profilis 0.2.0

High performance, non blocking profiler for Python web apps.

Profilis

A high performance, non-blocking profiler for Python web applications.

Overview

Profilis provides drop-in observability across APIs, functions, and database queries with minimal performance impact. It's designed to be:

• Non blocking: Async collection with configurable batching and backpressure handling
• Framework agnostic: Works with Flask and custom applications (FastAPI/Sanic planned)
• Database aware: Built-in support for SQLAlchemy (pyodbc/MongoDB/Neo4j planned)
• Production ready: Configurable sampling, error tracking, and multiple export formats

Features

• Request Profiling: Automatic HTTP request/response timing and status tracking
• Function Profiling: Decorator-based function timing with exception tracking
• Database Instrumentation: SQLAlchemy query performance monitoring with row counts
• Built-in UI: Real-time dashboard for monitoring and debugging
• Multiple Exporters: JSONL (with rotation), Console
• Runtime Context: Distributed tracing with trace/span ID management
• Configurable Sampling: Control data collection volume in production

Installation

Install the core package with optional dependencies for your specific needs:

Option 1: Using pip with extras (Recommended)

# Core package only
pip install profilis

# With Flask support
pip install profilis[flask]

# With database support
pip install profilis[flask,sqlalchemy]

# With all integrations
pip install profilis[all]

Option 2: Using requirements files

# Minimal setup (core only)
pip install -r requirements-minimal.txt

# Flask integration
pip install -r requirements-flask.txt

# SQLAlchemy integration
pip install -r requirements-sqlalchemy.txt

# All integrations
pip install -r requirements-all.txt

Option 3: Manual installation

# Core dependencies
pip install typing_extensions>=4.0

# Flask support
pip install flask[async]>=3.0

# SQLAlchemy support
pip install sqlalchemy>=2.0 aiosqlite greenlet

# Performance optimization
pip install orjson>=3.8

Quick Start

Flask Integration

from flask import Flask
from profilis.flask.adapter import ProfilisFlask
from profilis.exporters.jsonl import JSONLExporter
from profilis.core.async_collector import AsyncCollector

# Setup exporter and collector
exporter = JSONLExporter(dir="./logs", rotate_bytes=1024*1024, rotate_secs=3600)
collector = AsyncCollector(exporter, queue_size=2048, batch_max=128, flush_interval=0.1)

# Create Flask app and integrate Profilis
app = Flask(__name__)
profilis = ProfilisFlask(
    app,
    collector=collector,
    exclude_routes=["/health", "/metrics"],
    sample=1.0  # 100% sampling
)

@app.route('/api/users')
def get_users():
    return {"users": ["alice", "bob"]}

# Start the app
if __name__ == "__main__":
    app.run(debug=True)

Function Profiling

from profilis.decorators.profile import profile_function
from profilis.core.emitter import Emitter
from profilis.exporters.console import ConsoleExporter
from profilis.core.async_collector import AsyncCollector

# Setup profiling
exporter = ConsoleExporter(pretty=True)
collector = AsyncCollector(exporter, queue_size=128, flush_interval=0.2)
emitter = Emitter(collector)

@profile_function(emitter)
def expensive_calculation(n: int) -> int:
    """This function will be automatically profiled."""
    result = sum(i * i for i in range(n))
    return result

@profile_function(emitter)
async def async_operation(data: list) -> list:
    """Async functions are also supported."""
    processed = [item * 2 for item in data]
    return processed

# Use the profiled functions
result = expensive_calculation(1000)

Manual Event Emission

from profilis.core.emitter import Emitter
from profilis.exporters.jsonl import JSONLExporter
from profilis.core.async_collector import AsyncCollector
from profilis.runtime import use_span, span_id

# Setup
exporter = JSONLExporter(dir="./logs")
collector = AsyncCollector(exporter)
emitter = Emitter(collector)

# Create a trace context
with use_span(trace_id=span_id()):
    # Emit custom events
    emitter.emit_req("/api/custom", 200, dur_ns=15000000)  # 15ms
    emitter.emit_fn("custom_function", dur_ns=5000000)      # 5ms
    emitter.emit_db("SELECT * FROM users", dur_ns=8000000, rows=100)

# Close collector to flush remaining events
collector.close()

Built-in Dashboard

from flask import Flask
from profilis.flask.ui import make_ui_blueprint
from profilis.core.stats import StatsStore

app = Flask(__name__)
stats = StatsStore()  # 15-minute rolling window

# Mount the dashboard at /_profilis
ui_bp = make_ui_blueprint(stats, ui_prefix="/_profilis")
app.register_blueprint(ui_bp)

# Visit http://localhost:5000/_profilis to see the dashboard

Advanced Usage

Custom Exporters

from profilis.core.async_collector import AsyncCollector
from profilis.exporters.base import BaseExporter

class CustomExporter(BaseExporter):
    def export(self, events: list[dict]) -> None:
        for event in events:
            # Custom export logic
            print(f"Custom export: {event}")

# Use custom exporter
exporter = CustomExporter()
collector = AsyncCollector(exporter)

Runtime Context Management

from profilis.runtime import use_span, span_id, get_trace_id, get_span_id

# Create distributed trace context
with use_span(trace_id="trace-123", span_id="span-456"):
    current_trace = get_trace_id()  # "trace-123"
    current_span = get_span_id()    # "span-456"

    # Nested spans inherit trace context
    with use_span(span_id="span-789"):
        nested_span = get_span_id()  # "span-789"
        parent_trace = get_trace_id() # "trace-123"

Performance Tuning

from profilis.core.async_collector import AsyncCollector

# High-throughput configuration
collector = AsyncCollector(
    exporter,
    queue_size=8192,        # Large queue for high concurrency
    batch_max=256,          # Larger batches for efficiency
    flush_interval=0.05,    # More frequent flushing
    drop_oldest=True        # Drop events under backpressure
)

# Low-latency configuration
collector = AsyncCollector(
    exporter,
    queue_size=512,         # Smaller queue for lower latency
    batch_max=32,           # Smaller batches for faster processing
    flush_interval=0.01,    # Very frequent flushing
    drop_oldest=False       # Don't drop events
)

Configuration

Environment Variables

# Note: Environment variable support is planned for future releases
# Currently, all configuration is done programmatically

Sampling Strategies

# Random sampling
profilis = ProfilisFlask(app, collector=collector, sample=0.1)  # 10% of requests

# Route-based sampling
profilis = ProfilisFlask(
    app,
    collector=collector,
    exclude_routes=["/health", "/metrics", "/static"],
    sample=1.0
)

Exporters

JSONL Exporter

from profilis.exporters.jsonl import JSONLExporter

# With rotation
exporter = JSONLExporter(
    dir="./logs",
    rotate_bytes=1024*1024,  # 1MB per file
    rotate_secs=3600         # Rotate every hour
)

Console Exporter

from profilis.exporters.console import ConsoleExporter

# Pretty-printed output for development
exporter = ConsoleExporter(pretty=True)

# Compact output for production
exporter = ConsoleExporter(pretty=False)

Performance Characteristics

• Event Creation: ≤15µs per event
• Memory Overhead: ~100 bytes per event
• Throughput: 100K+ events/second on modern hardware
• Latency: Sub-millisecond collection overhead

Documentation

Full documentation is available at: Profilis Docs

Docs are written in Markdown under docs/ and built with MkDocs Material.

Available Documentation

• Getting Started - Quick setup and basic usage
• Configuration - Tuning and customization
• Flask Integration - Flask adapter documentation
• SQLAlchemy Support - Database instrumentation
• JSONL Exporter - Log file output
• Built-in UI - Dashboard documentation
• Architecture - System design

To preview locally:

pip install mkdocs mkdocs-material mkdocs-mermaid2-plugin
mkdocs serve

Development

• See Contributing and Development Guidelines.
• Branch strategy: trunk‑based (feat/*, fix/*, perf/*, chore/*).
• Commits follow Conventional Commits.

Roadmap

See Profilis – v0 Roadmap Project and docs/overview/roadmap.md.

License

MIT