Version: Cloud

Python in Docker

Supported Frameworks

Standard Library Modules

Django

Prerequisite

To enable tracing for an application developed by the Django framework, sf-elastic-apm and sf-apm-lib must be available in your environment. These libraries can be installed by the following methods:

Add the below-mentioned entries in the requirements.txt file.

sf-elastic-apm==6.7.2
sf-apm-lib==0.1.5

Install the libraries using CLI.

RUN pip install sf-elastic-apm==6.7.2 
RUN pip install sf-apm-lib==0.1.5

Configuration

Make sure that the project and the application are created in the SnappyFlow Server. Click here to know how to create a project and an application in SnappyFlow.

Add the following entries in settings.py file.

Add the following import statement.

from sf_apm_lib.snappyflow import Snappyflow 
import os

Add the following entry in the INSTALLED_APPS block.
```
'elasticapm.contrib.django'  
```

Add the following entry in the MIDDLEWARE block.

'elasticapm.contrib.django.middleware.TracingMiddleware'

Add the following source code to integrate a Django application with SnappyFlow.

try: 
    sf = Snappyflow() 
    # Add below part to manually configure the initialization 
    SF_PROJECT_NAME = os.getenv('SF_PROJECT_NAME') 
    SF_APP_NAME = os.getenv('SF_APP_NAME') 
    SF_PROFILE_KEY = os.getenv('SF_PROFILE_KEY') 
    sf.init(SF_PROFILE_KEY, SF_PROJECT_NAME, SF_APP_NAME) 
    # End of manual configuration 
   
    SFTRACE_CONFIG = sf.get_trace_config()
   
    ELASTIC_APM={ 
    # Specify your service name for tracing
        'SERVICE_NAME': "custom-service" ,  
        'SERVER_URL': SFTRACE_CONFIG.get('SFTRACE_SERVER_URL'), 
        'GLOBAL_LABELS': SFTRACE_CONFIG.get('SFTRACE_GLOBAL_LABELS'), 
        'VERIFY_SERVER_CERT': SFTRACE_CONFIG.get('SFTRACE_VERIFY_SERVER_CERT'), 
        'SPAN_FRAMES_MIN_DURATION': SFTRACE_CONFIG.get('SFTRACE_SPAN_FRAMES_MIN_DURATION'), 
        'STACK_TRACE_LIMIT': SFTRACE_CONFIG.get('SFTRACE_STACK_TRACE_LIMIT'), 
        'CAPTURE_SPAN_STACK_TRACES': SFTRACE_CONFIG.get('SFTRACE_CAPTURE_SPAN_STACK_TRACES'), 
        'DJANGO_TRANSACTION_NAME_FROM_ROUTE': True, 
        'CENTRAL_CONFIG': False, 
        'METRICS_INTERVAL': '0s'
    } 
    except Exception as error: 
    print("Error while fetching snappyflow tracing configurations", error)

Provide SF_PROJECT_NAME, SF_APP_NAME, SF_PROFILE_KEY as environment variables in the docker-compose.yml or docker stack deployment file or while using docker run command for deployment in the command line.
```
 docker run -d -t -i -e SF_PROJECT_NAME='' \  
 -e SF_APP_NAME='' \ 
 -e SF_PROFILE_KEY='' \ 
 -p 80:80 \ 
 --name <container_name> <dockerhub_id/image_name> 
```

For more information on setting environment variables in docker refer to: https://docs.docker.com/compose/environment-variables/

note

If your app is in debug mode (eg: settings.Debug = true), then the agent won’t send any tracing data to the SnappyFlow server. You can override it by adding 'Debug':True configuration in the ELASTIC_APM block.

Verification

Follow the below steps to verify and view the trace data.

Login into SnappyFlow.
Go to the Application tab.
In the Application tab, navigate to your Project > Application.
Click the Application's Dashboard icon.
Navigate to the Tracing section and click the View Transactions button.
You can view the traces in the Aggregate and the Real Time tabs.

Troubleshoot

If the trace data is unavailable in the SnappyFlow server, check the trace configuration in the settings.py file.
Add the key-value pair in the ELASTIC_APM block of the settings.py file to enable the debug logs.
```
'DEBUG':True
```

Sample Application Code

Click here to view the sample application for which the configuration mentioned in the above sections enables the tracing feature.

Flask

Prerequisite

To enable tracing for an application developed by the Flask framework, sf-elastic-apm and sf-apm-lib must be available in your environment. These libraries can be installed by the following methods:

Add the below-mentioned entries in the requirements.txt file.

sf-elastic-apm[flask]==6.7.2
sf-apm-lib==0.1.5

Install the libraries using CLI.

pip install sf-elastic-apm[flask]==6.7.2 
pip install sf-apm-lib==0.1.5

Configuration

Make sure that the project and application are created in the SnappyFlow Server. Click here to know how to create a project and an application in SnappyFlow.

Add the following entries in the app.py file.

Add the following import statements.

from elasticapm.contrib.flask import ElasticAPM 
from sf_apm_lib.snappyflow import Snappyflow 
import os

Add the following source code to integrate a Flask application with SnappyFlow.

sf = Snappyflow() 
# Add below part to manually configure the initialization 
SF_PROJECT_NAME = os.getenv('SF_PROJECT_NAME') 
SF_APP_NAME = os.getenv('SF_APP_NAME') 
SF_PROFILE_KEY = os.getenv('SF_PROFILE_KEY') 
sf.init(SF_PROFILE_KEY, SF_PROJECT_NAME, SF_APP_NAME) 
# End of manual configuration   
SFTRACE_CONFIG = sf.get_trace_config()
app.config['ELASTIC_APM'] = { 
# Specify your service name for tracing 
'SERVICE_NAME': 'flask-service', 
'SERVER_URL': SFTRACE_CONFIG.get('SFTRACE_SERVER_URL'), 
'GLOBAL_LABELS': SFTRACE_CONFIG.get('SFTRACE_GLOBAL_LABELS'), 
'VERIFY_SERVER_CERT': SFTRACE_CONFIG.get('SFTRACE_VERIFY_SERVER_CERT'), 
'SPAN_FRAMES_MIN_DURATION': SFTRACE_CONFIG.get('SFTRACE_SPAN_FRAMES_MIN_DURATION'), 
'STACK_TRACE_LIMIT': SFTRACE_CONFIG.get('SFTRACE_STACK_TRACE_LIMIT'), 
'CAPTURE_SPAN_STACK_TRACES': SFTRACE_CONFIG.get('SFTRACE_CAPTURE_SPAN_STACK_TRACES'), 
'METRICS_INTERVAL': '0s'
    } 
    apm = ElasticAPM(app) 

Provide SF_PROJECT_NAME, SF_APP_NAME, SF_PROFILE_KEY as environment variables in the docker-compose.yml or docker stack deployment file or while using docker run command for deployment in the command line.
```
   docker run -d -t -i -e SF_PROJECT_NAME='' \  
  -e SF_APP_NAME='' \ 
  -e SF_PROFILE_KEY='' \ 
  -p 80:80 \ 
  --name <container_name> <dockerhub_id/image_name> 
```

For more information on setting environment variables in docker refer to: https://docs.docker.com/compose/environment-variables/

note

If your app is in debug mode (eg: app.Debug = true), then the agent won’t send any tracing data to the SnappyFlow server. You can override it by adding 'Debug':True configuration in the ELASTIC_APM block.

Verification

Follow the below steps to verify and view the trace data.

Login into SnappyFlow.
Go to the Application tab.
In the Application tab, navigate to your Project > Application.
Click the Application's Dashboard icon.
Navigate to the Tracing section and click the View Transactions button.
You can view the traces in the Aggregate and the Real Time tabs.

Troubleshoot

If the trace data is unavailable in the SnappyFlow server, check the trace configuration in the app.py file.
Add the key-value pair in the app.config block of the app.py file to enable the debug logs.
```
'DEBUG':True
```

Celery

note

The Celery configuration explained below is based on redis broker.

Prerequisite

To enable tracing for an application developed by Celery, sf-elastic-apm, redis and sf-apm-lib must be available in your environment.

Install the following requirements.

pip install sf-elastic-apm==6.7.2 
pip install redis 
pip install sf-apm-lib==0.1.5

Configuration

To setup elastic apm client, add the following code at the start of the file where the Celery app is initialized.

from sf_apm_lib.snappyflow import Snappyflow 
from elasticapm import Client, instrument 
from elasticapm.contrib.celery import register_exception_tracking, register_instrumentation

instrument()

try: 
 sf = Snappyflow() # Initialize Snappyflow. By default intialization will take profileKey, projectName and appName from sfagent config.yaml

 # Add below part to manually configure the initialization 
 SF_PROJECT_NAME = '<SF_PROJECT_NAME>' # Replace with appropriate Snappyflow project name 
 SF_APP_NAME = '<SF_APP_NAME>' # Replace with appropriate Snappyflow app name 
 SF_PROFILE_KEY = '<SF_PROFILE_KEY>' # Replace Snappyflow Profile key 
 sf.init(SF_PROFILE_KEY, SF_PROJECT_NAME, SF_APP_NAME) 
 # End of manual configuration 
    
 SFTRACE_CONFIG = sf.get_trace_config() 
 apm_client = Client(
     service_name= '<Service_Name>', # Specify service name for tracing
     server_url= SFTRACE_CONFIG.get('SFTRACE_SERVER_URL'), 
     global_labels= SFTRACE_CONFIG.get('SFTRACE_GLOBAL_LABELS'), 
     verify_server_cert= SFTRACE_CONFIG.get('SFTRACE_VERIFY_SERVER_CERT')
 )
    
 register_exception_tracking(apm_client) 
 register_instrumentation(apm_client) 
 except Exception as error: 
 print("Error while fetching snappyflow tracing configurations", error)  

Verification

Once the instrumentation is done and the Celery worker is running, you can see a trace for each celery task in the Snappyflow server. Follow the below steps to verify and view the traces.

Login into SnappyFlow.
Go to the Application tab.
In the Application tab, navigate to your Project > Application.
Click the Application's Dashboard icon.
Navigate to the Tracing section and click the View Transactions button.
You can view the traces in the Aggregate and the Real Time tabs.

Reference Code

Refer complete code: https://github.com/snappyflow/tracing-reference-apps/blob/master/ref-celery/tasks.py

Python in Docker

Django​

Prerequisite​

Configuration​

Verification​

Troubleshoot​

Sample Application Code​

Flask​

Prerequisite​

Configuration​

Verification​

Troubleshoot​

Celery​

Prerequisite​

Configuration​

Verification​

Reference Code​

Django

Prerequisite

Configuration

Verification

Troubleshoot

Sample Application Code

Flask

Prerequisite

Configuration

Verification

Troubleshoot

Celery

Prerequisite

Configuration

Verification

Reference Code