Python ThreadPoolExecutor snippet

snippets/python/s/thread-pool-executor.md

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+---
+title: ThreadPoolExecutor
+type: snippet
+language: python
+tags: [list, performance]
+cover: case-study
+dateModified: 2023-09-05T15:20:07.382Z
+---
+
+This snippet demonstrates the difference in execution time between normal execution and execution using the `ThreadPoolExecutor` class from Python's `concurrent.futures` module.
+
+- Measure and compare the execution times for a set of tasks.
+- Use a thread pool to execute tasks concurrently and compare the results.
+
+```py
+import concurrent.futures
+import time
+
+# Define a function that represents a task to be executed
+def task_function(task_id):
+  print(f"Task {task_id} started")
+  # Simulate some time-consuming work
+  time.sleep(2)  # Sleep for 2 seconds to simulate work
+  result = task_id ** 2
+  print(f"Task {task_id} finished with result: {result}")
+  return result
+
+def normal_execution():
+  start_time = time.time()
+  results = [task_function(i) for i in range(1, 6)]
+  end_time = time.time()
+  execution_time = end_time - start_time
+  print("Normal Execution Time:", execution_time)
+  print("Results:", results)
+
+def thread_pool_execution():
+  with concurrent.futures.ThreadPoolExecutor(max_workers=3) as executor:
+    start_time = time.time()
+    # Submit tasks to the executor and store the Future objects
+    task_futures = [executor.submit(task_function, i) for i in range(1, 6)]
+
+    # Wait for all tasks to complete and retrieve their results
+    results = [future.result() for future in concurrent.futures.as_completed(task_futures)]
+    end_time = time.time()
+    execution_time = end_time - start_time
+    print("ThreadPoolExecutor Execution Time:", execution_time)
+    print("Results:", results)
+
+if __name__ == "__main__":
+  print("Normal Execution:")
+  normal_execution()
+  print("\nThreadPoolExecutor Execution:")
+  thread_pool_execution()
+```