APM for Flask and Python WSGI Apps: Request Tracing and Slow-Query Spans
Add request tracing and slow-query detection to Flask apps with OpenTelemetry and JustAnalytics. Gunicorn-ready.
Last Tuesday I watched a Flask app eat 47 seconds on a single request. The endpoint looked fine — just a user lookup and some JSON serialization. Logs showed nothing. Metrics showed a latency spike. But without tracing, I had no idea whether the slowness lived in the database query, the serialization, or somewhere in the middleware stack.
Turned out SQLAlchemy was running 23 separate queries because someone (me, six months ago) wrote a loop that triggered N+1 loads. One trace with proper spans would've shown that immediately. Instead I spent three hours adding debug prints and reading EXPLAIN output.
This tutorial walks through setting up request tracing and slow-query detection in a Flask WSGI app using OpenTelemetry — wired to JustAnalytics for traces, errors, and analytics in one dashboard. If you're evaluating options, our observability platform comparison covers how JustAnalytics stacks up against Datadog and New Relic. I'm not going to pretend this is glamorous work. But it'll save you from my Tuesday. By the end, you'll have automatic span generation for every request, database query spans with timing, and alerts when queries exceed your threshold.
(If you're on Django instead of Flask, we covered the equivalent setup in our Django middleware tutorial. The concepts overlap but the instrumentation differs.)
What you'll have by the end
A Flask application with:
- Automatic distributed tracing on every HTTP request
- SQLAlchemy query spans nested under request traces
- Slow-query detection that flags anything over 100ms (configurable)
- Gunicorn-compatible setup that survives worker forks
- Traces exported to JustAnalytics for correlation with errors and analytics
The whole setup adds about 80 lines of configuration code. Honestly, I wish it were less — OpenTelemetry's API is flexible but verbose. Tradeoff for no vendor lock-in, I guess. It's an open standard, so you can swap exporters later if needed.
Prerequisites
- Python 3.10+ and Flask 2.3+
- A JustAnalytics account — free tier gets you 100K events/month
- SQLAlchemy (we're using 2.0+ syntax, but 1.4+ works)
- Gunicorn or another WSGI server for production
pip install opentelemetry-api opentelemetry-sdk opentelemetry-instrumentation-flask opentelemetry-instrumentation-sqlalchemy opentelemetry-exporter-otlp
Step 1: Basic OpenTelemetry setup
Create a new file at tracing.py in your Flask app root. This is where we configure the tracer provider and exporter.
# tracing.py
import os
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.resources import Resource, SERVICE_NAME
def init_tracing(service_name: str = "flask-app"):
resource = Resource(attributes={
SERVICE_NAME: service_name,
"deployment.environment": os.getenv("FLASK_ENV", "development"),
})
provider = TracerProvider(resource=resource)
# JustAnalytics OTLP endpoint
exporter = OTLPSpanExporter(
endpoint=os.getenv("OTEL_EXPORTER_OTLP_ENDPOINT", "https://otel.justanalytics.app/v1/traces"),
headers={
"Authorization": f"Bearer {os.getenv('JUSTANALYTICS_API_KEY')}",
},
)
provider.add_span_processor(BatchSpanProcessor(exporter))
trace.set_tracer_provider(provider)
return trace.get_tracer(__name__)
The BatchSpanProcessor is important here — it queues spans and exports them in batches every 5 seconds by default. Without batching, you'd make an HTTP call for every span, which kills throughput on high-traffic apps.
Step 2: Instrument Flask and SQLAlchemy
Now wire the instrumentation into your Flask app. I'm assuming you've got a typical Flask setup with SQLAlchemy.
# app.py
import os
from flask import Flask
from flask_sqlalchemy import SQLAlchemy
from opentelemetry.instrumentation.flask import FlaskInstrumentor
from opentelemetry.instrumentation.sqlalchemy import SQLAlchemyInstrumentor
from tracing import init_tracing
# Initialize tracing BEFORE creating the app
tracer = init_tracing(service_name="my-flask-app")
app = Flask(__name__)
app.config["SQLALCHEMY_DATABASE_URI"] = os.getenv("DATABASE_URL", "postgresql://localhost/mydb")
db = SQLAlchemy(app)
# Instrument Flask
FlaskInstrumentor().instrument_app(app)
# Instrument SQLAlchemy — pass the engine after db.init_app
with app.app_context():
SQLAlchemyInstrumentor().instrument(engine=db.engine)
@app.route("/users/<int:user_id>")
def get_user(user_id):
user = db.session.execute(
db.select(User).filter_by(id=user_id)
).scalar_one_or_none()
if not user:
return {"error": "not found"}, 404
return {"id": user.id, "name": user.name}
At this point, every request to your Flask app creates a trace with spans for the HTTP request and any SQLAlchemy queries executed during that request. The query spans will show the SQL statement (with parameters redacted by default), timing, and database connection metadata.
Not earth-shattering yet. But we're getting there.
Step 3: Add slow-query detection
Here's where it gets useful. We'll create a custom SpanProcessor that tags spans exceeding a threshold.
# slow_query_processor.py
from opentelemetry.sdk.trace import SpanProcessor, ReadableSpan
from opentelemetry.trace import StatusCode
class SlowQueryProcessor(SpanProcessor):
def __init__(self, threshold_ms: float = 100.0):
self.threshold_ms = threshold_ms
def on_start(self, span, parent_context):
pass
def on_end(self, span: ReadableSpan):
# Only process database spans
if span.name.startswith("SELECT") or span.name.startswith("INSERT") or \
span.name.startswith("UPDATE") or span.name.startswith("DELETE"):
duration_ms = (span.end_time - span.start_time) / 1_000_000
if duration_ms > self.threshold_ms:
# Tag as slow query — this shows up in JustAnalytics alerts
span.set_attribute("db.slow_query", True)
span.set_attribute("db.duration_ms", round(duration_ms, 2))
def shutdown(self):
pass
def force_flush(self, timeout_millis=None):
pass
Add this processor to your tracer provider in tracing.py:
# Updated tracing.py
from slow_query_processor import SlowQueryProcessor
def init_tracing(service_name: str = "flask-app"):
# ... resource and provider setup ...
# Add slow query processor BEFORE the batch processor
provider.add_span_processor(SlowQueryProcessor(threshold_ms=100))
provider.add_span_processor(BatchSpanProcessor(exporter))
trace.set_tracer_provider(provider)
return trace.get_tracer(__name__)
Now any database query over 100ms gets tagged with db.slow_query=True. In the JustAnalytics dashboard, you can filter traces by this attribute and set up alerts when slow queries spike. I usually set the threshold at 100ms for OLTP workloads — if you're running reports or batch jobs, you might want 500ms or higher.
Step 4: Gunicorn-compatible initialization
Here's a gotcha that'll bite you in production. Gunicorn (and uWSGI, and most WSGI servers) pre-fork workers. If you initialize the tracer in the parent process, the child workers will inherit broken state — file handles, connections, the whole mess.
Create a Gunicorn config file:
# gunicorn.conf.py
import os
workers = int(os.getenv("GUNICORN_WORKERS", 4))
bind = os.getenv("GUNICORN_BIND", "0.0.0.0:8000")
worker_class = "sync"
timeout = 30
def post_fork(server, worker):
# Reinitialize tracing in each worker
from tracing import init_tracing
init_tracing(service_name="my-flask-app")
And update your app startup to handle both development and production:
# app.py (updated)
import os
# Only init tracing in main process for dev
# Gunicorn workers init via post_fork
if os.getenv("FLASK_ENV") == "development":
from tracing import init_tracing
tracer = init_tracing(service_name="my-flask-app")
Run with gunicorn -c gunicorn.conf.py app:app and each worker will have its own tracer instance with a clean connection to the OTLP exporter. I learned this the hard way when traces just... stopped appearing after deploys. Workers were holding stale exporter connections. Fun.
Step 5: Add custom spans for business logic
The auto-instrumentation covers HTTP and database, but sometimes you want spans around specific business logic — payment processing, external API calls, background tasks.
from opentelemetry import trace
tracer = trace.get_tracer(__name__)
@app.route("/checkout", methods=["POST"])
def checkout():
with tracer.start_as_current_span("validate_cart") as span:
cart = validate_cart(request.json)
span.set_attribute("cart.item_count", len(cart.items))
with tracer.start_as_current_span("charge_payment") as span:
span.set_attribute("payment.provider", "stripe")
result = stripe.charges.create(amount=cart.total, ...)
span.set_attribute("payment.success", result.paid)
with tracer.start_as_current_span("send_confirmation") as span:
send_order_email(cart.user_email)
return {"order_id": cart.order_id}
Each start_as_current_span creates a child span under the parent HTTP request span. In your trace waterfall, you'll see exactly how long each step took — and when the payment provider is slow, you'll have the data to prove it.
Common errors and how to fix them
"No module named 'opentelemetry.instrumentation.flask'" — You're missing the instrumentation package. Run pip install opentelemetry-instrumentation-flask. The core opentelemetry-api and opentelemetry-sdk packages don't include framework instrumentors.
Traces appear in dev but not production. Check your OTLP endpoint and API key environment variables in production. Gunicorn doesn't inherit env vars from your shell by default — you need to set them in your systemd service file, Docker compose, or whatever runs your workers. Also verify the post_fork hook is actually running (add a print statement temporarily).
SQLAlchemy spans aren't appearing. Make sure you call SQLAlchemyInstrumentor().instrument(engine=db.engine) AFTER the engine is created. If you're using Flask-SQLAlchemy, that means inside an app.app_context() block. Also check that you imported the instrumentation package — it's easy to forget.
Spans have null parent IDs. This usually means you're creating spans outside a request context. If you're tracing background tasks or Celery jobs, you need to propagate context explicitly or start a new root span. I wasted an afternoon on this one.
Memory usage keeps climbing. The BatchSpanProcessor holds spans in memory until export. If your export endpoint is slow or failing, the buffer grows. Set max_export_batch_size and max_queue_size in the BatchSpanProcessor constructor to cap memory usage. For memory profiling techniques specific to Python apps, see our Python memory debugging guide.
What this doesn't cover
I said I'd be straight with you, so here's the scope limit.
This setup gives you request tracing and database span visibility for a single Flask app. It doesn't give you distributed tracing across multiple services out of the box — you'll need to propagate trace context via HTTP headers (W3C Trace Context) when calling other services. The opentelemetry-instrumentation-requests package handles this for outgoing HTTP calls if you're using the requests library.
It also doesn't replace proper database monitoring. You can see slow queries, but you won't get query plans, index recommendations, or connection pool metrics. For that, look at your database's native tooling or something like pganalyze for PostgreSQL. (Annoying, I know. I keep hoping someone will unify all this.)
And if you're chasing microsecond-level latency, the overhead matters. Tracing adds 3-5ms at p99 in our tests. For most web apps that's noise. For high-frequency trading, it's not. (Also if you're building an HFT system in Flask, we should talk.)
Next steps
You've got request tracing and slow-query detection running. The natural next step is correlating traces with error tracking — when an exception fires, you want the full trace context attached. JustAnalytics bundles error tracking alongside APM, so your exception reports include the trace ID and span waterfall automatically. Check our error tracking for Laravel guide for the equivalent PHP setup (the correlation pattern is the same).
For teams consolidating their observability stack, JustAnalytics Pro at $49/month ($39/month billed annually) covers 1M events/month with APM, error tracking, session replay, uptime monitoring, and analytics — all in one under-5KB script. That's the pitch, anyway. Whether it's right for you depends on your scale. The reality is you're probably stitching together Datadog ($23/host/month for APM) plus Sentry ($26/month for Team) plus something for analytics. At some point the dashboard sprawl gets annoying. Or maybe you like having specialized tools. I don't know your life.
For click fraud monitoring alongside your Flask app's traffic analytics, ClickzProtect integrates well with the event stream — check their bot detection guide for filtering invalid traffic. If you're managing multiple Flask deployments across client projects, JustBrowser keeps your browser sessions separated during debugging. For development environment consistency across your team, DevOS handles the setup once so tracing configs don't drift between local machines.
The code from this tutorial lives in our examples repo.
Frequently Asked Questions
Does OpenTelemetry auto-instrumentation work with Flask extensions like Flask-SQLAlchemy?
Yes. The opentelemetry-instrumentation-sqlalchemy package hooks into the SQLAlchemy engine automatically, so Flask-SQLAlchemy queries get traced without extra configuration. Install both opentelemetry-instrumentation-flask and opentelemetry-instrumentation-sqlalchemy, and your database spans will appear nested under your HTTP request spans.
How do I trace slow queries without capturing every single database call?
Set a threshold in your span processor. The example in this tutorial shows filtering spans under 100ms before export. You can also use sampling — OpenTelemetry's ParentBasedTraceIdRatioBased sampler lets you trace 10% of requests while always capturing slow ones via custom logic in a SpanProcessor.
Will this setup work with uWSGI instead of Gunicorn?
Yes. OpenTelemetry's WSGI instrumentation works with any WSGI server. The only difference is the startup command and where you initialize the tracer. With uWSGI, use the @postfork decorator to initialize tracing after workers fork, just like the Gunicorn post_fork hook shown here.
What's the performance overhead of tracing every request?
In our tests on a Flask app doing 200 requests/second, full tracing added 3-5ms of p99 latency and about 2% CPU overhead. Most of that is serialization and export. Batch exporting with a 5-second interval (the default) keeps the overhead flat at high throughput. If you're chasing sub-millisecond latency, sample aggressively.
Try JustAnalytics
All-in-one observability in one under-5KB script: cookieless analytics + error tracking + APM + session replay + uptime + structured logs. Replaces GA4 + Sentry + Datadog + Pingdom + LogRocket. Free tier (100K events/mo), Pro $49/month ($39 annual).
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