(TimescaleDB engineer here) There are major feature and capabilities available in TimescaleDB that are not available in pg_partman.
On the query side we implement a whole bunch of planner and execution time optimizations that don't come with plain PostgreSQL (and pg_partman does not implement any query optimizations AFAIK). These include optimizations that have to do with ordering based on time_bucket/date_trunc, execution-time chunk exclusion, etc. These result in query speedups of more than 1000x on many common time-series queries.
TimescaleDB is much more automated than pg_partman and thus easier to maintain and administer. There are a lot less knobs to tune and a lot less things to go wrong in TimescaleDB.
We implement analytical features necessary for time-series analyis: gap-filling, common time-series functions liked time_bucket, first, last, etc.
We also implement a lot of data management functionality geared towards time-series data: scheduled data reordering, schedule data dropping/expiration, etc.
This past Monday we released major feature called continuous aggregates. That automatically maintain a materialized view of aggregates over your time-series data, updating it as new data comes in and correctly handling backfilled data as well.
The two projects are really not comparable in breadth or scope IMHO.
On the query side we implement a whole bunch of planner and execution time optimizations that don't come with plain PostgreSQL (and pg_partman does not implement any query optimizations AFAIK). These include optimizations that have to do with ordering based on time_bucket/date_trunc, execution-time chunk exclusion, etc. These result in query speedups of more than 1000x on many common time-series queries.
TimescaleDB is much more automated than pg_partman and thus easier to maintain and administer. There are a lot less knobs to tune and a lot less things to go wrong in TimescaleDB.
We implement analytical features necessary for time-series analyis: gap-filling, common time-series functions liked time_bucket, first, last, etc.
We also implement a lot of data management functionality geared towards time-series data: scheduled data reordering, schedule data dropping/expiration, etc.
This past Monday we released major feature called continuous aggregates. That automatically maintain a materialized view of aggregates over your time-series data, updating it as new data comes in and correctly handling backfilled data as well.
The two projects are really not comparable in breadth or scope IMHO.