333 lines
16 KiB
Markdown
333 lines
16 KiB
Markdown
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# Design Pastebin.com (or Bit.ly)
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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-interview#index-of-system-design-topics-1) to avoid duplication. Refer to the linked content for general talking points, tradeoffs, and alternatives.*
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**Design Bit.ly** - is a similar question, except pastebin requires storing the paste contents instead of the original unshortened url.
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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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* **User** enters a block of text and gets a randomly generated link
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* Expiration
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* Default setting does not expire
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* Can optionally set a timed expiration
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* **User** enters a paste's url and views the contents
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* **User** is anonymous
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* **Service** tracks analytics of pages
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* Monthly visit stats
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* **Service** deletes expired pastes
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* **Service** has high availability
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#### Out of scope
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* **User** registers for an account
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* **User** verifies email
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* **User** logs into a registered account
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* **User** edits the document
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* **User** can set visibility
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* **User** can set the shortlink
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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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* Following a short link should be fast
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* Pastes are text only
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* Page view analytics do not need to be realtime
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* 10 million users
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* 10 million paste writes per month
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* 100 million paste reads per month
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* 10:1 read to write ratio
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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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* Size per paste
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* 1 KB content per paste
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* `shortlink` - 7 bytes
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* `expiration_length_in_minutes` - 4 bytes
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* `created_at` - 5 bytes
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* `paste_path` - 255 bytes
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* total = ~1.27 KB
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* 12.7 GB of new paste content per month
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* 1.27 KB per paste * 10 million pastes per month
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* ~450 GB of new paste content in 3 years
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* 360 million shortlinks in 3 years
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* Assume most are new pastes instead of updates to existing ones
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* 4 paste writes per second on average
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* 40 read requests per second on average
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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: User enters a block of text and gets a randomly generated link
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We could use a [relational database](https://github.com/donnemartin/system-design-primer-interview#relational-database-management-system-rdbms) as a large hash table, mapping the generated url to a file server and path containing the paste file.
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Instead of managing a file server, we could use a managed **Object Store** such as Amazon S3 or a [NoSQL document store](https://github.com/donnemartin/system-design-primer-interview#document-store).
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An alternative to a relational database acting as a large hash table, we could use a [NoSQL key-value store](https://github.com/donnemartin/system-design-primer-interview#key-value-store). We should discuss the [tradeoffs between choosing SQL or NoSQL](https://github.com/donnemartin/system-design-primer-interview#sql-or-nosql). The following discussion uses the relational database approach.
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* The **Client** sends a create paste request to the **Web Server**, running as a [reverse proxy](https://github.com/donnemartin/system-design-primer-interview#reverse-proxy-web-server)
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* The **Web Server** forwards the request to the **Write API** server
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* The **Write API** server does does the following:
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* Generates a unique url
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* Checks if the url is unique by looking at the **SQL Database** for a duplicate
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* If the url is not unique, it generates another url
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* If we supported a custom url, we could use the user-supplied (also check for a duplicate)
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* Saves to the **SQL Database** `pastes` table
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* Saves the paste data to the **Object Store**
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* Returns the url
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**Clarify with your interviewer how much code you are expected to write**.
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The `pastes` table could have the following structure:
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```
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shortlink char(7) NOT NULL
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expiration_length_in_minutes int NOT NULL
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created_at datetime NOT NULL
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paste_path varchar(255) NOT NULL
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PRIMARY KEY(shortlink)
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```
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We'll create an [index](https://github.com/donnemartin/system-design-primer-interview#use-good-indices) on `shortlink ` and `created_at` to speed up lookups (log-time instead of scanning the entire table) and to keep the data in memory. 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>
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To generate the unique url, we could:
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* Take the [**MD5**](https://en.wikipedia.org/wiki/MD5) hash of the user's ip_address + timestamp
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* MD5 is a widely used hashing function that produces a 128-bit hash value
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* MD5 is uniformly distributed
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* Alternatively, we could also take the MD5 hash of randomly-generated data
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* [**Base 62**](https://www.kerstner.at/2012/07/shortening-strings-using-base-62-encoding/) encode the MD5 hash
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* Base 62 encodes to `[a-zA-Z0-9]` which works well for urls, eliminating the need for escaping special characters
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* There is only one hash result for the original input and and Base 62 is deterministic (no randomness involved)
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* Base 64 is another popular encoding but provides issues for urls because of the additional `+` and `/` characters
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* The following [Base 62 pseudocode](http://stackoverflow.com/questions/742013/how-to-code-a-url-shortener) runs in O(k) time where k is the number of digits = 7:
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```
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def base_encode(num, base=62):
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digits = []
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while num > 0
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remainder = modulo(num, base)
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digits.push(remainder)
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num = divide(num, base)
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digits = digits.reverse
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```
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* Take the first 7 characters of the output, which results in 62^7 possible values and should be sufficient to handle our constraint of 360 million shortlinks in 3 years:
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```
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url = base_encode(md5(ip_address+timestamp))[:URL_LENGTH]
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```
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We'll use a public [**REST API**](https://github.com/donnemartin/system-design-primer-interview##representational-state-transfer-rest):
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```
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$ curl -X POST --data '{ "expiration_length_in_minutes": "60", \
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"paste_contents": "Hello World!" }' https://pastebin.com/api/v1/paste
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```
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Response:
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```
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{
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"shortlink": "foobar"
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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-interview#remote-procedure-call-rpc).
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### Use case: User enters a paste's url and views the contents
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* The **Client** sends a get paste request to the **Web Server**
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* The **Web Server** forwards the request to the **Read API** server
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* The **Read API** server does the following:
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* Checks the **SQL Database** for the generated url
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* If the url is in the **SQL Database**, fetch the paste contents from the **Object Store**
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* Else, return an error message for the user
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REST API:
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```
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$ curl https://pastebin.com/api/v1/paste?shortlink=foobar
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```
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Response:
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```
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{
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"paste_contents": "Hello World"
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"created_at": "YYYY-MM-DD HH:MM:SS"
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"expiration_length_in_minutes": "60"
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}
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```
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### Use case: Service tracks analytics of pages
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Since realtime analytics are not a requirement, we could simply **MapReduce** the **Web Server** logs to generate hit counts.
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**Clarify with your interviewer how much code you are expected to write**.
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```
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class HitCounts(MRJob):
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def extract_url(self, line):
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"""Extract the generated url from the log line."""
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...
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def extract_year_month(self, line):
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"""Return the year and month portions of the timestamp."""
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...
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def mapper(self, _, line):
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"""Parse each log line, extract and transform relevant lines.
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Emit key value pairs of the form:
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(2016-01, url0), 1
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(2016-01, url0), 1
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(2016-01, url1), 1
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"""
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url = self.extract_url(line)
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period = self.extract_year_month(line)
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yield (period, url), 1
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def reducer(self, key, value):
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"""Sum values for each key.
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(2016-01, url0), 2
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(2016-01, url1), 1
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"""
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yield key, sum(values)
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```
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### Use case: Service deletes expired pastes
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To delete expired pastes, we could just scan the **SQL Database** for all entries whose expiration timestamp are older than the current timestamp. All expired entries would then be deleted (or marked as expired) from the table.
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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 do this iteratively: 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.
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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-interview#) for main talking points, tradeoffs, and alternatives:
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* [DNS](https://github.com/donnemartin/system-design-primer-interview#domain-name-system)
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* [CDN](https://github.com/donnemartin/system-design-primer-interview#content-delivery-network)
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* [Load balancer](https://github.com/donnemartin/system-design-primer-interview#load-balancer)
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* [Horizontal scaling](https://github.com/donnemartin/system-design-primer-interview#horizontal-scaling)
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* [Web server (reverse proxy)](https://github.com/donnemartin/system-design-primer-interview#reverse-proxy-web-server)
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* [API server (application layer)](https://github.com/donnemartin/system-design-primer-interview#application-layer)
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* [Cache](https://github.com/donnemartin/system-design-primer-interview#cache)
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* [Relational database management system (RDBMS)](https://github.com/donnemartin/system-design-primer-interview#relational-database-management-system-rdbms)
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* [SQL write master-slave failover](https://github.com/donnemartin/system-design-primer-interview#fail-over)
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* [Master-slave replication](https://github.com/donnemartin/system-design-primer-interview#master-slave-replication)
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* [Consistency patterns](https://github.com/donnemartin/system-design-primer-interview#consistency-patterns)
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* [Availability patterns](https://github.com/donnemartin/system-design-primer-interview#availability-patterns)
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The **Analytics Database** could use a data warehousing solution such as Amazon Redshift or Google BigQuery.
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An **Object Store** such as Amazon S3 can comfortably handle the constraint of 12.7 GB of new content per month.
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To address the 40 *average* read requests per second (higher at peak), traffic for popular content should be handled by the **Memory Cache** instead of the database. The **Memory Cache** is also useful for handling the unevenly distributed traffic and traffic spikes. The **SQL Read Replicas** should be able to handle the cache misses, as long as the replicas are not bogged down with replicating writes.
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4 *average* paste writes per second (with higher at peak) should be do-able for a single **SQL Write Master-Slave**. Otherwise, we'll need to employ additional SQL scaling patterns:
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* [Federation](https://github.com/donnemartin/system-design-primer-interview#federation)
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* [Sharding](https://github.com/donnemartin/system-design-primer-interview#sharding)
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* [Denormalization](https://github.com/donnemartin/system-design-primer-interview#denormalization)
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* [SQL Tuning](https://github.com/donnemartin/system-design-primer-interview#sql-tuning)
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We should also consider moving some data to a **NoSQL Database**.
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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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#### NoSQL
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* [Key-value store](https://github.com/donnemartin/system-design-primer-interview#)
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* [Document store](https://github.com/donnemartin/system-design-primer-interview#)
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* [Wide column store](https://github.com/donnemartin/system-design-primer-interview#)
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* [Graph database](https://github.com/donnemartin/system-design-primer-interview#)
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* [SQL vs NoSQL](https://github.com/donnemartin/system-design-primer-interview#)
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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-interview#client-caching)
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* [CDN caching](https://github.com/donnemartin/system-design-primer-interview#cdn-caching)
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* [Web server caching](https://github.com/donnemartin/system-design-primer-interview#web-server-caching)
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* [Database caching](https://github.com/donnemartin/system-design-primer-interview#database-caching)
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* [Application caching](https://github.com/donnemartin/system-design-primer-interview#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-interview#caching-at-the-database-query-level)
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* [Caching at the object level](https://github.com/donnemartin/system-design-primer-interview#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-interview#cache-aside)
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* [Write-through](https://github.com/donnemartin/system-design-primer-interview#write-through)
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* [Write-behind (write-back)](https://github.com/donnemartin/system-design-primer-interview#write-behind-write-back)
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* [Refresh ahead](https://github.com/donnemartin/system-design-primer-interview#refresh-ahead)
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### Asynchronism and microservices
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* [Message queues](https://github.com/donnemartin/system-design-primer-interview#)
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* [Task queues](https://github.com/donnemartin/system-design-primer-interview#)
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* [Back pressure](https://github.com/donnemartin/system-design-primer-interview#)
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* [Microservices](https://github.com/donnemartin/system-design-primer-interview#)
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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-interview#representational-state-transfer-rest)
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* Internal communications - [RPC](https://github.com/donnemartin/system-design-primer-interview#remote-procedure-call-rpc)
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* [Service discovery](https://github.com/donnemartin/system-design-primer-interview#service-discovery)
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### Security
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Refer to the [security section](https://github.com/donnemartin/system-design-primer-interview#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-interview#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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