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socket_timeout includes queueing time in a shared, unconfigurable, process-wide thread pool, causing misleading QueryTimeoutErrors on unrelated queries

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Évaluation

Difficulté
5/5
Temps estimé
Plus d'une semaine
Accessibilité débutants
35/100
Type d'issue
Bug
Clarté
Plutôt claire
Activité
Active
Stack technique
postgresql, python
Domaine
backend, databases, performance

Piste de recherche

Start with utils/decorators.py, especially timeout() and preserve_transaction_status_with_timeout(), then trace services_container.get_thread_pool("DriverDialectExecutor"). Reproduce the reported contention with concurrent pg_sleep(8) calls and SELECT 1. Done should establish and test an agreed fix for queueing time and executor concurrency, rather than merely documenting the behavior.

Rédigé par le modèle d'indexation à partir du texte de l'issue.

Description

bug
Describe the bug

socket_timeout is enforced by submitting the underlying driver call to a shared ThreadPoolExecutor and calling future.result(timeout=socket_timeout) (utils/decorators.py, timeout() / preserve_transaction_status_with_timeout()). That executor is a single, process-wide singleton — services_container.get_thread_pool("DriverDialectExecutor") — shared by every connection and every cursor operation in the process, not scoped per-connection and not related to the size of the caller's own DB connection pool. Its size is never set explicitly anywhere in the wrapper (get_thread_pool(name) is always called with no max_workers), so it falls back to Python's generic ThreadPoolExecutor default: min(32, os.cpu_count() + 4).

Because future.result(timeout=...) starts its clock at executor.submit(), not at the moment the submitted call actually starts running, any time a submission spends queued behind other in-flight driver calls counts fully against socket_timeout. If enough concurrent driver calls are in flight to fill that small, fixed-size pool (very plausible for any app that pools more DB connections than cpu_count + 4, which is a common and reasonable configuration), unrelated and otherwise-trivially-fast queries start timing out — not because they're slow, but because they never got a worker thread in time. The resulting QueryTimeoutError is indistinguishable from a genuine slow query/lock wait, which makes this very hard to diagnose: the error attributes to whatever SQL happened to be queued, with no indication the real cause is thread-pool contention from unrelated concurrent calls elsewhere in the process.

Expected Behavior

socket_timeout should bound the time the actual driver/socket operation takes to execute, not scheduling delay inside an internal, undocumented thread pool. At minimum, the pool used to enforce this timeout should either scale with the caller's configured connection concurrency (e.g. pool size) or be explicitly configurable, so that setting a connection pool size larger than cpu_count + 4 doesn't silently create a hidden concurrency ceiling that's lower than the connection pool itself.

What plugins are used? What other connection properties were set?

plugins="iam", wrapper_dialect="rds-pg", wrapper_driver_dialect="psycopg"

Current Behavior

Under a burst of concurrent database contention (in our case: multiple concurrent transactions blocked on a Postgres row lock, each occupying a worker thread in DriverDialectExecutor for up to their full socket_timeout while waiting on Cursor.execute), a completely unrelated query — targeting a different table with no lock contention of its own — also raised QueryTimeoutError on Cursor.execute, having exceeded its 5 second socket_timeout.

We confirmed that this specific statement showed negligible database-side load and an average execution latency of 0.84ms across the whole incident window — i.e., the statement itself was never slow at the database. This is consistent with the delay happening entirely client-side: the call was queued in the shared DriverDialectExecutor pool behind the other concurrently-blocked calls, and the queueing time alone exceeded the 5 second timeout before the query was ever dispatched to Postgres.

Reproduction Steps

This can be reproduced by saturating the shared executor with concurrent slow calls:

  1. On a host/container where os.cpu_count() is small (e.g. 2, giving a default pool size of min(32, 2+4) = 6), open N > 6 connections via AwsWrapperConnection.connect(psycopg.connect, ..., socket_timeout=5).
  2. On N - 1 of those connections, concurrently (e.g. one thread per connection) execute a query that runs longer than socket_timeout but well under the connection's own network timeout — e.g. SELECT pg_sleep(8) — so each of those calls occupies a DriverDialectExecutor worker thread for ~8 seconds.
  3. On one additional, otherwise-idle connection, concurrently execute a trivial, instantaneous query: SELECT 1.
  4. Observe: the SELECT 1 call raises aws_advanced_python_wrapper.QueryTimeoutError (Cursor.execute timeout) even though SELECT 1 never reaches the database in a way that would take anywhere near 5 seconds — it's queued behind the 6 pg_sleep(8) calls in the shared pool and its future.result(timeout=5) clock (started at submission) expires first.
Possible Solution

A few options, roughly in order of how much they'd change existing behavior:

  1. Expose the DriverDialectExecutor (and equivalent per-dialect) pool size as a configurable wrapper property (e.g. DRIVER_DIALECT_EXECUTOR_MAX_WORKERS), defaulting to something scaled to expected DB concurrency rather than cpu_count + 4.
  2. Size the pool based on the connection provider's own pool configuration (e.g. pool_size + max_overflow) when a pooled connection provider is in use, so it can't become a stricter bottleneck than the connection pool it's serving.
  3. At minimum, document clearly that socket_timeout includes time spent queued in a shared, process-wide thread pool of this default size, so callers can reason about whether their expected concurrent query volume can exceed it.
Additional Information/Context

No response

The AWS Advanced Python Wrapper version used

3.0.0

python version used

3.13.15

Operating System and version

Debian 13 (Trixie)

Langage dominant
Python
Étoiles
99
Forks
22
Merge moyen
1 j 7 h
PR mergées (30 j)
4

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