Python queue.Queue.put() Method: Usage and Examples

Queue.

Understanding the Concept

Queue.put(item, block=True, timeout=None) adds a work item, waiting for space when the bounded queue is full. Nonblocking calls raise queue.Full instead of waiting.

A bounded queue can keep memory use under control by slowing producers.

How to reason about concurrent execution

Workers may start and finish in a different order on every run. Focus on ownership of data, how work and errors move between workers, and what happens when one worker stops early. Locks protect invariants, queues exchange messages, and explicit shutdown logic prevents blocked or orphaned workers.

Concurrency adds overhead and failure modes. Begin with a working sequential solution, choose bounded worker counts and explicit timeouts, then measure whether overlap or parallel execution improves the target workload.

Worked Example

Read the code from the first line to the last. Identify which values are inputs, which operation is being demonstrated and what should happen before running it.

from queue import Queue, Full
queue = Queue(maxsize=1)
queue.put("first")
try:
    queue.put("second", block=False)
except Full:
    print("Queue is full")
print(queue.get_nowait())
queue.task_done()

Walkthrough: What Each Part Does

The central operation is queue.put('first'). Read its actual arguments: those values determine what is tested, logged, measured or scheduled. Then locate the line that inspects its result or handles a failure.
The imports make queue available. These are standard-library building blocks unless the example explicitly names an external tool.
The try/except or finally block states what happens when an operation fails; cleanup and error reporting are part of the example, not optional extras.
The last lines coordinate completion and inspect results. Actual worker scheduling may differ between executions.

This demonstration focuses on one practical behavior. Before changing several lines, vary one input or one configuration choice and check whether the result changes for the reason you expected.

Example Output

Queue is full
first

The output above was captured by running the example in the available Python environment. The order of concurrent events, durations and platform-dependent values can legitimately differ on your computer.

Important Behavior and Edge Cases

Calling blocking put on a full queue without a consumer, producing a program that appears frozen.

A bounded queue can keep memory use under control by slowing producers.

Practice Exercise

Try a timeout of 0.01 and handle Full without losing the item.

Write down the result you expect before running your changed code. Check at least one normal case and one boundary or failure case. If your example starts workers or opens resources, verify how they are cleaned up.

Further reading: official documentation and API reference.

Common Beginner Mistakes

Calling blocking put on a full queue without a consumer, producing a program that appears frozen.
Forgetting to handle worker errors and shutdown, even when the normal completion path appears correct.

Key Takeaways

Queue.
A bounded queue can keep memory use under control by slowing producers.
Practice: Try a timeout of 0.01 and handle Full without losing the item.