What is databaseperservice?

Database per Service is a design pattern used in micro‑service architectures where each individual service owns and manages its own separate database. Instead of sharing a single database across many services, every service has a dedicated data store that it can control, evolve, and scale independently.

Let's break it down

  • Micro‑service: a small, focused application that does one thing well and talks to other services through APIs.
  • Database: the place where a service keeps its data (SQL, NoSQL, file storage, etc.).
  • Ownership: the service is the only one that reads or writes to its database; other services must request data via the service’s API.
  • Isolation: because databases are separate, changes in one service’s schema or technology do not affect the others.

Why does it matter?

Having a private database per service reduces coupling between teams, lets each service choose the best data technology for its needs, and prevents a failure in one database from bringing down the whole system. It also makes it easier to deploy, scale, and upgrade services without coordinating massive database migrations.

Where is it used?

  • Large cloud‑native platforms like Netflix, Amazon, and Uber that run hundreds of micro‑services.
  • Companies adopting container orchestration (Kubernetes) and continuous delivery pipelines.
  • Any organization moving from a monolithic app to a micro‑service architecture to gain flexibility and speed.

Good things about it

  • Loose coupling: services are independent, so teams can work in parallel.
  • Technology freedom: each service can pick the database type that fits its data model (SQL, document, graph, etc.).
  • Scalability: you can scale a service’s database without impacting others.
  • Fault isolation: a crash or corruption in one database doesn’t corrupt data for other services.
  • Simpler schema evolution: changes affect only one service, reducing the risk of breaking others.

Not-so-good things

  • Operational overhead: managing many databases increases monitoring, backup, and security tasks.
  • Data duplication: the same information may be stored in multiple places, leading to consistency challenges.
  • Complex queries across services: reporting that needs data from several services may require additional aggregation layers or data warehouses.
  • Higher cost: more databases can mean higher infrastructure and licensing expenses.
  • Testing difficulty: integration tests must account for multiple independent data stores.