system-design-primer/solutions/system_design/sales_rank/README.md

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# Design Amazon's sales rank by category feature
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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.
> Ask questions to clarify use cases and constraints.
> 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 case
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* **Service** calculates the past week's most popular products by category
* **User** views the past week's most popular products by category
* **Service** has high availability
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#### Out of scope
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* The general e-commerce site
* Design components only for calculating sales rank
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### Constraints and assumptions
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#### State assumptions
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* Traffic is not evenly distributed
* Items can be in multiple categories
* Items cannot change categories
* There are no subcategories ie `foo/bar/baz`
* Results must be updated hourly
* More popular products might need to be updated more frequently
* 10 million products
* 1000 categories
* 1 billion transactions per month
* 100 billion read requests per month
* 100:1 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 transaction:
* `created_at` - 5 bytes
* `product_id` - 8 bytes
* `category_id` - 4 bytes
* `seller_id` - 8 bytes
* `buyer_id` - 8 bytes
* `quantity` - 4 bytes
* `total_price` - 5 bytes
* Total: ~40 bytes
* 40 GB of new transaction content per month
* 40 bytes per transaction * 1 billion transactions per month
* 1.44 TB of new transaction content in 3 years
* Assume most are new transactions instead of updates to existing ones
* 400 transactions per second on average
* 40,000 read requests per second on average
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Handy conversion guide:
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* 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
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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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![Imgur](http://i.imgur.com/vwMa1Qu.png)
## Step 3: Design core components
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> Dive into details for each core component.
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### Use case: Service calculates the past week's most popular products by category
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We could store the raw **Sales API** server log files on a managed **Object Store** such as Amazon S3, rather than managing our own distributed file system.
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**Clarify with your interviewer how much code you are expected to write**.
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We'll assume this is a sample log entry, tab delimited:
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```
timestamp product_id category_id qty total_price seller_id buyer_id
t1 product1 category1 2 20.00 1 1
t2 product1 category2 2 20.00 2 2
t2 product1 category2 1 10.00 2 3
t3 product2 category1 3 7.00 3 4
t4 product3 category2 7 2.00 4 5
t5 product4 category1 1 5.00 5 6
...
```
The **Sales Rank Service** could use **MapReduce**, using the **Sales API** server log files as input and writing the results to an aggregate table `sales_rank` in a **SQL Database**. 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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We'll use a multi-step **MapReduce**:
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* **Step 1** - Transform the data to `(category, product_id), sum(quantity)`
* **Step 2** - Perform a distributed sort
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```python
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class SalesRanker(MRJob):
def within_past_week(self, timestamp):
"""Return True if timestamp is within past week, False otherwise."""
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...
def mapper(self, _ line):
"""Parse each log line, extract and transform relevant lines.
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Emit key value pairs of the form:
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(category1, product1), 2
(category2, product1), 2
(category2, product1), 1
(category1, product2), 3
(category2, product3), 7
(category1, product4), 1
"""
timestamp, product_id, category_id, quantity, total_price, seller_id, \
buyer_id = line.split('\t')
if self.within_past_week(timestamp):
yield (category_id, product_id), quantity
def reducer(self, key, value):
"""Sum values for each key.
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(category1, product1), 2
(category2, product1), 3
(category1, product2), 3
(category2, product3), 7
(category1, product4), 1
"""
yield key, sum(values)
def mapper_sort(self, key, value):
"""Construct key to ensure proper sorting.
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Transform key and value to the form:
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(category1, 2), product1
(category2, 3), product1
(category1, 3), product2
(category2, 7), product3
(category1, 1), product4
The shuffle/sort step of MapReduce will then do a
distributed sort on the keys, resulting in:
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(category1, 1), product4
(category1, 2), product1
(category1, 3), product2
(category2, 3), product1
(category2, 7), product3
"""
category_id, product_id = key
quantity = value
yield (category_id, quantity), product_id
def reducer_identity(self, key, value):
yield key, value
def steps(self):
"""Run the map and reduce steps."""
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return [
self.mr(mapper=self.mapper,
reducer=self.reducer),
self.mr(mapper=self.mapper_sort,
reducer=self.reducer_identity),
]
```
The result would be the following sorted list, which we could insert into the `sales_rank` table:
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```
(category1, 1), product4
(category1, 2), product1
(category1, 3), product2
(category2, 3), product1
(category2, 7), product3
```
The `sales_rank` table could have the following structure:
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```
id int NOT NULL AUTO_INCREMENT
category_id int NOT NULL
total_sold int NOT NULL
product_id int NOT NULL
PRIMARY KEY(id)
FOREIGN KEY(category_id) REFERENCES Categories(id)
FOREIGN KEY(product_id) REFERENCES Products(id)
```
We'll create an [index](https://github.com/donnemartin/system-design-primer#use-good-indices) on `id `, `category_id`, and `product_id` 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#latency-numbers-every-programmer-should-know>1</a></sup>
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### Use case: User views the past week's most popular products by category
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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)
* The **Web Server** forwards the request to the **Read API** server
* The **Read API** server reads from the **SQL Database** `sales_rank` table
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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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```
$ curl https://amazon.com/api/v1/popular?category_id=1234
```
Response:
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```
{
"id": "100",
"category_id": "1234",
"total_sold": "100000",
"product_id": "50",
},
{
"id": "53",
"category_id": "1234",
"total_sold": "90000",
"product_id": "200",
},
{
"id": "75",
"category_id": "1234",
"total_sold": "80000",
"product_id": "3",
},
```
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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![Imgur](http://i.imgur.com/MzExP06.png)
**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:
* [DNS](https://github.com/donnemartin/system-design-primer#domain-name-system)
* [CDN](https://github.com/donnemartin/system-design-primer#content-delivery-network)
* [Load balancer](https://github.com/donnemartin/system-design-primer#load-balancer)
* [Horizontal scaling](https://github.com/donnemartin/system-design-primer#horizontal-scaling)
* [Web server (reverse proxy)](https://github.com/donnemartin/system-design-primer#reverse-proxy-web-server)
* [API server (application layer)](https://github.com/donnemartin/system-design-primer#application-layer)
* [Cache](https://github.com/donnemartin/system-design-primer#cache)
* [Relational database management system (RDBMS)](https://github.com/donnemartin/system-design-primer#relational-database-management-system-rdbms)
* [SQL write master-slave failover](https://github.com/donnemartin/system-design-primer#fail-over)
* [Master-slave replication](https://github.com/donnemartin/system-design-primer#master-slave-replication)
* [Consistency patterns](https://github.com/donnemartin/system-design-primer#consistency-patterns)
* [Availability patterns](https://github.com/donnemartin/system-design-primer#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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We might only want to store a limited time period of data in the database, while storing the rest in a data warehouse or in an **Object Store**. An **Object Store** such as Amazon S3 can comfortably handle the constraint of 40 GB of new content per month.
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To address the 40,000 *average* read requests per second (higher at peak), traffic for popular content (and their sales rank) 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. With the large volume of reads, the **SQL Read Replicas** might not be able to handle the cache misses. We'll probably need to employ additional SQL scaling patterns.
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400 *average* writes per second (higher at peak) might be tough for a single **SQL Write Master-Slave**, also pointing to a need for additional scaling techniques.
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SQL scaling patterns include:
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* [Federation](https://github.com/donnemartin/system-design-primer#federation)
* [Sharding](https://github.com/donnemartin/system-design-primer#sharding)
* [Denormalization](https://github.com/donnemartin/system-design-primer#denormalization)
* [SQL Tuning](https://github.com/donnemartin/system-design-primer#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
* [Key-value store](https://github.com/donnemartin/system-design-primer#key-value-store)
* [Document store](https://github.com/donnemartin/system-design-primer#document-store)
* [Wide column store](https://github.com/donnemartin/system-design-primer#wide-column-store)
* [Graph database](https://github.com/donnemartin/system-design-primer#graph-database)
* [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
* [Client caching](https://github.com/donnemartin/system-design-primer#client-caching)
* [CDN caching](https://github.com/donnemartin/system-design-primer#cdn-caching)
* [Web server caching](https://github.com/donnemartin/system-design-primer#web-server-caching)
* [Database caching](https://github.com/donnemartin/system-design-primer#database-caching)
* [Application caching](https://github.com/donnemartin/system-design-primer#application-caching)
* What to cache
* [Caching at the database query level](https://github.com/donnemartin/system-design-primer#caching-at-the-database-query-level)
* [Caching at the object level](https://github.com/donnemartin/system-design-primer#caching-at-the-object-level)
* When to update the cache
* [Cache-aside](https://github.com/donnemartin/system-design-primer#cache-aside)
* [Write-through](https://github.com/donnemartin/system-design-primer#write-through)
* [Write-behind (write-back)](https://github.com/donnemartin/system-design-primer#write-behind-write-back)
* [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)
* [Task queues](https://github.com/donnemartin/system-design-primer#task-queues)
* [Back pressure](https://github.com/donnemartin/system-design-primer#back-pressure)
* [Microservices](https://github.com/donnemartin/system-design-primer#microservices)
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### Communications
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* Discuss tradeoffs:
* External communication with clients - [HTTP APIs following REST](https://github.com/donnemartin/system-design-primer#representational-state-transfer-rest)
* Internal communications - [RPC](https://github.com/donnemartin/system-design-primer#remote-procedure-call-rpc)
* [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
* Scaling is an iterative process