yes re table definition.

To answer your question, for indexing there's a couple of options: - if you know which fields you want to index, use an expression index with a regular B-tree. - if you don't know which fields you want to index, use a GIN index on the whole field.

Blogpost which covers both cases: https://scalegrid.io/blog/using-jsonb-in-postgresql-how-to-e...

That said, I generally don't bother indexing the "raw" table, but instead create a parsed+summary table that breaks out the fields I want to index into 'real' sql columns and summarizes the data to reduce the number of records. In particular, I use a partitioned table for the raw data which keeps the data-per-table tiny enough to fit in RAM, and "real time" queries just hit the latest raw table(s). For big, real-time data, I sometimes use TABLESAMPLE queries to create estimates, or triggers to do exact counting (which is what NoSQL databases do, just without the nice structure of triggers).

In event sourcing, as described in DDD, the aggregate is more important. So there (also) has to be an aggregate Id, that's non-unique (one aggregate has many events).

Then, there's the optimistic locking system that you'll need if there's a lot of concurrency. For that, typically, a 'sequence' number is added. Simple, effective.

Last, you're quite often only interested in one, or a few, types when doing reporting. A stringly typed 'type' column is often Good Enough.

Created at is handy, but not crucial.

So, you'll want something like

{ Id: uuid, aggregate_id: ?, sequence: Int, type: String, data: jsonb }

Querying then fetches "everything for aggregate x, ordered by sequence". Possibly limited to type X (e.g. when rebuilding a query index). But the first is what happens every time an Aggregate is hydrated (i.e. ShoppingCart::load(”acme/1337”) ) so it has to be indexed for that, primarily.