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· 3 min read
Jeffrey Aven

We were looking to implement a variant of the %sql magic command in Jupyter without using the default sqlalchemy module (in our case, just using psycopg2 to connect to a local server - a StackQL postrges wire protocol server).

Create the extension module

We named our extension and cell magic command stackql, so start by creating a file named We made this file in a directory name ext in the Jupyter working directory.

Write the magic extension

Magic commands can be line-based or cell-based or line-or-cell-based; in this example, we will use line-or-cell-based magic, meaning the decorator %stackql will be used to evaluate a line of code and the %%stackql decorator will be used to evaluate the entire contents of the cell it is used in.

The bare-bones class and function definitions required for this extension are described below:

Create a Magic Class

We will need to define a magics class, which we will use to define the magic commands. The class name is arbitrary, but it must be a subclass of IPython.core.magic.Magics. An example is below:

from IPython.core.magic import (Magics, magics_class, line_cell_magic)

class StackqlMagic(Magics):

def stackql(self, line, cell=None):
if cell is None:
# do something with line
# do something with cell
return results

Load and register the extension

To register the magic functions in the StackqlMagic class we created above, use a function named load_ipython_extension, like the following:

def load_ipython_extension(ipython):

Complete extension code

The complete code for our extension is shown here:

from __future__ import print_function
import pandas as pd
import psycopg2, json
from psycopg2.extras import RealDictCursor
from IPython.core.magic import (Magics, magics_class, line_cell_magic)
from io import StringIO
from string import Template

conn = psycopg2.connect("dbname=stackql user=stackql host=localhost port=5444")

class StackqlMagic(Magics):

def get_rendered_query(self, data):
t = Template(StringIO(data).read())
rendered = t.substitute(
return rendered

def run_query(self, query):
cur = conn.cursor(cursor_factory=RealDictCursor)
rows = cur.fetchall()
return pd.read_json(json.dumps(rows))

def stackql(self, line, cell=None):
if cell is None:
results = self.run_query(self.get_rendered_query(line))
results = self.run_query(self.get_rendered_query(cell))
return results

def load_ipython_extension(ipython):

Load the magic extension

To use our extension, we need to use the %load_ext magic command referencing the extension we created.

%load_ext ext.stackql

Note that since our extension was a file named in a directory named ext we reference it using ext.stackql.

Use the magic function in a cell

To use the magic function in a cell (operating on all contents of the cell), we use the %% decorator, like:


Use the magic function on a line

To use the magic function on a line, we use the % decorator, like:

%stackql DESCRIBE aws.ec2.instances
Using Variable Expansion

In our example, we implemented variable expansion using the "batteries included" String templating capabilities in Python3. This allows for variables to be set globally in our notebooks and then used in our queries. For example, we can set a variable in a cell like:

project = 'stackql-demo'
zone = 'australia-southeast1-a'

Then use those variables in our queries like:

SELECT status, count(*) as num_instances
FROM google.compute.instances
WHERE project = '$project'
AND zone = '$zone'
GROUP BY status

An example is shown here:

Using a Custom Jupyter Magic Command

The complete code can be found at stackql/stackql-jupyter-demo.

if you have enjoyed this post, please consider buying me a coffee ☕ to help me keep writing!

· 3 min read
Yuncheng Yang

It is common to have a remote and dispersed team these days. As face to face meetings are less common and with geographically dispersed development teams not possible, it is challenging to have a clear picture of where your team is.

GitHub provides useful data to help us understand your development team's workload and progress. StackQL has an official GitHub provider which allows you to access this data using SQL.


StackQL is an open source project which enables you to query, analyze and interact with cloud and SaaS provider resources using SQL, see

In this example we will use the pystackql Python package (Python wrapper for StackQL) along with a Jupyter Notebook to retrieve data from GitHub using SQL, then sink the data into a cloud native data warehouse for long term storage and analytics at scale, in this example we have used BigQuery.

Step by Step Guide

This guide will walk you through the steps involved in capturing and analyzing developer data using StackQL, Python, Jupyter and BigQuery.

1. Create GitHub Personal Access Token

You will need to create a Personal Access Token in GitHub for a user which has access to the org or orgs in GitHub you will be analyzing. Follow this guide to create your GitHub token and store it somewhere safe.

2. Setup your Jupyter Notebook

You need to set up your Jupyter environment, you can either use the Docker, see stackql/stackql-jupyter-demo or:

  1. Create your Jupyter project
  2. Download and install StackQL
  3. Clone the pystackql repo

3. Setup StackQL Authentication to GitHub

You can find instructions on how to use your personal access token to authenticate to GitHub here. The following example shows how to do this in a Jupyter notebook cell using pystackql.

4. Retrieve data

Next, we will use StackQL SQL queries to get commits, pull requests and pull request reviews, then we will aggregate by usernames of contributors. You can use JOIN semantics in StackQL to do this as well.

Get Contributors, Commits, Pull Requests and Reviews

In the following cell we will query data from GitHub using StackQL:

Aggregate Data By Username

Now we will aggregate the data by each contributor, see the following example:

5. Store the Data in BigQuery

After the transformation of data, we will then upload it to BigQuery. First, we will store the data as a new line delimited json file, making the uploading process much easier and handling the nested schema better, as shown in the following cell:

Now we can see the table on BigQuery as shown here:

BigQuery User Activity Table

From here you can use the same process to append data to the table and use BigQuery to perform analytics at scale on the data.


The complete notebook for this article can be accessed at FabioYyc/stackql-github-notebook-bq