univocity-parsers | uniVocity-parsers is a suite of extremely fast and reliable parsers for Java. It provides a consiste | Parser library

Parsing CSV files correctly while supporting several data types (eg. floating-point numbers, integers, strings) and possibly ill-formed input files is clearly not easy, and doing so efficiently is actually pretty hard. Moreover, decoding UTF-8 strings is also much slower than reading directly ASCII strings. This is the reasons why most CSV libraries are pretty slow. Not to mention wrapping library in Python could introduce pretty big overheads regarding the input types (especially string).

Hopefully, if you need to read a CSV file containing a square matrix of integers that is assumed to be correctly formed, then you can write a much faster specific code dedicated to your needs (which does not care about floating-point numbers, strings, UTF-8, header decoding, error handling, etc.).

That being said, any call to a basic CPython function tends to introduce a huge overhead. Even a simple call to open+read is relatively slow (the binary mode is significantly faster than the text mode but unfortunately not so fast). The trick is to use Numpy to load the whole binary file in RAM with np.fromfile. This function is extremely fast: it just read the whole file at once, put its binary content in a raw memory buffer and return a view on it. When the file is in the operating system cache or a high-throughput NVMe SSD storage device, it can load the file at the speed of several GiB/s.

One the file is loaded, you can decode it with Numba (or Cython) so the decoding can be nearly as fast as a native code. Note that Numba does not support well/efficiently strings/bytes. Hopefully, the function np.fromfile produces a contiguous byte array and Numba can compute it very quickly. You can know the size of the matrix by just reading the first line and counting the number of comma. Then you can fill the matrix very efficiently by decoding integer on-the-fly, packing them in a flatten matrix and just consider end-of-line characters as regular separators. Note that \r and \n can both appear in the file since the file is read in binary mode.

Here is the resulting implementation:

The bintray service was shutdown starting from 1st of May. (Press release)

So Apache spark community has provided new repo to host all spark packages. You can add/replace below code snippet in your code and things should work.

Looks like the ObjectRowWriterProcessor approach works fine, but the type at runtime is BigDecimal instead of a Double.

So changing the following line does produce the desired result:

For the sake of completeness, I want to subsume at least the part of the solutions given by @axelclk and @IanGabes that worked. First of all, it seemed to be necessary to manually add all implicit dependencies plus the repositories they originate from to server's build.gradle, corresponding to the pom.xml files of matheclipse-core and of matheclipse-external:

Author of the library here. This is not supported, you'll have to split these sub-records manually.