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solutions/system_design/mint/README.md
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# Design Mint.com
*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.*
*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.*
## Step 1: Outline use cases and constraints
@ -88,9 +88,9 @@ Handy conversion guide:
### Use case: User connects to a financial account
We could store info on the 10 million users in a [relational database](https://github.com/donnemartin/system-design-primer-interview#relational-database-management-system-rdbms). 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 could store info on the 10 million users in a [relational database](https://github.com/donnemartin/system-design-primer#relational-database-management-system-rdbms). We should discuss the [use cases and tradeoffs between choosing SQL or NoSQL](https://github.com/donnemartin/system-design-primer#sql-or-nosql).
* 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 **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 **Accounts API** server
* The **Accounts API** server updates the **SQL Database** `accounts` table with the newly entered account info
@ -110,9 +110,9 @@ PRIMARY KEY(id)
FOREIGN KEY(user_id) REFERENCES users(id)
```
We'll create an [index](https://github.com/donnemartin/system-design-primer-interview#use-good-indices) on `id`, `user_id `, 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>
We'll create an [index](https://github.com/donnemartin/system-design-primer#use-good-indices) on `id`, `user_id `, 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#latency-numbers-every-programmer-should-know>1</a></sup>
We'll use a public [**REST API**](https://github.com/donnemartin/system-design-primer-interview##representational-state-transfer-rest):
We'll use a public [**REST API**](https://github.com/donnemartin/system-design-primer#representational-state-transfer-rest):
```
$ curl -X POST --data '{ "user_id": "foo", "account_url": "bar", \
@ -120,7 +120,7 @@ $ curl -X POST --data '{ "user_id": "foo", "account_url": "bar", \
https://mint.com/api/v1/account
```
For internal communications, we could use [Remote Procedure Calls](https://github.com/donnemartin/system-design-primer-interview#remote-procedure-call-rpc).
For internal communications, we could use [Remote Procedure Calls](https://github.com/donnemartin/system-design-primer#remote-procedure-call-rpc).
Next, the service extracts transactions from the account.
@ -137,7 +137,7 @@ Data flow:
* The **Client** sends a request to the **Web Server**
* The **Web Server** forwards the request to the **Accounts API** server
* The **Accounts API** server places a job on a **Queue** such as Amazon SQS or [RabbitMQ](https://www.rabbitmq.com/)
* Extracting transactions could take awhile, we'd probably want to do this [asynchronously with a queue](https://github.com/donnemartin/system-design-primer-interview#asynchronism), although this introduces additional complexity
* Extracting transactions could take awhile, we'd probably want to do this [asynchronously with a queue](https://github.com/donnemartin/system-design-primer#asynchronism), although this introduces additional complexity
* The **Transaction Extraction Service** does the following:
* Pulls from the **Queue** and extracts transactions for the given account from the financial institution, storing the results as raw log files in the **Object Store**
* Uses the **Category Service** to categorize each transaction
@ -160,7 +160,7 @@ PRIMARY KEY(id)
FOREIGN KEY(user_id) REFERENCES users(id)
```
We'll create an [index](https://github.com/donnemartin/system-design-primer-interview#use-good-indices) on `id`, `user_id `, and `created_at`.
We'll create an [index](https://github.com/donnemartin/system-design-primer#use-good-indices) on `id`, `user_id `, and `created_at`.
The `monthly_spending` table could have the following structure:
@ -174,7 +174,7 @@ PRIMARY KEY(id)
FOREIGN KEY(user_id) REFERENCES users(id)
```
We'll create an [index](https://github.com/donnemartin/system-design-primer-interview#use-good-indices) on `id` and `user_id `.
We'll create an [index](https://github.com/donnemartin/system-design-primer#use-good-indices) on `id` and `user_id `.
#### Category service
@ -331,27 +331,27 @@ class SpendingByCategory(MRJob):
**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.
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](https://github.com/donnemartin/system-design-primer/blob/master/solutions/system_design/scaling_aws/README.md) 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:
*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-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)
* [Asynchronism](https://github.com/donnemartin/system-design-primer-interview#aysnchronism)
* [Consistency patterns](https://github.com/donnemartin/system-design-primer-interview#consistency-patterns)
* [Availability patterns](https://github.com/donnemartin/system-design-primer-interview#availability-patterns)
* [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)
* [Asynchronism](https://github.com/donnemartin/system-design-primer#aysnchronism)
* [Consistency patterns](https://github.com/donnemartin/system-design-primer#consistency-patterns)
* [Availability patterns](https://github.com/donnemartin/system-design-primer#availability-patterns)
We'll add an additional use case: **User** accesses summaries and transactions.
@ -367,7 +367,7 @@ User sessions, aggregate stats by category, and recent transactions could be pla
* If the url is in the **SQL Database**, fetches the contents
* Updates the **Memory Cache** with the contents
Refer to [When to update the cache](https://github.com/donnemartin/system-design-primer-interview#when-to-update-the-cache) for tradeoffs and alternatives. The approach above describes [cache-aside](https://github.com/donnemartin/system-design-primer-interview#cache-aside).
Refer to [When to update the cache](https://github.com/donnemartin/system-design-primer#when-to-update-the-cache) for tradeoffs and alternatives. The approach above describes [cache-aside](https://github.com/donnemartin/system-design-primer#cache-aside).
Instead of keeping the `monthly_spending` aggregate table in the **SQL Database**, we could create a separate **Analytics Database** using a data warehousing solution such as Amazon Redshift or Google BigQuery.
@ -377,10 +377,10 @@ To address the 2,000 *average* read requests per second (higher at peak), traffi
200 *average* transaction writes per second (higher at peak) might be tough for a single **SQL Write Master-Slave**. We might 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)
* [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)
We should also consider moving some data to a **NoSQL Database**.
@ -390,50 +390,50 @@ We should also consider moving some data to a **NoSQL Database**.
#### 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#)
* [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)
### 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)
* [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-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)
* [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-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)
* [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)
### 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#)
* [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)
### 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)
* 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)
### Security
Refer to the [security section](https://github.com/donnemartin/system-design-primer-interview#security).
Refer to the [security section](https://github.com/donnemartin/system-design-primer#security).
### Latency numbers
See [Latency numbers every programmer should know](https://github.com/donnemartin/system-design-primer-interview#latency-numbers-every-programmer-should-know).
See [Latency numbers every programmer should know](https://github.com/donnemartin/system-design-primer#latency-numbers-every-programmer-should-know).
### Ongoing

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@ -1,6 +1,6 @@
# 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.*
*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.*
**Design Bit.ly** - is a similar question, except pastebin requires storing the paste contents instead of the original unshortened url.
@ -87,13 +87,13 @@ Handy conversion guide:
### 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.
We could use a [relational database](https://github.com/donnemartin/system-design-primer#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).
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#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.
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#key-value-store). We should discuss the [tradeoffs between choosing SQL or NoSQL](https://github.com/donnemartin/system-design-primer#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 **Client** sends a create paste 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 **Write API** server
* The **Write API** server does does the following:
* Generates a unique url
@ -116,7 +116,7 @@ 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.<sup><a href=https://github.com/donnemartin/system-design-primer-interview#latency-numbers-every-programmer-should-know>1</a></sup>
We'll create an [index](https://github.com/donnemartin/system-design-primer#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#latency-numbers-every-programmer-should-know>1</a></sup>
To generate the unique url, we could:
@ -146,7 +146,7 @@ def base_encode(num, base=62):
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):
We'll use a public [**REST API**](https://github.com/donnemartin/system-design-primer#representational-state-transfer-rest):
```
$ curl -X POST --data '{ "expiration_length_in_minutes": "60", \
@ -161,7 +161,7 @@ Response:
}
```
For internal communications, we could use [Remote Procedure Calls](https://github.com/donnemartin/system-design-primer-interview#remote-procedure-call-rpc).
For internal communications, we could use [Remote Procedure Calls](https://github.com/donnemartin/system-design-primer#remote-procedure-call-rpc).
### Use case: User enters a paste's url and views the contents
@ -239,26 +239,26 @@ To delete expired pastes, we could just scan the **SQL Database** for all entrie
**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.
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](https://github.com/donnemartin/system-design-primer/blob/master/solutions/system_design/scaling_aws/README.md) 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:
*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-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)
* [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)
The **Analytics Database** could use a data warehousing solution such as Amazon Redshift or Google BigQuery.
@ -268,10 +268,10 @@ To address the 40 *average* read requests per second (higher at peak), traffic f
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)
* [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)
We should also consider moving some data to a **NoSQL Database**.
@ -281,50 +281,50 @@ We should also consider moving some data to a **NoSQL Database**.
#### 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#)
* [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)
### 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)
* [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-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)
* [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-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)
* [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)
### 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#)
* [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)
### 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)
* 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)
### Security
Refer to the [security section](https://github.com/donnemartin/system-design-primer-interview#security).
Refer to the [security section](https://github.com/donnemartin/system-design-primer#security).
### Latency numbers
See [Latency numbers every programmer should know](https://github.com/donnemartin/system-design-primer-interview#latency-numbers-every-programmer-should-know).
See [Latency numbers every programmer should know](https://github.com/donnemartin/system-design-primer#latency-numbers-every-programmer-should-know).
### Ongoing

90
solutions/system_design/query_cache/README.md
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@ -1,6 +1,6 @@
# Design a key-value cache to save the results of the most recent web server queries
*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.*
*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.*
## Step 1: Outline use cases and constraints
@ -66,11 +66,11 @@ Handy conversion guide:
### Use case: User sends a request resulting in a cache hit
Popular queries can be served from a **Memory Cache** such as Redis or Memcached to reduce read latency and to avoid overloading the **Reverse Index Service** and **Document Service**. 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>
Popular queries can be served from a **Memory Cache** such as Redis or Memcached to reduce read latency and to avoid overloading the **Reverse Index Service** and **Document Service**. 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>
Since the cache has limited capacity, we'll use a least recently used (LRU) approach to expire older entries.
* 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 **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 **Query API** server
* The **Query API** server does the following:
* Parses the query
@ -206,7 +206,7 @@ The cache should be updated when:
The most straightforward way to handle these cases is to simply set a max time that a cached entry can stay in the cache before it is updated, usually referred to as time to live (TTL).
Refer to [When to update the cache](https://github.com/donnemartin/system-design-primer-interview#when-to-update-the-cache) for tradeoffs and alternatives. The approach above describes [cache-aside](https://github.com/donnemartin/system-design-primer-interview#cache-aside).
Refer to [When to update the cache](https://github.com/donnemartin/system-design-primer#when-to-update-the-cache) for tradeoffs and alternatives. The approach above describes [cache-aside](https://github.com/donnemartin/system-design-primer#cache-aside).
## Step 4: Scale the design
@ -216,22 +216,22 @@ Refer to [When to update the cache](https://github.com/donnemartin/system-design
**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.
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](https://github.com/donnemartin/system-design-primer/blob/master/solutions/system_design/scaling_aws/README.md) 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:
*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-interview#domain-name-system)
* [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)
* [Consistency patterns](https://github.com/donnemartin/system-design-primer-interview#consistency-patterns)
* [Availability patterns](https://github.com/donnemartin/system-design-primer-interview#availability-patterns)
* [DNS](https://github.com/donnemartin/system-design-primer#domain-name-system)
* [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)
* [Consistency patterns](https://github.com/donnemartin/system-design-primer#consistency-patterns)
* [Availability patterns](https://github.com/donnemartin/system-design-primer#availability-patterns)
### Expanding the Memory Cache to many machines
@ -239,7 +239,7 @@ To handle the heavy request load and the large amount of memory needed, we'll sc
* **Each machine in the cache cluster has its own cache** - Simple, although it will likely result in a low cache hit rate.
* **Each machine in the cache cluster has a copy of the cache** - Simple, although it is an inefficient use of memory.
* **The cache is [sharded](https://github.com/donnemartin/system-design-primer-interview#sharding) across all machines in the cache cluster** - More complex, although it is likely the best option. We could use hashing to determine which machine could have the cached results of a query using `machine = hash(query)`. We'll likely want to use [consistent hashing](https://github.com/donnemartin/system-design-primer-interview#consistent-hashing).
* **The cache is [sharded](https://github.com/donnemartin/system-design-primer#sharding) across all machines in the cache cluster** - More complex, although it is likely the best option. We could use hashing to determine which machine could have the cached results of a query using `machine = hash(query)`. We'll likely want to use [consistent hashing](https://github.com/donnemartin/system-design-primer#under-development).
## Additional talking points
@ -247,58 +247,58 @@ To handle the heavy request load and the large amount of memory needed, we'll sc
### SQL scaling patterns
* [Read replicas](https://github.com/donnemartin/system-design-primer-interview#master-slave)
* [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)
* [Read replicas](https://github.com/donnemartin/system-design-primer#master-slave)
* [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)
#### 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#)
* [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)
### 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)
* [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-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)
* [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-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)
* [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)
### 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#)
* [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)
### 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)
* 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)
### Security
Refer to the [security section](https://github.com/donnemartin/system-design-primer-interview#security).
Refer to the [security section](https://github.com/donnemartin/system-design-primer#security).
### Latency numbers
See [Latency numbers every programmer should know](https://github.com/donnemartin/system-design-primer-interview#latency-numbers-every-programmer-should-know).
See [Latency numbers every programmer should know](https://github.com/donnemartin/system-design-primer#latency-numbers-every-programmer-should-know).
### Ongoing

98
solutions/system_design/sales_rank/README.md
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@ -1,6 +1,6 @@
# 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.*
*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.*
## Step 1: Outline use cases and constraints
@ -95,7 +95,7 @@ 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).
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).
We'll use a multi-step **MapReduce**:
@ -196,15 +196,15 @@ 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>
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>
### 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 **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
We'll use a public [**REST API**](https://github.com/donnemartin/system-design-primer-interview##representational-state-transfer-rest):
We'll use a public [**REST API**](https://github.com/donnemartin/system-design-primer#representational-state-transfer-rest):
```
$ curl https://amazon.com/api/v1/popular?category_id=1234
@ -233,7 +233,7 @@ Response:
},
```
For internal communications, we could use [Remote Procedure Calls](https://github.com/donnemartin/system-design-primer-interview#remote-procedure-call-rpc).
For internal communications, we could use [Remote Procedure Calls](https://github.com/donnemartin/system-design-primer#remote-procedure-call-rpc).
## Step 4: Scale the design
@ -243,26 +243,26 @@ For internal communications, we could use [Remote Procedure Calls](https://githu
**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.
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](https://github.com/donnemartin/system-design-primer/blob/master/solutions/system_design/scaling_aws/README.md) 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:
*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-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)
* [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)
The **Analytics Database** could use a data warehousing solution such as Amazon Redshift or Google BigQuery.
@ -274,10 +274,10 @@ To address the 40,000 *average* read requests per second (higher at peak), traff
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)
* [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)
We should also consider moving some data to a **NoSQL Database**.
@ -287,50 +287,50 @@ We should also consider moving some data to a **NoSQL Database**.
#### 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#)
* [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)
### 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)
* [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-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)
* [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-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)
* [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)
### 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#)
* [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)
### 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)
* 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)
### Security
Refer to the [security section](https://github.com/donnemartin/system-design-primer-interview#security).
Refer to the [security section](https://github.com/donnemartin/system-design-primer#security).
### Latency numbers
See [Latency numbers every programmer should know](https://github.com/donnemartin/system-design-primer-interview#latency-numbers-every-programmer-should-know).
See [Latency numbers every programmer should know](https://github.com/donnemartin/system-design-primer#latency-numbers-every-programmer-should-know).
### Ongoing

110
solutions/system_design/scaling_aws/README.md
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@ -1,6 +1,6 @@
# Design a system that scales to millions of users on AWS
*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.*
*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.*
## Step 1: Outline use cases and constraints
@ -83,7 +83,7 @@ Handy conversion guide:
* **Web server** on EC2
* Storage for user data
* [**MySQL Database**](https://github.com/donnemartin/system-design-primer-interview#sql)
* [**MySQL Database**](https://github.com/donnemartin/system-design-primer#sql)
Use **Vertical Scaling**:
@ -96,7 +96,7 @@ Use **Vertical Scaling**:
*Trade-offs, alternatives, and additional details:*
* The alternative to **Vertical Scaling** is [**Horizontal scaling**](https://github.com/donnemartin/system-design-primer-interview#horizontal-scaling)
* The alternative to **Vertical Scaling** is [**Horizontal scaling**](https://github.com/donnemartin/system-design-primer#horizontal-scaling)
#### Start with SQL, consider NoSQL
@ -104,8 +104,8 @@ The constraints assume there is a need for relational data. We can start off us
*Trade-offs, alternatives, and additional details:*
* See the [Relational database management system (RDBMS)](https://github.com/donnemartin/system-design-primer-interview#relational-database-management-system-rdbms) section
* Discuss reasons to use [SQL or NoSQL](https://github.com/donnemartin/system-design-primer-interview#sql-or-nosql)
* See the [Relational database management system (RDBMS)](https://github.com/donnemartin/system-design-primer#relational-database-management-system-rdbms) section
* Discuss reasons to use [SQL or NoSQL](https://github.com/donnemartin/system-design-primer#sql-or-nosql)
#### Assign a public static IP
@ -118,7 +118,7 @@ Add a **DNS** such as Route 53 to map the domain to the instance's public IP.
*Trade-offs, alternatives, and additional details:*
* See the [Domain name system](https://github.com/donnemartin/system-design-primer-interview#domain-name-system) section
* See the [Domain name system](https://github.com/donnemartin/system-design-primer#domain-name-system) section
#### Secure the web server
@ -131,7 +131,7 @@ Add a **DNS** such as Route 53 to map the domain to the instance's public IP.
*Trade-offs, alternatives, and additional details:*
* See the [Security](https://github.com/donnemartin/system-design-primer-interview#security) section
* See the [Security](https://github.com/donnemartin/system-design-primer#security) section
## Step 4: Scale the design
@ -187,7 +187,7 @@ We've been able to address these issues with **Vertical Scaling** so far. Unfor
*Trade-offs, alternatives, and additional details:*
* See the [Security](https://github.com/donnemartin/system-design-primer-interview#security) section
* See the [Security](https://github.com/donnemartin/system-design-primer#security) section
### Users++
@ -201,18 +201,18 @@ Our **Benchmarks/Load Tests** and **Profiling** show that our single **Web Serve
* The following goals attempt to address the scaling issues with the **Web Server**
* Based on the **Benchmarks/Load Tests** and **Profiling**, you might only need to implement one or two of these techniques
* Use [**Horizontal Scaling**](https://github.com/donnemartin/system-design-primer-interview#horizontal-scaling) to handle increasing loads and to address single points of failure
* Add a [**Load Balancer**](https://github.com/donnemartin/system-design-primer-interview#load-balancer) such as Amazon's ELB or HAProxy
* Use [**Horizontal Scaling**](https://github.com/donnemartin/system-design-primer#horizontal-scaling) to handle increasing loads and to address single points of failure
* Add a [**Load Balancer**](https://github.com/donnemartin/system-design-primer#load-balancer) such as Amazon's ELB or HAProxy
* ELB is highly available
* If you are configuring your own **Load Balancer**, setting up multiple servers in [active-active](https://github.com/donnemartin/system-design-primer-interview#active-active) or [active-passive](https://github.com/donnemartin/system-design-primer-interview#active-passive) in multiple availability zones will improve availability
* If you are configuring your own **Load Balancer**, setting up multiple servers in [active-active](https://github.com/donnemartin/system-design-primer#active-active) or [active-passive](https://github.com/donnemartin/system-design-primer#active-passive) in multiple availability zones will improve availability
* Terminate SSL on the **Load Balancer** to reduce computational load on backend servers and to simplify certificate administration
* Use multiple **Web Servers** spread out over multiple availability zones
* Use multiple **MySQL** instances in [**Master-Slave Failover**](https://github.com/donnemartin/system-design-primer-interview#master-slave-replication) mode across multiple availability zones to improve redundancy
* Separate out the **Web Servers** from the [**Application Servers**](https://github.com/donnemartin/system-design-primer-interview#application-layer)
* Use multiple **MySQL** instances in [**Master-Slave Failover**](https://github.com/donnemartin/system-design-primer#master-slave-replication) mode across multiple availability zones to improve redundancy
* Separate out the **Web Servers** from the [**Application Servers**](https://github.com/donnemartin/system-design-primer#application-layer)
* Scale and configure both layers independently
* **Web Servers** can run as a [**Reverse Proxy**](https://github.com/donnemartin/system-design-primer-interview#reverse-proxy)
* **Web Servers** can run as a [**Reverse Proxy**](https://github.com/donnemartin/system-design-primer#reverse-proxy-web-server)
* For example, you can add **Application Servers** handling **Read APIs** while others handle **Write APIs**
* Move static (and some dynamic) content to a [**Content Delivery Network (CDN)**](https://github.com/donnemartin/system-design-primer-interview#content-delivery-network) such as CloudFront to reduce load and latency
* Move static (and some dynamic) content to a [**Content Delivery Network (CDN)**](https://github.com/donnemartin/system-design-primer#content-delivery-network) such as CloudFront to reduce load and latency
*Trade-offs, alternatives, and additional details:*
@ -232,13 +232,13 @@ Our **Benchmarks/Load Tests** and **Profiling** show that we are read-heavy (100
* The following goals attempt to address the scaling issues with the **MySQL Database**
* Based on the **Benchmarks/Load Tests** and **Profiling**, you might only need to implement one or two of these techniques
* Move the following data to a [**Memory Cache**](https://github.com/donnemartin/system-design-primer-interview#cache) such as Elasticache to reduce load and latency:
* Move the following data to a [**Memory Cache**](https://github.com/donnemartin/system-design-primer#cache) such as Elasticache to reduce load and latency:
* Frequently accessed content from **MySQL**
* First, try to configure the **MySQL Database** cache to see if that is sufficient to relieve the bottleneck before implementing a **Memory Cache**
* Session data from the **Web Servers**
* The **Web Servers** become stateless, allowing for **Autoscaling**
* 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>
* Add [**MySQL Read Replicas**](https://github.com/donnemartin/system-design-primer-interview#master-slave-replication) to reduce load on the write master
* 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>
* Add [**MySQL Read Replicas**](https://github.com/donnemartin/system-design-primer#master-slave-replication) to reduce load on the write master
* Add more **Web Servers** and **Application Servers** to improve responsiveness
*Trade-offs, alternatives, and additional details:*
@ -254,7 +254,7 @@ Our **Benchmarks/Load Tests** and **Profiling** show that we are read-heavy (100
*Trade-offs, alternatives, and additional details:*
* See the [Relational database management system (RDBMS)](https://github.com/donnemartin/system-design-primer-interview#relational-database-management-system-rdbms) section
* See the [Relational database management system (RDBMS)](https://github.com/donnemartin/system-design-primer#relational-database-management-system-rdbms) section
### Users++++
@ -317,14 +317,14 @@ We'll continue to address scaling issues due to the problem's constraints:
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)
* [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)
To further address the high read and write requests, we should also consider moving appropriate data to a [**NoSQL Database**](https://github.com/donnemartin/system-design-primer-interview#nosql) such as DynamoDB.
To further address the high read and write requests, we should also consider moving appropriate data to a [**NoSQL Database**](https://github.com/donnemartin/system-design-primer#nosql) such as DynamoDB.
We can further separate out our [**Application Servers**](https://github.com/donnemartin/system-design-primer-interview#application-layer) to allow for independent scaling. Batch processes or computations that do not need to be done in real-time can be done [**Asynchronously**](https://github.com/donnemartin/system-design-primer-interview#asynchronism) with **Queues** and **Workers**:
We can further separate out our [**Application Servers**](https://github.com/donnemartin/system-design-primer#application-layer) to allow for independent scaling. Batch processes or computations that do not need to be done in real-time can be done [**Asynchronously**](https://github.com/donnemartin/system-design-primer#asynchronism) with **Queues** and **Workers**:
* For example, in a photo service, the photo upload and the thumbnail creation can be separated:
* **Client** uploads photo
@ -344,58 +344,58 @@ We can further separate out our [**Application Servers**](https://github.com/don
### SQL scaling patterns
* [Read replicas](https://github.com/donnemartin/system-design-primer-interview#master-slave)
* [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)
* [Read replicas](https://github.com/donnemartin/system-design-primer#master-slave)
* [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)
#### 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#)
* [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)
### 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)
* [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-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)
* [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-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)
* [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)
### 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#)
* [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)
### 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)
* 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)
### Security
Refer to the [security section](https://github.com/donnemartin/system-design-primer-interview#security).
Refer to the [security section](https://github.com/donnemartin/system-design-primer#security).
### Latency numbers
See [Latency numbers every programmer should know](https://github.com/donnemartin/system-design-primer-interview#latency-numbers-every-programmer-should-know).
See [Latency numbers every programmer should know](https://github.com/donnemartin/system-design-primer#latency-numbers-every-programmer-should-know).
### Ongoing

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@ -1,6 +1,6 @@
# Design the data structures for a social network
*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.*
*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.*
## Step 1: Outline use cases and constraints
@ -99,9 +99,9 @@ class Graph(Graph):
return None
```
We won't be able to fit all users on the same machine, we'll need to [shard](https://github.com/donnemartin/system-design-primer-interview#sharding) users across **Person Servers** and access them with a **Lookup Service**.
We won't be able to fit all users on the same machine, we'll need to [shard](https://github.com/donnemartin/system-design-primer#sharding) users across **Person Servers** and access them with a **Lookup Service**.
* 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 **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 **Search API** server
* The **Search API** server forwards the request to the **User Graph Service**
* The **User Graph Service** does does the following:
@ -218,7 +218,7 @@ class UserGraphService(object):
return None
```
We'll use a public [**REST API**](https://github.com/donnemartin/system-design-primer-interview##representational-state-transfer-rest):
We'll use a public [**REST API**](https://github.com/donnemartin/system-design-primer#representational-state-transfer-rest):
```
$ curl https://social.com/api/v1/friend_search?person_id=1234
@ -244,7 +244,7 @@ Response:
},
```
For internal communications, we could use [Remote Procedure Calls](https://github.com/donnemartin/system-design-primer-interview#remote-procedure-call-rpc).
For internal communications, we could use [Remote Procedure Calls](https://github.com/donnemartin/system-design-primer#remote-procedure-call-rpc).
## Step 4: Scale the design
@ -254,31 +254,31 @@ For internal communications, we could use [Remote Procedure Calls](https://githu
**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.
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](https://github.com/donnemartin/system-design-primer/blob/master/solutions/system_design/scaling_aws/README.md) 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:
*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-interview#domain-name-system)
* [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)
* [Consistency patterns](https://github.com/donnemartin/system-design-primer-interview#consistency-patterns)
* [Availability patterns](https://github.com/donnemartin/system-design-primer-interview#availability-patterns)
* [DNS](https://github.com/donnemartin/system-design-primer#domain-name-system)
* [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)
* [Consistency patterns](https://github.com/donnemartin/system-design-primer#consistency-patterns)
* [Availability patterns](https://github.com/donnemartin/system-design-primer#availability-patterns)
To address the constraint of 400 *average* read requests per second (higher at peak), person data can be served from a **Memory Cache** such as Redis or Memcached to reduce response times and to reduce traffic to downstream services. This could be especially useful for people who do multiple searches in succession and for people who are well-connected. 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>
To address the constraint of 400 *average* read requests per second (higher at peak), person data can be served from a **Memory Cache** such as Redis or Memcached to reduce response times and to reduce traffic to downstream services. This could be especially useful for people who do multiple searches in succession and for people who are well-connected. 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>
Below are further optimizations:
* Store complete or partial BFS traversals to speed up subsequent lookups in the **Memory Cache**
* Batch compute offline then store complete or partial BFS traversals to speed up subsequent lookups in a **NoSQL Database**
* Reduce machine jumps by batching together friend lookups hosted on the same **Person Server**
* [Shard](https://github.com/donnemartin/system-design-primer-interview#Sharding) **Person Servers** by location to further improve this, as friends generally live closer to each other
* [Shard](https://github.com/donnemartin/system-design-primer#sharding) **Person Servers** by location to further improve this, as friends generally live closer to each other
* Do two BFS searches at the same time, one starting from the source, and one from the destination, then merge the two paths
* Start the BFS search from people with large numbers of friends, as they are more likely to reduce the number of [degrees of separation](https://en.wikipedia.org/wiki/Six_degrees_of_separation) between the current user and the search target
* Set a limit based on time or number of hops before asking the user if they want to continue searching, as searching could take a considerable amount of time in some cases
@ -290,58 +290,58 @@ Below are further optimizations:
### SQL scaling patterns
* [Read replicas](https://github.com/donnemartin/system-design-primer-interview#master-slave)
* [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)
* [Read replicas](https://github.com/donnemartin/system-design-primer#master-slave)
* [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)
#### 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#)
* [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)
### 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)
* [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-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)
* [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-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)
* [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)
### 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#)
* [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)
### 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)
* 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)
### Security
Refer to the [security section](https://github.com/donnemartin/system-design-primer-interview#security).
Refer to the [security section](https://github.com/donnemartin/system-design-primer#security).
### Latency numbers
See [Latency numbers every programmer should know](https://github.com/donnemartin/system-design-primer-interview#latency-numbers-every-programmer-should-know).
See [Latency numbers every programmer should know](https://github.com/donnemartin/system-design-primer#latency-numbers-every-programmer-should-know).
### Ongoing

100
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@ -1,6 +1,6 @@
# Design the Twitter timeline and search
*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.*
*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.*
**Design the Facebook feed** and **Design Facebook search** are similar questions.
@ -100,13 +100,13 @@ Handy conversion guide:
### Use case: User posts a tweet
We could store the user's own tweets to populate the user timeline (activity from the user) in a [relational database](https://github.com/donnemartin/system-design-primer-interview#relational-database-management-system-rdbms). 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 could store the user's own tweets to populate the user timeline (activity from the user) in a [relational database](https://github.com/donnemartin/system-design-primer#relational-database-management-system-rdbms). We should discuss the [use cases and tradeoffs between choosing SQL or NoSQL](https://github.com/donnemartin/system-design-primer#sql-or-nosql).
Delivering tweets and building the home timeline (activity from people the user is following) is trickier. Fanning out tweets to all followers (60 thousand tweets delivered on fanout per second) will overload a traditional [relational database](https://github.com/donnemartin/system-design-primer-interview#relational-database-management-system-rdbms). We'll probably want to choose a data store with fast writes such as a **NoSQL database** or **Memory Cache**. 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>
Delivering tweets and building the home timeline (activity from people the user is following) is trickier. Fanning out tweets to all followers (60 thousand tweets delivered on fanout per second) will overload a traditional [relational database](https://github.com/donnemartin/system-design-primer#relational-database-management-system-rdbms). We'll probably want to choose a data store with fast writes such as a **NoSQL database** or **Memory Cache**. 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>
We could store media such as photos or videos on an **Object Store**.
* The **Client** posts a tweet to the **Web Server**, running as a [reverse proxy](https://github.com/donnemartin/system-design-primer-interview#reverse-proxy-web-server)
* The **Client** posts a tweet 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 **Write API** server
* The **Write API** stores the tweet in the user's timeline on a **SQL database**
* The **Write API** contacts the **Fan Out Service**, which does the following:
@ -130,7 +130,7 @@ If our **Memory Cache** is Redis, we could use a native Redis list with the foll
The new tweet would be placed in the **Memory Cache**, which populates user's home timeline (activity from people the user is following).
We'll use a public [**REST API**](https://github.com/donnemartin/system-design-primer-interview##representational-state-transfer-rest):
We'll use a public [**REST API**](https://github.com/donnemartin/system-design-primer#representational-state-transfer-rest):
```
$ curl -X POST --data '{ "user_id": "123", "auth_token": "ABC123", \
@ -150,7 +150,7 @@ Response:
}
```
For internal communications, we could use [Remote Procedure Calls](https://github.com/donnemartin/system-design-primer-interview#remote-procedure-call-rpc).
For internal communications, we could use [Remote Procedure Calls](https://github.com/donnemartin/system-design-primer#remote-procedure-call-rpc).
### Use case: User views the home timeline
@ -207,7 +207,7 @@ The REST API would be similar to the home timeline, except all tweets would come
* Normalizes capitalization
* Converts the query to use boolean operations
* Queries the **Search Cluster** (ie [Lucene](https://lucene.apache.org/)) for the results:
* [Scatter gathers](https://github.com/donnemartin/system-design-primer-interview#scatter-gather) each server in the cluster to determine if there are any results for the query
* [Scatter gathers](https://github.com/donnemartin/system-design-primer#under-development) each server in the cluster to determine if there are any results for the query
* Merges, ranks, sorts, and returns the results
REST API:
@ -226,26 +226,26 @@ The response would be similar to that of the home timeline, except for tweets ma
**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.
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](https://github.com/donnemartin/system-design-primer/blob/master/solutions/system_design/scaling_aws/README.md) 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:
*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-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)
* [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)
The **Fanout Service** is a potential bottleneck. Twitter users with millions of followers could take several minutes to have their tweets go through the fanout process. This could lead to race conditions with @replies to the tweet, which we could mitigate by re-ordering the tweets at serve time.
@ -268,10 +268,10 @@ Although the **Memory Cache** should reduce the load on the database, it is unli
The high volume of writes would overwhelm a single **SQL Write Master-Slave**, also pointing to a need for additional scaling techniques.
* [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)
* [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)
We should also consider moving some data to a **NoSQL Database**.
@ -281,50 +281,50 @@ We should also consider moving some data to a **NoSQL Database**.
#### 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#)
* [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)
### 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)
* [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-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)
* [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-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)
* [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)
### 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#)
* [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)
### 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)
* 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)
### Security
Refer to the [security section](https://github.com/donnemartin/system-design-primer-interview#security).
Refer to the [security section](https://github.com/donnemartin/system-design-primer#security).
### Latency numbers
See [Latency numbers every programmer should know](https://github.com/donnemartin/system-design-primer-interview#latency-numbers-every-programmer-should-know).
See [Latency numbers every programmer should know](https://github.com/donnemartin/system-design-primer#latency-numbers-every-programmer-should-know).
### Ongoing

96
solutions/system_design/web_crawler/README.md
View File

@ -1,6 +1,6 @@
# Design a web crawler
*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.*
*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.*
## Step 1: Outline use cases and constraints
@ -81,7 +81,7 @@ We'll assume we have an initial list of `links_to_crawl` ranked initially based
We'll use a table `crawled_links` to store processed links and their page signatures.
We could store `links_to_crawl` and `crawled_links` in a key-value **NoSQL Database**. For the ranked links in `links_to_crawl`, we could use [Redis](https://redis.io/) with sorted sets to maintain a ranking of page links. 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 could store `links_to_crawl` and `crawled_links` in a key-value **NoSQL Database**. For the ranked links in `links_to_crawl`, we could use [Redis](https://redis.io/) with sorted sets to maintain a ranking of page links. We should discuss the [use cases and tradeoffs between choosing SQL or NoSQL](https://github.com/donnemartin/system-design-primer#sql-or-nosql).
* The **Crawler Service** processes each page link by doing the following in a loop:
* Takes the top ranked page link to crawl
@ -211,7 +211,7 @@ We might also choose to support a `Robots.txt` file that gives webmasters contro
### Use case: User inputs a search term and sees a list of relevant pages with titles and snippets
* 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 **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 **Query API** server
* The **Query API** server does does the following:
* Parses the query
@ -224,7 +224,7 @@ We might also choose to support a `Robots.txt` file that gives webmasters contro
* The **Reverse Index Service** ranks the matching results and returns the top ones
* Uses the **Document Service** to return titles and snippets
We'll use a public [**REST API**](https://github.com/donnemartin/system-design-primer-interview##representational-state-transfer-rest):
We'll use a public [**REST API**](https://github.com/donnemartin/system-design-primer#representational-state-transfer-rest):
```
$ curl https://search.com/api/v1/search?query=hello+world
@ -250,7 +250,7 @@ Response:
},
```
For internal communications, we could use [Remote Procedure Calls](https://github.com/donnemartin/system-design-primer-interview#remote-procedure-call-rpc).
For internal communications, we could use [Remote Procedure Calls](https://github.com/donnemartin/system-design-primer#remote-procedure-call-rpc).
## Step 4: Scale the design
@ -260,32 +260,32 @@ For internal communications, we could use [Remote Procedure Calls](https://githu
**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.
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](https://github.com/donnemartin/system-design-primer/blob/master/solutions/system_design/scaling_aws/README.md) 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:
*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-interview#domain-name-system)
* [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)
* [NoSQL](https://github.com/donnemartin/system-design-primer-interview#nosql)
* [Consistency patterns](https://github.com/donnemartin/system-design-primer-interview#consistency-patterns)
* [Availability patterns](https://github.com/donnemartin/system-design-primer-interview#availability-patterns)
* [DNS](https://github.com/donnemartin/system-design-primer#domain-name-system)
* [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)
* [NoSQL](https://github.com/donnemartin/system-design-primer#nosql)
* [Consistency patterns](https://github.com/donnemartin/system-design-primer#consistency-patterns)
* [Availability patterns](https://github.com/donnemartin/system-design-primer#availability-patterns)
Some searches are very popular, while others are only executed once. Popular queries can be served from a **Memory Cache** such as Redis or Memcached to reduce response times and to avoid overloading the **Reverse Index Service** and **Document Service**. The **Memory Cache** is also useful for handling the unevenly distributed traffic and traffic spikes. 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>
Some searches are very popular, while others are only executed once. Popular queries can be served from a **Memory Cache** such as Redis or Memcached to reduce response times and to avoid overloading the **Reverse Index Service** and **Document Service**. The **Memory Cache** is also useful for handling the unevenly distributed traffic and traffic spikes. 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>
Below are a few other optimizations to the **Crawling Service**:
* To handle the data size and request load, the **Reverse Index Service** and **Document Service** will likely need to make heavy use sharding and replication.
* DNS lookup can be a bottleneck, the **Crawler Service** can keep its own DNS lookup that is refreshed periodically
* The **Crawler Service** can improve performance and reduce memory usage by keeping many open connections at a time, referred to as [connection pooling](https://en.wikipedia.org/wiki/Connection_pool)
* Switching to [UDP](https://github.com/donnemartin/system-design-primer-interview#user-datagram-protocol-udp) could also boost performance
* Switching to [UDP](https://github.com/donnemartin/system-design-primer#user-datagram-protocol-udp) could also boost performance
* Web crawling is bandwidth intensive, ensure there is enough bandwidth to sustain high throughput
## Additional talking points
@ -294,58 +294,58 @@ Below are a few other optimizations to the **Crawling Service**:
### SQL scaling patterns
* [Read replicas](https://github.com/donnemartin/system-design-primer-interview#master-slave)
* [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)
* [Read replicas](https://github.com/donnemartin/system-design-primer#master-slave)
* [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)
#### 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#)
* [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)
### 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)
* [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-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)
* [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-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)
* [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)
### 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#)
* [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)
### 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)
* 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)
### Security
Refer to the [security section](https://github.com/donnemartin/system-design-primer-interview#security).
Refer to the [security section](https://github.com/donnemartin/system-design-primer#security).
### Latency numbers
See [Latency numbers every programmer should know](https://github.com/donnemartin/system-design-primer-interview#latency-numbers-every-programmer-should-know).
See [Latency numbers every programmer should know](https://github.com/donnemartin/system-design-primer#latency-numbers-every-programmer-should-know).
### Ongoing