feat: daily metrics db migration #5791
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kwasniew
commented
Jan 9, 2024
| feature_name VARCHAR(255), | ||
| app_name VARCHAR(255), | ||
| environment VARCHAR(100), | ||
| timestamp TIMESTAMP WITH TIME ZONE, |
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I'm thinking about using date here since we don't want the time section
kwasniew
commented
Jan 9, 2024
| feature_name VARCHAR(255), | ||
| app_name VARCHAR(255), | ||
| environment VARCHAR(100), | ||
| timestamp TIMESTAMP WITH TIME ZONE, |
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same here, date not time
kwasniew
commented
Jan 9, 2024
| feature_name VARCHAR(255), | ||
| app_name VARCHAR(255), | ||
| environment VARCHAR(100), | ||
| date DATE, |
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this is deliberately date not timestamp as the original hourly table
kwasniew
commented
Jan 9, 2024
| exports.up = function (db, cb) { | ||
| db.runSql( | ||
| ` | ||
| CREATE TABLE IF NOT EXISTS client_metrics_env_daily ( |
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copy of client_metrics_env but with date instead of timestamp
kwasniew
commented
Jan 9, 2024
| no INTEGER DEFAULT 0, | ||
| PRIMARY KEY (feature_name, app_name, environment, date) | ||
| ); | ||
| CREATE TABLE IF NOT EXISTS client_metrics_env_variants_daily ( |
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copy of metrics_env_variants but with date instead of timestamp
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About the changes
Business problem: we want to store up to 3 months of metrics data. Hourly storage is not feasible, so we go for the daily storage above 2 days.
Technical solution:
Adding 2 new tables to simulate time series DB in postgres. The idea is to store data in high resolution initially (the original tables) and then aggregate it to a lower resolution as it ages (2 new tables).
The original metrics tables contain hourly data, the new tables will contain daily data. The structure is the same except from the timestamp column changing to date column.
Calculations for 90 days of data: https://gist.github.com/kwasniew/864bb6e5111b7de516b6e4454963d2d4#data-requirements-for-the-extended-90-days-scenario. New table should take twice as much space as the hourly table.
In the next PR I will create a scheduler that uses those tables for aggregation: #5804
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