System Design Terminologies

Excelling in system design interviews often hinges on understanding key concepts and terminologies related to scalable and efficient systems. Familiarity with these essential terms can significantly enhance your performance. Here, we outline the top 50 crucial system design terminologies, complete with definitions, illustrative examples, and additional learning resources to help you master them.

1. Scalability: The capacity of a system to handle increased load by adding resources.
2. Load Balancer: Distributes incoming network traffic across multiple servers to prevent any single server from being overwhelmed.
3. Microservices: An architectural pattern that organizes an application as a collection of loosely coupled services.
4. CAP Theorem: In a distributed system, you can achieve at most two out of the three guarantees: Consistency, Availability, and Partition Tolerance.
5. Sharding: The process of dividing a large database into smaller, more manageable pieces called shards.
6. Latency: The time it takes for data to travel from one point to another.
7. Throughput: The amount of data a system can process within a given timeframe.
8. Cache: A hardware or software component that stores data to quickly serve future requests for the same data.
9. Content Delivery Network (CDN): A network of geographically distributed servers that delivers web content based on the user's location.
10. REST API: An architectural style for building web services where data is accessed and manipulated using HTTP requests.
11. GraphQL: A query language for APIs that offers a more powerful, efficient, and flexible alternative to REST.
12. ACID: A set of properties ensuring reliable database transactions: Atomicity, Consistency, Isolation, and Durability.
13. BASE: An alternative to ACID emphasizing Availability and Partition tolerance over strict Consistency. Stands for Basically Available, Soft state, Eventually consistent system.
14. NoSQL: A type of database designed for flexible data storage and retrieval, differing from relational databases.
15. SQL: The standard language for managing and querying relational databases.
16. Database Indexing: A data structure technique that improves the speed of data retrieval from a database.
17. Replication: The process of copying and maintaining database objects across multiple databases in a distributed system.
18. Failover: A backup mechanism where functions of a failed system component are taken over by another component.
19. API Gateway: A server that handles API requests, applies throttling and security policies, and forwards them to backend services.
20. Service Mesh: An infrastructure layer that facilitates service-to-service communication in a microservices architecture.
21. Serverless Computing: A cloud computing model where the cloud provider dynamically manages and allocates computing resources.
22. Event-Driven Architecture: A software design paradigm focused on the generation, detection, and reaction to events.
23. Monolithic Architecture: A software design where all components are combined into a single application and run as a single service.
24. Distributed Systems: Systems where components on networked computers communicate and coordinate actions by passing messages.
25. Message Queue: A method for asynchronous service-to-service communication in both serverless and microservices architectures.
26. Pub/Sub Model: A messaging pattern where publishers send messages that can be received by subscribers without knowing their identities.
27. Data Partitioning: Splitting a database into smaller, more manageable parts.
28. Horizontal Scaling: Increasing capacity by adding more machines or nodes to a system.
29. Vertical Scaling: Enhancing an existing machine's power, such as increasing CPU or RAM.
30. Rate Limiting: Controlling the rate of traffic sent or received by the network interface.
31. Circuit Breaker Pattern: A design pattern that detects failures and prevents repeated failures by encapsulating the failure logic.
32. Data Consistency: Ensuring that data remains uniform across multiple instances and is not corrupted.
33. Eventual Consistency: A consistency model where updates propagate over time and are reflected by all nodes eventually.
34. Strong Consistency : A model ensuring that every read operation retrieves the most recent write for a given data unit.
35. Containerization: Encapsulating an application and its dependencies into a container to run in any computing environment.
36. Kubernetes: An open-source platform that automates the deployment, scaling, and management of application containers.
37. Autoscaling: Automatically adjusting the number of computational resources based on user load.
38. Multi-Tenancy: Architecture where a single software instance serves multiple consumers or customers.
39. Load Shedding: Reducing or degrading service demand to maintain system health under high load conditions.
40. Idempotence: A property of operations where multiple executions have the same effect as a single execution.
41. Quorum: The minimum number of votes required to commit a distributed transaction.
42. Orchestration: A pattern where a central coordinator manages interactions between services.
43. Choreography: A service interaction pattern where services are self-contained and communicate through events without a central coordinator.
44. Service Registry: A database that tracks instances of microservices.
45. API Rate Limiting: Controlling the number of requests a client can make to an API within a specific timeframe.
46. Data Warehouse: A system designed for generating reports and business analytics; the core of Business Intelligence.
47. Data Lake: A repository where data is stored in its raw format, typically as object blobs or files.
48. OLAP: Online Analytical Processing, a category of software for analyzing data stored in databases.
49. OLTP: Online Transaction Processing, a class of systems designed for managing transaction-oriented applications.
50. Big Data: Large and complex data sets that cannot be efficiently managed by traditional data-processing software.

Remember, mastering these concepts requires ongoing learning and practice. Engage with the resources, participate in discussions, and apply these concepts in your projects to deepen your understanding.

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