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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pipeline examples\n",
    "\n",
    "This example show quickly how to use pipelines in `redis-py`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking that Redis is running"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import redis \n",
    "\n",
    "r = redis.Redis(decode_responses=True)\n",
    "r.ping()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a pipeline instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = r.pipeline()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding commands to the pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline<ConnectionPool<Connection<host=localhost,port=6379,db=0>>>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.set(\"a\", \"a value\")\n",
    "pipe.set(\"b\", \"b value\")\n",
    "\n",
    "pipe.get(\"a\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Executing the pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[True, True, 'a value']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.execute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The responses of the three commands are stored in a list. In the above example, the two first boolean indicates that the `set` commands were successfull and the last element of the list is the result of the `get(\"a\")` comand."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chained call\n",
    "\n",
    "The same result as above can be obtained in one line of code by chaining the opperations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[True, True, 'a value']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe = r.pipeline()\n",
    "pipe.set(\"a\", \"a value\").set(\"b\", \"b value\").get(\"a\").execute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance comparison\n",
    "\n",
    "Using pipelines can improve performance, for more informations, see [Redis documentation about pipelining](https://redis.io/docs/manual/pipelining/). Here is a simple comparison test of performance between basic and pipelined commands (we simply increment a value and measure the time taken by both method)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "incr_value = 100000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Without pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "r.set(\"incr_key\", \"0\")\n",
    "\n",
    "start = datetime.now()\n",
    "\n",
    "for _ in range(incr_value):\n",
    "    r.incr(\"incr_key\")\n",
    "res_without_pipeline = r.get(\"incr_key\")\n",
    "\n",
    "time_without_pipeline = (datetime.now() - start).total_seconds()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Without pipeline\n",
      "================\n",
      "Time taken:  21.759733\n",
      "Increment value:  100000\n"
     ]
    }
   ],
   "source": [
    "print(\"Without pipeline\")\n",
    "print(\"================\")\n",
    "print(\"Time taken: \", time_without_pipeline)\n",
    "print(\"Increment value: \", res_without_pipeline)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### With pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "r.set(\"incr_key\", \"0\")\n",
    "\n",
    "start = datetime.now()\n",
    "\n",
    "pipe = r.pipeline()\n",
    "for _ in range(incr_value):\n",
    "    pipe.incr(\"incr_key\")\n",
    "pipe.get(\"incr_key\")\n",
    "res_with_pipeline = pipe.execute()[-1]\n",
    "\n",
    "time_with_pipeline = (datetime.now() - start).total_seconds()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "With pipeline\n",
      "=============\n",
      "Time taken:  2.357863\n",
      "Increment value:  100000\n"
     ]
    }
   ],
   "source": [
    "print(\"With pipeline\")\n",
    "print(\"=============\")\n",
    "print(\"Time taken: \", time_with_pipeline)\n",
    "print(\"Increment value: \", res_with_pipeline)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using pipelines provides the same result in much less time."
   ]
  }
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