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.
The SPARQL query language is extensible by nature: it allows implementors to define their own custom operators if the standard set of operators is not sufficient for the needs of some application.
Sesame’s SPARQL engine has been designed with this extensibility in mind: it allows you to define your own custom functions and use them as part of your SPARQL queries, like any other function. In this new recipe, I’ll show how to create a simple custom function. Specifically, we are going to implement a boolean function that detects if some string literal is a palindrome. read more…