JSON is extremely fast these days. Gzipped JSON perhaps even more so.

I find that JSON blobs up to about 1 megabyte are very reasonable in most scenarios. You are looking at maybe a millisecond of latency overhead in exchange for much denser I/O for complex objects. If the system is very write-intensive, I would cap the blobs around 10-100kb.

> You are looking at maybe a millisecond of latency overhead [for 1 megabyte]

Considering the data transfer alone for 1 MB / 1 sec requires 8 Gbps, I have doubts. But for fun, I created a small table in Postgres 18 with an INT PK, and a few thousand JSONB blobs of various sizes, up to 1 MiB. Median timing was 4.7 msec for a simple point select, compared to 0.1 msec (blobs of 3 KiB), and 0.8 msec (blobs of 64 KiB). This was on a MBP M4 Pro, using Python with psycopg, so latency is quite low.

The TOAST/de-TOAST overhead is going to kill you for any blobs > 2 KiB (by default, adjustable). And for larger blobs, especially in cloud solutions where the disk is almost always attached over a network, the sheer number of pages you have to fetch (a 1 MiB blob will nominally consume 128 pages, modulo compression, row overhead, etc.) will add significant latency. All of this will also add pressure to actually useful pages that may be cached, so queries to more reasonable tables will be impacted as well.

RDBMS should not be used to store blobs; it's not a filesystem.

I adore contiguous reads that ideas like that yield. I'd rather push that out to a read-only end point, then getting sucked into the entropy of treating what is effectively an unschema-ed blob into editable data.