jupyter-duckdb 0.8.1.5

a basic wrapper kernel for DuckDB

DuckDB Kernel for Jupyter

This is a simple DuckDB wrapper kernel which accepts SQL as input, executes it using a previously loaded DuckDB instance and formats the output as a table. There are some magic commands that make teaching easier with this kernel.

Quick Start

Table of Contents

• Setup
  • Using pip
  • Using Docker
• Usage
  • A Note on Magic Commands
  • Load a Database
  • Schema Diagrams
  • Number of Rows
  • Ship Tests With Your Notebook
  • Relational Algebra
  • Domain Calculus

Setup

Using pip

Run pip to install the corresponding package from pypi after Jupyter is already installed.

pip install jupyter-duckdb

Register the kernel.

jupyter kernelspec install <path to the site-packages directory>/duckdb_kernel

Now start Jupyter the usual way and the kernel should be available.

Using Docker

Execute the following command to pull and run a prepared image.

docker run -p 8888:8888 troebs/jupyter:duckdb

This image can also be used with JupyterHub and the DockerSpawner / SwarmSpawner and probably with the kubespawner. You can also build your own image using the Dockerfile in the repository.

Usage

A detailed example can be found in the repository. The rest of this section describes the magic commands.

A Note on Magic Commands

Many Jupyter kernels make a difference between magic commands for a single line starting with one percent sign and others for a whole cell starting with two percent signs. The upcoming magic commands always apply to a whole cell. Therefore, it does not matter whether you use a single or two percent signs. However, the magic commands must always be used at the beginning of a cell.

It is also possible to use more than one magic command per cell.

Load a Database

To load the database two magic commands are available.

CREATE creates a new database and therefore overwrites files with the same name without prompting. Using the optional parameter OF you can either provide another DuckDB file or a file with SQL statements. In the first case the included tables will be copied to the new database, while in the second case the SQL statements are just executed. We find this feature very useful to work in a temporary copy of the data and therefore be able to restart at any time. The last optional parameter WITH_TESTS is described in detail below.

%CREATE data.duckdb OF my_statements.sql

LOAD on the other hand loads an existing database and returns an error if it does not exist. (That is why OF cannot be used with LOAD! WITH_TESTS on the other hand is available also with this magic command.)

%LOAD data.duckdb

Only one database can be open at any time. If a new database is created or loaded, the current one is closed first and saved to disk if necessary.

Please note that :memory: is also a valid file path for DuckDB. The data is then stored exclusively in the main memory. In combination with CREATE and OF this makes it possible to work on a temporary copy in memory.

Schema Diagrams

The magic command SCHEMA can be used to create a simple schema diagram of the loaded database, showing all created tables, their columns and data types, but without any views. Primary keys are printed in bold and unique keys are underlined. Foreign keys are also highlighted and the dependencies between the tables are shown by arrows.

The optional flag TD can be set to force a vertical layout. This representation requires more space, but can improve readability.

%SCHEMA TD

Graphviz (dot in PATH) is required to render schema diagrams.

Number of Rows

By default, only 20 rows are shown. All further lines are replaced by three dots. When hovering over the three dots using the cursor, the number of omitted lines is displayed. Of course, the number of lines displayed can be changed.

The magic command ALL_ROWS and its short form ALL can be used to display all rows of the query in the same cell. Caution: With large result sets this can lead to a frozen Jupyter instance.

%ALL_ROWS
SELECT *
FROM foo
-- all rows

The magic command QUERY_MAX_ROWS followed by an integer can be used to change the number of displayed rows for the current cell.

%QUERY_MAX_ROWS 50
SELECT *
FROM foo
-- 50 rows

The magic command MAX_ROWS followed by an integer can be used to change the number of displayed rows for all future queries including the current cell.

%MAX_ROWS 30
SELECT *
FROM foo
-- 30 rows

SELECT *
FROM bar
-- 30 rows

Ship Tests With Your Notebooks

Simple tests can be loaded together with the database with the help of the WITH_TESTS parameter. These tests are stored as a JSON file. Each test is assigned a unique name, a result set and whether the test should check the order of the result. A very simple test file looks like the following JSON object:

{
  "task1": {
    "ordered": false,
    "equals": [
      [
        1,
        "Name 1"
      ],
      [
        2,
        "Name 2"
      ]
    ]
  }
}

To bind a test to a cell, use the magic command TEST in combination with a name. After the cell is executed, the result is evaluated and then displayed below the query result.

%TEST task1
SELECT 2, 'Name 2'
UNION
SELECT 1, 'Name 1'

Disclaimer: The integrated testing is work-in-progress and thus subject to potentially incompatible changes and enhancements.

Relational Algebra

An interpreter for relational algebra queries is integrated in this kernel. The magic command RA activates the relational algebra mode for a single cell:

%RA
π a, b (σ c = 1 (R))

The supported operations are:

• Projection π
• Selection σ
• Rename ρ
• Union ∪
• Intersection ∩
• Difference -
• Natural Join ⋈
• Cross Product ×

The Dockerfile also installs the Jupyter Lab plugin jupyter-ra-extension. It adds the symbols mentioned above and some other supported symbols to the toolbar for insertion on click.

Domain Calculus

An interpreter for domain calculus queries is integrated in this kernel. The magic command DC activates the domain calculus mode for a single cell:

%DC
{ a, b | R(a, b, c) ∧ c = 1 }