While end-to-end(E2E) testing is a vital aspect of software development, another important part of testing that’s often overlooked is the seeding of testing databases. Not only do your applications need proper testing, they also need a solid testing foundation to shine in the real world. Seeding your test databases properly by creating an environment that acts just like the real world sets the stage for a realistic and consistent testing environment.
Test database seeding
Developers employ various methods to establish test databases for their testing environments. Manual seeding using SQL scripts or database management tools is probably the most common approach.
The drawbacks of conventional database seeding
Establishing a test database necessitates a deep grasp of data requirements to prevent making inaccurate assumptions and introducing biases. Additionally, handling and maintaining the seed files can be quite time-consuming and laborious. It is both complex and challenging.
Automated data generation through AI
Luckily, there are specialized tools and libraries (such as Snaplet’s seed and Copycat 🙏) that can automate the seeding process using artificial intelligence (AI). We’ll discuss Snaplet seed in more detail soon, but first, let's dive into some best practices for test database seeding:
Test database seeding best practices
1. Data relevance and realism
It's important to seed databases with realistic and relevant data that reflects a range of scenarios. For example, if testing a social media platform, you would need different names, ages, interests, and locations. Mirroring actual user inputs, such as creating profiles with various hobbies, uploading different types of content, and engaging in diverse interactions helps to create a diverse dataset. If the platform supports image sharing, your dataset should include images with different sizes, formats, and content to help identify potential issues or limitations.
2. Data consistency and stability
Ensuring that the data used in testing scenarios remains reliable and stable across different testing cycles is vital. This involves maintaining a consistent and unchanging dataset to validate the functionality and performance of the software accurately.
3. Fast and efficient seeding techniques
Efficient testing workflows, particularly within CI/CD pipelines, thrive on swift and dependable testing. The optimization of this process hinges on incorporating rapid and effective seeding techniques.
4. Isolation and transaction management
Proper isolation and transaction management are vital to prevent interference between concurrent or sequential test cases. Without it, a test's actions may impact the database, leading to unpredictable results. By prioritizing isolation and effective transaction management, testers ensure a consistent database state after each test, enhancing the reliability and accuracy of test results within a controlled testing environment.
5. Security and privacy considerations
Security and privacy considerations play a pivotal role in database seeding, requiring the careful exclusion of sensitive information. Any personally identifiable information (PII) such as customer names, addresses, or payment details should be anonymized to safeguard user privacy and comply with data protection regulations.
6. Documentation and maintenance
Thorough documentation is imperative for both onboarding new team members and troubleshooting. Well-documented procedures aids in efficient identification and resolution of issues. Streamlined onboarding and effective problem-solving both contribute to productivity and the success of your E2E tests.
Why developers sometimes fail to follow best practices
Even though these best practices for test database seeding make a lot of sense, there are times when teams don't follow them as closely as they should. One big reason is the constant pressure to develop and deploy things quickly. This rush can lead to using less realistic or relevant data for testing. Also, when it comes to continuous integration and deployment pipelines, the need for speedy testing might push teams to skip out on efficient seeding techniques, making the testing process less reliable.
In all fairness, transactions and database isolation is really hard to achieve, even with the help of automation tools such as Snaplet. Sometimes, security and privacy concerns get overlooked because there's this misconception that test environments don't need as much protection as production systems. In the midst of the hustle, teams often prioritize working on the project itself rather than dedicating ample time to creating comprehensive guides and documentation.
Real-world pressures and project demands can sometimes get in the way of doing things the best way possible. It need not be the case with seeding. We created Snaplet seed to automatically generate production-like data for your testing database so that you can focus on what you do best and enjoy most.
How Snaplet seed simplifies test database seeding
Automated values and relationships
Snaplet seed uses generative AI to automatically seed your testing database with realistic, production-like data based on your schema. The determination of values is all automated, making it unnecessary for you to define each value explicitly. Snaplet seed automatically creates relational entities, simplifying the management of IDs across tables. The data connection process ensures data integrity and consistency by automatically handling related data based on foreign keys and uniqueness constraints. This minimizes manual effort and reduces the risk of data inconsistencies. For example, consider a scenario with a "join table" in the database schema, connecting users and groups. Snaplet automates this process, adhering to constraints like the PRIMARY KEY in the group_member table, guaranteeing that a user is present only once in each group.
For a practical example, let's say you have 10 users and 2 groups, and you want to connect them using Snaplet:
// Setup user and group pools
const { users } = await seed.users(x => x(10));
const { groups } = await seed.groups(x => x(2));
// Create 15 group members, connecting them to users and groups
await seed.groupMembers(x => x(15), { connect: { users, groups } });
Snaplet automatically manages constraints, ensuring each user is placed in each group at most once. If constraints cannot be satisfied, Snaplet provides a warning. This simplicity extends to adding new foreign keys; Snaplet adapts to changes, reducing the need for script updates.
Type-safe and deterministic data generation
Snaplet seed creates a TypeScript client based on the database structure, ensuring type safety and soft documentation. This allows you to harness the power of TypeScript language while defining data values. We use Copycat for deterministic data generation, ensuring consistent outputs for the same inputs.
Efficiency in seeding techniques
Testing shouldn't feel like watching paint dry. Thanks to automation and AI, Snaplet seed makes your testing process faster and more reliable. Perfect for those CI/CD pipelines where every second counts.
Safe data
Snaplet seed lets you keep sensitive info under wraps during seeding. Because we generate realistic, production-like data, based on your schema only, there is no need for any actual production data to be used in testing.
To wrap it up, Snaplet seed can be your co-pilot when it comes to seeding your testing database. We take the complexity and effort out of test database seeding so you can make sure you follow best practices for better testing. If you want to see how we fit right into the testing workflow read here or watch this demo to see how Snaplet seed and Playwright plays well together. If you’re eager to keep your tests running smoothly, give Snaplet seed a spin!
Other relevant articles:
Snaplet seed has got your end-to-end
Need data to seed your testing database? Just Snaplet seed it
Snaplet seed plays right with Playwright
Unearthing the power of seed data in database management
