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S1

Fundamentals

74 stations Β· 10.0h

Scalability, latency, CAP, caching, load balancing and core building blocks. 74 concept lectures explained by codestorywithMIK (10.0h of video).

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All 74 stations in order

  1. 1.Back-Of-The-Envelope-Estimation πŸ•΅οΈπŸ“ˆπŸ“πŸ“
  2. 2.Twitter QPS & Storage Estimation πŸ¦πŸ”ŽπŸ“€
  3. 3.System Design Interview : 🚩 Red Flags & Myths πŸ’₯🀯🫒
  4. 4.Design News Feed System : step-1 of 4
  5. 5.Design News Feed System : step-2 of 4
  6. 6.Design News Feed System : step-3 of 4
  7. 7.Design News Feed System : Step 4 of 4
  8. 8.Design Rate Limiter : What is it β“πŸ€¨πŸ“ˆ
  9. 9.Design Rate Limiter : step-1 of 4 πŸ“ˆπŸ“βš™οΈπŸ—
  10. 10.Design Rate Limiter : Step-2 of 4 (PART-I) - Where To Put Rate Limiter
  11. 11.Design Rate Limiter : step-2 of 4 (PART-2) - Token Bucket Algorithm
  12. 12.Design Rate Limiter : step-2 of 4 (PART-3) - Leaky Bucket Algorithm πŸͺ£πŸ’¦πŸ’§
  13. 13.Design Rate Limiter : step-2 of 4 (PART-IV) - Fixed Window Counter Algorithm πŸ‘©β€πŸ”§πŸ—βš™οΈ
  14. 14.Design Rate Limiter : step-2 of 4 (PART-V) - Sliding Window Log Algorithm
  15. 15.Design Rate Limiter : step-2 of 4 (PART-VI) - High Level Architecture
  16. 16.Design Rate Limiter : step-3 of 4 (PART-I) - Design Deep Dive & LYFT πŸš•πŸš–
  17. 17.Design Rate Limiter : step-3 (PART-II) - Detailed Design πŸ•΅οΈπŸ”Žβš™οΈπŸ—
  18. 18.Design Rate Limiter : step-3 of 4 (PART-III) - Rate Limiter in Distributed Environment βš™οΈπŸ—πŸ€―
  19. 19.Design Rate Limiter - step-3 of 4 (PART-IV) - Performance Optimisation & Monitoring
  20. 20.Design Rate Limiter - step-4 of 4 - WRAP UP
  21. 21.Design Consistent Hashing - What’s the problem ? Why need it ? #οΈβƒ£πŸ•΅οΈ
  22. 22.Design Consistent Hashing - How It Works ? #οΈβƒ£πŸ—
  23. 23.Design Consistent Hashing - Two Problems with Basic Approach 🀯#οΈβƒ£πŸ•΅πŸ»
  24. 24.Design Consistent Hashing - Virtual Nodes οΏΌ#οΈβƒ£πŸ€―πŸ•΅πŸ»
  25. 25.Design Consistent Hashing - Wrap Up #️⃣πŸ₯³πŸ€©
  26. 26.Design Key-Value Store - (Part-I)
  27. 27.Design Key-Value Store - (PART-II) : Single Server Key-Value Store πŸ—βš™οΈπŸ’»πŸ’Ώ
  28. 28.Design Key-Value Store - (PART-III) : Distributed Key-Value Store and CAP Theorem 🀯
  29. 29.Design Key-Value Store - (PART-IV) : Data Partition & Data Replication πŸ’Ώβš™οΈπŸ—
  30. 30.Design Key-Value Store - (PART-V) : Consistency, Quorum, Tunable Consistency πŸ˜±πŸ€―βš™οΈ
  31. 31.Design Key-Value Store - (PART-VI) : Inconsistency Resolution and Vector ClocksπŸ˜±πŸ€―βš™οΈ
  32. 32.Design Key-Value Store - (PART-VII) : Handling Temporary Failures
  33. 33.Design Key-Value Store - (PART-VIII) : Handling Permanent Failures
  34. 34.Design Key-Value Store - (PART-IX) : System Architecture Diagram πŸ˜±βš™οΈπŸ—πŸ‘©β€πŸŽ¨πŸ‘·πŸ‘·β€β™€οΈ
  35. 35.Design Key-Value Store - (PART-X) : Read Path & Write Pathβš™οΈπŸ§‘πŸ»β€πŸ’»πŸ‘·πŸ—πŸ‘©β€πŸŽ¨
  36. 36.Design A Unique ID Generator In Distributed Systems - (Part-I) : Understand The Problem πŸ—πŸ‘·πŸ‘©πŸ»β€πŸ’»
  37. 37.Design A Unique ID Generator In Distributed Systems (Part-II): Multi Master Replication πŸ—πŸ‘·πŸ‘©πŸ»β€πŸ’»
  38. 38.Design A Unique ID Generator In Distributed Systems (Part-III): UUID πŸ—πŸ‘·πŸ‘©πŸ»β€πŸ’»
  39. 39.Design A Unique ID Generator In Distributed Systems (Part-IV): Ticket Server πŸ—πŸ‘·πŸ‘©πŸ»β€πŸ’»
  40. 40.Design A Unique ID Generator In Distributed Systems (Part-V): Twitter Snowflake Approach πŸ—πŸ‘·πŸ‘©πŸ»β€πŸ’»
  41. 41.Design A Unique ID Generator In Distributed Systems (Part-VI): Design Deep Dive πŸ—πŸ‘·πŸ‘©πŸ»β€πŸ’»
  42. 42.Design A Unique ID Generator In Distributed Systems (Part-VII): Wrap Up !!! πŸ—πŸ‘·πŸ‘©πŸ»β€πŸ’»
  43. 43.Design A URL Shortener (Part-I): Step-1 (Understand The Problem & Establish Design Scope) !!! πŸ—βš™οΈπŸ‘·
  44. 44.Design A URL Shortener (Part-II): Back Of The Envelope Estimation !!! πŸ—βš™οΈπŸ‘·
  45. 45.Design A URL Shortener (Part-III): STEP-2 : Propose High Level Design & Get Buy-In !!! πŸ—βš™οΈπŸ‘·
  46. 46.Design A URL Shortener (Part-IV): STEP-2 : 301 Redirect Vs 302 Redirect Status Code !!! πŸ—βš™οΈπŸ‘·
  47. 47.Design A URL Shortener (Part-V): STEP-2 : URL SHORTENING (HASH FUNCTION) !!! πŸ—βš™οΈπŸ‘·
  48. 48.Design A URL Shortener (Part-VI): STEP-3 : DESIGN DEEP DIVE (DATA MODEL) !!! πŸ—βš™οΈπŸ‘·
  49. 49.Design A URL Shortener (Part-VII): STEP-3 : DESIGN DEEP DIVE (HASH FUNCTION) !!! πŸ—βš™οΈπŸ‘·
  50. 50.Design A URL Shortener (Part-VIII): STEP-3 : DESIGN DEEP DIVE (HASH + COLLISION RESOLUTION) πŸ—βš™οΈπŸ‘·
  51. 51.Design A URL Shortener (Part-IX): STEP-3 : DESIGN DEEP DIVE (BASE-62 CONVERSION) πŸ—βš™οΈπŸ‘·
  52. 52.Design A URL Shortener (Part-X): STEP-3 : DESIGN DEEP DIVE (URL SHORTENING FLOW) πŸ—βš™οΈπŸ‘·
  53. 53.Design A URL Shortener (Part-XI): STEP-3&4 : DESIGN DEEP DIVE & WRAP UP (URL Redirecting Deep Dive)
  54. 54.Design Web Crawler (Part-I): (Google) : STEP-1 : Understand The Problem & Establish Design Scope
  55. 55.Design Web Crawler (Part-II) : Google, Amazon, Meta: STEP-1 : Back Of The Envelope Estimation πŸ—βš™οΈπŸ‘·
  56. 56.Design Web Crawler(Part-III):Google, Amazon, Meta: STEP-2 : Propose A High Level Design & Get Buy-in
  57. 57.Design Web Crawler (Part-IV) : Google, Amazon, Meta : STEP-3 : Design Deep Dive (BFS vs DFS) πŸ—βš™οΈπŸ‘·
  58. 58.Design Web Crawler (Part-V) : Google, Amazon, Meta : STEP-3 : Design Deep Dive (URL Frontier) πŸ—βš™οΈπŸ‘·
  59. 59.Design Web Crawler (Part-VI) : Google, Amazon, Meta : STEP-3 : Design Deep Dive Complete & WRAP-UP
  60. 60.Introduction - Why & what ?
  61. 61.Single Server Setup
  62. 62.Database & Multiple Servers
  63. 63.Which Database to use ?
  64. 64.Vertical Scaling vs Horizontal Scaling
  65. 65.Load Balancer βš–οΈ
  66. 66.Database Replication πŸ’ΏπŸ’ΏπŸ“šπŸ“–πŸ“’
  67. 67.Caching
  68. 68.CDN (Content Delivery Network) πŸššπŸ“¦
  69. 69.Stateful & Stateless Architecture πŸ‘·πŸ—
  70. 70.Data Centers & geoDNS
  71. 71.Message Queue
  72. 72.Logging, Metric, Automation πŸ€–βš™οΈ
  73. 73.Database Scaling, Sharding & Justin Bieber Problem πŸ§‘β€πŸŽ€πŸ’Ώ
  74. 74.Normalization vs Denormalization in RDBMS