⚡ pytrackio

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The fastest way to add performance tracking to any Python project. One decorator. Zero dependencies. No servers. No config. Just answers.

pip install pytrackio

from pytrackio import track, timer, counter, report

@track
def process_order(order_id):
    ...

with timer("db_query"):
    results = db.execute(query)

counter("api_calls").increment()
report()

================================================================================
  pytrackio - Performance Report         uptime: 4.21s
================================================================================
  Name                         Calls      Avg      Min      Max      p95      p99  Errors
--------------------------------------------------------------------------------
  process_order                   42   120.34    98.10   310.50   290.10   308.40       -
  db_query                        18    45.20    40.10    89.30    87.20    89.10       -
--------------------------------------------------------------------------------
  Counters
--------------------------------------------------------------------------------
  api_calls: 42
================================================================================

Why pytrackio?

Sometimes you just need to know how fast your code runs, without setting up servers, installing agents, or managing infrastructure.

pytrackio runs inside your process, requires zero setup, and gives you real answers in seconds.

Installation

pip install pytrackio

Requirements: Python 3.10+ and zero external dependencies.

Usage

@track - decorate any function

from pytrackio import track

@track
def fetch_user(user_id: int):
    ...

@track
async def send_notification(user_id: int):
    await mailer.send(...)

@track(name="payment_gateway")
def charge_card(amount: float):
    ...

Tracks call count, avg, min, max, p95, p99 latency, error count, and error rate.

timer() - track any code block

from pytrackio import timer

with timer("image_resize"):
    resized = resize_image(img, width=800)

async with timer("external_api"):
    result = await fetch_data()

counter() - named event counters

from pytrackio import counter

counter("cache_hits").increment()
counter("retries").increment(3)
counter("queue_size").decrement()
counter("requests").reset()

print(counter("cache_hits").value)

report() - print everything

from pytrackio import report

report()
report(show_counters=False)
report(colour=False)

Returns the report as a string for logging or alerting.

Export data

from pytrackio import export_json, export_csv, export_dict

export_json("metrics.json")
export_csv("metrics.csv")
data = export_dict()

Raw registry access

from pytrackio import get_registry

registry = get_registry()

for s in registry.all_summaries():
    if s.error_rate > 5.0:
        print(f"{s.name} error rate: {s.error_rate:.1f}%")

registry.reset()

Real-world example

from pytrackio import track, timer, counter, report

@track
def get_product(product_id: int) -> dict:
    counter("db_reads").increment()
    return db.query(Product).get(product_id)

@track
async def checkout(cart_id: int) -> str:
    with timer("payment"):
        result = await payment_gateway.charge(cart_id)
    counter("orders_placed").increment()
    return result["order_id"]

report()

Contributing

See CONTRIBUTING.md - PRs welcome.

Changelog

See CHANGELOG.md for version history.

Author

Deepanshu - Python Developer and Open Source Author. Creator of pytrackio. Building tools that solve real problems for real developers.

License

MIT - free to use, modify, and distribute.