AK Coding

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1 week ago | [YT] | 5

AK Coding

🔹 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗠𝗶𝗰𝗿𝗼𝗺𝗲𝘁𝗲𝗿 𝗧𝗿𝗮𝗰𝗶𝗻𝗴?

Micrometer Tracing is the modern distributed tracing solution in Spring Boot 3+, replacing Spring Cloud Sleuth. It helps you track requests across microservices using standard observability tools.
Micrometer Tracing is part of the Micrometer ecosystem that provides:

1. 𝘛𝘳𝘢𝘤𝘦 𝘐𝘋𝘴 & 𝘚𝘱𝘢𝘯 𝘐𝘋𝘴
2. 𝘈𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘤 𝘪𝘯𝘴𝘵𝘳𝘶𝘮𝘦𝘯𝘵𝘢𝘵𝘪𝘰𝘯
3. 𝘐𝘯𝘵𝘦𝘨𝘳𝘢𝘵𝘪𝘰𝘯 𝘸𝘪𝘵𝘩 𝘰𝘣𝘴𝘦𝘳𝘷𝘢𝘣𝘪𝘭𝘪𝘵𝘺 𝘣𝘢𝘤𝘬𝘦𝘯𝘥𝘴

👉 Think of it as:

Sleuth (old) → Micrometer Tracing (new standard)

🔹 𝗪𝗵𝘆 𝗦𝗹𝗲𝘂𝘁𝗵 𝘄𝗮𝘀 𝗿𝗲𝗽𝗹𝗮𝗰𝗲𝗱?
Spring Cloud Sleuth was tightly coupled with Spring.

Micrometer Tracing is:
• 𝘔𝘰𝘳𝘦 𝘴𝘵𝘢𝘯𝘥𝘢𝘳𝘥𝘪𝘻𝘦𝘥
• 𝘉𝘢𝘤𝘬𝘦𝘯𝘥-𝘢𝘨𝘯𝘰𝘴𝘵𝘪𝘤
• 𝘚𝘶𝘱𝘱𝘰𝘳𝘵𝘴 𝘖𝘱𝘦𝘯𝘛𝘦𝘭𝘦𝘮𝘦𝘵𝘳𝘺

🔹 𝗞𝗲𝘆 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻𝘀

Micrometer Tracing works with:

𝗧𝗿𝗮𝗰𝗶𝗻𝗴 𝗕𝗮𝗰𝗸𝗲𝗻𝗱𝘀

Zipkin
Jaeger
OpenTelemetry

𝗟𝗼𝗴𝗴𝗶𝗻𝗴 (𝗘𝗟𝗞)

Elasticsearch
Kibana



👉 You can search by:

𝘵𝘳𝘢𝘤𝘦𝘐𝘥: 𝘢𝘣𝘤123

🔹 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀

✔️ Works with:

• 𝘙𝘦𝘴𝘵𝘛𝘦𝘮𝘱𝘭𝘢𝘵𝘦

• 𝘞𝘦𝘣𝘊𝘭𝘪𝘦𝘯𝘵

• 𝘚𝘱𝘳𝘪𝘯𝘨 𝘔𝘝𝘊 / 𝘞𝘦𝘣𝘍𝘭𝘶𝘹

• 𝘒𝘢𝘧𝘬𝘢, 𝘙𝘢𝘣𝘣𝘪𝘵𝘔𝘘

✔️ No manual code needed


🔹 𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 ⚡

• 𝘜𝘴𝘦 𝘴𝘢𝘮𝘱𝘭𝘪𝘯𝘨 𝘪𝘯 𝘱𝘳𝘰𝘥𝘶𝘤𝘵𝘪𝘰𝘯 (𝘦.𝘨., 10%)
• 𝘈𝘭𝘸𝘢𝘺𝘴 𝘭𝘰𝘨 𝘵𝘳𝘢𝘤𝘦𝘐𝘥
• 𝘊𝘰𝘮𝘣𝘪𝘯𝘦 𝘸𝘪𝘵𝘩 𝘌𝘓𝘒 𝘧𝘰𝘳 𝘭𝘰𝘨𝘴 + 𝘵𝘳𝘢𝘤𝘪𝘯𝘨
• 𝘈𝘷𝘰𝘪𝘥 𝘵𝘳𝘢𝘤𝘪𝘯𝘨 𝘦𝘷𝘦𝘳𝘺 𝘳𝘦𝘲𝘶𝘦𝘴𝘵 𝘪𝘯 𝘩𝘪𝘨𝘩 𝘵𝘳𝘢𝘧𝘧𝘪𝘤 𝘴𝘺𝘴𝘵𝘦𝘮𝘴

#Java #Spring #springboot #Microservices

1 month ago | [YT] | 5

AK Coding

🏦 𝗭𝗲𝗿𝗼-𝗗𝗼𝘄𝗻𝘁𝗶𝗺𝗲 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 (𝗕𝗮𝗻𝗸𝗶𝗻𝗴 𝗦𝘆𝘀𝘁𝗲𝗺)
🔹 Key Components Explained

𝟭. 🌐 𝗟𝗼𝗮𝗱 𝗕𝗮𝗹𝗮𝗻𝗰𝗲𝗿
Using something like NGINX or cloud LB:

👉 Responsibilities:

Route traffic between Blue & Green
Enable instant traffic switch

𝟮. 🔵🟢 𝗗𝘂𝗮𝗹 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀 (𝗖𝗼𝗿𝗲 𝗖𝗼𝗻𝗰𝗲𝗽𝘁)
Using Blue-Green Deployment:

𝟯. 🚪 𝗔𝗣𝗜 𝗚𝗮𝘁𝗲𝘄𝗮𝘆 𝗟𝗮𝘆𝗲𝗿

Using Spring Cloud Gateway:
• 𝘈𝘶𝘵𝘩𝘦𝘯𝘵𝘪𝘤𝘢𝘵𝘪𝘰𝘯 (𝘑𝘞𝘛/𝘖𝘈𝘶𝘵𝘩2)
• 𝘙𝘰𝘶𝘵𝘪𝘯𝘨
• 𝘙𝘢𝘵𝘦 𝘭𝘪𝘮𝘪𝘵𝘪𝘯𝘨
👉 Stateless → easily scalable

𝟰. 🔧 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗟𝗮𝘆𝗲𝗿
  • 𝘗𝘢𝘺𝘮𝘦𝘯𝘵 𝘚𝘦𝘳𝘷𝘪𝘤𝘦
  • 𝘈𝘤𝘤𝘰𝘶𝘯𝘵 𝘚𝘦𝘳𝘷𝘪𝘤𝘦
  • 𝘛𝘳𝘢𝘯𝘴𝘢𝘤𝘵𝘪𝘰𝘯 𝘚𝘦𝘳𝘷𝘪𝘤𝘦

👉 Each service:
Independently deployed
Horizontally scalable

𝟱. 🗄️ 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗟𝗮𝘆𝗲𝗿 (𝗠𝗼𝘀𝘁 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹)

👉 Shared database between Blue & Green

⚠️ Risk:

Schema changes can break old version
Solution:
Use backward-compatible schema (Expand–Migrate–Contract)

𝟲. 🔁 𝗘𝘃𝗲𝗻𝘁 𝗟𝗮𝘆𝗲𝗿

Using Apache Kafka:

👉 Benefits:

Decoupling
Async processing
Safe retries

1 month ago | [YT] | 7

AK Coding

𝗝𝗩𝗠 𝗧𝘂𝗻𝗶𝗻𝗴 is the process of optimizing the Java Virtual Machine (JVM) settings to improve an application’s performance, memory usage, and stability.


👉 In simple terms:

You adjust how Java uses memory, CPU, and garbage collection to make your app faster and more efficient.

🔹 𝗪𝗵𝘆 𝗝𝗩𝗠 𝗧𝘂𝗻𝗶𝗻𝗴 𝗶𝘀 𝗡𝗲𝗲𝗱𝗲𝗱
By default, JVM settings are generic. In real-world systems:

High traffic 🚀
Large data 🧠

Microservices architecture

👉 Default configs are often not optimal

🔹 Key Areas of JVM Tuning

𝟭. 🧠 𝗛𝗲𝗮𝗽 𝗠𝗲𝗺𝗼𝗿𝘆 𝗧𝘂𝗻𝗶𝗻𝗴

JVM memory is divided into:

• 𝘏𝘦𝘢𝘱 (𝘠𝘰𝘶𝘯𝘨 + 𝘖𝘭𝘥 𝘎𝘦𝘯𝘦𝘳𝘢𝘵𝘪𝘰𝘯)

• 𝘚𝘵𝘢𝘤𝘬

• 𝘔𝘦𝘵𝘢𝘴𝘱𝘢𝘤𝘦

• 𝘐𝘮𝘱𝘰𝘳𝘵𝘢𝘯𝘵 𝘗𝘢𝘳𝘢𝘮𝘦𝘵𝘦𝘳𝘴:

• -𝘟𝘮𝘴512𝘮 # 𝘐𝘯𝘪𝘵𝘪𝘢𝘭 𝘩𝘦𝘢𝘱 𝘴𝘪𝘻𝘦

• -𝘟𝘮𝘹2𝘨 # 𝘔𝘢𝘹 𝘩𝘦𝘢𝘱 𝘴𝘪𝘻𝘦

👉 Best practice:

Keep Xms = Xmx in production to avoid resizing overhead



𝟮. ♻️ 𝗚𝗮𝗿𝗯𝗮𝗴𝗲 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻 (𝗚𝗖) 𝗧𝘂𝗻𝗶𝗻𝗴
Garbage Collector removes unused objects.

Common GC Options:

• 𝘚𝘦𝘳𝘪𝘢𝘭 𝘎𝘊 → 𝘴𝘮𝘢𝘭𝘭 𝘢𝘱𝘱𝘴

• 𝘗𝘢𝘳𝘢𝘭𝘭𝘦𝘭 𝘎𝘊 → 𝘵𝘩𝘳𝘰𝘶𝘨𝘩𝘱𝘶𝘵-𝘧𝘰𝘤𝘶𝘴𝘦𝘥

• 𝘎1 𝘎𝘊 → 𝘣𝘢𝘭𝘢𝘯𝘤𝘦𝘥 (𝘮𝘰𝘴𝘵 𝘶𝘴𝘦𝘥)

• 𝘡𝘎𝘊 → 𝘶𝘭𝘵𝘳𝘢-𝘭𝘰𝘸 𝘭𝘢𝘵𝘦𝘯𝘤𝘺

• -𝘟𝘟:+𝘜𝘴𝘦𝘎1𝘎𝘊

• -𝘟𝘟:𝘔𝘢𝘹𝘎𝘊𝘗𝘢𝘶𝘴𝘦𝘔𝘪𝘭𝘭𝘪𝘴=200

👉 Goal:

Reduce GC pauses

Improve application responsiveness

𝟯. 🧵 𝗧𝗵𝗿𝗲𝗮𝗱 & 𝗖𝗣𝗨 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻

• 𝘛𝘶𝘯𝘦 𝘵𝘩𝘳𝘦𝘢𝘥 𝘱𝘰𝘰𝘭𝘴

• 𝘈𝘷𝘰𝘪𝘥 𝘦𝘹𝘤𝘦𝘴𝘴𝘪𝘷𝘦 𝘵𝘩𝘳𝘦𝘢𝘥 𝘤𝘳𝘦𝘢𝘵𝘪𝘰𝘯

• 𝘙𝘦𝘥𝘶𝘤𝘦 𝘣𝘭𝘰𝘤𝘬𝘪𝘯𝘨 𝘤𝘢𝘭𝘭𝘴



𝟰. 📦 𝗠𝗲𝘁𝗮𝘀𝗽𝗮𝗰𝗲 𝗧𝘂𝗻𝗶𝗻𝗴

Stores class metadata:

-𝘟𝘟:𝘔𝘦𝘵𝘢𝘴𝘱𝘢𝘤𝘦𝘚𝘪𝘻𝘦=256𝘮
-𝘟𝘟:𝘔𝘢𝘹𝘔𝘦𝘵𝘢𝘴𝘱𝘢𝘤𝘦𝘚𝘪𝘻𝘦=512𝘮

𝟱. 🔍 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 & 𝗣𝗿𝗼𝗳𝗶𝗹𝗶𝗻𝗴

Use tools like:

• 𝘑𝘗𝘳𝘰𝘧𝘪𝘭𝘦𝘳
• 𝘝𝘪𝘴𝘶𝘢𝘭𝘝𝘔

👉 Identify:

Memory leaks
CPU hotspots
GC issues


For More Read my Medium Article 👉 lnkd.in/gzsShsHJ

#java #spring #jvm

1 month ago | [YT] | 4

AK Coding

🧠 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗙𝗿𝗮𝘂𝗱 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻?

👉 Fraud detection means:

Identifying and blocking suspicious transactions in real-time or near real-time

🎯 Types of Fraud in Credit Cards
  • Stolen card usage
  • Card-not-present fraud (online)
  • Transaction replay / duplicate
  • Unusual spending patterns

🏗️ 𝗛𝗼𝘄 𝘁𝗼 𝗗𝗲𝘀𝗶𝗴𝗻 𝗙𝗿𝗮𝘂𝗱 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 𝗦𝘆𝘀𝘁𝗲𝗺

🔥 𝟭. 𝗥𝘂𝗹𝗲-𝗕𝗮𝘀𝗲𝗱 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 (𝗙𝗶𝗿𝘀𝘁 𝗟𝗮𝘆𝗲𝗿)
👉 Fast and simple checks
Examples:
• 𝘛𝘳𝘢𝘯𝘴𝘢𝘤𝘵𝘪𝘰𝘯 𝘢𝘮𝘰𝘶𝘯𝘵 > 𝘵𝘩𝘳𝘦𝘴𝘩𝘰𝘭𝘥
• 𝘛𝘰𝘰 𝘮𝘢𝘯𝘺 𝘵𝘳𝘢𝘯𝘴𝘢𝘤𝘵𝘪𝘰𝘯𝘴 𝘪𝘯 𝘴𝘩𝘰𝘳𝘵 𝘵𝘪𝘮𝘦
• 𝘋𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘵 𝘤𝘰𝘶𝘯𝘵𝘳𝘺 𝘵𝘩𝘢𝘯 𝘶𝘴𝘶𝘢𝘭
• 𝘕𝘪𝘨𝘩𝘵-𝘵𝘪𝘮𝘦 𝘶𝘯𝘶𝘴𝘶𝘢𝘭 𝘢𝘤𝘵𝘪𝘷𝘪𝘵𝘺
• 𝘐𝘍 𝘢𝘮𝘰𝘶𝘯𝘵 > 1,00,000 𝘈𝘕𝘋 𝘭𝘰𝘤𝘢𝘵𝘪𝘰𝘯 != 𝘶𝘴𝘶𝘢𝘭_𝘭𝘰𝘤𝘢𝘵𝘪𝘰𝘯 → 𝘍𝘭𝘢𝘨

⚡ 𝟮. 𝗩𝗲𝗹𝗼𝗰𝗶𝘁𝘆 𝗖𝗵𝗲𝗰𝗸𝘀 (𝗩𝗲𝗿𝘆 𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁)

👉 Detect rapid transactions
Example:
5 𝘵𝘳𝘢𝘯𝘴𝘢𝘤𝘵𝘪𝘰𝘯𝘴 𝘪𝘯 1 𝘮𝘪𝘯𝘶𝘵𝘦
𝘚𝘢𝘮𝘦 𝘤𝘢𝘳𝘥 𝘶𝘴𝘦𝘥 𝘪𝘯 𝘮𝘶𝘭𝘵𝘪𝘱𝘭𝘦 𝘭𝘰𝘤𝘢𝘵𝘪𝘰𝘯𝘴 𝘲𝘶𝘪𝘤𝘬𝘭𝘺
👉 Prevents:
𝘉𝘰𝘵 𝘢𝘵𝘵𝘢𝘤𝘬𝘴
𝘊𝘢𝘳𝘥 𝘵𝘦𝘴𝘵𝘪𝘯𝘨 𝘧𝘳𝘢𝘶𝘥

🧠 𝟯. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹𝘀

👉 Advanced fraud detection
Models analyze:
  • 𝘜𝘴𝘦𝘳 𝘣𝘦𝘩𝘢𝘷𝘪𝘰𝘳
  • 𝘚𝘱𝘦𝘯𝘥𝘪𝘯𝘨 𝘱𝘢𝘵𝘵𝘦𝘳𝘯𝘴
  • 𝘋𝘦𝘷𝘪𝘤𝘦 𝘧𝘪𝘯𝘨𝘦𝘳𝘱𝘳𝘪𝘯𝘵
  • 𝘊𝘰𝘮𝘮𝘰𝘯 𝘔𝘓 𝘛𝘦𝘤𝘩𝘯𝘪𝘲𝘶𝘦𝘴:
  • 𝘓𝘰𝘨𝘪𝘴𝘵𝘪𝘤 𝘙𝘦𝘨𝘳𝘦𝘴𝘴𝘪𝘰𝘯
  • 𝘙𝘢𝘯𝘥𝘰𝘮 𝘍𝘰𝘳𝘦𝘴𝘵

Neural Networks

📊 Example Features for ML
  • 𝘛𝘳𝘢𝘯𝘴𝘢𝘤𝘵𝘪𝘰𝘯 𝘢𝘮𝘰𝘶𝘯𝘵
  • 𝘓𝘰𝘤𝘢𝘵𝘪𝘰𝘯
  • 𝘛𝘪𝘮𝘦
  • 𝘔𝘦𝘳𝘤𝘩𝘢𝘯𝘵 𝘤𝘢𝘵𝘦𝘨𝘰𝘳𝘺
  • 𝘋𝘦𝘷𝘪𝘤𝘦 𝘐𝘋
  • 𝘐𝘗 𝘢𝘥𝘥𝘳𝘦𝘴𝘴

🔐 𝟰. 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗘𝗻𝗴𝗶𝗻𝗲

👉 System flow:
  • 𝘛𝘳𝘢𝘯𝘴𝘢𝘤𝘵𝘪𝘰𝘯 𝘳𝘦𝘲𝘶𝘦𝘴𝘵 𝘤𝘰𝘮𝘦𝘴
  • 𝘎𝘰𝘦𝘴 𝘵𝘰 𝘍𝘳𝘢𝘶𝘥 𝘚𝘦𝘳𝘷𝘪𝘤𝘦
  • 𝘙𝘪𝘴𝘬 𝘴𝘤𝘰𝘳𝘦 𝘤𝘢𝘭𝘤𝘶𝘭𝘢𝘵𝘦𝘥

Decision:
  • ✅ 𝘈𝘱𝘱𝘳𝘰𝘷𝘦
  • ⚠️ 𝘍𝘭𝘢𝘨 (𝘖𝘛𝘗 / 2𝘍𝘈)
  • ❌ 𝘋𝘦𝘤𝘭𝘪𝘯𝘦

🧩 Architecture (High-Level)

𝘜𝘴𝘦𝘳 → 𝘗𝘢𝘺𝘮𝘦𝘯𝘵 𝘎𝘢𝘵𝘦𝘸𝘢𝘺 → 𝘍𝘳𝘢𝘶𝘥 𝘚𝘦𝘳𝘷𝘪𝘤𝘦 → 𝘋𝘦𝘤𝘪𝘴𝘪𝘰𝘯 𝘌𝘯𝘨𝘪𝘯𝘦 → 𝘉𝘢𝘯𝘬

🚀 𝟱. 𝗥𝗶𝘀𝗸 𝗦𝗰𝗼𝗿𝗶𝗻𝗴 𝗦𝘆𝘀𝘁𝗲𝗺
👉 Each transaction gets a score:
  • 𝘓𝘰𝘸 → 𝘈𝘱𝘱𝘳𝘰𝘷𝘦
  • 𝘔𝘦𝘥𝘪𝘶𝘮 → 𝘖𝘛𝘗 / 2𝘍𝘈
  • 𝘏𝘪𝘨𝘩 → 𝘉𝘭𝘰𝘤𝘬


📱 𝟲. 𝗗𝗲𝘃𝗶𝗰𝗲 𝗙𝗶𝗻𝗴𝗲𝗿𝗽𝗿𝗶𝗻𝘁𝗶𝗻𝗴

👉 Track:
  • 𝘋𝘦𝘷𝘪𝘤𝘦 𝘐𝘋
  • 𝘉𝘳𝘰𝘸𝘴𝘦𝘳
  • 𝘖𝘚

👉 Detect:

• 𝘕𝘦𝘸 / 𝘴𝘶𝘴𝘱𝘪𝘤𝘪𝘰𝘶𝘴 𝘥𝘦𝘷𝘪𝘤𝘦𝘴

🌍 𝟳. 𝗚𝗲𝗼-𝗕𝗮𝘀𝗲𝗱 𝗖𝗵𝗲𝗰𝗸𝘀

👉 Example:
  • 𝘊𝘢𝘳𝘥 𝘶𝘴𝘦𝘥 𝘪𝘯 𝘐𝘯𝘥𝘪𝘢
  • 𝘕𝘦𝘹𝘵 𝘵𝘳𝘢𝘯𝘴𝘢𝘤𝘵𝘪𝘰𝘯 𝘪𝘯 𝘜𝘚𝘈 𝘪𝘯 2 𝘮𝘪𝘯𝘶𝘵𝘦𝘴 ❌
  • 👉 𝘍𝘭𝘢𝘨 𝘪𝘮𝘮𝘦𝘥𝘪𝘢𝘵𝘦𝘭𝘺

🔁 𝟴. 𝗜𝗱𝗲𝗺𝗽𝗼𝘁𝗲𝗻𝗰𝘆 & 𝗗𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲 𝗣𝗿𝗲𝘃𝗲𝗻𝘁𝗶𝗼𝗻

👉 Prevent:

• 𝘋𝘰𝘶𝘣𝘭𝘦 𝘱𝘢𝘺𝘮𝘦𝘯𝘵
• 𝘙𝘦𝘱𝘭𝘢𝘺 𝘢𝘵𝘵𝘢𝘤𝘬𝘴

🔔 𝟵. 𝗔𝗹𝗲𝗿𝘁𝘀 & 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴

  • 𝘚𝘔𝘚 / 𝘌𝘮𝘢𝘪𝘭 𝘢𝘭𝘦𝘳𝘵𝘴
  • 𝘙𝘦𝘢𝘭-𝘵𝘪𝘮𝘦 𝘥𝘢𝘴𝘩𝘣𝘰𝘢𝘳𝘥𝘴
  • 𝘍𝘳𝘢𝘶𝘥 𝘢𝘯𝘢𝘭𝘺𝘵𝘪𝘤𝘴

🔐 Security Standard

👉 Follow:
PCI DSS

🎯 Interview Answer (Perfect)

Fraud detection in a payment system is handled using a multi-layered approach. Initially, rule-based checks and velocity checks are applied for quick filtering. Then, machine learning models analyze transaction patterns to assign a risk score. Based on this score, the system either approves, flags for additional authentication, or declines the transaction. The system must operate in real-time, be highly scalable, and comply with security standards like PCI-DSS.

🔥 Pro Tips (To Impress Interviewer)

Say these lines:

“I would use a risk scoring engine”
“Combine rule-based + ML approach”
“Ensure real-time processing with low latency”
“Use Kafka for async fraud analysis”

🏁 Final Summary
👉 Fraud detection = speed + intelligence + security

Rule-based → fast
ML → smart
Real-time → critical

1 month ago | [YT] | 7

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☁️ 𝗪𝗵𝗮𝘁 𝗶𝘀 𝟭𝟮-𝗙𝗮𝗰𝘁𝗼𝗿 𝗔𝗽𝗽 𝗺𝗲𝘁𝗵𝗼𝗱𝗼𝗹𝗼𝗴𝘆?

The Twelve-Factor App is a set of 12 best practices to build scalable, maintainable, and cloud-ready applications.

🚀 The 12 Factors (Simple + Interview Ready)
𝟭. 📦 𝗖𝗼𝗱𝗲𝗯𝗮𝘀𝗲
👉 One codebase tracked in version control (Git)
Multiple deploys (dev, QA, prod)

𝟮. 📚 𝗗𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝗶𝗲𝘀
👉 Explicitly declare dependencies

𝗨𝘀𝗲 𝗠𝗮𝘃𝗲𝗻 / 𝗚𝗿𝗮𝗱𝗹𝗲 (𝗝𝗮𝘃𝗮)
3. ⚙️ Config
👉 Store config in environment variables
No hardcoding
Example: DB URL, API keys

𝟰. 🧩 𝗕𝗮𝗰𝗸𝗶𝗻𝗴 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀
👉 Treat DB, cache, messaging as attached resources
Easily replaceable (e.g., MySQL → PostgreSQL)

𝟱. 🔨 𝗕𝘂𝗶𝗹𝗱, 𝗥𝗲𝗹𝗲𝗮𝘀𝗲, 𝗥𝘂𝗻
👉 Separate:
Build → compile
Release → config
Run → execution

𝟲. 🧱 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀
👉 App runs as stateless processes
No session stored in memory

𝟳. 🌐 𝗣𝗼𝗿𝘁 𝗕𝗶𝗻𝗱𝗶𝗻𝗴
👉 App exposes service via port
Example: Spring Boot runs on 8080

𝟴. ⚡ 𝗖𝗼𝗻𝗰𝘂𝗿𝗿𝗲𝗻𝗰𝘆
👉 Scale via multiple processes
Horizontal scaling (more instances)

𝟵. 🔄 𝗗𝗶𝘀𝗽𝗼𝘀𝗮𝗯𝗶𝗹𝗶𝘁𝘆
👉 Fast startup & graceful shutdown
Important for containers (Docker)

𝟭𝟬. 🧪 𝗗𝗲𝘃/𝗣𝗿𝗼𝗱 𝗣𝗮𝗿𝗶𝘁𝘆
👉 Keep dev, QA, prod environments similar
Avoid “works on my machine” issues

𝟭𝟭. 📊 𝗟𝗼𝗴𝘀
👉 Treat logs as event streams
Don’t store locally → use ELK / Splunk

𝟭𝟮. 🛠️ 𝗔𝗱𝗺𝗶𝗻 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀

👉 Run admin tasks as one-off processes
Example: DB migration scripts

#systemdesign #cloudnative #cloud #aws

1 month ago | [YT] | 8

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𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗙𝘂𝗹𝗹 𝗖𝗼𝘂𝗿𝘀𝗲 𝗣𝗹𝗮𝘆𝗹𝗶𝘀𝘁 | 𝗛𝗟𝗗 & 𝗟𝗟𝗗 𝗳𝗼𝗿 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 (Step-by-Step)
Master the art of 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 with this complete playlist! 🚀 Whether you’re preparing for 𝗙𝗔𝗔𝗡𝗚 interviews, building scalable applications, or just curious about how big tech systems work — this playlist has you covered.
👉 lnkd.in/gEgffGUQ
✅ What you’ll learn:
Core System Design fundamentals
𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 vs Performance explained
Designing systems for 1 million+ users
𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀, 𝗰𝗮𝗰𝗵𝗶𝗻𝗴, l𝗼𝗮𝗱 𝗯𝗮𝗹𝗮𝗻𝗰𝗶𝗻𝗴 & 𝘀𝗵𝗮𝗿𝗱𝗶𝗻𝗴
Real-world interview questions & scenarios
From beginner-friendly concepts to advanced architecture discussions, this playlist will help you think like a software architect and design systems that scale.

📌 Perfect for software 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀, 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀, and anyone preparing for 𝘀𝘆𝘀𝘁𝗲𝗺 𝗱𝗲𝘀𝗶𝗴𝗻 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀.

#SystemDesign #TechInterviews #SoftwareEngineering

1 month ago | [YT] | 7

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youtube.com/shorts/GSCLE5avEz...

2 months ago | [YT] | 2

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🚀 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝘃𝘀 𝗠𝗼𝗱𝘂𝗹𝗮𝗿 𝗠𝗼𝗻𝗼𝗹𝗶𝘁𝗵 + 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴
𝗪𝗵𝗮𝘁 𝗦𝗵𝗼𝘂𝗹𝗱 𝗬𝗼𝘂 𝗖𝗵𝗼𝗼𝘀𝗲 𝗶𝗻 𝟮𝟬𝟮𝟲 𝗮𝘀 𝗮 𝗦𝗽𝗿𝗶𝗻𝗴 𝗕𝗼𝗼𝘁 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿?

For years we were told:

👉 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 = 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 = 𝗠𝗼𝗱𝗲𝗿𝗻 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲

But in 2026… companies are asking a different question:

❓ “How do we make developers faster and systems simpler?”

⚠️ The Microservices Reality (What no one tells you)

Microservices are powerful… but they come with a cost:

❌ 𝘛𝘰𝘰 𝘮𝘢𝘯𝘺 𝘴𝘦𝘳𝘷𝘪𝘤𝘦𝘴 𝘵𝘰 𝘮𝘢𝘯𝘢𝘨𝘦
❌ 𝘊𝘰𝘮𝘱𝘭𝘦𝘹 𝘥𝘦𝘱𝘭𝘰𝘺𝘮𝘦𝘯𝘵𝘴 & 𝘊𝘐/𝘊𝘋 𝘱𝘪𝘱𝘦𝘭𝘪𝘯𝘦𝘴
❌ 𝘕𝘦𝘵𝘸𝘰𝘳𝘬 𝘭𝘢𝘵𝘦𝘯𝘤𝘺 & 𝘴𝘦𝘳𝘷𝘪𝘤𝘦-𝘵𝘰-𝘴𝘦𝘳𝘷𝘪𝘤𝘦 𝘧𝘢𝘪𝘭𝘶𝘳𝘦𝘴
❌ 𝘋𝘪𝘴𝘵𝘳𝘪𝘣𝘶𝘵𝘦𝘥 𝘵𝘳𝘢𝘤𝘪𝘯𝘨 & 𝘥𝘦𝘣𝘶𝘨𝘨𝘪𝘯𝘨 𝘩𝘦𝘢𝘥𝘢𝘤𝘩𝘦𝘴
❌ 𝘏𝘦𝘢𝘷𝘺 𝘋𝘦𝘷𝘖𝘱𝘴 𝘰𝘷𝘦𝘳𝘩𝘦𝘢𝘥

👉 Great for large-scale systems
👉 Overkill for most products

💡 𝗧𝗵𝗲 𝗖𝗼𝗺𝗲𝗯𝗮𝗰𝗸 𝗼𝗳 𝗠𝗼𝗱𝘂𝗹𝗮𝗿 𝗠𝗼𝗻𝗼𝗹𝗶𝘁𝗵 💥

Modern teams are rediscovering the Modular Monolith:

✔ Single deployable application
✔ Clear domain boundaries (DDD style)
✔ Easier debugging
✔ Lower infrastructure cost
✔ Faster development speed 🚀

It gives you the simplicity of a monolith + structure of microservices

⚡ 𝗘𝗻𝘁𝗲𝗿 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 (𝗧𝗵𝗲 𝗚𝗮𝗺𝗲 𝗖𝗵𝗮𝗻𝗴𝗲𝗿)

This is where the industry is heading 👇

Platform Engineering focuses on:

🔹 Internal Developer Platforms (IDP)
🔹 Self-service environments
🔹 Standardized deployment pipelines
🔹 Built-in observability, security & scaling

👉 Developers focus on business logic
👉 Platform handles infrastructure complexity

☕ 𝗪𝗵𝗲𝗿𝗲 𝗦𝗽𝗿𝗶𝗻𝗴 𝗕𝗼𝗼𝘁 𝗙𝗶𝘁𝘀 𝗜𝗻

Whether you choose:

👉 Modular Monolith
👉 Microservices
👉 Platform-based architecture

Spring Boot supports all of them:

✔ Build modular domains
✔ Scale to microservices when needed
✔ Integrate with cloud & DevOps platforms
✔ Plug in AI using Spring AI

🧠 𝗠𝘆 𝗥𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗮𝘁𝗶𝗼𝗻 (𝟮𝟬𝟮𝟲 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆)

👉 Start with Modular Monolith
👉 Add Platform Engineering practices
👉 Move to Microservices only when needed

This approach gives:

🔥 Faster delivery
🔥 Lower cost
🔥 Better developer productivity
🔥 Less operational chaos

💬 𝗟𝗲𝘁’𝘀 𝗗𝗶𝘀𝗰𝘂𝘀𝘀

If you start a new project today, what will you choose?

👉 MICROSERVICES
👉 MODULAR MONOLITH + PLATFORM ENGINEERING

Comment your answer 👇


#SpringBoot #Java #Microservices #ModularMonolith #PlatformEngineering #SystemDesign #SoftwareArchitecture #BackendDevelopment #CloudNative #DevOps #TechTrends #Developers #Coding #AI #FutureOfTech

3 months ago | [YT] | 4

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⚡ 𝗦𝗽𝗿𝗶𝗻𝗴 𝗖𝗹𝗼𝘂𝗱 𝗖𝗶𝗿𝗰𝘂𝗶𝘁 𝗕𝗿𝗲𝗮𝗸𝗲𝗿 — 𝗦𝗮𝘃𝗲 𝗬𝗼𝘂𝗿 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗳𝗿𝗼𝗺 𝗙𝗮𝗶𝗹𝘂𝗿𝗲!

In a microservices architecture, one failing service can bring down your entire system 😱

👉 That’s where Spring Cloud Circuit Breaker comes in.

🔥 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗦𝗽𝗿𝗶𝗻𝗴 𝗖𝗹𝗼𝘂𝗱 𝗖𝗶𝗿𝗰𝘂𝗶𝘁 𝗕𝗿𝗲𝗮𝗸𝗲𝗿?

It is a resilience library abstraction in Spring that helps you:

✔ 𝘗𝘳𝘦𝘷𝘦𝘯𝘵 𝘤𝘢𝘴𝘤𝘢𝘥𝘪𝘯𝘨 𝘧𝘢𝘪𝘭𝘶𝘳𝘦𝘴
✔ 𝘐𝘮𝘱𝘳𝘰𝘷𝘦 𝘴𝘺𝘴𝘵𝘦𝘮 𝘴𝘵𝘢𝘣𝘪𝘭𝘪𝘵𝘺
✔ 𝘗𝘳𝘰𝘷𝘪𝘥𝘦 𝘧𝘢𝘭𝘭𝘣𝘢𝘤𝘬 𝘳𝘦𝘴𝘱𝘰𝘯𝘴𝘦𝘴

It internally integrates with modern resilience libraries like:

➡️ Resilience4j
➡️ (Earlier: Netflix Hystrix – now deprecated)

🧠 𝗛𝗼𝘄 𝗖𝗶𝗿𝗰𝘂𝗶𝘁 𝗕𝗿𝗲𝗮𝗸𝗲𝗿 𝗪𝗼𝗿𝗸𝘀 (𝗥𝗲𝗮𝗹-𝗹𝗶𝗳𝗲 𝗮𝗻𝗮𝗹𝗼𝗴𝘆)

Think of it like an electric fuse:

⚡ Too many failures → circuit opens
🔁 After some time → it retries
✅ If successful → closes again

🔄 𝗖𝗶𝗿𝗰𝘂𝗶𝘁 𝗕𝗿𝗲𝗮𝗸𝗲𝗿 𝗦𝘁𝗮𝘁𝗲𝘀

1️⃣ Closed → everything normal
2️⃣ Open → calls are blocked (fallback returned)
3️⃣ Half-Open → limited calls to test recovery

💻 Simple Spring Boot Example

@𝘊𝘪𝘳𝘤𝘶𝘪𝘵𝘉𝘳𝘦𝘢𝘬𝘦𝘳(𝘯𝘢𝘮𝘦 = "𝘶𝘴𝘦𝘳𝘚𝘦𝘳𝘷𝘪𝘤𝘦", 𝘧𝘢𝘭𝘭𝘣𝘢𝘤𝘬𝘔𝘦𝘵𝘩𝘰𝘥 = "𝘧𝘢𝘭𝘭𝘣𝘢𝘤𝘬𝘜𝘴𝘦𝘳")
𝘱𝘶𝘣𝘭𝘪𝘤 𝘚𝘵𝘳𝘪𝘯𝘨 𝘨𝘦𝘵𝘜𝘴𝘦𝘳() {
𝘳𝘦𝘵𝘶𝘳𝘯 𝘳𝘦𝘴𝘵𝘛𝘦𝘮𝘱𝘭𝘢𝘵𝘦.𝘨𝘦𝘵𝘍𝘰𝘳𝘖𝘣𝘫𝘦𝘤𝘵("𝘩𝘵𝘵𝘱://𝘜𝘚𝘌𝘙-𝘚𝘌𝘙𝘝𝘐𝘊𝘌/𝘢𝘱𝘪/𝘶𝘴𝘦𝘳𝘴", 𝘚𝘵𝘳𝘪𝘯𝘨.𝘤𝘭𝘢𝘴𝘴);
}

public String fallbackUser(Exception ex) {
return "User service is down. Please try later.";
}

🚀 𝗪𝗵𝘆 𝗜𝘁’𝘀 𝗠𝗨𝗦𝗧-𝗛𝗔𝗩𝗘 𝗶𝗻 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀

✔ 𝘗𝘳𝘦𝘷𝘦𝘯𝘵𝘴 𝘥𝘰𝘮𝘪𝘯𝘰 𝘧𝘢𝘪𝘭𝘶𝘳𝘦 𝘪𝘯 𝘮𝘪𝘤𝘳𝘰𝘴𝘦𝘳𝘷𝘪𝘤𝘦𝘴
✔ 𝘐𝘮𝘱𝘳𝘰𝘷𝘦𝘴 𝘶𝘴𝘦𝘳 𝘦𝘹𝘱𝘦𝘳𝘪𝘦𝘯𝘤𝘦 𝘥𝘶𝘳𝘪𝘯𝘨 𝘥𝘰𝘸𝘯𝘵𝘪𝘮𝘦
✔ 𝘞𝘰𝘳𝘬𝘴 𝘱𝘦𝘳𝘧𝘦𝘤𝘵𝘭𝘺 𝘸𝘪𝘵𝘩 𝘚𝘱𝘳𝘪𝘯𝘨 𝘉𝘰𝘰𝘵 & 𝘖𝘱𝘦𝘯𝘍𝘦𝘪𝘨𝘯
✔ 𝘚𝘶𝘱𝘱𝘰𝘳𝘵𝘴 𝘳𝘦𝘵𝘳𝘺, 𝘳𝘢𝘵𝘦 𝘭𝘪𝘮𝘪𝘵𝘦𝘳, 𝘣𝘶𝘭𝘬𝘩𝘦𝘢𝘥, 𝘵𝘪𝘮𝘦𝘰𝘶𝘵

🔥 𝗥𝗲𝗮𝗹 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀

✔ 𝘗𝘢𝘺𝘮𝘦𝘯𝘵 𝘴𝘦𝘳𝘷𝘪𝘤𝘦 𝘧𝘢𝘪𝘭𝘶𝘳𝘦 𝘧𝘢𝘭𝘭𝘣𝘢𝘤𝘬
✔ 𝘌𝘹𝘵𝘦𝘳𝘯𝘢𝘭 𝘈𝘗𝘐 𝘵𝘪𝘮𝘦𝘰𝘶𝘵 𝘩𝘢𝘯𝘥𝘭𝘪𝘯𝘨
✔ 𝘌𝘮𝘢𝘪𝘭/𝘚𝘔𝘚 𝘴𝘦𝘳𝘷𝘪𝘤𝘦 𝘳𝘦𝘵𝘳𝘺 𝘮𝘦𝘤𝘩𝘢𝘯𝘪𝘴𝘮
✔ 𝘖𝘳𝘥𝘦𝘳 𝘴𝘦𝘳𝘷𝘪𝘤𝘦 𝘳𝘦𝘴𝘪𝘭𝘪𝘦𝘯𝘤𝘦 𝘪𝘯 𝘦-𝘤𝘰𝘮𝘮𝘦𝘳𝘤𝘦

⚙️ 𝗪𝗵𝗲𝗻 𝘁𝗼 𝗨𝘀𝗲 𝗜𝘁?

Use Circuit Breaker when:

👉 𝘊𝘢𝘭𝘭𝘪𝘯𝘨 𝘦𝘹𝘵𝘦𝘳𝘯𝘢𝘭 𝘴𝘦𝘳𝘷𝘪𝘤𝘦𝘴
👉 𝘊𝘢𝘭𝘭𝘪𝘯𝘨 𝘢𝘯𝘰𝘵𝘩𝘦𝘳 𝘮𝘪𝘤𝘳𝘰𝘴𝘦𝘳𝘷𝘪𝘤𝘦
👉 𝘞𝘰𝘳𝘬𝘪𝘯𝘨 𝘸𝘪𝘵𝘩 𝘶𝘯𝘴𝘵𝘢𝘣𝘭𝘦 𝘯𝘦𝘵𝘸𝘰𝘳𝘬/𝘈𝘗𝘐

💬 𝗟𝗲𝘁’𝘀 𝗗𝗶𝘀𝗰𝘂𝘀𝘀

Do you use Circuit Breaker in your microservices?

Comment 👇
YES / NO / PLANNING TO USE


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