Learning SPARQL, 2nd Edition

Chapter 7. Query Efficiency and Debugging

A query asks for a set of information. Sometimes, there are different ways that a query can ask for the same set of information, and some ways are more efficient than others. When you keep in mind the amount of work that each part of your query asks a SPARQL processor to perform (or, in computer science jargon, how “expensive” each part is in processor cycles), it helps you create queries that run faster. Debugging techniques and tools can also help you tune your query as well as fix a query that isn’t doing what you want it to.

In this chapter, we’ll learn about:

The WHERE clause is the heart of any query, and the ordering of its components and the choice of functions it calls can speed things up or slow things down.

Efficiency Outside the WHERE Clause

Once a WHERE clause has returned values from a dataset, there are several things that a query can do with those queries, and some are more expensive than others.

Debugging

Debugging of SPARQL queries starts with classic techniques that you’d use with any development language and can also take advantage of specialized tool features.

Efficiency Inside the WHERE Clause

Before a SPARQL processor can list, sort, delete, or insert the data described in your query or update request, it usually must first find the data you’re interested in by matching the triple patterns in your query’s WHERE clause against the triples in the dataset that you’re querying. While the order of a graph pattern’s triple patterns ...