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As a Java/backend engineer, at one point these system design concepts become inescapable. Understand these concepts properly to be a better engineer and also to perform better during interviews: → APIs (Application Programming Interfaces): A set of rules and protocols that allows different software applications to communicate with each other → API Gateways: A management tool that sits between a client and a collection of backend services, acting as a reverse proxy to handle all API calls → JWTs (JSON Web Tokens): A compact, URL-safe means of representing claims to be transferred between two parties, commonly used for authorization → Webhooks: Automated messages sent from apps when something happens, delivered to a unique URL → REST vs GraphQL: REST is an architectural style for building APIs using standard HTTP methods, while GraphQL is a query language for APIs that allows clients to request exactly the data they need → Load Balancing: The process of distributing network traffic across multiple servers to ensure no single server becomes overwhelmed. → Proxy vs Reverse Proxy: A proxy acts on behalf of a client to fetch resources, while a reverse proxy acts on behalf of a server to handle incoming requests. → Scalability: The ability of a system to handle a growing amount of work by adding resources. → Availability: The percentage of time that a system is operational and accessible to users. → SPOF (Single Point of Failure): A part of a system that, if it fails, will stop the entire system from working. → CAP Theorem: It is impossible for a distributed data store to simultaneously provide more than two out of three guarantees: Consistency, Availability, and Partition tolerance. → SQL vs NoSQL: SQL databases are relational and use structured query language, while NoSQL databases are non-relational and have dynamic schemas for unstructured data. → ACID Transactions: A set of properties (Atomicity, Consistency, Isolation, Durability) that guarantee database transactions are processed reliably. → Database Indexes: Data structures that improve the speed of data retrieval operations on a database table. → Database Sharding: The process of breaking up a large database into smaller, more manageable parts called shards → Consistent Hashing: A hashing technique that minimizes the number of keys that need to be remapped when a hash table is resized, useful in distributed systems. → CDC (Change Data Capture): The process of observing all data modifications in a database and then extracting them in a form that can be used by other systems → Caching: The process of storing copies of files in a temporary storage location (cache) to be accessed more quickly. → Caching Strategies: Different methods for managing a cache to optimize performance and data consistency, such as cache-aside, read-through, and write-through. → Cache Eviction Policies: Algorithms used to decide which items to remove from a cache when it is full, such as Least Recently Used (LRU). → CDN (Content Delivery Network): A geographically distributed network of proxy servers that work together to provide fast delivery of Internet content. → Rate Limiting Algorithms: Techniques used to control the amount of incoming and outgoing traffic to or from a network. → Message Queues: A form of asynchronous service-to-service communication used in serverless and microservices architectures. → Bloom Filters: A space-efficient probabilistic data structure used to test whether an element is a member of a set. → Idempotency: A property of certain operations that they can be applied multiple times without changing the result beyond the initial application. → Concurrency vs Parallelism: Concurrency is about dealing with lots of things at once, while parallelism is about doing lots of things at once. → Long Polling vs WebSockets: Long polling involves the server holding a client's request until new data is available, while WebSockets provide a persistent, two-way communication channel. → Stateful vs. Stateless Architecture: A stateful architecture remembers client data from one request to the next, while a stateless architecture does not. → Batch vs Stream Processing: Batch processing handles data in large blocks, while stream processing handles data in real-time as it is generated. → Geohashing: A geocoding system that encodes a geographic location into a short string of letters and digits.

makakmayum_sid's tweet photo. As a Java/backend engineer, at one point these system design concepts become inescapable. Understand these concepts properly to be a better engineer and also to perform better during interviews:

→ APIs (Application Programming Interfaces): A set of rules and protocols that allows different software applications to communicate with each other

→ API Gateways: A management tool that sits between a client and a collection of backend services, acting as a reverse proxy to handle all API calls

→ JWTs (JSON Web Tokens): A compact, URL-safe means of representing claims to be transferred between two parties, commonly used for authorization

→ Webhooks: Automated messages sent from apps when something happens, delivered to a unique URL

→ REST vs GraphQL: REST is an architectural style for building APIs using standard HTTP methods, while GraphQL is a query language for APIs that allows clients to request exactly the data they need

→ Load Balancing: The process of distributing network traffic across multiple servers to ensure no single server becomes overwhelmed.

→ Proxy vs Reverse Proxy: A proxy acts on behalf of a client to fetch resources, while a reverse proxy acts on behalf of a server to handle incoming requests.

→ Scalability: The ability of a system to handle a growing amount of work by adding resources.

→ Availability: The percentage of time that a system is operational and accessible to users.

→ SPOF (Single Point of Failure): A part of a system that, if it fails, will stop the entire system from working.

→ CAP Theorem: It is impossible for a distributed data store to simultaneously provide more than two out of three guarantees: Consistency, Availability, and Partition tolerance.

→ SQL vs NoSQL: SQL databases are relational and use structured query language, while NoSQL databases are non-relational and have dynamic schemas for unstructured data.

→ ACID Transactions: A set of properties (Atomicity, Consistency, Isolation, Durability) that guarantee database transactions are processed reliably.

→ Database Indexes: Data structures that improve the speed of data retrieval operations on a database table.

→ Database Sharding: The process of breaking up a large database into smaller, more manageable parts called shards

→ Consistent Hashing: A hashing technique that minimizes the number of keys that need to be remapped when a hash table is resized, useful in distributed systems.

→ CDC (Change Data Capture): The process of observing all data modifications in a database and then extracting them in a form that can be used by other systems

→ Caching: The process of storing copies of files in a temporary storage location (cache) to be accessed more quickly.

→ Caching Strategies: Different methods for managing a cache to optimize performance and data consistency, such as cache-aside, read-through, and write-through.

→ Cache Eviction Policies: Algorithms used to decide which items to remove from a cache when it is full, such as Least Recently Used (LRU).

→ CDN (Content Delivery Network): A geographically distributed network of proxy servers that work together to provide fast delivery of Internet content.

→ Rate Limiting Algorithms: Techniques used to control the amount of incoming and outgoing traffic to or from a network.

→ Message Queues: A form of asynchronous service-to-service communication used in serverless and microservices architectures.

→ Bloom Filters: A space-efficient probabilistic data structure used to test whether an element is a member of a set.

→ Idempotency: A property of certain operations that they can be applied multiple times without changing the result beyond the initial application.

→ Concurrency vs Parallelism: Concurrency is about dealing with lots of things at once, while parallelism is about doing lots of things at once.

→ Long Polling vs WebSockets: Long polling involves the server holding a client's request until new data is available, while WebSockets provide a persistent, two-way communication channel.

→ Stateful vs. Stateless Architecture: A stateful architecture remembers client data from one request to the next, while a stateless architecture does not.

→ Batch vs Stream Processing: Batch processing handles data in large blocks, while stream processing handles data in real-time as it is generated.

→ Geohashing: A geocoding system that encodes a geographic location into a short string of letters and digits.

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