TimescaleDB has introduced several performance improvements in recent releases, including enhancements to query analytics. These improvements include vectorized query execution, compressed columnar storage, continuous aggregates, partial aggregates at the chunk level, optimized chunk exclusion, lighter locks during continuous aggregate refresh procedures, more decompression parallel workers, and faster real-time continuous aggregates. Additionally, TimescaleDB has made optimizations to its columnar mini-batches sorting process, which benefits queries that limit the number of returned tuples. These improvements aim to increase the performance and scalability of TimescaleDB for demanding workloads like time series, events, and analytics.