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[docs] simple web crawler example (ray-project#31900)
Signed-off-by: Edward Oakes <[email protected]>
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{ | ||
"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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