Controlling Log Levels for Synthetic Events in .with_span_events()
Many developers struggle with effectively managing the volume of logs generated by their applications, especially when dealing with synthetic events like those created using the .with_span_events() method. These events are invaluable for monitoring and debugging, but their default verbosity can lead to overwhelming log files and make critical information difficult to find. This article will guide you through the process of controlling the log level for synthetic events, enabling you to achieve a balance between detailed insights and manageable log sizes.
The Scenario: Unwanted Log Verbosity
Imagine you're building a web application using a framework like Flask or Django. You're using a tracing library to monitor request processing, and you've implemented the .with_span_events() method to capture specific events within your code. However, you soon realize that your log files are overflowing with information, making it challenging to pinpoint essential details.
from opentelemetry.instrumentation.flask import FlaskInstrumentor
from opentelemetry.trace import TracerProvider
from opentelemetry.sdk.trace import SpanProcessor, SimpleSpanProcessor
from opentelemetry.sdk.trace.export import BatchSpanExporter
from opentelemetry.sdk.resources import Resource
# ... your Flask app setup
FlaskInstrumentor().instrument_app(app)
trace_provider = TracerProvider(resource=Resource.create({}))
span_processor = SimpleSpanProcessor(BatchSpanExporter())
trace_provider.add_span_processor(span_processor)
@app.route('/')
def index():
# ... your code
with trace_provider.get_tracer(__name__).start_as_current_span("my-span"):
# ... your code
trace_provider.get_tracer(__name__).with_span_events(
"event_1",
attributes={"key": "value"},
message="My synthetic event",
)
# ... your code
return "Hello World!"
In the above example, the .with_span_events() method will log every time the "event_1" occurs, potentially generating a large number of log entries. This excessive logging might not be necessary, especially in production environments.
The Solution: Leveraging Log Levels
To control the log level of synthetic events, you need to understand the logging mechanisms provided by your chosen tracing library. Most libraries follow a hierarchical logging system, allowing you to set different verbosity levels for different components. The most common levels are:
- DEBUG: Logs everything, including detailed information.
- INFO: Logs general information and significant events.
- WARNING: Logs potential issues or warnings.
- ERROR: Logs errors and exceptions.
- CRITICAL: Logs severe errors and system failures.
Adjusting Log Levels:
To limit the verbosity of your logs, you can adjust the logging level for your tracing library. This can be done through configuration files or programmatically within your code.
Example with OpenTelemetry:
import logging
# ... your code
logging.basicConfig(level=logging.INFO) # Sets the default log level to INFO
# ... your Flask app setup
FlaskInstrumentor().instrument_app(app)
trace_provider = TracerProvider(resource=Resource.create({}))
# ... your other code
By setting the logging level to INFO, you will only see INFO, WARNING, ERROR, and CRITICAL level messages. This will significantly reduce the number of log entries generated by your application.
Remember:
- The default log level varies depending on the tracing library you're using.
- Refer to your library's documentation for specific instructions on how to adjust log levels.
- It's crucial to find a balance between detailed logging and manageable log sizes. Consider your application's needs and environment when deciding on the appropriate log level.
Beyond Log Levels: Filtering and Customizing
In addition to adjusting log levels, many tracing libraries allow you to filter synthetic events based on specific criteria. You can set up rules to exclude events that are not relevant to your monitoring or debugging needs.
For example, you might want to filter out events that occur within a specific time window or events that are associated with a specific user.
Furthermore, some libraries offer the ability to customize the format of log messages, allowing you to include specific data points or omit unnecessary information. This fine-grained control ensures that your logs are tailored to your needs.
Conclusion
Controlling the log level of synthetic events generated by .with_span_events() is essential for maintaining manageable and informative logs. By understanding the logging mechanisms of your tracing library and leveraging features like log levels, filtering, and customization, you can effectively manage the volume of your logs while preserving valuable insights. This will enable you to troubleshoot issues efficiently and monitor your application's performance without being overwhelmed by unnecessary data.
