1. Show us the PG version, view definition, the exact query that's slow, and the EXPLAIN (ANALYZE).
2. Presumably there's an index on each table's *_hash column?
On Tue, Feb 13, 2024 at 8:48 AM Adrian Garcia Badaracco <adrian@xxxxxxxxxxxx> wrote:
Thank you for the reply Ron.Yes there are many fewer (<1%) the number of rows in new_table.Thanks for making me think of normalization, I hadn’t seen it that way. Although there is no theoretical relationship between the rows in the other columns in the original table and the attributes column, in practice there is a strong correlation, so I guess what I am trying to capture here is taking advantage of that correlation, while not completely depending on it because it can be broken.In any case, whatever theoretical framework is put around this solution, I am also interested in the practical aspects, in particular that case of selecting a subset of columns from the view that I know doesn’t need the join but the query planner thinks does.On Tue, Feb 13, 2024 at 3:16 AM Ron Johnson <ronljohnsonjr@xxxxxxxxx> wrote:On Mon, Feb 12, 2024 at 10:12 PM Adrian Garcia Badaracco <adrian@xxxxxxxxxxxx> wrote:I am using Timescale so I'll be mentioning some timestamp stuff but I think this is a general postgres question for the most part.
I have a table with some fixed, small columns (id, timestamp, etc) and a large JSONB column (let's call it `attributes`). `attributes` has 1000s of schemas, but given a schema, there's a lot of duplication. Across all rows, more than 99% of the data is duplicated (as measured by `count(attributes)` vs `count(distinct attributes)`.I can't normalize `attributes` into real columns because it is quite variable (remember 1000s of schemas).My best idea is to make a table like `(day timestamptz, hash text, attributes jsonb)` and then in my original table replace `attributes` with a reference to `new_table`.Meaning that there are many fewer rows in new_table?I can then make a view that joins them `select original_table.timestamp, new_table.attributes from original join new_table on (time_bucket('1 day', timestamp) = day AND original.hash = new_table.hash)` or something like that. The idea of time bucketing into 1 day is to balance write and read speed (by relying on timescale to do efficient time partitioning, data retention, etc.).I recognize this is essentially creating a key-value store in postgres and also janky compression, so I am cautious about it.If my interpretation (that there are many fewer rows in new_table) is correct, then you've stumbled into the Second Normal Form of database design: https://en.wikipedia.org/wiki/Second_normal_form#Example
