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🚨 Full Database Sharding Course on Udemy: Database sharding explained from scratch, using one growing app to show exactly when a single database stops scaling and what replaces it. Chirp starts on one server and one database. Then it hits millions of users, the biggest machine the cloud will rent isn’t enough, and writes start failing during busy moments. Replicas rescue the slow reads but can’t touch the write problem. This course follows Chirp through every fix, so shard keys, hashing, hotspots and consistent hashing each show up right when the app needs them. 🧪 Try it yourself in the free lab: 📚 What you’ll learn: 1️⃣ Why read replicas speed up reads but can’t help once writes start failing 2️⃣ The five checks a shard key has to pass, and why user ID beats country, signup year and username 3️⃣ How hashing turns an email into a shard number, and why Python’s built-in hash() breaks sharding 4️⃣ How to stop a celebrity hotspot with a dedicated shard, a cache and salted keys 5️⃣ Why adding one shard can move 80% of your data, and how consistent hashing with virtual nodes prevents it 🧪 Free hands-on lab: ⏰ Timestamps: 00:00 - Intro 00:40 - Chirp: one app, one database 02:30 - Reads, writes and scaling up 04:00 - Read replicas 06:00 - Why writes can’t go to replicas 08:00 - What sharding actually is 09:30 - Splitting users across four shards 11:00 - Lab 1: Shard a 10,000-row users table 11:30 - What a shard key is 13:00 - Five properties of a good shard key 19:30 - Hashing non-numeric shard keys 22:00 - Why Python’s hash() fails for sharding 25:00 - Range, directory and geographic sharding 29:30 - Lab 2: Test different shard keys 30:00 - The routing layer: app, proxy or database 34:00 - Hotspots and the celebrity problem 37:30 - Salting hot keys 41:00 - Why adding one shard moves most of your data 43:30 - Consistent hashing and the hash ring 46:30 - Virtual nodes 🗄️ System Design Playlist: 🔔 Subscribe for more database and system design lessons built around real apps #DatabaseSharding #SystemDesign #KodeKloud #ConsistentHashing #Sharding #DatabaseScaling #HorizontalScaling #ShardKey #DistributedSystems #DatabaseReplication #Hashing #MongoDB #BackendEngineering #SystemDesignInterview #Scalability #DevOps

🚨 Full Database Sharding Course on Udemy: Database sharding explained from scratch, using one growing app to show exactly when a single database stops scaling and what replaces it. Chirp starts on one server and one database. Then it hits millions of users, the biggest machine the cloud will rent isn’t enough, and writes start failing during busy moments. Replicas rescue the slow reads but can’t touch the write problem. This course follows Chirp through every fix, so shard keys, hashing, hotspots and consistent hashing each show up right when the app needs them. 🧪 Try it yourself in the free lab: 📚 What you’ll learn: 1️⃣ Why read replicas speed up reads but can’t help once writes start failing 2️⃣ The five checks a shard key has to pass, and why user ID beats country, signup year and username 3️⃣ How hashing turns an email into a shard number, and why Python’s built-in hash() breaks sharding 4️⃣ How to stop a celebrity hotspot with a dedicated shard, a cache and salted keys 5️⃣ Why adding one shard can move 80% of your data, and how consistent hashing with virtual nodes prevents it 🧪 Free hands-on lab: ⏰ Timestamps: 00:00 - Intro 00:40 - Chirp: one app, one database 02:30 - Reads, writes and scaling up 04:00 - Read replicas 06:00 - Why writes can’t go to replicas 08:00 - What sharding actually is 09:30 - Splitting users across four shards 11:00 - Lab 1: Shard a 10,000-row users table 11:30 - What a shard key is 13:00 - Five properties of a good shard key 19:30 - Hashing non-numeric shard keys 22:00 - Why Python’s hash() fails for sharding 25:00 - Range, directory and geographic sharding 29:30 - Lab 2: Test different shard keys 30:00 - The routing layer: app, proxy or database 34:00 - Hotspots and the celebrity problem 37:30 - Salting hot keys 41:00 - Why adding one shard moves most of your data 43:30 - Consistent hashing and the hash ring 46:30 - Virtual nodes 🗄️ System Design Playlist: 🔔 Subscribe for more database and system design lessons built around real apps #DatabaseSharding #SystemDesign #KodeKloud #ConsistentHashing #Sharding #DatabaseScaling #HorizontalScaling #ShardKey #DistributedSystems #DatabaseReplication #Hashing #MongoDB #BackendEngineering #SystemDesignInterview #Scalability #DevOps

31,600 просмотров • 1 месяц назад

AWS SAA Question 2/65 🎯 Cross-account access in AWS confusing you? Scenario: Switch between multiple accounts (Dev, Prod, Security) while maintaining security Answer: IAM role switching with STS temporary credentials 🔐 👆 Watch to see why duplicate IAM users are a NO! #AWS #CloudComputing #TechTok #SolutionsArchitect #AWSCertification #CloudSecurity #TechEducation #ITJobs #CloudCareer #AWSPrep

AWS SAA Question 2/65 🎯 Cross-account access in AWS confusing you? Scenario: Switch between multiple accounts (Dev, Prod, Security) while maintaining security Answer: IAM role switching with STS temporary credentials 🔐 👆 Watch to see why duplicate IAM users are a NO! #AWS #CloudComputing #TechTok #SolutionsArchitect #AWSCertification #CloudSecurity #TechEducation #ITJobs #CloudCareer #AWSPrep

110,800 просмотров • 11 месяцев назад

🧪 Try Jev free with KodeKey: Jev by TypeSafe is a System One AI model that answers your code with calibrated probabilities instead of text, and it doesn't charge for output tokens. Everyone's talking about the model. Almost nobody explains why it works the way it does. This video covers what a System One model actually is, why a normal LLM can't reliably tell you how sure it is, and how Jev keeps its answers tied to reality, plus where it saves you real money. Then it goes hands-on: a Papers, Please style game where Jev goes head to head with GPT-5.5 and DeepSeek to clear an approval queue on KodeKey. Stick around for the end, where you get everything you need to build the same demo yourself. 📚 What you'll learn: 1️⃣ Why a confidence score from a regular LLM can't be trusted, and what calibration really means 2️⃣ How Jev splits probability across choice, score and yes/no questions in a single pass 3️⃣ Confidence-gated routing: which tickets your code can act on alone and which need a human 4️⃣ Why Jev's output tokens are free, and how speculative fan-out answers five questions for the price of one 5️⃣ A game build where Jev, GPT-5.5 and DeepSeek approve and reject papers side by side

🧪 Try Jev free with KodeKey: Jev by TypeSafe is a System One AI model that answers your code with calibrated probabilities instead of text, and it doesn't charge for output tokens. Everyone's talking about the model. Almost nobody explains why it works the way it does. This video covers what a System One model actually is, why a normal LLM can't reliably tell you how sure it is, and how Jev keeps its answers tied to reality, plus where it saves you real money. Then it goes hands-on: a Papers, Please style game where Jev goes head to head with GPT-5.5 and DeepSeek to clear an approval queue on KodeKey. Stick around for the end, where you get everything you need to build the same demo yourself. 📚 What you'll learn: 1️⃣ Why a confidence score from a regular LLM can't be trusted, and what calibration really means 2️⃣ How Jev splits probability across choice, score and yes/no questions in a single pass 3️⃣ Confidence-gated routing: which tickets your code can act on alone and which need a human 4️⃣ Why Jev's output tokens are free, and how speculative fan-out answers five questions for the price of one 5️⃣ A game build where Jev, GPT-5.5 and DeepSeek approve and reject papers side by side

14,600 просмотров • 1 месяц назад

Can your meeting notes turn into structured calendar events in seconds? 🤯 With LangChain, they can! ⬇️ Meeting notes → Action items → Calendar events — all linked by AI. 🚀 🔗 This is the POWER of AI chaining! #AIChaining #LangChain #ArtificialIntelligence #Python #CodeTutorial #AIWorkflow #Programming #AITools #Automation #AIEngineering #kodekloud

Can your meeting notes turn into structured calendar events in seconds? 🤯 With LangChain, they can! ⬇️ Meeting notes → Action items → Calendar events — all linked by AI. 🚀 🔗 This is the POWER of AI chaining! #AIChaining #LangChain #ArtificialIntelligence #Python #CodeTutorial #AIWorkflow #Programming #AITools #Automation #AIEngineering #kodekloud

12,000 просмотров • 1 год назад

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