dta-observability 0.0.18

Lightweight wrapper around OpenTelemetry core APIs

DTA Observability

A lightweight wrapper around OpenTelemetry for Python applications.

Overview

DTA Observability simplifies the use of OpenTelemetry by providing a streamlined interface for instrumentation. It handles configuration of tracing, metrics, and logging with minimal setup.

Features

• Single function initialization of all telemetry components
• Automatic instrumentation for Flask, FastAPI, Celery, and other frameworks
• Structured logging with trace context correlation
• Function decoration for easy span creation
• System and application metrics collection
• Support for OTLP, GCP Cloud, and console exporters
• Optional trace-only none exporter for log correlation without trace export
• Automatic resource detection
• Configuration via parameters or environment variables

Installation

pip install dta-observability

Or with Poetry:

poetry add dta-observability

Basic Usage

import dta_observability
from dta_observability import get_logger, traced

# Initialize telemetry
dta_observability.init_telemetry(
    service_name="my-service",
    service_version="1.0.0",
    otlp_endpoint="http://otel-collector:4317",
    exporter_type="otlp",  # Options: "otlp", "console", "gcp"
)

# Get a logger
logger = get_logger("my-service")

# Use the traced decorator
@traced(name="my_function")
def my_function():
    logger.info("Doing work")
    return "result"

Framework Integration

Flask

from flask import Flask
import dta_observability

app = Flask(__name__)

dta_observability.init_telemetry(
    service_name="flask-service",
    flask_app=app
)

FastAPI

from fastapi import FastAPI
import dta_observability

app = FastAPI()

dta_observability.init_telemetry(
    service_name="fastapi-service",
    fastapi_app=app
)

Celery

from celery import Celery
import dta_observability

app = Celery("tasks")

dta_observability.init_telemetry(
    service_name="worker-service",
    celery_app=app
)

GCP Integration

When using the GCP exporter type:

1. For traces: Uses Cloud Trace exporter
2. For metrics: Uses Cloud Monitoring exporter with workload.googleapis.com prefix
3. For logs: Uses GCP log format when log_format is set to "gcp", sending logs to stdout in the proper format

To use GCP integration:

dta_observability.init_telemetry(
    service_name="my-gcp-service",
    exporter_type="gcp",  # Uses GCP exporters for all signal types
    log_format="gcp"      # Formats logs for GCP
)

When log_format is set to "gcp", all logs will be formatted for Google Cloud Logging and sent to stdout, while metrics and traces will use their respective GCP exporters.

If you want GCP-formatted logs with trace/span correlation fields but do not want to export traces, set traces_exporter_type="none" and keep enable_traces=True:

dta_observability.init_telemetry(
    service_name="my-gcp-service",
    exporter_type="gcp",
    log_format="gcp",
    traces_exporter_type="none",
    enable_traces=True,
    enable_logs=True,
    enable_metrics=False,
)

This keeps spans active in-process for log correlation, but does not export them to Cloud Trace or to the console.

OTLP Integration

OTLP exporters send telemetry to an OpenTelemetry Collector:

dta_observability.init_telemetry(
    service_name="my-otlp-service",
    exporter_type="otlp",
    otlp_endpoint="http://otel-collector:4317"
)

Configuration

Configuration options available in init_telemetry():

Parameter	Environment Variable	Default	Description
service_name	SERVICE_NAME	unnamed-service	Name to identify the service
service_version	SERVICE_VERSION	0.0.0	Version of the service
service_instance_id	SERVICE_INSTANCE_ID		Unique identifier for this service instance
resource_attributes		None	Additional resource attributes (dictionary)
configure_auto_instrumentation	AUTO_INSTRUMENTATION_ENABLED	True	Whether to auto-instrument detected libraries
log_level	LOG_LEVEL	INFO	Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
log_format	LOG_FORMAT	default	Log format type (default or gcp)
flask_app		None	Flask application instance to instrument
fastapi_app		None	FastAPI application instance to instrument
celery_app		None	Celery application instance to instrument
safe_logging	SAFE_LOGGING	True	Whether to enable safe logging with complex data types
excluded_instrumentations	EXCLUDED_INSTRUMENTATIONS	None	Comma-separated list of instrumentations to exclude
otlp_endpoint	EXPORTER_OTLP_ENDPOINT	http://localhost:4317	OTLP exporter endpoint URL
otlp_insecure	EXPORTER_OTLP_INSECURE	True	Whether to use insecure connection for OTLP
batch_export_delay_ms	BATCH_EXPORT_SCHEDULE_DELAY	120000	Milliseconds between batch exports
enable_resource_detectors	RESOURCE_DETECTORS_ENABLED	True	Whether to enable automatic resource detection
enable_logging_instrumentation	LOGGING_INSTRUMENTATION_ENABLED	True	Whether to enable logging instrumentation
propagators	OTEL_PROPAGATORS	w3c,gcp,tracecontext	Comma-separated list of context propagators
exporter_type	EXPORTER_TYPE	otlp	Default exporter type for all signals (otlp, console, or gcp)
traces_exporter_type	TRACES_EXPORTER_TYPE	otlp	Exporter type for traces (otlp, console, gcp, or none)
metrics_exporter_type	METRICS_EXPORTER_TYPE	otlp	Exporter type for metrics (otlp, console, or gcp)
logs_exporter_type	LOGS_EXPORTER_TYPE	otlp	Exporter type for logs (otlp, console, or gcp)
enable_traces		True	Whether to enable trace collection
enable_metrics		True	Whether to enable metrics collection
enable_logs		True	Whether to enable logs collection
enable_system_metrics	SYSTEM_METRICS_ENABLED	True	Whether to enable system metrics collection

Environment variables can also be prefixed with OTEL_ or DTA_ (e.g., DTA_SERVICE_NAME).

Examples

The examples directory contains sample applications demonstrating usage with different frameworks.