The most frequent query applications make on time-series data is to find the most recent value for a given device or item. However, querying large time-series datasets can be challenging due to the impact of indexes and query patterns. Five methods are presented to access the most recent value in PostgreSQL: Naive GROUP BY, LATERAL JOIN, TimescaleDB SkipScan, Loose Index Scan, and Logging table with triggers. Each method has its advantages and disadvantages, and some may not perform well on large datasets or high-cardinality data. The choice of method depends on the specific use case and requirements of the application. It is recommended to test each option in an environment that closely resembles production to determine which approach works best for a particular use case. Additionally, using TimescaleDB can provide efficient querying capabilities, especially with SkipScan, and can help improve the performance of time-series data applications.