diff --git a/solutions/system_design/pastebin/README.md b/solutions/system_design/pastebin/README.md new file mode 100644 index 0000000..1d43804 --- /dev/null +++ b/solutions/system_design/pastebin/README.md @@ -0,0 +1,332 @@ +# Design Pastebin.com (or Bit.ly) + +*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.* + +**Design Bit.ly** - is a similar question, except pastebin requires storing the paste contents instead of the original unshortened url. + +## 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 + +* **User** enters a block of text and gets a randomly generated link + * Expiration + * Default setting does not expire + * Can optionally set a timed expiration +* **User** enters a paste's url and views the contents +* **User** is anonymous +* **Service** tracks analytics of pages + * Monthly visit stats +* **Service** deletes expired pastes +* **Service** has high availability + +#### Out of scope + +* **User** registers for an account + * **User** verifies email +* **User** logs into a registered account + * **User** edits the document +* **User** can set visibility +* **User** can set the shortlink + +### Constraints and assumptions + +#### State assumptions + +* Traffic is not evenly distributed +* Following a short link should be fast +* Pastes are text only +* Page view analytics do not need to be realtime +* 10 million users +* 10 million paste writes per month +* 100 million paste reads per month +* 10:1 read to write ratio + +#### Calculate usage + +**Clarify with your interviewer if you should run back-of-the-envelope usage calculations.** + +* Size per paste + * 1 KB content per paste + * `shortlink` - 7 bytes + * `expiration_length_in_minutes` - 4 bytes + * `created_at` - 5 bytes + * `paste_path` - 255 bytes + * total = ~1.27 KB +* 12.7 GB of new paste content per month + * 1.27 KB per paste * 10 million pastes per month + * ~450 GB of new paste content in 3 years + * 360 million shortlinks in 3 years + * Assume most are new pastes instead of updates to existing ones +* 4 paste writes per second on average +* 40 read requests per second on average + +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. + +![Imgur](http://i.imgur.com/BKsBnmG.png) + +## Step 3: Design core components + +> Dive into details for each core component. + +### Use case: User enters a block of text and gets a randomly generated link + +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. + +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). + +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. + +* 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) +* The **Web Server** forwards the request to the **Write API** server +* The **Write API** server does does the following: + * Generates a unique url + * Checks if the url is unique by looking at the **SQL Database** for a duplicate + * If the url is not unique, it generates another url + * If we supported a custom url, we could use the user-supplied (also check for a duplicate) + * Saves to the **SQL Database** `pastes` table + * Saves the paste data to the **Object Store** + * Returns the url + +**Clarify with your interviewer how much code you are expected to write**. + +The `pastes` table could have the following structure: + +``` +shortlink char(7) NOT NULL +expiration_length_in_minutes int NOT NULL +created_at datetime NOT NULL +paste_path varchar(255) NOT NULL +PRIMARY KEY(shortlink) +``` + +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.1 + +To generate the unique url, we could: + +* Take the [**MD5**](https://en.wikipedia.org/wiki/MD5) hash of the user's ip_address + timestamp + * MD5 is a widely used hashing function that produces a 128-bit hash value + * MD5 is uniformly distributed + * Alternatively, we could also take the MD5 hash of randomly-generated data +* [**Base 62**](https://www.kerstner.at/2012/07/shortening-strings-using-base-62-encoding/) encode the MD5 hash + * Base 62 encodes to `[a-zA-Z0-9]` which works well for urls, eliminating the need for escaping special characters + * There is only one hash result for the original input and and Base 62 is deterministic (no randomness involved) + * Base 64 is another popular encoding but provides issues for urls because of the additional `+` and `/` characters + * 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: + +``` +def base_encode(num, base=62): + digits = [] + while num > 0 + remainder = modulo(num, base) + digits.push(remainder) + num = divide(num, base) + digits = digits.reverse +``` + +* 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: + +``` +url = base_encode(md5(ip_address+timestamp))[:URL_LENGTH] +``` + +We'll use a public [**REST API**](https://github.com/donnemartin/system-design-primer-interview##representational-state-transfer-rest): + +``` +$ curl -X POST --data '{ "expiration_length_in_minutes": "60", \ + "paste_contents": "Hello World!" }' https://pastebin.com/api/v1/paste +``` + +Response: + +``` +{ + "shortlink": "foobar" +} +``` + +For internal communications, we could use [Remote Procedure Calls](https://github.com/donnemartin/system-design-primer-interview#remote-procedure-call-rpc). + +### Use case: User enters a paste's url and views the contents + +* The **Client** sends a get paste request to the **Web Server** +* The **Web Server** forwards the request to the **Read API** server +* The **Read API** server does the following: + * Checks the **SQL Database** for the generated url + * If the url is in the **SQL Database**, fetch the paste contents from the **Object Store** + * Else, return an error message for the user + +REST API: + +``` +$ curl https://pastebin.com/api/v1/paste?shortlink=foobar +``` + +Response: + +``` +{ + "paste_contents": "Hello World" + "created_at": "YYYY-MM-DD HH:MM:SS" + "expiration_length_in_minutes": "60" +} +``` + +### Use case: Service tracks analytics of pages + +Since realtime analytics are not a requirement, we could simply **MapReduce** the **Web Server** logs to generate hit counts. + +**Clarify with your interviewer how much code you are expected to write**. + +``` +class HitCounts(MRJob): + + def extract_url(self, line): + """Extract the generated url from the log line.""" + ... + + def extract_year_month(self, line): + """Return the year and month portions of the timestamp.""" + ... + + def mapper(self, _, line): + """Parse each log line, extract and transform relevant lines. + + Emit key value pairs of the form: + + (2016-01, url0), 1 + (2016-01, url0), 1 + (2016-01, url1), 1 + """ + url = self.extract_url(line) + period = self.extract_year_month(line) + yield (period, url), 1 + + def reducer(self, key, value): + """Sum values for each key. + + (2016-01, url0), 2 + (2016-01, url1), 1 + """ + yield key, sum(values) +``` + +### Use case: Service deletes expired pastes + +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. + +## Step 4: Scale the design + +> Identify and address bottlenecks, given the constraints. + +![Imgur](http://i.imgur.com/4edXG0T.png) + +**Important: Do not simply jump right into the final design from the initial design!** + +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. + +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) +* [CDN](https://github.com/donnemartin/system-design-primer-interview#content-delivery-network) +* [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) +* [Relational database management system (RDBMS)](https://github.com/donnemartin/system-design-primer-interview#relational-database-management-system-rdbms) +* [SQL write master-slave failover](https://github.com/donnemartin/system-design-primer-interview#fail-over) +* [Master-slave replication](https://github.com/donnemartin/system-design-primer-interview#master-slave-replication) +* [Consistency patterns](https://github.com/donnemartin/system-design-primer-interview#consistency-patterns) +* [Availability patterns](https://github.com/donnemartin/system-design-primer-interview#availability-patterns) + +The **Analytics Database** could use a data warehousing solution such as Amazon Redshift or Google BigQuery. + +An **Object Store** such as Amazon S3 can comfortably handle the constraint of 12.7 GB of new content per month. + +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. + +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: + +* [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) + +We should also consider moving some data to a **NoSQL Database**. + +## Additional talking points + +> Additional topics to dive into, depending on the problem scope and time remaining. + +#### 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 diff --git a/solutions/system_design/pastebin/__init__.py b/solutions/system_design/pastebin/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/solutions/system_design/pastebin/pastebin.png b/solutions/system_design/pastebin/pastebin.png new file mode 100644 index 0000000..1841f0a Binary files /dev/null and b/solutions/system_design/pastebin/pastebin.png differ diff --git a/solutions/system_design/pastebin/pastebin.py b/solutions/system_design/pastebin/pastebin.py new file mode 100644 index 0000000..7cb1f20 --- /dev/null +++ b/solutions/system_design/pastebin/pastebin.py @@ -0,0 +1,46 @@ +# -*- coding: utf-8 -*- + +from mrjob.job import MRJob + + +class HitCounts(MRJob): + + def extract_url(self, line): + """Extract the generated url from the log line.""" + pass + + def extract_year_month(self, line): + """Return the year and month portions of the timestamp.""" + pass + + def mapper(self, _, line): + """Parse each log line, extract and transform relevant lines. + + Emit key value pairs of the form: + + (2016-01, url0), 1 + (2016-01, url0), 1 + (2016-01, url1), 1 + """ + url = self.extract_url(line) + period = self.extract_year_month(line) + yield (period, url), 1 + + def reducer(self, key, value): + """Sum values for each key. + + (2016-01, url0), 2 + (2016-01, url1), 1 + """ + yield key, sum(values) + + def steps(self): + """Run the map and reduce steps.""" + return [ + self.mr(mapper=self.mapper, + reducer=self.reducer) + ] + + +if __name__ == '__main__': + HitCounts.run() diff --git a/solutions/system_design/pastebin/pastebin_basic.png b/solutions/system_design/pastebin/pastebin_basic.png new file mode 100644 index 0000000..4cee694 Binary files /dev/null and b/solutions/system_design/pastebin/pastebin_basic.png differ