful goodies to speed things up, such as TAB-completion, allowing someone to only partially type a command or filename and let the shell complete it by pressing the TAB-key.

  • Convenient integration. Besides running a single command-line instruction to do a specific task, I also often combine various instructions to accomplish something more difficult. For example, the following chain of commands:

    $ wc -l $(find . -name \*.h -or -name \*.c) | head -n -1 | sort -n -r

    comes in handy when I want to generate an overview of lines of code per source file implemented in the C programming language and sort them in reverse order (that is the biggest source file first).

    As you may notice, I integrate four command-line instructions: the find instruction seeks for all relevant C source files in the current working directory, the wc -l command calculates the lines of code per file, the head command chops off the last line displaying the total values and the sort command does a numeric sort of the output in reverse order.

    Integration is done by pipes (using the | operator) which make the output of one process the input of another and command substitution (using the $(...) construct) that substitutes the invocation by its output.

  • Automation. I often find myself repeating common sets of shell instructions to accomplish certain tasks. I can conveniently turn them into shell scripts to make my life easier.

Recommendations for developing command-line utilities

Something that I consider an appealing property of the automation and integration aspects is that people can develop "their own" commands in nearly any programming language (using any kind of technology) in a straight forward way and easily integrate them with other commands.

I have developed many command-line utilities -- some of them have been developed for private use by my past and current employers (I described one of them in my bachelor's thesis for example), and others are publicly available as free and open source software, such as the tools part of the IFF file format experiments project, Disnix, NiJS and the reengineered npm2nix.

Although developing simple shell scripts as command-line utilities is a straight forward task, implementing good quality command-line tools is often more complicated and time consuming. I typically take the following properties in consideration while developing them:

Interface conventions

There are various ways to influence the behaviour of processes invoked from the command-line. The most common ways are through command-line options, environment variables and files.

I expose anything that is configurable as command-line options. I mostly follow the conventions of the underlying OS/platform:

  • On Linux and most other UNIX-like systems (e.g. Mac OS X, FreeBSD) I follow GNU's convention for command line parameters.

    For each parameter, I always define a long option (e.g. --help) and for the most common parameters also a short option (e.g. -h). I only implement a purely short option interface if the target platform does not support long options, such as classic UNIX-like operating systems.
  • On DOS/Windows, I follow the DOS convention. That is: command line options are single character only and prefixed by a slash, e.g. /h.

I always implement two command-line options, namely a help option displaying a help page that summarizes how the command can be used and a version option displaying the name and version of the package, and optionally a copyright + license statement.

I typically reserve non-option parameters to file names/paths only.

If certain options are crosscutting among multiple command line tools, I also make them configurable through environment variables in addition to command-line parameters.

For example, Disnix exposes each deployment activity (e.g. building a system, distributing and activating it) as a separate command-line tool. Disnix has the ability to deploy a system to multiple target environments (called: a profile). The following chain of command-line invocations to deploy a system make more sense to specify a profile:

$ export DISNIX_PROFILE=testenv
$ manifest=$(disnix-manifest -s services.nix -i infrastructure.nix \
    -d distribution.nix)
$ disnix-distribute $manifest
$ disnix-activate $manifest
$ disnix-set $manifest

than passing -p testenv parameter four times (to each command-line invocation).

I typically use files to process or produce arbitrary sets of data:

  • If a tool takes one single file as input or produces one single output or a combination of both, I also allow it to read from the standard input or write to the standard output so that it can be used as a component in a pipe. In most cases supporting these special file descriptors is almost as straight forward as opening arbitrary files.

    For example, the ILBM image viewer's primary purpose is just to view an ILBM image file stored on disk. However, because I also allow it to read from the standard input I can also do something like this:

    $ iffjoin picture1.ILBM picture2.ILBM | ilbmviewer

    In the above example, I concatenate two ILBM files into a single IFF container file and invoke the viewer to view both of them without storing the immediate result on disk first. This can be useful for a variety of purposes.

  • A tool that produces output writes to the standard output data that can be parsed/processed by another process in a pipeline. All other output, e.g. errors, debug messages, notifications go the the standard error.

Finally, every process should return an exit status when it finishes. By convention, if everything went OK it should return 0, and if some error occurs a non-zero exit status that uniquely identifies the error.

Command-line option parsing

As explained in the previous section, I typically follow the command-line option conventions of the platform. However, parsing them is usually not that straight forward. Some things we must take into account are:

  • We must know which parameters are command-line option flags (e.g. starting with -, -- or /) and which are non-option parameters.
  • Some command-line options have a required argument, some take an optional argument and some take none.

Luckily, there are many libraries available for a variety of programming languages/platforms implementing such a parser. So far, I have used the following:

I have never implemented a sophisticated command-line parser myself. The only case in which I ended up implementing a custom parser is in the native Windows and AmigaOS ports of the IFF libraries projects -- I could not find any libraries supporting their native command-line option style. Fortunately, the command-line interfaces were quite simple, so it did not take me that much effort.

Validation and error reporting

Besides parsing the command-line options (and non-options), we must also check whether all mandatory parameters are set, whether they have the right format and set default values for unspecified parameters if needed.

Furthermore, in case of an error regarding the inputs: we must report it to the caller and exit the process with a non-zero exit status.


Another important concern while developing a command-line utility (that IMO is often overlooked) is providing documentation about its usage.

For every command-line tool, I typically implement an help option parameter displaying a help page on the terminal. This help page contains the following sections:

  • A usage line describing briefly in what ways the command-line tool can be invoked and what their mandatory parameters are.
  • A brief description explaining what the tool does.
  • The command-line options. For each option, I document the following properties:

    • The short and long option identifiers.
    • Whether the option requires no parameter, an optional parameter, or a required parameter.
    • A brief description of the option
    • What the default value is, if appropriate

    An example of an option that I have documented for the disnix-distribute utility is:

    -m, --max-concurrent-transfers=NUM  Maximum amount of concurrent closure
                                        transfers. Defauls to: 2

    that specifies that the amount of concurrent transfers can be specified through the -m short or --max-concurrent-transfers long option, requires a numeric argument and defaults to 2 if the option is unspecified.

  • An environment section describing which environment variables can be configured and their meanings.
  • I only document the exit statuses if any of them has a special meaning. Zero in case of success and non-zero in case of a failure is too obvious.

Besides concisely writing a help page, there are more practical issues with documentation -- the help page is typically not the only source of information that describes how command-line utilities can be used.

For projects that are a bit more mature, I also want to provide a manpage of every command-line utility that more or less contain the same stuff as a help page. In large and more complicated projects, such as Disnix, I also provide a Docbook manual that (besides detailed instructions and background information) includes the help pages of the command-line utilities in the appendix.

I used to write these manpages and docbook help pages by hand, but it's quite tedious to write the same stuff multiple times. Moreover, it is even more tedious to keep them all up-to-date and consistent.

Fortunately, we can also generate the latter two artifacts. GNU's help2man utility comes in quite handy to generate a manual page by invoking the --help and --version options of an existing command-line utility. For example, by following GNU's convention for writing help pages, I was able to generate a reasonably good manual page for the disnix-distribute tool, by simply running:

$ help2man --output=disnix-distribute.1 --no-info --name \
  'Distributes intra-dependency closures of services to target machines' \
  --libtool ./disnix-distribute

If needed, additional information can be augmented to the generated manual page.

I also discovered a nice tool called doclifter that allows me to generate Docbook help pages from manual pages. I can run the following command to generate a Docbook man page section from the earlier generated manpage:

$ doclifter -x disnix-distribute.1

The above command-line instruction generates a Docbook 5 XML file (disnix-distribute.1.xml) from the earlier manual page and the result looks quite acceptable to me. The only thing I had to do is manually replacing some xml:id attributes so that a command's help page can be properly referenced from the other Docbook sections.


When implementing a command-line utility many concerns need to be implemented besides the primary tasks that it needs to do. In this blog post I have mentioned the following:

  • Interface conventions
  • Command-line option parsing
  • Validation and error reporting
  • Documentation

I tend to separate the command-line interface implementing the above concerns into a separate module/file (typically called main.c), unless the job that the command-line tool should do is relatively simple (e.g. it can be expressed in 1-10 lines of code), because I consider modularization a good practice and a module should preferably not grow too big or do too many things.


When I implement a toolset of command-line utilities, such as Disnix, I often see that these tools have many common parameters, common validation procedures and a common procedure displaying the tool version.

I typically abstract them away in a common module, file or library so that I don't find myself duplicating them making maintenance more difficult.


In this blog post, I have explained that I prefer the command-line for many development and system administration tasks. Moreover, I have also explained that I have developed many command-line utilities.

Creating good quality command-line tools is not straight forward. To make myself and other people that I happen to work with aware of it, I have written down some of the aspects that I take into consideration.

Although the command-line has some appealing properties, it is obviously not perfect. Some command-line tools are weird, may significantly lack quality and cannot be integrated in a straight forward way through pipes. Furthermore, many shells (e.g. bash) implement a programming language having weird features and counter-intuitive traits.

For example, one of my favorite pitfalls in bash is its behaviour when executing shell scripts, such as the following script (to readers that are unfamiliar with the commands: false is a command that always fails):

echo "hello"
false | cat
echo "world"
echo "!!!"

When executing the script like this:

$ bash script.bash

bash simply executes everything despite the fact that two commands fail. However, when I add the -e command line parameter, the execution is supposed to be stopped if any command returns a non-zero exit status:

$ bash -e script.bash

However, there is still one oddity -- the pipe (false | cat) still succeeds, because the exit status of the pipe corresponds to the exit status of the last component of the pipe only! There is another way to check the status of the other components (through $PIPESTATUS), but this feels counter-intuitive to me! Fortunately, bash 3.0 and onwards have an additional setting that makes the behaviour come closer to what I expect:

$ bash -e -o pipefail script.bash

Despite a number of oddities, the overall idea of the command-line is good IMO: it is a general purpose environment that can be interactive, programmed and extended with custom commands implemented in nearly any programming language that can be integrated with each other in a straight forward manner.

Meanwhile, now that I have made myself aware of some important concerns, I have adapted the development versions of all my free and open source projects to properly reflect them.

Thursday, April 30, 2015

An evaluation and comparison of Snappy Ubuntu

A few months ago, I noticed that somebody was referring to my "On Nix and GNU Guix" blog post from the Ask Ubuntu forum. The person who started the topic wanted to know how Snappy Ubuntu compares to Nix and GNU Guix.

Unfortunately, he did not read my blog post (or possibly one of the three Nix and NixOS explanation recipes) in detail.

Moreover, I was hoping that somebody else involved with Snappy Ubuntu would do a more in depth comparison and write a response, but this still has not happened yet. As a matter of fact, there is still no answer as of today.

Because of these reasons, I have decided to take a look at Snappy Ubuntu Core and do an evaluation myself.

What is Snappy Ubuntu?

Snappy is Ubuntu's new mechanism for delivering applications and system upgrades. It is used as the basis of their upcoming cloud and mobile distributions and supposed to be offered alongside the Debian package manager that Ubuntu currently uses for installing and upgrading software in their next generation desktop distribution.

Besides the ability to deploy packages, Snappy also has a number of interesting non-functional properties. For example, the website says the following:

The snappy approach is faster, more reliable, and lets us provide stronger security guarantees for apps and users -- that's why we call them "snappy" applications.

Snappy apps and Ubuntu Core itself can be upgraded atomically and rolled back if needed -- a bulletproof approach that is perfect for deployments where predictability and reliability are paramount. It's called "transactional" or "image-based" systems management, and we’re delighted to make it available on every Ubuntu certified cloud.

The text listed above contains a number of interesting quality aspects that have a significant overlap with Nix -- reliability, atomic upgrades and rollbacks, predictability, and being "transactional" are features that Nix also implements.

Package organization

The Snappy Ubuntu Core distribution uses mostly a FHS compliant filesystem layout. One notable deviation is the folder in which applications are installed.

For application deployment the /app folder is used in which files belonging to a specific application version reside in separate folders. Application folders use the following naming convention:


Each application is identified by its name, version identifier and optionally a developer identifier, as shown below:

$ ls -l /apps
drwxr-xr-x 2 root ubuntu 4096 Apr 25 20:38 bin
drwxr-xr-x 3 root root   4096 Apr 25 15:56 docker
drwxr-xr-x 3 root root   4096 Apr 25 20:34 go-example-webserver.canonical
drwxr-xr-x 3 root root   4096 Apr 25 20:31 hello-world.canonical
drwxr-xr-x 3 root root   4096 Apr 25 20:38 webcam-demo.canonical
drwxr-xr-x 3 root ubuntu 4096 Apr 23 05:24 webdm.sideload

For example, the /app/webcam-demo.canonical/1.0.1 refers to a package named: webcam version 1.0.1 that is delivered by Canonical.

There are almost no requirements on the contents of an application folder. I have observed that the example packages seem to follow some conventions though. For example:

$ cd /apps/webcam-demo.canonical/1.0.1
$ find . -type f ! -iname ".*"

Binaries are typically stored inside the bin/ sub folder, while libraries are stored inside the lib/ sub folder. Moreover, the above example also ships binaries for two kinds of system architectures (x86_64 and ARM) that reside inside bin/x86_64-linux, bin/arm-linux-gnueabihf, and lib/x86_64-linux, arm-linux-gnueabihf sub folders.

The only sub folder that has a specific purpose is meta/ that is supposed to contain at least two files -- the readme.md file contains documentation in which the first heading and the first paragraph have a specific meaning, and the package.yaml file contains various meta attributes related to the deployment of the package.

Snappy's package storing convention also makes it possible to store multiple versions of a package next to each other, as shown below:

$ ls -l /apps/webcam-demo.canonical
drwxr-xr-x 7 clickpkg clickpkg 4096 Apr 24 19:38 1.0.0
drwxr-xr-x 7 clickpkg clickpkg 4096 Apr 25 20:38 1.0.1
lrwxrwxrwx 1 root     root        5 Apr 25 20:38 current -> 1.0.1

Moreover, every application folder contains a symlink named: current/ that refers to the version that is currently in use. This approach makes it possible to do atomic upgrades and rollbacks by merely flipping the target of the current/ symlink. As a result, the system always refers to an old or new version of the package, but never to an inconsistent mix of the two.

Apart from storing packages in isolation, they also must be made accessible to end users. For each binary that is declared in the package.yaml file, e.g.:

# for debugging convience we also make the binary available as a command
 - name: bin/webcam-webui

a wrapper script is placed inside /apps/bin that is globally accessible by the users of a system through the PATH environment variable.

Each wrapper script contains the app name. For example, the webcam-webui binary (shown earlier) must be started as follows:

$ webcam-demo.webcam-webui

Besides binaries, package configurations can also declare services from which systemd jobs are composed. The corresponding configuration files are put into the /etc/systemd/system folder and also use a naming convention containing the package name.

Unprivileged users can also install their own packages. The corresponding application files are placed inside $HOME/app and are organized in the same way as the global /app folder.

Snappy's package organization has many similarities with Nix's package organization -- Nix also stores files files belonging to a package in isolated folders in a special purpose directory called the Nix store.

However, Nix uses a more powerful way of identifying packages. Whereas Snappy only identifies packages with their names, version numbers and vendor identifiers, Nix package names are prefixed with unique hash codes (such as /nix/store/wan65mpbvx2a04s2s5cv60ci600mw6ng-firefox-with-plugins-27.0.1) that are derived from all build time dependencies involved to build the package, such as compilers, libraries and the build scripts themselves.

The purpose of using hash codes is to make a distinction between any variant of the same package. For example, when a package is compiled with a different version of GCC, linked against a different library dependency, when debugging symbols are enabled or disabled or certain optional features enabled or disabled, or the build procedure has been modified, a package with a different hash is built that is safely stored next to existing variants.

Moreover, Nix also uses symlinking to refer to specific versions of packages, but the corresponding mechanism is more powerful. Nix generates so-called Nix profiles which synthesize the contents of a collection of installed packages in the Nix store into a symlink tree so that their files (such as executables) can be referenced from a single location. A second symlink indirection refers to Nix profile containing the desired versions of the packages.

Nix profiles also allow unprivileged users to manage their own set of private packages that do not conflict with other user's private packages or the system wide installed packages. However, partly because of Nix's package naming convention, also the packages of unprivileged users can be safely stored in the global Nix store, so that common dependencies can be shared among users in a secure way.

Dependency management

Software packages are rarely self contained -- they typically have dependencies on other packages, such as shared libraries. If a dependency is missing or incorrect, a package may not work properly or not at all.

I observed that in the Snappy example packages, all dependencies are bundled statically. For example, in the webcam-demo package, the lib/ sub folder contains the following files:


As can be seen in the above output, all the library dependencies, including the libraries' dependencies (even libc) are bundled into the package. When running an executable or starting a systemd job, a container (essentially an isolated/restricted environment) is composed in which the process runs (with some restrictions) where it can find its dependencies in the "common FHS locations", such as /lib.

Besides static bundling, there seems to be a primitive mechanism that provides some form of sharing. According to the packaging format specification, it also possible to declare dependencies on frameworks in the package.yaml file:

frameworks: docker, foo, bar # list of frameworks required

Frameworks are managed like ordinary packages in /app, but they specify additional required system privileges and require approval from Canonical to allow them to be redistributed.

Although it is not fully clear to me from the documentation how these dependencies are addressed, I suspect that the contents of the frameworks is made available to packages inside the containers in which they run.

Moreover, I noticed that dependencies are only addressed by their names and that they refer to the current versions of the corresponding frameworks. In the documentation, there seems to be no way (yet) to refer to other versions or variants of frameworks.

The Nix-way of managing dependencies is quite different -- Nix packages are constructed from source and the corresponding build procedures are executed in isolated environments in which only the specified build-time dependencies can be found.

Moreover, when constructing Nix packages, runtime dependencies are bound statically to executables, for example by modifying the RPATH of an ELF executable or wrapping executables in scripts that set environment variables allowing it to find its dependencies (such as CLASSPATH or PERL5LIB). A subset of the buildtime dependencies are identified by Nix as runtime dependencies by scanning for hash occurrences in the build result.

Because dependencies are statically bound to executables, there is no need to compose containers to allow executables to find them. Furthermore, they can also refer to different versions or variants of library dependencies of a package without conflicting with other package's dependencies. Sharing is also supported, because two packages can refer to the same dependency with the same hash prefix in the Nix store.

As a sidenote: with Nix you can also use a containerized approach by composing isolated environments (e.g. a chroot environment or container) in which packages can find their dependencies from common locations. A prominent Nix package that uses this approach is Steam, because it is basically a deployment tool conflicting with Nix's deployment properties. Although such an approach is also possible, it is only used in very exceptional cases.

System organization

Besides applications and frameworks, the base system of the Snappy Ubuntu Core distribution can also be upgraded and downgraded. However, a different mechanism is used to accomplish this.

According to the filesystem layout & updates guide, the Snappy Ubuntu Core distribution follows a specific partition layout:

  • boot partition. This is a very tiny partition used for booting and should be big enough to contain a few kernels.
  • system-a partition. This partition contains a minimal working base system. This partition is mounted read-only.
  • system-b partition. An alternative partition containing a minimal working base system. This partition is mounted read-only as well.
  • writable partition. A writable partition that stores everything else including the applications and frameworks.

Snappy uses an "A/B system partitions mechanism" to allow a base system to be updated as a single unit by applying a new system image. It is also used to roll back to the "other" base system in case of problems with the most recently-installed system by making the bootloader switch root filesystems.

NixOS (the Linux distribution built around the Nix package manager) approaches system-level upgrades in a different way and is much more powerful. In NixOS, a complete system configuration is composed from packages residing in isolation in the Nix store (like ordinary packages) and these are safely stored next to existing versions. As a result, it is possible to roll back to any previous system configuration that has not been garbage collected yet.

Creating packages

According to the packaging guide, creating Snap files is very simple. It is just creating a directory, putting some files in there, creating a meta/ sub folder with a readme.md and package.yaml file, and running:

$ snappy build .

The above file generates a Snap file, which is basically just a tarball file containing the contents of the folder.

In my opinion, creating Snap packages is not that easy -- the above process demonstrates that delivering files from one machine to another is straight forward, but getting a package right is another thing.

Many packages on Linux systems are constructed from source code. To properly do that, you need to have the required development tools and libraries deployed first, a process that is typically easier said than done.

Snappy does not provide facilities to make that process manageable. With Snappy, it is the packager's own responsibility.

In contrast, Nix is a source package manager and provides a DSL that somebody can use construct isolated environments in which builds are executed and automatically deploys all buildtime dependencies that are required to build a package.

The build facilities of Nix are quite accessible. For example, you can easily construct your own private set of Nix packages or a shell session containing all development dependencies.

Moreover, Nix also implements transparent binary deployment -- if a particular Nix package with an identical hash exists elsewhere, we can download it from a remote location instead of building it from source ourselves.


Another thing the Snappy Ubuntu Core distribution does with containers (besides using them to let a package find its dependencies) is restricting the things programs are allowed to do, such as the TCP/UDP ports they are allowed to bind to.

In Nix and NixOS, it is not a common practice to restrict the runtime behaviour of programs by default. However, it is still possible to impose restrictions on running programs, by composing a systemd job for a program yourself in a system's NixOS configuration.


The following table summarizes the conceptual differences between the Snappy Ubuntu Core and Nix/NixOS covered in this blog post:

Snappy Ubuntu Core Nix/NixOS
Concept Binary package manager Source package manager (with transparent binary deployment)
Dependency addressing By name Exact (using hash codes)
Dependency binding Container composition Static binding (e.g. by modifying RPATH or wrapping executables)
Systems composition management "A/B" partitions Package compositions
Construction from source Unmanaged Managed
Unprivileged user installations Supported without sharing Supported with sharing
Runtime isolation Part of package configuration Supported optionally, by manually composing a systemd job


Snappy shares some interesting traits with Nix that provide a number of huge deployment benefits -- by deviating from the FHS and storing packages in isolated folders, it becomes easier to store multiple versions of packages next to each other and to perform atomic upgrades and rollbacks.

However, something that I consider a huge drawback of Snappy is the way dependencies are managed. In the Snappy examples, all library dependencies are bundled statically consuming more disk space (and RAM at runtime) than needed.

Moreover, packaging shared dependencies as frameworks is not very convenient and require approval from Canonical if they must be distributed. As a consequence, I think it will not be very encouraging to modularize systems, which is generally considered a good practice.

According to the framework guide the purpose of frameworks is to extend the base system, but not to be a sharing mechanism. Also the guide says:

Frameworks exist primarily to provide mediation of shared resources (eg, device files, sensors, cameras, etc)

So it appears that sharing in general is discouraged. In many common Linux distributions (including Debian and derivatives such as Ubuntu), it is common that the reuse-degree is raised to almost a maximum. For example, each library is packaged individually and sometimes libraries are even split into binary, development and documentation sub packages. I am not sure how Snappy is going to cope with such a fine granularity of reuse. Is Snappy going to be improved to support reuse as well, or is it now considered a good thing to package huge monolithic blobs?

Also, Snappy does only binary deployment and is not really helpful to alleviate the problem of constructing packages from source which is also quite a challenge in my opinion. I see lots of room for improvement in this area as well.

Another funny observation is the fact that Snappy Ubuntu Core relies on advanced concepts such as containers to make programs work, while there are also simpler solutions available, such as static linking.

Finally, something that Nix/NixOS could learn from the Snappy approach is the runtime isolation of programs out-of-the-box. Currently, doing this with Nix/NixOS this is not as convenient as with Snappy.


This is not the only comparison I have done between Nix/NixOS and another deployment approach. A few years ago while I was still a PhD student, I also did a comparison between the deployment properties of GoboLinux and NixOS.

Interestingly enough, GoboLinux addresses packages in a similar way as Snappy, supports sharing, does not provide runtime isolation of programs, but does have a very powerful source construction mechanism that Snappy lacks.

Friday, March 13, 2015

Disnix 0.3 release announcement

In the previous blog post, I have explained the differences between Disnix (which does service deployment) and NixOps (which does infrastructure deployment), and shown that both tools can be used together to address both concerns in a deployment process.

Furthermore, I raised a couple of questions and intentionally left one unmentioned question unanswered. The unmentioned question that I was referring to is the following: "Is Disnix still alive or is it dead?".

The answer is that Disnix's development was progressing at a very low pace for some time after I left academia -- I made minor changes once in a while, but nothing really interesting happened.

However, for the last few months, I am using it on a daily basis and made many big improvements. Moreover, I have reached a stable point and decided that this is a good moment to announce the next release!

New features

So what is new in this release?

Visualization tool

I have added a new tool to the Disnix toolset named: disnix-visualize that generates Graphviz images to visualize a particular deployment scenario. An example image is shown below:

The above picture shows a deployment scenario of the StaffTracker Java example in which services are divided over two machines in a network and have all kinds of complex dependency relationships as denoted by the arrows.

The tool was already included in the development versions for a while, but has never been part of any release.


I also made a major change in Disnix's architecture. As explained in my old blog post about Disnix, activating and deactivating services cannot be done generically and I have developed a plugin system to take care of that.

This plugin system package (formerly known as Disnix activation scripts) has now become an independent tool named Dysnomia and can also be applied in different contexts. Furthermore, it can also be used as a standalone tool.

For example, a running MySQL DBMS instance (called a container in Dysnomia terminology) could be specified in a configuration file (such as ~/mysql-production) as follows:


A database can be encoded as an SQL file (~/test-database/createdb.sql) creating the schema:

create table author
  FirstName  VARCHAR(255)  NOT NULL,
  LastName   VARCHAR(255)  NOT NULL,

create table books
( ISBN       VARCHAR(255)  NOT NULL,
  Title      VARCHAR(255)  NOT NULL,
  FOREIGN KEY(AUTHOR_ID) references author(AUTHOR_ID)
    on update cascade on delete cascade

We can use the following command-line instruction to let Dysnomia deploy the database to the MySQL DBMS container we have just specified earlier:

$ dysnomia --operation activate --component ~/test-database \
  --container ~/mysql-production

When Disnix has to execute deployment operations, two external tools are consulted -- Nix takes care of all deployment operations of the static parts of a system, and Dysnomia takes care of performing the dynamic activation and deactivation steps.

Concurrent closure transfers

In the previous versions of Disnix, only one closure (of a collection of services and its intra-dependencies) is transferred to a target machine at the time. If a target machine has more network bandwidth than the coordinator, this is usually fine, but in all other cases, it slows the deployment process down.

In the new version, two closures are transferred concurrently by default. The amount of concurrent closure transfers can be adjusted as follows:

$ disnix-env -s services.nix -i infrastructure.nix \
  -d distribution.nix --max-concurrent-transfers 4

The last command-line argument states that 4 closures should be transferred concurrently.

Concurrent service activation and deactivation

In the old Disnix versions, the activation and deactivation steps of a service on a target machine were executed sequentially, i.e. one service on a machine at the time. In all my old testcases these steps were quite cheap/quick, but now that I have encountered systems that are much bigger, I noticed that there is a lot of deployment time that we can save.

In the new implementation, Disnix tries to concurrently activate or deactivate one service per machine. The amount of services that can be concurrently activated or deactivated per machine can be raised in the infrastructure model:

  test1 = {
    hostname = "test1";
    numOfCores = 2;

In the above infrastructure model, the numOfCores attribute states that two services can be concurrently activated/deactivated on machine test1. If this attribute has been omitted, it defaults to 1.

Multi connection protocol support

By default, Disnix uses an SSH protocol wrapper to connect to the target machines. There is also an extension available, called DisnixWebService, that uses SOAP + MTOM instead.

In the old version, changing the connection protocol means that every target machine should be reached with it. In the new version, you can also specify the target property and client interface in the infrastructure model to support multi connection protocol deployments:

  test1 = {
    hostname = "test1";
    targetProperty = "hostname";
    clientInterface = "disnix-ssh-client";
  test2 = {
    hostname = "test2";
    targetEPR = http://test2:8080/DisnixWebService/services/DisnixWebService;
    targetProperty = "targetEPR";
    clientInterface = "disnix-soap-client";

The above infrastructure model states the following:

  • To connect to machine: test1, the hostname attribute contains the address and the disnix-ssh-client tool should be invoked to connect it.
  • To connect to machine: test2, the targetEPR attribute contains the address and the disnix-soap-client tool should be invoked to connect it.

NixOps integration

As described in my previous blog post, Disnix does service deployment and can integrate NixOS' infrastructure deployment features with an extension called DisnixOS.

DisnixOS can now also be used in conjunction with NixOps -- NixOps can be used to instantiate and deploy a network of virtual machines:

$ nixops create ./network.nix ./network-ec2.nix -d ec2
$ nixops deploy -d ec2

and DisnixOS can be used to deploy services to them:

$ disnixos-env -s services.nix -n network.nix -d distribution.nix \

Omitted features

There are also a couple of features described in some older blog posts, papers, and my PhD thesis, which have not become part of the new Disnix release.

Dynamic Disnix

This is an extended framework built on top of Disnix supporting self-adaptive redeployment. Although I promised to make it part of the new release a long time ago, it did not happen. However, I did update the prototype to work with the current Disnix implementation, but it still needs refinements, documentation and other small things to make it usable.

Brave people who are eager to try it can pull the Dynamic Disnix repository from my GitHub page.

Snapshot/restore features of Dysnomia

In a paper I wrote about Dysnomia I also proposed state snapshotting/restoring facilities. These have not become part of the released versions of Dysnomia and Disnix yet.

The approach I have described is useful in some scenarios but also has a couple of very big drawbacks. Moreover, it also significantly alters the behavior of Disnix. I need to find a way to properly integrate these features in such a way that they do not break the standard approach. Moreover, these techniques must be selectively applied as well.


In this blog post, I have announced the availability of the next release of Disnix. Perhaps I should give it the codename: "Disnix Forever!" or something :-). Also, the release date (Friday the 13th) seems to be appropriate.

Moreover, the previous release was considered an advanced prototype. Although I am using Disnix on a daily basis now to eat my own dogfood, and the toolset has become much more usable, I would not yet classify this release as something that is very mature yet.

Disnix can be obtained by installing NixOS, through Nixpkgs or from the Disnix release page.

I have also updated the Disnix homepage a bit, which should provide you more information.

Monday, March 9, 2015

On NixOps, Disnix, service deployment and infrastructure deployment

I have written many software deployment related blog posts covering tools that are part of the Nix project. However, there is one tool that I have not elaborated about so far, namely: NixOps, which has become quite popular these days.

Although NixOps is quite popular, its availability also leads to a bit of confusion with another tool. Some Nix users, in particular newbies, are suffering from this. The purpose of this blog post is to clear up that confusion.


NixOps is something that is advertised as the "NixOS-based cloud deployment tool". It basically expands NixOS' (a Linux distribution built around the Nix package manager) approach of deploying a complete system configuration from a declarative specification to networks of machines and instantiates and provisions the required machines (e.g. in an IaaS cloud environment, such as Amazon EC2) automatically if requested.

A NixOps deployment process is driven by one or more network models that encapsulate multiple (partial) NixOS configurations. In a standard NixOps workflow, network models are typically split into a logical network model capturing settings that are machine independent and a physical network model capturing machine specific properties.

For example, the following code fragment is a logical network model consisting of three machines capturing the configuration properties of a Trac deployment, a web-based issue tracker system:

  network.description = "Trac deployment";

  storage =
    { pkgs, ... }:

    { services.nfs.server.enable = true;
      services.nfs.server.exports = ''
      services.nfs.server.createMountPoints = true;

  postgresql =
    { pkgs, ... }:

    { services.postgresql.enable = true;
      services.postgresql.package = pkgs.postgresql;
      services.postgresql.enableTCPIP = true;
      services.postgresql.authentication = ''
        local all all                trust
        host  all all   trust
        host  all all ::1/128        trust
        host  all all trust

  webserver =
    { pkgs, ... }:

    { fileSystems = [
        { mountPoint = "/repos";
          device = "storage:/repos";
          fsType = "nfs";
      services.httpd.enable = true;
      services.httpd.adminAddr = "root@localhost";
      services.httpd.extraSubservices = [
        { serviceType = "trac"; }
      environment.systemPackages = [

The three machines in the above example have the following purpose:

  • The first machine, named storage, is responsible for storing Subversion source code repositories in the following folder: /repos and makes the corresponding folder available as a NFS mount.
  • The second machine, named postgresql, runs a PostgreSQL database server storing the tickets.
  • The third machine, named webserver, runs the Apache HTTP server hosting the Trac web application front-end. Moreover, it mounts the /repos folder as a network file system connecting to the storage machine so that the Trac web application can view the Subversion repositories stored inside it.

The above specification can be considered a logical network model, because it captures the configuration we want to deploy, without any machine specific characteristics. Regardless of what kind of machine we intend deploy, we want these services to be available.

However, a NixOS configuration cannot be deployed without any machine specific settings. These remaining settings can be specified by writing a second model, the physical network model, capturing these:

  storage = 
    { pkgs, ...}:
    { boot.loader.grub.version = 2;
      boot.loader.grub.device = "/dev/sda";

      fileSystems = [
        { mountPoint = "/";
          label = "root";

      swapDevices = [
        { label = "swap"; }

      networking.hostName = "storage";

  postgresql = ...

  webserver = ...

The above partial network model specifies the following physical characteristics for the storage machine:

  • GRUB version 2 should be used as bootloader and should installed on the MBR of the hard drive partition: /dev/sda.
  • The hard drive partition with label: root should be mounted as root partition.
  • The hard drive partition with label: swap should be mounted as swap partition.
  • The hostname of the system should be: 'storage'

By invoking NixOps with the two network models shown earlier as parameters, we can create a NixOps deployment -- an environment containing a set of machines that belong together:

$ nixops create ./network-logical.nix ./network-physical.nix -d test

The above command creates a deployment named: test. We can run the following command to actually deploy the system configurations:

$ nixops deploy -d test

What the above command does is invoking the Nix package manager to build all the machine configurations, then it transfers their corresponding package closures to the target machines and finally activates the NixOS configurations. The end result is a collection of machines running the new configuration, if all previous steps have succeeded.

If we adapt any of the network models, and run the deploy command again, the system is upgraded. In case of an upgrade, only the packages that have been changed are built and transferred, making this phase as efficient as possible.

We can also replace the physical network model shown earlier with the following model:

  storage = {
    deployment.targetEnv = "virtualbox";
    deployment.virtualbox.memorySize = 1024;

  postgresql = ...

  webserver = ...

The above physical network configuration states that the storage machine is a VirtualBox Virtual Machine (VM) requiring 1024 MiB of RAM.

When we instantiate a new deployment with the above physical network model and deploy it:

$ nixops create ./network-logical.nix ./network-vbox.nix -d vbox
$ nixops deploy -d vbox

NixOps does an extra step before doing the actual deployment of the system configurations -- it first instantiates the VMs by consulting VirtualBox and populates them with a basic NixOS disk image.

Similarly, we can also create a physical network model like this:

  region = "us-east-1";
  accessKeyId = "ABCD..."; # symbolic name looked up in ~/.ec2-keys

  ec2 =
    { resources, ... }:
    { deployment.targetEnv = "ec2";
      deployment.ec2.accessKeyId = accessKeyId;
      deployment.ec2.region = region;
      deployment.ec2.instanceType = "m1.medium";
      deployment.ec2.keyPair = resources.ec2KeyPairs.my-key-pair;
      deployment.ec2.securityGroups = [ "my-security-group" ];
  storage = ec2;

  postgresql = ec2;

  webserver = ec2;

  resources.ec2KeyPairs.my-key-pair = {
    inherit region accessKeyId;

The above physical network configuration states that the storage machine is a virtual machine residing in the Amazon EC2 cloud.

Running the following commands:

$ nixops create ./network-logical.nix ./network-ec2.nix -d ec2
$ nixops deploy -d ec2

Automatically instantiate the virtual machines in EC2, populates them with basic NixOS AMI images and finally deploys the machines to run our desired Trac deployment.

(In order to make EC2 deployment work, you need to create the security group (e.g. my-security-group) through the Amazon EC2 console first and you must set the AWS_SECRET_ACCESS_KEY environment variable to contain the secret access key that you actually need to connect to the Amazon services).

Besides physical machines, VirtualBox, and Amazon EC2, NixOps also supports the Google Computing Engine (GCE) and Hetzner. Moreover, preliminary Azure support is also available in the development version of NixOps.

With NixOps you can also do multi-cloud deployment -- it is not required to deploy all VMs in the same IaaS environment. For example, you could also deploy the first machine to Amazon EC2, the second to Hetzner and the third to a physical machine.

In addition to deploying system configurations, NixOps can be used to perform many other kinds of system administration tasks that work on machine level.


Readers who happen to know me a bit, may probably notice that many NixOps features are quite similar to things I did in the past -- while I was working for Delft University of Technology as a PhD student, I was investigating distributed software deployment techniques and developed a tool named: Disnix that also performs distributed deployment tasks using the Nix package manager as underlying technology.

I have received quite a few questions from people asking me things such as: "What is the difference between Disnix and NixOps?", "Is NixOps the successor of/a replacement for Disnix?", "Are Disnix and NixOps in competition with each other?"

The short answer is: while both tools perform distributed deployment tasks and use the Nix package manager as underlying (local) deployment technology, they are designed for different purposes and address different concerns. Furthermore, they can also be effectively used together to automate deployment processes for certain kinds of systems.

In the next chapters I will try to clarify the differences and explain how they can be used together.

Infrastructure and service deployment

NixOps does something that I call infrastructure deployment -- it manages configurations that work on machine level and deploys entire system configurations as a whole.

What Disnix does is service deployment -- Disnix is primarily designed for deploying service-oriented systems. What "service-oriented system" is exactly supposed to mean has always been an open debate, but a definition I have seen in the literature is "systems composed of platform-independent entities that can be loosely coupled and automatically discovered" etc.

Disnix expects a system to be decomposed into distributable units (called services in Disnix terminology) that can be built and deployed independently to machines in a network. These components can be web services (systems composed of web services typically qualify themselves as service-oriented systems), but this is not a strict requirement. Services in a Disnix-context can be any unit that can be deployed independently, such as web services, UNIX processes, web applications, and databases. Even entire NixOS configurations can be considered a "service" by Disnix, since they are also a unit of deployment, although they are quite big.

Whereas NixOps builds, transfers and activates entire Linux system configurations, Disnix builds, transfers and activates individual services on machines in a network and manages/controls them and their dependencies individually. Moreover, the target machines to which Disnix deploys are neither required to run NixOS nor Linux. They can run any operating system and system distribution capable of running the Nix package manager.

Being able to deploy services to heterogeneous networks of machines is useful for service-oriented systems. Although services might manifest themselves as platform independent entities (e.g. because of their interfaces), they still have an underlying implementation that might be bound to technology that only works on a certain operating system. Furthermore, you might also want to have the ability to experiment with the portability of certain services among operating systems, or effectively use a heterogeneous network of operating systems to use their unique selling points effectively for the appropriate services (e.g. a Linux, Windows, OpenBSD hybrid).

For example, although I have mainly used Disnix to deploy services to Linux machines, I also once did an experiment with deploying services implemented with .NET technology to Windows machines running Windows specific system services (e.g. IIS and SQL server), because our industry partner in our research project was interested in this.

To be able to do service deployment with Disnix one important requirement must be met -- Disnix expects preconfigured machines to be present running a so-called "Disnix service" providing remote access to deployment operations and some other system services. For example, to allow Disnix to deploy Java web applications, a predeployed Servlet container, such as Apache Tomcat, must already be present on the target machine. Also other container services, such as a DBMS, may be required.

Disnix does not automate the deployment of machine configurations (that include the Disnix service and containers) requiring people to deploy a network of machines by other means first and writing an infrastructure model that reflects the machine configurations accordingly.

Combining service and infrastructure deployment

To be able to deploy a service-oriented system into a network of machines using Disnix, we must first deploy a collection of machines running some required system services first. In other words: infrastructure deployment is a prerequisite for doing service deployment.

Currently, there are two Disnix extensions that can be used to integrate service deployment and infrastructure deployment:

  • DisnixOS is an extension that complements Disnix with NixOS' deployment features to do infrastructure deployment. With this extension you can do tasks such as deploying a network of machines with NixOps first and then do service deployment inside the deployed network with Disnix.

    Moreover, with DisnixOS you can also spawn a network of NixOS VMs using the NixOS test driver and run automated tests inside them.

    A major difference from a user perspective between Disnix and DisnixOS is that the latter works with network models (i.e. networked NixOS configurations used by NixOps and the NixOS test driver) instead of infrastructure models and does the conversion between these models automatically.

    A drawback of DisnixOS is that service deployment is effectively tied to NixOS, which is a Linux distribution. DisnixOS is not very helpful if a service-oriented system must be deployed in a heterogeneous network running multiple kinds of operating systems.
  • Dynamic Disnix. With this extension, each machine in the network is supposed to publish its configuration and a discovery service running on the coordinator machine generates an infrastructure model from the supplied settings. For each event in the network, e.g. a crashing machine, a newly added machine or a machine upgrade, a new infrastructure model is generated that can be used to trigger a redeployment.

    The Dynamic Disnix approach is more powerful and not tied to NixOS specifically. Any infrastructure deployment approach (e.g. Norton Ghost for Windows machines) that includes the deployment of the Disnix service and container services can be used. Unfortunately, the Dynamic Disnix framework is still a rough prototype and needs to become more mature.

Is service deployment useful?

Some people have asked me: "Is service deployment really needed?", since it also possible to deploy services as part of a machine's configuration.

In my opinion it depends on the kinds of systems that you want to deploy and what problems you want to solve. For some kinds of distributed systems, Disnix is not really helpful. For example, if you want to deploy a cluster of DBMSes that are specifically tuned for specific underlying hardware, you cannot really make a decomposition into "distributable units" that can be deployed independently. Same thing with filesystem services, as shown in the Trac example -- doing an NFS mount is a deployment operation, but not a really an independent unit of deployment.

As a sidenote: That does not imply that you cannot do such things with Disnix. With Disnix you could still encapsulate an entire (or partial) machine specific configuration as a service and deploy that, or doing a network mount by deploying a script performing the mount, but that defeats its purpose.

At the same time, service-oriented systems can also be deployed on infrastructure level, but this sometimes leads to a number of inconveniences and issues. Let me illustrate that by giving an example:

The above picture reflects the architecture of one of the toy systems (Staff Tracker Java version) I have created for Disnix for demonstration purposes. The architecture consists of three layers:

  • Each component in the upper layer is a MySQL database storing certain kinds of information.
  • The middle layer encapsulates web services (implemented as Java web applications) responsible for retrieving and modifying data stored in the databases. An exception is the GeolocationService, which retrieves data by other means.
  • The bottom layer contains a Java web application that is consulted by end-users.

Each component in the above architecture is a distributable service and each arrow denotes dependency relationships between them which manifest themselves as HTTP and TCP connections. Because components are distributable, we could, for example, deploy all of them to one single machine, but we can also run each of them on a separate machine.

If we want to deploy the example system on infrastructure level, we may end up composing a networked machine configuration that looks as follows:

The above picture shows a deployment scenario in which the services are divided over two machines a network:

  • The MySQL databases are hosted inside a MySQL DBMS running on the first machine.
  • The web application front-end and one of the web services granting access to the databases are deployed inside the Apache Tomcat Servlet container running on the first machine.
  • The remaining web services are deployed inside an Apache Tomcat container running on the second machine.

When we use NixOps to deploy the above machine configurations, then the entire machine configurations are deployed and activated as a whole, which have a number of implications. For example, the containers have indirectly become dependent on each other as can be seen in the picture below in which I have translated the dependencies from service level to container level:

In principle, Apache Tomcat does not depend on MySQL, so under normal circumstances, these containers can be activated in any order. However, because we host a Java web application that requires a database, suddenly the order in which these services are activated does matter. If we would activate them in the wrong order then the web service (and also indirectly the web application front-end) will not work. (In extreme cases: if a system has been poorly developed, it may even crash and need to be manually reactivated again!)

Moreover, there is another implication -- the web application front-end also depends on services that are deployed to the second machine, and two of these services require access to databases deployed to the first machine. On container level, you could clearly see that this situation leads to two machines having a cyclic dependency on each other. That means that you cannot solve activation problems by translating service-level dependencies to machine-level dependencies.

As a matter of fact: NixOps allows cyclic dependencies between machines and activates their configurations in arbitrary order and is thus incapable of dealing with temporarily or permanent inconsistency issues (because of broken dependencies) while deploying a system as shown in the example.

Another practical issue with deploying such a system on infrastructure level is that it is tedious to do redeployment, for example when a requirement changes. You need to adapt machine configurations as a whole -- you cannot easily specify a different distribution scenario for services to machines.

As a final note, in some organisations (including a company that I have worked for in the past) it is common practice that infrastructure and service deployment are separated. For example, one department is responsible for providing machines and system services and another department (typically a development group) responsible for building the services and deploying them to the provided machines.


In this blog post, I have described NixOps and elaborated on the differences between NixOps and Disnix -- the former tool does infrastructure deployment, while the latter does service deployment. Infrastructure deployment is a prerequisite of doing service deployment and both tools can actually be combined to automate both concerns.

Service deployment is particularly useful for distributed systems that can be decomposed into "distributable units" (such as service-oriented systems), but not all kinds of distributed systems.

Moreover, NixOps is a tool that has been specifically designed to deploy NixOS configurations, while Disnix can deploy services to machines running any operating system capable of running the Nix package manager.

Finally, I have been trying to answer three questions, which I mentioned somewhere in the middle of this blog post. There is another question I have intentionally avoided that obviously needs an answer as well! I will elaborate more on this in the next blog post.


More information about Disnix, service and infrastructure deployment, and in particular: integrating deployment concerns can be found in my PhD thesis.

Interestingly enough, during my PhD thesis defence there was also a question about the difference between service and infrastructure deployment. This blog post is a more elaborate version of the answer I gave earlier. :-)

Saturday, February 7, 2015

A sales pitch explanation of NixOS

Exactly one week ago, I have visited FOSDEM for the seventh time. In this year's edition, we had a NixOS stand to promote NixOS and its related sub projects. Promoting NixOS is a bit of challenge, because properly explaining its underlying concepts (the Nix package manager) and their benefits is often not that straight forward.

Explaining Nix

Earlier I have written two recipes explaining the Nix package manager, each having its pros and cons. The first recipe is basically explaining Nix from a system administrator's perspective -- it starts by explaining what the disadvantages of conventional approaches are and then what Nix does differently: namely storing packages in isolation in separate directories in the Nix store using hash codes as prefixes. Usually when I show this to people, there is always a justification process involved, because these hash codes look weird and counter-intuitive. Sometimes it still works out despite the confusion, sometimes it does not.

The other recipe explains Nix from a programming language perspective, since Nix borrows its underlying concepts from purely functional programming languages. In this explanation recipe, I first explain in what way purely functional programming languages are different compared to conventional programming languages. Then I draw an analogy to package managers. I find this the better explanation recipe, because the means used to make Nix purely functional (e.g. using hash codes) make sense in this context. The only drawback is that a large subset of the people using package managers are often not programmers and typically do not understand nor appreciate the programming language aspect.

To summarize: advertising the Nix concepts is tough. While I was at the NixOS stand, I had to convince people passing by in just a few minutes that it is worth to give NixOS (or any of its sub projects) a try. In the following section, I will transcribe my "sales pitch explanation" of NixOS.

The pitch

NixOS is a Linux distribution built around the Nix package manager solving package and configuration management problems in its own unique way. When installing systems running Linux distributions by conventional means, it is common to do activities, such as installing the distribution itself, then installing additional custom packages, modifying configuration files and so on, which is often a tedious, time consuming and error prone process.

In NixOS the deployment of an entire system is fully automated. Deployment is driven by a declarative configuration file capturing the desired properties of a system, such as the harddrive partitions, services to run (such as OpenSSH, the Apache webserver), the desktop (e.g. KDE, GNOME or Xfce) and end-user packages (e.g. Emacs and Mozilla Firefox). With just one single command-line instruction, an entire system configuration can be deployed. By adapting the declarative configuration file and running the same command-line instruction again, an existing configuration can be upgraded.

NixOS has a couple of other nice properties as well. Upgrading is always safe, so there is no reason to be worried that an interruption will break a system. Moreover, older system configurations are retained by default, and if an upgrade, for example, makes a system unbootable, you can always switch back to any available older configuration. Also configurations can be reproduced on any system by simply providing the declarative configuration file to someone else.

Several tools in the Nix project extend this deployment approach to other areas: NixOps can be used to deploy a network of NixOS machines in the cloud, Hydra is the Nix-based continuous integration server, Disnix deploys services into a network of a machines. Furthermore, the Nix package manager -- that serves as the basis for all of these tools -- can also be used on any Linux distribution and a few other operating systems as well, such as Mac OS X.

Concluding remarks

The above pitch does not reveal much about its technical aspects, but simply focuses itself on its key aspect -- fully automated deployment and some powerful quality properties. This often leads to more questions from people passing by, but I consider that a good thing.

This year's FOSDEM was a very nice experience. I'd like to thank all the fellow Nixers who did all the organisation work for the stand. As a matter of fact, apart from doing some promotion work at the stand I was not involved in any of its organizational aspects. Besides having a stand to promote our project, Nicolas Pierron gave a talk about NixOS in the distributions devroom. I also enjoyed Larry Wall's talk about Perl 6 very much:

I'm looking forward to see what next year's FOSDEM will bring us!

Thursday, January 29, 2015

Agile software development: my experiences

In a couple of older blog posts, I've reported about my experiences with companies, such as the people to whom I talked to at the LAC conference and my job searching month. One of the things that I have noticed is that nearly all of them were doing "Agile software development", or at least they claim to do so.

At the LAC conference, Agile seemed to be one of the hottest buzzwords and every company had its own success story in which they explained how much Agile methodologies have improved their business and the quality of the systems that they deliver.

The most popular Agile software development methodology nowadays is probably Scrum. All the companies that I visited in my job searching month, claimed that they have implemented it in their organisation. In fact, I haven't seen any company recently, that is intentionally not using Scrum or any other Agile software development methodology.

Although many companies claim to be Agile, I still have the impression that the quality of software systems and the ability to deliver software in time and within the budget haven't improved that much in general, although there are some exceptions, of course.

What is Agile?

I'm not an expert in Agile software development. One of the first things I wanted to discover is what "Agile" actually means. My feeling says that only a few people have an exact idea, especially non-native English speakers, such as people living in my country -- the Netherlands. To me, it looks like most of the developers with a Dutch mother tongue use this buzzword as if it's something as common as ordering a hamburger in a restaurant without consciously thinking about its meaning.

According to the Merriam Webster dictionary, Agile means:
: marked by ready ability to move with quick easy grace <an agile dancer>
: having a quick resourceful and adaptable character <an agile mind>

The above definition is a bit abstract, but contains a number of interesting keywords. To me it looks like if some person or object is Agile, then it has a combination of the following characteristics: quick, easy, resourceful, and adaptable.

Why do we want/need to be Agile in software development?

It's generally known that many software development projects partially or completely fail, because of many reasons, such as:
  • The resulting system is not delivered in time or cannot be delivered at all.
  • The resulting system does not do what the customer expects, a.k.a. mismatch of expectations. Apart from customers, this also happens internally in a development team -- developers may implement something totally different as a designer has intended.
  • There is a significant lack of quality, such as in performance or security.
  • The overall project costs (way) too much.

These issues are caused by many factors, such as:

  • Wrong estimations. It is difficult (or sometimes impossible) to estimate how much time something will take to implement. For example, I have encountered a few cases in my past career in which something took double or even ten times the amount of time that was originally estimated.
  • Unclarity. Sometimes a developer thinks he has a good understanding of what a particular feature should look like, but after implementing it, it turns out that many requirements were incorrectly interpreted (or sometimes even overlooked), requiring many revisions and extra development time.
  • Interaction problems among team members. For example, one particular developer cannot complete his task because of a dependency on another developer's task which has not been completed yet. Also, there could be a mismatch of expectations among team members. For example, a missing feature that has been overlooked by one developer blocking another developer.
  • Changing requirements/conditions. In highly competitive environments, it may be possible that a competitor implements missing features that a certain class of customers want making it impossible to sell your product. Another example could be Apple changing its submission requirements for the Apple Appstore making it impossible to distribute an application to iPhone/iPad users unless the new requirements have been met.
  • Unpredictable incidents and problems. For example, a team member gets sick and is unavailable for a while. The weather conditions are so bad (e.g. lots of snowfall) that people can't make it to the office. A production server breaks down and needs to be replaced by a new instance forcing the organisation to invest money to buy a new one and time to get it configured.
  • Lack of resources. There is not enough manpower to do the job. Specialized knowledge is missing. A server application requires much more system resources than expected, e.g. more RAM, more diskspace etc.

Ideally, in a software development project, these problems should be prevented. However, since this ideal is hard to achieve, it is also highly desirable to be able to respond to them as quickly as possible without too much effort, to prevent the corresponding problems to grow out of hand. That is why being Agile (e.g. quick, easy, resourceful, and adaptable) in software development is often not only wanted, but also necessary, in my opinion.

Agile manifesto

The "definition of Agile" has been "translated" to software development by a group of practitioners, into something that is known as the Agile manifesto. This manifesto states the following:

We are uncovering better ways of developing software by doing it and helping others do it. Through this work we have come to value:

Individuals and interactions over processes and tools
Working software over comprehensive documentation
Customer collaboration over contract negotiation
Responding to change over following a plan

That is, while there is value in the items on the right, we value the items on the left more.

The Agile manifesto looks very interesting, but when I compare it to the definition of Agile provided by the Merriam Webster dictionary, I don't see any of its characterizing keywords (such as adaptable and easy) in the text at all, which looks quite funny to me. The only piece that has some kind of connection is Responding to change (that has a connection to adaptable), but that is pretty much everything I can see that it has in common.


This observation makes me wonder: How is the Agile manifesto going to help us to become more agile in software development and more importantly, how should we interpret it?

Because it states that the items on the left have more value than the items on the right, I have seen many people considering the right items not to be relevant at all. As a consequence, I have seen the following things happen in practice:

  • Not thinking about a process. For example, in one of my past projects, it was common to create Git branches for all kinds of weirdly related tasks in an unstructured manner because that nicely integrated with the issue tracker system. Furthermore, merging was also done at unpredictable moments. As a consequence, it often came together with painful merge conflicts that were hard to resolve making the integration process tedious and much more time consuming than necessary.
  • Not documenting anything at all. This is not about writing down every detail per se, but rather about documenting a system from a high level perspective to make the basics clear to everyone involved in a development process, such as a requirements document.

    I have been involved in quite a few projects in which we just started implementing something without writing anything down at all and "trust" that it eventually gets right. So far, it always took us many more iterations than if most of the simple, basic details would be clear from the beginning. For example, some basic domain knowledge that may sound obvious, may turn out not be that obvious at all.
  • Not having an agreement with the customer. Some of the companies I worked for did integration with third party (e.g. customer's) systems. What, for example, if you are developing the front-end and some error occurs because of a bug in the customer's system? Who's going to get blamed? Typically, it's you unless you can prove otherwise. Moreover, unclear communication may also result in wrong expectations typically extending the development time.
  • Not having a plan at all. Of course being flexible with regard to changes is good, but sometimes you also have to stick yourself to something, because it might completely alter the current project's objectives otherwise. A right balance must be found between these two, or you might end up in a situation like this. It also happened to me a few times when I was developing web sites as a teenager.

From my perspective, the Agile manifesto does not say that the emphasis should lie on the left items only. In fact, I think the right items are also still important. However, in situations where things are unclear or when pressure arises, then the item on the left should take precedence. I'm not sure if this is something the authors of the manifesto have intended to communicate though.

For example, while developing a certain aspect of a system, it would still make sense to me to write their corresponding requirements down so that everyone involved knows about it. However, writing every possible detail down often does not make sense because they are typically not known or subject to change anyway. In these kind of situations, it would be better to proceed working on an implementation, validate that with the stakeholders and refine the requirements later.

Same thing, for example, applies to customer collaboration (in my opinion). An agreement should be made, but of course, there are always unforeseen things that both parties did not know of. In such situations it is good to be flexible, but it should not come at any price.

Why agile?

What is exactly Agile about finding a right balance between these items? I think in ideal situations, having a formalized processed that exactly describes the processes, documentation that catches everything, a solid contract that does not have to be changed and a plan of which you know that works is the quickest and easiest path to get software implemented.

However, since unpredictable and unforeseen things always happen, these might get in your way and you have to be flexible. In such cases, you must be adaptable by giving the items on the left precedence. I don't see, however, what's resourceful about all of this. :-)

So is this manifesto covering enough to consider software development "Agile" if it is done properly? Not everybody agrees! For example, there is also the More Agile Manifesto that covers organisations, not teams. Kent Beck, one of the signatories of the Agile manifesto, wrote an evolved version. Zed Shaw considers it all to be nonsense and simply says that people should do programming and nothing should get in their way.

I'm not really a strong believer in anything. I want to focus myself on facts, rather than on an idealism.


As I have explained earlier, nearly all the companies that I visited during my job searching month as well as my current employer have implemented (or claim to have implemented) Scrum in their organisation. According to the Scrum guide, Scrum is actually not a methodology, but rather a process framework.

In a process implementing Scrum, development is iterative and divided into so-called sprints (that take up to 2-4 weeks). At the end of each sprint an increment is delivered that is considered "done". Each sprint has the following activities:

  • The Sprint planning is held at the beginning of each sprint in which the team discusses how and when to implement certain items from the product backlog.
  • Daily scrum is a short meeting held at the beginning of every development day in which team members briefly discuss the progress made the day before, the work that needs to be done the next 24 hours and any potential problems.
  • The Sprint review activity is held at the end of the sprint in which stakeholders review, reflect and demonstrate what is done. Furthermore, future goals are set during this meeting.
  • Finally, the Sprint retrospective meeting is held in which team members discuss what can be improved with regards to people, relationships, process, and tools in future sprints.

In a Scrum process, two kinds of "lists" are used. The product back log contains a list of items that need to be implemented to complete the product. The sprint back log contains a list of items reflecting the work that needs to be done to deliver the increment.

Teams typically consist of 3-9 persons. Scrum only defines three kinds of team member roles, namely the product owner (responsible for maintaining the product back log and validating it), the Scrum master (who guards to process and takes away anything that blocks developers) and developers.

The Scrum guide makes no distinction between specific developer roles, because (ideally) every team member should be able to take over each other's work if needed. Moreover, teams are self-organizing meaning that it's up to the developers themselves (and nobody else) to decide who does what and how things are done.

Why agile?

I have encountered quite a few people saying "Hey, we're doing Scrum in our company, so we're Agile!", because they appear to have some sort of a process reflecting the above listed traits. This makes me wonder: How is Scrum going to help and what is so agile about it?

In my opinion, most of its aspects facilitate transparency (such as the four activities) to prevent that certain things to go wrong or that too much time is wasted because of misunderstandings. It also facilitates reflection with the purpose to adapt and optimize the development process in future sprints.

Since Scrum only loosely defines a process, the activities defined by it (sort of) make sense to me, but also deliberately leaves some things open. As I have mentioned earlier, a completely predictable process would be the quickest and easiest way to do software development, but since that ideal is hard to achieve because of unpredictable/unforeseen events, we need some flexibility too. We must find a balance and that is what Scrum is (sort of doing) by providing a framework that still gives an adopter some degree of freedom.

A few things that came into my mind with regards to a process implementation are:

  • How to specify "items" on the product and sprint backlogs? Although the Scrum guide does not say anything on how to do this, I have seen many people using a so-called "user-story format" in which they describe items in a formalism like "As a <user role> I want to <do some activity / see something etc. >".

    From my point of view, a user story (sort of) reflects a functional or non-functional requirement or a combination of both. However, it is typically only an abstract definition of something that might not cover all relevant details. Moreover, it can also be easily misinterpreted.

    Some people have told me that writing more formal requirements (e.g. by adhering to a standard, such as the IEEE 830-1998 standard for software requirement specifications) is way too formal, too time consuming and "unagile".

    IMHO, I think it really depends on the context. In some projects, the resulting product has only simple requirements (that do not even have to be met fully) and in others more difficult ones. In the latter case, I think it pays off to think about requirements more thoroughly, than having to revise to product many times. Of course, a right balance must be found between specifying and implementing.
  • When is something considered "done"? I believe this is one of the biggest ongoing discussions within the Scrum community, because the Scrum guide intentionally leaves the meaning of this definition open to the implementer.

    Some questions that I sometimes think about are: Should something be demonstrated to stakeholders? Should it also be tested thoroughly (e.g. all automated test cases must pass and the coverage should be acceptable)? Can we simply run a prototype on a development machine or does the increment have to be deployed to a production environment?

    All these questions cannot be uniformly answered. If the sprint goal is a prototype then the meaning of this definition is probably different than a mission critical product. Furthermore, accomplishing all the corresponding tasks to consider something done might be more complicated than expected, e.g. software deployment is often a more difficult problem than people think.
  • How to effectively divide work among team members and how to compose teams? If for example, people have to work on a huge monolithic code base, then it is typically difficult to, for example, compose two teams working on it simultaneously because they might apply conflicting changes that slow things down and may break a system. This could also happen between individual team members. To counter this, modularization of a big codebase helps, but accomplishing this is all but trivial.
  • According to the Scrum guide, each developer is considered equal, but how can we ensure that one developer is capable of taking over another developer's work? That person needs to have the right skills and domain specific knowledge. For the latter aspect it is also important to have something documented, I guess.
  • How to respond to unpredictable events during a sprint? Should it be cancelled? Should the scope be altered?

In practice, I have not seen that many people consciously thinking about the implementation of certain aspects in a Scrum process at all. They are either too much concerned with the measurable aspects of a process, (e.g. is the burndown chart, that reflects the amount of work remaining, looking ok?), or the tools that are being used (e.g. should we add another user story?).

IMHO, Scrum solves and facilitates certain things that helps you to be Agile. But actually being Agile is a much broader and more difficult question to answer. Moreover, this question also needs to be continuously evaluated.


In this blog post, I have written about my experiences with Agile software development. I'm by no means an expert or a believer in any Agile methodology.

In my opinion, what being agile actually means and how to accomplish this is a difficult question to answer and must be continuously evaluated. There is no catch-all solution for being it.


I gained most of my inspiration for this blog post from my former colleague's (Rini van Solingen) video log named: "Groeten uit Delft" that covers many Scrum and Agile related topics. I used to work for the same research group (SERG) at Delft University of Technology.