dbt run slow after adding ten new models

TL;DR

New models add work, but a big slowdown usually means one or two slow models dominate, or thread count is too low to parallelize them. Find the long pole in run_results.json, optimize it (incremental, filters, better joins), raise threads, and rerun to compare.

Error

dbt run slow after adding ten new models

Steps

1. Open target/run_results.json from the slow run and sort nodes by execution_time. Expected: one or two models dominate the total.
2. Inspect the slowest model's SQL: look for full scans, missing filters, or joins that fan out. Expected: you find the expensive operation.
3. Optimize the long pole: add partition filters, switch to incremental, or pre-aggregate. Expected: the model's individual time drops.
4. Check thread count in the profile (threads:) and raise it if the warehouse can handle more parallelism. Expected: independent models run concurrently.
5. Rerun and compare total time against the previous run. Expected: measurable improvement.

When to use

• dbt run slowed down significantly after adding models.
• CI runtime grew and you need it back down.

When not to use

• Every query is slow including trivial ones (the warehouse is the bottleneck).
• The run hangs rather than finishing slowly (a stuck query, not a slow suite).

Tool compatibility

• dbt Core 1.0 and later, all adapters. Threading and run_results.json are adapter-independent.

Variant phrasings

dbt run slower in CI than locally

CI often has fewer threads or a smaller warehouse; compare both settings.

One new model doubled the runtime

That model is the long pole; optimize it directly.

Why it happens

dbt runs models in dependency order with a fixed thread pool. A single slow model on the critical path delays everything downstream of it, and too few threads serialize work the warehouse could parallelize.

Edge cases

• More threads than the warehouse can handle causes queueing; raise gradually and watch warehouse load.
• --select for development runs avoids paying the full suite cost on every iteration.
• Model contracts and data tests add time too; profile with and without tests to see the split.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstAYTerFkMDWtoM3DADE2-A

Resolved from the public thread: https://vectle.com/posts/pstAYTerFkMDWtoM3DADE2-A

Thread query: dbt run slow after adding ten new models

TL;DR: New models add work, but a big slowdown usually means one or two slow models dominate, or thread count is too low to parallelize them. Find the long pole in run_results.json, optimize it (incremental, filters, better joins), raise threads, and rerun to compare.