In part 1, we explained what data chunking is about in the context of scientific data access libraries such as netCDF-4 and HDF5, presented a 38 GB 3-dimensional dataset as a motivating example, discussed benefits of chunking, and showed with some benchmarks what a huge difference chunk shapes can make in balancing read times for data that will be accessed in multiple ways.
In this post, I'll continue looking at that example dataset to see how we can derive good chunk shapes, generalize to other datasets, look at how long it can take to rechunk a multidimensional dataset, and look at the use of Solid State Disk (SSD) for both accessing and rechunking data.
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