Lately, I've been working with Julia a lot, partly because of my new book which was released a couple of weeks ago and partly because it's fun. If you are somewhat experienced with data handling, you'll probably familiar with the JLD and JLD2 packages that are built on top of the FileIO one. Of these, JLD2 is faster and generally better but it seems that it's no longer maintained, while the files it produces are quite bulky. This may not be an issue with a smaller dataset but when you are dealing with lots of data, it can be quite time-consuming to load or save data using this package.
Enter the CDF framework, which is short for Compressed Data Format. This is still work in progress so don’t expect miracles here! However, I made sure it can handle the most common data types, such as scalars of any type (including complex string variables), the most common arrays (including BitArrays) for up to two dimensions, and of course dictionaries containing the above data structures. It does not support yet tensors or any kind of Boolean array (which for some bizarre reason is distinct from the BitArray data structure).
CDF has two types of functions within it: those converting data into a large text string (and vice versa) and those dealing with IO operations, using binary files. The compression algorithm employed is the Deflate one from the CodecZlib package as it seems to have better performance than the other algorithms in the package. Remember that all of these algorithms take a text variable as an input, which is why everything is converted into a string before compressed and stored into a data file.
The CDF framework doesn't have many of the options JLD2 has but for the files created with it, the performance edge is quite obvious. A typical dataset can be around 20 times smaller and many times faster to read or write, as the compression algorithm is fairly fast and there is fewer data to process with the computationally heavy IO commands. As a codebase, CDF is significantly simpler than other packages while the main functions are easy as pie. In fact, the whole framework is quite intuitive that there is not really any need for documentation for it.
If you are interested in using this framework and/or extending it, feel free to contact me through this blog. Be sure to mention any affiliation(s) of yours too. Cheers!
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Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.