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content/blog/pyfutures.md

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+---
+title: "Quick Python: Concurrent Futures"
+date: 2020-04-11T20:40:28-04:00
+draft: false
+tags: ["python", "concurrency"]
+---
+
+Another way to perform concurrency in python is to use the `concurrent.futures` module.
+
+```python
+from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
+def add(x, y):
+    return x + y
+with ThreadPoolExecutor(max_workers=4) as executor:
+    future = executor.submit(add, 1, 2)
+    result = future.result(timeout=30) # unit: seconds
+```
+
+If `max_workers=None` then it will default to the number of processors on the machine multiplied by 5.
+
+If `timeout=None` then there is no time limit applied.
+
+You can also apply a function to a list or iterables
+
+```python
+def double(x):
+    return 2 * x
+with ThreadPoolExecutor() as executor:
+    future = executor.submit(function_handle, [1, 2, 3])
+    result = future.result()
+```
+
+Instead of threads, it is also possible to spawn processes to side-step the global interpreter lock. The documentation warns that only picklable objects can be executed and returned though.
+
+```python
+def add(x, y):
+    return x + y
+with ProcessPoolExecutor() as executor:
+    future = executor.submit(add, 1, 2)
+    result = future.result()
+```
+