System Design Glossary

A set of protocols and tools that allows different software applications to communicate with each other. REST, GraphQL, and gRPC are common API styles.

The percentage of time a system is operational and accessible. Measured in "nines" (e.g., 99.99% = "four nines" = 52 minutes downtime/year).

A communication pattern where the sender doesn't wait for a response before continuing. Enables better scalability and decoupling.

Binary Large Object storage for unstructured data like images, videos, and files. Examples: AWS S3, Azure Blob Storage, Google Cloud Storage.

An intermediary that receives messages from producers and delivers them to consumers. Examples: Kafka, RabbitMQ, Redis Pub/Sub.

A high-speed data storage layer that stores a subset of data for faster access. Reduces database load and improves response times.

States that a distributed system can only guarantee 2 of 3 properties: Consistency, Availability, and Partition Tolerance.

A geographically distributed network of servers that delivers content from locations closest to users, reducing latency.

A group of servers working together as a single system to provide high availability and load distribution.

The ability to handle multiple tasks or requests simultaneously. Different from parallelism (executing tasks at the exact same time).

All nodes in a distributed system see the same data at the same time. Strong consistency ensures reads always return the latest write.

A lightweight, standalone package that includes everything needed to run an application. Docker is the most popular containerization platform.

A data structure that improves the speed of data retrieval operations on a database table at the cost of additional storage and write overhead.

A queue that stores messages that couldn't be processed successfully after multiple retries. Used for debugging and recovery.

The process of adding redundant data to a database to improve read performance at the cost of write complexity.

The internet's phone book that translates human-readable domain names (google.com) to IP addresses.

A consistency model where replicas will eventually converge to the same state, but not immediately after a write.

A design pattern where the flow of the program is determined by events (messages) rather than sequential logic.

The automatic switching to a backup system when the primary system fails. Critical for high availability.

A messaging pattern where a single message is delivered to multiple consumers. Used in notification systems and social media feeds.

A high-performance RPC (Remote Procedure Call) framework that uses Protocol Buffers for serialization. Faster than REST for service-to-service communication.

Periodic signals sent between systems to indicate they are operational. Used for health monitoring and failure detection.

Adding more machines to handle increased load (scaling out). Contrast with vertical scaling (adding resources to existing machine).

A condition where a disproportionate amount of traffic or load is directed to a single node or partition.

A property where an operation produces the same result regardless of how many times it's executed. Critical for retry logic.

The time it takes for a request to travel from sender to receiver and back. Measured in milliseconds (ms).

A component that distributes incoming network traffic across multiple servers to ensure no single server is overwhelmed.

A technique where the client makes a request and the server holds it open until new data is available, simulating real-time updates.

A form of asynchronous communication where messages are stored in a queue until they can be processed. Decouples producers from consumers.

An architectural style where an application is composed of small, independent services that communicate via APIs.

An architectural style where all components of an application are tightly coupled into a single deployable unit.

A category of databases that don't use the traditional relational model. Types include document (MongoDB), key-value (Redis), column (Cassandra), and graph (Neo4j).

A division of a database or message queue topic. Also known as sharding. Enables horizontal scaling.

A system's ability to continue operating despite network partitions (communication failures between nodes).

A replication pattern where one node (primary) handles writes and replicates data to read-only replicas. Also called master-slave.

A messaging pattern where publishers send messages to topics and subscribers receive messages from topics they're interested in.

A metric measuring the number of queries a system can handle per second. Used for capacity planning.

The minimum number of nodes that must agree on an operation for it to be considered successful. Common formula: (N/2) + 1.

Controlling the number of requests a user or client can make in a given time period. Prevents abuse and ensures fair usage.

Duplication of critical components to increase reliability. If one fails, others can take over.

Copying data across multiple nodes to improve availability and read performance. Synchronous or asynchronous.

Representational State Transfer. An architectural style for APIs that uses HTTP methods (GET, POST, PUT, DELETE) to perform CRUD operations.

Splitting data across multiple databases based on a shard key. Each shard contains a subset of the data.

A contract defining the expected level of service, including availability, response time, and support.

A service that doesn't store client session data between requests. Each request contains all information needed to process it.

A communication pattern where the sender waits for a response before continuing. Simpler but can create bottlenecks.

The number of operations or data transferred per unit time. Measured in requests/second or bytes/second.

A mechanism that limits the lifespan of data. Commonly used in caching and DNS to expire stale data.

Adding more resources (CPU, RAM, storage) to an existing machine (scaling up). Has hardware limits.

A protocol providing full-duplex communication channels over a single TCP connection. Used for real-time applications.

A technique where changes are written to a log before being applied to the database. Ensures durability and crash recovery.