diff --git a/src/roadmaps/system-design/content/101-performance-vs-scalability.md b/src/roadmaps/system-design/content/101-performance-vs-scalability.md index 607e3547b..fa30059de 100644 --- a/src/roadmaps/system-design/content/101-performance-vs-scalability.md +++ b/src/roadmaps/system-design/content/101-performance-vs-scalability.md @@ -1 +1,13 @@ -# Performance vs scalability \ No newline at end of file +# Performance vs Scalability + +A service is scalable if it results in increased performance in a manner proportional to resources added. Generally, increasing performance means serving more units of work, but it can also be to handle larger units of work, such as when datasets grow.1 + +Another way to look at performance vs scalability: + +- If you have a performance problem, your system is slow for a single user. +- If you have a scalability problem, your system is fast for a single user but slow under heavy load. + +To learn more, visit the following links: + +- [Scalability, Availability & Stability Patterns](https://www.slideshare.net/jboner/scalability-availability-stability-patterns/) +- [A Word on Scalability](https://www.allthingsdistributed.com/2006/03/a_word_on_scalability.html) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/102-latency-vs-throughput.md b/src/roadmaps/system-design/content/102-latency-vs-throughput.md index 097eff568..0ab7ad328 100644 --- a/src/roadmaps/system-design/content/102-latency-vs-throughput.md +++ b/src/roadmaps/system-design/content/102-latency-vs-throughput.md @@ -1 +1,7 @@ -# Latency vs throughput \ No newline at end of file +# Latency vs Throughput + +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. + +Learn more from the following links: + +- [Understanding Latency versus Throughput](https://community.cadence.com/cadence_blogs_8/b/fv/posts/understanding-latency-vs-throughput) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/104-consistency-patterns/100-weak-consistency.md b/src/roadmaps/system-design/content/104-consistency-patterns/100-weak-consistency.md index 03973d84c..6c007e8b6 100644 --- a/src/roadmaps/system-design/content/104-consistency-patterns/100-weak-consistency.md +++ b/src/roadmaps/system-design/content/104-consistency-patterns/100-weak-consistency.md @@ -1 +1,8 @@ -# Weak consistency \ No newline at end of file +# Weak Consistency + +After a write, reads may or may not see it. A best effort approach is taken. This approach is seen in systems such as memcached. Weak consistency works well in real time use cases such as VoIP, video chat, and realtime multiplayer games. For example, if you are on a phone call and lose reception for a few seconds, when you regain connection you do not hear what was spoken during connection loss. + +To learn more, visit the following links: +- [Introduction to Weak Consistency](https://github.com/donnemartin/system-design-primer) +- [Guide to Weak Consistency](https://iq.opengenus.org/consistency-patterns-in-system-design/) + diff --git a/src/roadmaps/system-design/content/104-consistency-patterns/101-eventual-consistency.md b/src/roadmaps/system-design/content/104-consistency-patterns/101-eventual-consistency.md index d956dddfe..afdb55819 100644 --- a/src/roadmaps/system-design/content/104-consistency-patterns/101-eventual-consistency.md +++ b/src/roadmaps/system-design/content/104-consistency-patterns/101-eventual-consistency.md @@ -1 +1,9 @@ -# Eventual consistency \ No newline at end of file +# Eventual Consistency + +After a write, reads will eventually see it (typically within milliseconds).Data is replicated asynchronously. This approach is seen in systems such as DNS and email. Eventual consistency works well in highly available systems. + + +To learn more, visit the following links: + +- [Eventual Consistency Patterns](https://github.com/donnemartin/system-design-primer) +- [System Design Concepts – Eventual Consistency](https://www.acodersjourney.com/eventual-consistency/) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/104-consistency-patterns/102-strong-consistency.md b/src/roadmaps/system-design/content/104-consistency-patterns/102-strong-consistency.md index 9b94eee9b..2faee7576 100644 --- a/src/roadmaps/system-design/content/104-consistency-patterns/102-strong-consistency.md +++ b/src/roadmaps/system-design/content/104-consistency-patterns/102-strong-consistency.md @@ -1 +1,8 @@ -# Strong consistency \ No newline at end of file +# Strong Consistency + +After a write, reads will see it. Data is replicated synchronously. This approach is seen in file systems and RDBMSes. Strong consistency works well in systems that need transactions. + +To learn more, visit the following links: + +- [Strong Consistency Patterns](https://github.com/donnemartin/system-design-primer) +- [Get started with Strong Consistency](https://www.geeksforgeeks.org/eventual-vs-strong-consistency-in-distributed-databases/) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/104-consistency-patterns/index.md b/src/roadmaps/system-design/content/104-consistency-patterns/index.md index bf146a0d0..15fda777c 100644 --- a/src/roadmaps/system-design/content/104-consistency-patterns/index.md +++ b/src/roadmaps/system-design/content/104-consistency-patterns/index.md @@ -10,4 +10,4 @@ Each of these patterns has its own advantages and disadvantages, and the choice Have a look at the following resources to learn more: -- [Consistency Patterns in Distributed Systems](https://cs.fyi/guide/consistency-patterns-week-strong-eventual/) +- [Consistency Patterns in Distributed Systems](https://cs.fyi/guide/consistency-patterns-week-strong-eventual/) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/105-availability-patterns/100-fail-over.md b/src/roadmaps/system-design/content/105-availability-patterns/100-fail-over.md index 552b099d1..d1663cb51 100644 --- a/src/roadmaps/system-design/content/105-availability-patterns/100-fail-over.md +++ b/src/roadmaps/system-design/content/105-availability-patterns/100-fail-over.md @@ -1 +1,19 @@ -# Fail over \ No newline at end of file +# Fail-Over + +## Active-passive + +With active-passive fail-over, heartbeats are sent between the active and the passive server on standby. If the heartbeat is interrupted, the passive server takes over the active's IP address and resumes service. +The length of downtime is determined by whether the passive server is already running in 'hot' standby or whether it needs to start up from 'cold' standby. Only the active server handles traffic. Active-passive failover can also be referred to as master-slave failover. + +## Active-active +In active-active, both servers are managing traffic, spreading the load between them. If the servers are public-facing, the DNS would need to know about the public IPs of both servers. If the servers are internal-facing, application logic would need to know about both servers. Active-active failover can also be referred to as master-master failover. + +## Disadvantages of Failover + + - Fail-over adds more hardware and additional complexity. + - There is a potential for loss of data if the active system fails before any newly written data can be replicated to the passive. + + To learn more visit the following links: + + - [Getting started with Fail-Over in System Design](https://github.com/donnemartin/system-design-primer) + - [System Design — Availabiliy Patterns](https://medium.com/must-know-computer-science/system-design-redundancy-and-replication-e9946aa335ba) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/105-availability-patterns/101-replication.md b/src/roadmaps/system-design/content/105-availability-patterns/101-replication.md index 0516dcfd9..329f4d13a 100644 --- a/src/roadmaps/system-design/content/105-availability-patterns/101-replication.md +++ b/src/roadmaps/system-design/content/105-availability-patterns/101-replication.md @@ -1 +1,34 @@ -# Replication \ No newline at end of file +# Replication + +Replication is futher derived in two components: + + - Master-Slave Replication + - Master-Master Replication + +## Master-Slave Replication + +The master serves reads and writes, replicating writes to one or more slaves, which serve only reads. Slaves can also replicate to additional slaves in a tree-like fashion. If the master goes offline, the system can continue to operate in read-only mode until a slave is promoted to a master or a new master is provisioned. + +## Disadvantages Master-Slave replication + +Following are the disadvantages: + - Additional logic is needed to promote a slave to a master. + +## Master-Master Replication + +Both masters serve reads and writes and coordinate with each other on writes. If either master goes down, the system can continue to operate with both reads and writes. + +## Disadvantages of Master-Master replication + +Following are the disadvantages of master-master replication: + + - A load balancer or you'll need to make changes to your application logic to determine where to write. + - Most master-master systems are either loosely consistent (violating ACID) or have increased write latency due to synchronization. + - Conflict resolution comes more into play as more write nodes are added and as latency increases. + - See Disadvantage(s): replication for points related to both master-slave and master-master. + + +Visi the following links for more resources: + +- [Replication - Master-Slave](https://github.com/donnemartin/system-design-primer#master-slave-replication) +- [Master- Master Replication](https://github.com/donnemartin/system-design-primer#master-master-replication) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/105-availability-patterns/102-availability-in-numbers.md b/src/roadmaps/system-design/content/105-availability-patterns/102-availability-in-numbers.md index 269130355..84e838131 100644 --- a/src/roadmaps/system-design/content/105-availability-patterns/102-availability-in-numbers.md +++ b/src/roadmaps/system-design/content/105-availability-patterns/102-availability-in-numbers.md @@ -1 +1,42 @@ -# Availability in numbers \ No newline at end of file +# Availability In Numbers + +Availability is often quantified by uptime (or downtime) as a percentage of time the service is available. Availability is generally measured in number of 9s--a service with 99.99% availability is described as having four 9s. + +## 99.9% Availability - Three 9s: + +| Duration | Acceptable downtime | +| ------------- | ------------- | +| Downtime per year | 8h 45min 57s | +| Downtime per month | 43m 49.7s | +| Downtime per week | 10m 4.8s | +| Downtime per day | 1m 26.4s | + +## 99.99% Availability - Four 9s + +| Duration | Acceptable downtime | +| ------------- | ------------- | +| Downtime per year | 52min 35.7s | +| Downtime per month | 43m 49.7s | +| Downtime per week | 1m 5s | +| Downtime per day | 8.6s | + +## Availability in parallel vs in sequence + +If a service consists of multiple components prone to failure, the service's overall availability depends on whether the components are in sequence or in parallel. + +### In sequence + +Overall availability decreases when two components with availability < 100% are in sequence: +Availability (Total) = Availability (Foo) * Availability (Bar) +If both Foo and Bar each had 99.9% availability, their total availability in sequence would be 99.8%. + +### In parallel + +Overall availability increases when two components with availability < 100% are in parallel: +Availability (Total) = 1 - (1 - Availability (Foo)) * (1 - Availability (Bar)) +If both Foo and Bar each had 99.9% availability, their total availability in parallel would be 99.9999%. + +To learn more, visit the following links: + +- [Getting started with Availability in Numbers](https://github.com/donnemartin/system-design-primer) +- [Availability in System Design](https://www.enjoyalgorithms.com/blog/availability-system-design-concept/) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/105-availability-patterns/index.md b/src/roadmaps/system-design/content/105-availability-patterns/index.md index f797acfa7..e44b4c48d 100644 --- a/src/roadmaps/system-design/content/105-availability-patterns/index.md +++ b/src/roadmaps/system-design/content/105-availability-patterns/index.md @@ -1 +1,32 @@ -# Availability patterns \ No newline at end of file +# Availability Patterns + +There are three Availability Patterns which are: + +- Fail-Over +- Replication +- Availability in Numbers + +## Fail-Over + +### Active-passive +With active-passive fail-over, heartbeats are sent between the active and the passive server on standby. If the heartbeat is interrupted, the passive server takes over the active's IP address and resumes service. + +## Active-active +In active-active, both servers are managing traffic, spreading the load between them. If the servers are public-facing, the DNS would need to know about the public IPs of both servers. If the servers are internal-facing, application logic would need to know about both servers. + +## Replication + +Replication is futher derived in two components: + + - Master-Slave Replication - The master serves reads and writes, replicating writes to one or more slaves, which serve only reads. + - Master-Master Replication - Both masters serve reads and writes and coordinate with each other on writes. If either master goes down, the system can continue to operate with both reads and writes. + + ## Availability In Numbers + +Availability is often quantified by uptime (or downtime) as a percentage of time the service is available. Availability is generally measured in number of 9s--a service with 99.99% availability is described as having four 9s. + +To learn more, visit the following links: + + - [Getting started with Availability Patterns](https://github.com/donnemartin/system-design-primer) + - [Availability in System Design](https://www.enjoyalgorithms.com/blog/availability-system-design-concept) + - [System Design: Availability](https://dev.to/karanpratapsingh/system-design-availability-38bd) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/106-background-jobs/100-event-driven.md b/src/roadmaps/system-design/content/106-background-jobs/100-event-driven.md index e192cded1..231f16f0d 100644 --- a/src/roadmaps/system-design/content/106-background-jobs/100-event-driven.md +++ b/src/roadmaps/system-design/content/106-background-jobs/100-event-driven.md @@ -1 +1,11 @@ -# Event driven \ No newline at end of file +# Event Driven + +Event-driven architecture (EDA) is a design pattern that focuses on the flow of events through a system, rather than the flow of data or control. It is based on the idea that a system should respond to external events and trigger the appropriate actions. + +In an event-driven system, events are generated by external sources, such as user input, sensors, or other systems, and are passed through the system to be handled by the appropriate components. These events can trigger various actions, such as updating the state of the system, sending a message to another system, or triggering a computation. + +Learn more from the following links: + +- [What is an Event-Driven Architecture?](https://aws.amazon.com/event-driven-architecture/) +- [Event-Driven Architecture - Everything You Need to Know](https://blog.hubspot.com/website/event-driven-architecture) +- [System Design: Event-Driven Architecture (EDA)](https://dev.to/karanpratapsingh/system-design-event-driven-architecture-eda-3m72) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/106-background-jobs/101-schedule-driven.md b/src/roadmaps/system-design/content/106-background-jobs/101-schedule-driven.md index 974e4b7be..279a1c9fc 100644 --- a/src/roadmaps/system-design/content/106-background-jobs/101-schedule-driven.md +++ b/src/roadmaps/system-design/content/106-background-jobs/101-schedule-driven.md @@ -1 +1,15 @@ -# Schedule driven \ No newline at end of file +# Schedule Driven + +Schedule-driven systems are systems that are designed to perform specific tasks or actions at predetermined times or intervals. These schedules can be defined by the system itself or can be set by an external agent, such as a user or another system. + +Examples of schedule-driven systems include: + +- Cron jobs +- Scheduled batch jobs +- Recurring events +- Automated trading systems + +Learn more from the following links: + +- [System Design - Job Scheduling System?](https://aws.amazon.com/event-driven-architecture/) +- [Scheduler System Design](https://atul-agrawal.medium.com/scheduler-as-a-service-9c5d0414ec6d) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/106-background-jobs/102-returning-results.md b/src/roadmaps/system-design/content/106-background-jobs/102-returning-results.md index 586fe502f..ddaaa22bc 100644 --- a/src/roadmaps/system-design/content/106-background-jobs/102-returning-results.md +++ b/src/roadmaps/system-design/content/106-background-jobs/102-returning-results.md @@ -1 +1,7 @@ -# Returning results \ No newline at end of file +# Returning Results + +Returning results in a system design refers to the process of providing the output or outcome of a specific task or action to the requesting entity. This can include providing a response to a user request, returning a result of a computation or analysis, or sending a notification or message to another system. + +Learn more from the following links: + +- [Overview of Return Statement](https://press.rebus.community/programmingfundamentals/chapter/return-statement/) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/106-background-jobs/index.md b/src/roadmaps/system-design/content/106-background-jobs/index.md index 1915bd27e..301ced319 100644 --- a/src/roadmaps/system-design/content/106-background-jobs/index.md +++ b/src/roadmaps/system-design/content/106-background-jobs/index.md @@ -1 +1,14 @@ -# Background jobs \ No newline at end of file +# Background Jobs + +Background jobs in system design refer to tasks that are executed in the background, independently of the main execution flow of the system. These tasks are typically initiated by the system itself, rather than by a user or another external agent. + +Background jobs can be used for a variety of purposes, such as: + +- Performing maintenance tasks: such as cleaning up old data, generating reports, or backing up the database. +- Processing large volumes of data: such as data import, data export, or data transformation. +- Sending notifications or messages: such as sending email notifications or push notifications to users. +- Performing long-running computations: such as machine learning or data analysis. + +Learn more from the following links: + +- [Intro of Background job system](https://www.codementor.io/projects/tool/background-job-system-atx32exogo) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/107-domain-name-system.md b/src/roadmaps/system-design/content/107-domain-name-system.md index 3f038299b..415b45092 100644 --- a/src/roadmaps/system-design/content/107-domain-name-system.md +++ b/src/roadmaps/system-design/content/107-domain-name-system.md @@ -1 +1,16 @@ -# Domain name system \ No newline at end of file +# Domain Name System + +A Domain Name System (DNS) translates a domain name such as www.example.com to an IP address. + +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). + +- NS record (name server) - Specifies the DNS servers for your domain/subdomain. +- MX record (mail exchange) - Specifies the mail servers for accepting messages. +- A record (address) - Points a name to an IP address. +- CNAME (canonical) - Points a name to another name or CNAME (example.com to www.example.com) or to an A record. + +To learn more, visit the following links: + +- [Getting started with Domain Name System](https://github.com/donnemartin/system-design-primer#domain-name-system) +- [Intro to DNS Architecture](https://learn.microsoft.com/en-us/previous-versions/windows/it-pro/windows-server-2008-R2-and-2008/dd197427(v=ws.10)?redirectedfrom=MSDNs) +- [DNS articles](https://support.dnsimple.com/categories/dns/) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/108-content-delivery-networks/100-push-cdns.md b/src/roadmaps/system-design/content/108-content-delivery-networks/100-push-cdns.md index ff4c11218..e97c02d78 100644 --- a/src/roadmaps/system-design/content/108-content-delivery-networks/100-push-cdns.md +++ b/src/roadmaps/system-design/content/108-content-delivery-networks/100-push-cdns.md @@ -1 +1,10 @@ -# Push cdns \ No newline at end of file +# Push CDNs + +Push CDNs receive new content whenever changes occur on your server. You take full responsibility for providing content, uploading directly to the CDN and rewriting URLs to point to the CDN. You can configure when content expires and when it is updated. Content is uploaded only when it is new or changed, minimizing traffic, but maximizing storage. + +Sites with a small amount of traffic or sites with content that isn't often updated work well with push CDNs. Content is placed on the CDNs once, instead of being re-pulled at regular intervals. + +To learn more, visit the following links: + +- [Introduction on Push CDNs](https://github.com/donnemartin/system-design-primer#content-delivery-network) +- [Why use a CDN?](https://dev.to/karanpratapsingh/system-design-content-delivery-network-cdn-bof) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/108-content-delivery-networks/101-pull-cdns.md b/src/roadmaps/system-design/content/108-content-delivery-networks/101-pull-cdns.md index b478124c3..83d00940b 100644 --- a/src/roadmaps/system-design/content/108-content-delivery-networks/101-pull-cdns.md +++ b/src/roadmaps/system-design/content/108-content-delivery-networks/101-pull-cdns.md @@ -1 +1,11 @@ -# Pull cdns \ No newline at end of file +# Pull CDNs + +Pull CDNs grab new content from your server when the first user requests the content. You leave the content on your server and rewrite URLs to point to the CDN. This results in a slower request until the content is cached on the CDN. + +A time-to-live (TTL) determines how long content is cached. Pull CDNs minimize storage space on the CDN, but can create redundant traffic if files expire and are pulled before they have actually changed. Sites with heavy traffic work well with pull CDNs, as traffic is spread out more evenly with only recently-requested content remaining on the CDN. + +To learn more, visit the following links: + +- [The Differences Between Push And Pull CDNss](http://www.travelblogadvice.com/technical/the-differences-between-push-and-pull-cdns/) +- [Brief aout Content delivery network](https://en.wikipedia.org/wiki/Content_delivery_network) +- [What is Globally distributed content delivery?](https://figshare.com/articles/journal_contribution/Globally_distributed_content_delivery/6605972) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/108-content-delivery-networks/index.md b/src/roadmaps/system-design/content/108-content-delivery-networks/index.md index 41028e6ea..015d0794e 100644 --- a/src/roadmaps/system-design/content/108-content-delivery-networks/index.md +++ b/src/roadmaps/system-design/content/108-content-delivery-networks/index.md @@ -1 +1,27 @@ -# Content delivery networks \ No newline at end of file +# Content Delivery Networks + +A content delivery network (CDN) is a globally distributed network of proxy servers, serving content from locations closer to the user. Generally, static files such as HTML/CSS/JS, photos, and videos are served from CDN, although some CDNs such as Amazon's CloudFront support dynamic content. The site's DNS resolution will tell clients which server to contact. + +Serving content from CDNs can significantly improve performance in two ways: + + - Users receive content from data centers close to them + - Your servers do not have to serve requests that the CDN fulfills + +## Push CDNs +Push CDNs receive new content whenever changes occur on your server. You take full responsibility for providing content, uploading directly to the CDN and rewriting URLs to point to the CDN. You can configure when content expires and when it is updated. Content is uploaded only when it is new or changed, minimizing traffic, but maximizing storage. + +Sites with a small amount of traffic or sites with content that isn't often updated work well with push CDNs. Content is placed on the CDNs once, instead of being re-pulled at regular intervals. + +## Pull CDNs +Pull CDNs grab new content from your server when the first user requests the content. You leave the content on your server and rewrite URLs to point to the CDN. This results in a slower request until the content is cached on the CDN. + +A time-to-live (TTL) determines how long content is cached. Pull CDNs minimize storage space on the CDN, but can create redundant traffic if files expire and are pulled before they have actually changed. Sites with heavy traffic work well with pull CDNs, as traffic is spread out more evenly with only recently-requested content remaining on the CDN. + +## Disadvantages of CDN +- CDN costs could be significant depending on traffic, although this should be weighed with additional costs you would incur not using a CDN. +- Content might be stale if it is updated before the TTL expires it. +- CDNs require changing URLs for static content to point to the CDN. + +- [The Differences Between Push And Pull CDNss](http://www.travelblogadvice.com/technical/the-differences-between-push-and-pull-cdns/) +- [Brief aout Content delivery network](https://en.wikipedia.org/wiki/Content_delivery_network) +- [What is Globally distributed content delivery?](https://figshare.com/articles/journal_contribution/Globally_distributed_content_delivery/6605972) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/109-load-balancers/100-horizontal-scaling.md b/src/roadmaps/system-design/content/109-load-balancers/100-horizontal-scaling.md index 381bbeb9e..1349be0f7 100644 --- a/src/roadmaps/system-design/content/109-load-balancers/100-horizontal-scaling.md +++ b/src/roadmaps/system-design/content/109-load-balancers/100-horizontal-scaling.md @@ -1 +1,15 @@ -# Horizontal scaling \ No newline at end of file +# 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. + +## Disadvantages of 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 (SQL, NoSQL) or a persistent cache (Redis, Memcached) +- Downstream servers such as caches and databases need to handle more simultaneous connections as upstream servers scale out. + +To learn more, visit the following links: + +- [Introduction to Horizontal Scaling](https://github.com/donnemartin/system-design-primer#horizontal-scaling) +- [System Design – Horizontal and Vertical Scaling](https://www.geeksforgeeks.org/system-design-horizontal-and-vertical-scaling/) +- [Getting started with Horizontal and Vertical Scaling](https://www.codingninjas.com/blog/2021/08/25/system-design-horizontal-and-vertical-scaling/) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/109-load-balancers/101-layer-4-load-balancing.md b/src/roadmaps/system-design/content/109-load-balancers/101-layer-4-load-balancing.md index 740cf49d6..caf6f95ef 100644 --- a/src/roadmaps/system-design/content/109-load-balancers/101-layer-4-load-balancing.md +++ b/src/roadmaps/system-design/content/109-load-balancers/101-layer-4-load-balancing.md @@ -1 +1,8 @@ -# Layer 4 load balancing \ No newline at end of file +# Layer 4 Load Balancing + +Layer 4 load balancers look at info at the transport layer 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). + +To learn more, visit the following links: + +- [What is Layer 4 Load Balancing?](https://github.com/donnemartin/system-design-primer#communication) +- [Getting Started with Layer 4 Load Balancing](https://www.nginx.com/resources/glossary/layer-4-load-balancing/) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/109-load-balancers/102-layer-7-load-balancing.md b/src/roadmaps/system-design/content/109-load-balancers/102-layer-7-load-balancing.md index f24e1eb07..194c7e8ce 100644 --- a/src/roadmaps/system-design/content/109-load-balancers/102-layer-7-load-balancing.md +++ b/src/roadmaps/system-design/content/109-load-balancers/102-layer-7-load-balancing.md @@ -1 +1,10 @@ -# Layer 7 load balancing \ No newline at end of file +# Layer 7 Load Balancing + +Layer 7 load balancers look at the application layer to decide how to distribute requests. This can involve contents of the header, message, and cookies. Layer 7 load balancers terminate 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. + +Learn more from the following links: + +- [Introduction to Layer 7 Load Balancing](https://github.com/donnemartin/system-design-primer#layer-7-load-balancing) +- [A Brief of Layer 7 Balancing](https://github.com/donnemartin/system-design-primer#communication) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/109-load-balancers/103-load-balancing-algorithms.md b/src/roadmaps/system-design/content/109-load-balancers/103-load-balancing-algorithms.md index 79d1e431f..8dd6d8713 100644 --- a/src/roadmaps/system-design/content/109-load-balancers/103-load-balancing-algorithms.md +++ b/src/roadmaps/system-design/content/109-load-balancers/103-load-balancing-algorithms.md @@ -1 +1,8 @@ -# Load balancing algorithms \ No newline at end of file +# Load Balancing Algorithms + +Load balancing is the process of distributing incoming network traffic across multiple servers in order to optimize resource usage, minimize response time, and avoid overloading any single server. There are several algorithms that can be used to achieve this, each with its own advantages and disadvantages. + +To learn more, visit the following links: + +- [Concept of load balancing algorithms](https://www.enjoyalgorithms.com/blog/load-balancers-in-system-design) +- [Types of load balancing algorithms](https://www.cloudflare.com/learning/performance/types-of-load-balancing-algorithms/) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/109-load-balancers/104-lb-vs-reverse-proxy.md b/src/roadmaps/system-design/content/109-load-balancers/104-lb-vs-reverse-proxy.md index 07dfe9661..669aec5a7 100644 --- a/src/roadmaps/system-design/content/109-load-balancers/104-lb-vs-reverse-proxy.md +++ b/src/roadmaps/system-design/content/109-load-balancers/104-lb-vs-reverse-proxy.md @@ -1 +1,14 @@ -# Lb vs reverse proxy \ No newline at end of file +# 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. + +## Disadvantages of 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) further increases complexity + +To learn more visit the following links: + +- [What is a Reverse Proxy vs. Load Balancer?](https://www.nginx.com/resources/glossary/reverse-proxy-vs-load-balancer/) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/109-load-balancers/index.md b/src/roadmaps/system-design/content/109-load-balancers/index.md index 52e95f44f..9f147859b 100644 --- a/src/roadmaps/system-design/content/109-load-balancers/index.md +++ b/src/roadmaps/system-design/content/109-load-balancers/index.md @@ -1 +1,31 @@ -# Load balancers \ No newline at end of file +# Load Balancers + +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 to eliminate a single point of failure + +Load balancers can be implemented with hardware (expensive) or with software such as HAProxy. 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 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 or active-active mode. Load balancers can route traffic based on various metrics, including: + +## Layer 4 load balancing +Layer 4 load balancers look at info at the transport layer 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). + +## Layer 7 load balancing +Layer 7 load balancers look at the application layer to decide how to distribute requests. This can involve contents of the header, message, and cookies. Layer 7 load balancers terminate 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. + +## Disadvantages of 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 a single point of failure results in increased complexity. +A single load balancer is a single point of failure, configuring multiple load balancers further increases complexity. + +To learn more, visit the following links: + +- [What is Load balancing (computing)?](https://en.wikipedia.org/wiki/Load_balancing_(computing)) +- [Introduction to Load Balancing](https://github.com/donnemartin/system-design-primer#layer-7-load-balancing) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/110-application-layer/100-microservices.md b/src/roadmaps/system-design/content/110-application-layer/100-microservices.md index 3ace82ef2..2658508c8 100644 --- a/src/roadmaps/system-design/content/110-application-layer/100-microservices.md +++ b/src/roadmaps/system-design/content/110-application-layer/100-microservices.md @@ -1 +1,10 @@ -# Microservices \ No newline at end of file +# Microservices + +Related to this discussion are 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-defined, lightweight mechanism to serve a business goal. 1 + +Pinterest, for example, could have the following microservices: user profile, follower, feed, search, photo upload, etc. + +To learn more, visit the following links: + +- [Intro to Microservice](https://github.com/donnemartin/system-design-primer#microservices) +- [Building Microservices](https://cloudncode.wordpress.com/2016/07/22/msa-getting-started/) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/110-application-layer/101-service-discovery.md b/src/roadmaps/system-design/content/110-application-layer/101-service-discovery.md index 96b2c0fe2..2c205c307 100644 --- a/src/roadmaps/system-design/content/110-application-layer/101-service-discovery.md +++ b/src/roadmaps/system-design/content/110-application-layer/101-service-discovery.md @@ -1 +1,8 @@ -# Service discovery \ No newline at end of file +# Service Discovery + +Systems such as Consul, Etcd, and Zookeeper can help services find each other by keeping track of registered names, addresses, and ports. Health checks help verify service integrity and are often done using an HTTP endpoint. Both Consul and Etcd have a built in key-value store that can be useful for storing config values and other shared data. + +Visit the following links to learn more: + +- [What is Service-oriented architecture?](https://en.wikipedia.org/wiki/Service-oriented_architecture) +- [Intro to Service Discovery](https://github.com/donnemartin/system-design-primer#Service%20Discovery) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/110-application-layer/index.md b/src/roadmaps/system-design/content/110-application-layer/index.md index 1ae28ea71..5b4a768aa 100644 --- a/src/roadmaps/system-design/content/110-application-layer/index.md +++ b/src/roadmaps/system-design/content/110-application-layer/index.md @@ -1 +1,7 @@ -# Application layer \ No newline at end of file +# Application Layer + +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. 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. + +For more resources, visit the following links: + +- [Getting started with Application Layer](https://github.com/donnemartin/system-design-primer#Application%20layer) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/111-databases/100-rdbms/100-replication.md b/src/roadmaps/system-design/content/111-databases/100-rdbms/100-replication.md index 0516dcfd9..ca0d67b2e 100644 --- a/src/roadmaps/system-design/content/111-databases/100-rdbms/100-replication.md +++ b/src/roadmaps/system-design/content/111-databases/100-rdbms/100-replication.md @@ -1 +1,11 @@ -# Replication \ No newline at end of file +# Replication + +## Master-slave replication: +The master serves reads and writes, replicating writes to one or more slaves, which serve only reads. Slaves can also replicate to additional slaves in a tree-like fashion. If the master goes offline, the system can continue to operate in read-only mode until a slave is promoted to a master or a new master is provisioned. + +## Master-master replication: +Both masters serve reads and writes and coordinate with each other on writes. If either master goes down, the system can continue to operate with both reads and writes. + +To learn more, visit the following links: + +- [Getting started with Replication](https://github.com/donnemartin/system-design-primer#replication) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/111-databases/100-rdbms/101-sharding.md b/src/roadmaps/system-design/content/111-databases/100-rdbms/101-sharding.md index f837bc837..8646526b7 100644 --- a/src/roadmaps/system-design/content/111-databases/100-rdbms/101-sharding.md +++ b/src/roadmaps/system-design/content/111-databases/100-rdbms/101-sharding.md @@ -1 +1,10 @@ -# Sharding \ No newline at end of file +# Sharding + +Sharding distributes data across different databases such that each database can only manage a subset of the data. Taking a users database as an example, as the number of users increases, more shards are added to the cluster. + +Similar to the advantages of federation, sharding results in less read and write traffic, less replication, and more cache hits. Index size is also reduced, which generally improves performance with faster queries. If one shard goes down, the other shards are still operational, although you'll want to add some form of replication to avoid data loss. Like federation, there is no single central master serializing writes, allowing you to write in parallel with increased throughput. + +Learn more from the following links: + +- [The coming of the Shard](http://highscalability.com/blog/2009/8/6/an-unorthodox-approach-to-database-design-the-coming-of-the.html) +- [Shard (database architecture)](https://en.wikipedia.org/wiki/Shard_(database_architecture)) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/111-databases/100-rdbms/102-federation.md b/src/roadmaps/system-design/content/111-databases/100-rdbms/102-federation.md index d759f7acb..634f3da49 100644 --- a/src/roadmaps/system-design/content/111-databases/100-rdbms/102-federation.md +++ b/src/roadmaps/system-design/content/111-databases/100-rdbms/102-federation.md @@ -1 +1,7 @@ -# Federation \ No newline at end of file +# Federation + +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. + +Learn more from the following links: + +- [Intro to Federation](https://github.com/donnemartin/system-design-primer#federation) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/111-databases/100-rdbms/103-denormalization.md b/src/roadmaps/system-design/content/111-databases/100-rdbms/103-denormalization.md index bfe8a01ce..dd4ee4683 100644 --- a/src/roadmaps/system-design/content/111-databases/100-rdbms/103-denormalization.md +++ b/src/roadmaps/system-design/content/111-databases/100-rdbms/103-denormalization.md @@ -1 +1,10 @@ -# Denormalization \ No newline at end of file +# Denormalization + +Denormalization attempts 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 and Oracle support materialized views which handle the work of storing redundant information and keeping redundant copies consistent. + +Once data becomes distributed with techniques such as federation and sharding, managing joins across data centers further increases complexity. Denormalization might circumvent the need for such complex joins. + +To learn more, visit the following links: + +- [Guide to Denormalization](https://github.com/donnemartin/system-design-primer#denormalization) +- [Denormalization](https://en.wikipedia.org/wiki/Denormalization) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/111-databases/100-rdbms/104-sql-tuning.md b/src/roadmaps/system-design/content/111-databases/100-rdbms/104-sql-tuning.md index 538f9d3c1..66db5b6f0 100644 --- a/src/roadmaps/system-design/content/111-databases/100-rdbms/104-sql-tuning.md +++ b/src/roadmaps/system-design/content/111-databases/100-rdbms/104-sql-tuning.md @@ -1 +1,13 @@ -# Sql tuning \ No newline at end of file +# SQL Tuning + +SQL tuning is a broad topic and many books have been written as reference. It's important to benchmark and profile to simulate and uncover bottlenecks. + + - Benchmark - Simulate high-load situations with tools such as ab. + - Profile - Enable tools such as the slow query log to help track performance issues. + +Benchmarking and profiling might point you to the following optimizations. + +To learn more, visit the following links: + +- [What is SQL Tuning?](https://github.com/donnemartin/system-design-primer#sql-tuning) +- [Optimizing MySQL Queries](https://aiddroid.com/10-tips-optimizing-mysql-queries-dont-suck/) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/111-databases/100-rdbms/index.md b/src/roadmaps/system-design/content/111-databases/100-rdbms/index.md index b4f713379..3b246d596 100644 --- a/src/roadmaps/system-design/content/111-databases/100-rdbms/index.md +++ b/src/roadmaps/system-design/content/111-databases/100-rdbms/index.md @@ -1 +1,14 @@ -# Rdbms \ No newline at end of file +# RDBMS + +A relational database like SQL is a collection of data items organized in tables. ACID is a set of properties of relational database transactions. + + - **Atomicity** - Each transaction is all or nothing + - **Consistency** - Any transaction will bring the database from one valid state to another + - **Isolation** - Executing transactions concurrently has the same results as if the transactions were executed serially + - **Durability** - Once a transaction has been committed, it will remain so + +There are many techniques to scale a relational database: master-slave replication, master-master replication, federation, sharding, denormalization, and SQL tuning. + +To learn more, visit the following links: + +- [Guide to RDBMS?](https://github.com/donnemartin/system-design-primer#relational-database-management-system-rdbms) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/111-databases/101-nosql/100-key-value-store.md b/src/roadmaps/system-design/content/111-databases/101-nosql/100-key-value-store.md index 49a59b60c..f2c7395df 100644 --- a/src/roadmaps/system-design/content/111-databases/101-nosql/100-key-value-store.md +++ b/src/roadmaps/system-design/content/111-databases/101-nosql/100-key-value-store.md @@ -1 +1,10 @@ -# Key value store \ No newline at end of file +# Key Value Store + +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, 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. + +To learn more, visit the following links: + +- [Key–value database](https://en.wikipedia.org/wiki/Key%E2%80%93value_database) +- [What are the disadvantages of using a key/value table?](https://stackoverflow.com/questions/4056093/what-are-the-disadvantages-of-using-a-key-value-table-over-nullable-columns-or) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/111-databases/101-nosql/101-document-store.md b/src/roadmaps/system-design/content/111-databases/101-nosql/101-document-store.md index df7f9e2b5..53cf39077 100644 --- a/src/roadmaps/system-design/content/111-databases/101-nosql/101-document-store.md +++ b/src/roadmaps/system-design/content/111-databases/101-nosql/101-document-store.md @@ -1 +1,10 @@ -# Document store \ No newline at end of file +# Document Store + +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. + +Based on the underlying implementation, documents are organized by collections, tags, metadata, or directories. Although documents can be organized or grouped together, documents may have fields that are completely different from each other. + +To learn more, visit the following links: + +- [Getting started with Document Store](https://github.com/donnemartin/system-design-primer#document-store) +- [Document-oriented database](https://en.wikipedia.org/wiki/Document-oriented_database) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/111-databases/101-nosql/102-wide-column-store.md b/src/roadmaps/system-design/content/111-databases/101-nosql/102-wide-column-store.md index 676178990..90e00af50 100644 --- a/src/roadmaps/system-design/content/111-databases/101-nosql/102-wide-column-store.md +++ b/src/roadmaps/system-design/content/111-databases/101-nosql/102-wide-column-store.md @@ -1 +1,10 @@ -# Wide column store \ No newline at end of file +# Wide Column Store + +A wide column store's basic unit of data is a column (name/value pair). A column can be grouped in column families (analogous to a SQL table). Super column families further group column families. You can access each column independently with a row key, and columns with the same row key form a row. Each value contains a timestamp for versioning and for conflict resolution. + +Google introduced Bigtable as the first wide column store, which influenced the open-source HBase often-used in the Hadoop ecosystem, and Cassandra from Facebook. Stores such as BigTable, HBase, and Cassandra maintain keys in lexicographic order, allowing efficient retrieval of selective key ranges. + +Learn more from the following links: + +- [A brief of Wide Column Store](https://github.com/donnemartin/system-design-primer#Wide%20column%20store) +- [Bigtable architecture](https://www.read.seas.harvard.edu/~kohler/class/cs239-w08/chang06bigtable.pdf) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/111-databases/101-nosql/103-graph-databases.md b/src/roadmaps/system-design/content/111-databases/101-nosql/103-graph-databases.md index 54a5a1640..be340da15 100644 --- a/src/roadmaps/system-design/content/111-databases/101-nosql/103-graph-databases.md +++ b/src/roadmaps/system-design/content/111-databases/101-nosql/103-graph-databases.md @@ -1 +1,10 @@ -# Graph databases \ No newline at end of file +# Graph Databases + +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. + +Learn more from the following links: + +- [Graph database](https://en.wikipedia.org/wiki/Graph_database) +- [Introduction to NoSQL](https://www.youtube.com/watch?v=qI_g07C_Q5I) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/111-databases/101-nosql/index.md b/src/roadmaps/system-design/content/111-databases/101-nosql/index.md index a6b54c76c..a6b6108db 100644 --- a/src/roadmaps/system-design/content/111-databases/101-nosql/index.md +++ b/src/roadmaps/system-design/content/111-databases/101-nosql/index.md @@ -1 +1,15 @@ -# Nosql \ No newline at end of file +# NoSQL + +NoSQL is a collection of data items represented in a key-value store, document store, wide column store, or a graph database. Data is denormalized, and joins are generally done in the application code. Most NoSQL stores lack true ACID transactions and favor eventual consistency. + +BASE is often used to describe the properties of NoSQL databases. In comparison with the CAP Theorem, BASE chooses availability over consistency. + +- Basically available - the system guarantees availability. +- Soft state - the state of the system may change over time, even without input. +- Eventual consistency - the system will become consistent over a period of time, given that the system doesn't receive input during that period. + +Learn more from the following links: + +- [SQL or noSQL?](https://github.com/donnemartin/system-design-primer#sql-or-nosql) +- [Brief of noSQL Patterns](http://horicky.blogspot.com/2009/11/nosql-patterns.html) +- [Introduction to NoSQL](https://www.youtube.com/watch?v=qI_g07C_Q5I) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/111-databases/102-sql-vs-nosql.md b/src/roadmaps/system-design/content/111-databases/102-sql-vs-nosql.md index e621b40cb..46c611479 100644 --- a/src/roadmaps/system-design/content/111-databases/102-sql-vs-nosql.md +++ b/src/roadmaps/system-design/content/111-databases/102-sql-vs-nosql.md @@ -1 +1,32 @@ -# Sql vs nosql \ No newline at end of file +# SQL vs noSQL + +## Reasons for SQL: +- Structured data +- Strict schema +- Relational data +- Need for complex joins +- Transactions +- Clear patterns for scaling +- More established: developers, community, code, tools, etc +- Lookups by index are very fast + + +## Reasons for NoSQL: +- Semi-structured data +- Dynamic or flexible schema +- Non-relational data +- No need for complex joins +- Store many TB (or PB) of data +- Very data intensive workload +- Very high throughput for IOPS + +## Sample data well-suited for NoSQL: +- Rapid ingest of clickstream and log data +- Leaderboard or scoring data +- Temporary data, such as a shopping cart +- Frequently accessed ('hot') tables +- Metadata/lookup tables + +Learn more from the followinw links: +- [SQL vs NoSQL: The Differences](https://www.sitepoint.com/sql-vs-nosql-differences/) +- [Scaling up to your first 10 million users](https://www.youtube.com/watch?v=kKjm4ehYiMs) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/111-databases/index.md b/src/roadmaps/system-design/content/111-databases/index.md index 602f6d863..6f0cb03dd 100644 --- a/src/roadmaps/system-design/content/111-databases/index.md +++ b/src/roadmaps/system-design/content/111-databases/index.md @@ -1 +1,15 @@ -# Databases \ No newline at end of file +# Databases + +A database is a collection of data that is organized and stored in a structured way, allowing for efficient retrieval and manipulation of the data. Databases are used in many different types of systems to store and manage data, from small personal applications to large enterprise systems. + +There are many different types of databases available, each with their own strengths and weaknesses. Some of the most common types of databases are: + +- Relational databases +- NoSQL databases +- Graph databases +- Time-series databases + +Learn more from the following links: + +- [Intro to Databases](https://github.com/donnemartin/system-design-primer#database) +- [Database design](https://en.wikipedia.org/wiki/Database_design) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/112-caching/100-client-caching.md b/src/roadmaps/system-design/content/112-caching/100-client-caching.md index 7bac9297d..5eb3b34d9 100644 --- a/src/roadmaps/system-design/content/112-caching/100-client-caching.md +++ b/src/roadmaps/system-design/content/112-caching/100-client-caching.md @@ -1 +1,8 @@ -# Client caching \ No newline at end of file +# Client caching + +Caches can be located on the client side (OS or browser), server side, or in a distinct cache layer. + +To learn more, visit the following links: + +- [Intro to Client Caching](https://github.com/donnemartin/system-design-primer#client%20caching) +- [Server side Client Caching](https://github.com/donnemartin/system-design-primer#reverse-proxy-web-server) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/112-caching/101-cdn-caching.md b/src/roadmaps/system-design/content/112-caching/101-cdn-caching.md index b142ac918..0f150abeb 100644 --- a/src/roadmaps/system-design/content/112-caching/101-cdn-caching.md +++ b/src/roadmaps/system-design/content/112-caching/101-cdn-caching.md @@ -1 +1,10 @@ -# Cdn caching \ No newline at end of file +# CDN caching + +CDNs are considered a type of cache. + +A content delivery network (CDN) is a globally distributed network of proxy servers, serving content from locations closer to the user. Generally, static files such as HTML/CSS/JS, photos, and videos are served from CDN, although some CDNs such as Amazon's CloudFront support dynamic content. The site's DNS resolution will tell clients which server to contact. + +To learn more, visit the following links: + +- [What is CDN Cache?](https://github.com/donnemartin/system-design-primer#CDN%20cache) +- [CDN Caching](https://github.com/donnemartin/system-design-primer#content-delivery-network) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/112-caching/102-web-server-caching.md b/src/roadmaps/system-design/content/112-caching/102-web-server-caching.md index 2c49af2fd..bf441be01 100644 --- a/src/roadmaps/system-design/content/112-caching/102-web-server-caching.md +++ b/src/roadmaps/system-design/content/112-caching/102-web-server-caching.md @@ -1 +1,7 @@ -# Web server caching \ No newline at end of file +# Web Server Caching + +Reverse proxies and caches such as Varnish can serve static and dynamic content directly. Web servers can also cache requests, returning responses without having to contact application servers. + +To learn more, visit the following links: + +- [Intro to Web Server Caching?](https://github.com/donnemartin/system-design-primer#web-server-caching) diff --git a/src/roadmaps/system-design/content/112-caching/103-database-caching.md b/src/roadmaps/system-design/content/112-caching/103-database-caching.md index ab93b48a5..75a8ac1e8 100644 --- a/src/roadmaps/system-design/content/112-caching/103-database-caching.md +++ b/src/roadmaps/system-design/content/112-caching/103-database-caching.md @@ -1 +1,7 @@ -# Database caching \ No newline at end of file +# Database Caching + +Your database usually includes some level of caching in a default configuration, optimized for a generic use case. Tweaking these settings for specific usage patterns can further boost performance. + +To learn more, visit the following links: + +- [Intro to Database-Caching?](https://github.com/donnemartin/system-design-primer#database-caching) diff --git a/src/roadmaps/system-design/content/112-caching/104-application-caching.md b/src/roadmaps/system-design/content/112-caching/104-application-caching.md index d944829c2..93f73c0e5 100644 --- a/src/roadmaps/system-design/content/112-caching/104-application-caching.md +++ b/src/roadmaps/system-design/content/112-caching/104-application-caching.md @@ -1 +1,7 @@ -# Application caching \ No newline at end of file +# Application Caching + +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 algorithms such as least recently used (LRU) can help invalidate 'cold' entries and keep 'hot' data in RAM. + +Visit the following links to learn more: + +- [Intro to Application Caching](https://github.com/donnemartin/system-design-primer#application-caching) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/112-caching/105-caching-strategies/100-cache-aside.md b/src/roadmaps/system-design/content/112-caching/105-caching-strategies/100-cache-aside.md index be33eca0f..f5904afa2 100644 --- a/src/roadmaps/system-design/content/112-caching/105-caching-strategies/100-cache-aside.md +++ b/src/roadmaps/system-design/content/112-caching/105-caching-strategies/100-cache-aside.md @@ -1 +1,16 @@ -# Cache aside \ No newline at end of file +# Cache-aside + + +The application is responsible for reading and writing from storage. The cache does not interact with storage directly. The application does the following: + +- Look for entry in cache, resulting in a cache miss +- Load entry from the database +- Add entry to cache +- Return entry + +Memcached is generally used in this manner. Subsequent reads of data added to cache are fast. Cache-aside is also referred to as lazy loading. Only requested data is cached, which avoids filling up the cache with data that isn't requested. + +Learn more from the following links: + +- [Getting started with Cache-aside](https://github.com/donnemartin/system-design-primer#cache-aside) +- [What is Memcached?](https://memcached.org/) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/112-caching/105-caching-strategies/101-write-through.md b/src/roadmaps/system-design/content/112-caching/105-caching-strategies/101-write-through.md index 898b414c7..81d77b2ef 100644 --- a/src/roadmaps/system-design/content/112-caching/105-caching-strategies/101-write-through.md +++ b/src/roadmaps/system-design/content/112-caching/105-caching-strategies/101-write-through.md @@ -1 +1,13 @@ -# Write through \ No newline at end of file +# Write-through + +The application uses the cache as the main data store, reading and writing data to it, while the cache is responsible for reading and writing to the database: + +- Application adds/updates entry in cache +- Cache synchronously writes entry to data store +- Return + +Write-through is a slow overall operation due to the write operation, but subsequent reads of just written data are fast. Users are generally more tolerant of latency when updating data than reading data. Data in the cache is not stale. + +To learn more, visit the following links: + +- [Getting started with Write-through](https://github.com/donnemartin/system-design-primer#Write-through) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/112-caching/105-caching-strategies/102-write-behind.md b/src/roadmaps/system-design/content/112-caching/105-caching-strategies/102-write-behind.md index aa7b747f2..e2164a5f4 100644 --- a/src/roadmaps/system-design/content/112-caching/105-caching-strategies/102-write-behind.md +++ b/src/roadmaps/system-design/content/112-caching/105-caching-strategies/102-write-behind.md @@ -1 +1,15 @@ -# Write behind \ No newline at end of file +# Write-behind + +In write-behind, the application does the following: + +- Add/update entry in cache +- Asynchronously write entry to the data store, improving write performance + +## Disadvantages of write-behind: + +- There could be data loss if the cache goes down prior to its contents hitting the data store. +- It is more complex to implement write-behind than it is to implement cache-aside or write-through. + +To learn more, visit the following links: + +- [Getting started with Write-behind](https://github.com/donnemartin/system-design-primer#Write-behind) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/112-caching/105-caching-strategies/103-refresh-ahead.md b/src/roadmaps/system-design/content/112-caching/105-caching-strategies/103-refresh-ahead.md index 096336545..c53f9063d 100644 --- a/src/roadmaps/system-design/content/112-caching/105-caching-strategies/103-refresh-ahead.md +++ b/src/roadmaps/system-design/content/112-caching/105-caching-strategies/103-refresh-ahead.md @@ -1 +1,10 @@ -# Refresh ahead \ No newline at end of file +# Refresh-ahead + +You can configure the cache to automatically refresh any recently accessed cache entry prior to its expiration. Refresh-ahead can result in reduced latency vs read-through if the cache can accurately predict which items are likely to be needed in the future. + +## Disadvantage of refresh-ahead: +- Not accurately predicting which items are likely to be needed in the future can result in reduced performance than without refresh-ahead. + +To learn more, visit the following links: + +- [Getting started with Refresh-ahead](https://github.com/donnemartin/system-design-primer#refresh-ahead) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/112-caching/105-caching-strategies/index.md b/src/roadmaps/system-design/content/112-caching/105-caching-strategies/index.md index b9d251089..562abaac8 100644 --- a/src/roadmaps/system-design/content/112-caching/105-caching-strategies/index.md +++ b/src/roadmaps/system-design/content/112-caching/105-caching-strategies/index.md @@ -1 +1,24 @@ -# Caching strategies \ No newline at end of file +# Caching Strategies + +Caching improves page load times and can reduce the load on your servers and databases. In this model, the dispatcher will first lookup if the request has been made before and try to find the previous result to return, in order to save the actual execution. + +Databases often benefit from a uniform distribution of reads and writes across its partitions. Popular items can skew the distribution, causing bottlenecks. Putting a cache in front of a database can help absorb uneven loads and spikes in traffic. + +## Client caching +Caches can be located on the client side (OS or browser), server side, or in a distinct cache layer. + +## CDN caching +CDNs are considered a type of cache. + +## Web server caching +Reverse proxies and caches such as Varnish can serve static and dynamic content directly. Web servers can also cache requests, returning responses without having to contact application servers. + +## Database caching +Your database usually includes some level of caching in a default configuration, optimized for a generic use case. Tweaking these settings for specific usage patterns can further boost performance. + +## Application caching +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 algorithms such as least recently used (LRU) can help invalidate 'cold' entries and keep 'hot' data in RAM. + +To learn more, visit the following links: + +- [Getting started with Cache](https://github.com/donnemartin/system-design-primer#cache) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/112-caching/index.md b/src/roadmaps/system-design/content/112-caching/index.md index 07f88797a..c8695f1e4 100644 --- a/src/roadmaps/system-design/content/112-caching/index.md +++ b/src/roadmaps/system-design/content/112-caching/index.md @@ -1 +1,25 @@ -# Caching \ No newline at end of file +# Caching + +Caching improves page load times and can reduce the load on your servers and databases. In this model, the dispatcher will first lookup if the request has been made before and try to find the previous result to return, in order to save the actual execution. + +Databases often benefit from a uniform distribution of reads and writes across its partitions. Popular items can skew the distribution, causing bottlenecks. Putting a cache in front of a database can help absorb uneven loads and spikes in traffic. + +## Client caching +Caches can be located on the client side (OS or browser), server side, or in a distinct cache layer. + +## CDN caching +CDNs are considered a type of cache. + +## Web server caching +Reverse proxies and caches such as Varnish can serve static and dynamic content directly. Web servers can also cache requests, returning responses without having to contact application servers. + +## Database caching +Your database usually includes some level of caching in a default configuration, optimized for a generic use case. Tweaking these settings for specific usage patterns can further boost performance. + +## Application caching +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 algorithms such as least recently used (LRU) can help invalidate 'cold' entries and keep 'hot' data in RAM. + +To learn more, visit the following links: + +- [Getting started with Cache](https://github.com/donnemartin/system-design-primer#cache) +- [From cache to in-memory data grid](https://www.slideshare.net/tmatyashovsky/from-cache-to-in-memory-data-grid-introduction-to-hazelcast) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/113-asynchronism/100-message-queues.md b/src/roadmaps/system-design/content/113-asynchronism/100-message-queues.md index bdefdc0a3..9b09ece3d 100644 --- a/src/roadmaps/system-design/content/113-asynchronism/100-message-queues.md +++ b/src/roadmaps/system-design/content/113-asynchronism/100-message-queues.md @@ -1 +1,23 @@ -# Message queues \ No newline at end of file +# Message Queues + +Message queues receive, hold, and deliver messages. If an operation is too slow to perform inline, you can use a message queue with the following workflow: + +- An application publishes a job to the queue, then notifies the user of job status +- A worker picks up the job from the queue, processes it, then signals the job is complete + +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. + +## Redis +It is useful as a simple message broker but messages can be lost. + +## RabbitMQ +This is popular but requires you to adapt to the 'AMQP' protocol and manage your own nodes. + +## Amazon SQS +Amazon SQS is hosted but can have high latency and has the possibility of messages being delivered twice. + +To learn more, visit the following links: + +- [What is Redis?](https://redis.io/) +- [RabbitMQ in Message Queues](https://www.rabbitmq.com/) +- [Overview of Amazon SQS](https://aws.amazon.com/sqs/) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/113-asynchronism/101-task-queues.md b/src/roadmaps/system-design/content/113-asynchronism/101-task-queues.md index 91111c14d..34458e312 100644 --- a/src/roadmaps/system-design/content/113-asynchronism/101-task-queues.md +++ b/src/roadmaps/system-design/content/113-asynchronism/101-task-queues.md @@ -1 +1,10 @@ -# Task queues \ No newline at end of file +# Task Queues + +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. + +Celery has support for scheduling and primarily has python support. + +To learn more, visit the following links: + +- [Overview of Task Queues](https://github.com/donnemartin/system-design-primer#task%20queues) +- [Celery - Distributed Task Queue](https://docs.celeryq.dev/en/stable/) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/113-asynchronism/102-back-pressure.md b/src/roadmaps/system-design/content/113-asynchronism/102-back-pressure.md index 70cfb71ec..543e48dc9 100644 --- a/src/roadmaps/system-design/content/113-asynchronism/102-back-pressure.md +++ b/src/roadmaps/system-design/content/113-asynchronism/102-back-pressure.md @@ -1 +1,7 @@ -# Back pressure \ No newline at end of file +# Back Pressure + +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 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. + +To learn more, visit the following links: + +- [Overview of Back Pressure](https://github.com/donnemartin/system-design-primer#back%20pressure) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/113-asynchronism/index.md b/src/roadmaps/system-design/content/113-asynchronism/index.md index da1006540..dea66290a 100644 --- a/src/roadmaps/system-design/content/113-asynchronism/index.md +++ b/src/roadmaps/system-design/content/113-asynchronism/index.md @@ -1 +1,8 @@ -# Asynchronism \ No newline at end of file +# Asynchronism + +Asynchronous workflows help reduce request times for expensive operations that would otherwise be performed in-line. They can also help by doing time-consuming work in advance, such as periodic aggregation of data. + +To learn more, visit the following links: + +- [Overview of Asynchronism](https://github.com/donnemartin/system-design-primer#Asynchronism) +- [What is the difference between a message queue and a task queue?](https://www.quora.com/What-is-the-difference-between-a-message-queue-and-a-task-queue-Why-would-a-task-queue-require-a-message-broker-like-RabbitMQ-Redis-Celery-or-IronMQ-to-function) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/114-idempotent-operations.md b/src/roadmaps/system-design/content/114-idempotent-operations.md index ad337234c..adddcd802 100644 --- a/src/roadmaps/system-design/content/114-idempotent-operations.md +++ b/src/roadmaps/system-design/content/114-idempotent-operations.md @@ -1 +1,8 @@ -# Idempotent operations \ No newline at end of file +# Idempotent Operations + +An idempotent operation is an operation, action, or request that can be applied multiple times without changing the result, i.e. the state of the system, beyond the initial application. EXAMPLES (WEB APP CONTEXT): IDEMPOTENT: Making multiple identical requests has the same effect as making a single request. + +To learn more, visit the following links: + +- [What is an idempotent operation?](https://stackoverflow.com/questions/1077412/what-is-an-idempotent-operation) +- [Overview of Idempotent Operation](https://www.baeldung.com/cs/idempotent-operations) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/115-communication/100-http.md b/src/roadmaps/system-design/content/115-communication/100-http.md index b0e41f6d5..5edc4bab2 100644 --- a/src/roadmaps/system-design/content/115-communication/100-http.md +++ b/src/roadmaps/system-design/content/115-communication/100-http.md @@ -1 +1,8 @@ -# Http \ No newline at end of file +# 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. + +To learn more, visit the following links: + +- [What Is HTTP?](https://www.nginx.com/resources/glossary/http/) +- [What is the difference between HTTP protocol and TCP protocol?](https://www.quora.com/What-is-the-difference-between-HTTP-protocol-and-TCP-protocol) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/115-communication/101-tcp.md b/src/roadmaps/system-design/content/115-communication/101-tcp.md index 2ff51ffd4..b486a1498 100644 --- a/src/roadmaps/system-design/content/115-communication/101-tcp.md +++ b/src/roadmaps/system-design/content/115-communication/101-tcp.md @@ -1 +1,13 @@ -# Tcp \ No newline at end of file +# TCP + +TCP is a connection-oriented protocol over an IP network. Connection is established and terminated using a handshake. All packets sent are guaranteed to reach the destination in the original order and without corruption through: + +- Sequence numbers and checksum fields for each packet +- Acknowledgement 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 and congestion control. These guarantees cause delays and generally result in less efficient transmission than UDP. + +To learn more, visit the following links: + +- [What Is TCP?](https://github.com/donnemartin/system-design-primer#TCP) +- [What is the difference between HTTP protocol and TCP protocol?](https://www.quora.com/What-is-the-difference-between-HTTP-protocol-and-TCP-protocol) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/115-communication/102-udp.md b/src/roadmaps/system-design/content/115-communication/102-udp.md index 88c788fb4..f01e13681 100644 --- a/src/roadmaps/system-design/content/115-communication/102-udp.md +++ b/src/roadmaps/system-design/content/115-communication/102-udp.md @@ -1 +1,18 @@ -# Udp \ No newline at end of file +# UDP + +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 because the client has not yet received an IP address, thus preventing a way for TCP to stream without the IP address. + +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 + +To learn more, visit the following link: + +- [What Is UDP?](https://github.com/donnemartin/system-design-primer#UDP) +- [Difference between TCP and UDP?](https://stackoverflow.com/questions/5970383/difference-between-tcp-and-udp) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/115-communication/103-rpc.md b/src/roadmaps/system-design/content/115-communication/103-rpc.md index 82d418a48..205ca03e0 100644 --- a/src/roadmaps/system-design/content/115-communication/103-rpc.md +++ b/src/roadmaps/system-design/content/115-communication/103-rpc.md @@ -1 +1,7 @@ -# Rpc \ No newline at end of file +# RPC + +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, Thrift, and Avro. + +To learn more, visit the following links: + +- [What Is RPC?](https://github.com/donnemartin/system-design-primer#RPC) diff --git a/src/roadmaps/system-design/content/115-communication/104-rest.md b/src/roadmaps/system-design/content/115-communication/104-rest.md index 5088a865c..99fc65ac9 100644 --- a/src/roadmaps/system-design/content/115-communication/104-rest.md +++ b/src/roadmaps/system-design/content/115-communication/104-rest.md @@ -1 +1,15 @@ -# Rest \ No newline at end of file +# REST + +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. + +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 (HTML interface for HTTP) - your web service should be fully accessible in a browser. + +To learn more, visit the following links: + +- [What Is REST?](https://github.com/donnemartin/system-design-primer#REST) +- [What are the drawbacks of using RESTful APIs?](https://www.quora.com/What-are-the-drawbacks-of-using-RESTful-APIs) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/115-communication/105-grpc.md b/src/roadmaps/system-design/content/115-communication/105-grpc.md index 5d543f43c..f36b64680 100644 --- a/src/roadmaps/system-design/content/115-communication/105-grpc.md +++ b/src/roadmaps/system-design/content/115-communication/105-grpc.md @@ -1 +1,7 @@ -# Grpc \ No newline at end of file +# gRPC + +gRPC is a high-performance, open-source framework for building remote procedure call (RPC) APIs. It is based on the Protocol Buffers data serialization format and supports a variety of programming languages, including C#, Java, and Python. + +Learn more from the following links: + +- [What Is gRPC?](https://www.wallarm.com/what/the-concept-of-grpc) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/115-communication/106-graphql.md b/src/roadmaps/system-design/content/115-communication/106-graphql.md index 92b738b69..53928a758 100644 --- a/src/roadmaps/system-design/content/115-communication/106-graphql.md +++ b/src/roadmaps/system-design/content/115-communication/106-graphql.md @@ -1 +1,8 @@ -# Graphql \ No newline at end of file +# GraphQL + +GraphQL is a query language and runtime for building APIs. It allows clients to define the structure of the data they need and the server will return exactly that. This is in contrast to traditional REST APIs, where the server exposes a fixed set of endpoints and the client must work with the data as it is returned. + +To learn more, visit the following links: + +- [GraphQL Server](https://www.howtographql.com/basics/3-big-picture/) +- [What is GraphQL?](https://www.redhat.com/en/topics/api/what-is-graphql) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/115-communication/index.md b/src/roadmaps/system-design/content/115-communication/index.md index 7e37400e2..4aec47310 100644 --- a/src/roadmaps/system-design/content/115-communication/index.md +++ b/src/roadmaps/system-design/content/115-communication/index.md @@ -1 +1,28 @@ -# Communication \ No newline at end of file +# Communication + +## Hypertext transfer protocol (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. + + +## Transmission control protocol (TCP) +TCP is a connection-oriented protocol over an IP network. Connection is established and terminated using a handshake. All packets sent are guaranteed to reach the destination in the original order and without corruption through: + +- Sequence numbers and checksum fields for each packet +- Acknowledgement packets and automatic retransmission + + +## User datagram protocol (UDP) +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 because the client has not yet received an IP address, thus preventing a way for TCP to stream without the IP address. + + +## Remote procedure call (RPC) +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, Thrift, and Avro. + +## Representational state transfer (REST) +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. + +To learn more, visit the following links: + +- [Getting started with Communication](https://github.com/donnemartin/system-design-primer) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/116-performance-antipatterns/100-busy-database.md b/src/roadmaps/system-design/content/116-performance-antipatterns/100-busy-database.md index 5f308313b..9117f6e35 100644 --- a/src/roadmaps/system-design/content/116-performance-antipatterns/100-busy-database.md +++ b/src/roadmaps/system-design/content/116-performance-antipatterns/100-busy-database.md @@ -1 +1,8 @@ -# Busy database \ No newline at end of file +# Busy Database + +A busy database in system design refers to a database that is handling a high volume of requests or transactions, this can occur when a system is experiencing high traffic or when a database is not properly optimized for the workload it is handling. This can lead to Performance degradation, Increased resource utilization, Deadlocks and contention, Data inconsistencies. To address a busy database, a number of approaches can be taken such as Scaling out, Optimizing the schema, Caching, and Indexing. + +To learn more, visit the following links: + +- [Busy Database antipattern](https://learn.microsoft.com/en-us/azure/architecture/antipatterns/busy-database/) +- [Database Design](https://www.sciencedirect.com/topics/computer-science/database-design) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/116-performance-antipatterns/101-busy-frontend.md b/src/roadmaps/system-design/content/116-performance-antipatterns/101-busy-frontend.md index 7b21e7646..e7303a4c4 100644 --- a/src/roadmaps/system-design/content/116-performance-antipatterns/101-busy-frontend.md +++ b/src/roadmaps/system-design/content/116-performance-antipatterns/101-busy-frontend.md @@ -1 +1,8 @@ -# Busy frontend \ No newline at end of file +# Busy Frontend + +A busy frontend in system design refers to a frontend that is handling a high volume of requests or traffic, this can occur when a system is experiencing high traffic or when a frontend is not properly optimized for the workload it is handling. This can lead to Performance degradation, Increased resource utilization, Increased error rates, and Poor user experience. To address a busy frontend, a number of approaches can be taken such as Scaling out, Optimizing the code, Caching, and Load balancing. + +To learn more, visit the following link: + +- [Busy Front End antipattern](https://learn.microsoft.com/en-us/azure/architecture/antipatterns/busy-front-end/) +- [What is Front end system design?](https://www.youtube.com/watch?v=XPNMiWyHBAU) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/116-performance-antipatterns/102-chatty-io.md b/src/roadmaps/system-design/content/116-performance-antipatterns/102-chatty-io.md index b57cd5513..6492afeb0 100644 --- a/src/roadmaps/system-design/content/116-performance-antipatterns/102-chatty-io.md +++ b/src/roadmaps/system-design/content/116-performance-antipatterns/102-chatty-io.md @@ -1 +1,8 @@ -# Chatty io \ No newline at end of file +# Chat IO + +Chat IO in system design refers to the design of a chat system, which allows real-time communication between multiple users. A chat system typically consists of the following components: Client, Server, Messaging protocol, Message store, and Notification. To design a chat system, there are several key considerations to keep in mind such as Scalability, Reliability, and Security. + +To learn more, visit the following links: + +- [Chat Applications System Design](https://javascript.plainenglish.io/chat-applications-system-design-6a070c60c8cd) +- [Design A Chat System](https://bytebytego.com/courses/system-design-interview/design-a-chat-system) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/116-performance-antipatterns/103-extraneous-fetching.md b/src/roadmaps/system-design/content/116-performance-antipatterns/103-extraneous-fetching.md index b5a513626..0b5af4656 100644 --- a/src/roadmaps/system-design/content/116-performance-antipatterns/103-extraneous-fetching.md +++ b/src/roadmaps/system-design/content/116-performance-antipatterns/103-extraneous-fetching.md @@ -1 +1,15 @@ -# Extraneous fetching \ No newline at end of file +# Extraneous Fetching + +Extraneous fetching in system design refers to the practice of retrieving more data than is needed for a specific task or operation. This can occur when a system is not optimized for the specific workload or when the system is not properly designed to handle the data requirements. + +Extraneous fetching can lead to a number of issues, such as: + +- Performance degradation +- Increased resource utilization +- Increased network traffic +- Poor user experience + +Visit the following links to learn more: + +- [Extraneous Fetching antipattern](https://learn.microsoft.com/en-us/azure/architecture/antipatterns/extraneous-fetching/) +- [What’s the difference between extraneous and confounding variables?](https://www.scribbr.com/frequently-asked-questions/extraneous-vs-confounding-variables/) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/116-performance-antipatterns/104-improper-instantiation.md b/src/roadmaps/system-design/content/116-performance-antipatterns/104-improper-instantiation.md index f2daa5409..9346afe4f 100644 --- a/src/roadmaps/system-design/content/116-performance-antipatterns/104-improper-instantiation.md +++ b/src/roadmaps/system-design/content/116-performance-antipatterns/104-improper-instantiation.md @@ -1 +1,7 @@ -# Improper instantiation \ No newline at end of file +# Improper Instantiation + +Improper instantiation in system design refers to the practice of creating unnecessary instances of an object, class or service, which can lead to performance and scalability issues. This can happen when the system is not properly designed, when the code is not written in an efficient way, or when the code is not optimized for the specific use case. + +Learn more from the following links: +- [Improper Instantiation antipattern](https://learn.microsoft.com/en-us/azure/architecture/antipatterns/improper-instantiation/) +- [What is Instantiation?](https://www.techtarget.com/whatis/definition/instantiation) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/116-performance-antipatterns/105-monolithic-persistence.md b/src/roadmaps/system-design/content/116-performance-antipatterns/105-monolithic-persistence.md index 64dfb0df3..b4d767e06 100644 --- a/src/roadmaps/system-design/content/116-performance-antipatterns/105-monolithic-persistence.md +++ b/src/roadmaps/system-design/content/116-performance-antipatterns/105-monolithic-persistence.md @@ -1 +1,8 @@ -# Monolithic persistence \ No newline at end of file +# Monolithic Persistence + +Monolithic Persistence in system design refers to the use of a single, monolithic database to store all of the data for an application or system. This approach can be used for simple, small-scale systems but as the system grows and evolves it can become a bottleneck, resulting in poor scalability, limited flexibility, and increased complexity. To address these limitations, a number of approaches can be taken such as Microservices, Sharding, and NoSQL databases. + +To learn more, visit the following links: + +- [Monolithic Persistence antipattern](https://learn.microsoft.com/en-us/azure/architecture/antipatterns/monolithic-persistence/) +- [System Design: Monoliths and Microservices](https://dev.to/karanpratapsingh/system-design-monoliths-and-microservices-24jn) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/116-performance-antipatterns/106-no-caching.md b/src/roadmaps/system-design/content/116-performance-antipatterns/106-no-caching.md index d011521d1..23ddfed7f 100644 --- a/src/roadmaps/system-design/content/116-performance-antipatterns/106-no-caching.md +++ b/src/roadmaps/system-design/content/116-performance-antipatterns/106-no-caching.md @@ -1 +1,15 @@ -# No caching \ No newline at end of file +# No Caching + +Monolithic persistence in system design refers to the use of a single, monolithic database to store all of the data for an application or system. This approach can be used for simple, small-scale systems, but as the system grows and evolves, it can become a bottleneck, resulting in poor scalability, limited flexibility, and increased complexity. + +A monolithic persistence can have several disadvantages: + +- Scalability +- Limited Flexibility +- Increased Complexity +- Single Point of Failure + +Learn from the following links: + +- [What is Caching in system design?](enjoyalgorithms.com/blog/caching-system-design-concept) +- [No Caching antipattern](https://learn.microsoft.com/en-us/azure/architecture/antipatterns/no-caching/) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/116-performance-antipatterns/107-noisy-neighbor.md b/src/roadmaps/system-design/content/116-performance-antipatterns/107-noisy-neighbor.md index 0d6f984d5..ccb32e6ca 100644 --- a/src/roadmaps/system-design/content/116-performance-antipatterns/107-noisy-neighbor.md +++ b/src/roadmaps/system-design/content/116-performance-antipatterns/107-noisy-neighbor.md @@ -1 +1,14 @@ -# Noisy neighbor \ No newline at end of file +# Noisy Neighbor + +Noisy neighbor in system design refers to a situation in which one or more components of a system are utilizing a disproportionate amount of shared resources, leading to resource contention and reduced performance for other components. This can occur when a system is not properly designed or configured to handle the workload, or when a component is behaving unexpectedly. + +Examples of noisy neighbor scenarios include: + +- One user on a shared server utilizing a large amount of CPU or memory, leading to reduced performance for other users on the same server. +- One process on a shared server utilizing a large amount of I/O, causing other processes to experience slow I/O and increased latency. +- One application consuming a large amount of network bandwidth, causing other applications to experience reduced throughput. + +Learn from the following links: + +- [Noisy Neighbor](https://docs.aws.amazon.com/wellarchitected/latest/saas-lens/noisy-neighbor.html) +- [Get started with Noisy Neighbor antipattern](https://learn.microsoft.com/en-us/azure/architecture/antipatterns/noisy-neighbor/noisy-neighbor) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/116-performance-antipatterns/108-retry-storm.md b/src/roadmaps/system-design/content/116-performance-antipatterns/108-retry-storm.md index 7665b25d0..573b562b0 100644 --- a/src/roadmaps/system-design/content/116-performance-antipatterns/108-retry-storm.md +++ b/src/roadmaps/system-design/content/116-performance-antipatterns/108-retry-storm.md @@ -1 +1,8 @@ -# Retry storm \ No newline at end of file +# Retry Storm + + Retry Storm in system design refers to a situation in which a large number of retries are triggered in a short period of time, leading to a significant increase in traffic and resource usage. This can occur when a system is not properly designed to handle failures or when a component is behaving unexpectedly. This can lead to Performance degradation, Increased resource utilization, Increased network traffic, and Poor user experience. To address retry storms, a number of approaches can be taken such as Exponential backoff, Circuit breaking, and Monitoring and alerting. + +To learn more, visit the following links: + +- [Retry Storm antipattern](https://learn.microsoft.com/en-us/azure/architecture/antipatterns/retry-storm/s) +- [How To Avoid Retry Storms In Distributed Systems](https://faun.pub/how-to-avoid-retry-storms-in-distributed-systems-91bf34f43c7f) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/116-performance-antipatterns/109-synchronous-io.md b/src/roadmaps/system-design/content/116-performance-antipatterns/109-synchronous-io.md index 6ef680246..251392fe0 100644 --- a/src/roadmaps/system-design/content/116-performance-antipatterns/109-synchronous-io.md +++ b/src/roadmaps/system-design/content/116-performance-antipatterns/109-synchronous-io.md @@ -1 +1,11 @@ -# Synchronous io \ No newline at end of file +# Synchronous IO + +In system design, synchronous IO refers to a type of input/output (IO) operation where the program execution is blocked or halted until the IO operation completes. This means that the program will wait for the IO operation to finish before it can continue executing the next instruction. Synchronous IO can be used in a variety of scenarios, such as: + +- **Reading and writing files:** When a program needs to read or write a file, it can use synchronous IO to ensure that the operation completes before continuing. +- **Communicating with a database:** When a program needs to query or update a database, it can use synchronous IO to ensure that the operation completes before continuing. +- **Networking:** When a program needs to send or receive data over a network, it can use synchronous IO to ensure that the operation completes before continuing. + +To learn more, visit the following links: + +- [What is Synchronous I/O antipattern?](https://learn.microsoft.com/en-us/azure/architecture/antipatterns/synchronous-io/) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/116-performance-antipatterns/index.md b/src/roadmaps/system-design/content/116-performance-antipatterns/index.md index c03eba612..fc7a374c0 100644 --- a/src/roadmaps/system-design/content/116-performance-antipatterns/index.md +++ b/src/roadmaps/system-design/content/116-performance-antipatterns/index.md @@ -1 +1,16 @@ -# Performance antipatterns \ No newline at end of file +# Performance Antipatterns + +what is Performance Antipatterns in system design +Performance antipatterns in system design refer to common mistakes or suboptimal practices that can lead to poor performance in a system. These patterns can occur at different levels of the system and can be caused by a variety of factors such as poor design, lack of optimization, or lack of understanding of the workload. + +Examples of performance antipatterns include: + +- **N+1 queries:** This occurs when a system makes multiple queries to a database to retrieve related data, instead of using a single query to retrieve all the necessary data. +- **Chatty interfaces:** This occurs when a system makes too many small and frequent requests to an external service or API, instead of making fewer, larger requests. +- **Unbounded data:** This occurs when a system retrieves or processes more data than is necessary for the task at hand, leading to increased resource usage and reduced performance. +- **Inefficient algorithms:** This occurs when a system uses an algorithm that is not well suited to the task at hand, leading to increased resource usage and reduced performance. + +Learn more from the following links: + +- [Performance antipatterns for cloud applications](https://learn.microsoft.com/en-us/azure/architecture/antipatterns/) +- [Guide to Software Performance Antipatterns](http://www.perfeng.com/papers/antipat.pdf) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/117-monitoring/100-health-monitoring.md b/src/roadmaps/system-design/content/117-monitoring/100-health-monitoring.md index f748928bf..fb0861f4c 100644 --- a/src/roadmaps/system-design/content/117-monitoring/100-health-monitoring.md +++ b/src/roadmaps/system-design/content/117-monitoring/100-health-monitoring.md @@ -1 +1,7 @@ -# Health monitoring \ No newline at end of file +# Health Monitoring + +A health monitoring system is a system that is designed to collect, store, and analyze health-related data from a variety of sources, such as wearable devices, medical devices, and electronic health records. The goal of a health monitoring system is to provide healthcare professionals and individuals with real-time insights into their health, allowing them to make informed decisions about their care. + +Learn more from the following: + +- [Design of Wearable Health Monitoring Systems](https://link.springer.com/chapter/10.1007/978-3-319-23341-3_6) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/117-monitoring/101-availability-monitoring.md b/src/roadmaps/system-design/content/117-monitoring/101-availability-monitoring.md index 1ddd8f451..2c59527e2 100644 --- a/src/roadmaps/system-design/content/117-monitoring/101-availability-monitoring.md +++ b/src/roadmaps/system-design/content/117-monitoring/101-availability-monitoring.md @@ -1 +1,14 @@ -# Availability monitoring \ No newline at end of file +# Availability Monitoring + +Availability monitoring in system design refers to the practice of monitoring the availability of a system, service or application, to ensure that it is functioning correctly and is accessible to users when they need it. This is an important aspect of ensuring that a system is reliable and performs well. + +Availability monitoring typically includes the following components: + +- Heartbeat monitoring +- Transaction monitoring +- Alerts and notifications +- Root cause analysis + +Learn more from the following: + +- [System Monitoring, Alerting and Availability](https://www.aits.uillinois.edu/services/network_and_desktop_services/system_monitoring__alerting_and_availability) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/117-monitoring/102-performance-monitoring.md b/src/roadmaps/system-design/content/117-monitoring/102-performance-monitoring.md index bd947a650..a0c7bae70 100644 --- a/src/roadmaps/system-design/content/117-monitoring/102-performance-monitoring.md +++ b/src/roadmaps/system-design/content/117-monitoring/102-performance-monitoring.md @@ -1 +1,7 @@ -# Performance monitoring \ No newline at end of file +# Performance Monitoring + +Performance monitoring in system design refers to the practice of monitoring the performance of a system, service, or application, in order to ensure that it is performing well and meeting the needs of users. This is an important aspect of ensuring that a system is reliable and performs well. + +Learn more from following links: + +- [Get More on Performance Monitoring Systems](https://www.solarwinds.com/server-application-monitor/use-cases/performance-monitoring-system) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/117-monitoring/103-security-monitoring.md b/src/roadmaps/system-design/content/117-monitoring/103-security-monitoring.md index 27cfee735..0f864a454 100644 --- a/src/roadmaps/system-design/content/117-monitoring/103-security-monitoring.md +++ b/src/roadmaps/system-design/content/117-monitoring/103-security-monitoring.md @@ -1 +1,15 @@ -# Security monitoring \ No newline at end of file +# Security Monitoring + +Security monitoring in system design refers to the practice of monitoring the security of a system, service, or application, in order to detect and respond to security threats and vulnerabilities. This is an important aspect of ensuring that a system is secure and protected against unauthorized access, data breaches, and other security incidents. + +Security monitoring typically includes the following components: + +- Event collection +- Event analysis and correlation +- Alerts and notifications +- Incident response +- Compliance and audit + +Visit the following to learn more: + +- [Intro to Security Monitoring](https://www.sciencedirect.com/topics/computer-science/security-monitoring) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/117-monitoring/104-usage-monitoring.md b/src/roadmaps/system-design/content/117-monitoring/104-usage-monitoring.md index e20356ab9..6d0a7f161 100644 --- a/src/roadmaps/system-design/content/117-monitoring/104-usage-monitoring.md +++ b/src/roadmaps/system-design/content/117-monitoring/104-usage-monitoring.md @@ -1 +1,14 @@ -# Usage monitoring \ No newline at end of file +# Usage Monitoring + +Usage monitoring in system design refers to the practice of monitoring the usage of a system, service, or application, in order to understand how it is being used and identify any potential issues or areas for improvement. This is an important aspect of ensuring that a system is meeting the needs of users and providing value. + +Usage monitoring typically includes the following components: + +- Data collection +- Data analysis and visualization +- Alerts and notifications +- Trend analysis + +Learn more from the following links: + +- [What is Usage Monitoring?](https://patterns.arcitura.com/cloud-computing-patterns/design_patterns/usage_monitoring) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/117-monitoring/105-instrumentation.md b/src/roadmaps/system-design/content/117-monitoring/105-instrumentation.md index f7bf6e87e..32917654c 100644 --- a/src/roadmaps/system-design/content/117-monitoring/105-instrumentation.md +++ b/src/roadmaps/system-design/content/117-monitoring/105-instrumentation.md @@ -1 +1,7 @@ -# Instrumentation \ No newline at end of file +# Instrumentation + + + +Learn more from the following links: + +- [Instrumentation System Docs](http://eolss.net/Sample-Chapters/C05/E6-39A-04-08.pdf) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/117-monitoring/106-visualization-and-alerts.md b/src/roadmaps/system-design/content/117-monitoring/106-visualization-and-alerts.md index 068f16d3e..b64c8f85a 100644 --- a/src/roadmaps/system-design/content/117-monitoring/106-visualization-and-alerts.md +++ b/src/roadmaps/system-design/content/117-monitoring/106-visualization-and-alerts.md @@ -1 +1,14 @@ -# Visualization and alerts \ No newline at end of file +# Visualization and Alerts + +Instrumentation in system design refers to the process of adding monitoring and measurement capabilities to a system, service, or application. This allows developers and operations teams to observe the behavior of the system, measure its performance, and identify any issues or areas for improvement. + +Instrumentation can be used to monitor a wide variety of aspects of a system, such as: + +- Performance: Instrumentation can be used to measure the performance of a system, such as response time, throughput, and resource utilization. +- Errors: Instrumentation can be used to detect and diagnose errors, such as exceptions and stack traces. +- Security: Instrumentation can be used to monitor for security-related events, such as authentication attempts and network traffic. +- Usage: Instrumentation can be used to monitor usage-related data, such as the number of users and requests. + +To learn more, visit the following links: + +- [Visualize Data and Raise Alerts](https://learn.microsoft.com/en-us/azure/architecture/framework/devops/monitor-visualize-data) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/117-monitoring/index.md b/src/roadmaps/system-design/content/117-monitoring/index.md index 2ddbe89cb..2118261a7 100644 --- a/src/roadmaps/system-design/content/117-monitoring/index.md +++ b/src/roadmaps/system-design/content/117-monitoring/index.md @@ -1 +1,8 @@ -# Monitoring \ No newline at end of file +# Monitoring + +System monitoring involves the continuous monitoring of an infrastructure – aka an IT system – by an IT manager. It includes the monitoring of CPU, server memory, routers, switches, bandwidth, and applications, as well as the performance and availability of important network devices. + +Visit the following to learn more: + +- [Design and implement a monitoring system](https://www.tdh.ch/sites/default/files/tdh_gmm_en_nouvelleversion_ang.pdf) +- [System Design — Design a Monitoring System](https://gongybable.medium.com/system-design-design-a-monitoring-system-f0f0cbafc895) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/100-asynchronous-request-reply.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/100-asynchronous-request-reply.md index efa41a702..eca59dd00 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/100-asynchronous-request-reply.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/100-asynchronous-request-reply.md @@ -1 +1,8 @@ -# Asynchronous request reply \ No newline at end of file +# Asynchronous Request Reply + +Asynchronous Request-Reply in system design refers to a pattern where a client sends a request to a server and the server responds asynchronously, allowing the client to continue processing other tasks or requests without waiting for the server's response. This can improve the performance and scalability of a system by allowing multiple requests to be processed concurrently. It can be implemented using callbacks, promises or event-based models. + +Learn more from the following links: + +- [Asynchronous Request-Reply pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/async-request-reply) +- [Intro to Asynchronous Request-Response](https://codeopinion.com/asynchronous-request-response-pattern-for-non-blocking-workflows/) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/101-claim-check.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/101-claim-check.md index 6eee64524..118d1bd1a 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/101-claim-check.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/101-claim-check.md @@ -1 +1,8 @@ -# Claim check \ No newline at end of file +# Claim Check + +Claim check in system design is a pattern where large or complex data is replaced with a small token or reference, which is passed along with a message or request. This can help to reduce the size and complexity of messages, and improve the performance and scalability of a system. The large or complex data is stored in a separate location, and a token generator is used to create a unique token for the actual data. + +Learn more from the following links: + +- [An Introduction to Claim-Check Pattern and Its Uses](https://aws.plainenglish.io/an-introduction-to-claim-check-pattern-and-its-uses-b018649a380d) +- [Claim Check - Cloud Design patterns](https://learn.microsoft.com/en-us/azure/architecture/patterns/) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/102-choreography.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/102-choreography.md index 35724c0b2..72004e869 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/102-choreography.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/102-choreography.md @@ -1 +1,8 @@ -# Choreography \ No newline at end of file +# Choreography + +Choreography in system design refers to the design and coordination of interactions between autonomous systems or services, without the use of a central controlling entity. Each system or service is responsible for its own behavior and communication with other systems or services, and there is no central point of control or coordination. Choreography can be used to improve the scalability, flexibility, and resilience of a system, by allowing each service to evolve and scale independently. It can be implemented using event-based, message-based or API-based models. + +Learn more from the following links: + +- [Choreography pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/choreography) +- [Service choreography](https://en.wikipedia.org/wiki/Service_choreography) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/103-competing-consumers.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/103-competing-consumers.md index 9102d77cc..a9017e9f1 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/103-competing-consumers.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/103-competing-consumers.md @@ -1 +1,8 @@ -# Competing consumers \ No newline at end of file +# Competing Consumers + +Competing Consumers in system design is a pattern that allows multiple consumers to process messages concurrently from a shared message queue. This approach can be used to improve the performance and scalability of a system by allowing multiple consumers to process messages in parallel. This pattern can be used in scenarios like load balancing and fault tolerance. It can be implemented using a variety of messaging technologies such as message queues, message brokers, and publish-subscribe systems. + +Learn more from the following links: + +- [Competing Consumers pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/competing-consumers) +- [Competing Consumers Pattern - Explained](https://medium.com/event-driven-utopia/competing-consumers-pattern-explained-b338d54eff2b) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/104-pipes-and-filters.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/104-pipes-and-filters.md index 21c4bcfaa..794cc1418 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/104-pipes-and-filters.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/104-pipes-and-filters.md @@ -1 +1,8 @@ -# Pipes and filters \ No newline at end of file +# Pipes and Filters + +Pipes and Filters in system design is a pattern that separates the processing of a task into a series of smaller, independent components, connected together in a pipeline. Each component, or filter, performs a specific task, and the output of one filter is passed as the input to the next filter. This approach can be used to build modular and extensible systems, by allowing filters to be added, removed, or replaced easily. Pipes and Filters pattern can be used in scenarios like data processing and data transformation. It can be implemented using a variety of technologies such as streams, generators, and iterators. + +Learn more from the following links: + +- [Pipe and Filter Architectural Style](https://cs.uwaterloo.ca/~m2nagapp/courses/CS446/1181/Arch_Design_Activity/PipeFilter.pdf) +- [Pipes and Filters pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/pipes-and-filters) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/105-priority-queue.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/105-priority-queue.md index 825597ddb..735e46864 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/105-priority-queue.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/105-priority-queue.md @@ -1 +1,7 @@ -# Priority queue \ No newline at end of file +# Priority Queue + +A priority queue in system design is a data structure that stores items with a priority value, and allows for efficient retrieval and manipulation of the items based on their priority. The items with the highest priority are retrieved first. This pattern is useful in situations where certain items or tasks are more important than others and should be processed first. Priority Queue can be used in scenarios like scheduling and real-time systems. It can be implemented using various data structures such as heap, linked list, and array. + +Learn more from the following links: + +- [Priority Queue pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/priority-queue) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/106-publisher-subscriber.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/106-publisher-subscriber.md index ce940b3d4..e1e95d3ae 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/106-publisher-subscriber.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/106-publisher-subscriber.md @@ -1 +1,8 @@ -# Publisher subscriber \ No newline at end of file +# Publisher Subscriber + +Publisher-Subscriber in system design is a pattern that allows multiple subscribers to receive updates from a single publisher, without the publisher and subscribers being aware of each other's existence. This pattern allows for decoupling of the publisher and subscribers, and can be used to build scalable and flexible systems. It can be used in scenarios like event-driven architecture and data streaming. It can be implemented using a variety of technologies such as message queues, message brokers, and event buses. + +Learn more from the following links: + +- [What is Pub/Sub Messaging?](https://aws.amazon.com/pub-sub-messaging/) +- [Publisher Subscriber - Pattern](https://www.enjoyalgorithms.com/blog/publisher-subscriber-pattern) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/107-queue-based-load-leveling.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/107-queue-based-load-leveling.md index 0a40a26b1..8f4f67a07 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/107-queue-based-load-leveling.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/107-queue-based-load-leveling.md @@ -1 +1,8 @@ -# Queue based load leveling \ No newline at end of file +# Queue Based Load Leveling + +Queue-based load leveling in system design is a pattern that allows for the buffering of incoming requests, and the processing of those requests at a controlled rate. This pattern can be used to prevent overloading of a system, and to ensure that the system can handle a variable rate of incoming requests. It can be used in scenarios like traffic spikes and variable workloads. It can be implemented using various data structures such as linked list, array, and heap. + +Learn more from the following links: + +- [Queue-Based Load Leveling pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/queue-based-load-leveling) +- [Design Patterns: Queue-Based Load Leveling Pattern](https://blog.cdemi.io/design-patterns-queue-based-load-leveling-pattern/) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/108-scheduling-agent-supervisor.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/108-scheduling-agent-supervisor.md index a808a52b6..0b36cfd37 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/108-scheduling-agent-supervisor.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/108-scheduling-agent-supervisor.md @@ -1 +1,7 @@ -# Scheduling agent supervisor \ No newline at end of file +# Scheduling Agent Supervisor + +Scheduling Agent Supervisor in system design is a pattern that allows for the scheduling and coordination of tasks or processes by a central entity, known as the Scheduling Agent. The Scheduling Agent is responsible for scheduling tasks, monitoring their execution, and handling errors or failures. This pattern can be used to build robust and fault-tolerant systems, by ensuring that tasks are executed as intended and that any errors or failures are handled appropriately. + +Learn more from the following links: + +- [Scheduler Agent Supervisor pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/scheduler-agent-supervisor) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/109-sequential-convoy.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/109-sequential-convoy.md index 66fed8761..a6d998d21 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/109-sequential-convoy.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/109-sequential-convoy.md @@ -1 +1,8 @@ -# Sequential convoy \ No newline at end of file +# Sequential Convoy + +Sequential Convoy in system design is a pattern that allows for the execution of a series of tasks, or convoy, in a specific order. This pattern can be used to ensure that a set of dependent tasks are executed in the correct order and to handle errors or failures during the execution of the tasks. It can be used in scenarios like workflow and transaction. It can be implemented using a variety of technologies such as state machines, workflows, and transactions. + +Learn more from the following links: + +- [What is Sequential Convoy?](https://learn.microsoft.com/en-us/biztalk/core/sequential-convoys) +- [Overview - Sequential Convoy pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/sequential-convoy) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/index.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/index.md index d2c5ab579..4c077f388 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/index.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/100-messaging/index.md @@ -1 +1,8 @@ -# Messaging \ No newline at end of file +# Messaging + +Messaging in system design is a pattern that allows for the communication and coordination between different components or systems, using messaging technologies such as message queues, message brokers, and event buses. This pattern allows for decoupling of the sender and receiver, and can be used to build scalable and flexible systems. Messaging pattern can be used in scenarios like asynchronous communication, loose coupling, and scalability. It can be implemented using a variety of technologies such as message queues, message brokers, and event buses. + +Learn more from the following links: + +- [System Design — Message Queues](https://medium.com/must-know-computer-science/system-design-message-queues-245612428a22) +- [Intro to System Design - Message Queues](https://dev.to/karanpratapsingh/system-design-message-queues-k9a) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/100-cache-aside.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/100-cache-aside.md index be33eca0f..bcaf0e6d0 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/100-cache-aside.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/100-cache-aside.md @@ -1 +1,7 @@ -# Cache aside \ No newline at end of file +# Cache Aside + +Cache-Aside in system design is a pattern that allows for the caching of data, in order to improve the performance and scalability of a system. This pattern is typically used in systems where data is read more frequently than it is written. It can be used to reduce the load on a primary data store, and to improve the responsiveness of a system by reducing the latency of data access. Cache-Aside pattern can be used in scenarios like read-heavy workloads and latency-sensitive workloads. It can be implemented using various caching technologies such as in-memory cache, distributed cache, and file-based cache. + +Learn more from the following links: + +- [Cache-Aside pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/cache-aside) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/101-cqrs.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/101-cqrs.md index 53209f872..f50328995 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/101-cqrs.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/101-cqrs.md @@ -1 +1,9 @@ -# Cqrs \ No newline at end of file +# CQRS + +CQRS (Command Query Responsibility Segregation) in system design is a pattern that separates the responsibilities of handling read and write operations in a system. This pattern allows for the separation of concerns between the read and write operations, and can be used to improve the scalability, performance, and maintainability of a system. + +In this pattern, the read and write operations are handled by different components in the system. The write operations, known as commands, are handled by a Command component that updates the state of the system. The read operations, known as queries, are handled by a Query component that retrieves the current state of the system. + +Learn more from the following links: + +- [CQRS pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/cqrs) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/102-event-sourcing.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/102-event-sourcing.md index 51d7c5005..fc389c18c 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/102-event-sourcing.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/102-event-sourcing.md @@ -1 +1,8 @@ -# Event sourcing \ No newline at end of file +# Event Sourcing + +Event Sourcing in system design is a pattern that stores the state of a system as a sequence of events, rather than the current state. Each change to the state of the system is recorded as an event, which is stored in an event store. The current state of the system can be derived from the events in the event store. Event sourcing can be used for various purposes such as tracking history, reconstruct state, recover from failures, and auditing. It is often implemented in conjunction with CQRS (Command Query Responsibility Segregation) pattern, which separates the responsibilities of handling read and write operations in a system. + +Learn more from the following links: + +- [Event Sourcing pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/event-sourcing) +- [Overview of Event Sourcing](https://microservices.io/patterns/data/event-sourcing.html) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/103-index-table.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/103-index-table.md index 0e633c58b..020437ddf 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/103-index-table.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/103-index-table.md @@ -1 +1,8 @@ -# Index table \ No newline at end of file +# Index Table + + An index table in system design is a data structure that allows for efficient lookup of data in a larger data set. It is used to improve the performance of searching, sorting, and retrieving data, by allowing for quick access to specific records or data elements. There are several types of index tables such as B-Tree, Hash table, and Trie each with its own strengths and weaknesses. Index tables can be used in a variety of scenarios such as searching, sorting, and retrieving. + +Learn more from the following links: + +- [System Design — Indexes](https://medium.com/must-know-computer-science/system-design-indexes-f6ad3de9925d) +- [Overview of Index Table](https://dev.to/karanpratapsingh/system-design-indexes-2574) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/104-materialized-view.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/104-materialized-view.md index 79f6f17e2..d49d11710 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/104-materialized-view.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/104-materialized-view.md @@ -1 +1,8 @@ -# Materialized view \ No newline at end of file +# Materialized View + +A Materialized View in system design is a pre-computed and stored version of a query result, which is used to improve the performance of frequently executed queries. It can be used to improve the performance of read-heavy workloads, by providing a pre-computed version of the data that can be quickly accessed. Materialized views can be used in scenarios like complex queries, large datasets, and real-time analytics. A materialized view can be created by executing a query and storing the result in a table. The data in the materialized view is typically updated periodically, to ensure that it stays up-to-date with the underlying data.s + +Learn more from the following links: + +- [Materialized View pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/materialized-view) +- [Overview of Materialized View Pattern](https://medium.com/design-microservices-architecture-with-patterns/materialized-view-pattern-f29ea249f8f8) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/105-sharding.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/105-sharding.md index f837bc837..54cb843bf 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/105-sharding.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/105-sharding.md @@ -1 +1,8 @@ -# Sharding \ No newline at end of file +# Sharding + +Sharding in system design is a technique used to horizontally partition a large data set across multiple servers, in order to improve the performance, scalability, and availability of a system. This is done by breaking the data set into smaller chunks, called shards, and distributing the shards across multiple servers. Each shard is self-contained and can be managed and scaled independently of the other shards. Sharding can be used in scenarios like scalability, availability, and geo-distribution. Sharding can be implemented using several different algorithms such as range-based sharding, hash-based sharding, and directory-based sharding. + +Learn more from the following links: + +- [Database Sharding: Concepts and Examples](https://www.mongodb.com/features/database-sharding-explained) +- [Database Sharding – System Design Interview Concept](https://www.geeksforgeeks.org/database-sharding-a-system-design-concept/) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/106-static-content-hosting.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/106-static-content-hosting.md index c37c1560b..07cd2694d 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/106-static-content-hosting.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/106-static-content-hosting.md @@ -1 +1,8 @@ -# Static content hosting \ No newline at end of file +# Static Content Hosting + +Static Content Hosting in system design is a technique used to serve static resources such as images, stylesheets, and JavaScript files, from a dedicated server or service, rather than from the main application server. This approach can be used to improve the performance, scalability, and availability of a system. Static content hosting can be used in scenarios like performance, scalability, and availability. Static content hosting can be implemented using several different techniques such as Content Delivery Network (CDN), Object Storage and File Server. + +Learn more from the following links: + +- [The pros and cons of the Static Content Hosting](https://www.redhat.com/architect/pros-and-cons-static-content-hosting-architecture-pattern) +- [Static Content Hosting pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/static-content-hosting) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/107-valet-key.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/107-valet-key.md index ee69aec14..54485fb58 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/107-valet-key.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/107-valet-key.md @@ -1 +1,10 @@ -# Valet key \ No newline at end of file +# Valet Key + +A valet key in system design is a type of security feature that allows a user to grant limited access to a resource. It is commonly used in the automotive industry, where a valet key is used to allow a valet parking attendant to drive and park a car, but not to access the trunk or the glove compartment of the car. + +In system design, a valet key can be used as a security feature to allow a user to grant limited access to a resource, such as a file or a service, to a third party. The third party can access the resource, but only with the limited permissions that have been granted by the valet key. + +Learn more from the following links: + +- [Valet Key pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/valet-key) +- [Explanation of Valet Key](https://www.youtube.com/watch?v=sapu2CE1W8s) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/index.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/index.md index d1cc2861f..e26752b82 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/index.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/101-data-management/index.md @@ -1 +1,8 @@ -# Data management \ No newline at end of file +# Data Management + +Data management in cloud system design refers to the process of designing, implementing and maintaining the data infrastructure and data management processes in a cloud computing environment. This includes designing and configuring data storage systems, data replication, data backup and disaster recovery, data security and access control, and data governance policies. It's a set of actions that aims to ensure the data is properly managed, stored and protected in a cloud environment. + +Learn more from the following links: + +- [Data Management in the Cloud: Promises, State-of-the-art](https://link.springer.com/article/10.1007/s13222-010-0033-3) +- [Data Management: What It Is, Importance, And Challenges](https://www.tableau.com/learn/articles/what-is-data-management) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/100-ambassador.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/100-ambassador.md index 5468e2568..eaf0334ff 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/100-ambassador.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/100-ambassador.md @@ -1 +1,8 @@ -# Ambassador \ No newline at end of file +# Ambassador + +Ambassador in system design is a type of software that acts as a facade for other services or applications. It's a reverse proxy and service mesh that allows to control access to services, and provide features such as authentication, rate limiting, and observability. Ambassador can be used to route requests, authenticate and authorize requests, provide observability, and rate limiting. Ambassador is designed to work with Kubernetes and other cloud-native platforms. + +To learn more, visit the following links: + +- [Design System Ambassadors](https://medium.com/sprout-social-design/design-system-ambassadors-c240e480baf6) +- [Ambassador pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/ambassador) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/101-anti-corruption-layer.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/101-anti-corruption-layer.md index 80cfde029..a2e33ce5f 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/101-anti-corruption-layer.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/101-anti-corruption-layer.md @@ -1 +1,7 @@ -# Anti corruption layer \ No newline at end of file +# Anti-orruption Layer + +An Anti-Corruption Layer (ACL) in system design is a software pattern that acts as a buffer between a system and external systems or legacy systems that use incompatible data formats or protocols. It's purpose is to protect the internal system from being affected by changes or inconsistencies in the external systems, and to provide a stable and consistent interface for the internal system to interact with the external systems. It can be used in scenarios like integration with legacy systems, integration with external systems, and isolation of dependencies. An ACL can be implemented using several different techniques such as data mapping, data validation, and error handling. + +To learn more, visit the following links: + +- [Anti-corruption Layer pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/anti-corruption-layer) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/102-backends-for-frontend.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/102-backends-for-frontend.md index 815c92bf5..70a5a3532 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/102-backends-for-frontend.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/102-backends-for-frontend.md @@ -1 +1,8 @@ -# Backends for frontend \ No newline at end of file +# Backends for Frontend + +Backends for Frontend (BFF) in system design is a pattern that involves creating a separate backend service for each frontend client. This allows each client to have its own API, tailored to its specific needs, while still sharing a common set of underlying services and data. BFF can be used to provide a tailored API, decouple frontend and backend, and reduce complexity. BFF can be implemented using several different techniques such as Microservices and API Gateway. + +To learn more, visit the following links: + +- [Why “Backend For Frontend” Application Architecture?](https://www.mobilelive.ca/blog/why-backend-for-frontend-application-architecture) +- [what is Backend for frontend (BFF) pattern](https://medium.com/mobilepeople/backend-for-frontend-pattern-why-you-need-to-know-it-46f94ce420b0) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/103-cqrs.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/103-cqrs.md index 53209f872..f50328995 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/103-cqrs.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/103-cqrs.md @@ -1 +1,9 @@ -# Cqrs \ No newline at end of file +# CQRS + +CQRS (Command Query Responsibility Segregation) in system design is a pattern that separates the responsibilities of handling read and write operations in a system. This pattern allows for the separation of concerns between the read and write operations, and can be used to improve the scalability, performance, and maintainability of a system. + +In this pattern, the read and write operations are handled by different components in the system. The write operations, known as commands, are handled by a Command component that updates the state of the system. The read operations, known as queries, are handled by a Query component that retrieves the current state of the system. + +Learn more from the following links: + +- [CQRS pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/cqrs) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/104-compute-resource-consolidation.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/104-compute-resource-consolidation.md index d17bc949e..70d0b2b43 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/104-compute-resource-consolidation.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/104-compute-resource-consolidation.md @@ -1 +1,8 @@ -# Compute resource consolidation \ No newline at end of file +# Compute Resource Consolidation + +Compute resource consolidation in system design is the process of combining multiple servers, storage devices, and network resources into a smaller number of more powerful and efficient systems. This approach can be used to reduce costs, improve performance, and simplify the management and maintenance of the IT infrastructure. Compute resource consolidation can be achieved through several different techniques such as Virtualization, Cloud computing, and Containers. It can also be used to reduce costs, improve performance, and simplify management and maintenance. + +To learn more, visit the following links: + +- [Compute Resource Consolidation pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/compute-resource-consolidation) +- [Tutorial - The Compute Resource Consolidation Pattern](https://www.youtube.com/watch?v=XzBmJvu6gpQ) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/105-external-configuration-store.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/105-external-configuration-store.md index 5821822c5..c82dbb234 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/105-external-configuration-store.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/105-external-configuration-store.md @@ -1 +1,8 @@ -# External configuration store \ No newline at end of file +# External Configuration Store + +An External Configuration Store (ECS) in system design is a centralized, external location where system configuration settings are stored and managed. It separates the configuration of a system from the code of the system, making it easier to manage and update the configuration settings. It can be used in scenarios such as centralized configuration management, dynamic configuration and environment-specific configuration. It can be implemented using techniques such as environment variables, configuration files, and distributed key-value stores. + +To learn more, visit the following links: + +- [External Configuration Store pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/external-configuration-store) +- [External Configuration Store Pattern - Azure Cloud Design Patterns](https://www.youtube.com/watch?v=e-x1G4fRzf8) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/106-gateway-aggregation.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/106-gateway-aggregation.md index ca25162e9..717533fe1 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/106-gateway-aggregation.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/106-gateway-aggregation.md @@ -1 +1,8 @@ -# Gateway aggregation \ No newline at end of file +# Gateway Aggregation + +Gateway Aggregation in system design is a pattern that involves using a single gateway to aggregate multiple services or microservices into a single endpoint. This allows for a simplified client-side API and can also provide additional functionality such as authentication, rate limiting, and observability. It can be used to simplify the client-side API, provide additional functionality, and decouple the client and services. It can be implemented using techniques such as API Gateway and Service Mesh. + +To learn more, visit the following links: + +- [Gateway Aggregation pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/gateway-aggregation) +- [Overview of Gateway Aggregation Pattern](https://medium.com/design-microservices-architecture-with-patterns/gateway-aggregation-pattern-9ff92e1771d0) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/107-gateway-offloading.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/107-gateway-offloading.md index 2ca6c0397..93e0772f6 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/107-gateway-offloading.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/107-gateway-offloading.md @@ -1 +1,7 @@ -# Gateway offloading \ No newline at end of file +# Gateway Offloading + +Gateway Offloading in system design is a pattern that involves using a gateway to offload certain tasks or processing from the backend services or microservices to the gateway itself. This can be used to reduce the load on the backend services, improve performance, and provide additional functionality such as caching, compression, and encryption. It can be implemented using techniques such as API Gateway and Edge Computing. + +To learn more, visit the following links: + +- [Gateway Offloading pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/gateway-offloading) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/108-gateway-routing.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/108-gateway-routing.md index a63f3a4d5..84ab4d018 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/108-gateway-routing.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/108-gateway-routing.md @@ -1 +1,8 @@ -# Gateway routing \ No newline at end of file +# Gateway Routing + +Gateway Routing in system design is a pattern that involves using a gateway to route requests to the appropriate backend service or microservice. The gateway acts as a single entry point for all incoming requests and routes them to the correct service based on the request's information such as the endpoint, headers, or payload. It can be used to decouple the client and services, provide additional functionality, and scale the system. It can be implemented using techniques such as API Gateway and Service Mesh. + +To learn more, visit the following links: + +- [Gateway Routing pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/gateway-routing) +- [Iverview of Gateway Routing Pattern](https://medium.com/design-microservices-architecture-with-patterns/gateway-routing-pattern-f40eb56a2dd9) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/109-leader-election.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/109-leader-election.md index 3b7c08ee2..6de11a45c 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/109-leader-election.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/109-leader-election.md @@ -1 +1,8 @@ -# Leader election \ No newline at end of file +# Leader Election + +Leader Election in system design is a pattern that is used to elect a leader among a group of distributed nodes in a system. The leader is responsible for coordinating the activities of the other nodes and making decisions on behalf of the group. Leader Election is important in distributed systems, as it ensures that there is a single point of coordination and decision-making, reducing the risk of conflicting actions or duplicate work. Leader Election can be used to ensure a single point of coordination, provide fault tolerance, and scalability. There are several algorithms such as Raft, Paxos, and Zab that can be used to implement Leader Election in distributed systems. + +To learn more, visit the following links: + +- [Overview of Leader Election](https://aws.amazon.com/builders-library/leader-election-in-distributed-systems/) +- [What is Leader Election in system design?](https://www.enjoyalgorithms.com/blog/leader-election-system-design) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/110-pipes-and-filters.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/110-pipes-and-filters.md index 21c4bcfaa..49de55e77 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/110-pipes-and-filters.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/110-pipes-and-filters.md @@ -1 +1,8 @@ -# Pipes and filters \ No newline at end of file +# Pipes and Filters + +Pipes and Filters in system design is a pattern that is used to decompose a large system into smaller, reusable components that can be combined in different ways to perform different tasks. It is based on the idea of data flowing through a series of connected "pipes", where each "pipe" represents a processing step or "filter" that performs a specific task on the data. It can be used to decompose a large system, increase flexibility and increase reusability. It can be implemented in several ways such as pipeline and Chain of Responsibility pattern. + +To learn more, visit the following links: + +- [Pipe and Filter Architectural Style](https://cs.uwaterloo.ca/~m2nagapp/courses/CS446/1181/Arch_Design_Activity/PipeFilter.pdf) +- [What are Pipes and Filters?](https://syedhasan010.medium.com/pipe-and-filter-architecture-bd7babdb908) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/111-sidecar.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/111-sidecar.md index b16bea4e2..dd8aed1d4 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/111-sidecar.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/111-sidecar.md @@ -1 +1,8 @@ -# Sidecar \ No newline at end of file +# Sidecar + +A Sidecar in system design is a pattern that involves running an additional process alongside a primary process, in order to provide additional functionality to the primary process. The sidecar process is typically used to manage cross-cutting concerns such as logging, monitoring, security, or networking. It is used to provide additional functionality, decouple the primary process from the additional functionality, and allow for easier upgrades. It can be implemented in several ways such as Service Mesh and Sidecar Container. + +To learn more, visit the following links: + +- [Sidecar pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/sidecar) +- [What is Sidecar Pattern?](https://www.oreilly.com/library/view/designing-distributed-systems/9781491983638/ch02.html) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/112-static-content-hosting.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/112-static-content-hosting.md index c37c1560b..07cd2694d 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/112-static-content-hosting.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/112-static-content-hosting.md @@ -1 +1,8 @@ -# Static content hosting \ No newline at end of file +# Static Content Hosting + +Static Content Hosting in system design is a technique used to serve static resources such as images, stylesheets, and JavaScript files, from a dedicated server or service, rather than from the main application server. This approach can be used to improve the performance, scalability, and availability of a system. Static content hosting can be used in scenarios like performance, scalability, and availability. Static content hosting can be implemented using several different techniques such as Content Delivery Network (CDN), Object Storage and File Server. + +Learn more from the following links: + +- [The pros and cons of the Static Content Hosting](https://www.redhat.com/architect/pros-and-cons-static-content-hosting-architecture-pattern) +- [Static Content Hosting pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/static-content-hosting) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/113-strangler-fig.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/113-strangler-fig.md index 7cb1f3edb..4e410069d 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/113-strangler-fig.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/113-strangler-fig.md @@ -1 +1,8 @@ -# Strangler fig \ No newline at end of file +# Strangler fig + +Strangler Fig in system design is a pattern that is used to gradually replace a monolithic application with a microservices-based architecture. It's based on the idea of a "strangler fig" vine slowly wrapping around and strangling a tree, gradually replacing it. This pattern can be used to gradually replace a monolithic application, reduce the impact of changes, and preserve existing functionality. It can be implemented in several ways such as API Gateway and Service Mesh. + +To learn more, visit the following links: + +- [The Sstrangler fig pattern](https://docs.aws.amazon.com/prescriptive-guidance/latest/modernization-aspnet-web-services/fig-pattern.html) +- [What is Strangler fig?](https://learn.microsoft.com/en-us/azure/architecture/patterns/strangler-fig) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/index.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/index.md index a28e10816..1033d7366 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/index.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/102-design-and-implementation/index.md @@ -1 +1,8 @@ -# Design and implementation \ No newline at end of file +# Design and Implementation + +Design and Implementation in system design refers to the process of creating a system that meets the requirements and goals of the stakeholders. It involves several steps such as requirements gathering, design, implementation, testing, deployment, and maintenance. The design phase involves creating a high-level plan for the system, including the overall architecture, the components that will be used, and the interfaces between them. The implementation phase involves taking the design and creating a working system using the chosen technologies and tools. + +To learn more, visit the following links: + +- [What is Design and Implementation?](https://www.marketlinks.org/good-practice-center/value-chain-wiki/design-and-implementation-overview) +- [Overview of System Design and Implementation](https://www.tutorialspoint.com/operating-system-design-and-implementation) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/100-availability/deployment-stamps.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/100-availability/deployment-stamps.md index eea1feb8b..82f242b3a 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/100-availability/deployment-stamps.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/100-availability/deployment-stamps.md @@ -1 +1,8 @@ -# Deployment stamps \ No newline at end of file +# Deployment Stamps + +Deployment Stamps in system design refers to a technique used to manage the deployment of a system across different environments, such as development, staging, and production. A deployment stamp is a set of environment-specific configurations and settings that are applied to the system during the deployment process. It allows to manage environment-specific configurations, ensure consistency across environments, and simplify the deployment process. It can be implemented in several different ways such as Configuration files, Environment variables and Deployment script. + +To learn more visit the following links: + +- [Deployment Stamps pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/deployment-stamp) +- [Deployment Stamps 101](https://blog.devgenius.io/deployment-stamps-101-7c04a6f704a2) diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/100-availability/geodes.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/100-availability/geodes.md index f544ee454..d2a6ec960 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/100-availability/geodes.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/100-availability/geodes.md @@ -1 +1,8 @@ -# Geodes \ No newline at end of file +# Geodes + +Geodes in system design refers to a technique of partitioning a large dataset into smaller chunks, called geodes, that can be stored and processed more efficiently. Geodes are similar to shards in database partitioning, but the term is often used in the context of distributed systems and data processing. It allows to Scale the system, Improve performance and balance the load. It can be implemented in several different ways such as Hashing and Range-based partitioning. + +To learn more visit the following links: + +- [Geode pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/geodes) +- [Geode Formation, Types & Appearance | What is a Geode?](https://study.com/academy/lesson/geode-formation-types-appearance.ht) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/100-availability/health-endpoint-monitoring.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/100-availability/health-endpoint-monitoring.md index 05c137bb9..edb34eb22 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/100-availability/health-endpoint-monitoring.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/100-availability/health-endpoint-monitoring.md @@ -1 +1,8 @@ -# Health endpoint monitoring \ No newline at end of file +# Health Endpoint Monitoring + +Health Endpoint Monitoring in system design refers to a technique for monitoring the health of a system by periodically sending requests to a specific endpoint, called a "health endpoint", on the system. The health endpoint returns a response indicating the current status of the system, such as whether it is running properly or if there are any issues. It allows to Monitor the overall health of the system, Provide insight into the system's performance, and automate the process of monitoring. It can be implemented in several different ways such as Periodic requests and Event-based monitoring. + +To learn more visit the following links: + +- [Health Endpoint Monitoring pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/health-endpoint-monitoring) +- [Explaining the health endpoint monitoring pattern](https://www.oreilly.com/library/view/java-ee-8/9781788830621/5012c01e-90ca-4809-a210-d3736574f5b3.xhtml) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/100-availability/index.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/100-availability/index.md index 9ae1629ae..5cff57264 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/100-availability/index.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/100-availability/index.md @@ -1 +1,8 @@ -# Availability \ No newline at end of file +# Availability + +Availability in system design refers to the ability of a system to perform its intended function without interruption. High availability is desired as it means that the system is less likely to experience downtime, and when it does, it can quickly recover. To increase the availability of a system, several methods can be used such as Redundancy, Load balancing, Failover, Monitoring, and Automated recovery. + +To learn more visit the following links: + +- [System Design: Availability](https://dev.to/karanpratapsingh/system-design-availability-38bd) +- [Concept of Availability in system design](https://www.enjoyalgorithms.com/blog/availability-system-design-concept) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/100-availability/queue-based-load-leveling.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/100-availability/queue-based-load-leveling.md index 0a40a26b1..026abf013 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/100-availability/queue-based-load-leveling.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/100-availability/queue-based-load-leveling.md @@ -1 +1,7 @@ -# Queue based load leveling \ No newline at end of file +# Queue-Based load leveling + +Queue-based load leveling in system design refers to a technique for managing the workload of a system by using a queue to buffer incoming requests and process them at a steady pace. By using a queue, the system can handle bursts of incoming requests without being overwhelmed, as well as prevent idle periods where there are not enough requests to keep the system busy. It allows to smooth out bursts of incoming requests, prevent idle periods, Provide a way to prioritize requests, and provide a way to monitor requests. It can be implemented in several different ways such as In-memory queue and Persistent queue. + +To learn more visit the following links: + +- [Queue-Based Load Leveling pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/queue-based-load-leveling) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/100-availability/throttling.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/100-availability/throttling.md index 315dbf1b5..08a4a7967 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/100-availability/throttling.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/100-availability/throttling.md @@ -1 +1,7 @@ -# Throttling \ No newline at end of file +# Throttling + +Throttling in system design refers to a technique for limiting the rate at which requests are processed by a system. This is often used to prevent the system from being overwhelmed by a high volume of requests, or to ensure that resources are used efficiently. Throttling can be applied to incoming requests, outgoing requests or both, and can be implemented at different levels of the system, such as at the network, application, or service level. It allows to prevent system overload, ensure efficient resource usage, provide Quality of Service (QoS) and prevent Denial of Service (DoS). It can be implemented in several different ways such as Rate limiting, Leaking bucket and Token bucket. + +To learn more visit the following links: + +- [Throttling pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/throttling) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/101-high-availability/bulkhead.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/101-high-availability/bulkhead.md index e158980d6..e4d4e6156 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/101-high-availability/bulkhead.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/101-high-availability/bulkhead.md @@ -1 +1,8 @@ -# Bulkhead \ No newline at end of file +# Bulkhead + +Bulkhead in system design refers to a technique for isolating different parts of a system to prevent one part from affecting the performance of the whole system. The term "bulkhead" is used to refer to the partitions or walls that are used to separate different parts of the system. It allows to Isolate critical parts of the system, prevent cascading failures and provide isolation for different types of requests. It can be implemented in several different ways such as Thread pools, Circuit breakers, and Workers. + +Learn more from the following links: + +- [Bulkhead pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/bulkhead) +- [Get started with Bulkhead](https://dzone.com/articles/resilient-microservices-pattern-bulkhead-pattern) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/101-high-availability/circuit-breaker.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/101-high-availability/circuit-breaker.md index 56427e3fa..ff66afacf 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/101-high-availability/circuit-breaker.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/101-high-availability/circuit-breaker.md @@ -1 +1,8 @@ -# Circuit breaker \ No newline at end of file +# Circuit Breaker + +Circuit Breaker in system design is a pattern that is used to prevent an application from repeatedly trying to perform an action that is likely to fail. By tripping the circuit breaker when an operation fails a certain number of times, the system can prevent cascading failures, provide fallback behavior, and monitor system health. It can be implemented in several different ways such as State machine, and Hystrix (library for Java). + +Learn more from the following links: + +- [Circuit breaker design pattern](https://en.wikipedia.org/wiki/Circuit_breaker_design_pattern) +- [Overview of Circuit Breaker](https://medium.com/geekculture/design-patterns-for-microservices-circuit-breaker-pattern-276249ffab33) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/101-high-availability/deployment-stamps.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/101-high-availability/deployment-stamps.md index eea1feb8b..82f242b3a 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/101-high-availability/deployment-stamps.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/101-high-availability/deployment-stamps.md @@ -1 +1,8 @@ -# Deployment stamps \ No newline at end of file +# Deployment Stamps + +Deployment Stamps in system design refers to a technique used to manage the deployment of a system across different environments, such as development, staging, and production. A deployment stamp is a set of environment-specific configurations and settings that are applied to the system during the deployment process. It allows to manage environment-specific configurations, ensure consistency across environments, and simplify the deployment process. It can be implemented in several different ways such as Configuration files, Environment variables and Deployment script. + +To learn more visit the following links: + +- [Deployment Stamps pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/deployment-stamp) +- [Deployment Stamps 101](https://blog.devgenius.io/deployment-stamps-101-7c04a6f704a2) diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/101-high-availability/geodes.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/101-high-availability/geodes.md index f544ee454..d2a6ec960 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/101-high-availability/geodes.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/101-high-availability/geodes.md @@ -1 +1,8 @@ -# Geodes \ No newline at end of file +# Geodes + +Geodes in system design refers to a technique of partitioning a large dataset into smaller chunks, called geodes, that can be stored and processed more efficiently. Geodes are similar to shards in database partitioning, but the term is often used in the context of distributed systems and data processing. It allows to Scale the system, Improve performance and balance the load. It can be implemented in several different ways such as Hashing and Range-based partitioning. + +To learn more visit the following links: + +- [Geode pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/geodes) +- [Geode Formation, Types & Appearance | What is a Geode?](https://study.com/academy/lesson/geode-formation-types-appearance.ht) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/101-high-availability/health-endpoint-monitoring.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/101-high-availability/health-endpoint-monitoring.md index 05c137bb9..edb34eb22 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/101-high-availability/health-endpoint-monitoring.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/101-high-availability/health-endpoint-monitoring.md @@ -1 +1,8 @@ -# Health endpoint monitoring \ No newline at end of file +# Health Endpoint Monitoring + +Health Endpoint Monitoring in system design refers to a technique for monitoring the health of a system by periodically sending requests to a specific endpoint, called a "health endpoint", on the system. The health endpoint returns a response indicating the current status of the system, such as whether it is running properly or if there are any issues. It allows to Monitor the overall health of the system, Provide insight into the system's performance, and automate the process of monitoring. It can be implemented in several different ways such as Periodic requests and Event-based monitoring. + +To learn more visit the following links: + +- [Health Endpoint Monitoring pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/health-endpoint-monitoring) +- [Explaining the health endpoint monitoring pattern](https://www.oreilly.com/library/view/java-ee-8/9781788830621/5012c01e-90ca-4809-a210-d3736574f5b3.xhtml) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/101-high-availability/index.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/101-high-availability/index.md index 3f7dd4fea..3b6f4302e 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/101-high-availability/index.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/101-high-availability/index.md @@ -1 +1,8 @@ -# High availability \ No newline at end of file +# High availability + +High availability in system design refers to the ability of a system to continue operating even in the event of a failure or outage. This is often achieved by designing the system to be redundant, meaning that multiple copies of the system are running at the same time, and if one copy fails, the others can take over. It can be achieved by using Redundancy, Load balancing, and Failover. It can be measured using metrics such as Mean Time Between Failures (MTBF), Mean Time To Recovery (MTTR) and Availability. + +Learn more from the following links: + +- [What is High availability (HA)?](https://www.techtarget.com/searchdatacenter/definition/high-availability) +- [Introduction to High Availability Architecture](https://www.filecloud.com/blog/an-introduction-to-high-availability-architecture/) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/bulkhead.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/bulkhead.md index e158980d6..e4d4e6156 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/bulkhead.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/bulkhead.md @@ -1 +1,8 @@ -# Bulkhead \ No newline at end of file +# Bulkhead + +Bulkhead in system design refers to a technique for isolating different parts of a system to prevent one part from affecting the performance of the whole system. The term "bulkhead" is used to refer to the partitions or walls that are used to separate different parts of the system. It allows to Isolate critical parts of the system, prevent cascading failures and provide isolation for different types of requests. It can be implemented in several different ways such as Thread pools, Circuit breakers, and Workers. + +Learn more from the following links: + +- [Bulkhead pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/bulkhead) +- [Get started with Bulkhead](https://dzone.com/articles/resilient-microservices-pattern-bulkhead-pattern) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/circuit-breaker.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/circuit-breaker.md index 56427e3fa..ff66afacf 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/circuit-breaker.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/circuit-breaker.md @@ -1 +1,8 @@ -# Circuit breaker \ No newline at end of file +# Circuit Breaker + +Circuit Breaker in system design is a pattern that is used to prevent an application from repeatedly trying to perform an action that is likely to fail. By tripping the circuit breaker when an operation fails a certain number of times, the system can prevent cascading failures, provide fallback behavior, and monitor system health. It can be implemented in several different ways such as State machine, and Hystrix (library for Java). + +Learn more from the following links: + +- [Circuit breaker design pattern](https://en.wikipedia.org/wiki/Circuit_breaker_design_pattern) +- [Overview of Circuit Breaker](https://medium.com/geekculture/design-patterns-for-microservices-circuit-breaker-pattern-276249ffab33) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/compensating-transaction.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/compensating-transaction.md index 55256f4dd..959d24a88 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/compensating-transaction.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/compensating-transaction.md @@ -1 +1,8 @@ -# Compensating transaction \ No newline at end of file +# Compensating Transaction + +A Compensating Transaction in system design refers to a mechanism for reversing or undoing the effects of a previously executed transaction in a system. It can be used to ensure that the system remains in a consistent state, even if a subsequent transaction fails or is rolled back. Typically used in systems that implement the principles of ACID transactions, it can be implemented in several different ways such as undo logs, savepoints. + +Learn more from the following resources: + +- [Compensating Transaction pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/compensating-transaction) +- [Intro to Compensation Transaction](https://en.wikipedia.org/wiki/Compensating_transaction) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/health-endpoint-monitoring.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/health-endpoint-monitoring.md index 05c137bb9..edb34eb22 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/health-endpoint-monitoring.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/health-endpoint-monitoring.md @@ -1 +1,8 @@ -# Health endpoint monitoring \ No newline at end of file +# Health Endpoint Monitoring + +Health Endpoint Monitoring in system design refers to a technique for monitoring the health of a system by periodically sending requests to a specific endpoint, called a "health endpoint", on the system. The health endpoint returns a response indicating the current status of the system, such as whether it is running properly or if there are any issues. It allows to Monitor the overall health of the system, Provide insight into the system's performance, and automate the process of monitoring. It can be implemented in several different ways such as Periodic requests and Event-based monitoring. + +To learn more visit the following links: + +- [Health Endpoint Monitoring pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/health-endpoint-monitoring) +- [Explaining the health endpoint monitoring pattern](https://www.oreilly.com/library/view/java-ee-8/9781788830621/5012c01e-90ca-4809-a210-d3736574f5b3.xhtml) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/index.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/index.md index 7efb356b2..50e218654 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/index.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/index.md @@ -1 +1,7 @@ -# Resiliency \ No newline at end of file +# Resilience + +Resilience in system design refers to the ability of a system to withstand and recover from disruptions, failures or unexpected conditions. It means the system can continue to function and provide service even when faced with stressors such as high traffic, failures or unexpected changes. Resilience can be achieved by designing the system to be redundant, fault-tolerant, scalable, having automatic recovery, and monitoring and alerting mechanisms. It can be measured by Recovery Time Objective (RTO), Recovery Point Objective (RPO), Mean time to failure (MTTF), and Mean time to recovery (MTTR). + +Learn more from the following links: + +- [System Resilience: What Exactly is it?](https://insights.sei.cmu.edu/blog/system-resilience-what-exactly-is-it/) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/leader-election.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/leader-election.md index 3b7c08ee2..6de11a45c 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/leader-election.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/leader-election.md @@ -1 +1,8 @@ -# Leader election \ No newline at end of file +# Leader Election + +Leader Election in system design is a pattern that is used to elect a leader among a group of distributed nodes in a system. The leader is responsible for coordinating the activities of the other nodes and making decisions on behalf of the group. Leader Election is important in distributed systems, as it ensures that there is a single point of coordination and decision-making, reducing the risk of conflicting actions or duplicate work. Leader Election can be used to ensure a single point of coordination, provide fault tolerance, and scalability. There are several algorithms such as Raft, Paxos, and Zab that can be used to implement Leader Election in distributed systems. + +To learn more, visit the following links: + +- [Overview of Leader Election](https://aws.amazon.com/builders-library/leader-election-in-distributed-systems/) +- [What is Leader Election in system design?](https://www.enjoyalgorithms.com/blog/leader-election-system-design) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/queue-based-load-leveling.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/queue-based-load-leveling.md index 0a40a26b1..026abf013 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/queue-based-load-leveling.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/queue-based-load-leveling.md @@ -1 +1,7 @@ -# Queue based load leveling \ No newline at end of file +# Queue-Based load leveling + +Queue-based load leveling in system design refers to a technique for managing the workload of a system by using a queue to buffer incoming requests and process them at a steady pace. By using a queue, the system can handle bursts of incoming requests without being overwhelmed, as well as prevent idle periods where there are not enough requests to keep the system busy. It allows to smooth out bursts of incoming requests, prevent idle periods, Provide a way to prioritize requests, and provide a way to monitor requests. It can be implemented in several different ways such as In-memory queue and Persistent queue. + +To learn more visit the following links: + +- [Queue-Based Load Leveling pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/queue-based-load-leveling) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/retry.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/retry.md index 1ba28f640..90656178b 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/retry.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/retry.md @@ -1 +1,8 @@ -# Retry \ No newline at end of file +# Retry + +Retry in system design refers to the process of automatically re-executing a failed operation in the hopes of getting a successful outcome. Retries are used to handle transient failures such as network errors, temporary unavailability of a service, or other issues that may be resolved quickly. Retries can be an effective way of dealing with these types of failures, as they can help to ensure that the system continues to function, even in the face of temporary disruptions. + +Learn more from the following resources: + +- [Introducing Retry](https://engineering.grab.com/designing-resilient-systems-part-2) +- [Retry pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/retry) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/scheduler-agent-supervisor.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/scheduler-agent-supervisor.md index b12084317..0b36cfd37 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/scheduler-agent-supervisor.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/102-resiliency/scheduler-agent-supervisor.md @@ -1 +1,7 @@ -# Scheduler agent supervisor \ No newline at end of file +# Scheduling Agent Supervisor + +Scheduling Agent Supervisor in system design is a pattern that allows for the scheduling and coordination of tasks or processes by a central entity, known as the Scheduling Agent. The Scheduling Agent is responsible for scheduling tasks, monitoring their execution, and handling errors or failures. This pattern can be used to build robust and fault-tolerant systems, by ensuring that tasks are executed as intended and that any errors or failures are handled appropriately. + +Learn more from the following links: + +- [Scheduler Agent Supervisor pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/scheduler-agent-supervisor) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/103-security/federated-identity.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/103-security/federated-identity.md index e8f5ce0f8..5d6ccec72 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/103-security/federated-identity.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/103-security/federated-identity.md @@ -1 +1,7 @@ -# Federated identity \ No newline at end of file +# Federated Identity + + +To learn more, visit the following links: + +- []() +- []() \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/103-security/gatekeeper.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/103-security/gatekeeper.md index b0bc3a206..a74657b7e 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/103-security/gatekeeper.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/103-security/gatekeeper.md @@ -1 +1,8 @@ -# Gatekeeper \ No newline at end of file +# Gatekeeper + +A Gatekeeper in system design is a pattern that is used to control access to a system or service. It acts as a central point of control and decision-making for requests to the system or service and is responsible for enforcing policies, rules, and constraints that are used to govern access. It can be implemented in different ways such as API Gateway, Service Mesh, and Load Balancer. It can be useful for Authentication and Authorization, Traffic Management, and Observability. + +Learn more from the following resources: + +- [Gatekeeper pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/gatekeeper) +- [Overview of Gatekeeper](https://www.techtarget.com/searchunifiedcommunications/definition/gatekeeper) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/103-security/index.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/103-security/index.md index f76df344c..634a321f1 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/103-security/index.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/103-security/index.md @@ -1 +1,8 @@ -# Security \ No newline at end of file +# Security + +Security in system design refers to the measures and techniques that are used to protect a system from unauthorized access, use, disclosure, disruption, modification, or destruction. It involves identifying and mitigating the risks that the system faces and implementing controls to prevent and detect security incidents. Security in system design can be divided into several areas such as Access Control, Data security, Network security, and Application security. It can be achieved by implementing best practices and standards such as Defense in depth, Least privilege, Separation of duties, and Monitoring and incident response. + +Learn more from the following links: + +- [Security in Infrastructure System Design](https://medium.com/cermati-tech/security-in-software-development-and-infrastructure-system-design-7b675c2323fc) +- [Security By Design: What Is It and How to Do It Right?](https://www.spiceworks.com/it-security/cyber-risk-management/articles/what-is-security-by-design/) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/103-security/valet-key.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/103-security/valet-key.md index ee69aec14..54485fb58 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/103-security/valet-key.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/103-security/valet-key.md @@ -1 +1,10 @@ -# Valet key \ No newline at end of file +# Valet Key + +A valet key in system design is a type of security feature that allows a user to grant limited access to a resource. It is commonly used in the automotive industry, where a valet key is used to allow a valet parking attendant to drive and park a car, but not to access the trunk or the glove compartment of the car. + +In system design, a valet key can be used as a security feature to allow a user to grant limited access to a resource, such as a file or a service, to a third party. The third party can access the resource, but only with the limited permissions that have been granted by the valet key. + +Learn more from the following links: + +- [Valet Key pattern](https://learn.microsoft.com/en-us/azure/architecture/patterns/valet-key) +- [Explanation of Valet Key](https://www.youtube.com/watch?v=sapu2CE1W8s) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/index.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/index.md index 77b16aaf4..7b3e6aab2 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/index.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/103-reliability-patterns/index.md @@ -1 +1,8 @@ -# Reliability patterns \ No newline at end of file +# Reliability Patterns + +Reliability patterns in system design are solutions to common problems that arise when building systems that need to be highly available and fault-tolerant. These patterns provide a way to design and implement systems that can withstand failures, maintain high levels of performance, and recover quickly from disruptions. Some common reliability patterns include Failover, Circuit Breaker, Retry, Bulkhead, Backpressure, Cache-Aside, Idempotent Operations and Health Endpoint Monitoring. + +Learn more from the following links: + +- [Reliability Patterns: A Survey](http://laccei.org/LACCEI2019-MontegoBay/full_papers/FP53.pdf) +- [Get started with Reliability Patterns](https://learn.microsoft.com/en-us/azure/architecture/framework/resiliency/reliability-patterns) \ No newline at end of file diff --git a/src/roadmaps/system-design/content/118-cloud-design-patterns/index.md b/src/roadmaps/system-design/content/118-cloud-design-patterns/index.md index 17c562494..dae9d86fe 100644 --- a/src/roadmaps/system-design/content/118-cloud-design-patterns/index.md +++ b/src/roadmaps/system-design/content/118-cloud-design-patterns/index.md @@ -1 +1,8 @@ -# Cloud design patterns \ No newline at end of file +# Cloud Design Patterns + +Cloud design patterns in system design are solutions to common problems that arise when building systems that run on a cloud platform. These patterns provide a way to design and implement systems that can take advantage of the unique characteristics of the cloud, such as scalability, elasticity, and pay-per-use pricing. Some common cloud design patterns include Scalability, Elasticity, Fault Tolerance, Microservices, Serverless, Data Management, Front-end and Back-end separation and Hybrid. + +To learn more, visit the following links: + +- [AWS Cloud Design Patterns](https://www.bmc.com/blogs/aws-cloud-design-patterns/) +- [Get started with Cloud Design Patterns](https://learn.microsoft.com/en-us/azure/architecture/patterns/) \ No newline at end of file