RUSLI: Real-time Updatable Spline Learned Index

Machine learning algorithms have accelerated data access through ‘learned index’, where a set of data items is indexed by a model learned on the pairs of data key and the corresponding record’s position in the memory. Most of the learned indexes require retraining of the model for new data insertions in the data set. The retraining is expensive and takes as much time as the model training. So, today, learned indexes are updated by retraining on batch inserts to amortize the cost. However, real-time applications, such as data-driven recommendation applications need to access users’ feature store in real-time both for reading data of existing users and adding new users as well.