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Lossy TimeSeries DataBase
35 Commits 1 Branch 8 Tags 284 KiB
Python 92.8%
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Peter J. Holzer b10b62e77d Make expiry exponential 
Choosing the element to expire from a uniform random distribution tends
to expire data points much too early. We want to always keep the oldest
observation and have a roughly exponential distribution between the
newest and the oldest observation.
 		2022-12-17 17:49:19 +01:00
clients Measure HTTP(S) response code and time 2022-11-27 10:18:23 +01:00
doc Think about scheduling measurements and processing them 2022-11-27 10:19:37 +01:00
templates Format timestamps explicitely 2022-11-26 23:36:00 +01:00
app.py Make logging configurable 2022-12-11 22:57:41 +01:00
ltsdb-json Maintain a single lossy timeseries (PoC) 2022-08-20 17:39:12 +02:00
ltsdb_json.py Make expiry exponential 2022-12-17 17:49:19 +01:00
ltsdb_test Implement json prototype of LTsDb 2022-08-21 11:58:31 +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 Process queue 2022-12-11 22:58:26 +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