Logging app events with Python

This part of the Python Bookshelf app tutorial shows how an app can incorporate detailed logging to help with detecting, debugging, and monitoring potential issues. Logging app events can help you identify issues and solve problems, both during development and after your app is in production.

This page is part of a multipage tutorial. To start from the beginning and read the setup instructions, go to Python Bookshelf app.

You can use the Python standard logging handlers introduced in Understanding the code on the App Engine flexible environment, Compute Engine, or Google Kubernetes Engine (GKE).

Configuring settings

This section uses code in the 5-logging directory. Edit the files and run commands in this directory.

  1. Open the config.py file for editing and replace the following values:
    • Set the value of [PROJECT_ID] to your project ID, which is visible in the GCP Console.
    • Set the value of [DATA_BACKEND] to the same value you used during the Using structured data tutorial.
    • If you are using Cloud SQL or MongoDB, set the values under the Cloud SQL or Mongo section to the same values you used during the Using structured data step.
    • Set the value of [CLOUD_STORAGE_BUCKET] to your Cloud Storage bucket name.
    • Under the OAuth2 configuration section, set the values of [GOOGLE_OAUTH2_CLIENT_ID] and [GOOGLE_OAUTH2_CLIENT_SECRET] to the application client ID and secret that you created previously.

  2. Save and close the config.py file.

If you are using Cloud SQL:

  1. Open the app.yaml file for editing.
  2. Set the value of cloudsql-instance to the same value used for [CLOUDSQL_CONNECTION_NAME] in the config.py file. Use the format project:region:cloudsql-instance. Uncomment this entire line.
  3. Save and close the app.yaml file.

Installing dependencies

To create a virtual environment and install dependencies, use the following commands:

Linux/macOS

virtualenv -p python3 env
source env/bin/activate
pip install -r requirements.txt

Windows

virtualenv -p python3 env
env\scripts\activate
pip install -r requirements.txt

Running the app on your local machine

  1. Start a local web server:

    python main.py
    
  2. In your browser, enter the following address:

    [http://localhost:8080](http://localhost:8080){: target="_blank"}
    

Press Control+C to exit the worker and then the local web server.

Deploying the app to the App Engine flexible environment

  1. Deploy the sample app:

    gcloud app deploy
    
  2. In your browser, enter the following address. Replace [YOUR_PROJECT_ID] with your GCP project ID:

    https://[YOUR_PROJECT_ID].appspot.com
    

If you update your app, you deploy the updated version by entering the same command that you used to deploy the app. The deployment creates a new version of your app and promotes it to the default version. The earlier versions of your app remain, as do their associated virtual machine (VM) instances. All of these app versions and VM instances are billable resources. To reduce costs, delete the non-default versions of your app.

To delete an app version:

  1. In the GCP Console, go to the Versions page for App Engine.

    Go to the Versions page

  2. Select the checkbox for the non-default app version you want to delete.
  3. Click Delete to delete the app version.

For more information about cleaning up billable resources, see the Cleaning up section in the final step of this tutorial.

Understanding the code

After you deploy an app, it's important to understand how well the app is working. Google Cloud Platform (GCP) provides logging and monitoring tools in the Google Cloud Platform Console that show detailed activity within your app, helping you quickly identify critical issues or trends.

The Bookshelf sample app uses Python's Logging facility for Python to manage logging.

The app can also log important events from anywhere by using the following code.

logging.info("Something happened");
logging.error("Something bad happened");

When the sample app is running in the App Engine flexible environment, anything logged to stderr and stdout is automatically collected by Stackdriver Logging. Use the logs viewer in the GCP Console to view, search, and export log content.

Go to the logs viewer in the GCP Console

Viewing logs

While the sample app runs, it collects logging data. You can analyze this data by using the log monitoring tools in the GCP Console.

Go to the log monitoring tools

The following image shows logged events in the GCP Console.

Logging in the GCP Console

For a more detailed analysis, you can stream or import the app's logs into BigQuery or export them to a Cloud Storage bucket. You can use the GCP Console to do both.

Using Python logging handlers

By default, the log messages captured in stdout and stderr aren't captured with severity metadata. If you query for log statements that contain WARN, the results set includes statements that contain the string WARN, regardless of the severity of the logging call. However, if metadata is correctly reported, you can use a metadata.severity >= WARN logging filter to return only log statements that are logged at WARN or higher.

The Cloud Client Libraries for Python include Python standard logging handlers, that ensure that all logging statements are reported to Logging with the correct metadata. The following code attaches the default Logging handlers, which vary depending on your deployment environment, to the root logger. In the App Engine flexible environment, Logging reports logs by using the fluentd agent that accompanies the runtime. For more information, see the Python Stackdriver Logging API client library.

if not app.testing:
    client = google.cloud.logging.Client(app.config['PROJECT_ID'])
    # Attaches a Google Stackdriver logging handler to the root logger
    client.setup_logging(logging.INFO)
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