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Seeding test databases in 2021 - best practices

ยท 19 min read

Seeding test databases in 2021 - best practices

In this tutorial, we'll learn how to design the Prisma data model for a basic message board and how to seed databases with the open-source tool synth and generate mock data to test our code.

The code for the example we are working with here can be accessed in the examples repository on GitHub.

Data modeling is not boring#

What is a data model?#

Data modeling (in the context of databases and this tutorial) refers to the practice of formalizing a collection of entities, their properties and relations between one another. It is an almost mathematical process (borrowing a lot of language from set theory) but that should not scare you. When it comes down to it, it is exceedingly simple and quickly becomes more of an art than a science.

The crux of the problem of data modeling is to summarize and write down what constitutes useful entities and how they relate to one another in a graph of connections.

You may wonder what constitutes a useful entity. It is indeed the toughest question to answer. It is very difficult to tackle it without a good combined idea of what you are building, the database you are building on top of and what the most common queries, operations and aggregate statistics are. There are many resources out there that will guide you through answering that question. Here we'll start with the beginning: why is it needed?

Why do I need a data model?#

Often times, getting the data model of your application right is crucial to its performance. A bad data model for your backend can mean it gets crippled by seemingly innocuous tasks. On the other hand, a good grasp on data modeling will make your life as a developer 1000 times easier. A good data model is not a source of constant pain, letting you develop and expand without slowing you down. It just is one of those things that pays out compounding returns.

Plus, there are nowadays many open-source tools that make building applications on top of data models really enjoyable. One of them is Prisma.

Prisma is awesome#

Prisma is an ORM, an object relational mapping. It is a powerful framework that lets you specify your data model using a database agnostic domain specific language (called the Prisma schema). It uses pluggable generators to build a nice javascript API and typescript bindings for your data model. Hook that up to your IDE and you get amazing code completion that is tailored to your data model, in addition to a powerful query engine.

Let's walk through a example. We want to get a sense for what it'll take to design the data model for a simple message board a little like Reddit or YCombinator's Hacker News. At the very minimum, we want to have a concept of users: people should be able to register for an account. Beyond that, we need a concept of posts: some structure, attached to users, that holds the content they publish.

Using the Prisma schema language, which is very expressive even if you haven't seen it before, our first go at writing down a User entity might look something like this:

model User {  objectId  Bytes    @id @map("_id")  id        Int      @unique @default(autoincrement())  createdAt DateTime @default(now())  email     String   @unique  nickname  String  posts     Post[]}

In other words, our User entity has properties id (a database internal unique identifier), createdAt (a timestamp, defaulting to now if not specified, that marks the creation time of the user's account), email (the user-specified email address, given on registration) which is required to be unique (no two users can share an email address) and nickname (the user specified display name, given on registration).

In addition, it has a property posts which links a user with its posts through the Post entity. We may come up with something like this for the Post entity:

model Post {  objectId  Bytes    @id @map("_id")  id        Int      @unique @default(autoincrement())  postedAt  DateTime @default(now())  title     String  author    User     @relation(fields: [authorId], references: [id])  authorId  Int}

In other words, our Post entity has properties id (a database internal unique identifier); postedAt (a timestamp, defaulting to now if not specified, that marks the time at which the user created the post and published it) ; title (the title of the post); author and authorId which specify a one-to-many relationship between users and posts.


You may have noticed that the User and Post models have an attribute which we haven't mentioned. The objectId property is an internal unique identifier used by mongoDB (the database we're choosing to implement our data model on in this tutorial).

Let's look closer at these last two properties author and authorId. There is a significant difference between them with respect to how they are implemented in the database. Remember that, at the end of the day, our data model will need to be realized into a database. Because we're using Prisma, a lot of these details are abstracted away from us. In this case, the prisma code-generator will handle author and authorId slightly differently.

The @relation(...) attribute on the author property is Prisma's way of declaring that authorId is a foreign key field. Because the type of the author property is a User entity, Prisma understands that posts are linked to users via the foreign key authorId which maps to the user's id, the associated primary key. This is an example of a one-to-many relation.

How that relation is implemented is left to Prisma and depends on the database you choose to use. Since we are using mongodb here, this is implemented by direct object id references.

Because our data model encodes the relation between posts and users, looking up a user's posts is inexpensive. This is the benefit of designing a good data model for an application: operations you have designed and planned for at this stage, are optimized for.

To get us started using this Prisma data model in an actual application, let's create a new npm project in an empty directory:

$ npm init

When prompted to specify the entry point, use src/index.js. Install some nice typescript bindings for node with:

$ npm install --save-dev @types/node typescript

Then you can initialize the typescript compiler with

$ npx tsc --init

This creates a tsconfig.json file which configures the behavior of the typescript compiler. Create a directory src/ and add the following index.ts:

import {PrismaClient} from '@prisma/client'
const prisma = new PrismaClient()
const main = async () => {    const user = await prisma.user.findFirst()    if (user === null) {        throw Error("No user data.")    }    console.log(`found username: ${user.nickname}`)    process.exit(0)}
main().catch((e) => {    console.error(e)    process.exit(1)})

Then create a prisma/ directory and add a schema.prisma file containing the Prisma code for the two entities User and Post.

Finally, to our schema.prisma file, we need to add configuration for our local dev database and the generation of the client:

datasource db {  provider = "mongodb"  url      = "mongodb://localhost:27017/board"}
generator client {  provider = "prisma-client-js"  previewFeatures = ["mongodb"]}

Head over to the repository to see an example of the complete file, including the extra configuration.

To build the Prisma client, run

$ npx prisma generate

Finally, to run it all, edit your package.json file (at the root of your project's directory). Look for the "script" field and modify the "test" script with:

{  ...  "test": "tsc --project ./ && node ."  ... }

Now all we need is for an instance of mongoDB to be running while we're working. We can run that straight from the official docker image:

$ docker run -d --name message-board-example -p 27017:27017 --rm mongo

To run the example do

$ npm run test
> message-board-example@1.0.0 test /tmp/message-board-example> tsc --project ./ && node .
Error: No user data.

You should see something close to the output of the snippet: our simple code failed because it is looking for a user that does not exist (yet) in our dev database. We will fix that in a little bit . But first, here's a secret.

The secret to writing good code#

Actually it's no secret at all. It is one of those things that everybody with software engineering experience knows. The key to writing good code is learning from your mistakes!

When coding becomes tedious is when it is hard to learn from errors. Usually this is caused by a lengthy process to go from writing the code to testing it. This can happen for many reasons: having to wait for the deployment of a backend in docker compose, sitting idly by while your code compiles just to fail at the end because of a typo, the strong integration of a system with components external to it, and many more.

The process that goes from the early stages of designing something to verifying its functionalities and rolling it out, that is what is commonly called the development cycle.

It should indeed be a cycle. Once the code is out there, deployed and running, it gets reviewed for quality and purpose. More often than not this happens because users break it and give feedback. The outcome of that gets folded in planning and designing for the next iteration or release. The agile philosophy is built on the idea that this cycle should be as short as possible.

So that brings the question: how do you make the development cycle as quick as possible? The faster the cycle is, the better your productivity becomes.

Testing, testing and more testing#

One of the keys to shortening a development cycle is making testing easy. When playing with databases and data models, it is something that is often hacky. In fact there are very few tools that let you iterate quickly on data models, much less developer-friendly tools.

The core issue at hand is that between iterations on ideas and features, we will need to make small and quick changes to our data model. What happens to our databases and the data in them in that case? Migration is sometimes an option but is notoriously hard and may not work at all if our changes are significant.

For development purposes the quickest solution is seeding our new data model with mock data. That way we can test our changes quickly and idiomatically.

Generate data for your data model#

At Synth we are building a declarative test data generator. It lets you write your data model in plain zero-code JSON and seed many relational and non-relational databases with mock data. It is completely free and open-source.

Let's take our data model and seed a development mongoDB database instance with Synth. Then we can make our development cycle very short by using an npm script that sets it all up for us whenever we need it.

Installing synth#

We'll need the synth command-line tool to get started. From a terminal, run:

$ curl -sSL | sh

This will run you through an install script for the binary release of synth. If you prefer installing from source, we got you: head on over to the Installation pages of the official documentation.

Once the installer script is done, try running

$ synth versionsynth 0.5.4

to make sure everything works. If it doesn't work, add $HOME/.local/bin to your $PATH environment variable with

$ export PATH=$HOME/.local/bin:$PATH

and try again.

Synth schema#

Just like Prisma and its schema DSL, synth lets you write down your data model with zero code.

There is one main difference: the synth schema is aimed at the generation of data. This means it lets you specify the semantics of your data model in addition to its entities and relations. The synth schema has an understanding of what an email, a username, an address are; whereas the Prisma schema only cares about top-level types (strings, integers, etc).

Let's navigate to the top of our example project's directory and create a new directory called synth/ for storing our synth schema files.

โ”œโ”€โ”€ package.jsonโ”œโ”€โ”€ package-lock.jsonโ”œโ”€โ”€ tsconfig.jsonโ”œโ”€โ”€ prisma/โ”œโ”€โ”€ synth/โ””โ”€โ”€ src/

Each file we will put in the synth/ directory that ends in .json will be opened by synth, parsed and interpreted as part of our data model. The structure of these files is simple: each one represents a collection in our database.


A collection is a single JSON schema file, stored in a namespace directory. Because collections are formed of many elements, their Synth schema type is that of arrays.

To get started, let's create a User.json file in the synth/ directory:

{  "type": "array",  "length": 1,  "content": {    "type": "null"  }}

Then run

$ synth generate synth/{"users":[null]}

Let's break this down. Our User.json collection schema is a JSON object with three fields. The "type" represents the kind of generator we want. As we said above, collections must generate arrays. The "length" and "content" fields are the parameters we need to specify an array generator. The "length" field specifies how many elements the generated array must have. The "content" specifies from what the elements of the array are generated.

For now the value of "content" is a generator of the null type. Which is why our array has null as a single element. But we will soon change this.

Note that the value of "length" can be another generator. Of course, because the length of an array is non-negative number, it cannot be just any generator. But it can be any kind that will generate non-negative numbers. For example

{  "type": "array",  "length": {    "type": "number",    "range": {      "low": 5,      "high": 10,      "step": 1    }  },  "content": {    "type": "null"  }}

This now makes our users collection variable length. Its length will be decided by the result of generating a new random integer between 5 and 10.

If you now run

$ synth generate synth/{"users":[null,null,null,null,null]}

you can see the result of that change.


By default synth fixes the seed of its internal PRNG. This means that, by default, running synth many times on the same input schemas will give the same output data. If you want to randomize the seed - and thus randomize the result, simply add the flag --random:

$ synth generate synth/ --random{"users":[null,null,null,null,null,null,null]}
$ synth generate synth/ --random{"users":[null,null,null,null,null,null,null,null,null]}

Schema nodes#

Before we can get our users collection to match our User Prisma model, we need to understand how to generate more kinds of data with synth.

Everything that goes into a schema file is a schema node. Schema nodes can be identified by the "type" field which specifies which kind of node it is. The documentation pages have a complete taxonomy of schema nodes and their "type".

Generating ids#

Let's look back at our User model. It has four properties:

  • id
  • createdAt
  • email
  • nickname

Let's start with id. How can we generate that?

The type of the id property in the User model is Int:

  id        Int      @unique @default(autoincrement())

and the attribute indicates that the field is meant to increment sequentially, going through values 0, 1, 2 etc.

The synth schema type for numbers is number. Within number there are three varieties of generators:

What decides the variant is the presence of the "range", "constant"or "id" field in the node's specification.

For example, a range variant would look like

{    "type": "number",    "range": {        "low": 5,        "high": 10,        "step": 1    }}

whereas a constant variant would look like

{    "type": "number",    "constant": 42}

For the id field we should use the id variant, which is auto-incrementing. Here is an example of id used in an array so we can see it behaves as expected:

{    "type": "array",    "length": 10,    "content": {        "type": "number",        "id": {}    }}

Generating emails#

Let us now look at the email field of our User model:

  email     String   @unique

Its type in the data model is that of a String. The synth schema type for that is string.

There are many different variants of string and they are all exhaustively documented. The different variants are identified by the presence of a distinguishing field which can be

Since we are interested in generating email addresses, we will be using the "faker" variant which leverages a preset collection of generators for common properties like usernames, addresses and emails:

{    "type": "string",    "faker": {        "generator": "safe_email"    }}

Generating objects#

OK, so we now know how to generate the id and the email properties of our User model. But we do not yet know how to put them together in one object. For that we need the object type:

{    "type": "object",    "id": {        "type": "number",        "id": {}    },    "email": {        "type": "string",        "faker": {            "generator": "safe_email"        }    }}

Leverage the docs#

Now we have everything we need to finish writing down our User model as a synth schema. A quick lookup of the documentation pages will tell us how to generate the createdAt and nickname fields.

Here is the finished result for our User.json collection:

``json synth[expect = "unknown variant date_time`"] { "type": "array", "length": 3, "content": { "type": "object", "id": { "type": "number", "id": {} }, "createdAt": { "type": "string", "date_time": { "format": "%Y-%m-%d %H:%M:%S", "begin": "2020-01-01 12:00:00" } }, "email": { "type": "string", "faker": { "generator": "safe_email" } }, "nickname": { "type": "string", "faker": { "generator": "username" } } } }

[`date_time`][synth-datetime] is now a generator on its own and is no longer a subtype of the `string` generator
### Making sure our constraints are satisfied
Looking back at the [`User` model](#prisma-is-awesome) we started from, there'sone thing that we did not quite address yet. The `email` field in the Prismaschema has the `@unique` attribute:
```graphql  email     String   @unique

This means that, in our data model, no two users can share the same email address. Yet, we haven't added that constraint anywhere in our final synth schema for the User.json collection.

What we need to use here is modifiers. A modifier is an attribute that we can add to any synth schema type to modify the way it behaves. There are two modifiers currently supported:

The optional modifier is an easy way to make a schema node randomly generate something or nothing:

{    "type": "number",    "optional": true,    "constant": 42}

Whereas the unique modifier is an easy way to enforce the constraint that the values generated have no duplication. So all we need to do, to represent our data model correctly, is to add the unique modifier to the email field:

{    "type": "string",    "unique": true,    "faker": {        "generator": "safe_email"    }}

The completed end result for the User.json collection can be viewed on GitHub here.

How to deal with relations#

Now that we have set up our User.json collection, let's turn our attention to the Post model and write out the synth schema for the Post.json collection.

Here is the end result:

``json synth[expect = "unknown variant date_time`"] { "type": "array", "length": 5, "content": { "type": "object", "id": { "type": "number", "id": {} }, "postedAt": { "type": "string", "date_time": { "format": "%Y-%m-%d %H:%M:%S", "begin": "2020-01-01 12:00:00" } }, "title": { "type": "string", "faker": { "generator": "bs" } }, "authorId": "" } }

[`date_time`][synth-datetime] is now a generator on its own and is no longer a subtype of the `string` generator
It all looks pretty similar to the `User.json` collection, except for oneimportant difference at the line
```json synth    "authorId": ""

The syntax @... is synth's way of specifying relations between collections. Here we are creating a many-to-1 relation between the field authorId of the Post.json collection and the field id of the User.json collection.

The final Post.json collection schema can be viewed on GitHub here.

Synth generate#

Now that our data model is implemented in Synth, we're ready to seed our test database with mock data. Here we'll use the offical mongo Docker image, but if you are using a relational database like Postgres or MySQL, you can follow the same process.

To start the mongo image in the background (if you haven't done so already), run

$ docker run -d -it -p 27017:27017 --rm mongo

Then, to seed the database with synth just run

$ synth generate synth/ --size 1000 --to mongodb://localhost:27017/board

That's it! Our test mongo instance is now seeded with the data of around 100 users. Head over to the examples repository to see the complete working example.

What's next#

Synth is completely free and built in the open by an amazing and fast growing community of contributors.

Join us in our mission to make test data easy and painless! We also have a very active Discord server where many members of the community would be happy to help if you encounter an issue!