cortisol 0.3.0

Accurately forecast log costs pre-production with Cortisol for Datadog, New Relic, and Grafana 💰📉

Cortisol, accurately forecast log costs pre-production.

cortisol

Cortisol is an open-source command-line tool designed specifically for web services. It offers easy-to-use cost estimation and forecasting capabilities tailored to main observability tools like Datadog, New Relic, Grafana and GCP Cloud Logging. Cortisol assists users in planning and optimizing their log costs before deploying their web services. It operates on a foundation inspired by Locust, allowing users to define user behavior using a regular Python script 💰📉.

For detailed reference to Cortisol commands please go to: Read the Docs

Installation

Prerequisites

Cortisol requires one of the following Python versions: 3.8, 3.9, 3.10 or 3.11

Install cortisol

At the command line:

pip install cortisol

If you have an Apple M1 CPU, we suggest installing using Poetry as a dependency management. Otherwise, the underline gevent library may not work.

Getting started

First things first! We need a RESTful service and so you'll need to do the following steps:

1. Clone this example repo https://github.com/CortisolAI/getting-started-example
2. cd getting-started-example
3. mkvirtualenv getting-started-cortisol
4. python -m app.main which will make the service available at http://127.0.0.1:8080/

And, now, it's time to create your first cortisol file. Copy and paste the following in a file named cortisolfile.py:

from locust import task

from cortisol.cortisollib.users import CortisolHttpUser


class WebsiteUser(CortisolHttpUser):
    @task
    def my_task(self):
        self.client.get("/")

Go to the virtualenv where the cortisol library is installed and run the following command in the terminal. Make sure to change the base path for the --log-file argument:

cortisol logs cost-estimate --host http://127.0.0.1:8080 --users 10 --spawn-rate 5 --run-time 10s --cortisol-file cortisolfile.py --log-file /some/path/getting-started-example/cortisol_app.log

Commands

Log Cost Estimate

Name

Forecast log costs

Synopsis

cortisol logs cost-estimate --host HOST --log-file LOG_FILE --users NUM_USERS --spawn-rate SPAWN_RATE --run-time RUN_TIME -cortisol-file CORTISOL_PYTHON_FILE

Description

Forecast log costs pre-production with Cortisol for Datadog, New Relic, and Grafana

Example

cortisol logs cost-estimate --host http://10.20.31.32:8000 --users 10 --spawn-rate 5 --run-time 10s --cortisol-file ./examples/cortisolfile.py --log-file /app/playground_app.log

Required Flags - Option 1

-f, --cortisol-file PATH Path to the CORTISOL_FILE

-h, --host TEXT Host in the following format: http://10.20.31.32 or http://10.20.31.32:8000

-l, --log-file PATH Path to log file

-u, --users INTEGER Peak number of concurrent users

-r, --spawn-rate INTEGER Rate to spawn users at (users per second)

-t, --run-time TEXT Stop after the specified amount of time, e.g. (50, 30s, 200m, 5h, 2h30m, etc.). Default unit in seconds.

Required Flags - Option 2

All the latter options plus the following in case your application run in a Docker container:

-c, --container-id TEXT Optional docker container id where your application runs

Example

cortisol logs cost-estimate --host http://127.0.0.1:8080 --users 100 --spawn-rate 5 --run-time 10s --cortisol-file ./examples/cortisolfile.py --log-file /app/playground_app.log --container-id 1212aa67e530af75b3310e1e5b30261b36844a6748df1d321088c4d48a20ebd0

Required Flags - Option 3

--config PATH Path to config file (YAML or JSON) containing the long version of flags from option 1

Optional Flags

--stats-file PATH Path where to store the cortisol statistics output as a csv

Here's a YAML example:

host: "http://10.20.31.32:8000"
log-file: "/path/to/logfile"
users: 100
spawn-rate: 30
run-time: "20m"
cortisol-file: "some_cortisol_file.py"
stats-file: "cortisol_stats.csv"

Here's a YAML example with docker container id:

host: "http://10.20.31.32:8000"
log-file: "/path/to/logfile"
users: 100
spawn-rate: 30
run-time: "20m"
cortisol-file: "some_cortisol_file.py"
container-id: "80f1bc1e7feb"
stats-file: "cortisol_stats.csv"

and a JSON example:

{
  "host": "http://10.20.31.32:8000",
  "log_file": "/path/to/logfile",
  "users": 100,
  "spawn_rate": 30,
  "run_time": "20m",
  "cortisol_file": "some_cortisol_file.py",
  "container_id": "80f1bc1e7feb",
  "stats-file": "cortisol_stats.csv"
}