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[docs] simple web crawler example (#31900)
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,244 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "source": [ | ||
| "# Speed up your web crawler by parallelizing it with Ray" | ||
| ], | ||
| "metadata": { | ||
| "collapsed": false | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "source": [ | ||
| "In this example we'll quickly demonstrate how to build a simple web scraper in Python and\n", | ||
| "parallelize it with Ray Tasks with minimal code changes.\n", | ||
| "\n", | ||
| "To run this example locally on your machine, please first install `ray` and `beautifulsoup` with\n", | ||
| "\n", | ||
| "```\n", | ||
| "pip install \"beautifulsoup4==4.11.1\" \"ray>=2.2.0\"\n", | ||
| "```\n", | ||
| "\n", | ||
| "First, we'll define a function called `find_links` which takes a starting page (`start_url`) to crawl,\n", | ||
| "and we'll take the Ray documentation as example of such a starting point.\n", | ||
| "Our crawler simply extracts all available links from the starting URL that contain a given `base_url`\n", | ||
| "(e.g. in our example we only want to follow links on `http://docs.ray.io`, not any external links).\n", | ||
| "The `find_links` function is then called recursively with all the links we found this way, until a\n", | ||
| "certain depth is reached.\n", | ||
| "\n", | ||
| "To extract the links from HTML elements on a site, we define a little helper function called\n", | ||
| "`extract_links`, which takes care of handling relative URLs properly and sets a limit on the\n", | ||
| "number of links returned from a site (`max_results`) to control the runtime of the crawler more easily.\n", | ||
| "\n", | ||
| "Here's the full implementation:" | ||
| ], | ||
| "metadata": { | ||
| "collapsed": false | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 154, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import requests\n", | ||
| "from bs4 import BeautifulSoup\n", | ||
| "\n", | ||
| "def extract_links(elements, base_url, max_results=100):\n", | ||
| " links = []\n", | ||
| " for e in elements:\n", | ||
| " url = e[\"href\"]\n", | ||
| " if \"https://\" not in url:\n", | ||
| " url = base_url + url\n", | ||
| " if base_url in url:\n", | ||
| " links.append(url)\n", | ||
| " return set(links[:max_results])\n", | ||
| "\n", | ||
| "\n", | ||
| "def find_links(start_url, base_url, depth=2):\n", | ||
| " if depth == 0:\n", | ||
| " return set()\n", | ||
| "\n", | ||
| " page = requests.get(start_url)\n", | ||
| " soup = BeautifulSoup(page.content, \"html.parser\")\n", | ||
| " elements = soup.find_all(\"a\", href=True)\n", | ||
| " links = extract_links(elements, base_url)\n", | ||
| "\n", | ||
| " for url in links:\n", | ||
| " new_links = find_links(url, base_url, depth-1)\n", | ||
| " links = links.union(new_links)\n", | ||
| " return links" | ||
| ], | ||
| "metadata": { | ||
| "collapsed": false | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "source": [ | ||
| "Let's define a starting and base URL and crawl the Ray docs to a `depth` of 2." | ||
| ], | ||
| "metadata": { | ||
| "collapsed": false | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 162, | ||
| "outputs": [], | ||
| "source": [ | ||
| "base = \"https://docs.ray.io/en/latest/\"\n", | ||
| "docs = base + \"index.html\"" | ||
| ], | ||
| "metadata": { | ||
| "collapsed": false | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 163, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "CPU times: user 19.3 s, sys: 340 ms, total: 19.7 s\n", | ||
| "Wall time: 25.8 s\n" | ||
| ] | ||
| }, | ||
| { | ||
| "data": { | ||
| "text/plain": "591" | ||
| }, | ||
| "execution_count": 163, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "%time len(find_links(docs, base))" | ||
| ], | ||
| "metadata": { | ||
| "collapsed": false | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "source": [ | ||
| "As you can see, crawling the documentation root recursively like this returns a\n", | ||
| "total of `591` pages and the wall time comes in at around 25 seconds.\n", | ||
| "\n", | ||
| "Crawling pages can be parallelized in many ways.\n", | ||
| "Probably the simplest way is to simple start with multiple starting URLs and call\n", | ||
| "`find_links` in parallel for each of them.\n", | ||
| "We can do this with [Ray Tasks](https://docs.ray.io/en/latest/ray-core/tasks.html) in a straightforward way.\n", | ||
| "We simply use the `ray.remote` decorator to wrap the `find_links` function in a task called `find_links_task` like this:" | ||
| ], | ||
| "metadata": { | ||
| "collapsed": false | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 157, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import ray\n", | ||
| "\n", | ||
| "@ray.remote\n", | ||
| "def find_links_task(start_url, base_url, depth=2):\n", | ||
| " return find_links(start_url, base_url, depth)" | ||
| ], | ||
| "metadata": { | ||
| "collapsed": false | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "source": [ | ||
| "To use this task to kick off a parallel call, the only thing you have to do is use\n", | ||
| "`find_links_tasks.remote(...)` instead of calling the underlying Python function directly.\n", | ||
| "\n", | ||
| "Here's how you run six crawlers in parallel, the first three (redundantly) crawl\n", | ||
| "`docs.ray.io` again, the other three crawl the main entry points of the Ray RLlib,\n", | ||
| "Tune, and Serve libraries, respectively:" | ||
| ], | ||
| "metadata": { | ||
| "collapsed": false | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 160, | ||
| "outputs": [], | ||
| "source": [ | ||
| "links = [find_links_task.remote(f\"{base}{lib}/index.html\", base)\n", | ||
| " for lib in [\"\", \"\", \"\", \"rllib\", \"tune\", \"serve\"]]" | ||
| ], | ||
| "metadata": { | ||
| "collapsed": false | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 161, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "591\n", | ||
| "591\n", | ||
| "105\n", | ||
| "204\n", | ||
| "105\n", | ||
| "CPU times: user 65.5 ms, sys: 47.8 ms, total: 113 ms\n", | ||
| "Wall time: 27.2 s\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "%time for res in ray.get(links): print(len(res))" | ||
| ], | ||
| "metadata": { | ||
| "collapsed": false | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "source": [ | ||
| "This parallel run crawls around four times the number of pages in roughly the same time as the initial, sequential run.\n", | ||
| "Note the use of `ray.get` in the timed run to retrieve the results from Ray (the `remote` call promise gets resolved with `get`).\n", | ||
| "\n", | ||
| "Of course, there are much smarter ways to create a crawler and efficiently parallelize it, and this example\n", | ||
| "gives you a starting point to work from." | ||
| ], | ||
| "metadata": { | ||
| "collapsed": false | ||
| } | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 2 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython2", | ||
| "version": "2.7.6" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 0 | ||
| } |
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