This content originally appeared on DEV Community and was authored by Manas Sharma
Introduction to OpenTelemetry & OpenObserve
OpenTelemetry and OpenObserve? Game changers in the observability landscape!
In today’s fast-paced world of software development, monitoring your application is essential to ensure it’s running smoothly. OpenTelemetry, an open-source observability framework, allows developers to collect logs, metrics, and traces in a standardized way. OpenObserve steps in as the perfect partner, offering a user-friendly interface to analyze this telemetry data effectively. It’s like having a magnifying glass to scrutinize the inner workings of your application.
Logging in OpenTelemetry
Logging is not just about recording events; it’s about gaining insights.
OpenTelemetry simplifies logging by integrating with existing logging libraries. This unified approach lets you capture logs, traces, and metrics with consistent metadata, making it easier to correlate data across your application. Imagine tracing a single request from start to finish across various services—that’s the power of OpenTelemetry.
Here's a quick example to get you started:
import logging
from opentelemetry import trace
tracer = trace.get_tracer_provider().get_tracer(__name__)
with tracer.start_as_current_span("demo"):
current_span = trace.get_current_span()
current_span.add_event("This is a span event")
logging.getLogger().error("This is a log message")
Collecting telemetry data is only the first step - the real value lies in the ability to effectively analyze and derive insights from this data. This is where OpenObserve steps in as a powerful solution.
OpenObserve Key Features
Easy to deploy & Lightweight: Single binary that can run on a laptop or as a container/Kubernetes cluster
User-friendly interface: More intuitive than tools like Grafana
Flexible querying: Supports both SQL and PromQL
Built-in alerting: Eliminates the need for additional setup
Significant cost savings: Up to 140x less storage costs compared to Elasticsearch
Collecting Logs with OpenTelemetry
Unified data collection with OpenTelemetry? Yes, please!
OpenTelemetry is a versatile tool that allows you to collect logs either directly or via existing log files. You can configure the OpenTelemetry Collector to process these logs and export them to a platform like OpenObserve. This setup ensures that your logs, traces, and metrics are seamlessly integrated, providing a holistic view of your application.
Demo Architecture
Getting Started
For this demo, we will perform all the tasks in a Windows environment. You can also do these in other environment(ubuntu/Mac).
Step 1: Setting Up the OpenTelemetry Collector
First things first, let’s set up the OpenTelemetry Collector:
- Run the commands below to install the opentelemetry-collector-contrib for Windows. Make sure you check that you are using the latest version.
cd .\otel-collector\
Invoke-WebRequest -Uri "https://github.com/open-telemetry/opentelemetry-collector-releases/releases/download/v0.107.0/otelcol-contrib_0.107.0_windows_amd64.tar.gz" -OutFile "otelcol-contrib.tar.gz"
tar -xzf otelcol-contrib.tar.gz
- Run the OpenTelemetry Collector:
./otelcol-contrib --config "C:\Users\lenovo\Desktop\Fast-API Otel logging\config.yaml"
You will get an output similar to the screenshot below:
The collector is now up and running, ready to process logs and traces like a pro.
Step 2: Setting Up OpenObserve
Now, let's get OpenObserve running:
- Download and Set Up OpenObserve:
Invoke-WebRequest -Uri "https://github.com/openobserve/openobserve/releases/download/v0.10.7/openobserve-v0.10.7-windows-amd64.zip" -OutFile "openobserve.zip"
Expand-Archive -Path openobserve.zip -DestinationPath .
- Run OpenObserve:
$env:ZO_ROOT_USER_EMAIL = "root@example.com"
$env:ZO_ROOT_USER_PASSWORD = "Complexpass#123"
.\openobserve.exe
Boom! OpenObserve is live at http://localhost:5080. Time to dive into some data!
Step 3: Setting Up the FastAPI Application
Next, we set up a FastAPI application that logs all requests:
main.py
from fastapi import FastAPI
from opentelemetry import trace
from opentelemetry.instrumentation.fastapi import FastAPIInstrumentor
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
import logging
resource = Resource(attributes={"service.name": "fastapi-service"})
tracer_provider = TracerProvider(resource=resource)
trace.set_tracer_provider(tracer_provider)
# Set up OTLP exporter for traces
otlp_exporter = OTLPSpanExporter(endpoint="http://localhost:4317", insecure=True)
span_processor = BatchSpanProcessor(otlp_exporter)
tracer_provider.add_span_processor(span_processor)
# Set up logging
logging.basicConfig(
filename='app.log',
filemode='a',
format='%(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s',
datefmt='%H:%M:%S',
level=logging.DEBUG
)
logger = logging.getLogger(__name__)
app = FastAPI()
# Instrument FastAPI
FastAPIInstrumentor.instrument_app(app)
@app.get("/")
async def root():
logger.info("Root endpoint accessed")
return {"message": "Hello World"}
@app.get("/items/{item_id}")
async def read_item(item_id: int):
logger.info(f"Item details requested for item_id: {item_id}")
return {"item_id": item_id}
Run your FastAPI application:
uvicorn main:app --reload
Your API is now up and running, ready to log and trace every request!
Step 4: Configuring OpenTelemetry Collector
Next, we need to configure the OpenTelemetry Collector using a config.yaml file. This file defines how logs are received, processed, and exported. Here's a breakdown of the key components:
- Receivers:
-
filelog: Monitors your application log file. -
otlp: Supports gRPC and HTTP protocols for receiving traces.
-
- Processors:
-
batch: Batches log entries for efficient transmission.
-
- Exporters:
-
otlphttp/openobserve: Sends all telemetry data to OpenObserve. To set this up, log in to OpenObserve, click on theIngestionbutton from the sidebar, and selectCustomand thenOtel Collectorto get the endpoint and the token.
-
config.yaml
receivers:
filelog:
include: path\to\the\file\app.log
otlp:
protocols:
grpc:
http:
processors:
batch:
exporters:
otlphttp/openobserve:
endpoint: `http://localhost:5080/api/default/`
headers:
Authorization: Basic cm9vdEBleGFtcGxlLmNvbTpNM3FCR2tvaFhGNjBjOXFM
stream-name: default
service:
pipelines:
logs:
receivers: [filelog]
processors: [batch]
exporters: [otlphttp/openobserve]
traces:
receivers: [otlp]
processors: [batch]
exporters: [otlphttp/openobserve]
Step 5: Testing and Analyzing
Let’s put everything to the test!
Test Your API:
- Use a browser or curl to test the endpoints:
curl http://localhost:8000/
curl http://localhost:8000/items/45
- Check the Logs & Traces in OpenObserve:
Head over to http://localhost:5080 and explore your logs. You can filter, search, and dive deep into the data to see how your application is performing.
OpenObserve Dashboard
Logs-Spans
Traces
Traces Dashboard
Common Pitfalls and Troubleshooting
Common Configuration Mistakes
Misconfigured Logging
Setting up OpenTelemetry logging can often present challenges, particularly when it comes to ensuring that all components are correctly configured. In our architecture, it is crucial to ensure that the config.yaml file is properly structured and that all necessary receiver, processor, and exporter components are configured accurately.
Best Practices
To avoid similar issues, consider the following best practices:
-
Double-check Configuration: Always review your
config.yamlfor accuracy, ensuring that all paths, endpoints, and tokens are correct. - Use Environment Variables: For sensitive information like authentication tokens, consider using environment variables to keep your configuration secure and flexible.
If you encounter issues with logs not being ingested or exported, double-check your configuration and consult the OpenTelemetry Discussions forum for help and insights from the community.
Conclusion and Next Steps
Observability? Check!
By combining OpenTelemetry with OpenObserve, you’ve set up a powerful monitoring solution for your FastAPI application. This setup ensures that you have all the tools you need to keep a close eye on your application’s performance, from logging to tracing, all in one place.
Now go forth and monitor like a pro! 🚀
Acknowledgements
This content originally appeared on DEV Community and was authored by Manas Sharma
Manas Sharma | Sciencx (2024-09-01T17:12:03+00:00) Monitoring Your FastAPI Application with OpenTelemetry and OpenObserve. Retrieved from https://www.scien.cx/2024/09/01/monitoring-your-fastapi-application-with-opentelemetry-and-openobserve/
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