Kishan Singh

In system design interviews, discuss cache eviction strategies like LRU (Least Recently Used), LFU (Least Frequently Used), and FIFO (First In, First Out). These methods determine which data to remove when the cache is full, balancing memory usage and ensuring high cache hit rates for optimal performance in high-traffic systems. _ Subscribe to my weekly newsletter. Learn a software engineering concept simplified like you're five, in just 5 minutes, every week! (Link in bio)

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Understanding Kafka working Apache Kafka is a distributed event streaming platform designed for high-throughput, fault-tolerant, and scalable data processing. It is commonly used for building real-time data pipelines and streaming applications. Here are the key components and concepts that define how Kafka works: 1. Producers: These are applications that publish (write) data to Kafka topics. Producers send records to specific topics, which are essentially categories or feeds to which messages are published. 2. Topics: A topic is a named stream of records. Each topic can have multiple partitions, which allows Kafka to scale horizontally and handle large volumes of data. 3. Partitions: Each topic is divided into partitions, which are ordered, immutable sequences of records. Partitions enable Kafka to distribute data across multiple brokers and provide parallelism for processing. 4. Consumers: These are applications that subscribe to (read) data from Kafka topics. Consumers can read from one or more topics and can be part of a consumer group, which allows for load balancing and fault tolerance. 5. Consumer Groups: A consumer group consists of one or more consumers that work together to consume messages from a topic. Each partition in a topic is consumed by only one consumer in a group, ensuring that messages are processed in order. 6. Brokers: Kafka runs as a cluster of one or more servers, known as brokers. Each broker is responsible for storing data and serving client requests. Brokers work together to manage the partitions of topics. 7. Zookeeper: Kafka uses Apache ZooKeeper to manage distributed brokers and maintain metadata about topics, partitions, and consumer groups. However, newer versions of Kafka are moving towards removing the dependency on ZooKeeper. 8. Replication: Kafka provides data durability and fault tolerance through replication. Each partition can be replicated across multiple brokers, ensuring that data is not lost in case of broker failure. 9. Message Retention: Kafka retains messages for a configurable amount of time, allowing consumers to read messages at their own pace. This retention policy can be based on time or size. 10. Stream Processing: Kafka can be integrated with stream processing frameworks like Apache Flink or Kafka Streams, enabling real-time data processing and analytics. In summary, Kafka is designed to handle real-time data feeds with high throughput and reliability, making it suitable for various applications such as log aggregation, event sourcing, and real-time analytics. 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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The Ultimate Redis 101 Redis is one of the most popular data stores in the world and is packed with features. Here are 8 simple steps that can help you understand the fundamentals of Redis. What is Redis? Redis (Remote Dictionary Server) is a multi-modal database that provides sub-millisecond latency. The core idea behind Redis is that a cache can also act as a full-fledged database. Redis Adoption High-traffic internet websites like Airbnb, Uber, Slack, and many others have adopted Redis in their technology stack. How Redis Changed the Database Game? Redis supported main memory read/writes while still supporting fully durable storage. Read and writes are served from the main memory but the data is also persisted to the disk. This is done using snapshots (RDB) and AOF. Redis Data Structures Redis stores data in key-value format. It supports various data structures such as strings, bitmaps, lists, sets, sorted sets, hash, JSON, etc. Basic Redis Commands Some of the most used Redis commands are SET, GET, DELETE, INCR, HSET, etc. There are many more commands available. Redis Modules Redis modules are add-ons that extend Redis functionality beyond its core features. Some prominent modules are RediSearch, RedisJSON, RedisGraph, RedisBloom, RedisAI, RedisTimeSeries, RedisGears, RedisML, and so on. Redis Pub/Sub Redis also supports even-driven architecture using a publish-subscribe communication model. Redis Use Cases Top Redis use cases are Distributed Caching, Session Storage, Message Queue, Rate Limiting, High-Speed Database, etc. What else will you add to get a better understanding of Redis? 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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AWS Graviton Weekly # 85: https://lnkd.in/dqjwPsQT Question of the week: If you want to start with Amazon DynamoDB today, which is your top recommended resource? The DynamoDB Book by Alex DeBrie https://lnkd.in/dyVDH6hg Highlights of the week ​🗞️ Amazon EC2 simplifies visibility into your active AMIs​ ​🗞️ Amazon EC2 now protects your AMIs from accidental deregistration​ ​🗞️ Announcing the general availability of Amazon Web Services (AWS) Local Zones in Honolulu, Hawaii​ ​🗞️ Amazon DynamoDB now supports an AWS FIS action to pause global table replication​ ​🗞️ Amazon EMR Serverless introduces Shuffle-optimized disks delivering improved performance for I/O intensive workloads​ ​🗞️ Amazon’s generative AI business has hit a multibillion-dollar run rate that’s reaccelerated cloud growth ​⛏️ AWS introduces new PyTorch DLAMI for AWS Graviton-based instances such as Amazon EC2 G5g Instances, by Markus Adhiwiyogo​​ ​ ​⛏️ ​How Upstox built the NextGen trading platform using Amazon EKS, Karpenter, and spot instances, by Pranav Kapoor, Jitendra Shihani, and Jayesh Vartak​​ ​ ​​⛏️ Transforming Cybersecurity with AWS Graviton, with Mark Vlamna​ ​ ​⛏️ Guaranteed Safety using Blue Green with ECS and CDK, by Benjamen Pyle ​ ​⛏️ Improving inclusion and accessibility through automated document translation with an open source app using Amazon Translate, by Philip Whiteside​​ ​ ​⛏️ ​Blockchain node deployment on AWS: A comprehensive guide, by Ian Holtz, Kristian C., James Burdon, and Yue Ning ​ ​⛏️ Surprisingly Powerful – Serverless WASM with Rust (Part I), by Benjamin Pyle​​ ​ ​⛏️ ​Careful with Pair-of-Registers instructions on Apple Silicon, by Daniel Lemire​​ ​ ​​⛏️ Kubernetes — A Journey Has Just Begun, by Konstantinos Stavropoulos 🎙️ Pinterest and AWS Graviton: more performance and lower carbon footprint, with Sierra Hutto (she/her) (Pinterest) ​ ​🎙️ Cloud-native opportunities to accelerate embedded product development, with Raffaele Menolascino (Accenture) ​ 🎙️ ​Level-up Cloud Security with Palo Alto Networks’ VM-Series for AWS Graviton, with Meghna Muralinath, Steven Cacciaroni, and Antal (Anti) Gyori #aws #awsgraviton #cloudsilicon

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If you are into AWS Serverless and one of those people who loves learning by building, here are 5 highly recommended AWS workshops 🛠 👷‍♂️ I have done all the workshops listed below and highly recommend if you are into AWS Serverless. ---- [1] Getting Started with Serverless In this tutorial, you'll create a simple serverless web application that implements a "Todo app" with an API to store and retrieve tasks in a cloud database ➡ https://lnkd.in/g-GBwKkE --- [2] Build your first CRUD API in 45 minutes or less! In this workshop, you will create a CRUD API that Creates, Reads, Updates, and Deletes items from a DynamoDB table. The API will run serverless. ➡ https://lnkd.in/gstb9vyz -- [3] CICD for Serverless Application In this workshop, you will learn how to start a new Serverless application from scratch using the AWS Serverless Application Model (AWS SAM) and how to fully automate builds and deployments.  ➡ https://lnkd.in/dU3fpANn -- [4] The AWS Step Functions Workshop In this workshop, you will learn how to use the primary features of AWS Step functions through a series of interactive modules. https://lnkd.in/gsDviydn -- [5] CDK Workshop for Beginners  ➡️ https://cdkworkshop.com/ #aws #serverless #awscommunity

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Single-Table Design is a common pattern in Amazon's #DynamoDB that overcomes the relational limitations of DynamoDB by modeling common relational patterns within a single DynamoDB table. Single-Table Design is great for transactional access patterns when that data stays in DynamoDB. But what happens when you need to normalize that data for other use cases, like analytics? Then, Single-Table Design can become a bit of a headache. In Tinybird, there's a great approach for this. Instead of querying the denormalized table directly, you parse out the raw data into several, normalized tables using Pipes and Materialized Views. Each Materialized View has its own schema optimized for the items in the table (and the expected analytical queries over those items). Gonzalo J. Gómez Ortiz has written an excellent guide walking through an example of normalizing a DynamoDB Single-Table Design in Tinybird. The end result is sub-second queries that are faster and more efficient than querying the raw table directly. https://tbrd.co/ddb-std

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An API Gateway is a server that acts as a single entry point for managing, aggregating, and routing API requests. It's the key to efficiently handling API traffic, ensuring security, and optimizing performance. 🚀 The API Gateway Architecture consists of several layers, each serving a unique purpose. Here are top 4 layers - 1️⃣ 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗟𝗮𝘆𝗲𝗿 🛡️: This layer takes care of protecting your API infrastructure from threats. It enforces security policies such as SSL/TLS, DDoS protection, and rate limiting, ensuring that your APIs are not vulnerable to unauthorized access or misuse. 2️⃣ 𝗔𝗱𝗺𝗶𝗻𝗶𝘀𝘁𝗿𝗮𝘁𝗶𝘃𝗲 𝗟𝗮𝘆𝗲𝗿 🛠️: Managing APIs is no small task. That's where the Administrative Layer comes in. It provides features for API versioning, monitoring, logging, and analytics. With these tools, you can keep track of API usage, diagnose issues, and gain insights into how your APIs are performing. 3️⃣ 𝗔𝗰𝗰𝗲𝘀𝘀 𝗟𝗮𝘆𝗲𝗿 🗝️: Controlling who gets access to your APIs is crucial. The Access Layer handles authentication, authorization, and access control policies. Whether it's through API keys, OAuth tokens, or other mechanisms, you have the power to decide who can access your APIs and what they can do. 4️⃣ 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿 🔄: APIs come in all shapes and sizes, and it's not always easy to make them work together. The Transformation Layer helps by providing tools for data transformation and protocol conversion. Whether you need to convert JSON to XML, handle different authentication schemes, or support legacy systems, this layer has got you covered. 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀 𝗼𝗳 𝗮𝗻 𝗔𝗣𝗜 𝗚𝗮𝘁𝗲𝘄𝗮𝘆 🚀 Performance Boost 🛡️ Enhanced Security 🧩 Simplified Microservices 💡 Unified API Management 𝗧𝘆𝗽𝗲𝘀 𝗼𝗳 𝗔𝗣𝗜 𝗚𝗮𝘁𝗲𝘄𝗮𝘆 ✅ Edge Gateways: Placed on the outer layer of your network, they handle public APIs, focusing on load balancing, security, and caching. ✅ Internal Gateways: Catering to internal APIs, they help optimize inter-service communication and manage internal API traffic. ✅ Micro-Gateways: Lightweight and tailored for specific use cases, they're deployed in close proximity to specific services, often within containerized environments. 𝗣𝗼𝗽𝘂𝗹𝗮𝗿 𝗔𝗣𝗜 𝗚𝗮𝘁𝗲𝘄𝗮𝘆 𝗧𝗼𝗼𝗹𝘀 🔵 Amazon API Gateway 🟢 Kong 🔴 Apigee Edge 🔶 Azure API Management 🟣 Tyk 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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Prometheus On Kubernetes From the content: Kube Prometheus Stack has 8 components by default, and we’ll learn what each one does and how it's useful. Node exporter is a Prometheus exporter that provides data about the underlying OS and hardware of your cluster....[read more] https://buff.ly/4iaE2a2 Follow #techbeatly @techbeatly gmsocial, tbsocial

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Orient DB - Features 🌟 OrientDB is a multi-model NoSQL database that supports graph, document, key-value, and object-oriented storage models. Here’s a rundown of its key features: 1. Multi-Model Support ▪ Graph Database: Allows the use of graphs to manage complex relationships between data entities. Supports both property graphs and schema-less graphs. ▪ Document Database: Stores data as documents, similar to JSON. Documents can be nested and are schema-less, allowing flexible data structures. ▪ Key-Value Store: Provides key-value storage, which is useful for simple data retrieval scenarios. ▪ Object-Oriented Database: Allows you to work with objects directly, bridging the gap between databases and object-oriented programming. 2. Schema-less, Schema-Full, and Schema-Mixed Modes ▪ Offers flexibility in defining schemas. You can use schema-less mode for dynamic structures, schema-full for strict data models, or a combination of both. 3. ACID Transactions ▪ Supports ACID-compliant transactions, ensuring reliable and safe data operations, crucial for enterprise applications. 4. Distributed Architecture ▪ Designed for distributed environments with features like sharding, replication, and multi-master architecture, ensuring high availability and horizontal scalability. 5. SQL-like Query Language ▪ Uses a SQL-like query language, making it easier for developers familiar with SQL to work with the database. The language is extended to support graph and document queries. 6. Full-Text Indexing and Search ▪ Provides full-text search capabilities, making it easier to perform complex searches on data, including text indexing and searching within documents. 7. Pluggable Storage Engine ▪ OrientDB allows you to plug in custom storage engines, offering flexibility for various use cases and optimization needs. 8. Security Features ▪ Includes support for role-based access control (RBAC), encryption, auditing, and other security mechanisms to protect data. 9. RESTful API ▪ Provides a RESTful API for interacting with the database, which is useful for web and mobile applications. 10. Embedded and Server Modes ▪ Can be used in embedded mode for lightweight applications or in server mode for larger, distributed applications. These features make OrientDB a versatile and powerful database system, suitable for various use cases, especially where complex data relationships and distributed environments are involved. #techvalens #techpost #OrientDB #NoSQL #GraphDatabase #DocumentDatabase #MultiModelDB #OpenSource #BigData #DataManagement #DatabaseFeatures #HighPerformanceDB

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What is Amazon ElastiCache? Amazon ElastiCache is a fully managed in-memory caching service that supports Redis and Memcached. It enables applications to retrieve data faster by storing frequently accessed data in memory instead of querying slower backend databases, reducing latency and improving throughput. Key Features of Amazon ElastiCache 1. Blazing Fast Performance: • Sub-millisecond response times, ideal for real-time applications like gaming, leaderboards, and financial trading. 2. Fully Managed Service: • AWS handles provisioning, patching, backups, monitoring, and scaling, reducing operational overhead. 3. Flexible Caching Engines: • Choose between Redis (high availability and advanced features) and Memcached (simple and distributed caching). 4. High Availability: • Supports Multi-AZ deployments with automatic failover to ensure reliability and uptime. 5. Scalability: • Dynamically scale capacity up or down to meet application demands, with support for sharding and read replicas. 6. Advanced Security: • Includes VPC isolation, encryption in transit and at rest, and IAM authentication for secure data access. 7. Analytics Integration: • Monitor performance and usage through CloudWatch and integrated metrics. 8. Seamless Integration: • Works effortlessly with other AWS services like RDS, DynamoDB, and Lambda for a cohesive ecosystem. Why Use Amazon ElastiCache? 1. Reduce Latency and Improve Speed: • Cache frequently accessed data to accelerate application performance and reduce database load. 2. Cost Efficiency: • Offload read-heavy queries from backend databases, reducing database costs while improving user experience. 3. Flexibility and Customization: • Use Redis for advanced features like pub/sub messaging and Memcached for simpler caching needs. 4. Reliability and Scalability: • Scale to handle millions of requests per second with built-in fault tolerance. 5. Real-Time Use Cases: • Perfect for real-time gaming, live leaderboards, and session management. Common Use Cases for Amazon ElastiCache 1. Gaming Applications: • Power real-time features like leaderboards, matchmaking, and game state caching. 2. E-Commerce Platforms: • Cache product catalogs, user sessions, and shopping carts for faster browsing and checkout experiences. 3. Real-Time Analytics: • Process and deliver insights on streaming data with low-latency caching. 4. Machine Learning: • Store precomputed models or features for quick retrieval during inference. 5. Content Management Systems (CMS): • Accelerate dynamic content delivery by caching frequently accessed pages or assets. Real-World Impact • Social Networking Platform: Accelerated feed generation by caching frequently accessed user data, reducing latency by 80%. • E-Commerce Giant: #devops #sre #dataops #cloudops #aws #career #jobs #ElastiCache #data #cache #caching #application #development #software #codexio_tech Codexio Tech

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