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Getting Started

This page will show you how to get started with OpenTelemetry in Python.

You will learn how you can instrument a simple application automatically, in such a way that traces, metrics and logs are emitted to the console.

Prerequisites

Ensure that you have the following installed locally:

Example Application

The following example uses a basic Flask application. If you are not using Flask, that's ok — you can use OpenTelemetry Python with other web frameworks as well, such as Django and FastAPI. For a complete list of libraries for supported frameworks, see the registry.

For more elaborate examples, see examples.

Installation

To begin, set up an environment in a new directory:

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mkdir otel-getting-started
cd otel-getting-started
python3 -m venv .
source ./bin/activate

Now install Flask:

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pip install flask

Create and launch an HTTP Server

Create a file app.py and add the following code to it:

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from random import randint
from flask import Flask

app = Flask(__name__)

@app.route("/rolldice")
def roll_dice():
    return str(do_roll())

def do_roll():
    return randint(1, 6)

Run the application with the following command and open http://localhost:8080/rolldice in your web browser to ensure it is working.

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flask run -p 8080

Instrumentation

Automatic instrumentation will generate telemetry data on your behalf. There are several options you can take, covered in more detail in Automatic Instrumentation. Here we'll use the opentelemetry-instrument agent.

Install the opentelemetry-distro package, which contains the OpenTelemetry API, SDK and also the tools opentelemetry-bootstrap and opentelemetry-instrument you will use below.

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pip install opentelemetry-distro

Run the opentelemetry-bootstrap command:

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opentelemetry-bootstrap -a install

This will install Flask instrumentation.

Run the instrumented app

You can now run your instrumented app with opentelemetry-instrument and have it print to the console for now:

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opentelemetry-instrument \
    --traces_exporter console \
    --metrics_exporter console \
    --logs_exporter console \
    flask run -p 8080

Open http://localhost:8080/rolldice in your web browser and reload the page a few times. After a while you should see the spans printed in the console, such as the following:

View example output 
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{
  "name": "/rolldice",
  "context": {
    "trace_id": "0xdcd253b9501348b63369d83219da0b14",
    "span_id": "0x886c05bc23d2250e",
    "trace_state": "[]"
  },
  "kind": "SpanKind.SERVER",
  "parent_id": null,
  "start_time": "2022-04-27T23:53:11.533109Z",
  "end_time": "2022-04-27T23:53:11.534097Z",
  "status": {
    "status_code": "UNSET"
  },
  "attributes": {
    "http.method": "GET",
    "http.server_name": "127.0.0.1",
    "http.scheme": "http",
    "net.host.port": 5000,
    "http.host": "localhost:5000",
    "http.target": "/rolldice",
    "net.peer.ip": "127.0.0.1",
    "http.user_agent": "curl/7.68.0",
    "net.peer.port": 52538,
    "http.flavor": "1.1",
    "http.route": "/rolldice",
    "http.status_code": 200
  },
  "events": [],
  "links": [],
  "resource": {
    "attributes": {
      "telemetry.sdk.language": "python",
      "telemetry.sdk.name": "opentelemetry",
      "telemetry.sdk.version": "1.14.0",
      "telemetry.auto.version": "0.35b0",
      "service.name": "unknown_service"
    },
    "schema_url": ""
  }
}

The generated span tracks the lifetime of a request to the /rolldice route.

Send a few more requests to the endpoint, and then either wait for a little bit or terminate the app and you'll see metrics in the console output, such as the following:

View example output 
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{
  "resource_metrics": [
    {
      "resource": {
        "attributes": {
          "service.name": "unknown_service",
          "telemetry.auto.version": "0.34b0",
          "telemetry.sdk.language": "python",
          "telemetry.sdk.name": "opentelemetry",
          "telemetry.sdk.version": "1.13.0"
        },
        "schema_url": ""
      },
      "schema_url": "",
      "scope_metrics": [
        {
          "metrics": [
            {
              "data": {
                "aggregation_temporality": 2,
                "data_points": [
                  {
                    "attributes": {
                      "http.flavor": "1.1",
                      "http.host": "localhost:5000",
                      "http.method": "GET",
                      "http.scheme": "http",
                      "http.server_name": "127.0.0.1"
                    },
                    "start_time_unix_nano": 1666077040061693305,
                    "time_unix_nano": 1666077098181107419,
                    "value": 0
                  }
                ],
                "is_monotonic": false
              },
              "description": "measures the number of concurrent HTTP requests that are currently in-flight",
              "name": "http.server.active_requests",
              "unit": "requests"
            },
            {
              "data": {
                "aggregation_temporality": 2,
                "data_points": [
                  {
                    "attributes": {
                      "http.flavor": "1.1",
                      "http.host": "localhost:5000",
                      "http.method": "GET",
                      "http.scheme": "http",
                      "http.server_name": "127.0.0.1",
                      "http.status_code": 200,
                      "net.host.port": 5000
                    },
                    "bucket_counts": [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                    "count": 1,
                    "explicit_bounds": [
                      0, 5, 10, 25, 50, 75, 100, 250, 500, 1000
                    ],
                    "max": 1,
                    "min": 1,
                    "start_time_unix_nano": 1666077040063027610,
                    "sum": 1,
                    "time_unix_nano": 1666077098181107419
                  }
                ]
              },
              "description": "measures the duration of the inbound HTTP request",
              "name": "http.server.duration",
              "unit": "ms"
            }
          ],
          "schema_url": "",
          "scope": {
            "name": "opentelemetry.instrumentation.flask",
            "schema_url": "",
            "version": "0.34b0"
          }
        }
      ]
    }
  ]
}

Add manual instrumentation to automatic instrumentation

Automatic instrumentation captures telemetry at the edges of your systems, such as inbound and outbound HTTP requests, but it doesn't capture what's going on in your application. For that you'll need to write some manual instrumentation. Here's how you can easily link up manual instrumentation with automatic instrumentation.

Traces

First, modify app.py to include code that initializes a tracer and uses it to create a trace that's a child of the one that's automatically generated:

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# These are the necessary import declarations
from opentelemetry import trace

from random import randint
from flask import Flask

# Acquire a tracer
tracer = trace.get_tracer("diceroller.tracer")

app = Flask(__name__)

@app.route("/rolldice")
def roll_dice():
    return str(do_roll())

def do_roll():
    # This creates a new span that's the child of the current one
    with tracer.start_as_current_span("do_roll") as rollspan:
        res = randint(1, 6)
        rollspan.set_attribute("roll.value", res)
        return res

Now run the app again:

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opentelemetry-instrument \
    --traces_exporter console \
    --metrics_exporter console \
    flask run -p 8080

When you send a request to the server, you'll see two spans in the trace emitted to the console, and the one called do_roll registers its parent as the automatically created one:

View example output 
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{
    "name": "do_roll",
    "context": {
        "trace_id": "0x48da59d77e13beadd1a961dc8fcaa74e",
        "span_id": "0x40c38b50bc8da6b7",
        "trace_state": "[]"
    },
    "kind": "SpanKind.INTERNAL",
    "parent_id": "0x84f8c5d92970d94f",
    "start_time": "2022-04-28T00:07:55.892307Z",
    "end_time": "2022-04-28T00:07:55.892331Z",
    "status": {
        "status_code": "UNSET"
    },
    "attributes": {
        "roll.value": 4
    },
    "events": [],
    "links": [],
    "resource": {
        "attributes": {
            "telemetry.sdk.language": "python",
            "telemetry.sdk.name": "opentelemetry",
            "telemetry.sdk.version": "1.14.0",
            "telemetry.auto.version": "0.35b0",
            "service.name": "unknown_service"
        },
        "schema_url": ""
    }
}
{
    "name": "/rolldice",
    "context": {
        "trace_id": "0x48da59d77e13beadd1a961dc8fcaa74e",
        "span_id": "0x84f8c5d92970d94f",
        "trace_state": "[]"
    },
    "kind": "SpanKind.SERVER",
    "parent_id": null,
    "start_time": "2022-04-28T00:07:55.891500Z",
    "end_time": "2022-04-28T00:07:55.892552Z",
    "status": {
        "status_code": "UNSET"
    },
    "attributes": {
        "http.method": "GET",
        "http.server_name": "127.0.0.1",
        "http.scheme": "http",
        "net.host.port": 5000,
        "http.host": "localhost:5000",
        "http.target": "/rolldice",
        "net.peer.ip": "127.0.0.1",
        "http.user_agent": "curl/7.68.0",
        "net.peer.port": 53824,
        "http.flavor": "1.1",
        "http.route": "/rolldice",
        "http.status_code": 200
    },
    "events": [],
    "links": [],
    "resource": {
        "attributes": {
            "telemetry.sdk.language": "python",
            "telemetry.sdk.name": "opentelemetry",
            "telemetry.sdk.version": "1.14.0",
            "telemetry.auto.version": "0.35b0",
            "service.name": "unknown_service"
        },
        "schema_url": ""
    }
}

The parent_id of do_roll is the same is the span_id for /rolldice, indicating a parent-child relationship!

Metrics

Now modify app.py to include code that initializes a meter and uses it to create a counter instrument which counts the number of rolls for each possible roll value:

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# These are the necessary import declarations
from opentelemetry import trace
from opentelemetry import metrics

from random import randint
from flask import Flask

tracer = trace.get_tracer("diceroller.tracer")
# Acquire a meter.
meter = metrics.get_meter("diceroller.meter")

# Now create a counter instrument to make measurements with
roll_counter = meter.create_counter(
    "roll_counter",
    description="The number of rolls by roll value",
)

app = Flask(__name__)

@app.route("/rolldice")
def roll_dice():
    return str(do_roll())

def do_roll():
    with tracer.start_as_current_span("do_roll") as rollspan:
        res = randint(1, 6)
        rollspan.set_attribute("roll.value", res)
        # This adds 1 to the counter for the given roll value
        roll_counter.add(1, {"roll.value": res})
        return res

Now run the app again:

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opentelemetry-instrument \
    --traces_exporter console \
    --metrics_exporter console \
    flask run -p 8080

When you send a request to the server, you'll see the roll counter metric emitted to the console, with separate counts for each roll value:

View example output 
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{
  "resource_metrics": [
    {
      "resource": {
        "attributes": {
          "telemetry.sdk.language": "python",
          "telemetry.sdk.name": "opentelemetry",
          "telemetry.sdk.version": "1.12.0rc1",
          "telemetry.auto.version": "0.31b0",
          "service.name": "unknown_service"
        },
        "schema_url": ""
      },
      "scope_metrics": [
        {
          "scope": {
            "name": "app",
            "version": "",
            "schema_url": null
          },
          "metrics": [
            {
              "name": "roll_counter",
              "description": "The number of rolls by roll value",
              "unit": "",
              "data": {
                "data_points": [
                  {
                    "attributes": {
                      "roll.value": 4
                    },
                    "start_time_unix_nano": 1654790325350232600,
                    "time_unix_nano": 1654790332211598800,
                    "value": 3
                  },
                  {
                    "attributes": {
                      "roll.value": 6
                    },
                    "start_time_unix_nano": 1654790325350232600,
                    "time_unix_nano": 1654790332211598800,
                    "value": 4
                  },
                  {
                    "attributes": {
                      "roll.value": 5
                    },
                    "start_time_unix_nano": 1654790325350232600,
                    "time_unix_nano": 1654790332211598800,
                    "value": 1
                  },
                  {
                    "attributes": {
                      "roll.value": 1
                    },
                    "start_time_unix_nano": 1654790325350232600,
                    "time_unix_nano": 1654790332211598800,
                    "value": 2
                  },
                  {
                    "attributes": {
                      "roll.value": 3
                    },
                    "start_time_unix_nano": 1654790325350232600,
                    "time_unix_nano": 1654790332211598800,
                    "value": 1
                  }
                ],
                "aggregation_temporality": 2,
                "is_monotonic": true
              }
            }
          ],
          "schema_url": null
        }
      ],
      "schema_url": ""
    }
  ]
}

Send telemetry to an OpenTelemetry Collector

The OpenTelemetry Collector is a critical component of most production deployments. Some examples of when it's beneficial to use a collector:

  • A single telemetry sink shared by multiple services, to reduce overhead of switching exporters
  • Aggregating traces across multiple services, running on multiple hosts
  • A central place to process traces prior to exporting them to a backend

Unless you have just a single service or are experimenting, you'll want to use a collector in production deployments.

Configure and run a local collector

First, save the following collector configuration code to a file in the /tmp/ directory:

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# /tmp/otel-collector-config.yaml
receivers:
  otlp:
    protocols:
      grpc:
exporters:
  logging:
    loglevel: debug
processors:
  batch:
service:
  pipelines:
    traces:
      receivers: [otlp]
      exporters: [logging]
      processors: [batch]
    metrics:
      receivers: [otlp]
      exporters: [logging]
      processors: [batch]

Then run the docker command to acquire and run the collector based on this configuration:

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docker run -p 4317:4317 \
    -v /tmp/otel-collector-config.yaml:/etc/otel-collector-config.yaml \
    otel/opentelemetry-collector:latest \
    --config=/etc/otel-collector-config.yaml

You will now have an collector instance running locally, listening on port 4317.

Modify the command to export spans and metrics via OTLP

The next step is to modify the command to send spans and metrics to the collector via OTLP instead of the console.

To do this, install the OTLP exporter package:

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pip install opentelemetry-exporter-otlp

The opentelemetry-instrument agent will detect the package you just installed and default to OTLP export when it's run next.

Run the application

Run the application like before, but don't export to the console:

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opentelemetry-instrument flask run -p 8080

By default, opentelemetry-instrument exports traces and metrics over OTLP/gRPC and will send them to localhost:4317, which is what the collector is listening on.

When you access the /rolldice route now, you'll see output in the collector process instead of the flask process, which should look something like this:

View example output 
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2022-06-09T20:43:39.915Z        DEBUG   loggingexporter/logging_exporter.go:51  ResourceSpans #0
Resource labels:
     -> telemetry.sdk.language: STRING(python)
     -> telemetry.sdk.name: STRING(opentelemetry)
     -> telemetry.sdk.version: STRING(1.12.0rc1)
     -> telemetry.auto.version: STRING(0.31b0)
     -> service.name: STRING(unknown_service)
InstrumentationLibrarySpans #0
InstrumentationLibrary app
Span #0
    Trace ID       : 7d4047189ac3d5f96d590f974bbec20a
    Parent ID      : 0b21630539446c31
    ID             : 4d18cee9463a79ba
    Name           : do_roll
    Kind           : SPAN_KIND_INTERNAL
    Start time     : 2022-06-09 20:43:37.390134089 +0000 UTC
    End time       : 2022-06-09 20:43:37.390327687 +0000 UTC
    Status code    : STATUS_CODE_UNSET
    Status message :
Attributes:
     -> roll.value: INT(5)
InstrumentationLibrarySpans #1
InstrumentationLibrary opentelemetry.instrumentation.flask 0.31b0
Span #0
    Trace ID       : 7d4047189ac3d5f96d590f974bbec20a
    Parent ID      :
    ID             : 0b21630539446c31
    Name           : /rolldice
    Kind           : SPAN_KIND_SERVER
    Start time     : 2022-06-09 20:43:37.388733595 +0000 UTC
    End time       : 2022-06-09 20:43:37.390723792 +0000 UTC
    Status code    : STATUS_CODE_UNSET
    Status message :
Attributes:
     -> http.method: STRING(GET)
     -> http.server_name: STRING(127.0.0.1)
     -> http.scheme: STRING(http)
     -> net.host.port: INT(5000)
     -> http.host: STRING(localhost:5000)
     -> http.target: STRING(/rolldice)
     -> net.peer.ip: STRING(127.0.0.1)
     -> http.user_agent: STRING(curl/7.82.0)
     -> net.peer.port: INT(53878)
     -> http.flavor: STRING(1.1)
     -> http.route: STRING(/rolldice)
     -> http.status_code: INT(200)

2022-06-09T20:43:40.025Z        INFO    loggingexporter/logging_exporter.go:56  MetricsExporter {"#metrics": 1}
2022-06-09T20:43:40.025Z        DEBUG   loggingexporter/logging_exporter.go:66  ResourceMetrics #0
Resource labels:
     -> telemetry.sdk.language: STRING(python)
     -> telemetry.sdk.name: STRING(opentelemetry)
     -> telemetry.sdk.version: STRING(1.12.0rc1)
     -> telemetry.auto.version: STRING(0.31b0)
     -> service.name: STRING(unknown_service)
InstrumentationLibraryMetrics #0
InstrumentationLibrary app
Metric #0
Descriptor:
     -> Name: roll_counter
     -> Description: The number of rolls by roll value
     -> Unit:
     -> DataType: Sum
     -> IsMonotonic: true
     -> AggregationTemporality: AGGREGATION_TEMPORALITY_CUMULATIVE
NumberDataPoints #0
Data point attributes:
     -> roll.value: INT(5)
StartTimestamp: 2022-06-09 20:43:37.390226915 +0000 UTC
Timestamp: 2022-06-09 20:43:39.848587966 +0000 UTC
Value: 1

Next steps

There are several options available for automatic instrumentation and Python. See Automatic Instrumentation to learn about them and how to configure them.

There's a lot more to manual instrumentation than just creating a child span. To learn details about initializing manual instrumentation and many more parts of the OpenTelemetry API you can use, see Manual Instrumentation.

Finally, there are several options for exporting your telemetry data with OpenTelemetry. To learn how to export your data to a preferred backend, see Exporters.