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FEAT : maj doc

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Raymond Bourges 2026-03-12 19:41:44 +01:00
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notes.md
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@ -8,8 +8,7 @@
```bash ```bash
ssh grab-4 ssh grab-4
docker exec -t client-demo-postgres-1 pg_dump -Fc -U postgres -d postgres -f /tmp/backup_postgres.dump docker exec client-demo-postgres-1 pg_dump -Fc -U postgres postgres > /tmp/backup_postgres.dump
docker cp client-demo-postgres-1:/tmp/backup_postgres.dump /tmp/backup_postgres.dump
exit exit
cd /tmp cd /tmp
@ -25,11 +24,11 @@ pg_restore -c -x -I mesure_idx2 -d postgres -h localhost -U postgres -W backup_
CREATE TABLE mesure_ng ( CREATE TABLE mesure_ng (
"date" TIMESTAMPTZ, "date" TIMESTAMPTZ,
id_captation INT NOT NULL, id_captation INT NOT NULL,
valeur float4 NOT NULL, valeur float4 NOT NULL
) WITH ( ) WITH (
tsdb.hypertable, tsdb.hypertable,
timescaledb.segmentby = 'id_captation', timescaledb.segmentby = 'id_captation',
timescaledb.orderby='time DESC' timescaledb.orderby='date DESC'
) )
INSERT INTO mesure_ng ("date", id_captation, valeur) INSERT INTO mesure_ng ("date", id_captation, valeur)
@ -37,6 +36,11 @@ INSERT INTO mesure_ng ("date", id_captation, valeur)
FROM mesure FROM mesure
``` ```
### Compression des données (stockage en colonnes)
`SELECT add_columnstore_policy('mesure_ng', INTERVAL '15 days')`
### Ex. de requête ### Ex. de requête
```sql ```sql
@ -59,10 +63,50 @@ ORDER BY
heure; heure;
``` ```
### Exploit
```sql
SELECT *
FROM timescaledb_information.chunks
WHERE hypertable_name = 'mesure_ng'
```
`SELECT * FROM timescaledb_information.jobs`
### Continuous aggregates (WIP) ### Continuous aggregates (WIP)
<https://www.tigerdata.com/docs/use-timescale/latest/continuous-aggregates/create-a-continuous-aggregate> <https://www.tigerdata.com/docs/use-timescale/latest/continuous-aggregates/create-a-continuous-aggregate>
* CREATE MATERIALIZED VIEW * CREATE MATERIALIZED VIEW
* SELECT add_continuous_aggregate_policy * CALL add_continuous_aggregate_policy
* supprimer les "vieilles" données de mesure_ng * supprimer les "vieilles" données de mesure_ng (add_retention_policy ?)
#### Exemples
##### Gauges
CREATE MATERIALIZED VIEW sensor_hourly
WITH (timescaledb.continuous)
AS
SELECT
time_bucket('1 hour', time) AS bucket,
sensor_id,
average(
time_weight('linear', "date", valeur)
) AS moyenne_temporelle,
percentile_agg(value) AS pct
FROM sensor_data
GROUP BY bucket, sensor_id;
Puis
SELECT
bucket,
sensor_id,
approx_percentile(0.95, pct) AS p95,
approx_percentile(0.99, pct) AS p99
FROM sensor_hourly;
##### Index
delta(counter_agg(time, value))