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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 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." ] } ], "metadata": { "interpreter": { "hash": "84048e2f8e89effc8610b2fb270e4858ef00e9403d223856d62b05266db287ca" }, "kernelspec": { "display_name": "Python 3.9.2 ('.venv': venv)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.2" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }