Π·Π΅ΡΠΊΠ°Π»ΠΎ ΠΈΠ· https://github.com/mozilla/opmon.git
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This makes it easier to visualize Opmon aggregate metrics in Grafana: Grafana's Bigquery integration is fundamentally `TIMESTAMP` shaped, not `DATE` shaped, and this avoids writing nested queries. |
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opmon | ||
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CODE_OF_CONDUCT.md | ||
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LICENSE | ||
README.md | ||
mypy.ini | ||
platform_config.toml | ||
pyproject.toml | ||
requirements.in | ||
requirements.txt | ||
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README.md
Operational Monitoring
Operational Monitoring (OpMon) is a self-service tool that aggregates and summarizes operational metrics that indicate the health of software. OpMon can be used to continuously monitor rollouts, experiments (including experiments with continuous enrollments) or the population of a specific product (for example, Firefox Desktop).
For more information on how to set up an Operational Monitoring project, see the documentation on dtmo.
Local installation
# Create and activate a python virtual environment.
python3 -m venv venv/
source venv/bin/activate
pip install -r requirements.txt
pip install .
The opmon
CLI tool will be available to run locally:
$ opmon --help
Usage: opmon [OPTIONS] COMMAND [ARGS]...
Initialize CLI.
Options:
--log_project_id, --log-project-id TEXT
GCP project to write logs to
--log_dataset_id, --log-dataset-id TEXT
Dataset to write logs to
--log_table_id, --log-table-id TEXT
Table to write logs to
--log_to_bigquery, --log-to-bigquery
--help Show this message and exit.
Commands:
backfill Backfill a specific project.
run Execute the monitoring ETL for a specific date.
validate_config Validate config files.
Documentation
User documentation is available on dtmo.
Developer documentation is available in the docs/
directory.