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    "info": {
        "author": "Robert Lechte",
        "author_email": "robert.lechte@dpc.vic.gov.au",
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        "classifiers": [
            "Programming Language :: Python :: 3",
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        "description": "# ratemate\n\nThere's a bunch of python rate limiting modules out there, but they all seem to suffer from similar problems:\n\n- Weird APIs, usually inflexible decorators that you need to wrap your calls in\n- Lack of `multiprocessing` support (eg, two processes will be unaware of each other, and thus double the intended rate)\n- Unnecessary coupling to other libraries\n\n`ratemate`, meanwhile, gives you a simple `RateLimit` object that avoids all these problems.\n\nIt works like this. Declare a `RateLimit` as follows:\n\n    from ratemate import RateLimit\n\n    rate_limit = RateLimit(max_count=2, per=5)  # 2 requests per 5 seconds\n\nThen call `.wait()` appropriately when you need to limit the rate.\n\nFor instance, here's an example when creating multiple threads with `concurrent.futures`. First the original rate-unlimited code:\n\n```python\n\nfrom concurrent.futures import ThreadPoolExecutor, as_completed\n\n\ndef task(n):\n    print(f\"  task {n} called\")\n    return n\n\nfutures = []\n\nwith ThreadPoolExecutor() as executor:\n    for i in range(20):\n        future = executor.submit(task, i)\n        futures.append(future)\n\n    for completed in as_completed(futures):\n        result = completed.result()\n        print('completed')\n```\n\nAdd rate-limiting simply by adding a wait at the appropriate time, either at task creation:\n\n```python\nfor i in range(20):\n    rate_limit.wait()  # wait before creating the task\n    future = executor.submit(task, i)\n    futures.append(future)\n```\n\nOr at the start of the task itself:\n\n```python\ndef task(n):\n    waited_time = rate_limit.wait()  # wait at start of task\n    print(f\"  task {n}: waited for {waited_time} secs\")\n    return n\n```\n\nBecause `ratemate` uses multi-process-aware shared memory to track its state, you can also use `ProcessPoolExecutor` and everything will still work nicely.\n\n\n## Greedy mode\n\nThe default (aka non-greedy aka patient) rate limiting mode spaces out calls evenly. First instance, max_count=10 and per=60 will result in one call every 6 seconds.\n\nYou may instead wish for calls to happen as fast as possible, only slowing down if the limit would be exceeded. Enable this with greedy=True, eg:\n\n```\nrate_limit = RateLimit(max_count=20, per=60, greedy=True)\n```\n\n## Further enhancements\n\nRate limit coordination between truly independent processes (not just subprocesses), possibly using Python 3.8's new shared memory or Redis or PostgreSQL or whatever.\n\n",
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        "maintainer": "Robert Lechte",
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