Use Dash to Build to Web Apps on Sentry Data

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData API Driver for Python, the pandas module, and the Dash framework, you can build Sentry-connected web applications for Sentry data. This article shows how to connect to Sentry with the CData Connector and use pandas and Dash to build a simple web app for visualizing Sentry data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Sentry data in Python. When you issue complex SQL queries from Sentry, the driver pushes supported SQL operations, like filters and aggregations, directly to Sentry and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Sentry Data

Connecting to Sentry data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

Using API Key Authentication

Sentry uses token-based authentication. To obtain an Auth Token:

1. Log in to your Sentry account at https://sentry.io
2. Navigate to Settings > Auth Tokens
3. Click "Create New Token"
4. Select the required scopes and click "Create Token"
5. Copy the generated token (it will only be shown once)

After obtaining your Auth Token, set the following connection properties:

• AuthScheme: Set this to APIKey.

Set the following in the ProfileSettings connection property:

• APIKey: Set this to your Sentry Auth Token.
• OrganizationId: Set this to your Sentry organization slug or ID.

Example Connection String

Profile=C:\profiles\Sentry.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_auth_token;OrganizationId=your_org_slug";

Connecting to Sentry

Once the authentication is configured, you can connect to Sentry and query data from any of the available tables such as Organizations, Projects, Issues, and Events.

After installing the CData Sentry Connector, follow the procedure below to install the other required modules and start accessing Sentry through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install pandas
pip install dash
pip install dash-daq

Visualize Sentry Data in Python

Once the required modules and frameworks are installed, we are ready to build our web app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import os
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import cdata.api as mod
import plotly.graph_objs as go

You can now connect with a connection string. Use the connect function for the CData Sentry Connector to create a connection for working with Sentry data.

cnxn = mod.connect("Profile=C:\profiles\Sentry.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_auth_token;OrganizationId=your_org_slug";")

Execute SQL to Sentry

Use the read_sql function from pandas to execute any SQL statement and store the result set in a DataFrame.

df = pd.read_sql("SELECT ,  FROM UserOrganizations WHERE  = ''", cnxn)

Configure the Web App

With the query results stored in a DataFrame, we can begin configuring the web app, assigning a name, stylesheet, and title.

app_name = 'dash-apiedataplot'

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.title = 'CData + Dash'

Configure the Layout

The next step is to create a bar graph based on our Sentry data and configure the app layout.

trace = go.Bar(x=df., y=df., name='')

app.layout = html.Div(children=[html.H1("CData Extension + Dash", style={'textAlign': 'center'}),
	dcc.Graph(
		id='example-graph',
		figure={
			'data': [trace],
			'layout':
			go.Layout(title='Sentry UserOrganizations Data', barmode='stack')
		})
], className="container")

Set the App to Run

With the connection, app, and layout configured, we are ready to run the app. The last lines of Python code follow.

if __name__ == '__main__':
    app.run_server(debug=True)

Now, use Python to run the web app and a browser to view the Sentry data.

python api-dash.py

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps with connectivity to Sentry data. Reach out to our Support Team if you have any questions.

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Full Source Code

import os
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import cdata.api as mod
import plotly.graph_objs as go

cnxn = mod.connect("Profile=C:\profiles\Sentry.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_auth_token;OrganizationId=your_org_slug";")

df = pd.read_sql("SELECT ,  FROM UserOrganizations WHERE  = ''", cnxn)
app_name = 'dash-apidataplot'

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.title = 'CData + Dash'
trace = go.Bar(x=df., y=df., name='')

app.layout = html.Div(children=[html.H1("CData Extension + Dash", style={'textAlign': 'center'}),
	dcc.Graph(
		id='example-graph',
		figure={
			'data': [trace],
			'layout':
			go.Layout(title='Sentry UserOrganizations Data', barmode='stack')
		})
], className="container")

if __name__ == '__main__':
    app.run_server(debug=True)