How Memgraph Uses Skip Lists for Fast Indexes and Unique Constraints

Memgraph utilizes skip lists to enhance the efficiency of indexes and uniqueness constraints, providing a probabilistic data structure that facilitates fast search, insertion, and deletion operations with O(log n) time complexity, akin to binary search trees but with a simpler implementation. This design choice allows Memgraph to efficiently handle large datasets by reducing the traversal needed for data retrieval, as demonstrated through improved query times when indexes are applied. However, the use of skip lists introduces trade-offs, such as increased memory consumption due to additional pointers and nodes, and a slight overhead on write operations because multiple skip lists need updating with each data modification. By strategically selecting which properties to index, Memgraph balances query performance with resource usage, optimizing both speed and storage efficiency. Uniqueness constraints, also built on skip lists, ensure data integrity by preventing duplicate entries, though they similarly impact memory and write speed. Overall, Memgraph's approach provides significant performance benefits for read operations while requiring careful consideration of memory and write operation costs.