The Sesame Native Store is reported to scale up to about 100-150 million triples (depending on hardware and dataset characteristics). However, getting that number of triples into the store is not always a trivial task, so I wanted to go over several possible strategies you can employ to get best performance when trying to upload large datasets into the Sesame Native Store.

In this recipe, we will look at simple uploading and its limitations, splitting your input data into several files (and how to deal with blank node identity), as well programmatically chunked uploads and several tweaks you can emply to improve performance.

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