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@ -13,19 +13,19 @@ _[Source: Intro to architecting systems for scale](http://lethain.com/introducti
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Separating out the web layer from the application layer (also known as platform layer) allows you to scale and configure both layers independently. Adding a new API results in adding application servers without necessarily adding additional web servers.
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Separating out the web layer from the application layer (also known as platform layer) allows you to scale and configure both layers independently. Adding a new API results in adding application servers without necessarily adding additional web servers.
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The single responsibility principle advocates for small and autonomous services that work together. Small teams with small services can plan more aggressively for rapid growth.
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The single responsibility principle advocates for small and autonomous services that work together. Small teams with small services can plan more aggressively for rapid growth.
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Workers in the application layer also help enable [asynchronism](https://github.com/donnemartin/system-design-primer#asynchronism) .
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Workers in the application layer also help enable [asynchronism](https://github.com/donnemartin/system-design-primer#asynchronism) .
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### Microservices
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### Microservices
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Related to this discussion are [microservices](https://en.wikipedia.org/wiki/Microservices) , which can be described as a suite of independently deployable, small, modular services. Each service runs a unique process and communicates through a well-definied, lightweight mechanism to serve a business goal. [1](https://smartbear.com/learn/api-design/what-are-microservices)
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Related to this discussion are [microservices](https://en.wikipedia.org/wiki/Microservices) , which can be described as a suite of independently deployable, small, modular services. Each service runs a unique process and communicates through a well-definied, lightweight mechanism to serve a business goal. [1](https://smartbear.com/learn/api-design/what-are-microservices)
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Pinterest, for example, could have the following microservices: user profile, follower, feed, search, photo upload, etc.
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Pinterest, for example, could have the following microservices: user profile, follower, feed, search, photo upload, etc.
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### Service Discovery
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### Service Discovery
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Systems such as [Zookeeper](http://www.slideshare.net/sauravhaloi/introduction-to-apache-zookeeper) can help services find each other by keeping track of registered names, addresses, ports, etc.
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Systems such as [Zookeeper](http://www.slideshare.net/sauravhaloi/introduction-to-apache-zookeeper) can help services find each other by keeping track of registered names, addresses, ports, etc.
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### Disadvantage(s) : application layer
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### Disadvantage(s) : application layer
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@ -19,9 +19,9 @@ Message queues receive, hold, and deliver messages. If an operation is too slow
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The user is not blocked and the job is processed in the background. During this time, the client might optionally do a small amount of processing to make it seem like the task has completed. For example, if posting a tweet, the tweet could be instantly posted to your timeline, but it could take some time before your tweet is actually delivered to all of your followers.
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The user is not blocked and the job is processed in the background. During this time, the client might optionally do a small amount of processing to make it seem like the task has completed. For example, if posting a tweet, the tweet could be instantly posted to your timeline, but it could take some time before your tweet is actually delivered to all of your followers.
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Redis is useful as a simple message broker but messages can be lost.
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Redis is useful as a simple message broker but messages can be lost.
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RabbitMQ is popular but requires you to adapt to the 'AMQP' protocol and manage your own nodes.
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RabbitMQ is popular but requires you to adapt to the 'AMQP' protocol and manage your own nodes.
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Amazon SQS, is hosted but can have high latency and has the possibility of messages being delivered twice.
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Amazon SQS, is hosted but can have high latency and has the possibility of messages being delivered twice.
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@ -29,11 +29,11 @@ Amazon SQS, is hosted but can have high latency and has the possibility of messa
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Tasks queues receive tasks and their related data, runs them, then delivers their results. They can support scheduling and can be used to run computationally-intensive jobs in the background.
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Tasks queues receive tasks and their related data, runs them, then delivers their results. They can support scheduling and can be used to run computationally-intensive jobs in the background.
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Celery has support for scheduling and primarily has python support.
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Celery has support for scheduling and primarily has python support.
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### Back pressure
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### Back pressure
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If queues start to grow significantly, the queue size can become larger than memory, resulting in cache misses, disk reads, and even slower performance. [Back pressure](http://mechanical-sympathy.blogspot.com/2012/05/apply-back-pressure-when-overloaded.html) can help by limiting the queue size, thereby maintaining a high throughput rate and good response times for jobs already in the queue. Once the queue fills up, clients get a server busy or HTTP 503 status code to try again later. Clients can retry the request at a later time, perhaps with [exponential backoff](https://en.wikipedia.org/wiki/Exponential_backoff) .
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If queues start to grow significantly, the queue size can become larger than memory, resulting in cache misses, disk reads, and even slower performance. [Back pressure](http://mechanical-sympathy.blogspot.com/2012/05/apply-back-pressure-when-overloaded.html) can help by limiting the queue size, thereby maintaining a high throughput rate and good response times for jobs already in the queue. Once the queue fills up, clients get a server busy or HTTP 503 status code to try again later. Clients can retry the request at a later time, perhaps with [exponential backoff](https://en.wikipedia.org/wiki/Exponential_backoff) .
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### Disadvantage(s) : asynchronism
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### Disadvantage(s) : asynchronism
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@ -39,7 +39,7 @@ _[Source: Scalability, availability, stability, patterns](http://www.slideshare.
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### Disadvantage(s) : master-slave replication
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### Disadvantage(s) : master-slave replication
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- Additional logic is needed to promote a slave to a master.
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- Additional logic is needed to promote a slave to a master.
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- See [Disadvantage(s) : replication](https://github.com/donnemartin/system-design-primer#disadvantages-replication) for points related to both master-slave and master-master.
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- See [Disadvantage(s) : replication](https://github.com/donnemartin/system-design-primer#disadvantages-replication) for points related to both master-slave and master-master.
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### Master-master replication
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### Master-master replication
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@ -53,7 +53,7 @@ _[Source: Scalability, availability, stability, patterns](http://www.slideshare.
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- You'll need a load balancer or you'll need to make changes to your application logic to determine where to write.
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- You'll need a load balancer or you'll need to make changes to your application logic to determine where to write.
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- Most master-master systems are either loosely consistent (violating ACID) or have increased write latency due to synchronization.
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- Most master-master systems are either loosely consistent (violating ACID) or have increased write latency due to synchronization.
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- Conflict resolution comes more into play as more write nodes are added and as latency increases.
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- Conflict resolution comes more into play as more write nodes are added and as latency increases.
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- See [Disadvantage(s) : replication](https://github.com/donnemartin/system-design-primer#disadvantages-replication) for points related to both master-slave and master-master.
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- See [Disadvantage(s) : replication](https://github.com/donnemartin/system-design-primer#disadvantages-replication) for points related to both master-slave and master-master.
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### Disadvantage(s) : replication
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### Disadvantage(s) : replication
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@ -7,14 +7,14 @@ isdraft = False
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### CAP theorem
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### CAP theorem
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[ ](https://camo.githubusercontent.com/13719354da7dcd34cd79ff5f8b6306a67bc18261/687474703a2f2f692e696d6775722e636f6d2f62674c4d4932752e706e67)
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[ ](https://camo.githubusercontent.com/13719354da7dcd34cd79ff5f8b6306a67bc18261/687474703a2f2f692e696d6775722e636f6d2f62674c4d4932752e706e67)
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_[Source: CAP theorem revisited](http://robertgreiner.com/2014/08/cap-theorem-revisited) _
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_[Source: CAP theorem revisited](http://robertgreiner.com/2014/08/cap-theorem-revisited) _
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In a distributed computer system, you can only support two of the following guarantees:
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In a distributed computer system, you can only support two of the following guarantees:
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- Consistency - Every read receives the most recent write or an error
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- Consistency - Every read receives the most recent write or an error
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- Availability - Every request receives a response, without guarantee that it contains the most recent version of the information
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- Availability - Every request receives a response, without guarantee that it contains the most recent version of the information
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- Partition Tolerance - The system continues to operate despite arbitrary partitioning due to network failures
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- Partition Tolerance - The system continues to operate despite arbitrary partitioning due to network failures
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_Networks aren't reliable, so you'll need to support partition tolerance. You'll need to make a software tradeoff between consistency and availability._
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_Networks aren't reliable, so you'll need to support partition tolerance. You'll need to make a software tradeoff between consistency and availability._
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Responses return the most recent version of the data, which might not be the latest. Writes might take some time to propagate when the partition is resolved.
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Responses return the most recent version of the data, which might not be the latest. Writes might take some time to propagate when the partition is resolved.
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AP is a good choice if the business needs allow for [eventual consistency](https://github.com/donnemartin/system-design-primer#eventual-consistency) or when the system needs to continue working despite external errors.
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AP is a good choice if the business needs allow for [eventual consistency](https://github.com/donnemartin/system-design-primer#eventual-consistency) or when the system needs to continue working despite external errors.
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### Source(s) and further reading
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### Source(s) and further reading
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---
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---
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## Introduction of base 62
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## Introduction of base 62
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- Encodes to `[a-zA-Z0-9]` which works well for urls, eliminating the need for escaping special characters
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- Encodes to `[a-zA-Z0-9]` which works well for urls, eliminating the need for escaping special characters
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- Only one hash result for the original input and and the operation is deterministic (no randomness involved)
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- Only one hash result for the original input and and the operation is deterministic (no randomness involved)
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- Base 64 is another popular encoding but provides issues for urls because of the additional `+` and `/` characters
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- Base 64 is another popular encoding but provides issues for urls because of the additional `+` and `/` characters
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# Cache locations
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# Cache locations
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### Client caching
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### Client caching
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Caches can be located on the client side (OS or browser) , [server side](https://github.com/donnemartin/system-design-primer#reverse-proxy) , or in a distinct cache layer.
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Caches can be located on the client side (OS or browser) , [server side](https://github.com/donnemartin/system-design-primer#reverse-proxy) , or in a distinct cache layer.
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### CDN caching
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### CDN caching
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### Web server caching
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### Web server caching
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[Reverse proxies](https://github.com/donnemartin/system-design-primer#reverse-proxy-web-server) and caches such as [Varnish](https://www.varnish-cache.org/) can serve static and dynamic content directly. Web servers can also cache requests, returning responses without having to contact application servers.
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[Reverse proxies](https://github.com/donnemartin/system-design-primer#reverse-proxy-web-server) and caches such as [Varnish](https://www.varnish-cache.org/) can serve static and dynamic content directly. Web servers can also cache requests, returning responses without having to contact application servers.
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### Database caching
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### Database caching
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### Application caching
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### Application caching
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In-memory caches such as Memcached and Redis are key-value stores between your application and your data storage. Since the data is held in RAM, it is much faster than typical databases where data is stored on disk. RAM is more limited than disk, so [cache invalidation](https://en.wikipedia.org/wiki/Cache_algorithms) algorithms such as [least recently used (LRU) ](https://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used) can help invalidate 'cold' entries and keep 'hot' data in RAM.
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In-memory caches such as Memcached and Redis are key-value stores between your application and your data storage. Since the data is held in RAM, it is much faster than typical databases where data is stored on disk. RAM is more limited than disk, so [cache invalidation](https://en.wikipedia.org/wiki/Cache_algorithms) algorithms such as [least recently used (LRU) ](https://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used) can help invalidate 'cold' entries and keep 'hot' data in RAM.
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Redis has the following additional features:
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Redis has the following additional features:
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- Persistence option
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- Persistence option
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- Built-in data structures such as sorted sets and lists
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- Built-in data structures such as sorted sets and lists
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There are multiple levels you can cache that fall into two general categories: database queries and objects:
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There are multiple levels you can cache that fall into two general categories: database queries and objects:
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- Row level
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- Row level
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- Query-level
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- Query-level
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### Disadvantage(s) : cache
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### Disadvantage(s) : cache
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- Need to maintain consistency between caches and the source of truth such as the database through [cache invalidation](https://en.wikipedia.org/wiki/Cache_algorithms) .
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- Need to maintain consistency between caches and the source of truth such as the database through [cache invalidation](https://en.wikipedia.org/wiki/Cache_algorithms) .
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- Need to make application changes such as adding Redis or memcached.
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- Need to make application changes such as adding Redis or memcached.
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- Cache invalidation is a difficult problem, there is additional complexity associated with when to update the cache.
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- Cache invalidation is a difficult problem, there is additional complexity associated with when to update the cache.
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Communication
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Communication
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-------------
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-------------
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---
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---
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[ ](https://camo.githubusercontent.com/1d761d5688d28ce1fb12a0f1c8191bca96eece4c/687474703a2f2f692e696d6775722e636f6d2f354b656f6351732e6a7067)
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[ ](https://camo.githubusercontent.com/1d761d5688d28ce1fb12a0f1c8191bca96eece4c/687474703a2f2f692e696d6775722e636f6d2f354b656f6351732e6a7067)
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_[Source: OSI 7 layer model](http://www.escotal.com/osilayer.html) _
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_[Source: OSI 7 layer model](http://www.escotal.com/osilayer.html) _
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## Introduction
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## Introduction
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With multiple copies of the same data, we are faced with options on how to synchronize them so clients have a consistent view of the data. Recall the definition of consistency from the [CAP theorem](https://github.com/donnemartin/system-design-primer#cap-theorem) - Every read receives the most recent write or an error.
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With multiple copies of the same data, we are faced with options on how to synchronize them so clients have a consistent view of the data. Recall the definition of consistency from the [CAP theorem](https://github.com/donnemartin/system-design-primer#cap-theorem) - Every read receives the most recent write or an error.
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### Weak consistency
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### Weak consistency
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- Isolation - Excuting transactions concurrently has the same results as if the transactions were executed serially
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- Isolation - Excuting transactions concurrently has the same results as if the transactions were executed serially
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- Durability - Once a transaction has been committed, it will remain so
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- Durability - Once a transaction has been committed, it will remain so
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There are many techniques to scale a relational database: master-slave replication, master-master replication, federation, sharding, denormalization, and SQL tuning.
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There are many techniques to scale a relational database: master-slave replication, master-master replication, federation, sharding, denormalization, and SQL tuning.
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noatcards = True
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isdraft = False
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# Denormalization
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## Denormalization introduction
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Denormalization attemps to improve read performance at the expense of some write performance. Redundant copies of the data are written in multiple tables to avoid expensive joins. Some RDBMS such as [PostgreSQL](https://en.wikipedia.org/wiki/PostgreSQL) and Oracle support [materialized views](https://en.wikipedia.org/wiki/Materialized_view) which handle the work of storing redudant information and keeping redundant copies consistent.
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Once data becomes distributed with techniques such as [federation](https://github.com/donnemartin/system-design-primer#federation) and [sharding](https://github.com/donnemartin/system-design-primer#sharding) , managing joins across data centers further increases complexity. Denormalization might circumvent the need for such complex joins.
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In most systems, reads can heavily number writes 100:1 or even 1000:1. A read resulting in a complex database join can be very expensive, spending a significant amount of time on disk operations.
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## Disadvantage(s) : denormalization
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- Data is duplicated.
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- Constraints can help redundant copies of information stay in sync, which increases complexity of the database design.
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- A denormalized database under heavy write load might perform worse than its normalized counterpart.
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## Source(s) and further reading: denormalization
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- [Denormalization](https://en.wikipedia.org/wiki/Denormalization)
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# Document store
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## Document Store Abstraction: key-value store with documents stored as values
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A document store is centered around documents (XML, JSON, binary, etc) , where a document stores all information for a given object. Document stores provide APIs or a query language to query based on the internal structure of the document itself. _Note, many key-value stores include features for working with a value's metadata, blurring the lines between these two storage types._
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Based on the underlying implementation, documents are organized in either collections, tags, metadata, or directories. Although documents can be organized or grouped together, documents may have fields that are completely different from each other.
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Some document stores like [MongoDB](https://www.mongodb.com/mongodb-architecture) and [CouchDB](https://blog.couchdb.org/2016/08/01/couchdb-2-0-architecture/) also provide a SQL-like language to perform complex queries.[DynamoDB](http://www.read.seas.harvard.edu/~kohler/class/cs239-w08/decandia07dynamo.pdf) supports both key-values and documents.
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Document stores provide high flexibility and are often used for working with occasionally changing data.
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## Source(s) and further reading: document store
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- [Document-oriented database](https://en.wikipedia.org/wiki/Document-oriented_database)
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- [MongoDB architecture](https://www.mongodb.com/mongodb-architecture)
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- [CouchDB architecture](https://blog.couchdb.org/2016/08/01/couchdb-2-0-architecture/)
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- [Elasticsearch architecture](https://www.elastic.co/blog/found-elasticsearch-from-the-bottom-up)
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# Domain name system
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## Introduction Domain Name System
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_[Source: DNS security presentation](http://www.slideshare.net/srikrupa5/dns-security-presentation-issa) _
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A Domain Name System (DNS) translates a domain name such as [www.example.com](http://www.example.com/) to an IP address.
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DNS is hierarchical, with a few authoritative servers at the top level. Your router or ISP provides information about which DNS server(s) to contact when doing a lookup. Lower level DNS servers cache mappings, which could become stale due to DNS propagation delays. DNS results can also be cached by your browser or OS for a certain period of time, determined by the [time to live (TTL) ](https://en.wikipedia.org/wiki/Time_to_live) .
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- NS record (name server) - Specifies the DNS servers for your domain/subdomain.
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- MX record (mail exchange) - Specifies the mail servers for accepting messages.
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- A record (address) - Points a name to an IP address.
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- CNAME (canonical) - Points a name to another name or `CNAME` (example.com to [www.example.com](http://www.example.com/)) or to an `A`record.
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||||||
|
Services such as [CloudFlare](https://www.cloudflare.com/dns/) and [Route 53](https://aws.amazon.com/route53/) provide managed DNS services. Some DNS services can route traffic through various methods:
|
||||||
|
|
||||||
|
- [Weighted round robin](http://g33kinfo.com/info/archives/2657)
|
||||||
|
- Prevent traffic from going to servers under maintenance
|
||||||
|
- Balance between varying cluster sizes
|
||||||
|
- A/B testing
|
||||||
|
- Latency-based
|
||||||
|
- Geolocation-based
|
||||||
|
|
||||||
|
### Disadvantage(s) : DNS
|
||||||
|
|
||||||
|
- Accessing a DNS server introduces a slight delay, although mitigated by caching described above.
|
||||||
|
- DNS server management could be complex, although they are generally managed by [governments, ISPs, and large companies](http://superuser.com/questions/472695/who-controls-the-dns-servers/472729) .
|
||||||
|
- DNS services have recently come under DDoS attack, preventing users from accessing websites such as Twitter without knowing Twitter's IP address(es) .
|
||||||
|
|
||||||
|
### Source(s) and further reading
|
||||||
|
|
||||||
|
- [DNS architecture](https://technet.microsoft.com/en-us/library/dd197427(v=ws.10) .aspx)
|
||||||
|
- [Wikipedia](https://en.wikipedia.org/wiki/Domain_Name_System)
|
||||||
|
- [DNS articles](https://support.dnsimple.com/categories/dns/)
|
||||||
|
|
@ -0,0 +1,25 @@
|
||||||
|
+++
|
||||||
|
noatcards = True
|
||||||
|
isdraft = False
|
||||||
|
+++
|
||||||
|
|
||||||
|
# Federation
|
||||||
|
|
||||||
|
## Introduction about Federation
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
_[Source: Scaling up to your first 10 million users](https://www.youtube.com/watch?v=vg5onp8TU6Q)_
|
||||||
|
|
||||||
|
Federation (or functional partitioning) splits up databases by function. For example, instead of a single, monolithic database, you could have three databases: forums,users, and products, resulting in less read and write traffic to each database and therefore less replication lag. Smaller databases result in more data that can fit in memory, which in turn results in more cache hits due to improved cache locality. With no single central master serializing writes you can write in parallel, increasing throughput.
|
||||||
|
|
||||||
|
## Disadvantage(s) : federation
|
||||||
|
|
||||||
|
- Federation is not effective if your schema requires huge functions or tables.
|
||||||
|
- You'll need to update your application logic to determine which database to read and write.
|
||||||
|
- Joining data from two databases is more complex with a [server link](http://stackoverflow.com/questions/5145637/querying-data-by-joining-two-tables-in-two-database-on-different-servers) .
|
||||||
|
- Federation adds more hardware and additional complexity.
|
||||||
|
|
||||||
|
## Source(s) and further reading: federation
|
||||||
|
|
||||||
|
- [Scaling up to your first 10 million users](https://www.youtube.com/watch?v=vg5onp8TU6Q)
|
||||||
|
|
@ -0,0 +1,24 @@
|
||||||
|
+++
|
||||||
|
noatcards = True
|
||||||
|
isdraft = False
|
||||||
|
+++
|
||||||
|
|
||||||
|
# Graph database
|
||||||
|
|
||||||
|
## Abstraction: graph
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
_[Source: Graph database](https://en.wikipedia.org/wiki/File:GraphDatabase_PropertyGraph.png)_
|
||||||
|
|
||||||
|
|
||||||
|
In a graph database, each node is a record and each arc is a relationship between two nodes. Graph databases are optimized to represent complex relationships with many foreign keys or many-to-many relationships.
|
||||||
|
|
||||||
|
Graphs databases offer high performance for data models with complex relationships, such as a social network. They are relatively new and are not yet widely-used; it might be more difficult to find development tools and resources. Many graphs can only be accessed with [REST APIs](https://github.com/donnemartin/system-design-primer#representational-state-transfer-rest) .
|
||||||
|
|
||||||
|
## Source(s) and further reading: graph
|
||||||
|
|
||||||
|
- [Graph database](https://en.wikipedia.org/wiki/Graph_database)
|
||||||
|
- [Neo4j](https://neo4j.com/)
|
||||||
|
- [FlockDB](https://blog.twitter.com/2010/introducing-flockdb)
|
||||||
|
|
@ -0,0 +1,28 @@
|
||||||
|
+++
|
||||||
|
noatcards = True
|
||||||
|
isdraft = False
|
||||||
|
+++
|
||||||
|
|
||||||
|
# Hypertext transfer protocol (HTTP)
|
||||||
|
|
||||||
|
## Introduction about HTTP
|
||||||
|
|
||||||
|
HTTP is a method for encoding and transporting data between a client and a server. It is a request/response protocol:
|
||||||
|
clients issue requests and servers issue responses with relevant content and completion status info about the request.
|
||||||
|
HTTP is self-contained, allowing requests and responses to flow through many intermediate routers and servers that
|
||||||
|
perform load balancing, caching, encryption, and compression.
|
||||||
|
|
||||||
|
A basic HTTP request consists of a verb (method) and a resource (endpoint) . Below are common HTTP verbs:
|
||||||
|
|
||||||
|
| Verb | Description | Idempotent* | Safe | Cacheable |
|
||||||
|
|---|---|---|---|---|
|
||||||
|
| GET | Reads a resource | Yes | Yes | Yes |
|
||||||
|
| POST | Creates a resource or trigger a process that handles data | No | No | Yes if response contains freshness info |
|
||||||
|
| PUT | Creates or replace a resource | Yes | No | No |
|
||||||
|
| PATCH | Partially updates a resource | No | No | Yes if response contains freshness info |
|
||||||
|
| DELETE | Deletes a resource | Yes | No | No |
|
||||||
|
|
||||||
|
HTTP is an application layer protocol relying on lower-level protocols such as TCP and UDP.
|
||||||
|
|
||||||
|
- [HTTP](https://www.nginx.com/resources/glossary/http/)
|
||||||
|
- [README](https://www.quora.com/What-is-the-difference-between-HTTP-protocol-and-TCP-protocol)
|
||||||
|
|
@ -0,0 +1,26 @@
|
||||||
|
+++
|
||||||
|
noatcards = True
|
||||||
|
isdraft = False
|
||||||
|
+++
|
||||||
|
|
||||||
|
# Key-value store
|
||||||
|
|
||||||
|
## Abstraction: hash table
|
||||||
|
|
||||||
|
A key-value store generally allows for O(1) reads and writes and is often backed by memory or SSD. Data stores can
|
||||||
|
maintain keys in [lexicographic order](https://en.wikipedia.org/wiki/Lexicographical_order) , allowing efficient
|
||||||
|
retrieval of key ranges. Key-value stores can allow for storing of metadata with a value.
|
||||||
|
|
||||||
|
Key-value stores provide high performance and are often used for simple data models or for rapidly-changing data, such
|
||||||
|
as an in-memory cache layer. Since they offer only a limited set of operations, complexity is shifted to the application
|
||||||
|
layer if additional operations are needed.
|
||||||
|
|
||||||
|
A key-value store is the basis for more complex system systems such as a document store, and in some cases, a graph
|
||||||
|
database.
|
||||||
|
|
||||||
|
## Source(s) and further reading: key-value store
|
||||||
|
|
||||||
|
- [Key-value database](https://en.wikipedia.org/wiki/Key-value_database)
|
||||||
|
- [Disadvantages of key-value stores](http://stackoverflow.com/questions/4056093/what-are-the-disadvantages-of-using-a-key-value-table-over-nullable-columns-or)
|
||||||
|
- [Redis architecture](http://qnimate.com/overview-of-redis-architecture/)
|
||||||
|
- [Memcached architecture](https://www.adayinthelifeof.nl/2011/02/06/memcache-internals/)
|
||||||
|
|
@ -0,0 +1,46 @@
|
||||||
|
### Latency numbers every programmer should know
|
||||||
|
---
|
||||||
|
Latency Comparison Numbers
|
||||||
|
--------------------------
|
||||||
|
L1 cache reference 0.5 ns
|
||||||
|
Branch mispredict 5 ns
|
||||||
|
L2 cache reference 7 ns 14x L1 cache
|
||||||
|
Mutex lock/unlock 100 ns
|
||||||
|
Main memory reference 100 ns 20x L2 cache, 200x L1 cache
|
||||||
|
Compress 1K bytes with Zippy 10,000 ns 10 us
|
||||||
|
Send 1 KB bytes over 1 Gbps network 10,000 ns 10 us
|
||||||
|
Read 4 KB randomly from SSD- 150,000 ns 150 us ~1GB/sec SSD
|
||||||
|
Read 1 MB sequentially from memory 250,000 ns 250 us
|
||||||
|
Round trip within same datacenter 500,000 ns 500 us
|
||||||
|
Read 1 MB sequentially from SSD- 1,000,000 ns 1,000 us 1 ms ~1GB/sec SSD, 4X memory
|
||||||
|
Disk seek 10,000,000 ns 10,000 us 10 ms 20x datacenter roundtrip
|
||||||
|
Read 1 MB sequentially from 1 Gbps 10,000,000 ns 10,000 us 10 ms 40x memory, 10X SSD
|
||||||
|
Read 1 MB sequentially from disk 30,000,000 ns 30,000 us 30 ms 120x memory, 30X SSD
|
||||||
|
Send packet CA->Netherlands->CA 150,000,000 ns 150,000 us 150 ms
|
||||||
|
|
||||||
|
Notes
|
||||||
|
-----
|
||||||
|
1 ns = 10^-9 seconds
|
||||||
|
1 us = 10^-6 seconds = 1,000 ns
|
||||||
|
1 ms = 10^-3 seconds = 1,000 us = 1,000,000 ns
|
||||||
|
|
||||||
|
|
||||||
|
Handy metrics based on numbers above:
|
||||||
|
|
||||||
|
- Read sequentially from disk at 30 MB/s
|
||||||
|
- Read sequentially from 1 Gbps Ethernet at 100 MB/s
|
||||||
|
- Read sequentially from SSD at 1 GB/s
|
||||||
|
- Read sequentially from main memory at 4 GB/s
|
||||||
|
- 6-7 world-wide round trips per second
|
||||||
|
- 2,000 round trips per second within a data center
|
||||||
|
|
||||||
|
#### [](https://github.com/donnemartin/system-design-primer#latency-numbers-visualized) Latency numbers visualized
|
||||||
|
|
||||||
|
[ ](https://camo.githubusercontent.com/77f72259e1eb58596b564d1ad823af1853bc60a3/687474703a2f2f692e696d6775722e636f6d2f6b307431652e706e67)
|
||||||
|
|
||||||
|
#### [](https://github.com/donnemartin/system-design-primer#sources-and-further-reading-14) Source(s) and further reading
|
||||||
|
|
||||||
|
- [Latency numbers every programmer should know - 1](https://gist.github.com/jboner/2841832)
|
||||||
|
- [Latency numbers every programmer should know - 2](https://gist.github.com/hellerbarde/2843375)
|
||||||
|
- [Designs, lessons, and advice from building large distributed systems](http://www.cs.cornell.edu/projects/ladis2009/talks/dean-keynote-ladis2009.pdf)
|
||||||
|
- [Software Engineering Advice from Building Large-Scale Distributed Systems](https://static.googleusercontent.com/media/research.google.com/en//people/jeff/stanford-295-talk.pdf)
|
||||||
|
|
@ -0,0 +1,17 @@
|
||||||
|
+++
|
||||||
|
noatcards = True
|
||||||
|
isdraft = False
|
||||||
|
+++
|
||||||
|
|
||||||
|
# Latency vs throughput
|
||||||
|
|
||||||
|
## Latency vs throughput define
|
||||||
|
Latency is the time to perform some action or to produce some result.
|
||||||
|
|
||||||
|
Throughput is the number of such actions or results per unit of time.
|
||||||
|
|
||||||
|
Generally, you should aim for maximal throughput with acceptable latency.
|
||||||
|
|
||||||
|
## Source(s) and further reading
|
||||||
|
|
||||||
|
- [Understanding latency vs throughput](https://community.cadence.com/cadence_blogs_8/b/sd/archive/2010/09/13/understanding-latency-vs-throughput)
|
||||||
|
|
@ -0,0 +1,78 @@
|
||||||
|
+++
|
||||||
|
noatcards = True
|
||||||
|
isdraft = False
|
||||||
|
+++
|
||||||
|
|
||||||
|
# Load balancer
|
||||||
|
|
||||||
|
## Load Balancer Introduction
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
_[Source: Scalable system design patterns](http://horicky.blogspot.com/2010/10/scalable-system-design-patterns.html)_
|
||||||
|
|
||||||
|
Load balancers distribute incoming client requests to computing resources such as application servers and databases. In each case, the load balancer returns the response from the computing resource to the appropriate client. Load balancers are effective at:
|
||||||
|
|
||||||
|
- Preventing requests from going to unhealthy servers
|
||||||
|
- Preventing overloading resources
|
||||||
|
- Helping eliminate single points of failure
|
||||||
|
|
||||||
|
Load balancers can be implemented with hardware (expensive) or with software such as HAProxy.
|
||||||
|
|
||||||
|
## Load Balancer benefits
|
||||||
|
|
||||||
|
Additional benefits include:
|
||||||
|
|
||||||
|
- SSL termination: Decrypt incoming requests and encrypt server responses so backend servers do not have to perform these potentially expensive operations
|
||||||
|
- Removes the need to install [X.509 certificates](https://en.wikipedia.org/wiki/X.509) on each server
|
||||||
|
- Session persistence: Issue cookies and route a specific client's requests to same instance if the web apps do not keep track of sessions
|
||||||
|
|
||||||
|
To protect against failures, it's common to set up multiple load balancers, either in [active-passive](https://github.com/donnemartin/system-design-primer#active-passive) or [active-active](https://github.com/donnemartin/system-design-primer#active-active) mode.
|
||||||
|
|
||||||
|
## Load Balancer route traffic
|
||||||
|
|
||||||
|
Load balancers can route traffic based on various metrics, including:
|
||||||
|
|
||||||
|
- Random
|
||||||
|
- Least loaded
|
||||||
|
- Seesion/cookies
|
||||||
|
- [Round robin or weighted round robin](http://g33kinfo.com/info/archives/2657)
|
||||||
|
- [Layer 4](https://github.com/donnemartin/system-design-primer#layer-4-load-balancing)
|
||||||
|
- [Layer 7](https://github.com/donnemartin/system-design-primer#layer-7-load-balancing)
|
||||||
|
|
||||||
|
## Layer 4 load balancing
|
||||||
|
|
||||||
|
Layer 4 load balancers look at info at the [transport layer](https://github.com/donnemartin/system-design-primer#communication) to decide how to distribute requests. Generally, this involves the source, destination IP addresses, and ports in the header, but not the contents of the packet. Layer 4 load balancers forward network packets to and from the upstream server, performing [Network Address Translation (NAT) ](https://www.nginx.com/resources/glossary/layer-4-load-balancing/) .
|
||||||
|
|
||||||
|
## layer 7 load balancing
|
||||||
|
|
||||||
|
Layer 7 load balancers look at the [application layer](https://github.com/donnemartin/system-design-primer#communication) to decide how to distribute requests. This can involve contents of the header, message, and cookies. Layer 7 load balancers terminates network traffic, reads the message, makes a load-balancing decision, then opens a connection to the selected server. For example, a layer 7 load balancer can direct video traffic to servers that host videos while directing more sensitive user billing traffic to security-hardened servers.
|
||||||
|
|
||||||
|
At the cost of flexibility, layer 4 load balancing requires less time and computing resources than Layer 7, although the performance impact can be minimal on modern commodity hardware.
|
||||||
|
|
||||||
|
## Horizontal scaling
|
||||||
|
|
||||||
|
Load balancers can also help with horizontal scaling, improving performance and availability. Scaling out using commodity machines is more cost efficient and results in higher availability than scaling up a single server on more expensive hardware, called Vertical Scaling. It is also easier to hire for talent working on commodity hardware than it is for specialized enterprise systems.
|
||||||
|
|
||||||
|
## Disadvantage(s) : horizontal scaling
|
||||||
|
|
||||||
|
- Scaling horizontally introduces complexity and involves cloning servers
|
||||||
|
* Servers should be stateless: they should not contain any user-related data like sessions or profile pictures
|
||||||
|
* Sessions can be stored in a centralized data store such as a [database](https://github.com/donnemartin/system-design-primer#database) (SQL, NoSQL) or a persistent [cache](https://github.com/donnemartin/system-design-primer#cache) (Redis, Memcached)
|
||||||
|
- Downstream servers such as caches and databases need to handle more simultaneous connections as upstream servers scale out
|
||||||
|
|
||||||
|
## Disadvantage(s) : load balancer
|
||||||
|
|
||||||
|
- The load balancer can become a performance bottleneck if it does not have enough resources or if it is not configured properly.
|
||||||
|
- Introducing a load balancer to help eliminate single points of failure results in increased complexity.
|
||||||
|
- A single load balancer is a single point of failure, configuring multiple load balancers further increases complexity.
|
||||||
|
|
||||||
|
## Source(s) and further reading
|
||||||
|
|
||||||
|
- [NGINX architecture](https://www.nginx.com/blog/inside-nginx-how-we-designed-for-performance-scale/)
|
||||||
|
- [HAProxy architecture guide](http://www.haproxy.org/download/1.2/doc/architecture.txt)
|
||||||
|
- [Scalability](http://www.lecloud.net/post/7295452622/scalability-for-dummies-part-1-clones)
|
||||||
|
- [Wikipedia](https://en.wikipedia.org/wiki/Load_balancing_(computing))
|
||||||
|
- [Layer 4 load balancing](https://www.nginx.com/resources/glossary/layer-4-load-balancing/)
|
||||||
|
- [Layer 7 load balancing](https://www.nginx.com/resources/glossary/layer-7-load-balancing/)
|
||||||
|
- [ELB listener config](http://docs.aws.amazon.com/elasticloadbalancing/latest/classic/elb-listener-config.html)
|
||||||
|
|
@ -0,0 +1,50 @@
|
||||||
|
# Remote procedure call (RPC)
|
||||||
|
|
||||||
|
## Remote procedure call introduction
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
_[Source: Crack the system design interview](http://www.puncsky.com/blog/2016/02/14/crack-the-system-design-interview/)_
|
||||||
|
|
||||||
|
In an RPC, a client causes a procedure to execute on a different address space, usually a remote server. The procedure is coded as if it were a local procedure call, abstracting away the details of how to communicate with the server from the client program. Remote calls are usually slower and less reliable than local calls so it is helpful to distinguish RPC calls from local calls. Popular RPC frameworks include [Protobuf](https://developers.google.com/protocol-buffers/) , [Thrift](https://thrift.apache.org/) , and [Avro](https://avro.apache.org/docs/current/) .
|
||||||
|
|
||||||
|
## Remote procedure call in detail
|
||||||
|
RPC is a request-response protocol:
|
||||||
|
|
||||||
|
- Client program - Calls the client stub procedure. The parameters are pushed onto the stack like a local procedure call.
|
||||||
|
- Client stub procedure - Marshals (packs) procedure id and arguments into a request message.
|
||||||
|
- Client communication module - OS sends the message from the client to the server.
|
||||||
|
- Server communication module - OS passes the incoming packets to the server stub procedure.
|
||||||
|
- Server stub procedure - Unmarshalls the results, calls the server procedure matching the procedure id and passes the given arguments.
|
||||||
|
- The server response repeats the steps above in reverse order.
|
||||||
|
|
||||||
|
Sample RPC calls:
|
||||||
|
```
|
||||||
|
GET /someoperation?data=anId
|
||||||
|
|
||||||
|
POST /anotheroperation
|
||||||
|
{
|
||||||
|
"data":"anId";
|
||||||
|
"anotherdata": "another value"
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
|
## Remote procedure call under behavior view
|
||||||
|
|
||||||
|
RPC is focused on exposing behaviors. RPCs are often used for performance reasons with internal communications, as you can hand-craft native calls to better fit your use cases.
|
||||||
|
|
||||||
|
Choose a Native Library aka SDK when:
|
||||||
|
|
||||||
|
- You know your target platform.
|
||||||
|
- You want to control how your "logic" is accessed
|
||||||
|
- You want to control how error control happens off your library
|
||||||
|
- Performance and end user experience is your primary concern
|
||||||
|
|
||||||
|
HTTP APIs following REST tend to be used more often for public APIs.
|
||||||
|
|
||||||
|
#### Disadvantage(s) : RPC
|
||||||
|
|
||||||
|
- RPC clients become tightly coupled to the service implementation.
|
||||||
|
- A new API must be definied for every new operation or use case.
|
||||||
|
- It can be difficult to debug RPC.
|
||||||
|
- You might not be able to leverage existing technologies out of the box. For example, it might require additional effort to ensure [RPC calls are properly cached](http://etherealbits.com/2012/12/debunking-the-myths-of-rpc-rest/) on caching servers such as [Squid](http://www.squid-cache.org/) .
|
||||||
|
|
@ -0,0 +1,35 @@
|
||||||
|
+++
|
||||||
|
noatcards = True
|
||||||
|
isdraft = False
|
||||||
|
+++
|
||||||
|
|
||||||
|
# Representational state transfer (REST)
|
||||||
|
|
||||||
|
## Representational state transfer introduction
|
||||||
|
|
||||||
|
REST is an architectural style enforcing a client/server model where the client acts on a set of resources managed by the server. The server provides a representation of resources and actions that can either manipulate or get a new representation of resources. All communication must be stateless and cacheable.
|
||||||
|
|
||||||
|
## RESTful interface
|
||||||
|
|
||||||
|
There are four qualities of a RESTful interface:
|
||||||
|
|
||||||
|
- Identify resources (URI in HTTP) - use the same URI regardless of any operation.
|
||||||
|
- Change with representations (Verbs in HTTP) - use verbs, headers, and body.
|
||||||
|
- Self-descriptive error message (status response in HTTP) - Use status codes, don't reinvent the wheel.
|
||||||
|
- [HATEOAS](http://restcookbook.com/Basics/hateoas/) (HTML interface for HTTP) - your web service should be fully accessible in a browser.
|
||||||
|
|
||||||
|
Sample REST calls:
|
||||||
|
|
||||||
|
```
|
||||||
|
GET /someresources/anId
|
||||||
|
|
||||||
|
PUT /someresources/anId
|
||||||
|
{"anotherdata": "another value"}
|
||||||
|
```
|
||||||
|
|
||||||
|
REST is focused on exposing data. It minimizes the coupling between client/server and is often used for public HTTP APIs. REST uses a more generic and uniform method of exposing resources through URIs, [representation through headers](https://github.com/for-GET/know-your-http-well/blob/master/headers.md) , and actions through verbs such as GET, POST, PUT, DELETE, and PATCH. Being stateless, REST is great for horizontal scaling and partitioning.
|
||||||
|
|
||||||
|
## Disadvantage(s) : REST
|
||||||
|
|
||||||
|
- With REST being focused on exposing data, it might not be a good fit if resources are not naturally organized or accessed in a simple hierarchy. For example, returning all updated records from the past hour matching a particular set of events is not easily expressed as a path. With REST, it is likely to be implemented with a combination of URI path, query parameters, and possibly the request body.
|
||||||
|
- REST typically relies on a few verbs (GET, POST, PUT, DELETE, and PATCH) which sometimes doesn't fit your use case. For example, moving expired documents to the archive folder might not cleanly fit within these verbs.
|
||||||
|
|
@ -0,0 +1,48 @@
|
||||||
|
+++
|
||||||
|
noatcards = True
|
||||||
|
isdraft = False
|
||||||
|
+++
|
||||||
|
|
||||||
|
# Reverse proxy (web server)
|
||||||
|
|
||||||
|
## Reverse proxy (web server) introduction
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
_[Source: Wikipedia](https://commons.wikimedia.org/wiki/File:Proxy_concept_en.svg) _
|
||||||
|
|
||||||
|
A reverse proxy is a web server that centralizes internal services and provides unified interfaces to the public. Requests from clients are forwarded to a server that can fulfill it before the reverse proxy returns the server's response to the client.
|
||||||
|
|
||||||
|
## Reverse Proxy benefit
|
||||||
|
|
||||||
|
Additional benefits include:
|
||||||
|
|
||||||
|
- Increased security - Hide information about backend servers, blacklist IPs, limit number of connections per client
|
||||||
|
- Increased scalability and flexibility - Clients only see the reverse proxy's IP, allowing you to scale servers or change their configuration
|
||||||
|
- SSL termination - Decrypt incoming requests and encrypt server responses so backend servers do not have to perform these potentially expensive operations
|
||||||
|
- Removes the need to install [X.509 certificates](https://en.wikipedia.org/wiki/X.509) on each server
|
||||||
|
- Compression - Compress server responses
|
||||||
|
- Caching - Return the response for cached requests
|
||||||
|
- Static content - Serve static content directly
|
||||||
|
- HTML/CSS/JS
|
||||||
|
- Photos
|
||||||
|
- Videos
|
||||||
|
- Etc
|
||||||
|
|
||||||
|
## Load balancer vs reverse proxy
|
||||||
|
|
||||||
|
- Deploying a load balancer is useful when you have multiple servers. Often, load balancers route traffic to a set of servers serving the same function.
|
||||||
|
- Reverse proxies can be useful even with just one web server or application server, opening up the benefits described in the previous section.
|
||||||
|
- Solutions such as NGINX and HAProxy can support both layer 7 reverse proxying and load balancing.
|
||||||
|
|
||||||
|
## Disadvantage(s) : reverse proxy
|
||||||
|
|
||||||
|
- Introducing a reverse proxy results in increased complexity.
|
||||||
|
- A single reverse proxy is a single point of failure, configuring multiple reverse proxies (ie a [failover](https://en.wikipedia.org/wiki/Failover)) further increases complexity.
|
||||||
|
|
||||||
|
## Source(s) and further reading
|
||||||
|
|
||||||
|
- [Reverse proxy vs load balancer](https://www.nginx.com/resources/glossary/reverse-proxy-vs-load-balancer/)
|
||||||
|
- [NGINX architecture](https://www.nginx.com/blog/inside-nginx-how-we-designed-for-performance-scale/)
|
||||||
|
- [HAProxy architecture guide](http://www.haproxy.org/download/1.2/doc/architecture.txt)
|
||||||
|
- [Wikipedia](https://en.wikipedia.org/wiki/Reverse_proxy)
|
||||||
|
|
@ -0,0 +1,29 @@
|
||||||
|
+++
|
||||||
|
noatcards = True
|
||||||
|
isdraft = False
|
||||||
|
+++
|
||||||
|
|
||||||
|
# Transmission control protocol (TCP)
|
||||||
|
|
||||||
|
## TCP Introduction
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
_[Source: How to make a multiplayer game](http://www.wildbunny.co.uk/blog/2012/10/09/how-to-make-a-multi-player-game-part-1/)_
|
||||||
|
|
||||||
|
TCP is a connection-oriented protocol over an [IP network](https://en.wikipedia.org/wiki/Internet_Protocol) . Connection is established and terminated using a [handshake](https://en.wikipedia.org/wiki/Handshaking) . All packets sent are guaranteed to reach the destination in the original order and without corruption through:
|
||||||
|
|
||||||
|
- Sequence numbers and [checksum fields](https://en.wikipedia.org/wiki/Transmission_Control_Protocol#Checksum_computation) for each packet
|
||||||
|
- [Acknowledgement](https://en.wikipedia.org/wiki/Acknowledgement_(data_networks)) packets and automatic retransmission
|
||||||
|
|
||||||
|
If the sender does not receive a correct response, it will resend the packets. If there are multiple timeouts, the connection is dropped. TCP also implements [flow control](https://en.wikipedia.org/wiki/Flow_control_(data)) and [congestion control](https://en.wikipedia.org/wiki/Network_congestion#Congestion_control) . These guarantees cause delays and generally results in less efficient transmission than UDP.
|
||||||
|
|
||||||
|
To ensure high throughput, web servers can keep a large number of TCP connections open, resulting in high memory usage. It can be expensive to have a large number of open connections between web server threads and say, a [memcached](https://github.com/donnemartin/system-design-primer#memcached) server. [Connection pooling](https://en.wikipedia.org/wiki/Connection_pool) can help in addition to switching to UDP where applicable.
|
||||||
|
|
||||||
|
|
||||||
|
## Use TCP over UDP when:
|
||||||
|
|
||||||
|
TCP is useful for applications that require high reliability but are less time critical. Some examples include web servers, database info, SMTP, FTP, and SSH.
|
||||||
|
|
||||||
|
- You need all of the data to arrive in tact
|
||||||
|
- You want to automatically make a best estimate use of the network throughput
|
||||||
|
|
@ -0,0 +1,36 @@
|
||||||
|
+++
|
||||||
|
noatcards = True
|
||||||
|
isdraft = False
|
||||||
|
+++
|
||||||
|
|
||||||
|
# User datagram protocol (UDP)
|
||||||
|
|
||||||
|
## User datagram protocol (UDP) introduction
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
_[Source: How to make a multiplayer game](http://www.wildbunny.co.uk/blog/2012/10/09/how-to-make-a-multi-player-game-part-1/) _
|
||||||
|
|
||||||
|
UDP is connectionless. Datagrams (analogous to packets) are guaranteed only at the datagram level. Datagrams might reach their destination out of order or not at all. UDP does not support congestion control. Without the guarantees that TCP support, UDP is generally more efficient.
|
||||||
|
|
||||||
|
UDP can broadcast, sending datagrams to all devices on the subnet. This is useful with [DHCP](https://en.wikipedia.org/wiki/Dynamic_Host_Configuration_Protocol) because the client has not yet received an IP address, thus preventing a way for TCP to stream without the IP address.
|
||||||
|
|
||||||
|
|
||||||
|
## Use UDP over TCP when
|
||||||
|
|
||||||
|
UDP is less reliable but works well in real time use cases such as VoIP, video chat, streaming, and realtime multiplayer games.
|
||||||
|
|
||||||
|
Use UDP over TCP when:
|
||||||
|
|
||||||
|
- You need the lowest latency
|
||||||
|
- Late data is worse than loss of data
|
||||||
|
- You want to implement your own error correction
|
||||||
|
|
||||||
|
## Source(s) and further reading: TCP and UDP
|
||||||
|
|
||||||
|
- [Networking for game programming](http://gafferongames.com/networking-for-game-programmers/udp-vs-tcp/)
|
||||||
|
- [Key differences between TCP and UDP protocols](http://www.cyberciti.biz/faq/key-differences-between-tcp-and-udp-protocols/)
|
||||||
|
- [Difference between TCP and UDP](http://stackoverflow.com/questions/5970383/difference-between-tcp-and-udp)
|
||||||
|
- [Transmission control protocol](https://en.wikipedia.org/wiki/Transmission_Control_Protocol)
|
||||||
|
- [User datagram protocol](https://en.wikipedia.org/wiki/User_Datagram_Protocol)
|
||||||
|
- [Scaling memcache at Facebook](http://www.cs.bu.edu/~jappavoo/jappavoo.github.com/451/papers/memcache-fb.pdf)
|
||||||
Loading…
Reference in New Issue