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354 lines
17 KiB
Markdown
354 lines
17 KiB
Markdown
# Design a web crawler
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*Note: This document links directly to relevant areas found in the [system design topics](https://github.com/donnemartin/system-design-primer#index-of-system-design-topics) to avoid duplication. Refer to the linked content for general talking points, tradeoffs, and alternatives.*
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## Step 1: Outline use cases and constraints
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> Gather requirements and scope the problem.
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> Ask questions to clarify use cases and constraints.
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> Discuss assumptions.
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Without an interviewer to address clarifying questions, we'll define some use cases and constraints.
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### Use cases
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#### We'll scope the problem to handle only the following use cases
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* **Service** crawls a list of urls:
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* Generates reverse index of words to pages containing the search terms
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* Generates titles and snippets for pages
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* Title and snippets are static, they do not change based on search query
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* **User** inputs a search term and sees a list of relevant pages with titles and snippets the crawler generated
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* Only sketch high level components and interactions for this use case, no need to go into depth
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* **Service** has high availability
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#### Out of scope
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* Search analytics
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* Personalized search results
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* Page rank
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### Constraints and assumptions
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#### State assumptions
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* Traffic is not evenly distributed
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* Some searches are very popular, while others are only executed once
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* Support only anonymous users
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* Generating search results should be fast
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* The web crawler should not get stuck in an infinite loop
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* We get stuck in an infinite loop if the graph contains a cycle
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* 1 billion links to crawl
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* Pages need to be crawled regularly to ensure freshness
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* Average refresh rate of about once per week, more frequent for popular sites
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* 4 billion links crawled each month
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* Average stored size per web page: 500 KB
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* For simplicity, count changes the same as new pages
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* 100 billion searches per month
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Exercise the use of more traditional systems - don't use existing systems such as [solr](http://lucene.apache.org/solr/) or [nutch](http://nutch.apache.org/).
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#### Calculate usage
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**Clarify with your interviewer if you should run back-of-the-envelope usage calculations.**
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* 2 PB of stored page content per month
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* 500 KB per page * 4 billion links crawled per month
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* 72 PB of stored page content in 3 years
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* 1,600 write requests per second
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* 40,000 search requests per second
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Handy conversion guide:
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* 2.5 million seconds per month
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* 1 request per second = 2.5 million requests per month
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* 40 requests per second = 100 million requests per month
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* 400 requests per second = 1 billion requests per month
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## Step 2: Create a high level design
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> Outline a high level design with all important components.
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## Step 3: Design core components
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> Dive into details for each core component.
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### Use case: Service crawls a list of urls
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We'll assume we have an initial list of `links_to_crawl` ranked initially based on overall site popularity. If this is not a reasonable assumption, we can seed the crawler with popular sites that link to outside content such as [Yahoo](https://www.yahoo.com/), [DMOZ](http://www.dmoz.org/), etc
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We'll use a table `crawled_links` to store processed links and their page signatures.
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We could store `links_to_crawl` and `crawled_links` in a key-value **NoSQL Database**. For the ranked links in `links_to_crawl`, we could use [Redis](https://redis.io/) with sorted sets to maintain a ranking of page links. We should discuss the [use cases and tradeoffs between choosing SQL or NoSQL](https://github.com/donnemartin/system-design-primer#sql-or-nosql).
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* The **Crawler Service** processes each page link by doing the following in a loop:
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* Takes the top ranked page link to crawl
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* Checks `crawled_links` in the **NoSQL Database** for an entry with a similar page signature
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* If we have a similar page, reduces the priority of the page link
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* This prevents us from getting into a cycle
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* Continue
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* Else, crawls the link
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* Adds a job to the **Reverse Index Service** queue to generate a [reverse index](https://en.wikipedia.org/wiki/Search_engine_indexing)
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* Adds a job to the **Document Service** queue to generate a static title and snippet
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* Generates the page signature
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* Removes the link from `links_to_crawl` in the **NoSQL Database**
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* Inserts the page link and signature to `crawled_links` in the **NoSQL Database**
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**Clarify with your interviewer how much code you are expected to write**.
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`PagesDataStore` is an abstraction within the **Crawler Service** that uses the **NoSQL Database**:
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```python
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class PagesDataStore(object):
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def __init__(self, db);
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self.db = db
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...
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def add_link_to_crawl(self, url):
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"""Add the given link to `links_to_crawl`."""
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...
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def remove_link_to_crawl(self, url):
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"""Remove the given link from `links_to_crawl`."""
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...
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def reduce_priority_link_to_crawl(self, url)
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"""Reduce the priority of a link in `links_to_crawl` to avoid cycles."""
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...
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def extract_max_priority_page(self):
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"""Return the highest priority link in `links_to_crawl`."""
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...
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def insert_crawled_link(self, url, signature):
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"""Add the given link to `crawled_links`."""
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...
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def crawled_similar(self, signature):
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"""Determine if we've already crawled a page matching the given signature"""
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...
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```
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`Page` is an abstraction within the **Crawler Service** that encapsulates a page, its contents, child urls, and signature:
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```python
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class Page(object):
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def __init__(self, url, contents, child_urls, signature):
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self.url = url
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self.contents = contents
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self.child_urls = child_urls
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self.signature = signature
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```
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`Crawler` is the main class within **Crawler Service**, composed of `Page` and `PagesDataStore`.
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```python
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class Crawler(object):
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def __init__(self, data_store, reverse_index_queue, doc_index_queue):
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self.data_store = data_store
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self.reverse_index_queue = reverse_index_queue
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self.doc_index_queue = doc_index_queue
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def create_signature(self, page):
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"""Create signature based on url and contents."""
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...
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def crawl_page(self, page):
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for url in page.child_urls:
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self.data_store.add_link_to_crawl(url)
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page.signature = self.create_signature(page)
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self.data_store.remove_link_to_crawl(page.url)
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self.data_store.insert_crawled_link(page.url, page.signature)
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def crawl(self):
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while True:
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page = self.data_store.extract_max_priority_page()
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if page is None:
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break
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if self.data_store.crawled_similar(page.signature):
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self.data_store.reduce_priority_link_to_crawl(page.url)
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else:
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self.crawl_page(page)
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```
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### Handling duplicates
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We need to be careful the web crawler doesn't get stuck in an infinite loop, which happens when the graph contains a cycle.
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**Clarify with your interviewer how much code you are expected to write**.
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We'll want to remove duplicate urls:
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* For smaller lists we could use something like `sort | unique`
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* With 1 billion links to crawl, we could use **MapReduce** to output only entries that have a frequency of 1
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```python
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class RemoveDuplicateUrls(MRJob):
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def mapper(self, _, line):
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yield line, 1
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def reducer(self, key, values):
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total = sum(values)
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if total == 1:
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yield key, total
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```
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Detecting duplicate content is more complex. We could generate a signature based on the contents of the page and compare those two signatures for similarity. Some potential algorithms are [Jaccard index](https://en.wikipedia.org/wiki/Jaccard_index) and [cosine similarity](https://en.wikipedia.org/wiki/Cosine_similarity).
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### Determining when to update the crawl results
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Pages need to be crawled regularly to ensure freshness. Crawl results could have a `timestamp` field that indicates the last time a page was crawled. After a default time period, say one week, all pages should be refreshed. Frequently updated or more popular sites could be refreshed in shorter intervals.
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Although we won't dive into details on analytics, we could do some data mining to determine the mean time before a particular page is updated, and use that statistic to determine how often to re-crawl the page.
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We might also choose to support a `Robots.txt` file that gives webmasters control of crawl frequency.
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### Use case: User inputs a search term and sees a list of relevant pages with titles and snippets
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* The **Client** sends a request to the **Web Server**, running as a [reverse proxy](https://github.com/donnemartin/system-design-primer#reverse-proxy-web-server)
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* The **Web Server** forwards the request to the **Query API** server
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* The **Query API** server does the following:
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* Parses the query
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* Removes markup
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* Breaks up the text into terms
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* Fixes typos
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* Normalizes capitalization
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* Converts the query to use boolean operations
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* Uses the **Reverse Index Service** to find documents matching the query
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* The **Reverse Index Service** ranks the matching results and returns the top ones
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* Uses the **Document Service** to return titles and snippets
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We'll use a public [**REST API**](https://github.com/donnemartin/system-design-primer#representational-state-transfer-rest):
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```
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$ curl https://search.com/api/v1/search?query=hello+world
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```
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Response:
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```
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{
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"title": "foo's title",
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"snippet": "foo's snippet",
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"link": "https://foo.com",
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},
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{
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"title": "bar's title",
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"snippet": "bar's snippet",
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"link": "https://bar.com",
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},
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{
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"title": "baz's title",
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"snippet": "baz's snippet",
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"link": "https://baz.com",
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},
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```
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For internal communications, we could use [Remote Procedure Calls](https://github.com/donnemartin/system-design-primer#remote-procedure-call-rpc).
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## Step 4: Scale the design
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> Identify and address bottlenecks, given the constraints.
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**Important: Do not simply jump right into the final design from the initial design!**
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State you would 1) **Benchmark/Load Test**, 2) **Profile** for bottlenecks 3) address bottlenecks while evaluating alternatives and trade-offs, and 4) repeat. See [Design a system that scales to millions of users on AWS](../scaling_aws/README.md) as a sample on how to iteratively scale the initial design.
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It's important to discuss what bottlenecks you might encounter with the initial design and how you might address each of them. For example, what issues are addressed by adding a **Load Balancer** with multiple **Web Servers**? **CDN**? **Master-Slave Replicas**? What are the alternatives and **Trade-Offs** for each?
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We'll introduce some components to complete the design and to address scalability issues. Internal load balancers are not shown to reduce clutter.
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*To avoid repeating discussions*, refer to the following [system design topics](https://github.com/donnemartin/system-design-primer#index-of-system-design-topics) for main talking points, tradeoffs, and alternatives:
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* [DNS](https://github.com/donnemartin/system-design-primer#domain-name-system)
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* [Load balancer](https://github.com/donnemartin/system-design-primer#load-balancer)
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* [Horizontal scaling](https://github.com/donnemartin/system-design-primer#horizontal-scaling)
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* [Web server (reverse proxy)](https://github.com/donnemartin/system-design-primer#reverse-proxy-web-server)
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* [API server (application layer)](https://github.com/donnemartin/system-design-primer#application-layer)
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* [Cache](https://github.com/donnemartin/system-design-primer#cache)
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* [NoSQL](https://github.com/donnemartin/system-design-primer#nosql)
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* [Consistency patterns](https://github.com/donnemartin/system-design-primer#consistency-patterns)
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* [Availability patterns](https://github.com/donnemartin/system-design-primer#availability-patterns)
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Some searches are very popular, while others are only executed once. Popular queries can be served from a **Memory Cache** such as Redis or Memcached to reduce response times and to avoid overloading the **Reverse Index Service** and **Document Service**. The **Memory Cache** is also useful for handling the unevenly distributed traffic and traffic spikes. Reading 1 MB sequentially from memory takes about 250 microseconds, while reading from SSD takes 4x and from disk takes 80x longer.<sup><a href=https://github.com/donnemartin/system-design-primer#latency-numbers-every-programmer-should-know>1</a></sup>
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Below are a few other optimizations to the **Crawling Service**:
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* To handle the data size and request load, the **Reverse Index Service** and **Document Service** will likely need to make heavy use sharding and replication.
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* DNS lookup can be a bottleneck, the **Crawler Service** can keep its own DNS lookup that is refreshed periodically
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* The **Crawler Service** can improve performance and reduce memory usage by keeping many open connections at a time, referred to as [connection pooling](https://en.wikipedia.org/wiki/Connection_pool)
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* Switching to [UDP](https://github.com/donnemartin/system-design-primer#user-datagram-protocol-udp) could also boost performance
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* Web crawling is bandwidth intensive, ensure there is enough bandwidth to sustain high throughput
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## Additional talking points
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> Additional topics to dive into, depending on the problem scope and time remaining.
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### SQL scaling patterns
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* [Read replicas](https://github.com/donnemartin/system-design-primer#master-slave-replication)
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* [Federation](https://github.com/donnemartin/system-design-primer#federation)
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* [Sharding](https://github.com/donnemartin/system-design-primer#sharding)
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* [Denormalization](https://github.com/donnemartin/system-design-primer#denormalization)
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* [SQL Tuning](https://github.com/donnemartin/system-design-primer#sql-tuning)
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#### NoSQL
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* [Key-value store](https://github.com/donnemartin/system-design-primer#key-value-store)
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* [Document store](https://github.com/donnemartin/system-design-primer#document-store)
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* [Wide column store](https://github.com/donnemartin/system-design-primer#wide-column-store)
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* [Graph database](https://github.com/donnemartin/system-design-primer#graph-database)
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* [SQL vs NoSQL](https://github.com/donnemartin/system-design-primer#sql-or-nosql)
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### Caching
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* Where to cache
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* [Client caching](https://github.com/donnemartin/system-design-primer#client-caching)
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* [CDN caching](https://github.com/donnemartin/system-design-primer#cdn-caching)
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* [Web server caching](https://github.com/donnemartin/system-design-primer#web-server-caching)
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* [Database caching](https://github.com/donnemartin/system-design-primer#database-caching)
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* [Application caching](https://github.com/donnemartin/system-design-primer#application-caching)
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* What to cache
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* [Caching at the database query level](https://github.com/donnemartin/system-design-primer#caching-at-the-database-query-level)
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* [Caching at the object level](https://github.com/donnemartin/system-design-primer#caching-at-the-object-level)
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* When to update the cache
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* [Cache-aside](https://github.com/donnemartin/system-design-primer#cache-aside)
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* [Write-through](https://github.com/donnemartin/system-design-primer#write-through)
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* [Write-behind (write-back)](https://github.com/donnemartin/system-design-primer#write-behind-write-back)
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* [Refresh ahead](https://github.com/donnemartin/system-design-primer#refresh-ahead)
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### Asynchronism and microservices
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* [Message queues](https://github.com/donnemartin/system-design-primer#message-queues)
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* [Task queues](https://github.com/donnemartin/system-design-primer#task-queues)
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* [Back pressure](https://github.com/donnemartin/system-design-primer#back-pressure)
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* [Microservices](https://github.com/donnemartin/system-design-primer#microservices)
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### Communications
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* Discuss tradeoffs:
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* External communication with clients - [HTTP APIs following REST](https://github.com/donnemartin/system-design-primer#representational-state-transfer-rest)
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* Internal communications - [RPC](https://github.com/donnemartin/system-design-primer#remote-procedure-call-rpc)
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* [Service discovery](https://github.com/donnemartin/system-design-primer#service-discovery)
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### Security
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Refer to the [security section](https://github.com/donnemartin/system-design-primer#security).
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### Latency numbers
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See [Latency numbers every programmer should know](https://github.com/donnemartin/system-design-primer#latency-numbers-every-programmer-should-know).
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### Ongoing
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* Continue benchmarking and monitoring your system to address bottlenecks as they come up
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* Scaling is an iterative process
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