Set a timeout on every production call you make with requests. Use one number when the same limit is acceptable for connection and response data, or a tuple such as (3.05, 27) when connection setup and waiting for response bytes need different limits. Catch requests.exceptions.Timeout (or its ConnectTimeout and ReadTimeout subclasses), and treat retries as a separate policy. A Requests timeout is not an end-to-end deadline for downloading an entire response.
What a Requests timeout actually controls
Requests has no default timeout. If you omit timeout, a stalled connection can wait indefinitely, which is why the official Quickstart says nearly all production requests should specify it. The value controls socket inactivity: how long the underlying connection may go without receiving data.
That distinction matters. A server can send a few bytes periodically and keep a request alive beyond your intended user-facing latency budget. Likewise, a large response can take longer than the timeout while remaining active. If you need a strict wall-clock deadline, enforce one outside Requests (for example, with a worker deadline or an application-level timer) rather than treating timeout as a total download cap.
See the Requests Quickstart and Advanced Usage timeout guidance for the documented behavior.
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Choose separate connection and read limits
One value
requests.get(url, timeout=10) applies 10 seconds to both connection establishment and waiting for response data. It is concise, but it hides which phase is slow.
A tuple
Pass (connect_timeout, read_timeout) to express the two phases directly:
import requests
response = requests.get(
"https://api.example.com/data",
timeout=(3.05, 27),
)
response.raise_for_status()
print(response.json())Here, the client allows about 3.05 seconds to establish a connection and 27 seconds between response-data arrivals. Those values are examples, not universal recommendations. Choose them from the service’s normal latency, your caller’s latency budget, and the cost of waiting.
Why connect time can exceed the number
Connect and read values are not wall-clock guarantees. A hostname may resolve to multiple IP addresses; connection attempts can occur in sequence, so the effective setup time can exceed the configured connect value. Account for this when setting an outer deadline.
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Timeout exceptions are transport failures, not HTTP status failures. Requests exposes a hierarchy that lets you handle all timeouts together or distinguish their cause.
Rank #2
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ConnectTimeout: connection establishment did not complete in time. Requests documents this case as safe to retry.ReadTimeout: the server did not provide response data within the read interval.Timeout: common superclass for both; use it when the distinction is not needed.ConnectionError: broader network failures such as DNS errors or refused connections.HTTPError: raised byraise_for_status()for an unsuccessful HTTP status; it is unrelated to a transport timeout.
import requests
try:
response = requests.get(
"https://api.example.com/data",
timeout=(3.05, 27),
)
response.raise_for_status()
except requests.exceptions.ConnectTimeout:
# No connection was established in the connect interval.
print("Connection timed out")
except requests.exceptions.ReadTimeout:
# No response bytes arrived in the read interval.
print("Read timed out")
except requests.exceptions.Timeout:
# Handles either timeout subtype if not handled above.
print("Request timed out")
except requests.exceptions.ConnectionError as exc:
print(f"Network error: {exc}")
except requests.exceptions.HTTPError as exc:
print(f"HTTP failure: {exc}")Put the specific subclasses before Timeout; otherwise the superclass branch catches them first. Keep status handling separate: a response with a 500 status is still a response, while a timeout means the expected transport step did not complete.
Use a session and apply a consistent policy
A small wrapper prevents one forgotten call from reintroducing an infinite wait:
from typing import Any
import requests
session = requests.Session()
def get_json(url: str, *, params: dict[str, Any] | None = None) -> Any:
try:
response = session.get(url, params=params, timeout=(3.05, 20))
response.raise_for_status()
return response.json()
except requests.exceptions.Timeout:
# Record the URL and operation, then let the caller decide what to do.
raiseUse a shorter read timeout for interactive requests and a longer one for a deliberately slow report endpoint. Do not silently return partial data after a timeout. If you use stream=True, remember that iterating over the body is a separate stage; the same inactivity rule applies while reading chunks.
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Requests does not retry failed connections by default. For controlled retries, mount an HTTPAdapter configured with urllib3.util.Retry:
import requests
from requests.adapters import HTTPAdapter
from urllib3.util import Retry
retry = Retry(
total=3,
connect=3,
read=0,
status=2,
backoff_factor=0.5,
status_forcelist=[429, 500, 502, 503, 504],
allowed_methods={"GET", "HEAD", "OPTIONS"},
respect_retry_after_header=True,
)
session = requests.Session()
adapter = HTTPAdapter(max_retries=retry)
session.mount("https://", adapter)
session.mount("http://", adapter)
response = session.get(
"https://api.example.com/data",
timeout=(3.05, 20),
)
response.raise_for_status()The counts and backoff above are an example policy. Adjust them to the service and operation. A timeout can happen after a server has received a request; retrying a non-idempotent POST can create a duplicate action. Restrict automatic retries to operations you can safely repeat, or use an idempotency key supplied by the API. The adapter’s basic integer retry behavior covers failed DNS lookups, socket connections, and connection timeouts, not cases where request data reached the server. Consult the HTTPAdapter reference and urllib3 retry documentation for the version you deploy.
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Design timeouts from an actual latency budget
Interactive web request
Reserve most of the budget for the remote service but keep connection setup small, for example (2, 5). Return a useful fallback when the deadline is exceeded rather than blocking the user interface.
Background synchronization
A longer read interval can be appropriate, but still set a finite value and log elapsed time. Let a job runner enforce its own overall deadline so one repeatedly slow endpoint cannot consume the whole worker.
Large or streamed responses
The timeout does not cap total transfer time. Consume the stream incrementally, enforce your own total deadline if required, and close the response in a context manager:
import requests
with requests.get(
"https://example.com/archive.zip",
stream=True,
timeout=(3.05, 30),
) as response:
response.raise_for_status()
with open("archive.zip", "wb") as output:
for chunk in response.iter_content(chunk_size=1024 * 64):
if chunk:
output.write(chunk)Diagnose a timeout instead of guessing
- Identify the phase. A
ConnectTimeoutpoints to DNS, routing, firewall, TLS, or an overloaded listener; aReadTimeoutpoints to server processing or a stalled response. - Record context. Log the operation name, host, timeout tuple, attempt number, and elapsed time. Avoid logging authorization headers or sensitive query values.
- Check status separately. If a response exists, inspect
response.status_codeor callraise_for_status(); do not classify every failure as a timeout. - Reproduce outside the application. Compare the endpoint with a command-line client or service health check, then inspect DNS, proxy, TLS, and firewall behavior.
- Check retry consequences. Verify whether the server could have completed the operation before deciding to repeat it.
Common mistakes and fixes
Omitting timeout
Symptom: workers remain stuck. Fix: pass a finite value on every external call or centralize calls in a wrapper.
Using a timeout as a total deadline
Symptom: a download exceeds the expected maximum despite a small timeout. Fix: remember that the value limits socket inactivity; add an outer deadline for total elapsed time.
Rank #4
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Retrying every exception
Symptom: duplicate writes or traffic spikes. Fix: retry only transient, safe operations and selected status codes.
Catching only HTTPError
Symptom: transport failures escape the handler. Fix: catch Timeout and other connection exceptions separately from HTTP status errors.
Setting an unrealistically low read value
Symptom: healthy but slow endpoints fail intermittently. Fix: measure normal latency, include network variation, and choose a value that matches the caller’s real budget.
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Quick decision checklist
- Did every external Requests call receive an explicit timeout?
- Should connection and read limits differ? If so, use a tuple.
- Is your required limit an overall wall-clock deadline? Add an outer deadline.
- Is the operation safe to repeat after a possible server-side execution?
- Are timeout, connection, and HTTP-status errors logged and handled separately?
- Are streamed downloads closed and monitored while chunks are consumed?
Frequently Asked Questions
What is the default timeout in Python Requests?
There is no default timeout. Without an explicit value, a request can wait indefinitely.
Is timeout=10 a ten-second total limit?
No. It applies to connection setup and socket inactivity while reading, not total response-download time.
When should I catch ConnectTimeout instead of Timeout?
Catch ConnectTimeout when connection-specific handling or retry decisions matter; use Timeout for common handling of both connection and read timeouts.
Does Requests retry a timed-out request automatically?
No. Configure an HTTPAdapter with urllib3 Retry, and limit retries to safe operations and deliberately selected conditions.
Quick Recap
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