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    "Async/Await and Non-Blocking Execution\n",
    "=======================================\n",
    "\n",
    "Dask integrates natively with concurrent applications using the [Tornado](https://www.tornadoweb.org/en/stable/) or [Asyncio](https://docs.python.org/3/library/asyncio.html) frameworks, and can make use of Python's `async` and `await` keywords.\n",
    "\n",
    "This example shows a small example how to start up a Dask Client in asynchronous mode."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `asynchronous=True` parameter\n",
    "---------------------------------\n",
    "\n",
    "Dask LocalCluster and Client objects can operate in async-await mode if you pass the `asynchronous=True` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dask.distributed import Client\n",
    "client = await Client(asynchronous=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inc(x: int) -> int:\n",
    "    return x + 1\n",
    "\n",
    "future = client.submit(inc, 10)\n",
    "future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "await future"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Collections\n",
    "-----------\n",
    "\n",
    "Note that blocking operations like the `.compute()` method aren't ok to use in asynchronous mode.  Instead you'll have to use the `Client.compute` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dask\n",
    "df = dask.datasets.timeseries()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.persist()             # persist is non-blocking, so it's ok"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "total = df[['x', 'y']].sum()  # lazy computations are also ok"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# total.compute()             # but compute is bad, because compute blocks until done"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "future = client.compute(total)\n",
    "future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "await future"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Within a script\n",
    "---------------\n",
    "\n",
    "Running async/await code in Jupyter is a bit atypical.  Jupyter already has an event loop running, so it's easy to use async/await syntax directly within it.  In a normal Python script this won't be the case.  Here is an example script that should run within a normal Python interpreter or as a script.\n",
    "\n",
    "```python\n",
    "import asyncio\n",
    "from dask.distributed import Client\n",
    "\n",
    "\n",
    "def inc(x: int) -> int:\n",
    "    return x + 1\n",
    "\n",
    "\n",
    "async def f():\n",
    "    async with Client(asynchronous=True) as client:\n",
    "        future = client.submit(inc, 10)\n",
    "        result = await future\n",
    "        print(result)\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    asyncio.get_event_loop().run_until_complete(f())\n",
    "```"
   ]
  }
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