From edc404ec5cfede05ec116bd4f610d998e99bf12c Mon Sep 17 00:00:00 2001 From: Donne Martin Date: Sat, 4 Mar 2017 21:06:17 -0800 Subject: [PATCH] Add Sales Rank solution --- solutions/system_design/sales_rank/README.md | 338 ++++++++++++++++++ .../system_design/sales_rank/__init__.py | 0 .../system_design/sales_rank/sales_rank.png | Bin 0 -> 217898 bytes .../sales_rank/sales_rank_basic.png | Bin 0 -> 80084 bytes .../sales_rank/sales_rank_mapreduce.py | 77 ++++ 5 files changed, 415 insertions(+) create mode 100644 solutions/system_design/sales_rank/README.md create mode 100644 solutions/system_design/sales_rank/__init__.py create mode 100644 solutions/system_design/sales_rank/sales_rank.png create mode 100644 solutions/system_design/sales_rank/sales_rank_basic.png create mode 100644 solutions/system_design/sales_rank/sales_rank_mapreduce.py diff --git a/solutions/system_design/sales_rank/README.md b/solutions/system_design/sales_rank/README.md new file mode 100644 index 00000000..137cd0ce --- /dev/null +++ b/solutions/system_design/sales_rank/README.md @@ -0,0 +1,338 @@ +# 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.1 + +### 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 diff --git 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b/solutions/system_design/sales_rank/sales_rank_mapreduce.py new file mode 100644 index 00000000..bbe844b4 --- /dev/null +++ b/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()