TimescaleDB, a time-series database built on PostgreSQL, efficiently manages time-series data by utilizing ordered indexes to enhance query performance. This approach allows storing relational and time-series data side-by-side, enabling complex queries using SQL. By breaking data into chunks based on time intervals and indexing each chunk, TimescaleDB optimizes queries significantly, which can be crucial for devices tracking metrics across numerous locations. Different types of indexes, such as composite indexes, are explored to improve query efficiency by organizing data based on columns like store_id, device_id, and time. The importance of index order is highlighted, as it directly influences query speed, and examples demonstrate how composite indexes can reduce query times by orders of magnitude when appropriately structured. The text encourages users to consider the trade-offs of index space versus performance benefits and points to community resources for further learning and support with TimescaleDB.