Setting Up Seed Data for Microservices

Microservices architecture has become a popular approach for building scalable and maintainable applications. In a microservices setup, the app is split into independently deployable services that each focus on specific capabilities.

When developing microservices, one question that arises is how to handle seed data the initial data required for the app to function properly. Seed data setup is important to populate databases, create admin accounts, insert reference data, etc. But with data and capabilities distributed across services, how do we handle seed data initialization in a microservices system?

In this post, we’ll explore some effective techniques for setting up seed data in microservices-based applications.

The Challenges of Seed Data in Microservices

First, let’s consider why seed data can be challenging with microservices:

• Distributed Responsibilities: With business functions decentralized across services, each service may require different types of seed data. Coordinating this distributed seed data is difficult.
• Data Consistency: Services depending on each other’s data need to ensure consistency as seed data gets inserted across databases. Failing to maintain data integrity can cause issues.
• Ordering Dependencies: Some seed data is order-dependent if a service relies on another service’s entities to be populated first. Honoring these dependencies gets complex.
• Shared Reference Data: Lookup or static reference data needed by multiple services requires consistent setup across the app.
• No Central Database: A core benefit of microservices is decentralized data ownership, so there is no central DB to handle universal seed setup.

Given these challenges, we need an approach tailored to Microservices Architectures.

Strategies for Setting Up Seed Data

Here are some effective techniques for initializing seed data in a microservices application:

1. Scripts Per Service

Each service can own its own seed data script that handles initializing data specific to that service. For example, the User service would have a setupUserSeedData.js script that inserts admin users, and the Product service would have its own setupProductSeedData.js script to populate the product catalog.

The seed data scripts should run within the context of the service on startup before the app is deployed. This encapsulates a service-specific setup and allows each team to manage its own seed data.

2. Idempotent Scripts

Seed data scripts should be idempotent, meaning they can be run repeatedly without adverse effects. For example, users should be created only if they don’t already exist rather than blindly inserted on each run. This avoids inconsistencies when seed data scripts execute more than once.

Idempotent seed data scripts are more robust and prevent duplicate or conflicting data issues.

3. Honor Dependencies

If service B’s seed data is dependent on service A having its data initialized first, enforce an execution order in the scripts. For example, service B could make an API call to service A to validate that its seed data was loaded. Alternatively, a scheduler or orchestrator can coordinate running service B’s script after service A’s.

Honoring dependencies ensures services have the required data populated before their scripts attempt to consume it.

4. Centralized Reference Data

For static reference data needed across services like currency codes, country lists, etc., you can centralize it in a distributed cache like Redis rather than duplicating it across services. Services can then reference the cache for shared seed data.

This avoids inconsistencies and reduces duplication for non-service-specific reference data.

5. Teardown and Recreation

An alternative seed data technique is to wipe and rebuild databases from scratch on each deployment completely. This ensures a consistent starting state, avoiding partial updates.

The teardown recreate approach works well when you have automated tests to validate app functionality after seeding and can afford downtime during repopulation.

6. Synthetic Test Data Generation

Rather than hand-crafting seed data, you can use a library like Faker.js to auto-generate dummy data for services. While not accurate, it can quickly populate databases with synthetic data for testing purposes.

Automated dummy data helps accelerate environment setup but should not be used in production. Hand-curated seed data is still recommended for production apps.

Final Words

Setting up robust and reliable seed data is an important concern when adopting a microservices architecture. With distributed services owning data setups, we need an approach that emphasizes encapsulation, script idempotency, dependency ordering, centralized reference data, and test automation. Using these patterns helps build reusable, maintainable seed data processes that initialize microservices apps consistently and reliably.