Hi Peter,

Thanks a lot for the suggestion.This would be nice if it performs better.

Is the idea to split one request into smaller parts or will "Select+Delete IDs" just perform better?

And regarding the latter option - is this possible in one SQL request? So something like
( SELECT id FROM mytable WHERE created_at < some_fixed_millis OFFSET 0 ROWS FETCH NEXT 1000 ROWS ONLY )

And then loop through the results via changing OFFSET and ROWS? (Btw: the column created_at is indexed)

Or would you recommend doing this as 2 separate statements in Java/JDBC? Or via maybe even just issuing the original DELETE request more frequent?


On 06.10.19 03:50, Peter Ondruška wrote:
Peter, try this if it makes a difference:

1. Select entries to be deleted, note their primary keys.
2. Issue delete using keys to be deleted (1.) and use short transaction batches.

On Sun, 6 Oct 2019, 01:33 Peter, <tableyourtime@gmail.com> wrote:

I have a table "mytable" with columns "id", "created_at" and "json"
(VARCHAR, BIGINT, LONG VARCHAR), where data is coming in like new 200k
entries every hour and I would like to keep only entries of the last 1
or 2 hours. It is expected behaviour for the user if too old entries
gets lost as it is some kind of a LRU cache.

The current solution is to delete entries older than 4 hours every 30

DELETE FROM mytable WHERE created_at < ?

I'm using this in a prepared statement where ? is "4 hours ago" in
milliseconds (new DateTime().getMillis()).

This works, but some (not all) INSERT statement get a bigger delay in
the same order (2-5 seconds) that this DELETE takes, which is ugly.
These INSERT statements are executed independently (using different
threads) of the DELETE.

Is there a better way? Can I somehow avoid locking the unrelated INSERT

What helps a bit is when I make those deletes more frequently than the
delays will get smaller, but then the number of those delayed requests
will increase.

What also helps a bit (currently have not seen a negative impact) is
increasing the page size for the Derby Network Server: