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System Design

System Design#

Table of Contents

Application Architecture Types#

Monolithic / N-Tier / Layered#

  • monolithic aka n-Tier app
  • separates/segregates concerns and decompose code base into functional components/layers
    • e.g. Data Access Layer, Presentation Layer, Business Logic Layer
    • or, MVC (Model, View, Controller) Framework
    • IDEA: to work with any component of the architecture independent of the other

Cons#

  • long term maintainability (cumbersome codebase)
  • decoupling issue
  • code change --> whole code base change
  • deployment --> whole application deplyment
  • difficulty adopting new technologies
  • less agile (tight coupled features/code/team) -> takes more time
  • scalling --> scale whole application --> more cost
  • ...

Pros#

  • simple/fast development (in initial days)
  • performant/fast (as no network calls)
  • fewer intercommunication pains

PS: Not outdated yet.

SOA (hybrid?)#

Service-based architectur

  • was the evolution to the limitation of the Monolithic architecture
  • decouples the application in smaller modules
  • all the smaller modeules/services then work with an aggregation layer/bus/message queue etc.
  • involves 2 main roles
    • service provider
    • service consumer
  • idea/concept
    • reusable modules
    • easily integrateable modules

Cons#

  • complex management
    • communication between each services
  • scalling/mgnt of the communication bus/message queue
    • e.g. Kafka?
  • cost
    • more human resource
    • tech
    • development
  • decreased response time
    • network calls
    • more running codes
      • more request/response validation

Pros#

  • better resuablity
  • better maintainability
  • better testability/debugability
    • better reliability
  • agile
    • parallel development
  • separates internal & external elements

PS: better for complex systems, where decomposition is not quite feasible/suitable.

Microservice#

  • a kind of SOA
  • are the evolution to some limitation of the SOA architecture
  • idea/concept
    • encouraging autonomus services/components
    • to gain agility to priorities business
    • to gain more capabilities in many terms
    • reduce duplication
    • increase cohesion
    • reduce tight-coupling
      • thus easier to understand, maintain, scale system
  • need experience to get it right
    • too granular or too broad can destroy the architecture
  • could be defined by
    • business case
    • hierchy
    • domain separation
  • inter-service communication is through some light weight protocol
    • REST over HTTP? really?
    • an organized way of doing that is through API Proxy (by configuring service communication)
    • or, by service mesh (like Istio)

Frameworks#

  • Sidecar
  • Ambassador
  • Adapter

Cons#

  • complex overall system
    • overhead of inter-service communication
    • automation to manage multiple small services
  • careful planning,
  • skilled & experience human resource
  • harder to achieve data consistency & transaction management
    • network calls
      • latency
        • can affect other service
    • individual DBs
  • more security concerns
    • coz of more small public service/APIs
  • multiple programing lang
    • double-edged sword

Pros#

  • easy to develop, test, deploy
    • autonomous/independent
  • increased agility
  • ability to scale hosrizontally
  • a way towards cloud-native model/approach

PS: Good but not required always.

Serverless#

  • application running on a server managed by a 3rd party/cloud vendor etc
  • are event-driven
  • idea
    • deligate the server maintainance to 3rd party
      • reduce devops cost
      • server, db management/maintainance
      • application scalability
    • focus on feature dev/business logic only
  • methods
    • FaaS
      • function as a service
      • a piece of code / fuctions runs on the vendor-managed server
    • BaaS
      • backend as a service
      • backend (db, storage, user authn service) managed by the vendor
      • only need to consume those in some app/code
    • MBaaS
      • Mobile-Backend as a service
      • functions for mobile applications
  • vendors
    • AWS Lambda
    • Google Cloud Functions
    • Azure Functions
    • IBM OpenWhisk
    • Oracle Fn Project
    • Kubeless
    • ...

Cons#

  • vendor lock-in
    • all things are stored there only
    • migration from one vendor to another could be challenging
  • not for long-term tasks

Pros#

  • easy to deploy
  • lower cost
    • optimized infra cost by 3rd party
  • enhanced scalability
    • automatic & seamless scaling managed by 3rd party
  • better for client-heavy apps
  • better for apps which need exponential/limitless scaling
    • really growing fast

Cloud Native#

  • for applications planning to deploy in cloud
  • applies to SOA & Microservices (and to monolithic & serverless as well)
  • an approach to
    • deploy on public clouds (aws, gcp, azure, some one else's data center etc)
    • make services ready to be containerized
      • i.e. orchestration
    • thus ready for CI/CD
  • services are usually stateless
  • services communicates through HTTP or messaging queue
  • envolves
    • multiple node of the service
      • thus a centralized DB/session state/config server
    • CI/CD
    • API gateway
    • container registry
    • message-oriented middleware (MOM: publish/subscribe)
    • service mesh (like Istio)
    • orchestration (kubernetes/mesos)
  • containerization
    • brings container orchestration
      • brings mesos, kubernetes

Cons#

  • complexity
  • experience

Pros#

  • agility
  • flexible
  • resilient

Cloud Based#

  • somewhere in between Cloud Enabled & Cloud Native
  • running on cloud, vendor is auto scaling server/db, managing backups/log etc

Cloud Enabled#

  • legacy monolithic apps, migrated to cloud
  • depends on local services
  • can't take advantage of shared services blah blah

Microservices#

aka Distributed System

A distributed system is a model in which components located on networked computers communicate and coordinate their actions by passing messages

A distributed system is one where different parts run on different physical or logical machines, communicating using non-local mechanisms.

A detailed view on this hot/major topic.

Principles#

A few principle microservice architecture follows are ^8:

  • Scalability
    • Availability
    • Resiliency/Fault-tolerant/Reliability
    • Consistency
  • Maintainability
    • Independent, autonomous
    • Decentralized governance
    • Auto-Provisioning
    • Continuous delivery through DevOps
  • Serviceability
    • Failure isolation
      • Failure detection
      • Recovery
  • Observability

Patterns/Architecture (https://microservices.io/patterns/microservices.html)#

  • Domain-driven-design
  • CQRS (command-query responsibity segragation)
  • Event-driven
  • Database per service
  • Decomposition patterns
    • Decompose by business capability
    • Decompose by subdomain
  • The Database per Service pattern describes how each service has its own database in order to ensure loose coupling.
  • The API Gateway pattern defines how clients access the services in a microservice architecture.
  • The Client-side Discovery and Server-side Discovery patterns are used to route requests for a client to an available service instance in a microservice architecture.
  • The Messaging and Remote Procedure Invocation patterns are two different ways that services can communicate.
  • The Single Service per Host and Multiple Services per Host patterns are two different deployment strategies.
  • Cross-cutting concerns patterns: Microservice chassis pattern and Externalized configuration
  • Testing patterns: Service Component Test and Service Integration Contract Test
  • Circuit Breaker
  • Access Token

as per ^8

  • Decomposition Patterns
    • Decompose by Business Capability
    • Decompose by Subdomain
    • Strangler Pattern
  • Integration Patterns
    • API Gateway Pattern
    • Aggregator Pattern
    • Client-Side UI Composition Pattern
  • Database Patterns
    • Database per Service
    • Shared Database per Service
    • Command Query Responsibility Segregation (CQRS)
    • Saga Pattern
  • Observability Patterns
    • Log Aggregation
    • Performance Metrics
    • Distributed Tracing
    • Health Check
  • Cross-Cutting Concern Patterns
    • External Configuration
    • Service Discovery Pattern
    • Circuit Breaker Pattern
    • Blue-Green Deployment Pattern

A few common properties of a service/software#

  • High Performance
  • Low Latency
  • High Throughput

Trades-Off#

Scalability vs Performance#

A detailed view on this hot/major topic^6.

Performance:

  1. about speed
  2. capability of a service w.r.t its response time (how fast)
  3. serving # of unit work over a period of time
  4. how fast the service is if there is only 1 user (1 traffic only)

Scalability:

  1. about handling large load/traffic or doing large unit of work
  2. capability of a service to perform directly proportional to the hardwares/resources added
  3. increament in performance of the service when added more resources
  4. service is fast for 1 user, but how fast the service is under heavy load (high traffic)

  5. scale up aka vertical scaling

    • increase server's power (cpu. ram, disk, network speed)
  6. scale out aka horizontal scaling
    • increase # of servers/instances

scalability gives - availability - resiliency/fault-tolerance/reliability - performance/responsiveness - low latency - high throughput

Latency vs Throughput#

strive for high throughput with acceptable latency

In networks..

Latency: Time taken by a packet to travel from its source to destination.

Throughput: The number of packets processed in a specific time duration.

In softwares..

Latency: The time taken by a software to process a request and respond.

Throughput: The number of requests processed in a specific time duration.

Availability vs Consistency#

CAP Theorem#

asserts that in a distributed system (networked data-shared) can have only 2 of the 3 desireable properties at a time.^5

  • consistency & partition tolerance
  • or, availability & partition tolerance

e.g. what NoSQL's BASE principle says: "basically available, soft state, eventually consistent"

Note: The definition of consistency used in CAP theorem is different to the definition of consistency used in ACID.

Partition Tolerance#

ability to tolerate network partitions

Scalability#

Availability#

^12

  • Service is operational/available/running whenever needed
  • % of time service is operational
  • uptime divided by total time (uptime plus downtime)
  • Or, each request to the service receives a response

Techniques to improve availability:

Fail Over#

A fall back mechanism

^10

asynchronous calls#

dependency isolations#

set timeout#
use circuit breaker#

set current limit#

load balancing?#

Resiliency/Fault-tolerant/Reliability#

Resiliency: Service is functional with expected results for given input/environment (even in case of a fault/error) for the required time duration.^1

Reliability: - involves availabilityi - if a system is reliable, you can say it is available. However, if it is available, it may not necessarily be reliable. - service is functional for a specific time interval without a failure - % of time the service works correctly - the ability of a distributed system to deliver its services even when one or several of its software or hardware components fail

Say there are multiple available servers, but not responding.. then redirect the request to other server.

It’s impossible to eliminate failure in microservice applications — the cost of that would be infinite! Instead, your focus needs to be on designing microservices that are tolerant of dependency failures and able to gracefully recover from them or mitigate the impact of those failures on their own responsibilities ^13.

Techniques#
  • step - 1 ensuring Availability
  • Applying retries, rate limits, circuit breakers, health checks, and caching to mitigate interservice communication issues
  • Applying safe communication standards across many services
  • circuit breaker
  • service mesh

Redundancy#

  • duplication of critical components (nodes, processes) with the aim to achieve reliability ^9
  • The (additional/copies/redundant) resources which are NOT strictly necessary for the distributed system to work correctly but good to have to avoid followings:
    • failovers
    • single point of failure
  • Improves reliability
Techniques#

Consistency#

All nodes see the same data (and behaviour? as well) at the same time.

patterns to achieve consistency:

Replication#

  • includes redundancy
  • but also involves

    • synchronization of state between different/redundant nodes in a distributed system. yes. yes.
    • sharing information to ensure consistency between redundant resources. ^9
  • improves availability

  • improves reliability
    • by implicit redundancy
  • improves performance
    • if geographycally distributed
Techniques#
  • active (push)
  • passive (pull)
  • Master Slave
  • Master Master
  • Tree Replication
  • Buddy Replication

Maintainability#

Independent, autonomous#

Decentralized governance#

Auto-Provisioning#

Continuous delivery through DevOps#

Serviceability#

Failure isolation#

Failure detection#

Recovery#

Observability#

Log aggregation#

Application metrics#

Audit logging#

Distributed tracing#

Exception tracking#

Health check API#

Log deployments and changes#

Unclassified Scalability Patterns/Techniques#

There are various techniques/methods/patterns to achieve so:

Load Balancing / Horizonal Scaling#

  • scalability
    • availability

Routing#

  • performance (or may be just a business use case)

Clustering#

database cluster? - scalability - availability - reliability

Stateless application#

  • scalability
    • consistency

Loose coupling#

  • maintainability
  • serviceability

Asynchrony/Concurrency#

  • performance
  • scalability
    • availability

Parallelism#

  • performance

Lazy loading#

  • performance

Caching#

  • performance

Distributed Caching#

HTTP Caching#

Reverse proxy#
CDN#

Master/Slave#

  • scalability
    • reliability

whether its database or some service node.. slave could take over in case of master failure

Partitioning / Sharding#

  • performance
  • scalability
    • availability

Database Scalability Pattern/Techniques#

NoSQL#

RDBMS#

paritioning#
sharding#
replication#

Confusing terms#

  • availability vs reliability
  • redundancy vs replication
  • partitioning vs sharding
  • scale up vs scale out

Pratice Problems#

  • Designing a URL Shortening service like TinyURL
  • How do you design an Elevator of the Lift system?
  • How do you design the Vending Machine in Java?
  • How to design an ATM machine?
  • Design a LRU Cache System
    • https://www.geeksforgeeks.org/lru-cache-implementation/
  • Design & implement LFU cache
  • Design phone book: search by name, phone number and print 10 most dialled numbers.
  • Design a TODO list cloud application, multiple client support, millions of users, synchronize updates across multiple clients.
  • Design an Authorization Service.
  • How would you design a Parking Lot system?
  • How do you design a traffic control system?
  • How do you design a website like Pastebin?
  • How do you design an API Rate Limiter?
  • How do you design a Search Engine?
  • What is required to design a garbage collection system?
  • Design of Review Systems
  • How do you design a recommendation system?
  • How do you design a web crawler, and when should it be used?
  • How do you design a chat application like WhatsApp or Facebook Messenger?
  • How to design a global video streaming service like YouTube or NetFlix?
  • How do you design global file sharing and storage apps like Google Drive or Dropbox?
  • how would you evaluate a software?

    • must fulfill basic requirements
    • cost
    • vendor / license / open-source
    • adoptability in team
      • brainstorm within team
    • maintainability
      • ease of use / document / customer-community support
      • modifiability as per special need
    • scalability
    • installation / setup
  • system design

    • type

      • hld
        • high level problem solve
      • lld
        • ludo
          • class design
          • no other things
    • expectations

      • not expected to complete uber or whatsapp
      • hld: how many use cases you could think of
      • lld: build fault tolerant, scalable
    • by example

      • whatsapp
        • features
          • chat
          • call
          • group chat
          • group call
        • try define requirements at your own/ come up with requirement
          • I'll solve this things
          • confirm requirements first
          • then try start solving the problem
      • uber
        • come up with requirements
    • Q:

      • how much time to define the requirements? -> 5 to 10 minutes
      • expected to code the same system design? -> No, very rare
      • approach to solve uber?
        • define req.
        • user should be able to see drivers
        • send request.
        • asked to include toll in the prices
      • SOLID principles? Most imp.
      • Design Patterns? Most imp.
      • how much to talk about lld vs hld? whatsapp? protocol? if you it--> it is a +ve point
      • 2-3yrs exp -> good lld, no system design; >=4 yrs -> both (some time they skip lld) hld->partitioning, sharding, replication, load-balancing, rolling upgrade, rolling logs, high scalability, high intensive system, horizontal vs vertical scaling
      • resources for preparation?
        • hld
          • start with core concept
      • how can play safe?
        • you can lead the interview
          • start with req.
          • if you know something, make interviewer ask those question
            • always start with high level components
            • same very wage, high level thing-> like say DB, don't say sql/nosql
          • practice many problems
          • prepare basic concepts
      • 3 imp point of sys design
        • partitioning
        • availability
        • sql/nosql; load balance
      • top 3 design patterns
        • interface segregation
        • compositions
        • SOLID
      • freshers?
        • only coding, os, dbms; no lld/hld-
      • discuss tech stack?
        • not imp, no brand name req.
        • only basic concept like quue, cache (not redis vs mem; kafka vs rabitmq)
      • how to handle opinion mis-match
        • say sql vs no-sql
          • back your decision
            • 1 million data, need horizontal partitioning etc.
          • use decision approach
          • don't be rigid with your decision
            • try to seek interviews feedback and try to mold your solution, try to incorporate
      • req.? who tells?
        • 75% we need to define and confirm
      • other skills to deveope? leadership, mgmnt? imp for higher position
      • how knowing big data helps sys-d interviews?
        • helps how to setup queue, high vol. db etc
      • when async, batch vs real-time?
      • learn paritionning in nosql
      • take examples of problems (to learn)
    • CP & DP

      • start writting recursive solution
        • then memoize it, but first need to be confortable in recurr
      • C++ STL, Java, Python
      • For working profs: Facebook hacker, Google Code Jam, Codechef lo.. etc.
      • 1 year, 2 prob each day is enough to be a good CP/DP
      • DS, Algo, OS, N/W, DBMS
      • Microsoft, Google, Amazon - CP, DS, Algo
      • Explain from Zero
      • Don't go blank in interview
      • 1st & 2nd year of college - CP
    • Ref: The course for LLD (Object Oriented Design & Design Patterns): https://practice.geeksforgeeks.org/co...

      The course for System Design: https://practice.geeksforgeeks.org/co...

      Our courses : https://practice.geeksforgeeks.org/co...

      LinkedIn Profiles of the people in the video:

      Shashi Bhushan Kumar https://www.linkedin.com/in/shashi-bh...

      Udit Agarwal https://www.linkedin.com/in/anomaly2104/

      You can follow Udit's YouTube Channel for System Design videos: https://www.youtube.com/c/UditAgarwal21

References#

FIXME: footnote link

[^2]: How to design a system to scale to first 100 million users [^3]: System Design Primer