A picture is worth a thousand words: 9 best practices for developing microservices.

When we develop microservices, we need to follow the following best practices:

1. Use separate data storage for each microservice
2. Keep code at a similar level of maturity
3. Separate build for each microservice
4. Assign each microservice with a single responsibility
5. Deploy into containers
6. Design stateless services
7. Adopt domain-driven design
8. Design micro frontend
9. Orchestrating microservices

#microservices #architecture
#application

Think like an architect: a mental model for designing your softwares

🍏What You'll Learn:
- How to transition your architectural knowledge to serverless
- The importance of modularity in serverless design and how to implement it
- A step-by-step example of moving from business requirements to a high-level architecture, and finally to a detailed serverless architecture

#microservices #architecture
#application #CloudComputing
#DevOps #CloudArchitect #ArchitecturePatterns
#AWSServices #CloudSolutions

🧩part1
https://www.youtube.com/watch?v=9gEtY4Pb_8g

🧩part2
https://www.youtube.com/watch?v=Gp_Cmv1Tams

Advanced Kubernetes: From GitOps to Resource Optimization and Quantum Computing

#infrastructure #kubernetes #gitops

https://youtube.com/live/aCWq-CYLHuI

🌱These tips will give you an understanding of what characteristics a reliable application should meet.

#Architecture #application #SystemsDesign

https://12factor.net/

🦶🏼How do message queue architectures evolve?
🔹 IBM MQ - was launched in 1993. It was originally called MQSeries and was renamed WebSphere MQ in 2002. It was renamed to IBM MQ in 2014. IBM MQ is a very successful product widely used in the financial sector. Its revenue still reached 1 billion dollars in 2020.

🔹 RabbitMQ - architecture differs from IBM MQ and is more similar to Kafka concepts. The producer publishes a message to an exchange with a specified exchange type. It can be direct, topic, or fanout. The exchange then routes the message into the queues based on different message attributes and the exchange type. The consumers pick up the message accordingly.

#infrastructure #DevOps #Architecture
#application #microservices

🔹 Kafka
In early 2011, LinkedIn open sourced Kafka, which is a distributed event streaming platform. It was named after Franz Kafka. As the name suggested, Kafka is optimized for writing. It offers a high-throughput, low-latency platform for handling real-time data feeds. It provides a unified event log to enable event streaming and is widely used in internet companies.

Kafka defines producer, broker, topic, partition, and consumer. Its simplicity and fault tolerance allow it to replace previous products like AMQP-based message queues.

🔹 Pulsar
Pulsar, developed originally by Yahoo, is an all-in-one messaging and streaming platform. Compared with Kafka, Pulsar incorporates many useful features from other products and supports a wide range of capabilities. Also, Pulsar architecture is more cloud-native, providing better support for cluster scaling and partition migration, etc.

There are two layers in Pulsar architecture: the serving layer and the persistent layer. Pulsar natively supports tiered storage, where we can leverage cheaper object storage like AWS S3 to persist messages for a longer term.

#infrastructure #DevOps #Architecture
#application #microservices

#DevOps #application #linux #humor

Why gRPC is Popular 🚀

Here's why:
🔍 Speed - gRPC is built on HTTP/2 and Protobufs for maximum throughput and minimal latency. Much faster than JSON over HTTP.
🔍 Efficiency The compact Protobuf binary format means smaller payloads than JSON. Less data sent = better performance.
🔍 Type Safety Protobufs are strongly typed, eliminating many bugs.
🔍 Polyglot Write services in many popular languages, the IDL works across them all.
🔍 Streaming gRPC supports bidirectional streaming for real-time data transmission.
🔍 Great Ecosystem Tons of tools exist for code gen, load balancing, monitoring, and more.

Of course, gRPC has some drawbacks:
🖍 More Complex Defining Protobuf schemas and setting up gRPC can have a learning curve.
🖍 Not Human Readable Protobufs are binary, so not as easy to troubleshoot as JSON. Tools like grpcurl help a little bit.
🖍 Limited Browser Support gRPC works best for backend microservices, not front-end apps.

#DevOps #Architecture #application #microservices #Architecture

#infrastructure #devops #terraform

Book: 📚 Development Techniques for Serverless and Kubernetes Applications

#infrastructure #aws #devops

🧑🏼‍💻Leveraging Cilium in its Kubernetes platform has lowered Kakao’s network and performance costs
🏃🏻‍♂️and enabled faster debugging of network issues.

#cloudnative #network
#kubernetes #cilium

https://www.cncf.io/case-studies/kakao/?utm_content=303207023&utm_medium=social&utm_source=linkedin&hss_channel=lcp-12893459

How can Redis be used?

There is more to Redis than just caching.

Redis can be used in a variety of scenarios as shown in the diagram.

🔹Session
We can use Redis to share user session data among different services.
🔹Cache
We can use Redis to cache objects or pages, especially for hotspot data.
🔹Distributed lock
We can use a Redis string to acquire locks among distributed services.
🔹Counter
We can count how many likes or how many reads for articles.
🔹Rate limiter
We can apply a rate limiter for certain user IPs.
🔹Global ID generator
We can use Redis Int for global ID.
🔹Shopping cart
We can use Redis Hash to represent key-value pairs in a shopping cart.
🔹Calculate user retention
We can use Bitmap to represent the user login daily and calculate user retention.
🔹Message queue
We can use List for a message queue.
🔹Ranking
We can use ZSet to sort the articles.

#redis #application #devops

Top 10 kubernetes Design Patterns

#kubernetes #infrastructure
#systemDesign

10 Key Data Structures We Use Every Day

🔸list: keep your Twitter feeds
🔸stack: support undo/redo of the word editor
🔸queue: keep printer jobs, or send user actions in-game
🔸hash table: cashing systems
🔸Array: math operations
🔸heap: task scheduling
🔸tree: keep the HTML document, or for AI decision
🔸suffix tree: for searching string in a document
🔸graph: for tracking friendship, or path finding
🔸r-tree: for finding the nearest neighbor
🔸 vertex buffer: for sending data to GPU for rendering

#devops #algorithms

Grafana LOKI one of the best tools for log collection, with good and reliable distributed architecture 🧗‍♂

#grafana #devops #loki

https://youtu.be/0B-yQdSXFJE?si=pNp00yotpJCcmT-n

For those who are not familiar with Linux🤓, I suggest you start getting to know it through this free course from Linux Foundation 🎁

#devops #linux #training #learning #freecourses

https://training.linuxfoundation.org/training/introduction-to-linux/

📚10 books Every Software Engineer should read

#books #learning_path #devops
#software #engineering

👥 Monolithic VS Microservices architecture

#devops #infrastructure

👉 𝗛𝗲𝗿𝗲'𝘀 𝗮 𝗯𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻 𝗼𝗳 𝗵𝗼𝘄 𝗗𝗼𝗰𝗸𝗲𝗿 𝘄𝗼𝗿𝗸𝘀:

➡️𝗗𝗼𝗰𝗸𝗲𝗿 𝗰𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝘀 are self-contained units that package your application code, runtime, system tools, settings, and libraries
➡️𝗗𝗼𝗰𝗸𝗲𝗿 𝗶𝗺𝗮𝗴𝗲𝘀 are blueprints that contain instructions for creating containers.
➡️𝗗𝗼𝗰𝗸𝗲𝗿 𝗱𝗮𝗲𝗺𝗼𝗻 (𝗱𝗼𝗰𝗸𝗲𝗿𝗱) is the engine that builds, runs, and manages Docker containers.
➡️𝗗𝗼𝗰𝗸𝗲𝗿 𝗖𝗟𝗜 is the tool you use to interact with the Docker daemon.
➡️𝗗𝗼𝗰𝗸𝗲𝗿 𝗿𝗲𝗴𝗶𝘀𝘁𝗿𝗶𝗲𝘀 are repositories that store and share Docker images.
➡️𝗗𝗼𝗰𝗸𝗲𝗿 𝘀𝘁𝗼𝗿𝗮𝗴𝗲 𝗱𝗿𝗶𝘃𝗲𝗿𝘀 manage how data is stored and persisted within containers.
➡️𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿𝘀 like Kubernetes that help you manage and scale large deployments of Docker containers. They take care of provisioning, scheduling, and healing your containers, making it easier to run complex applications in production.

#devops #docker #containers
#architecture #infrastructure

✨7 must-know strategies to scale your database.

1️⃣ Indexing:
Check the query patterns of your application and create the right indexes.
2️⃣ Materialized Views:
Pre-compute complex query results and store them for faster access.
3️⃣ Denormalization:
Reduce complex joins to improve query performance.
4️⃣ Vertical Scaling
Boost your database server by adding more CPU, RAM, or storage.
5️⃣ Caching
Store frequently accessed data in a faster storage layer to reduce database load.
6️⃣ Replication
Create replicas of your primary database on different servers for scaling the reads.
7️⃣ Sharding
Split your database tables into smaller pieces and spread them across servers. Used for scaling the writes as well as the reads.

#architecture #systemdesign #optimization #highloads #databases