I don’t know a single engineer (ops or not) who enjoys writing YAML, yet it is utterly unavoidable.
I’ve lost count of the amount of bugs and broken deploys that have happened because of YAML, or because of a type error caused by it.
I don’t know a single engineer (ops or not) who enjoys writing YAML, yet it is utterly unavoidable.
I’ve lost count of the amount of bugs and broken deploys that have happened because of YAML, or because of a type error caused by it.
Whatever format it happens to be written in (now YAML is the apparently trendy way) it is guilty by association.
To illustrate that, here's something I developed and wrote about a while ago, and have used regularly with great success to collaborate with non-technical people who are comfortable with spreadsheets (but whose heads would explode if I asked them to read or write JSON, YAML or XML):
Representing and Editing JSON with Spreadsheets
I’ve been developing a convenient way of representing and editing JSON in spreadsheets, that I’m very happy with, and would love to share!
https://medium.com/@donhopkins/representing-and-editing-json...
Here is the question I’m trying to answer:
How can you conveniently and compactly represent, view and edit JSON in spreadsheets, using the grid instead of so much punctuation?
My goal is to be able to easily edit JSON data in any spreadsheet, conveniently copy and paste grids of JSON around as TSV files (the format that Google Sheets puts on your clipboard), and efficiently export and import those spreadsheets as JSON.
So I’ve come up with a simple format and convenient conventions for representing and editing JSON in spreadsheets, without any sigils, tabs, quoting, escaping or trailing comma problems, but with comments, rich formatting, formulas, and leveraging the full power of the spreadsheet.
It’s especially powerful with Google Sheets, since it can run JavaScript code to export, import and validate JSON, provide colorized syntax highlighting, error feedback, interactive wizard dialogs, and integrations with other services. Then other apps and services can easily retrieve those live spreadsheets as TSV files, which are super-easy to parse into 2D arrays of strings to convert to JSON.
e.g. calling another google service with the JSON using a token minted BY THE USER CURRENTLY USING THE SHEET
The spreadsheet itself brings a lot of power to the table. (Pun not intended!)
There are some cool things you can do using spreadsheet expressions, like make random values that change every time you download the CSV sheet, which is great for testing. But expressions have their limitations: they can't add new rows and columns and structures, for example. However, named ranges are useful for pointing to data elsewhere in other sheets, and you can easily change their number of rows and columns.
For convenience and expressivity, I've defined ways of including other named sheets and named ranges by reference, and 2d arrays of uniformly typed values, and also define compact tables of identical nested JSON object/array structures by using declarative headers (one object per row, which I described in the article, but it's not so simple, and needs more examples and documentation).
At which point Terraform has a weak templating engine already, but its generally enough for building reusable infra. Additional features can be provided within the spreadsheet using reusable libraries. One pain point with embedding functional dataprocessing in a spreadsheet for JSON data, is a decent way of writing tree expressions, to which I would turn to the de facto JSON tooling jq for inspiration.
if you want to take this further, I am up for building some infra for continuous deployment spreadhseets through terraform. tom <dot> larkworthy <at> futurice.com
But I would not embed stuff inline with the JSON. I would have a pure sheet dedicated to stuff going in, and a compute sheet for stuff join out. And the definition for stuff going out should basically be a JQ expression, that can "shell out" to sheets expressions https://github.com/sloanlance/jq/issues/1
Here is a side-by-side comparison of data in TOML versus YAML: https://gist.github.com/oconnor663/9aeb4ed56394cb013a20
And some comments that resonate with me:
The yaml spec is overly complex and parsing it properly
is a nightmare. I rather prefer TOML because of it's
simplicity. Unless one really need the gazillion extra
features which yaml provides (which one probably doesn't),
I'd say sticking with TOML seems to be the saner choice.
I've recently kind of changed my mind on unquoted strings.
They're nice when you're editing config files by hand, but
they run into parsing issues in simple cases like when the
string looks like an int, or of course when the string
contains quotation marks itself.JSON is much more human readable in that respect because the structure is explicit so there's no ambiguity. I'd say TOML is somewhere in-between. But both are vastly preferable to YAML because they don't have Javascript-style type insanity.
https://en.wikipedia.org/wiki/Don%27t_repeat_yourself
If you write procedural configurations with a real Turing Complete, but not Turing-Tarpit language like Python or JavaScript, then you don't need to repeat yourself ridiculous numbers of times, and manage thousands of lines of hand written eye-sore almost-but-not-quite-entirely-unlike-tea YAML. Plus you can implement validation, warnings and error checking, and support multiple input and output formats.
But so many DevOps dogmatically go out of their way to avoid writing any code, even when the alternative means writing a hell of a lot more brittle unmaintainable YAML where you have to carefully check every line for correctness, and make sure that you're actually repeating the same thing in every location required without any typos, and don't let any of those repetitions slip through the cracks when you make changes.
With real Turing complete code, macros, and templates, you can accomplish the same task as with pure un-DRY YAML data, much more easily and efficiently, with orders of magnitudes fewer lines of code, that you can actually understand, maintain, validate, and test, so you can be sure it actually works without meticulously checking every line of output by hand.
It's better to combine DRY combinations of different non-Turing-complete data formats like CSV, YAML, JSON, INI, XML, using the most appropriate formats depending on the type of data. Spreadsheets are much better than JSON or YAML or XML for many different kinds of data with repeating structure (so you don't repeat key names), and JSON and YAML are much better for irregular tree-shaped data (since you're not restricted to rigid structures), and XML for tree structured data and documents including text.
You end up needing different formats as input as well as output. So you need a real Turing Complete language to read them all in, combine and validate them, and shit out the various other formats required by your tools and environment.
Well, this is the real problem, is that DevOps people should be writing code in my opinion, especially code to automate deployments and handle configuration. But many times it's just a new job label for people with the same ol' ops and sysadmin skillset who don't want to write code.
It doesn't help that when they make unmaintainable piles of configuration that nobody understands, it typically adds to their job security.
There are good DevOps and bad DevOps. Personally, I'm a Dev who necessarily knows how to do Ops, because nobody else is there to put out the fires and wipe my ass for me. Good DevOps should not have such disdain for writing code, and should not be so territorial and focused on job security, and should work more closely with developers and code.
And good developers should understand operations, and shouldn't be so helpless and ignorant when it comes to deploying and maintaining systems themselves, so they can design their code to be easily configurable and dovetail into operational systems.
For the same reasons, it's also important for programmers developing tools and content pipelines for use by artists to understand how the art tools and artists work, and how to use them (enough to create placeholder programmer art to test functionality), even if they don't have any artistic ability.
And for artists and designers to have some understanding of how computers and programming works, and how to use spreadsheets and outliners and databases to understand and create specifications and configurations, so they don't design things that are impossible or extremely inefficient to implement, and make intractable demands of computers and programmers.
The realization that I really needed to understand what happens in operations for me came around '09, when the Xbox Operations Center called me and told me my code wasn't working, and we had such a wall between us that I couldn't see what was going on, and they couldn't describe it either.
I ended up writing automated publishing pipelines for them to take the most risky parts of their dozens of pages word doc and writing tools to do this for them automatically. Most people didn't even think this was a thing that could be done, let alone should be done. Problem solved!
I think people who are territorial are inherently insecure in their skills and therefore fear getting out of their comfort zone. Generalists are far better than specialists in my opinion. You want someone to go where the problems are, rather than people who invent new problems for others in their own little empire. I think a lot of big companies are so big they can have people silo'd all day, so people don't even think about the people and systems they are affecting.
If you want an example of a dynamic configuration, look at RPM's .spec. That's a monstrosity. What you're asking for is more of that, and that's insane.
You could also use something like Python to do all your typing, build a dictionary, and just dump it to a yaml file if you think writing yaml by hand is too error prone (which I personally disagree with).
https://en.wikipedia.org/wiki/Turing_tarpit
Any time you take a language like bash that's ALREADY deeply muddled in the Turing Tarpit, and then try to make up for its weaknesses by wrapping it up in yet another arbitrary syntax that you just pulled out of your ass like .spec, you're even worse off than you were when you started. Why pick a terrible language like bash that's no good for reading and writing and manipulating structured data formats like CSV, JSON, YAML, INI, or XML, and then try to "fix" it, when there are so many much better well supported off-the-shelf alternatives that don't require re-inventing the square wheel, like Python, JavaScript, or Lua?
For most programs though, I would prefer a YAML config file. It's easy to serialize/deserialize for many languages, and you can adjust your init scripts / systemd units to spit out a new config on startup if you so choose. Or you can use something like ansible and some templates to generate that config once when you deploy your application (we're all using immutable infrastructure now, right?), although trying to template YAML files in jinja2 is a real PITA; I'd probably just write an application specific ansible module to dump out my config and skip the yaml jinja template part.
That's the really nice thing about ansible, you can make it do all sorts of interesting stuff.
I've tried to work with YAML a few times. The tree structure and extra data types are great. Everything else is a huge pain. There's at least 3 versions of the spec, and the latest one is nearly 100 pages. The parts I need are always in some "extension", so there's even more that I need to support. It has a system for serializing native Objects, so you have to be careful with untrusted data because there are some interesting security issues. It's so complex, I have trouble knowing what to quote, or how. It's not feasible to write your own parser in any reasonable amount of time. Worst of all, every parsing library is slightly different, so (not unlike SOAP) you kind of have to know that it's going to be parsed with (say) PyYAML.
Complicated configuration is indeed a problem in any format, but YAML makes even simple things complex. From the beginning, I really wanted to like YAML. Unfortunately, I think their goals (human-readable text, language-agnostic, rich data types, efficient, extensible, easy to implement, easy to use) are impossible. You simply can't achieve all of them at once.
I share your frustration and was motivated by it to build this. Why should I spend ages writing up everything as config files when I have a script that already works, is easy to change and debug, and can handle any custom thing I need?
I think config files to describe devops processes are a good approach for huge companies with huge teams, lots of churn etc. The approach perhaps has simplicity & stability benefits - works for everyone everywhere without understanding any detail, changes are a bit easier to track, etc. But for small teams wanting control, speed and the flexibility of just writing code to do what you want it can often be an inefficient approach. At least in my experience.
You should check Box CI out. Launching very soon!
JSON is a terrible config format, and an ok data interchange format.
But maybe I misunderstood. Can you give an example of a comment that makes sense / is required in a configuration JSON?
Mostly because I don't know whether the person customizing my code will read the README, but they'll definitely see the comments I wrote right before the configuration option itself.
With comments and some extra whitespace, they can go through the file line by line, read what a specific option does, configure it properly, and move on to the next one. No back and forth to the README required.
Something like http://www.relaxedjson.org/ is a JSON we need. An implicit root object would make it almost perfect for writing configuration.
And clearly YAML is the wrong tool for infra-as-code since CloudFormation has to build a macro system, conditionality, referencing, and a couple different systems for defining and calling functions (templates being one and their implicit functions being another). We also see tools like Troposphere and CDK which are effectively different ways to generate CloudFormation YAML via programming languages (or more precisely programming languages that were designed for humans).
And it's not just limitations inherent to CloudFormation--Helm has long had templates for generating YAML, but those also weren't sufficiently powerful/expressive/ergonomic so Helm3 is supporting Lua as well. And as I understand it, Terraform is constantly adding more and more powerful features into HCL.
So what's the solution? It's pretty simple--we should keep the YAML around, but it should be the intermediate representation (IR), not the human interface. The human interface should be something like a functional language[^1] (or an imperative language that is written in a functional style) that evaluates to that YAML IR layer. The IR is then passed to something like Kubernetes or Terraform or CloudFormation which understand it, but it's not the human interface.
As for the high-level language, something like [Starlark][0] would work well. It's purpose-built for being an evaluated configuration language. However, I would argue that a static type system (at least an optional static type system) is important--it's easy enough to imagine someone extending Starlark with type annotations and building a static type checker (which is much easier for Starlark since it's a subset of Python which is intended to be amenable to static analysis).
This, I think, is the proper direction for infrastructure-as-code tooling.
[^1]: Functional in that it is declarative instead of imperative--not necessarily that the syntax should be as hard to read as OCaml or Haskell. Also, while YAML is also declarative, it doesn't have a notion of evaluation or variables.
[0]: https://docs.bazel.build/versions/master/skylark/language.ht...
It has multiline strings and comments among other features...
That question does not make any sense. I am talking about Scheme the programming language: https://schemers.org/
NixOS is a GNU/Linux distribution built on top of the Nix package manager. The Nix package manager has its own custom configuration language. GuixSD is a GNU/Linux distribution built on top of the Guix package manager. GuixSD uses Guile Scheme as the package configuration language, system configuration language, scripting language, and implementation language for many of the system services (such as init). GuixSD does more things better with an existing standard programming language than NixOS does with its own custom programming language.
> Also, I detest CMake, but by your own standard (longevity, popularity), it is better than Nix or Guix.
What does CMake have to do with anything?
> Yes, they have been around for a while
What are you talking about? The first stable version of NixOS, 13.10, was released in 2013. GuixSD 1.0 was only released this May.
Your post is hard to make sense of.
Functional? Check. Type annotations? Check. Not Turing complete? Check. Compiles to multiple config formats? Check.
The one place I could write YAML is AWS CodeBuild buildspecs. I write them in JSON, when they’re not being assembled on the fly from Pulumi.
You can do it too—just pick better tools for it.
You might be interested in https://www.jetbrains.com/teamcity/
It has a Kotlin strongly typed DSL for pipelines, i absolutely despite Jenkins and Groovy, so many bugs causing huge issues due to weak typing and lack of testing :(
In contrast ansible has a lot of declarative building blocks (iirc all are unit-tested!), which either fail or do the specified job. And yeah, the specified job might be copying and templating a config-file for some oldschool service (and you might be shocked: people still use these!), which is inherently not really testable (except by integration tests. or maybe you write a test to get the file and to check whether you got to type the name right twice) - I'm not sure how pulumi can help you with that?
And yes, I would love a concise, descriptive DSL (compare for example spack, which does this nicely) over/as an alternative to the loops-crafted-on-YAML-mess of ansible but I take the latter any day over some "we-can-call-cloud-providers-apis-and-kubernetes-but-why-would-you-copy-files?"-stuff like pulumi.
You can write instance CM yourself, I'd you want. Pulumi isn't a "cloud thing", it's a lifecycle management tool. But it's typed, and that absolves it of many, many sins given the incredibly positive surface it presents to a developer.
Maybe the only comparable thing that comes to mind that I prefer is jsonnet.
Objectives:
TOML aims to be a minimal configuration file format
that's easy to read due to obvious semantics. TOML is
designed to map unambiguously to a hash table. TOML
should be easy to parse into data structures in a wide
variety of languages.
A reddit thread titled "YAML vs TOML" has some merit [2]. YAML downsides:
- Implicit typing causes surprise type changes. (e.g.
put 3 where you previously had a string and it will
magically turn into an int).
- A bunch of nasty "hidden features" like node anchors
and references that make it look unclear (although to
be fair a lot of people don't use this).
TOML downsides:
- Noisier syntax (especially with multiline strings).
- The way arrays/tables are done is confusing, especially
arrays of tables.
Rust uses TOML to configure its build system, Cargo [3].[1]: https://github.com/toml-lang/toml
[2]: https://www.reddit.com/r/devops/comments/6f82nu/yaml_vs_toml...
[3]: https://doc.rust-lang.org/cargo/getting-started/first-steps....
Why not just read python, like how webpack uses javascript as its "config".
I actually struggled with YAML at first when starting with Ansible because I didn't know it was YAML, and I didn't understand lists vs dictionaries in YAML (seems obvious in hindsight, but just didn't click for whatever reason).
> I’ve lost count of the amount of bugs and broken deploys that have happened because of YAML, or because of a type error caused by it.
YAML can be linted. Also, golang might be helpful to read i and deserialize your YAML files against a known-type so you can catch these type errors. This should probably be done during CI when you create a pull request or merge request. This obviously requires doing things on your end to make it all fit together.