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https://github.com/cortisolai/cortisol
Accurately forecast log costs pre-production with Cortisol for Datadog, New Relic, Grafana and GCP Cloud Logging 💰📉
https://github.com/cortisolai/cortisol
aws-cloudwatch cloud-cost-estimates cost-estimates cost-management datadog datadog-logs devops finops gcp-logging grafana grafana-loki log-analysis log-costs loki metric-costs monitoring new-relic new-relic-logs observability trace-costs
Last synced: 3 months ago
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Accurately forecast log costs pre-production with Cortisol for Datadog, New Relic, Grafana and GCP Cloud Logging 💰📉
- Host: GitHub
- URL: https://github.com/cortisolai/cortisol
- Owner: CortisolAI
- License: apache-2.0
- Created: 2023-07-25T21:20:22.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-10-15T11:19:36.000Z (over 1 year ago)
- Last Synced: 2024-09-27T15:41:00.587Z (4 months ago)
- Topics: aws-cloudwatch, cloud-cost-estimates, cost-estimates, cost-management, datadog, datadog-logs, devops, finops, gcp-logging, grafana, grafana-loki, log-analysis, log-costs, loki, metric-costs, monitoring, new-relic, new-relic-logs, observability, trace-costs
- Language: Python
- Homepage:
- Size: 1.28 MB
- Stars: 36
- Watchers: 2
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
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](https://www.datadoghq.com/), [New Relic](https://newrelic.com/), [Grafana](https://grafana.com/) and [GCP Cloud Logging](https://cloud.google.com/logging). Cortisol assists users in planning and optimizing their log costs before deploying their web services. It operates on a foundation inspired by [Locust](https://locust.io/), allowing users to define user behavior using a regular Python script 💰📉.
For detailed reference to Cortisol commands please go to: [Read the Docs](https://cortisolai.github.io/cortisol/)
## 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`:
```python
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:
```terminal
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:
```YAML
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:
```YAML
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:
```JSON
{
"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"
}
```