Postgres sample data

This guide describes how to download and install sample data for use with Neon.

Prerequisites

wget for downloading datasets, unless otherwise instructed. If your system does not support wget, you can paste the source file address in your browser's address bar.
A psql client for connecting to your Neon database and loading data. This client is included with a standalone PostgreSQL installation. See PostgreSQL Downloads.
A pg_restore client if you are loading the employees or postgres_air database. The pg_restore client is included with a standalone PostgreSQL installation. See PostgreSQL Downloads.
A Neon database connection string. After creating a database, you can obtain the connection string from the Connection Details widget on the Neon Dashboard. In the instructions that follow, replace postgres://[user]:[password]@[neon_hostname]/[dbname] with your connection string.
A Neon Pro account if you intend to install a dataset larger than 3 GB.
Instructions for each dataset require that you create a database. You can do so from a client such as psql or from the Neon SQL Editor.

note

You can also load sample data using the Neon CLI. See Load sample data with the Neon CLI.

Sample data

Sample datasets are listed in order of the smallest to largest installed size. Please be aware that the Neon Free Tier has a storage limit of 3 GB per branch. Datasets larger than 3 GB cannot be loaded on the Free Tier.

Name	Tables	Records	Source file size	Installed size
Periodic table data	1	118	17 KB	7.2 MB
World Happiness Index	1	156	9.4 KB	7.2 MB
Titanic passenger data	1	1309	220 KB	7.5 MB
Netflix data	1	8807	3.2 MB	11 MB
Pagila database	33	62322	3 MB	15 MB
Chinook database	11	77929	1.8 MB	17 MB
Lego database	8	633250	13 MB	42 MB
Employees database	6	3919015	34 MB	333 MB
Wikipedia vector embeddings	1	25000	1.7 GB	850 MB
Postgres air	10	67228600	1.2 GB	6.7 GB

note

Installed size is measured using the query: SELECT pg_size_pretty(pg_database_size('your_database_name')). The reported size for small datasets may appear larger than expected due to inherent Postgres storage overhead.

Periodic table data

A table containing data about the periodic table of elements.

Create a periodic_table database:
```
CREATE DATABASE periodic_table;
```

Download the source file:

wget https://raw.githubusercontent.com/neondatabase/postgres-sample-dbs/main/periodic_table.sql

Navigate to the directory where you downloaded the source file, and run the following command:

psql -d "postgres://[user]:[password]@[neon_hostname]/periodic_table" -f periodic_table.sql

Connect to the periodic_table database:

psql postgres://[user]:[password]@[neon_hostname]/periodic_table

Look up the the element with the Atomic Number 10:

SELECT * FROM periodic_table WHERE "AtomicNumber" = 10;

Source: https://github.com/andrejewski/periodic-table
License: ISC License
Copyright (c) 2017, Chris Andrejewski <christopher.andrejewski@gmail.com>

World Happiness Index

A dataset with multiple indicators for evaluating the happiness of countries of the world.

Create a world_happiness database:
```
CREATE DATABASE world_happiness;
```

Download the source file:

wget https://raw.githubusercontent.com/neondatabase/postgres-sample-dbs/main/happiness_index.sql

Navigate to the directory where you downloaded the source file, and run the following command:

psql -d "postgres://[user]:[password]@[neon_hostname]/happiness_index" -f happiness_index.sql

Connect to the titanic database:

psql postgres://[user]:[password]@[neon_hostname]/world_happiness_index

Find the countries where the happiness score is above average but the GDP per capita is below average:

SELECT 
    country_or_region,
    score,
    gdp_per_capita
FROM 
    "2019"
WHERE 
    score > (SELECT AVG(score) FROM "2019") 
    AND 
    gdp_per_capita < (SELECT AVG(gdp_per_capita) FROM "2019")
ORDER BY 
    score DESC;

Source: https://www.kaggle.com/datasets/unsdsn/world-happiness
License: CC0: Public Domain

Titanic passenger data

A dataset containing information on the passengers aboard the RMS Titanic, which sank on its maiden voyage in 1912.

Create a titanic database:
```
CREATE DATABASE titanic;
```

Download the source file:

wget https://raw.githubusercontent.com/neondatabase/postgres-sample-dbs/main/titanic.sql

Navigate to the directory where you downloaded the source file, and run the following command:
```
psql -d "postgres://[user]:[password]@[neon_hostname]/titanic" -f titanic.sql
```

Connect to the titanic database:

psql postgres://[user]:[password]@[neon_hostname]/titanic

Query passengers with the most expensive fares:

SELECT name, fare
FROM passenger
ORDER BY fare DESC
LIMIT 10;

Source: https://www.kaggle.com/datasets/ibrahimelsayed182/titanic-dataset
License: Unknown

Netflix data

A dataset containing information about movies and tv shows on Netflix.

Create a netflix database:
```
CREATE DATABASE netflix;
```

Download the source file:

wget https://raw.githubusercontent.com/neondatabase/postgres-sample-dbs/main/netflix.sql

Navigate to the directory where you downloaded the source file, and run the following command:
```
psql -d "postgres://[user]:[password]@[neon_hostname]/netflix" -f netflix.sql
```

Connect to the netflix database:

psql postgres://[user]:[password]@[neon_hostname]/netflix

Find the directors with the most movies in the database:

SELECT 
    director,
    COUNT(*) AS "Number of Movies"
FROM 
    netflix_shows
WHERE 
    type = 'Movie'
GROUP BY 
    director
ORDER BY 
    "Number of Movies" DESC
LIMIT 5;

Source: https://www.kaggle.com/datasets/shivamb/netflix-shows
License: CC0: Public Domain

Pagila database

Sample data for a fictional DVD rental store. Pagila includes tables for films, actors, film categories, stores, customers, payments, and more.

Create a pagila database:
```
CREATE DATABASE pagila;
```

Download the source file:

wget https://raw.githubusercontent.com/neondatabase/postgres-sample-dbs/main/pagila.sql

Navigate to the directory where you downloaded the source file, and run the following command:
```
psql -d "postgres://[user]:[password]@[neon_hostname]/pagila" -f pagila.sql
```

Connect to the pagila database:

psql postgres://[user]:[password]@[neon_hostname]/pagila

Find the top 10 most popular film categories based on rental frequency:

SELECT c.name AS category_name, COUNT(r.rental_id) AS rental_count
FROM category c
JOIN film_category fc ON c.category_id = fc.category_id
JOIN inventory i ON fc.film_id = i.film_id
JOIN rental r ON i.inventory_id = r.inventory_id
GROUP BY c.name
ORDER BY rental_count DESC
LIMIT 10;

Source: https://github.com/devrimgunduz/pagila
License: LICENSE.txt
Copyright (c) Devrim Gündüz <devrim@gunduz.org>

Chinook database

A sample database for a digital media store, including tables for artists, albums, media tracks, invoices, customers, and more.

Create a chinook database:
```
CREATE DATABASE chinook;
```

Download the source file:

wget https://raw.githubusercontent.com/neondatabase/postgres-sample-dbs/main/chinook.sql

Navigate to the directory where you downloaded the source file, and run the following command:
```
psql -d "postgres://[user]:[password]@[neon_hostname]/chinook" -f chinook.sql
```

Connect to the chinook database:

psql postgres://[user]:[password]@[neon_hostname]/chinook

Find out the most sold item by track title:

SELECT 
T."Name" AS "Track Title",
SUM(IL."Quantity") AS "Total Sold"
FROM 
    "Track" T
JOIN 
    "InvoiceLine" IL ON T."TrackId" = IL."TrackId"
GROUP BY 
    T."Name"
ORDER BY 
    "Total Sold" DESC
LIMIT 1;

Source: https://github.com/lerocha/chinook-database
License: LICENSE.md
Copyright (c) 2008-2017 Luis Rocha

Lego database

A dataset containing information about various LEGO sets, their themes, parts, colors, and other associated data.

Create a lego database:
```
CREATE DATABASE lego;
```

Download the source file:

wget https://raw.githubusercontent.com/neondatabase/postgres-sample-dbs/main/lego.sql

Navigate to the directory where you downloaded the source file, and run the following command:
```
psql -d "postgres://[user]:[password]@[neon_hostname]/lego" -f lego.sql
```

Connect to the lego database:

psql postgres://[user]:[password]@[neon_hostname]/lego

Find the top 5 LEGO themes by the number of sets:

SELECT lt.name AS theme_name, COUNT(ls.set_num) AS number_of_sets
FROM lego_themes lt
JOIN lego_sets ls ON lt.id = ls.theme_id
GROUP BY lt.name
ORDER BY number_of_sets DESC
LIMIT 5;

Source: https://www.kaggle.com/datasets/rtatman/lego-database
License: CC0: Public Domain

Employees database

A dataset containing details about employees, their departments, salaries, and more.

Create the database and schema:

CREATE DATABASE employees;
\c employees
CREATE SCHEMA employees;

Download the source file:

wget https://raw.githubusercontent.com/neondatabase/postgres-sample-dbs/main/employees.sql.gz

Navigate to the directory where you downloaded the source file, and run the following command:
```
pg_restore -d postgres://[user]:[password]@[neon_hostname]/employees -Fc employees.sql.gz -c -v --no-owner --no-privileges
```
Database objects are created in the employees schema rather than the public schema.

Connect to the employees database:

psql postgres://[user]:[password]@[neon_hostname]/employees

Find the top 5 departments with the highest average salary:

SELECT d.dept_name, AVG(s.amount) AS average_salary
FROM employees.salary s
JOIN employees.department_employee de ON s.employee_id = de.employee_id
JOIN employees.department d ON de.department_id = d.id
WHERE s.to_date > CURRENT_DATE AND de.to_date > CURRENT_DATE
GROUP BY d.dept_name
ORDER BY average_salary DESC
LIMIT 5;

Source: The initial dataset was created by Fusheng Wang and Carlo Zaniolo from Siemens Corporate Research. Designing the relational schema was undertaken by Giuseppe Maxia while Patrick Crews was responsible for transforming the data into a format compatible with MySQL. Their work can be accessed here: https://github.com/datacharmer/test_db. Subsequently, this information was adapted to a format suitable for PostgreSQL: https://github.com/h8/employees-database. The data was generated, and there are inconsistencies.
License: This work is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/3.0/ or send a letter to Creative Commons, 171 Second Street, Suite 300, San Francisco, California, 94105, USA.

Wikipedia vector embeddings

An OpenAI example dataset containing pre-computed vector embeddings for 25000 Wikipedia articles. It is intended for use with the pgvector Postgres extension, which you must install first to create a table with vector type columns. For a Jupyter Notebook that uses this dataset with Neon, refer to the following GitHub repository: neon-vector-search-openai-notebooks

Download the zip file (~700MB):

wget https://cdn.openai.com/API/examples/data/vector_database_wikipedia_articles_embedded.zip

Navigate to the directory where you downloaded the zip file, and run the following command to extract the source file:
```
unzip vector_database_wikipedia_articles_embedded.zip
```
Create a wikipedia database:
```
CREATE DATABASE wikipedia;
```

Connect to the wikipedia database:

psql postgres://[user]:[password]@[neon_hostname]/wikipedia

Install the pgvector extension:
```
CREATE EXTENSION vector;
```

Create the following table in your database:

CREATE TABLE IF NOT EXISTS public.articles (
    id INTEGER NOT NULL PRIMARY KEY,
    url TEXT,
    title TEXT,
    content TEXT,
    title_vector vector(1536),
    content_vector vector(1536),
    vector_id INTEGER
);

Create vector search indexes:

CREATE INDEX ON public.articles USING ivfflat (content_vector) WITH (lists = 1000);

CREATE INDEX ON public.articles USING ivfflat (title_vector) WITH (lists = 1000);

Navigate to the directory where you extracted the source file, and run the following command:

psql -d "postgres://[user]:[password]@[neon_hostname]/wikipedia" -c "\COPY public.articles (id, url, title, content, title_vector, content_vector, vector_id) FROM 'vector_database_wikipedia_articles_embedded.csv' WITH (FORMAT CSV, HEADER true, DELIMITER ',');"

note

If you encounter a memory error related to the maintenance_work_mem setting, refer to Indexing vectors for how to increase this setting.

Source: OpenAI
License: MIT License

Postgres air database

An airport database containing information about airports, aircraft, bookings, passengers, and more.

Download the file (1.3 GB) from Google drive
Create a postgres_air database:
```
CREATE DATABASE postgres_air;
```
Navigate to the directory where you downloaded the source file, and run the following command:
```
pg_restore -d postgres://[user]:[password]@[neon_hostname]/postgres_air -Fc postgres_air_2023.backup -c -v --no-owner --no-privileges
```
Database objects are created in a postgres_air schema rather than the public schema.

Connect to the postgres_air database:

psql postgres://[user]:[password]@[neon_hostname]/wikipedia

Find the aircraft type with the most flights:

SELECT ac.model, COUNT(f.flight_id) AS number_of_flights
FROM postgres_air.aircraft ac
JOIN postgres_air.flight f ON ac.code = f.aircraft_code
GROUP BY ac.model
ORDER BY number_of_flights DESC
LIMIT 10;

Source: https://github.com/hettie-d/postgres_air
License: BSD 3-Clause License

Load sample data with the Neon CLI

You can load data with the Neon CLI by passing the --psql option, which calls the psql command line utility.

The Neon CLI and psql must be installed on your system. For installation instructions, see:

Neon CLI — Install and connect
PostgreSQL Downloads for psql

If you have multiple Neon projects or branches, we recommend setting your Neon CLI project and branch context so that you don't have to specify them explicitly when running a Neon CLI command. See Neon CLI commands — set-context.

To load sample data:

Download one of the data files listed above. For example:
```
wget https://raw.githubusercontent.com/neondatabase/postgres-sample-dbs/main/periodic_table.sql
```
Alternatively, supply your own data file.
Load the data using one of the following Neon CLI commands (projects, branches, or connection-string):
- Create a new Neon project, connect to it with psql, and run the .sql file.
```
neonctl projects create --psql -- -f periodic_table.sql
```
- Create a branch, connect to it with psql, and run the an .sql file.
```
neonctl branches create --psql -- -f periodic_table.sql
```
- Get a connection string, connect with psql, and run the .sql file.
```
neonctl connection-string --psql -- -f periodic_table.sql
```

Need help?

Join our Discord Server to ask questions or see what others are doing with Neon. Users on paid plans can open a support ticket from the console. For more detail, see Getting Support.