Vitalii Petkanych

Here are 12 best practices for developing and managing microservices: 1. Single Responsibility Principle: Each microservice should focus on a specific business capability or function, ensuring that it has a single responsibility. 2. Decentralized Data Management: Each microservice should manage its own database to avoid tight coupling and allow for independent scaling and deployment. 3. API-First Design: Design APIs before implementing the microservices to ensure clear communication and integration points between services. 4. Automated Testing: Implement automated testing at various levels (unit, integration, end-to-end) to ensure the reliability and functionality of each microservice. 5. Continuous Integration and Continuous Deployment (CI/CD): Use CI/CD pipelines to automate the deployment process, allowing for faster and more reliable releases. 6. Service Discovery: Implement service discovery mechanisms to allow microservices to find and communicate with each other dynamically. 7. Monitoring and Logging: Establish comprehensive monitoring and logging practices to track the performance and health of microservices, enabling quick identification of issues. 8. Resilience and Fault Tolerance: Design microservices to handle failures gracefully, using patterns like circuit breakers and retries to maintain system stability. 9. Versioning: Implement versioning for APIs to manage changes and ensure backward compatibility, allowing clients to adapt to updates without disruption. 10. Security: Incorporate security measures at every layer, including authentication, authorization, and data encryption, to protect microservices and their data. 11. Containerization: Use containers (e.g., Docker) to package microservices, ensuring consistency across different environments and simplifying deployment. 12. Documentation: Maintain clear and up-to-date documentation for each microservice, including API specifications, deployment instructions, and operational guidelines. By following these best practices, organizations can effectively leverage the benefits of microservices architecture while minimizing potential challenges. Want to know more? Follow me or connect🥂 Please don't forget to like❤️ and comment💭 and repost♻️, thank you🌹🙏 #backend #Csharp #github #EFCore #dotnet #dotnetCore

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Why is Kafka fast? There are many design decisions that contributed to Kafka’s performance. In this post, we’ll focus on two. We think these two carried the most weight. 1️. The first one is Kafka’s reliance on Sequential I/O. 2️. The second design choice that gives Kafka its performance advantage is its focus on efficiency: zero copy principle.   The diagram below illustrates how the data is transmitted between producer and consumer, and what zero-copy means.   🔹Step 1.1 - 1.3: Producer writes data to the disk    🔹Step 2: Consumer reads data without zero-copy 2.1: The data is loaded from disk to OS cache 2.2 The data is copied from OS cache to Kafka application 2.3 Kafka application copies the data into the socket buffer  2.4 The data is copied from socket buffer to network card 2.5 The network card sends data out to the consumer   🔹Step 3: Consumer reads data with zero-copy 3.1: The data is loaded from disk to OS cache 3.2 OS cache directly copies the data to the network card via sendfile() command 3.3 The network card sends data out to the consumer   Zero copy is a shortcut to save multiple data copies between the application context and kernel context. – Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages): https://bit.ly/3KCnWXq #systemdesign #coding #interviewtips .

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Why is Kafka fast? There are many design decisions that contributed to Kafka’s performance. In this post, we’ll focus on two. We think these two carried the most weight. 1️. The first one is Kafka’s reliance on Sequential I/O.   2️. The second design choice that gives Kafka its performance advantage is its focus on efficiency: zero copy principle.   The diagram below illustrates how the data is transmitted between producer and consumer, and what zero-copy means.   🔹Step 1.1 - 1.3: Producer writes data to the disk    🔹Step 2: Consumer reads data without zero-copy 2.1: The data is loaded from disk to OS cache 2.2 The data is copied from OS cache to Kafka application 2.3 Kafka application copies the data into the socket buffer  2.4 The data is copied from socket buffer to network card 2.5 The network card sends data out to the consumer   🔹Step 3: Consumer reads data with zero-copy 3.1: The data is loaded from disk to OS cache 3.2 OS cache directly copies the data to the network card via sendfile() command 3.3 The network card sends data out to the consumer   Zero copy is a shortcut to save multiple data copies between the application context and kernel context. --  Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages): https://bit.ly/3KCnWXq #systemdesign #coding #interviewtips  .

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System Design Acronyms In the context of system design, particularly in distributed systems and databases, "BASE" is an acronym that stands for: - Basically Available: The system guarantees availability of data, meaning that it will always respond to requests, even if some of the data might be stale or incomplete. - Soft State: The state of the system may change over time, even without new input, due to eventual consistency. This implies that the system does not need to be in a stable state at all times, allowing for more flexibility in how data is stored and accessed. - Eventual Consistency: The system guarantees that if no new updates are made to a given piece of data, eventually all accesses to that data will return the last updated value. This contrasts with strong consistency models where immediate consistency is required. The acronym SOLID represents a set of five design principles intended to make software designs more understandable, flexible, and maintainable. These principles are primarily applicable in object-oriented design and programming. Here's what each letter stands for: 1. S - Single Responsibility Principle (SRP): A class should have one, and only one, reason to change. This means that a class should have only one job or responsibility. This makes the system easier to understand, maintain, and test. 2. O - Open/Closed Principle (OCP): Software entities (classes, modules, functions, etc.) should be open for extension but closed for modification. This means that you should be able to add new functionality to a system without altering existing code, which reduces the risk of introducing bugs. 3. L - Liskov Substitution Principle (LSP): Objects of a superclass should be replaceable with objects of a subclass without affecting the correctness of the program. This principle ensures that a subclass can stand in for its superclass, guaranteeing that the subclass can be used in all the same contexts as the superclass. 4. I - Interface Segregation Principle (ISP): Clients should not be forced to depend on interfaces they do not use. This means that many client-specific interfaces are better than one general-purpose interface, promoting a design that is more decoupled and easier to refactor and maintain. 5. D - Dependency Inversion Principle (DIP): High-level modules should not depend on low-level modules. Both should depend on abstractions (e.g., interfaces). Additionally, abstractions should not depend on details. Details (concrete implementations) should depend on abstractions. This principle helps reduce the coupling between different components of the system. Want to know more? Follow me or connect🥂 Please don't forget to like❤️ and comment💭 and repost♻️, thank you🌹🙏 #backend #fullStack #developer #Csharp #github #EFCore #dotnet #dotnetCore #programmer #azure #visualstudio

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😯 Very few developers know that in DDD! 𝐓𝐰𝐨 𝐒𝐜𝐡𝐨𝐨𝐥𝐬 𝐨𝐟 𝐓𝐡𝐨𝐮𝐠𝐡𝐭 𝐨𝐧 𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐞 𝐑𝐨𝐨𝐭𝐬 𝐢𝐧 𝐃𝐨𝐦𝐚𝐢𝐧-𝐃𝐫𝐢𝐯𝐞𝐧 𝐃𝐞𝐬𝐢𝐠𝐧 (𝐃𝐃𝐃) In the world of DDD, there’s an intriguing discussion about how we perceive Aggregate Roots (AR). I’ve been reflecting several years ago on this, and I wanted to share insights from two prominent schools of thought: 1. 🔥 Udi Dahan’s Perspective: • Contextual Aggregate Roots: According to Udi Dahan, an entity can serve as an Aggregate Root in one use case and not in another—even within the same bounded context. This means the exact same class might act differently depending on the scenario. • Non-Structural Property: He argues that being an Aggregate Root is not a structural property of the system. Instead, it’s determined by the specific needs of EACH use case. • Avoid Rigid Annotations: Annotating a class permanently as an AggregateRoot might limit flexibility. Since an entity’s role can change, rigid annotations could hinder the model’s adaptability. Udi Dahan introduced this idea to the community around 2009 with his article « Don’t create Aggregate Roots » (google for it), and it has significantly influenced how some practitioners handle immediate consistency and model complex domains. 2. 💦 Vaughn Vernon and Eric Evans’s Perspective: • Fixed Aggregate Roots: In contrast, Vaughn Vernon and Eric Evans advocate that an Aggregate Root is a structural property of the domain. An entity designated as an AR remains so across all use cases within the bounded context. • Consistent Boundaries: This approach emphasizes clear and consistent aggregate boundaries, which can simplify understanding and enforcing invariants. • Structural Annotations: Using annotations like AggregateRoot aligns with this perspective, as it reflects the entity’s consistent role in the domain model. This school of thought focuses on stability and predictability within the domain model, which can be advantageous for maintaining invariants and ensuring clarity. Why I Favor Udi Dahan’s Vision 🔥: I’ve leaned towards Udi Dahan’s approach for a few reasons: • Flexibility in Modeling: It allows for more adaptable models that can cater to varying business processes and transactional requirements. • Simplified Consistency Handling: Immediate consistency across related entities becomes more manageable without complex coordination between fixed aggregates. ****That point is so GREAT! 🔥 I can assert**** • Optimized Performance: By adjusting aggregate boundaries per use case, we can optimize further for performance and scalability, loading only what’s strictly necessary. If you have any question, feel free to ask me. #ddd #aggregate #aggregateroots #schools #tips

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Monolith vs. microservices: which side are you on? If you’re currently deciding whether to migrate to microservices, the ebook we published on 10 critical challenges to consider will help you out ⬇️ https://bit.ly/3ULrnjM The 10-part ebook guides you through the process of re-architecting your tech stack & organizational structure during a monolith to microservices migration: • 80+ pages of in-depth content • 10 migration examples from Uber, Spotify, and Netflix • 25+ tools and technologies examined (I’m an ex-Googler and software executive with 20+ years of experience who has gone through this complex shift multiple times) I covered all the potential challenges and ways to overcome them, including service boundaries, monolith decomposition, decentralized data management, interservice communication, load balancing, monitoring, observability, and much more. Check out the ebook in the comments. PS: It’s free & created for the dev community.

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WARNING ⚠ : Are you about to design or work on a microservices project? If so, you really need to read this first. I will be publishing a series of articles about the dark side of microservices projects, and how some tiny design mistakes can grow into huge problems 🌋. My point of views are up for debate. I'm not saying I'm right, rather I'm showcasing some of the problems I went through. If you have an opposing opinion please share it with me as these discussions can help both of us learn more 📚.

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Choosing the right tools and packages can make a big difference in your projects. Here are some of my favorites that I use regularly: 1. ramda 2. generic-pool 3. nestjs 4. kafkajs 5. mongoose 6. luxon 7. opossum 8. jest 9. playwright 10. ajv 11. cdktf 12. commander 13. ulidx 14. ioredis What about you? What are some of your go-to tools and packages that you simply can't do without? #nodejs #softwareengineering

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🚀 Essential System Design Acronyms You Should Know! 🚀 🔑 CAP (Consistency, Availability, Partition Tolerance): In distributed systems, you can only guarantee two out of these three. Which one do you prioritize? 🔑 BASE (Basically Available, Soft state, Eventually consistent): A model used in distributed databases. It prioritizes availability over consistency but ensures eventual consistency. 🔑 SOLID (Single Responsibility, Open-Closed, Liskov Substitution, Interface Segregation, Dependency Inversion): A set of 5 principles to ensure better design and maintainability in object-oriented programming. 🔑 KISS (Keep It Simple, Stupid): A reminder that simple designs are often the best. Avoid overcomplicating systems to ensure efficiency and reliability! 💡 Mastering these principles is crucial for building scalable, efficient, and maintainable systems Which one do you find most challenging in practice? #systemdesign #captheorem #basemodel #SOLIDPrinciples #kissprinciple #techinnovation #softwaredeveloper development #scalability#innovation #webdevelopment #digitalsolutions #techindustry #softwarecompany #reelsinstagram #digitalsolutions #artiplesolutions #devops

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Kube Controller Manager: A Quick Guide 🚀 So, what is a controller? Controllers are programs that run continuous control loops. This means they run indefinitely, monitoring the actual and desired states of objects via the API server. The Controller pattern is a fundamental design concept in Kubernetes. Controllers use the Kubernetes API to track changes in the state of the cluster and take action when a change is detected. If there’s a difference between the actual and desired state, the controller ensures the Kubernetes resource/object reaches the desired state. 📌 Let’s look at an example: If you want to create a deployment, you specify the desired state in the manifest YAML file (declarative approach). For instance, you may define 2 replicas, one volume mount, a configmap, etc. The built-in deployment controller ensures that the deployment stays in the desired state at all times. If a user updates the deployment to 5 replicas, the deployment controller detects this change and ensures the desired state becomes 5 replicas. Kube Controller Manager is a component that manages all the Kubernetes controllers. Kubernetes resources/objects like pods, namespaces, jobs, and replicasets are managed by their respective controllers. Here are some examples of core built-in Kubernetes controllers: - Deployment Controller - Replicaset Controller - DaemonSet Controller - Job Controller - Node Controller Here is what you should know about the Kube Controller Manager: ✅ It manages all controllers, and these controllers work to keep the cluster in the desired state. ✅ You can extend Kubernetes with custom controllers associated with custom resource definitions. ✅ Controllers use the Kubernetes API to get the current state of the cluster and update it by creating, updating, or deleting resources. ✅ If there are multiple instances of the Kube Controller Manager (Control Plane HA), it runs in leader election mode. One instance is elected as the leader to make changes to the cluster at any given time. This prevents conflicts and ensures the cluster remains in a consistent state. In the next post, we will explore the Cloud Controller Manager component in detail. You can check my previous Kubernetes learning series posts here: 𝗞𝟴𝘀 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗦𝗲𝗿𝗶𝗲𝘀: https://lnkd.in/g9u8T3QM In the next post, we will explore custom contollers in detail. ♻️ PS: Repost and share with the community if it is helpful :) #DevOps #kubernetes

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Casandra DB - Features Cassandra is a distributed NoSQL database known for its scalability, high availability, and fault tolerance. Here are some of its key features: 1. Distributed Architecture: Cassandra is designed to be distributed across multiple nodes, allowing it to handle large amounts of data and provide high throughput and low latency. 2. Linear Scalability: Cassandra's decentralized architecture enables it to scale linearly as more nodes are added to the cluster, without any single point of failure. 3. High Availability: Data in Cassandra is replicated across multiple nodes, ensuring that it remains available even if some nodes fail. It supports automatic data replication and partitioning for fault tolerance. 4. Fault Tolerance: Cassandra is fault-tolerant by design, with its distributed nature allowing it to continue functioning even if some nodes in the cluster fail. 5. Tunable Consistency: Cassandra offers tunable consistency levels, allowing developers to choose between consistency and availability based on their application requirements. It supports eventual consistency, strong consistency, and various levels in between. 6. Flexible Schema: Cassandra is schema-agnostic, allowing developers to store structured, semi-structured, or unstructured data without needing a fixed schema definition. This flexibility simplifies data modeling and accommodates evolving data requirements. 7. Query Language: Cassandra Query Language (CQL) provides a SQL-like interface for interacting with the database. It supports standard database operations like CRUD (Create, Read, Update, Delete) as well as more complex queries. 8. Wide Column Store: Cassandra organizes data in columns rather than rows, making it efficient for read-heavy workloads and analytical queries. It is particularly well-suited for time-series data, IoT data, and other use cases with large datasets. 9. Integrated Caching: Cassandra includes an integrated caching mechanism to improve read performance by caching frequently accessed data in memory. 10. Compression and Compaction: Cassandra supports data compression to reduce storage requirements and network bandwidth. It also performs compaction to reclaim disk space and optimize data storage. These features make Cassandra a popular choice for use cases requiring massive scalability, high availability, and fault tolerance, such as real-time analytics, IoT, and online transaction processing (OLTP) applications. #techvalens #techpost #CassandraFeatures #NoSQL #ScalableData #DistributedDatabase #HighAvailability #FaultTolerance #LinearScalability #DataReplication #SchemaFlexibility #TunableConsistency #Analytics #RealTimeData #ContinuousAvailability

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Why is Kafka fast? There are many design decisions that contributed to Kafka’s performance. In this post, we’ll focus on two. We think these two carried the most weight. 1️. The first one is Kafka’s reliance on Sequential I/O.   2️. The second design choice that gives Kafka its performance advantage is its focus on efficiency: zero copy principle.   The diagram below illustrates how the data is transmitted between producer and consumer, and what zero-copy means.   🔹Step 1.1 - 1.3: Producer writes data to the disk    🔹Step 2: Consumer reads data without zero-copy 2.1: The data is loaded from disk to OS cache 2.2 The data is copied from OS cache to Kafka application 2.3 Kafka application copies the data into the socket buffer  2.4 The data is copied from socket buffer to network card 2.5 The network card sends data out to the consumer   🔹Step 3: Consumer reads data with zero-copy 3.1: The data is loaded from disk to OS cache 3.2 OS cache directly copies the data to the network card via sendfile() command 3.3 The network card sends data out to the consumer   Zero copy is a shortcut to save multiple data copies between the application context and kernel context. --  Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages): https://bit.ly/3KCnWXq    #systemdesign #coding #interviewtips  .

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Tsynamo simplifies the DynamoDB API so that you don't have to write commands with raw expressions and hassle with the attribute names and values. Moreover, Tsynamo makes sure you use correct types in your DynamoDB expressions, and the queries are nicer to write with autocompletion!

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How do we Perform Pagination in API Design? Pagination is crucial in API design to handle large datasets efficiently and improve performance. Here are six popular pagination techniques: 1-Offset-based Pagination: This technique uses an offset and a limit parameter to define the starting point and the number of records to return. - Example: GET /orders?offset=0&limit=3 - Pros: Simple to implement and understand. - Cons: Can become inefficient for large offsets, as it requires scanning and skipping rows. 2-Cursor-based Pagination: This technique uses a cursor (a unique identifier) to mark the position in the dataset. Typically, the cursor is an encoded string that points to a specific record. Example: GET /orders?cursor=xxx - Pros: More efficient for large datasets, as it doesn't require scanning skipped records. - Cons: Slightly more complex to implement and understand. 3-Page-based Pagination: This technique specifies the page number and the size of each page. Example: GET /items?page=2&size=3 - Pros: Easy to implement and use. - Cons: Similar performance issues as offset-based pagination for large page numbers. 4-Keyset-based Pagination: This technique uses a key to filter the dataset, often the primary key or another indexed column. Example: GET /items?after_id=102&limit=3 - Pros: Efficient for large datasets and avoids performance issues with large offsets. - Cons: Requires a unique and indexed key, and can be complex to implement. 5-Time-based Pagination: This technique uses a timestamp or date to paginate through records. Example: GET /items?start_time=xxx&end_time=yyy - Pros: Useful for datasets ordered by time, ensures no records are missed if new ones are added. - Cons: Requires a reliable and consistent timestamp. 6-Hybrid Pagination: This technique combines multiple pagination techniques to leverage their strengths. Example: Combining cursor and time-based pagination for efficient scrolling through time-ordered records. Example: GET /items?cursor=abc&start_time=xxx&end_time=yyy - Pros: Can offer the best performance and flexibility for complex datasets. - Cons: More complex to implement and requires careful design. blog.bytebytego.com Want to know more? Follow me or connect🥂 Please don't forget to like❤️ and comment💭 and repost♻️, thank you🌹🙏 #backend #fullStack #developer #Csharp #github #EFCore #dotnet #dotnetCore #programmer #azure #visualstudio

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🌐 What is an API Gateway and Why is it So Important in Microservices? 🌐 An API Gateway is an intermediary server that acts as the single entry point for all client requests to microservices. Instead of clients interacting directly with individual microservices, they send their requests to the API Gateway, which then routes them to the appropriate microservice. 🔑 Key Functions of an API Gateway 🔑 1️⃣ Request Routing: It receives client requests and directs them to the correct microservice. 2️⃣ Response Aggregation: Combines responses from multiple microservices into a single response for the client. 3️⃣ Rate Limiting: Controls the number of requests a client can make in a given time period. 4️⃣ Protocol Transformation: Converts protocols like HTTP to others more optimized for microservices, such as gRPC. 5️⃣ CORS Handling: Manages CORS policies to allow or restrict API access from different origins. 🚀 Why is it so Important in Microservices? 🚀 1️⃣ Simplifies Communication Between Services: Clients only need to interact with a single endpoint, which simplifies management and reduces complexity. 2️⃣ Centralized Logic: Tasks such as authentication, authorization, and logging can be handled centrally, avoiding repetition across microservices. 3️⃣ Scalability and Flexibility: It facilitates scalability by allowing each microservice to remain independent. You can also add new features without affecting the existing architecture. 4️⃣ Performance Optimization: With features like caching and load balancing, it enhances the overall performance of the system. 5️⃣ Enhanced Security: API Gateways allow for better security management since all requests must pass through this component, making it easier to implement controls and monitoring. 💡 Examples of API Gateways: Nginx, Kong, Traefik, Apigee (Google Cloud ). ⭐️ Follow me on LinkedIn to see more posts like this. This is just one of many amazing posts I have for you! #Microservices #APIGateway #SoftwareArchitecture #WebDevelopment #NGINX #KongAPI #AWSAPI #Traefik #Apigee #SpringCloudGateway #Zuul #LoadBalancing #APISecurity #Docker #Kubernetes

Big O Notation 101: The Secret to Writing Efficient Algorithms From simple array operations to complex sorting algorithms, understanding the Big O Notation is critical for building high-performance software solutions. O(1) This is the constant time notation. The runtime remains steady regardless of input size. For example, accessing an element in an array by index and inserting/deleting an element in a hash table. O(n) Linear time notation. The runtime grows in direct proportion to the input size. For example, finding the max or min element in an unsorted array. O(log n) Logarithmic time notation. The runtime increases slowly as the input grows. For example, a binary search on a sorted array and operations on balanced binary search trees. O(n^2) Quadratic time notation. The runtime grows exponentially with input size. For example, simple sorting algorithms like bubble sort, insertion sort, and selection sort. O(n^3) Cubic time notation. The runtime escalates rapidly as the input size increases. For example, multiplying two dense matrices using the naive algorithm. O(n logn) Linearithmic time notation. This is a blend of linear and logarithmic growth. For example, efficient sorting algorithms like merge sort, quick sort, and heap sort O(2^n) Exponential time notation. The runtime doubles with each new input element. For example, recursive algorithms solve problems by dividing them into multiple subproblems. O(n!) Factorial time notation. Runtime skyrockets with input size. For example, permutation-generation problems. O(sqrt(n)) Square root time notation. Runtime increases relative to the input’s square root. For example, searching within a range such as the Sieve of Eratosthenes for finding all primes up to n. What else will you add to better understand the Big O Notation? ByteByteGo Want to know more? Follow me or connect🥂 Please don't forget to like❤️ and comment💭 and repost♻️, thank you🌹🙏 #backend #Csharp #EFCore #dotnet #dotnetCore

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