Add Sales Rank solution

solutions/system_design/sales_rank/README.md

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+# Design Amazon's sales rank by category feature
+
+*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 case
+
+* **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
+
+#### Out of scope
+
+* The general e-commerce site
+    * Design components only for calculating sales rank
+
+### Constraints and assumptions
+
+#### State assumptions
+
+* 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
+
+#### Calculate usage
+
+**Clarify with your interviewer if you should run back-of-the-envelope usage calculations.**
+
+* 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
+
+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/vwMa1Qu.png)
+
+## Step 3: Design core components
+
+> Dive into details for each core component.
+
+### Use case: Service calculates the past week's most popular products by category
+
+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.
+
+**Clarify with your interviewer how much code you are expected to write**.
+
+We'll assume this is a sample log entry, tab delimited:
+
+```
+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-interview#sql-or-nosql).
+
+We'll use a multi-step **MapReduce**:
+
+* **Step 1** - Transform the data to `(category, product_id), sum(quantity)`
+* **Step 2** - Perform a distributed sort
+
+```
+class SalesRanker(MRJob):
+
+    def within_past_week(self, timestamp):
+        """Return True if timestamp is within past week, False otherwise."""
+        ...
+
+    def mapper(self, _ line):
+        """Parse each log line, extract and transform relevant lines.
+
+        Emit key value pairs of the form:
+
+        (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.
+
+        (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.
+
+        Transform key and value to the form:
+
+        (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:
+
+        (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."""
+        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:
+
+```
+(category1, 1), product4
+(category1, 2), product1
+(category1, 3), product2
+(category2, 3), product1
+(category2, 7), product3
+```
+
+The `sales_rank` table could have the following structure:
+
+```
+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-interview#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-interview#latency-numbers-every-programmer-should-know>1</a></sup>
+
+### Use case: User views the past week's most popular products by category
+
+* 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 **Read API** server
+* The **Read API** server reads from the **SQL Database** `sales_rank` table
+
+We'll use a public [**REST API**](https://github.com/donnemartin/system-design-primer-interview##representational-state-transfer-rest):
+
+```
+$ curl https://amazon.com/api/v1/popular?category_id=1234
+```
+
+Response:
+
+```
+{
+    "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-interview#remote-procedure-call-rpc).
+
+## Step 4: Scale the design
+
+> Identify and address bottlenecks, given the constraints.
+
+![Imgur](http://i.imgur.com/MzExP06.png)
+
+**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)
+* [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.
+
+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.
+
+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.
+
+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.
+
+SQL scaling patterns include:
+
+* [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

solutions/system_design/sales_rank/sales_rank_mapreduce.py

@@ -0,0 +1,77 @@
+# -*- coding: utf-8 -*-
+
+from mrjob.job import MRJob
+
+
+class SalesRanker(MRJob):
+
+    def within_past_week(self, timestamp):
+        """Return True if timestamp is within past week, False otherwise."""
+        ...
+
+    def mapper(self, _ line):
+        """Parse each log line, extract and transform relevant lines.
+
+        Emit key value pairs of the form:
+
+        (foo, p1), 2
+        (bar, p1), 2
+        (bar, p1), 1
+        (foo, p2), 3
+        (bar, p3), 10
+        (foo, p4), 1
+        """
+        timestamp, product_id, category, quantity = line.split('\t')
+        if self.within_past_week(timestamp):
+            yield (category, product_id), quantity
+
+    def reducer(self, key, value):
+        """Sum values for each key.
+
+        (foo, p1), 2
+        (bar, p1), 3
+        (foo, p2), 3
+        (bar, p3), 10
+        (foo, p4), 1
+        """
+        yield key, sum(values)
+
+    def mapper_sort(self, key, value):
+        """Construct key to ensure proper sorting.
+
+        Transform key and value to the form:
+
+        (foo, 2), p1
+        (bar, 3), p1
+        (foo, 3), p2
+        (bar, 10), p3
+        (foo, 1), p4
+
+        The shuffle/sort step of MapReduce will then do a
+        distributed sort on the keys, resulting in:
+
+        (category1, 1), product4
+        (category1, 2), product1
+        (category1, 3), product2
+        (category2, 3), product1
+        (category2, 7), product3
+        """
+        category, product_id = key
+        quantity = value
+        yield (category, quantity), product_id
+
+    def reducer_identity(self, key, value):
+        yield key, value
+
+    def steps(self):
+        """Run the map and reduce steps."""
+        return [
+            self.mr(mapper=self.mapper,
+                    reducer=self.reducer),
+            self.mr(mapper=self.mapper_sort,
+                    reducer=self.reducer_identity),
+        ]
+
+
+if __name__ == '__main__':
+    HitCounts.run()