We aim at using HDF5 for our data format. HDF5 has been selected because it is a hierarchical filesystem-like cross-platform data format and it supports large amounts of data.
The file will contain arrays and some parameters. The question is about how to store the parameters (which are not made up by large amounts of data), considering also file versioning issues and the efforts to build the library. Parameters inside the HDF5 could be stored as either (A) human-readable attribute/value pairs or (B) binary data in the form of HDF5 compound data types.
Just as an example, let's consider as a parameter a polygon with three vertex. Under case A we could have for instance a variable named
Polygon with the string representation of the series of vertices, e.g. for instance
(1, 2); (3, 4); (4, 1). Under case B, we could have instead a variable named
Polygon made up by a [2 x 3] matrix.
We have some idea, but it would be great to have inputs from people who have already worked with something similar. More precisely, could you please list pro/cons of A and B and also say under what circumstances which would be preferable?
Best How To :
Speaking as someone who's had to do exactly what you're talking about a number of time, rr got it basically right, but I would change the emphasis a little.
- For file versioning, text is basically the winner.
- Since you're using an hdf5 library, I assume both serializing and parsing are equivalent human-effort.
- text files are more portable. You can transfer the files across generations of hardware with the minimal risk.
text files are easier for humans to work with. If you want to extract a subset of the data and manipulate it, you can do that with many programs on many computers. If you are working with binary data, you will need a program that allows you to do so. Depending on how you see people working with your data, this can make a huge difference to the accessibility of the data and maintenance costs. You'll be able to sed, grep, and even edit the data in excel.
input and output of binary data (for large data sets) will be vastly faster than text.
- working with those binary files in a new environmnet (e.g. a 128 bit little endian computer in some sci-fi future) will require some engineering.
- similarly, if you write applications in other languages, you'll need to handle the encoding identically between applications. This will either mean engineering effort, or having the same libraries available on all platforms. Plain text this is easier...
- If you want others to write applications that work with your data, plain text is simpler. If you're providing binary files, you'll have to provide a file specification which they can follow. With plain text, anyone can just look at the file and figure out how to parse it.
- you can archive the text files with compression, so space concerns are primarily an issue for the data you are actively working with.
- debugging binary data storage is significantly more work than debugging plain-text storage.
So in the end it depends a little on your use case. Is it meaningful to look at the data in the myriad tools that handle plain-text? Is it only meaningful to look at it with big-data hdf5 viewers? Will writing plain text be onerous to you in terms of time and space?
In general, when I'm faced with this issue, I basically always do the same thing: I store the data in plain text until I realize the speed problems are more irritating than working with binary would be, and then I switch. If you don't know in advance if you're crossing that threshold start with plain-text, and write your interface to your persistence layer in such a way that it will be easy to switch later. This is tiny bit of additional work, which you will probably get back thanks to plain text being easier to debug.