Add Web Crawler solution
This commit is contained in:
Donne Martin
parent
e577dda5d9
commit
e60de1775e
353
solutions/system_design/web_crawler/README.md
Normal file
353
solutions/system_design/web_crawler/README.md
Normal file
@ -0,0 +1,353 @@
|
||||
# Design a web crawler
|
||||
|
||||
*Note: This document links directly to relevant areas found in the [system design topics](https://github.com/donnemartin/system-design-primer-interview#index-of-system-design-topics-1) to avoid duplication. Refer to the linked content for general talking points, tradeoffs, and alternatives.*
|
||||
|
||||
## Step 1: Outline use cases and constraints
|
||||
|
||||
> Gather requirements and scope the problem.
|
||||
> Ask questions to clarify use cases and constraints.
|
||||
> Discuss assumptions.
|
||||
|
||||
Without an interviewer to address clarifying questions, we'll define some use cases and constraints.
|
||||
|
||||
### Use cases
|
||||
|
||||
#### We'll scope the problem to handle only the following use cases
|
||||
|
||||
* **Service** crawls a list of urls:
|
||||
* Generates reverse index of words to pages containing the search terms
|
||||
* Generates titles and snippets for pages
|
||||
* Title and snippets are static, they do not change based on search query
|
||||
* **User** inputs a search term and sees a list of relevant pages with titles and snippets the crawler generated
|
||||
* Only sketch high level components and interactions for this use case, no need to go into depth
|
||||
* **Service** has high availability
|
||||
|
||||
#### Out of scope
|
||||
|
||||
* Search analytics
|
||||
* Personalized search results
|
||||
* Page rank
|
||||
|
||||
### Constraints and assumptions
|
||||
|
||||
#### State assumptions
|
||||
|
||||
* Traffic is not evenly distributed
|
||||
* Some searches are very popular, while others are only executed once
|
||||
* Support only anonymous users
|
||||
* Generating search results should be fast
|
||||
* The web crawler should not get stuck in an infinite loop
|
||||
* We get stuck in an infinite loop if the graph contains a cycle
|
||||
* 1 billion links to crawl
|
||||
* Pages need to be crawled regularly to ensure freshness
|
||||
* Average refresh rate of about once per week, more frequent for popular sites
|
||||
* 4 billion links crawled each month
|
||||
* Average stored size per web page: 500 KB
|
||||
* For simplicity, count changes the same as new pages
|
||||
* 100 billion searches per month
|
||||
|
||||
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/).
|
||||
|
||||
#### Calculate usage
|
||||
|
||||
**Clarify with your interviewer if you should run back-of-the-envelope usage calculations.**
|
||||
|
||||
* 2 PB of stored page content per month
|
||||
* 500 KB per page * 4 billion links crawled per month
|
||||
* 72 PB of stored page content in 3 years
|
||||
* 1,600 write requests per second
|
||||
* 40,000 search requests per second
|
||||
|
||||
Handy conversion guide:
|
||||
|
||||
* 2.5 million seconds per month
|
||||
* 1 request per second = 2.5 million requests per month
|
||||
* 40 requests per second = 100 million requests per month
|
||||
* 400 requests per second = 1 billion requests per month
|
||||
|
||||
## Step 2: Create a high level design
|
||||
|
||||
> Outline a high level design with all important components.
|
||||
|
||||
|
||||
|
||||
## Step 3: Design core components
|
||||
|
||||
> Dive into details for each core component.
|
||||
|
||||
### Use case: Service crawls a list of urls
|
||||
|
||||
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
|
||||
|
||||
We'll use a table `crawled_links` to store processed links and their page signatures.
|
||||
|
||||
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-interview#sql-or-nosql).
|
||||
|
||||
* The **Crawler Service** processes each page link by doing the following in a loop:
|
||||
* Takes the top ranked page link to crawl
|
||||
* Checks `crawled_links` in the **NoSQL Database** for an entry with a similar page signature
|
||||
* If we have a similar page, reduces the priority of the page link
|
||||
* This prevents us from getting into a cycle
|
||||
* Continue
|
||||
* Else, crawls the link
|
||||
* Adds a job to the **Reverse Index Service** queue to generate a [reverse index](https://en.wikipedia.org/wiki/Search_engine_indexing)
|
||||
* Adds a job to the **Document Service** queue to generate a static title and snippet
|
||||
* Generates the page signature
|
||||
* Removes the link from `links_to_crawl` in the **NoSQL Database**
|
||||
* Inserts the page link and signature to `crawled_links` in the **NoSQL Database**
|
||||
|
||||
**Clarify with your interviewer how much code you are expected to write**.
|
||||
|
||||
`PagesDataStore` is an abstraction within the **Crawler Service** that uses the **NoSQL Database**:
|
||||
|
||||
```
|
||||
class PagesDataStore(object):
|
||||
|
||||
def __init__(self, db);
|
||||
self.db = db
|
||||
...
|
||||
|
||||
def add_link_to_crawl(self, url):
|
||||
"""Add the given link to `links_to_crawl`."""
|
||||
...
|
||||
|
||||
def remove_link_to_crawl(self, url):
|
||||
"""Remove the given link from `links_to_crawl`."""
|
||||
...
|
||||
|
||||
def reduce_priority_link_to_crawl(self, url)
|
||||
"""Reduce the priority of a link in `links_to_crawl` to avoid cycles."""
|
||||
...
|
||||
|
||||
def extract_max_priority_page(self):
|
||||
"""Return the highest priority link in `links_to_crawl`."""
|
||||
...
|
||||
|
||||
def insert_crawled_link(self, url, signature):
|
||||
"""Add the given link to `crawled_links`."""
|
||||
...
|
||||
|
||||
def crawled_similar(self, signature):
|
||||
"""Determine if we've already crawled a page matching the given signature"""
|
||||
...
|
||||
```
|
||||
|
||||
`Page` is an abstraction within the **Crawler Service** that encapsulates a page, its contents, child urls, and signature:
|
||||
|
||||
```
|
||||
class Page(object):
|
||||
|
||||
def __init__(self, url, contents, child_urls, signature):
|
||||
self.url = url
|
||||
self.contents = contents
|
||||
self.child_urls = child_urls
|
||||
self.signature = signature
|
||||
```
|
||||
|
||||
`Crawler` is the main class within **Crawler Service**, composed of `Page` and `PagesDataStore`.
|
||||
|
||||
```
|
||||
class Crawler(object):
|
||||
|
||||
def __init__(self, data_store, reverse_index_queue, doc_index_queue):
|
||||
self.data_store = data_store
|
||||
self.reverse_index_queue = reverse_index_queue
|
||||
self.doc_index_queue = doc_index_queue
|
||||
|
||||
def create_signature(self, page):
|
||||
"""Create signature based on url and contents."""
|
||||
...
|
||||
|
||||
def crawl_page(self, page):
|
||||
for url in page.child_urls:
|
||||
self.data_store.add_link_to_crawl(url)
|
||||
page.signature = self.create_signature(page)
|
||||
self.data_store.remove_link_to_crawl(page.url)
|
||||
self.data_store.insert_crawled_link(page.url, page.signature)
|
||||
|
||||
def crawl(self):
|
||||
while True:
|
||||
page = self.data_store.extract_max_priority_page()
|
||||
if page is None:
|
||||
break
|
||||
if self.data_store.crawled_similar(page.signature):
|
||||
self.data_store.reduce_priority_link_to_crawl(page.url)
|
||||
else:
|
||||
self.crawl_page(page)
|
||||
```
|
||||
|
||||
### Handling duplicates
|
||||
|
||||
We need to be careful the web crawler doesn't get stuck in an infinite loop, which happens when the graph contains a cycle.
|
||||
|
||||
**Clarify with your interviewer how much code you are expected to write**.
|
||||
|
||||
We'll want to remove duplicate urls:
|
||||
|
||||
* For smaller lists we could use something like `sort | unique`
|
||||
* With 1 billion links to crawl, we could use **MapReduce** to output only entries that have a frequency of 1
|
||||
|
||||
```
|
||||
class RemoveDuplicateUrls(MRJob):
|
||||
|
||||
def mapper(self, _, line):
|
||||
yield line, 1
|
||||
|
||||
def reducer(self, key, values):
|
||||
total = sum(values)
|
||||
if total == 1:
|
||||
yield key, total
|
||||
```
|
||||
|
||||
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).
|
||||
|
||||
### Determining when to update the crawl results
|
||||
|
||||
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.
|
||||
|
||||
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.
|
||||
|
||||
We might also choose to support a `Robots.txt` file that gives webmasters control of crawl frequency.
|
||||
|
||||
### Use case: User inputs a search term and sees a list of relevant pages with titles and snippets
|
||||
|
||||
* The **Client** sends a request to the **Web Server**, running as a [reverse proxy](https://github.com/donnemartin/system-design-primer-interview#reverse-proxy-web-server)
|
||||
* The **Web Server** forwards the request to the **Query API** server
|
||||
* The **Query API** server does does the following:
|
||||
* Parses the query
|
||||
* Removes markup
|
||||
* Breaks up the text into terms
|
||||
* Fixes typos
|
||||
* Normalizes capitalization
|
||||
* Converts the query to use boolean operations
|
||||
* Uses the **Reverse Index Service** to find documents matching the query
|
||||
* The **Reverse Index Service** ranks the matching results and returns the top ones
|
||||
* Uses the **Document Service** to return titles and snippets
|
||||
|
||||
We'll use a public [**REST API**](https://github.com/donnemartin/system-design-primer-interview##representational-state-transfer-rest):
|
||||
|
||||
```
|
||||
$ curl https://search.com/api/v1/search?query=hello+world
|
||||
```
|
||||
|
||||
Response:
|
||||
|
||||
```
|
||||
{
|
||||
"title": "foo's title",
|
||||
"snippet": "foo's snippet",
|
||||
"link": "https://foo.com",
|
||||
},
|
||||
{
|
||||
"title": "bar's title",
|
||||
"snippet": "bar's snippet",
|
||||
"link": "https://bar.com",
|
||||
},
|
||||
{
|
||||
"title": "baz's title",
|
||||
"snippet": "baz's snippet",
|
||||
"link": "https://baz.com",
|
||||
},
|
||||
```
|
||||
|
||||
For internal communications, we could use [Remote Procedure Calls](https://github.com/donnemartin/system-design-primer-interview#remote-procedure-call-rpc).
|
||||
|
||||
## Step 4: Scale the design
|
||||
|
||||
> Identify and address bottlenecks, given the constraints.
|
||||
|
||||
|
||||
|
||||
**Important: Do not simply jump right into the final design from the initial design!**
|
||||
|
||||
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]() as a sample on how to iteratively scale the initial design.
|
||||
|
||||
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?
|
||||
|
||||
We'll introduce some components to complete the design and to address scalability issues. Internal load balancers are not shown to reduce clutter.
|
||||
|
||||
*To avoid repeating discussions*, refer to the following [system design topics](https://github.com/donnemartin/system-design-primer-interview#) for main talking points, tradeoffs, and alternatives:
|
||||
|
||||
* [DNS](https://github.com/donnemartin/system-design-primer-interview#domain-name-system)
|
||||
* [Load balancer](https://github.com/donnemartin/system-design-primer-interview#load-balancer)
|
||||
* [Horizontal scaling](https://github.com/donnemartin/system-design-primer-interview#horizontal-scaling)
|
||||
* [Web server (reverse proxy)](https://github.com/donnemartin/system-design-primer-interview#reverse-proxy-web-server)
|
||||
* [API server (application layer)](https://github.com/donnemartin/system-design-primer-interview#application-layer)
|
||||
* [Cache](https://github.com/donnemartin/system-design-primer-interview#cache)
|
||||
* [NoSQL](https://github.com/donnemartin/system-design-primer-interview#nosql)
|
||||
* [Consistency patterns](https://github.com/donnemartin/system-design-primer-interview#consistency-patterns)
|
||||
* [Availability patterns](https://github.com/donnemartin/system-design-primer-interview#availability-patterns)
|
||||
|
||||
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-interview#latency-numbers-every-programmer-should-know>1</a></sup>
|
||||
|
||||
Below are a few other optimizations to the **Crawling Service**:
|
||||
|
||||
* 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.
|
||||
* DNS lookup can be a bottleneck, the **Crawler Service** can keep its own DNS lookup that is refreshed periodically
|
||||
* 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)
|
||||
* Switching to [UDP](https://github.com/donnemartin/system-design-primer-interview#user-datagram-protocol-udp) could also boost performance
|
||||
* Web crawling is bandwidth intensive, ensure there is enough bandwidth to sustain high throughput
|
||||
|
||||
## Additional talking points
|
||||
|
||||
> Additional topics to dive into, depending on the problem scope and time remaining.
|
||||
|
||||
### SQL scaling patterns
|
||||
|
||||
* [Read replicas](https://github.com/donnemartin/system-design-primer-interview#master-slave)
|
||||
* [Federation](https://github.com/donnemartin/system-design-primer-interview#federation)
|
||||
* [Sharding](https://github.com/donnemartin/system-design-primer-interview#sharding)
|
||||
* [Denormalization](https://github.com/donnemartin/system-design-primer-interview#denormalization)
|
||||
* [SQL Tuning](https://github.com/donnemartin/system-design-primer-interview#sql-tuning)
|
||||
|
||||
#### NoSQL
|
||||
|
||||
* [Key-value store](https://github.com/donnemartin/system-design-primer-interview#)
|
||||
* [Document store](https://github.com/donnemartin/system-design-primer-interview#)
|
||||
* [Wide column store](https://github.com/donnemartin/system-design-primer-interview#)
|
||||
* [Graph database](https://github.com/donnemartin/system-design-primer-interview#)
|
||||
* [SQL vs NoSQL](https://github.com/donnemartin/system-design-primer-interview#)
|
||||
|
||||
### Caching
|
||||
|
||||
* Where to cache
|
||||
* [Client caching](https://github.com/donnemartin/system-design-primer-interview#client-caching)
|
||||
* [CDN caching](https://github.com/donnemartin/system-design-primer-interview#cdn-caching)
|
||||
* [Web server caching](https://github.com/donnemartin/system-design-primer-interview#web-server-caching)
|
||||
* [Database caching](https://github.com/donnemartin/system-design-primer-interview#database-caching)
|
||||
* [Application caching](https://github.com/donnemartin/system-design-primer-interview#application-caching)
|
||||
* What to cache
|
||||
* [Caching at the database query level](https://github.com/donnemartin/system-design-primer-interview#caching-at-the-database-query-level)
|
||||
* [Caching at the object level](https://github.com/donnemartin/system-design-primer-interview#caching-at-the-object-level)
|
||||
* When to update the cache
|
||||
* [Cache-aside](https://github.com/donnemartin/system-design-primer-interview#cache-aside)
|
||||
* [Write-through](https://github.com/donnemartin/system-design-primer-interview#write-through)
|
||||
* [Write-behind (write-back)](https://github.com/donnemartin/system-design-primer-interview#write-behind-write-back)
|
||||
* [Refresh ahead](https://github.com/donnemartin/system-design-primer-interview#refresh-ahead)
|
||||
|
||||
### Asynchronism and microservices
|
||||
|
||||
* [Message queues](https://github.com/donnemartin/system-design-primer-interview#)
|
||||
* [Task queues](https://github.com/donnemartin/system-design-primer-interview#)
|
||||
* [Back pressure](https://github.com/donnemartin/system-design-primer-interview#)
|
||||
* [Microservices](https://github.com/donnemartin/system-design-primer-interview#)
|
||||
|
||||
### Communications
|
||||
|
||||
* Discuss tradeoffs:
|
||||
* External communication with clients - [HTTP APIs following REST](https://github.com/donnemartin/system-design-primer-interview#representational-state-transfer-rest)
|
||||
* Internal communications - [RPC](https://github.com/donnemartin/system-design-primer-interview#remote-procedure-call-rpc)
|
||||
* [Service discovery](https://github.com/donnemartin/system-design-primer-interview#service-discovery)
|
||||
|
||||
### Security
|
||||
|
||||
Refer to the [security section](https://github.com/donnemartin/system-design-primer-interview#security).
|
||||
|
||||
### Latency numbers
|
||||
|
||||
See [Latency numbers every programmer should know](https://github.com/donnemartin/system-design-primer-interview#latency-numbers-every-programmer-should-know).
|
||||
|
||||
### Ongoing
|
||||
|
||||
* Continue benchmarking and monitoring your system to address bottlenecks as they come up
|
||||
* Scaling is an iterative process
|
||||
0
solutions/system_design/web_crawler/__init__.py
Normal file
0
solutions/system_design/web_crawler/__init__.py
Normal file
BIN
solutions/system_design/web_crawler/web_crawler.png
Normal file
BIN
solutions/system_design/web_crawler/web_crawler.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 194 KiB |
BIN
solutions/system_design/web_crawler/web_crawler_basic.png
Normal file
BIN
solutions/system_design/web_crawler/web_crawler_basic.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 108 KiB |
25
solutions/system_design/web_crawler/web_crawler_mapreduce.py
Normal file
25
solutions/system_design/web_crawler/web_crawler_mapreduce.py
Normal file
@ -0,0 +1,25 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
from mrjob.job import MRJob
|
||||
|
||||
|
||||
class RemoveDuplicateUrls(MRJob):
|
||||
|
||||
def mapper(self, _, line):
|
||||
yield line, 1
|
||||
|
||||
def reducer(self, key, values):
|
||||
total = sum(values)
|
||||
if total == 1:
|
||||
yield key, total
|
||||
|
||||
def steps(self):
|
||||
"""Run the map and reduce steps."""
|
||||
return [
|
||||
self.mr(mapper=self.mapper,
|
||||
reducer=self.reducer)
|
||||
]
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
RemoveDuplicateUrls.run()
|
||||
72
solutions/system_design/web_crawler/web_crawler_snippets.py
Normal file
72
solutions/system_design/web_crawler/web_crawler_snippets.py
Normal file
@ -0,0 +1,72 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
class PagesDataStore(object):
|
||||
|
||||
def __init__(self, db);
|
||||
self.db = db
|
||||
...
|
||||
|
||||
def add_link_to_crawl(self, url):
|
||||
"""Add the given link to `links_to_crawl`."""
|
||||
...
|
||||
|
||||
def remove_link_to_crawl(self, url):
|
||||
"""Remove the given link from `links_to_crawl`."""
|
||||
...
|
||||
|
||||
def reduce_priority_link_to_crawl(self, url)
|
||||
"""Reduce the priority of a link in `links_to_crawl` to avoid cycles."""
|
||||
...
|
||||
|
||||
def extract_max_priority_page(self):
|
||||
"""Return the highest priority link in `links_to_crawl`."""
|
||||
...
|
||||
|
||||
def insert_crawled_link(self, url, signature):
|
||||
"""Add the given link to `crawled_links`."""
|
||||
...
|
||||
|
||||
def crawled_similar(self, signature):
|
||||
"""Determine if we've already crawled a page matching the given signature"""
|
||||
...
|
||||
|
||||
|
||||
class Page(object):
|
||||
|
||||
def __init__(self, url, contents, child_urls):
|
||||
self.url = url
|
||||
self.contents = contents
|
||||
self.child_urls = child_urls
|
||||
self.signature = self.create_signature()
|
||||
|
||||
def create_signature(self):
|
||||
# Create signature based on url and contents
|
||||
...
|
||||
|
||||
|
||||
class Crawler(object):
|
||||
|
||||
def __init__(self, pages, data_store, reverse_index_queue, doc_index_queue):
|
||||
self.pages = pages
|
||||
self.data_store = data_store
|
||||
self.reverse_index_queue = reverse_index_queue
|
||||
self.doc_index_queue = doc_index_queue
|
||||
|
||||
def crawl_page(self, page):
|
||||
for url in page.child_urls:
|
||||
self.data_store.add_link_to_crawl(url)
|
||||
self.reverse_index_queue.generate(page)
|
||||
self.doc_index_queue.generate(page)
|
||||
self.data_store.remove_link_to_crawl(page.url)
|
||||
self.data_store.insert_crawled_link(page.url, page.signature)
|
||||
|
||||
def crawl(self):
|
||||
while True:
|
||||
page = self.data_store.extract_max_priority_page()
|
||||
if page is None:
|
||||
break
|
||||
if self.data_store.crawled_similar(page.signature):
|
||||
self.data_store.reduce_priority_link_to_crawl(page.url)
|
||||
else:
|
||||
self.crawl_page(page)
|
||||
page = self.data_store.extract_max_priority_page()
|
||||
Loading…
Reference in New Issue
Block a user