We present practical implementations of two different approaches for space efficient data structures that can be used to answer longest common extension queries. The first approach is based on Rabin-Karp fingerprints and the second approach uses string synchronizing sets. It is interesting to see that, despite being a very naive algorithm, scanning is still one of the fastest ways to answer longest common extension queries. However, our longest common extension data structure based on string synchronizing sets is faster, but requires a small memory overhead of 10%-20% of the input size.
Our implementations are available from the original repository and my fork that sometimes is ahead of the development compared to the original repository.