Lossy TimeSeries DataBase

Peter J. Holzer 3f21122769 Use random time stamps ... Don't remember what I wanted to test there ...		2023-08-18 21:14:15 +02:00
clients	Backport to Python 3.6	2023-05-06 00:13:52 +02:00
doc	Think about scheduling measurements and processing them	2022-11-27 10:19:37 +01:00
templates	Display min, max and last value in description	2022-12-31 17:55:25 +01:00
test_data	Rename data to test_data to prevent clash with live layout	2023-03-19 11:38:37 +01:00
tests	Allow arbitrary number of stops	2023-03-19 11:34:38 +01:00
app.py	Add new API endpoint /record as a (preferred) alias for /report	2023-01-07 12:55:21 +01:00
dashboard.py	Make log scales more robust	2023-08-18 21:11:01 +02:00
ltsdb-json	Maintain a single lossy timeseries (PoC)	2022-08-20 17:39:12 +02:00
ltsdb_json.py	Keep min and max values of dropped data points	2023-01-05 15:14:28 +01:00
ltsdb_test	Use random time stamps	2023-08-18 21:14:15 +02:00
predict_disk_full	Use a lossy timeseries to predict when a filesystem will be full (PoC)	2022-08-20 17:40:16 +02:00
predict_disk_full_home_bytes	Use a lossy timeseries to predict when a filesystem will be full (PoC)	2022-08-20 17:40:16 +02:00
predict_disk_full_var_bytes	Use a lossy timeseries to predict when a filesystem will be full (PoC)	2022-08-20 17:40:16 +02:00
process_queue	Smooth out old data to avoid false positives in disk full prediction	2023-08-18 21:07:57 +02:00
pyproject.toml	Add test case	2023-02-04 12:43:04 +01:00
record_df	Record disk usage in LTsDb	2022-08-21 12:00:07 +02:00
record_dus	Be quiet(er)	2022-08-21 13:15:01 +02:00
requirements.txt	Add gunicorn to requirements	2022-09-03 22:57:32 +02:00