How to seed a postgres database with node

This guide was originally posted on my blog richardhaines.dev

This guide will walk through seeding a Postgres database. It will cover creating a Node script to seed the data and touch on some of the pros and cons as to the chosen approach.

The source code for this guide can be found here: github.com/molebox/seed-postgres-database-tutorial

Prerequisites

• You must have Postgres installed on your machine
• You must have Node installed on your machine
• It is assumed that you have Postgres setup and know-how to access its databases, though the guide will cover some basic commands towards the end.
• This guide uses the default user postgres for accessing the database. If you have another user account you would prefer to use then swap in that.

The guide uses version 13.1 of Postgres and version 14.15.1 of Node. It was developed on a Windows machine.

What do we mean by seed?

The process of seeding (in the context of databases) is to insert or populate the initial data into the database. This can be either a manual or automated step in the setup of an application. Seeding can be used when testing different branches, for example, if you have a dev branch where you want to test some new sorting query against the database, seeding would be a good way to test against data that won't affect a production build. Of course, there are many reason one might choose to seed a database. In some instances, an applications database requires some form of data present before it will work properly, such as an admin account. But more often than not seeding would take place pre-install and thus allow the user to begin using the app without any issues.

The seed script

The seed script will aim to accomplish the following:

• Create a database with a table.
• Create a csv file and populate it with fake data using the faker library. It will default to 10 rows but allow the user to specify an amount if they like.
• Parse that data and insert it into the table - seed the database.

Begin by creating a schema.sql file at the root of your project. This file will enable you to lay the groundwork for how your database and its table will look.

schema.sql

-- Seeing as we will be testing out this script alot we can destroy the db before creating everything again
DROP DATABASE IF EXISTS translationsdb;

-- Create the db
CREATE DATABASE translationsdb;

-- Move into the db
\c translationsdb

-- Create our table if it doesn't already exist
CREATE TABLE IF NOT EXISTS Translations
(
    key character varying(100),
    lang character varying(5),
    content text
);

-- Changes the owner of the table to postgres which is the default when installing postgres
ALTER TABLE Translations
    OWNER to postgres;

db.js

To interact with the Postgres database, you can install the node-postgres package, a collection of modules made for interacting with Postgres. You'll use it to establish an initial connection to the database and insert some fake data. Create a new file src/db.js and add the following:

const { Pool } = require('pg');
const { host, user, database, password, port } = require('./config');

// Create a pool instance and pass in our config, which we set in our env vars
const pool = new Pool({
    host,
    user,
    database,
    password,
    port,
});

module.exports = {
    query: (text, params, callback) => {
        return pool.query(text, params, callback);
    },
    connect: (err, client, done) => {
        return pool.connect(err, client, done);
    },
};

The Pool class takes some optional config and the values passed in enable a connection with the database. They are set as environment variables (env vars) and imported from a separate config file. This file exports two functions. The query, which will be used to run an INSERT statement, and a connect function which will be used to connect to the database.

config.js

Storing all the env vars in one place and exporting them means you have one source of truth and can easily swap them out from one place instead of multiple files. Create a new file and name it config.js.

const dotenv = require('dotenv');
dotenv.config();
// Single source to handle all the env vars
module.exports = {
    host: process.env.PGHOST,
    user: process.env.PGUSER,
    database: process.env.PGDATABASE,
    password: process.env.PGPASSWORD,
    port: process.env.PGPORT,
};

An example of how your env vars might look:

PGUSER=postgres
PGHOST=localhost
PGPASSWORD=test1234
PGDATABASE=translationsdb
PGPORT=5432

main.js

In a real-world scenario, you would perhaps have some data stored in a csv file. This example will make use of the faker library, and some other packages. Install the following:

yarn add dotenv faker fast-csv minimist pg validator

Use the Faker library by creating a function that will mimic the shape of the table set in schema.sql. It will return a template literal string to be added to a csv file later on.

const faker = require('faker');

// Create some fake data using the faker lib. Returns a template string to be inserted into a csv file as a single line
function createTranslation() {
    const key = faker.address.country();
    const lang = faker.address.countryCode();
    const content = faker.random.word();

    return `${key},${lang},${content}\n`;
}

Next, you will need to import fs and create a stream. This will write to an as yet non-existent csv file.

// other imports..
const fs = require('fs');

// The path to write the csv file to
const output = './src/output.csv';

// other functions..

// Create a stream to write to the csv file
const stream = fs.createWriteStream(output);

Enabling the user to choose how many rows they would like to seed the database with is an extra and worthwhile step. The minimist package helps with parsing argument options. In the case of the script, it allows the user the option to pass in an amount, if the user chooses not to pass any additional arguments then you can set a default value. Create a new function that will write the fake data to the csv file.

// other imports..
const args = require('minimist')(process.argv.slice(2));

// other functions...

async function writeToCsvFile() {
    // The user can specify how many rows they want to create (yarn seed --rows=20), if they dont specify anything (yarn seed) then defaults to 10
    let rows = args['rows'] || 10;
    // Iterate x number of times and write a new line to the csv file using the createTranslation function
    for (let index = 0; index < rows; index++) {
        stream.write(createTranslation(), 'utf-8');
    }
    stream.end();
}

Now that the csv file is created and populated with fake data, you can begin the process of actually seeding that data into the Postgres database. fast-csv is a library for parsing and formatting csv files. You'll use it in combination with the validator library and node-postgres.

// other imports...
const fastcsv = require('fast-csv');
const db = require('./db');
const contains = require('validator/lib/contains');

// other functions...

function insertFromCsv() {
    let csvData = [];
    return (
        fastcsv
            .parse()
            // validate that the column key doesn't contain any commas, as some countries do. This will break our insertion as it would be treated as an extra column and our table expects only 3 columns
            .validate((data) => !contains(data[0], ','))
            // triggered when a new record is parsed, we then add it to the data array
            .on('data', (data) => {
                csvData.push(data);
            })
            .on('data-invalid', (row, rowNumber) =>
                console.log(
                    `Invalid [rowNumber=${rowNumber}] [row=${JSON.stringify(row)}]`
                )
            )
            // once parsing is finished and all the data is added to the array we can then insert it into the db table
            .on('end', () => {
                // The insert statement
                const query =
                    'INSERT INTO translations (key, lang, content) VALUES ($1, $2, $3)';
                // Connect to the db instance
                db.connect((err, client, done) => {
                    if (err) throw err;
                    try {
                        // loop over the lines stored in the csv file
                        csvData.forEach((row) => {
                            // For each line we run the insert query with the row providing the column values
                            client.query(query, row, (err, res) => {
                                if (err) {
                                    // We can just console.log any errors
                                    console.log(err.stack);
                                } else {
                                    console.log('inserted ' + res.rowCount + ' row:', row);
                                }
                            });
                        });
                    } finally {
                        done();
                    }
                });
            })
    );
}

The function first validates the contents of the row using the contains function from the validator library. This is necessary because some countries can have an extra comma in their name. An extra comma in a csv file equates to an extra column and the table created and defined in the schema.sql file dictates that only 3 columns will exist. If this check fails fast-csvwill not accept the row and throw an event, which is used to print a message to the console to inform the user.

If the row is accepted then it is added to an array. Once the parsing is finished and all the row data is added to the array the connection is established with the Postgres database. The data array is then iterated over, for each row in the array a client instance is acquired from the pool and an INSERT query is used as an argument, along with the row data. If the row is successfully inserted into the table then its corresponding data is printed to the console, if any errors occur they are also printed to the console. Finally, the done function is called to release the clients back to the pool.

The final function called seed is where the data is written to the csv file, a stream is created to read the data from the output file and then the INSERT function is piped to the stream.

// all the other code from main.js

async function seed() {
    await writeToCsvFile();
    let stream = fs.createReadStream(output);
    stream.pipe(insertFromCsv());
}

seed();

Add two scripts to the package.json file. The first create-db will ask the user to login and connect to their Postgres database and then run the commands in the schema.sql file. The second script will run the first before running the seed function.

The user may run the script with additional arguments to set the number of rows created in the table.

• Extra rows: yarn seed --rows=200
• Default 10 rows: yarn seed

  "scripts": {
    "create-db": "psql -U postgres < schema.sql",
    "seed": "yarn create-db && Node src/main.js"
  },

Check the database

To check the database table you can run the following commands from your terminal:

// login and connect to the database
psql -U postgres -d translationsdb

// get all the rows in the table
select *  from "translations";

Final thoughts

There are many ways this could have been accomplished, in fact, there are many libraries that support using Node with Postgres. This method was chosen for its relative simplicity. It's not a generic solution that would fit all scenarios but it could be built upon to incorporate extra features.

Positive take-aways

• The fact that no external API was used for the data removed the overhead of having to make any requests.
• Using Nodes inbuilt process.argv combined with a small parser library meant the user could add some level of configuration when seeding.

Possible improvements

• The function that creates the fake data could be added via config to match the schema definition of the table.
• It would also work quite nicely with a CLI, this would enable the end user much more configuration.