Introduction
Technological advancements are made possible through tools that offer the easiest ways to build services and a convenient way to connect them. It is established that APIs are the standard and secure choice for connecting services, while Python has acquired tremendous traction recently, becoming a highly sought-after programming language. A popular choice for creating fast and secure API services is FastAPI, a high-performance web framework with type safety built using rust-based libraries.
An essential part of the API service lifecycle, other than building and integrating, is monitoring. Monitoring enables administrative capabilities, ensuring APIs function seamlessly and behave as intended throughout their lifecycle. Developers may extract performant APIs by equipping themselves with techniques for API monitoring using Python and FastAPI.
What is API Monitoring?
Implementation of rigorous checks and tests to ensure API functionality, availability, performance, and more for delivery and operational excellence is API monitoring. API monitoring helps identify and eradicate anomalies such as bugs, slow response times and outages that can affect API efficiency and integrity.
Why is API Monitoring Important?
Applications undergo both stable and breaking changes as they evolve. Having CI/CD procedures in place to identify breaking changes and apply fixes is beneficial. However, despite safeguards, anomalies occasionally find their way into live applications. These anomalies can cause data leaks, performance bottlenecks, security breaches, and more if not handled meticulously.
Users report certain anomalies, while stakeholders report others. By proactive issue identification, the impact of anomalies can be minimized in advance with robust API monitoring in place. Before end users report problems, an ideal API monitoring solution can assist in identifying and resolving them.
5 API Monitoring Strategies
Standard API monitoring strategies guarantee secure and scalable API services with essential and promising capabilities. Let us understand strategies that can boost the delivery confidence of your FastAPI applications.
Health and Availability Monitoring
API endpoints are the crucial features of modern applications for querying repeatedly to perform logical backend operations. The endpoints are expected to be always healthy and available to deliver the desired functionality to the querying applications.
Continuously validating the response status codes and querying the endpoints on scheduled intervals deliver stats supporting the health and availability of an API endpoint.
from fastapi import FastAPI, BackgroundTasks, HTTPException
import httpx
import schedule
import time
#Endpoint to validate for health and availability
endpoint = "https://www.checklyhq.com/blog"
def endpoint_health_check():
try:
response = httpx.get(endpoint)
status = response.raise_for_status()
if status == None:
Health_check = "OK"
print(f"Health Status: {Health_check}")
except httpx.RequestError as e:
error = f"Error: {str(e)}"
print(f"Health Status: {error}")
except httpx.HTTPError as e:
error = f"HTTP Error: {e.response.status_code}"
print(f"Health Status: {error}")
except Exception as e:
error = f"Unexpected Error: {str(e)}"
print(f"Health Status: {error}")
def run_periodic_health_checks():
# Schedule the health check every 10 mins
schedule.every(6000).seconds.do(endpoint_health_check)
app = FastAPI()
@app.on_event("startup")
async def on_startup(background_health_check_init: BackgroundTasks):
# Starting background periodic health check
background_health_check_init.add_task(run_periodic_health_checks)
The scheduled health check delivers endpoint health and availability simultaneously. Curated alerting mechanisms can be articulated by leveraging the function response to implement responsive actions accordingly.
Error Rate Analysis
Error rate analysis corresponds to the percentage value of the total number of internal server errors returned by an endpoint with an error status message of “Internal Service Error” or “Service Unavailable” divided by the total number of API requests made to the endpoint in five minutes.
(error_count / total_requests) * 100.0
Error rate analysis is crucial to finding trends and patterns in the incidence of errors through a set of predefined checks. These checks ensure that the endpoints return successful payloads with minimized error responses.
from fastapi import FastAPI, HTTPException
app = FastAPI()
error_count = 0
total_requests = 0
@app.exception_handler(HTTPException)
async def http_exception_handler(rqst, excpn):
global error_count, total_requests
error_count += 1
total_requests += 1
return JSONResponse(
status_code=excpn.status_code,
content={"error": excpn.detail, "error_rate": calculate_error_rate()},
)
def error_rate_validation():
if total_requests == 0:
return 0.0
return (error_count / total_requests) * 100.0
Applying error rate logic helps capture the API error responses into logs for handling. Parsing the log files with custom logic helps set up alerting and notifications to react quickly to possible problems depending on real-time error types and rates.
import logging
from plyer import notification
logging.basicConfig(filename="API_error_analysis.log", level=logging.ERROR)
message = "Parsed item from log file"
def send_notification(message):
notification.notify(
title="Log Alert",
message=message,
timeout=10
)
Rate Limiting and Throttling
Rate limiting limits how many queries a client can make to an API in a predetermined time interval. In addition to limiting the number of requests, throttling ensures holding up or queuing excessive requests rather than simply rejecting them for sequential processing.
Endpoint-level rate limiting and throttling promise better performance and security at a granular level. Calculated throttle and rate limitation enables you to stop API abuse through request overloading and shields them from denial-of-service attacks.
By using FastAPIs simpleRateLimiter namespace, we can derive the rate_limits at specified intervals.
SimpleRateLimiter(rate_limit="500/minute", block=True)
An extra layer of protection can be added to data access based on user authentication levels. Also, requests can be filtered and routed at the subscription level to different API endpoints.
from fastapi import FastAPI, Depends
from fastapi.security import OAuth2PasswordBearer
from fastapi.throttling import Throttle, ThrottleRequest, SimpleRateLimiter
app = FastAPI()
# enabling throttle setting
limiter = SimpleRateLimiter(rate_limit="500/minute", block=True)
# validting users for redirection and limiting
oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
async def get_current_user(token: str = Depends(oauth2_scheme)):
return token
@app.get("/bounded-endpoint")
async def bounded_endpoint(current_user: str = Depends(get_current_user),
request: ThrottleRequest = Depends()):
await limiter.consume(request.client.host)
return {"message": "The rate limit is 500 per minute.
upgrade for more"}
Distributed Tracing
Distributed remote applications deployed and maintained across environments rely on performant and responsive endpoints. These endpoints have to traverse through proxies, gateways, and security groups to deliver the desired functionality and payload.
Through distributed tracing, which tracks the path of requests as they traverse various services, applications can be monitored and profiled at scale. It aids in comprehending a distributed system’s performance and bottlenecks.
Continuous Security Scanning
The method of continuously and automatically evaluating an API’s security throughout its lifecycle is known as continuous API security scanning. The objective is to methodically identify and mitigate security risks and vulnerabilities in an automated fashion.
This procedure is a component of the DevSecOps effort, which aims to include security in the API development and deployment process. Through continuous security scanning, SQL injection attacks, auth vulnerabilities, data mismanagement issues, misconfiguration, and more can be handled.
Best Practices
The monitoring strategies are necessary to develop robust and resilient API endpoints, while best practices ensure the endpoints are streamlined for performance and managed for efficiency.
Key Metrics Monitoring
Key monitoring metrics that exhibit the API functionality and status are very crucial. The enablement of critical metrics for logging exposes detailed insights into the API. Key metrics include response times, error rates, latency, throughput, request rates, concurrency, HTTP status codes, etc.
By examining these indicators, you can ensure that API security, performance, and dependability are at their best and can be identified and fixed early on. Building a responsive and well-optimized API architecture can be achieved through gathering and analyzing metrics using tools like Prometheus, Grafana, or Datadog.
Explicit Logging
When developing APIs, explicit logging should be a top priority to gain a thorough understanding of its functionality and performance. Explicit logging entails logging crucial data such as endpoint-specific error messages, request specifics, and important events. Timestamp, request method, endpoint, user data, and pertinent context should all be included in explicit logs. This information helps with thorough performance analysis, security audits, and debugging of all API events.
Regular log examination and evaluation help spot irregularities, solve problems, and enhance API security and dependability. Additionally, employing structured logging formats, such as JSON, makes log integration with monitoring tools like grafana and Prometheus easier, improving log readability.
Automated Incident Response
Custom scripts can be developed through accumulated metrics and logs to build automated incident response systems. These response systems can react to real-time events occurring throughout the application by applying mitigative solutions and triggering alerts based on criticality.
Unusual traffic patterns, security lapses, or degradation in performance are examples of incidents. Automated responses may include alerting appropriate teams, scaling resources, or blocking harmful IP addresses. Incorporating incident response into API operations yields a reduction in API downtime and improvement in overall security posture. In addition, anomalies are addressed with a prompt and consistent response.
Conclusion
Guaranteed security, performance, and dependability of APIs demand API monitoring. Organizations can leverage it to prevent security breaches, and performance bottlenecks while delivering responsive solutions at scale. Through API monitoring, logs and metrics can be captured, which in turn can be used to detect and address incidents proactively. Organizations can deliver performant APIs by implementing efficient monitoring strategies and applying proven best practices.