C++ is heavily customized C. The heavy customization make Redshift a columnar database and more ideal for querying large amounts of data quickly. How does Timescale help Postgres in this area?
By tuning the chunk sizes so their data fits in memory, many common queries gain a lot of efficiency. It's built around some assumptions of time-series data: Most inserts and queries are for recent data and are generally ordered.
I've had great experience with TimescaleDB for small-medium time-series loads such as sensor or analytics data; I've found it's pretty plug-and-play and have used it to store tables with ~1B time-series rows of geospatial data, sensor values, etc.