Database Replication
The process of copying data from one database server to one or more replicas to improve read performance, provide geographic distribution, and ensure data durability through redundancy. Replication can be synchronous or asynchronous.
Database replication creates copies of data across multiple servers. Synchronous replication guarantees that all replicas have identical data before confirming a write, ensuring strong consistency at the cost of increased write latency. Asynchronous replication confirms writes immediately and propagates changes to replicas in the background, offering better performance but risking temporary inconsistency between replicas.
For AI product teams, replication enables read-heavy analytics queries to run against replicas without impacting the primary database's performance for user-facing operations. Feature extraction pipelines, model training data exports, and product analytics queries can all target read replicas. Growth teams benefit because experiment analysis queries, which can be complex and resource-intensive, run against replicas without affecting production performance. Geographic replication also reduces read latency for users in different regions, which matters when AI features need to fetch user context data before generating responses. The trade-off between consistency models is important: if a user updates their preferences and immediately receives a recommendation based on stale replica data, the experience feels broken.
Related Terms
Content Delivery Network
A geographically distributed network of proxy servers that caches and delivers content from locations closest to end users. CDNs reduce latency, improve load times, and absorb traffic spikes by serving content from edge nodes rather than a single origin server.
Edge Computing
A distributed computing paradigm that processes data closer to the source of generation rather than in a centralized data center. Edge computing reduces latency, conserves bandwidth, and enables real-time processing for latency-sensitive applications.
Serverless Computing
A cloud execution model where the provider dynamically manages server allocation and scaling. Developers deploy functions or containers without provisioning infrastructure, paying only for actual compute time consumed rather than reserved capacity.
Function as a Service
A serverless computing category where developers deploy individual functions that execute in response to events. FaaS platforms like AWS Lambda, Google Cloud Functions, and Azure Functions handle all infrastructure management, scaling each function independently.
Platform as a Service
A cloud computing model that provides a complete development and deployment environment without managing underlying infrastructure. PaaS offerings like Heroku, Vercel, and Google App Engine handle servers, storage, networking, and runtime configuration.
Infrastructure as a Service
A cloud computing model that provides virtualized computing resources over the internet. IaaS offerings like AWS EC2, Google Compute Engine, and Azure Virtual Machines give teams full control over servers, storage, and networking without owning physical hardware.