This isn't YAML, this is just sparkling JSON
this post was submitted on 04 Jul 2025
191 points (98.5% liked)
Programmer Humor
25253 readers
1117 users here now
Welcome to Programmer Humor!
This is a place where you can post jokes, memes, humor, etc. related to programming!
For sharing awful code theres also Programming Horror.
Rules
- Keep content in english
- No advertisements
- Posts must be related to programming or programmer topics
founded 2 years ago
MODERATORS
All yaml is just sparkling JSON.
Always has been
How do you figure?
Valid JSON is valid YAML.
So valid YAML can contain JSON
That's a long way from all or most YAML being JSON-compatible. I wonder if more YAML files in the wild parse as Markdown than JSON.
Maybe use a real database for that? I'm a fan of simple tools (e.g. plaintext) for simple usecases but please use appropriate tools.
What is wrong with a file for this? Sounds more like a local log or debug output that a single thread in a single process would be creating. A file is fine for high volume append only data like this. The only big issue is the format of that data.
What benefit would a database bring here?
I think SQLite is a great middle ground. It saves the database as a single .db file, and can do everything an SQL database can do. Querying for data is a lot more flexible and a lot faster. The tools for manipulating the data in any way you want are very good and very robust.
However, I'm not sure how it would affect file size. It might be smaller because JSON/YAML wastes a lot of characters on redundant information (field names) and storing numbers as text, which the database would store as binary data in a defined structure. On the other hand, extra space is used to make common SQL operations happen much faster using fancy data structures. I don't know which effect is greater so file size could be bigger or smaller.
I didn't look to much at the data but I think csv might actually be an appropriate format for this?
Nice simple plaintext and very easy to parse into a datastructure for analysing/using it in python or similar
CSV would be fine. The big problem with the data as presented is it is a YAML list, so needs the whole file to be read into memory and decoded before you get and values out of it. Any line based encoding would be vastly better and allow line based processing to be done. CSV, json objects encoded into a single line, some other streaming binary format. Does not make much difference overall as long as it is line based or at least streamable.
SQLite would definitely be smaller, faster, and require less memory.
Thing is, it's 2025, roughly 20 years since anybody's given half a shit about storage efficiency, memory efficiency, or even CPU efficiency for anything so small. Presumably this is not something they need to query dynamically.
True (in most contexts, probably including this one), but I think that only makes the case for SQLite stronger. What people do still care about is a good flexible, usable and reliable interface. I'm not sure how to get that with YAML.
YAML is not a good format for this. But any line based or steamable format would be good enough for log data like this. Really easy to parse with any language or even directly with shell scripts. No need to even know SQL, any text processing would work fine.
Smaller file size, lower data rate, less computational overhead, no conversion loss.
A 64 bit float requires 64 bits to store.
ASCII representation of a 64 bit float (in the example above) is 21 characters or 168 bits.
Also, if every record is the same then there is a huge overhead for storing the name of each value. Plus the extra spaces, commas and braces.
So, you are at least doubling the file size and data throughput. And there is precision loss when converting float-string-float. Plus the computational overhead of doing those conversions.
Something like sqlite is lightweight, fast and will store the native data types.
It is widely supported, and allows for easy querying of the data.
Also makes it easy for 3rd party programs to interact with the data.
If you are ever thinking of implementing some sort of data storage in files, consider sqlite first.
Never said it had to be a text file. There are many binary serialization formats that could be used. But is a lot of situations the overhead you save is not worth the debugging effort of working with binary data. For something like this that is likely not going to be more then a GB or so, probably much less it really does not matter that much if you use binary or text formats. This is an export format that will likely just have one batch processing layer on. This type of thing is generally easiest for more people to work with in a plain text format. If you really need efficient querying of the data then it is trivial and quick to load it into a DB of your choice rather then being stuck with sqlite.
load more comments
(1 replies)
It's used to export tracking data to analyze later on. Something like SQLite seems like a much better choice to me.
If it's an export that will be consumed by a separate, unrelated program later, I think a CSV is most appropriate. Databases are persistence tools, not transport.
It's only intended to be used by the program itself. It's purely storage.
Ah so it's not really an export, it's just the backing store used by some other (locally-running) program that you're trying to reverse engineer?
In that case yeah an sqlite database is probably most appropriate, though I can see a CSV still being desirable to remove a potential sqlite dependency.
Because this is not log or debug data as OP said. In any case, what do you think would happen with this data? It will be analyzed by some sort of tool because no one could manually look at this much text data. In text, this can be like 1MB of data per second. So in a normal eye tracking session, probably hundreds of MB. The problem isn't the storage space, but the time it will take to read that in and analyze it each time, forcing you to wait for processing or use lots of memory while reading it. And anyway, in most languages, it's actually much easier to store the number values directly (in 8 bytes not the 30something this text representation uses) than to convert them to JSON, all languages have some built-in way to do that. And even if not, sqlite is piss-easy and does everything for you, being as simple as JSON.
There is just no reason to do it like that unless you just don't think about what you're doing or have no clue.
export tracking data to analyze later on
That is essentially log data or essentially equivalent. Log data does not have to be human readable, it is just a series of events that happen over time. Most log data, even what you would think of as traditional messages from a program, is not parsed by humans manually but analyzed by code later on. It is really not that hard to slow to process log data line by line. I have done this with TB of data before which does require a lot more effort to do. A simple file like this would take seconds to process at most, even if you were not very efficient about it. I also never said it needed to be stored as text, just a simple file is enough - no need for a full database. That file could be binary if you really need it to be but text serialization would also be good enough. Most of the web world is processed via text serialization.
The biggest problem with yaml like in OP is the need to decode the whole file at once since it is a single list. Line by line processing would be a lot easier to work with. But even then if it is only a few 100 MBs loading it all in memory once and analyzing it all in memory would not take long at all - it just does not scale very well.
Some order in the CSV data, if it weren't a logfile, which i didn't know.
I'm amazed at developers who don't grasp that you don't need to have absolutely everything under the sun in a human readable file format. This is such a textbook case...
Yeah this isn't even human readable even when it's in YAML. What am I going to do? Read the floats and understand that the person looked left?
It's human-readable enough for debugging. You might not be able to read whether a person look left, but you can read which field is null or missing or wildly out of range. You can also read if a value is duplicated when it shouldn't be.
Human-readable is primarily about the structure and less about the data being human readable.
load more comments
(4 replies)
Even if you want it to be human readable, you don't need to include the name into every field and use balanced separators.
Any CSV variant would be an improvement already.
Even using C#'s decimal type (128bit) would be an improvement! I count 22 characters per numbers here. So a minimum of 176bit.
That's it everyone, back to copybooks.
Exactly. All modern CPUs are so standardized that there is little reason to store all the data in ASCII text. It's so much faster and less complicated to just keep the raw binary on disk.
If you’re using a library to handle deserialization , the ugliness of the serial format doesn’t matter that much.
Just call yaml.load() and forget about it.
That works until you realize your calculations are all wrong due to floating point inaccuracies. YAML doesn't require any level of precision for floats, so different parsers on a document may give you different results.
What text based serialization formats do enforce numeric precision?
AFAIK it’s always left up to the writer (serializer)
Cuelang: https://cuelang.org/docs/reference/spec/#numeric-values
Implementation restriction: although numeric values have arbitrary precision in the language, implementations may implement them using an internal representation with limited precision. That said, every implementation must:
- Represent integer values with at least 256 bits.
- Represent floating-point values with a mantissa of at least 256 bits and a signed binary exponent of at least 16 bits.
- Give an error if unable to represent an integer value precisely.
- Give an error if unable to represent a floating-point value due to overflow.
- Round to the nearest representable value if unable to represent a floating-point value due to limits on precision. These requirements apply to the result of any expression except for builtin functions, for which an unusual loss of precision must be explicitly documented.
Thanks for teaching me something, but the obscurity of your answer just illustrates how rare that requirement is in human readable formats, and mostly limited to data formats designed for numeric precision, like HDF5, FITS or protobuf.
I don't think having well-defined precision is a rare requirement, it's more that most devs don't understand (and/or care) about the pitfalls of inaccuracy, because they usually aren't obvious. Also, languages like JavaScript/PHP make it hard to do things the right way. When I was working on an old PHP codebase, I ran into a popular currency library (Zend_Currency) that used floats for handling money, which I'm sure works fine up until the point the accountants call you up asking why they can't balance the books. The "right way" was to use the bcmath extension, which was a huge pain.
Technically, JSON enforces a specific numeric precision by enforcing that numbers are stored as JS-compatible floating point numbers with its associated precision.
Other than that, the best way to go if you want to have a specific precision is to cast to string before serialisation.
Fuck yaml. I'm not parsing data structured with spaces and newlines with my eyes. Use visible characters.
Does your viewer/editor not show space chars or indent levels?
i mean, json is valid yaml
Why you shouldn't use YAML
The best approach would be to never use yaml for anything
YAML doesn't require any level of accuracy for floating point numbers, and that doc appears to have numbers large enough to run into problems for single-precision floats (maybe double too). That means different parsers could give you different results.
I really like YAML but way too many people use it beyond its purpose... I work with Gitlabci and seeing complex bash scripts inline in YAML files makes me want to hurt people.
This is nasty to look at
Also let's represent all numbers in scientific notation, I'm sure that's going to make it easier to read...
I’d probably just use line delimited JSON or CSV for this use case. It plays nicely with cat and other standard tools and basically all the yaml is doing is wrapping raw json and adding extra parse time/complexity.
In the end consider converting this to parquet for analysis, you probably won’t get much from compression or row-group clustering, but you will get benefits from the column store format when reading the data.
Thanks for the advice, but this is just the format of some eyetracking software I had to use not something I develop myself
Ah, well, such is software dependencies.
view more: next ›