Batch query with large filter condition, upstream MV with lots of keys fails with CN OOM

[kwannoel]

Repro Scripts

create.sql.log
insert.sql.log
query.sql.log

python script to generate these: https://github.com/kwannoel/risingwave-dev-scripts/blob/main/wide_table.py.

You can generate versions to stdout with less row and columns to easily see the structure of the sql.

e.g.

# view usage
./wide_table.py
./wide_table.py create 4
./wide_table.py insert 3 3

You can also use explain on the batch query (query.sql.log)

Repro steps

./risedev d full
# open localhost:3001, look at CN memory usage
psql -f create.sql.log
psql -f insert.sql.log
psql -f query.sql.log
# mem usage should spike

Observations

1. Halving the number of filter conditions also halves the mem usage.
2. Can try varying the PK as well.

Suggested next steps

Get a heap profile

[kwannoel]

I guess I have found the root cause of batch query OOM:
We have too many scan_ranges in RowSeqScan somehow (861 ranges in my reproduction). For each range, we will issue a range scan concurrently. The problem is that we don’t limit the concurrency, so there will be 861 tasks running at the same time, each has multiple array builders. They will consume a total of 861 ranges x 128 columns x 1024 rows x 4 bytes = 450MB memory. If we consider parallelism, it can easily eat up several GB.

From @wangrunji0408