The N+1 query problem
The most common pattern: rendering a list and firing a separate query for each row to fetch a related record. With ten rows it is invisible; with ten thousand it is thousands of round trips and a page that hangs.
- Join or batch instead of looping
Fetch related data in a single query (a join or a batched IN query), not one query per row.
- Watch ORMs and lazy relations
Generated ORM code often triggers N+1 implicitly through lazy-loaded relations. Eager-load what the view needs.
Missing indexes and full-table scans
A filter or sort on an unindexed column forces Postgres to scan the entire table. It is fast when the table is small and degrades smoothly into unusable as it grows - the worst kind of bug because it passes every test.
- Index your filter and sort columns
Any column used in a WHERE, ORDER BY, or JOIN on a growing table needs an index.
- Use composite indexes for multi-column filters
A query filtering on two columns often needs a composite index, not two separate ones.
- Check the query plan
EXPLAIN reveals sequential scans on large tables - the signal to add an index.
Unbounded reads and connection limits
Two more patterns round out the set: queries that fetch an entire table with no limit (fine until the table is large, then a memory and latency problem), and exhausting the connection pool under concurrency. Paginate every collection read, and make sure serverless deployments use a pooler rather than opening a fresh connection per request.
The pre-launch checklist
- Eliminate N+1 queries
Join or batch related data; eager-load ORM relations.
- Index every filter, sort, and join column
On any table that grows.
- Add composite indexes for multi-column filters
Match the index to the query shape.
- Paginate or limit every collection read
No query should return an unbounded result set.
- Use a connection pooler in serverless
Avoid exhausting Postgres connections under load.
- Check query plans with EXPLAIN
Catch sequential scans before they reach production.
Run this checklist on your repo, automatically
PeakStack scores every commit for security, scalability, and cost - with the exact line and a fix.
Request accessFAQ
Why do AI-generated Postgres apps get slow at scale?
Because generated code favors patterns that work with little data - N+1 queries, missing indexes, and unbounded reads - that degrade silently as the table grows. Nothing errors; the app just gets slower until a query times out.
What is the single highest-impact Postgres fix?
Adding indexes to the columns you filter and sort on. A missing index turns a common query into a full-table scan, which is the most frequent cause of an app that mysteriously slows down as it grows.
How do I find these issues before they hit production?
PeakStack runs deterministic scalability analysis on every commit, flagging N+1 queries, unbounded reads, and likely missing indexes with the exact line - no load test required.