FEAT : maj doc

docker-compose_PG.yml

@@ -0,0 +1,7 @@
+services:
+  timescaledb:
+    image: postgres:18
+    ports:
+      - 5432:5432
+    environment:
+      - POSTGRES_PASSWORD=postgres

notes.md

@@ -8,8 +8,7 @@
 
 ```bash
 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
 
 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 (
   "date" TIMESTAMPTZ,
   id_captation INT NOT NULL,
-  valeur float4 NOT NULL,
+  valeur float4 NOT NULL
 ) WITH (
   tsdb.hypertable,
   timescaledb.segmentby = 'id_captation',
-  timescaledb.orderby='time DESC'
+  timescaledb.orderby='date DESC'
 )
 
 INSERT INTO mesure_ng ("date", id_captation, valeur)
@@ -37,6 +36,11 @@ INSERT INTO mesure_ng ("date", id_captation, valeur)
     FROM mesure
 ```
 
+### Compression des données (stockage en colonnes)
+
+`SELECT add_columnstore_policy('mesure_ng', INTERVAL '15 days')`
+
+
 ### Ex. de requête
 
 ```sql
@@ -59,10 +63,50 @@ ORDER BY
   heure;
 ```
 
+### Exploit
+
+```sql
+SELECT *
+FROM timescaledb_information.chunks
+WHERE hypertable_name = 'mesure_ng'
+```
+
+`SELECT * FROM timescaledb_information.jobs`
+
 ### Continuous aggregates (WIP)
 
 <https://www.tigerdata.com/docs/use-timescale/latest/continuous-aggregates/create-a-continuous-aggregate>
 
 * 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))