2. Capture Tags - Python

15 minutes

Let’s add some tags to our traces, so we can find out why some customers receive a poor experience from our application.

Identify Useful Tags

We’ll start by reviewing the code for the credit_check function of creditcheckservice (which can be found in the /home/splunk/workshop/tagging/creditcheckservice-py/main.py file):

@app.route('/check')
def credit_check():
    customerNum = request.args.get('customernum')

    # Get Credit Score
    creditScoreReq = requests.get("http://creditprocessorservice:8899/getScore?customernum=" + customerNum)
    creditScoreReq.raise_for_status()
    creditScore = int(creditScoreReq.text)

    creditScoreCategory = getCreditCategoryFromScore(creditScore)

    # Run Credit Check
    creditCheckReq = requests.get("http://creditprocessorservice:8899/runCreditCheck?customernum=" + str(customerNum) + "&score=" + str(creditScore))
    creditCheckReq.raise_for_status()
    checkResult = str(creditCheckReq.text)

    return checkResult

We can see that this function accepts a customer number as an input. This would be helpful to capture as part of a trace. What else would be helpful?

Well, the credit score returned for this customer by the creditprocessorservice may be interesting (we want to ensure we don’t capture any PII data though). It would also be helpful to capture the credit score category, and the credit check result.

Great, we’ve identified four tags to capture from this service that could help with our investigation. But how do we capture these?

Capture Tags

We start by adding importing the trace module by adding an import statement to the top of the creditcheckservice-py/main.py file:

import requests
from flask import Flask, request
from waitress import serve
from opentelemetry import trace  # <--- ADDED BY WORKSHOP
...

Next, we need to get a reference to the current span so we can add an attribute (aka tag) to it:

def credit_check():
    current_span = trace.get_current_span()  # <--- ADDED BY WORKSHOP
    customerNum = request.args.get('customernum')
    current_span.set_attribute("customer.num", customerNum)  # <--- ADDED BY WORKSHOP
...

That was pretty easy, right? Let’s capture some more, with the final result looking like this:

def credit_check():
    current_span = trace.get_current_span()  # <--- ADDED BY WORKSHOP
    customerNum = request.args.get('customernum')
    current_span.set_attribute("customer.num", customerNum)  # <--- ADDED BY WORKSHOP

    # Get Credit Score
    creditScoreReq = requests.get("http://creditprocessorservice:8899/getScore?customernum=" + customerNum)
    creditScoreReq.raise_for_status()
    creditScore = int(creditScoreReq.text)
    current_span.set_attribute("credit.score", creditScore)  # <--- ADDED BY WORKSHOP

    creditScoreCategory = getCreditCategoryFromScore(creditScore)
    current_span.set_attribute("credit.score.category", creditScoreCategory)  # <--- ADDED BY WORKSHOP

    # Run Credit Check
    creditCheckReq = requests.get("http://creditprocessorservice:8899/runCreditCheck?customernum=" + str(customerNum) + "&score=" + str(creditScore))
    creditCheckReq.raise_for_status()
    checkResult = str(creditCheckReq.text)
    current_span.set_attribute("credit.check.result", checkResult)  # <--- ADDED BY WORKSHOP

    return checkResult

Redeploy Service

Once these changes are made, let’s run the following script to rebuild the Docker image used for creditcheckservice and redeploy it to our Kubernetes cluster:

./5-redeploy-creditcheckservice.sh

Confirm Tag is Captured Successfully

After a few minutes, return to Splunk Observability Cloud and load one of the latest traces to confirm that the tags were captured successfully (hint: sort by the timestamp to find the latest traces):

Well done, you’ve leveled up your OpenTelemetry game and have added context to traces using tags.

Next, we’re ready to see how you can use these tags with Splunk Observability Cloud!