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Python tracing

The Python tracing automatically instruments APIs, frameworks and application servers. The sfAPM python agent collects and sends the tracing metrics and the correlated application logs to the SnappyFlow server.

Supported Python versions

Python 3.6, 3.7, 3.8, 3.9, 3.10, 3.11

Supported Web Frameworks

Django 1.11, 2.0, 2.1, 2.2, 3.0, 3.1, 3.2, 4.0
Flask 1.0, 1.1, 2.0

Supported Platforms​

Instances

Docker

Kubernetes

ECS

AWS Lambda

Supported Trace Features​

Below is the list of the supported trace features:

  • Distributed Tracing
  • Transaction Mapping
  • Log Correlation
  • Trace to Log Body
  • Service Map

Instances​

Supported Frameworks​

Django

Flask

Standard Library Modules​

Script

Celery

Django​

Follow the below steps to enable the tracing for the application based on Django Framework.

Configuration​

  1. Add the below mentioned entries in requirements.txt file to install sf-elastic-apm and sf-apm-lib in your environment.

    sf-elastic-apm==6.7.2
    sf-apm-lib==0.1.1

    or

    Install the below libraries using CLI.

    pip install sf-elastic-apm==6.7.2 
    pip install sf-apm-lib==0.1.1

    • If the agent is already installed in your instance, the trace agent picks up the profileKey, projectName, and appName from the config.yaml file. Add the below entries in the settings.py file.

      i. Add the import statement. 

      from sf_apm_lib.snappyflow import Snappyflow

      ii. Add the following entry in INSTALLED_APPS block.

      'elasticapm.contrib.django'

      iii. Add the following entry in MIDDLEWARE block.

      'elasticapm.contrib.django.middleware.TracingMiddleware'

      iv. Add the following source code to integrate the Django application to the SnappyFlow.
      try: 
         # Initialize Snappyflow. By default intialization will take profileKey, projectName and appName from sfagent config.yaml
         sf = Snappyflow()  
         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)

    • If the sfAgent is not installed in your instance, then follow the below steps:

      i. Make sure the project and application is created in the SnappyFlow Server. Click Here to know how to create the project and application in SnappyFlow.

      ii. Export SF_PROJECT_NAME, SF_APP_NAME, SF_PROFILE_KEY as the environment variables.

      # Update the below default values with proper values
      export SF_PROJECT_NAME=<SF_PROJECT_NAME>
      export SF_APP_NAME=<SF_APP_NAME>
      export SF_PROFILE_KEY=<SF_PROFILE_KEY>

      iii. Add the following entries in the settings.py file.

      1. Add the import statement. 

        from sf_apm_lib.snappyflow import Snappyflow

      2. Add the following entry in INSTALLED_APPS block.

        'elasticapm.contrib.django'

      3. Add the following entry in MIDDLEWARE block.

        'elasticapm.contrib.django.middleware.TracingMiddleware'

      4. Add the following source code to integrate the Django application to the 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)

Verification​

Once your application is up and running, follow the below steps to verfiy that the SnappyFlow has started to collect the traces.

  1. Make sure that the project and the application is created.
  2. In the app, click the View Dashboard icon.
  3. In the Dashboard window, go to Tracing section.
  4. In the Tracing section, click the View Transactions button.
  5. Now you can view the traces in Aggregate and Real Time tabs.

Troubleshoot Steps​

  1. If the trace data is not collected in the SnappyFlow server, then check the trace configuration in the settings.py.

  2. To enable the debug logs, add the below key-value pair in the ELASTIC_APM block of the settings.py.

    'DEBUG':'true'

Sample Application Code​

The below link contains the sample application with the trace enabled by following the configuration mentioned in the above sections.

Click Here to view the reference application.

Flask​

  1. Add 

    sf-elastic-apm[flask]==6.7.2
    sf-apm-lib==0.1.1

    entries in requirements.txt file and install these in your project environment

    or

    Install the below libraries using CLI.

    pip install sf-elastic-apm[flask]==6.7.2 
    pip install sf-apm-lib==0.1.1

  2. Provide SF_PROJECT_NAME, SF_APP_NAME, SF_PROFILE_KEY as an environment variable.

  3. Add following entries in app.py

    1. Add imports statement

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

    2. Get trace config

      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 = 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()

      # Start Trace to log feature section
      # Add below line of code to enable Trace to log feature:
      SFTRACE_CONFIG['SFTRACE_GLOBAL_LABELS'] += ',_tag_redact_body=true'
      # Option Configs for trace to log
      # Add below line to provide custom documentType (Default:"user-input"):
      SFTRACE_CONFIG['SFTRACE_GLOBAL_LABELS'] += ',_tag_documentType=<document-type>'
      # Add below line to provide destination index (Default:"log"):
      SFTRACE_CONFIG['SFTRACE_GLOBAL_LABELS'] += ',_tag_IndexType=<index-type>' # Applicable values(log, metric)
      # End trace to log section

    3. Initialize elastic apm and instrument it to flask app

      app.config['ELASTIC_APM'] = { 
          'SERVICE_NAME': '<SERVICE_NAME>', # Specify your 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'), 
          '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'), 
          'DEBUG': True,
          'METRICS_INTERVAL': '0s'
      } 
      apm = ElasticAPM(app)

    4. Once your server is up and running you can check trace in Snappyflow Server.

      For viewing trace in snappyflow server make sure project and app name is created or discovered with project name and app name specified in point no.2

      Once project and app name is created, Go to View dashboard -> Click on Tracing on lef side bar -> Click on view transaction -> Go to real time tab

    note

    'CAPTURE_BODY':'all' config should be present in apm agent code instrumentation for Trace to Log feature.

Script​

  1. Install following requirements

    pip install sf-elastic-apm==6.7.2 
    pip install sf-apm-lib==0.1.1

  2. Add following code at start of script file to setup elastic apm client

    import elasticapm 
    from sf_apm_lib.snappyflow import Snappyflow 

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

    # Add below part to manually configure the initialization 
    SF_PROJECT_NAME = '<Snappyflow Project Name>' 
    SF_APP_NAME = '<Snappyflow App Name>' 
    SF_PROFILE_KEY = '<Snappyflow Profile Key>' 
    sf.init(SF_PROFILE_KEY, SF_PROJECT_NAME, SF_APP_NAME) 
    # End of manual configuration

    trace_config = sf.get_trace_config() # Returns trace config 
    client = elasticapm.Client(
       service_name="<Service name> ",# Specify service name for tracing 
       server_url=trace_config['SFTRACE_SERVER_URL'], 
       verify_cert=trace_config['SFTRACE_VERIFY_SERVER_CERT'], 
       global_labels=trace_config['SFTRACE_GLOBAL_LABELS'] 
    ) 
    elasticapm.instrument()  # Only call this once, as early as possible.

  3. Once instrumentation is completed we can create custom transaction and span

    Example

    def main(): 
        sess = requests.Session() 
        for url in [ 'https://www.elastic.co', 'https://benchmarks.elastic.co' ]: 
            resp = sess.get(url) 
            time.sleep(1) 
    client.begin_transaction(transaction_type="script") 
    main() 
    # Record an exception 
    try: 
        1/0 
    except ZeroDivisionError: 
        ident = client.capture_exception() 
        print ("Exception caught; reference is %s" % ident) 
    client.end_transaction(name=__name__, result="success")

    Refer link to know more:

    https://www.elastic.co/guide/en/apm/agent/python/master/instrumenting-custom-code.html

  4. Now run you script and test your trace in snappyflow server.

    For viewing trace in snappyflow server make sure project and app name is created or discovered with project name and app name specified in point no.2

    Once project and app name is created, Go to View dashboard -> Click on Tracing on left side bar -> Click on view transaction -> Go to real time tab

  1. Refer complete script:

    https://github.com/snappyflow/tracing-reference-apps/blob/master/refapp-django/python_script_trace.py

Celery​

  1. Install following requirements (Following example is based on redis broker)

    pip install sf-elastic-apm==6.7.2 
    pip install redis 
    pip install sf-apm-lib==0.1.1

  2. Add following code at start of the file where celery app is initialized to setup elastic apm client

    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)

  3. Once instrumentation is done and celery worker is running we can see trace for each celery task in Snappyflow server.

    For viewing trace in snappyflow server make sure project and app name is created or discovered with project name and app name specified in point no.2

    Once project and app name is created, Go to View dashboard -> Click on Tracing on left side bar -> Click on view transaction -> Go to real time tab

  4. Refer complete code:

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

Kubernetes​

Django​

Follow the below steps to enable the tracing for the applications based on Django Framework.

Configuration​

  1. Add the below mentioned entries in requirements.txt file to install sf-elastic-apm and sf-apm-lib in your environment.

    sf-elastic-apm==6.7.2
    sf-apm-lib==0.1.1

    or

    Install the below libraries using CLI.

    pip install sf-elastic-apm==6.7.2 
    pip install sf-apm-lib==0.1.1

  2. Add the following entries in settings.py file.

    1. Add the import statements.

      from sf_apm_lib.snappyflow import Snappyflow
      import os

    2. Add the following entry in INSTALLED_APPS block.

      'elasticapm.contrib.django'

    3. Add the following entry in MIDDLEWARE block.

      'elasticapm.contrib.django.middleware.TracingMiddleware'

    4. Add the following source code to integrate the Django application to the 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)

  3. Provide SF_PROJECT_NAME, SF_APP_NAME, SF_PROFILE_KEY as an environment variables in Kubernetes deployment file.

    #deployment.yaml
    apiVersion: apps/v1
    kind: Deployment
    metadata:
    name: python-app
    labels:
       app: python-app
    spec:
    containers:
    - name: python-app
       image: imagename/tag:version
       env:
       - name: SF_PROFILE_KEY
          value: <profle-key>
       - name: SF_PROJECT_NAME
          value: <project_name>
       - name: SF_APP_NAME
          value: <app-name>

    If the deployment is with helm charts, provide the above variables in the values.yaml and use them in the deployment file of charts. 

    #values.yaml
    global:
    # update the sfappname, sfprojectname and key with the proper values
    sfappname: <app-name>
    sfprojectname: <project-name>
    key: <profile-key>

    replicaCount: 1
    image:
    repository: djangoapp
    pullPolicy: IfNotPresent
    tag: "latest"

    Pass the global section key-value from the value.yaml by setting the deployment.yaml as below :

    #deployment.yaml
    apiVersion: apps/v1
    kind: Deployment
    spec:
    containers:
       - name: {{ .Chart.Name }}
          image: "{{ .Values.image.repository }}:{{ .Values.image.tag }}"
          imagePullPolicy: {{ .Values.image.pullPolicy }}
          env:
          - name: SF_PROFILE_KEY
          value: {{ .Values.global.key }}
          - name: SF_PROJECT_NAME
          value: {{ .Values.global.sfprojectname }}
          - name: SF_APP_NAME
          value: {{ .Values.global.sfappname }}

Verification​

Once your application is up and running, follow the below steps to verfiy that the SnappyFlow has started to collect the traces.

  1. Make sure that the project and the application is created.
  2. In the app, click the View Dashboard icon.
  3. In the Dashboard window, go to Tracing section.
  4. In the Tracing section, click the View Transactions button.
  5. Now you can view the traces in Aggregate and Real Time tabs.

Sample Application Code​

The below link contains the sample application with the trace enabled by following the configuration mentioned in the above sections.

Click Here to view the reference application.

Flask​

  1. Add 

    sf-elastic-apm[flask]==6.7.2
    sf-apm-lib==0.1.1

    entries in requirements.txt file and install these in your project environment

    or

    Install through CLI using 

    RUN pip install sf-elastic-apm[flask]==6.7.2 
    RUN pip install sf-apm-lib==0.1.1

  2. Add following entries in app.py

    1. Add imports statement

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

    2. Get trace config

      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 = 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()

      # Start Trace to log feature section
      # Add below line of code to enable Trace to log feature:
      SFTRACE_CONFIG['SFTRACE_GLOBAL_LABELS'] += ',_tag_redact_body=true'
      # Option Configs for trace to log
      # Add below line to provide custom documentType (Default:"user-input"):
      SFTRACE_CONFIG['SFTRACE_GLOBAL_LABELS'] += ',_tag_documentType=<document-type>'
      # Add below line to provide destination index (Default:"log"):
      SFTRACE_CONFIG['SFTRACE_GLOBAL_LABELS'] += ',_tag_IndexType=<index-type>' # Applicable values(log, metric)
      # End trace to log section

    3. Initialize elastic apm and instrument it to flask app

      app.config['ELASTIC_APM'] = { 
         'SERVICE_NAME': '<SERVICE_NAME>', # Specify your 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'), 
         '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'), 
         'DEBUG': True,
         'METRICS_INTERVAL': '0s'
      } 
      apm = ElasticAPM(app)

  3. Provide SF_PROJECT_NAME, SF_APP_NAME, SF_PROFILE_KEY as an environment variables in Kubernetes deployment file.

    https://kubernetes.io/docs/tasks/inject-data-application/define-environment-variable-container/

    If deploying with helm provide above variables in values.yaml and use them in deployment file of charts.

    https://phoenixnap.com/kb/helm-environment-variables

  4. Once your server is up and running you can check trace in Snappyflow Server.

    For viewing trace in snappyflow server make sure project and app name is created or discovered with project name and app name specified in point no.2

    Once project and app name is created, Go to View dashboard -> Click on Tracing on lef side bar -> Click on view transaction -> Go to real time tab

    note

    'CAPTURE_BODY':'all' config should be present in apm agent code instrumentation for Trace to Log feature.

Celery​

  1. Install following requirements (Following example is based on redis broker)

    pip install sf-elastic-apm==6.7.2 
    pip install redis 
    pip install sf-apm-lib==0.1.1

  2. Add following code at start of the file where celery app is initialized to setup elastic apm client

    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)

  3. Once instrumentation is done and celery worker is running we can see trace for each celery task in Snappyflow server.

    For viewing trace in snappyflow server make sure project and app name is created or discovered with project name and app name specified in point no.2

    Once project and app name is created, Go to View dashboard -> Click on Tracing on left side bar -> Click on view transaction -> Go to real time tab

  4. Refer complete code:

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

Docker​

Django​

Follow the below steps to enable the tracing for the application based on Django Framework.

Configuration​

  1. Add below mentioned entries in requirements.txt file and install these in your project environment.

    sf-elastic-apm==6.7.2
    sf-apm-lib==0.1.1

    or

    Install the below libraries using CLI.

    RUN pip install sf-elastic-apm==6.7.2 
    RUN pip install sf-apm-lib==0.1.1

  2. Make sure the project and application is created in the SnappyFlow Server. Click Here to know how to create the project and application in SnappyFlow.

  3. Add the following entries in settings.py file.

    1. Add the import statement.

      from sf_apm_lib.snappyflow import Snappyflow 
      import os

    2. Add the following entry in INSTALLED_APPS block.

      'elasticapm.contrib.django'

    3. Add the following entry in MIDDLEWARE block.

      'elasticapm.contrib.django.middleware.TracingMiddleware'

    4. Add the following source code to integrate the Django application to the 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)

  4. Provide SF_PROJECT_NAME, SF_APP_NAME, SF_PROFILE_KEY as an environment variables in docker-compose.yml or docker stack deployment file or at command line when using docker run command for deployment.

    Follow the below referrence documentation:

    https://docs.docker.com/compose/environment-variables/

    Docker RUN: 

    docker run -d -t -i -e SF_PROJECT_NAME='' \  
    -e SF_APP_NAME='' \ 
    -e SF_PROFILE_KEY='' \ 
    -p 80:80 \ 
    --link redis:redis \   
    --name <container_name> <dockerhub_id/image_name>

Verification​

Once your application is up and running, follow the below steps to verfiy that the SnappyFlow has started to collect the traces.

  1. Make sure that the project and the application is created.
  2. In the app, click the View Dashboard icon.
  3. In the Dashboard window, go to Tracing section.
  4. In the Tracing section, click the View Transactions button.
  5. Now you can view the traces in Aggregate and Real Time tabs.

Troubleshoot Steps​

  1. If the trace data is not collected in the SnappyFlow server, then check the trace configuration in the settings.py.

  2. To enable the debug logs, add the below key-value pair in the ELASTIC_APM block of the settings.py.

    'DEBUG':'true'

Sample Application Code​

The below link contains the sample application with the trace enabled by following the configuration mentioned in the above sections.

Click Here to view the reference application.

Flask​

  1. Add 

    sf-elastic-apm[flask]==6.7.2
    sf-apm-lib==0.1.1

    entries in requirements.txt file and install these in your project environment

    or

    Install through CLI using

    RUN pip install sf-elastic-apm[flask]==6.7.2 
    RUN pip install sf-apm-lib==0.1.1

  2. Add following entries in app.py

    1. Add imports statement

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

    2. Get trace config

      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 
      # import os module
      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()

      # Start Trace to log feature section
      # Add below line of code to enable Trace to log feature:
      SFTRACE_CONFIG['SFTRACE_GLOBAL_LABELS'] += ',_tag_redact_body=true'
      # Option Configs for trace to log
      # Add below line to provide custom documentType (Default:"user-input"):
      SFTRACE_CONFIG['SFTRACE_GLOBAL_LABELS'] += ',_tag_documentType=<document-type>'
      # Add below line to provide destination index (Default:"log"):
      SFTRACE_CONFIG['SFTRACE_GLOBAL_LABELS'] += ',_tag_IndexType=<index-type>' # Applicable values(log, metric)
      # End trace to log section

    3. Initialize elastic apm and instrument it to flask app

      app.config['ELASTIC_APM'] = { 
         'SERVICE_NAME': '<SERVICE_NAME>', # Specify your 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'), 
         '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'), 
         'DEBUG': True,
         'METRICS_INTERVAL': '0s'
      } 
      apm = ElasticAPM(app)

  3. Provide SF_PROJECT_NAME, SF_APP_NAME, SF_PROFILE_KEY as an environment variables in docker-compose.yml or docker stack deployment file or at command line when using docker run command for deployment.

    Eg:

    Docker-compose and stack: https://docs.docker.com/compose/environment-variables/

    Docker run cli command: 

    docker run -d -t -i -e SF_PROJECT_NAME='<SF_PROJECT_NAME>' \  
    -e SF_APP_NAME='<SF_APP_NAME>' \ 
    -e SF_PROFILE_KEY='<snappyflow profile key>' \ 
    --name <container_name>  <dockerhub_id/image_name>

  4. Once your server is up and running you can check trace in Snappyflow Server.

    For viewing trace in snappyflow server make sure project and app name is created or discovered with project name and app name specified in point no.2

    Once project and app name is created, Go to View dashboard -> Click on Tracing on lef side bar -> Click on view transaction -> Go to real time tab

    note

    'CAPTURE_BODY':'all' config should be present in apm agent code instrumentation for Trace to Log feature.

Celery​

  1. Install following requirements (Following example is based on redis broker)

    pip install sf-elastic-apm==6.7.2 
    pip install redis 
    pip install sf-apm-lib==0.1.1

  2. Add following code at start of the file where celery app is initialized to setup elastic apm client

    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)

  3. Once instrumentation is done and celery worker is running we can see trace for each celery task in Snappyflow server.

    For viewing trace in snappyflow server make sure project and app name is created or discovered with project name and app name specified in point no.2

    Once project and app name is created, Go to View dashboard -> Click on Tracing on left side bar -> Click on view transaction -> Go to real time tab

  4. Refer complete code:

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

ECS​

Django​

Follow the below steps to enable the tracing for the applications based on Django Framework.

Configuration​

  1. Add the below mentioned entries in requirements.txt file to install sf-elastic-apm and sf-apm-lib in your environment.

    sf-elastic-apm==6.7.2
    sf-apm-lib==0.1.1

    or

    Install the below libraries using CLI.

    pip install sf-elastic-apm==6.7.2 
    pip install sf-apm-lib==0.1.1

  2. Make sure the project and application is created in the SnappyFlow Server. Click Here to know how to create the project and application in SnappyFlow.

  3. Add the following entries in settings.py file.

    1. Add the import statements.

      from sf_apm_lib.snappyflow import Snappyflow 
      import os

    2. Add the following entry in INSTALLED_APPS block.

      'elasticapm.contrib.django'

    3. Add the following entry in MIDDLEWARE block.

      'elasticapm.contrib.django.middleware.TracingMiddleware'

    4. Add the following source code to integrate the Django application to the 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)

  4. Provide SF_PROJECT_NAME, SF_APP_NAME, SF_PROFILE_KEY as an environment variables in add container section of task definitions.

    Refer the below documentation:

    https://catalog.us-east-1.prod.workshops.aws/workshops/c6bdf8dc-d2b2-4dbd-b673-90836e954745/en-US/container-migration/create-task-definition

Verification​

Once your application is up and running, follow the below steps to verfiy that the SnappyFlow has started to collect the traces..

  1. Make sure that the project and the application is created.
  2. In the app, click the View Dashboard icon.
  3. In the Dashboard window, go to Tracing section.
  4. In the Tracing section, click the View Transactions button.
  5. Now you can view the traces in Aggregate and Real Time tabs.

Troubleshoot Steps​

  1. If the trace data is not collected in the SnappyFlow server, then check the trace configuration in the settings.py.

  2. To enable the debug logs, add the below key-value pair in the ELASTIC_APM block of the settings.py.

    'DEBUG':'true'

Sample Application Code​

The below link contains the sample application with the trace enabled by following the configuration mentioned in the above sections.

Click Here to view the reference application.

Flask​

  1. Add 

    sf-elastic-apm[flask]==6.7.2
    sf-apm-lib==0.1.1

    entries in requirements.txt file and install these in your project environment

    or

    Install through CLI using

    RUN pip install sf-elastic-apm[flask]==6.7.2 
    RUN pip install sf-apm-lib==0.1.1

  2. Add following entries in app.py

    1. Add imports statement

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

    2. Get trace config

      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 
      # import os module
      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()

      # Start Trace to log feature section
      # Add below line of code to enable Trace to log feature:
      SFTRACE_CONFIG['SFTRACE_GLOBAL_LABELS'] += ',_tag_redact_body=true'
      # Option Configs for trace to log
      # Add below line to provide custom documentType (Default:"user-input"):
      SFTRACE_CONFIG['SFTRACE_GLOBAL_LABELS'] += ',_tag_documentType=<document-type>'
      # Add below line to provide destination index (Default:"log"):
      SFTRACE_CONFIG['SFTRACE_GLOBAL_LABELS'] += ',_tag_IndexType=<index-type>' # Applicable values(log, metric)
      # End trace to log section

    3. Initialize elastic apm and instrument it to flask app

      app.config['ELASTIC_APM'] = { 
         'SERVICE_NAME': '<SERVICE_NAME>', # Specify your 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'), 
         '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'), 
         'DEBUG': True,
         'METRICS_INTERVAL': '0s'
      } 
      apm = ElasticAPM(app)

  3. Provide SF_PROJECT_NAME, SF_APP_NAME, SF_PROFILE_KEY as an environment variables in add container section of task definitions.

    https://docs.aws.amazon.com/AmazonECS/latest/developerguide/taskdef-envfiles.html

  4. Once your server is up and running you can check trace in Snappyflow Server.

    For viewing trace in snappyflow server make sure project and app name is created or discovered with project name and app name specified in point no.2

    Once project and app name is created, Go to View dashboard -> Click on Tracing on lef side bar -> Click on view transaction -> Go to real time tab

    note

    'CAPTURE_BODY':'all' config should be present in apm agent code instrumentation for Trace to Log feature.

Celery​

  1. Install following requirements (Following example is based on redis broker)

    pip install sf-elastic-apm==6.7.2 
    pip install redis 
    pip install sf-apm-lib==0.1.1

  2. Add following code at start of the file where celery app is initialized to setup elastic apm client

    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)

  3. Once instrumentation is done and celery worker is running we can see trace for each celery task in Snappyflow server.

    For viewing trace in snappyflow server make sure project and app name is created or discovered with project name and app name specified in point no.2

    Once project and app name is created, Go to View dashboard -> Click on Tracing on left side bar -> Click on view transaction -> Go to real time tab

  4. Refer complete code:

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

AWS Lambda​

Script​

  1. Add these python libraries in requirements.txt file. Follow the AWS lambda doc on adding runtime dependency to lambda function. 

    sf-apm-lib==0.1.1 
    sf-elastic-apm==6.7.2

    Ref: https://docs.aws.amazon.com/lambda/latest/dg/python-package-create.html#python-package-create-with-dependency

  2. Instrument lambda function to enable tracing.

    1. Import Libraries

      import elasticapm 
      from sf_apm_lib.snappyflow import Snappyflow

    2. Add code to get SnappyFlow Trace config, outside lambda handler method. 

      sf = Snappyflow()
      SF_PROJECT_NAME = os.environ['SF_PROJECT_NAME'] 
      SF_APP_NAME = os.environ['SF_APP_NAME'] 
      SF_PROFILE_KEY = os.environ['SF_PROFILE_KEY'] 
      sf.init(SF_PROFILE_KEY, SF_PROJECT_NAME, SF_APP_NAME) 
      trace_config = snappyflow.get_trace_config()

    3. Add custom instrumentation in lambda handler function

      def lambda_handler(event, context): 
         client = elasticapm.Client(service_name="<SERVICE_NAME_CHANGEME>", 
            server_url=trace_config['SFTRACE_SERVER_URL'], 
            verify_cert=trace_config['SFTRACE_VERIFY_SERVER_CERT'], 
            global_labels=trace_config['SFTRACE_GLOBAL_LABELS'] 
            ) 
         elasticapm.instrument()  
         client.begin_transaction(transaction_type="script") 
         # DO SOME WORK. No return statements. 
         client.end_transaction(name=__name__, result="success") 
         # RETURN STATEMENT e.g. return response

  3. Deploy the Lambda function. Follow README to test sample app

    Sample code for reference:

    https://github.com/upendrasahu/aws-lambda-python-tracing-sample

  4. Configure Lambda function before trigger/invoke.

    1. Add the environment variable SF_PROFILE_KEY and set the value to your profile key copied from SnappyFlow.
    2. Add environment variables SF_APP_NAME and SF_PROJECT_NAME with appropriate values.

Trace to Log Body​

For the transactions that are HTTP requests which contain the request body, the sfPython trace agent capture the request body and store it in the SnappyFlow with the specific index and document type.

caution

Request bodies usually contain sensitive data like passwords and credit card numbers. If your service handles data like this, we advise you to enable this feature with care.

  1. Add the below values to enable this feature

    1. Update the ELASTIC_APM block with the following key-value pair in the settings.py.

      'CAPTURE_BODY': 'all'

    2. Add the below line in the try block of tracing instrumentation code in the settings.py.

      SFTRACE_CONFIG['SFTRACE_GLOBAL_LABELS'] += ',_tag_redact_body=true'

  2. Follow the below steps in the try block of settings.py to customize the document type and destination index. (Optional) 

    1. Add below line to customize the destination index (Default:"log"), Applicable values(log, metric).

      SFTRACE_CONFIG['SFTRACE_GLOBAL_LABELS'] += ',_tag_IndexType=<index-type>'

    2. Add the below line to customize the document type (Default:"user-input").

      SFTRACE_CONFIG['SFTRACE_GLOBAL_LABELS'] += ',_tag_documentType=user-input'

The overall configuration is below:

try: 
         
   SFTRACE_CONFIG['SFTRACE_GLOBAL_LABELS'] += ',_tag_redact_body=true'
   SFTRACE_CONFIG['SFTRACE_GLOBAL_LABELS'] += ',_tag_IndexType=log'
   SFTRACE_CONFIG['SFTRACE_GLOBAL_LABELS'] += ',_tag_documentType=user-input'
         
   ELASTIC_APM={ 
      'CAPTURE_BODY': 'all'
   } 
except Exception as error: 
   print("Error while fetching snappyflow tracing configurations", error)

Log Correlation​

For enabling the log correlation, follow the below instructions.

Django​

a. Add import statement in settings.py

from elasticapm.handlers.logging import Formatter

b. Add following logging configuration in settings.py.

LOGGING = {
    'version': 1,
    'disable_existing_loggers': True, // Disable existing logger
    'formatters': {
       'elastic': { // Add elastic formatter
            'format': '[%(asctime)s] [%(levelname)s] [%(message)s]',
            'class': 'elasticapm.handlers.logging.Formatter',
            'datefmt': "%d/%b/%Y %H:%M:%S"
        }
    },
    'handlers': {
        'elasticapm_log': {
            'level': 'INFO',
            'class': 'logging.handlers.RotatingFileHandler',
            'filename': '/var/log/trace/django.log', //specify you log file path
            'formatter': 'elastic'
        }
    },
    'loggers': {
        'elasticapm': {
            'handlers': ['elasticapm_log'],
            'level': 'INFO',
        }
    }
}

c. Usage:

import logging
log = logging.getLogger('elasticapm')

class ExampleView(APIView):
   def get(self, request):
      log.info('Get API called')

Refer code: https://github.com/snappyflow/tracing-reference-apps/blob/master/refapp-django

Flask​

  1. Add following code in app.py after import statements to set logger configuration
import logging
from elasticapm.handlers.logging import Formatter
fh = logging.FileHandler('/var/log/trace/flask.log') 

# we imported a custom Formatter from the Python Agent earlier 
formatter = Formatter("[%(asctime)s] [%(levelname)s] [%(message)s]", "%d/%b/%Y %H:%M:%S") 
fh.setFormatter(formatter) 
logging.getLogger().addHandler(fh)

# Once logging is configured get log object using following code  
log = logging.getLogger()
log.setLevel('INFO')

@app.route('/')
def home():
   log.info('Home API called')
   return 'Welcome to Home'

Refer code: https://github.com/snappyflow/tracing-reference-apps/blob/master/refapp-flask/app.py

Send log correlation data to snappyflow server​

Below are the modes for sending log correlated data to snappyflow server

For Appliance: ​

Install sfagent and create config file.

Refer: https://docs.snappyflow.io/docs/Integrations/os/linux/sfagent_linux

Add elasticApmLog plugin to sfagent config.yaml and restart sfagent service. Eg. Config.yaml

key: <SF_PROFILE_KEY>
tags:
  Name: <any-name>
  appName: <SF_APP_NAME>
  projectName: <SF_PROJECT_NAME>
logging:
  plugins:
    - name: elasticApmTraceLog
      enabled: true
      config:
         log_level:
            - error
            - warning
            - info
         log_path: /var/log/trace/ntrace.log  # Your app log file path

For Kubernetes: ​

Specify following values in metadata labels section of deployment file.

snappyflow/appname: <SF_APP_NAME>
snappyflow/projectname: <SF_PROJECT_NAME>
snappyflow/component: gen-elastic-apm-log # This is must for tracing log correlation

Sample deployment file​

apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    io.kompose.service: python-app
    snappyflow/appname: '<sf_app_name>'
    snappyflow/projectname: '<sf_project_name>'
    snappyflow/component: gen-elastic-apm-log
  name: python-app
spec:
  replicas: 1
  selector:
    matchLabels:
      io.kompose.service: python-app
  strategy: {}
  template:
    metadata:
      labels:
        io.kompose.service: python-app
        snappyflow/appname: '<sf_app_name>'
        snappyflow/projectname: '<sf_project_name>'
        snappyflow/component: gen-elastic-apm-log
    spec:
      containers:
        - env:
            - name: SF_APP_NAME
              value: '<sf_app_name>'
            - name: SF_PROFILE_KEY
              value: '<sf_profile_key>'
            - name: SF_PROJECT_NAME
              value: '<sf_project_name>'
          image: refapp-node:latest
          imagePullPolicy: Always
          name: python-app
          ports:
            - containerPort: 3000
          resources:
            requests:
              cpu: 10m
              memory: 10Mi
            limits:
              cpu: 50m
              memory: 50Mi
      restartPolicy: Always
note

For kubernetes mode we need sfagent pods to be running inside kubernetes cluster where your application pods are deployed.

For viewing trace and logs in Snappyflow server make sure project and app name is created or discovered. Once project and app name is created.

Go to: View App dashboard -> Click on Tracing on left side bar -> Click on view transaction -> Go to real time tab Then click on any trace and go to logs tab to see the correlated logs to trace.

# Note: To get trace in snappyflow server we need log entries to adhere following log format:
<date in following format>
[10/Aug/2021 10:51:16] [<log_level>] [<message>] | elasticapm transaction.id=<transaction_id> trace.id=<trace_id> span.id=<span_id>