We are testing a new application to try to find performance issues.
AWS RDS m4.large 500GB storage (SSD)
One table only, called Messages:
User id (Text)
Payload (JSON, up to 20kb)
UserID + Country (main index)
We inserted 160MM rows, around 2KB each. No partitioning.
Insert started at around 3.000 inserts per second, but (as expected)
started to slow down as the number of rows increased. In the end we got
around 500 inserts per second.
Queries by Userd_ID + Country took less than 2 seconds, but while the batch
insert was running the queries took over 20 seconds!!!
We had 20 Lambda getting messages from SQS and bulk inserting them into
The insert performance is important, but we would slow it down if needed in
order to ensure a more flat query performance. (Below 2 seconds). Each
query (userId + country) returns around 100 diferent messages, which are
filtered and order by the synchronous Lambda function. So we don't do any
special filtering, sorting, ordering or full text search in Postgres. In
some ways we use it more like a glorified file system. :)
We are going to limit the number of lambda workers to 1 or 2, and then run
some queries concurrently to see if the query performance is not affect too
much. We aim to get at least 50 queries per second (returning 100 messages
each) under 2 seconds, even when there is millions of messages on SQS being
inserted into PG.
We haven't done any performance tuning in the DB.
With all that said, the question is:
What can be done to ensure good query performance (UserID+ country) even
when the bulk insert is running (low priority).
We are limited to use AWS RDS at the moment.