---
title: Recommendation Service
linkTitle: Recommendation
aliases: [recommendationservice]
cSpell:ignore: cpython instrumentor NOTSET
---

This service is responsible to get a list of recommended products for the user
based on existing product IDs the user is browsing.

[Recommendation service source](https://github.com/open-telemetry/opentelemetry-demo/blob/main/src/recommendationservice/)

## Auto-instrumentation

This Python based service, makes use of the OpenTelemetry auto-instrumentor for
Python, accomplished by leveraging the `opentelemetry-instrument` Python wrapper
to run the scripts. This can be done in the `ENTRYPOINT` command for the
service's `Dockerfile`.

```dockerfile
ENTRYPOINT [ "opentelemetry-instrument", "python", "recommendation_server.py" ]
```

## Traces

### Initializing Tracing

The OpenTelemetry SDK is initialized in the `__main__` code block. This code
will create a tracer provider, and establish a Span Processor to use. Export
endpoints, resource attributes, and service name are automatically set by the
OpenTelemetry auto instrumentor based on environment variables.

```python
tracer = trace.get_tracer_provider().get_tracer("recommendationservice")
```

### Add attributes to auto-instrumented spans

Within the execution of auto-instrumented code you can get current span from
context.

```python
span = trace.get_current_span()
```

Adding attributes to a span is accomplished using `set_attribute` on the span
object. In the `ListRecommendations` function an attribute is added to the span.

```python
span.set_attribute("app.products_recommended.count", len(prod_list))
```

### Create new spans

New spans can be created and placed into active context using
`start_as_current_span` from an OpenTelemetry Tracer object. When used in
conjunction with a `with` block, the span will automatically be ended when the
block ends execution. This is done in the `get_product_list` function.

```python
with tracer.start_as_current_span("get_product_list") as span:
```

## Metrics

### Initializing Metrics

The OpenTelemetry SDK is initialized in the `__main__` code block. This code
will create a meter provider. Export endpoints, resource attributes, and service
name are automatically set by the OpenTelemetry auto instrumentor based on
environment variables.

```python
meter = metrics.get_meter_provider().get_meter("recommendationservice")
```

### Custom metrics

The following custom metrics are currently available:

- `app_recommendations_counter`: Cumulative count of # recommended products per
  service call

### Auto-instrumented metrics

The following metrics are available through auto-instrumentation, courtesy of
the `opentelemetry-instrumentation-system-metrics`, which is installed as part
of `opentelemetry-bootstrap` on building the recommendationservice Docker image:

- `runtime.cpython.cpu_time`
- `runtime.cpython.memory`
- `runtime.cpython.gc_count`

## Logs

### Initializing logs

The OpenTelemetry SDK is initialized in the `__main__` code block. The following
code creates a logger provider with a batch processor, an OTLP log exporter, and
a logging handler. Finally, it creates a logger for use throughout the
application.

```python
logger_provider = LoggerProvider(
    resource=Resource.create(
        {
            'service.name': service_name,
        }
    ),
)
set_logger_provider(logger_provider)
log_exporter = OTLPLogExporter(insecure=True)
logger_provider.add_log_record_processor(BatchLogRecordProcessor(log_exporter))
handler = LoggingHandler(level=logging.NOTSET, logger_provider=logger_provider)

logger = logging.getLogger('main')
logger.addHandler(handler)
```

### Create log records

Create logs using the logger. Examples can be found in `ListRecommendations` and
`get_product_list` functions.

```python
logger.info(f"Receive ListRecommendations for product ids:{prod_list}")
```

As you can see, after the initialization, log records can be created in the same
way as in standard Python. OpenTelemetry libraries automatically add a trace ID
and span ID for each log record and, in this way, enable correlating logs and
traces.

### Notes

Logs for Python are still experimental, and some changes can be expected. The
implementation in this service follows the
[Python log example](https://github.com/open-telemetry/opentelemetry-python/blob/stable/docs/examples/logs/example.py).
