FEAT : conf objets timescale OK
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Raymond Bourges
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notes.md
141
notes.md
@ -14,53 +14,9 @@ exit
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cd /tmp
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scp grab-4:/tmp/backup_postgres.dump .
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pg_restore -c -x -t mesure -d postgres -h localhost -U postgres -W backup_postgres.dump
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pg_restore -c -x -I mesure_idx1 -d postgres -h localhost -U postgres -W backup_postgres.dump
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pg_restore -c -x -I mesure_idx2 -d postgres -h localhost -U postgres -W backup_postgres.dump
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```
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### Création d'une hypertable
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```sql
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CREATE TABLE mesure_ng (
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"date" TIMESTAMPTZ,
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id_captation INT NOT NULL,
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valeur float4 NOT NULL
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) WITH (
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tsdb.hypertable,
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timescaledb.segmentby = 'id_captation',
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timescaledb.orderby='date DESC'
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)
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INSERT INTO mesure_ng ("date", id_captation, valeur)
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SELECT "date", id_captation, valeur
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FROM mesure
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```
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### Compression des données (stockage en colonnes)
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`SELECT add_columnstore_policy('mesure_ng', INTERVAL '15 days')`
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### Ex. de requête
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```sql
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SELECT
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time_bucket('1 hour', "date") AS heure,
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round(
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average(
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time_weight('linear', "date", valeur)
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)
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) AS moyenne_conso,
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COUNT(*) AS nombre_de_mesures
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FROM
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mesure_ng
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WHERE
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"date" BETWEEN '2026-03-01 00:00:00' AND '2026-03-31 23:59:59'
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and id_captation = 59 -- puissance PAC
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GROUP BY
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heure
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ORDER BY
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heure;
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# pg_restore -c -x -I mesure_idx1 -d postgres -h localhost -U postgres -W backup_postgres.dump
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# pg_restore -c -x -I mesure_idx2 -d postgres -h localhost -U postgres -W backup_postgres.dump
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pg_restore -c -x -t captation -d postgres -h localhost -U postgres -W backup_postgres.dump
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```
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### Exploit
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@ -73,40 +29,73 @@ WHERE hypertable_name = 'mesure_ng'
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`SELECT * FROM timescaledb_information.jobs`
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### Continuous aggregates (WIP)
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### WIP
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<https://www.tigerdata.com/docs/use-timescale/latest/continuous-aggregates/create-a-continuous-aggregate>
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#### Grafana
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* CREATE MATERIALIZED VIEW
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* CALL add_continuous_aggregate_policy
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* supprimer les "vieilles" données de mesure_ng (add_retention_policy ?)
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mixe de
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#### Exemples
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sur le range :
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##### Gauges
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CREATE MATERIALIZED VIEW sensor_hourly
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WITH (timescaledb.continuous)
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AS
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SELECT
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time_bucket('1 hour', time) AS bucket,
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sensor_id,
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average(
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time_weight('linear', "date", valeur)
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) AS moyenne_temporelle,
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percentile_agg(value) AS pct
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FROM sensor_data
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GROUP BY bucket, sensor_id;
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Puis
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```sql
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SELECT *
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FROM (
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SELECT
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bucket,
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sensor_id,
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approx_percentile(0.95, pct) AS p95,
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approx_percentile(0.99, pct) AS p99
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FROM sensor_hourly;
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"date" AS time,
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valeur
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FROM mesure_gauge
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WHERE $__timeFilter("date")
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AND $__range_s < 86400
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UNION ALL
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SELECT
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"date" AS time,
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average(valeur_temporelle)
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FROM mesure_gauge_heure
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WHERE $__timeFilter("date")
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AND $__range_s BETWEEN 86400 AND 2592000
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UNION ALL
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SELECT
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"date" AS time,
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average(valeur_temporelle)
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FROM mesure_gauge_jour
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WHERE $__timeFilter("date")
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AND $__range_s > 2592000
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) t
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ORDER BY time
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```
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et où est dans le passé
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```sql
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SELECT *
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FROM (
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-- données récentes → brut
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SELECT
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mg."date" AS time,
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mg.valeur
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FROM mesure_gauge mg
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WHERE $__timeFilter(mg."date")
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AND $__to > now() - interval '7 days'
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UNION ALL
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-- données anciennes → agrégées
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SELECT
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mgh."date" AS time,
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average(mgh.valeur_temporelle) AS valeur
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FROM mesure_gauge_heure mgh
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WHERE $__timeFilter(mgh."date")
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AND $__to <= now() - interval '7 days'
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) t
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ORDER BY time;
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```
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##### Index
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delta(counter_agg(time, value))
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3
sql/1_type_captation.sql
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3
sql/1_type_captation.sql
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@ -0,0 +1,3 @@
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-- pour différencier les capteurs (Gauges) des index (Compteurs)
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ALTER TABLE public.captation ADD "type" varchar DEFAULT 'G' NOT NULL;
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COMMENT ON COLUMN public.captation."type" IS 'G pour Gauge, C pour Compteur (index)';
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71
sql/2_gauge.sql
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71
sql/2_gauge.sql
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@ -0,0 +1,71 @@
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CREATE TABLE mesure_gauge ( -- TODO ; revoir précudes d'injection de données pour faire la différence entre gauge et compteur
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"date" TIMESTAMPTZ,
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id_captation INT NOT NULL,
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valeur float4 NOT NULL
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) WITH (
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tsdb.hypertable,
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timescaledb.segmentby = 'id_captation',
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timescaledb.orderby='date DESC'
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);
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INSERT INTO mesure_gauge ("date", id_captation, valeur)
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SELECT m."date", m.id_captation, m.valeur
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FROM mesure m
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WHERE m.id_captation IN (
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SELECT c.id_captation
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FROM captation c
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WHERE c."type" = 'G' -- TODO : prévoir cette info dans captation
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);
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SELECT add_retention_policy('mesure_gauge', INTERVAL '6 months'); -- on ne garde les données brutes que 6 mois
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CREATE MATERIALIZED VIEW mesure_gauge_heure -- valeurs par heure
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WITH (timescaledb.continuous) AS
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SELECT
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id_captation,
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time_bucket(INTERVAL '1 HOUR', mg."date") AS "date",
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time_weight('linear', mg."date", mg.valeur) AS valeur_temporelle,
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MAX(mg.valeur),
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MIN(mg.valeur)
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FROM mesure_gauge mg
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GROUP BY id_captation, time_bucket(INTERVAL '1 hour', mg."date");
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SELECT add_continuous_aggregate_policy( -- On met à jour les données tt les h en relisant les données brutes entre 1 mois et 1 jour
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'mesure_gauge_heure',
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start_offset => INTERVAL '1 month',
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end_offset => INTERVAL '1 day',
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schedule_interval => INTERVAL '1 hour'
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);
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SELECT add_retention_policy('mesure_gauge_heure', INTERVAL '5 years'); -- on ne garde les données par h que 5 ans
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CREATE MATERIALIZED VIEW mesure_gauge_jour -- valeurs par jour
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WITH (timescaledb.continuous) AS
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SELECT
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id_captation,
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time_bucket(INTERVAL '1 DAY', mg."date") AS "date",
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rollup(valeur_temporelle) AS valeur_temporelle,
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MAX(mg.max),
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MIN(mg.min)
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FROM mesure_gauge_heure mg
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GROUP BY id_captation, time_bucket(INTERVAL '1 DAY', mg."date");
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SELECT add_continuous_aggregate_policy( -- On met à jour les données tt les h en relisant les données brutes entre 1 mois et 1 jour
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'mesure_gauge_jour',
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start_offset => INTERVAL '1 month',
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end_offset => INTERVAL '1 day',
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schedule_interval => INTERVAL '1 hour'
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);
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SELECT add_retention_policy('mesure_gauge_jour', INTERVAL '20 years'); -- on ne garde les données par h que 20 ans
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-- ex. select
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select
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mgh.id_captation,
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time_bucket(INTERVAL '1 DAY', mgh."date") AS "date", -- 1 jour et pas seulement une heure
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average(rollup(valeur_temporelle)) AS moyenne -- rollup pour avour le concaténation des valeur_temporelle
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from mesure_gauge_heure mgh
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where mgh.id_captation = 59 -- puissance PAC
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group by mgh.id_captation, time_bucket(INTERVAL '1 DAY', mgh."date")
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order by "date" desc
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limit 1000;
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70
sql/3_compteur.sql
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70
sql/3_compteur.sql
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@ -0,0 +1,70 @@
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CREATE TABLE mesure_compteur ( -- TODO ; revoir précudes d'injection de données pour faire la différence entre gauge et compteur
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"date" TIMESTAMPTZ,
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id_captation INT NOT NULL,
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valeur float4 NOT NULL
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) WITH (
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tsdb.hypertable,
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timescaledb.segmentby = 'id_captation',
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timescaledb.orderby='date DESC'
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);
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INSERT INTO mesure_compteur ("date", id_captation, valeur)
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SELECT m."date", m.id_captation, m.valeur
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FROM mesure m
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WHERE m.id_captation IN (
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SELECT c.id_captation
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FROM captation c
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WHERE c."type" = 'C' -- TODO : prévoir cette info dans captation
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);
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SELECT add_retention_policy('mesure_compteur', INTERVAL '6 months'); -- on ne garde les données brutes que 6 mois
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CREATE MATERIALIZED VIEW mesure_compteur_heure -- valeurs par heure
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WITH (timescaledb.continuous) AS
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SELECT
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id_captation,
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time_bucket(INTERVAL '1 hour', mc."date") AS "date",
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counter_agg(mc."date", mc.valeur) AS valeur_temporelle
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FROM mesure_compteur mc
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GROUP BY
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id_captation,
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time_bucket(INTERVAL '1 hour', mc."date");
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SELECT add_continuous_aggregate_policy('mesure_compteur_heure', -- On met à jour les données tt les h en relisant les données brutes entre 1 mois et 1 jour
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start_offset => INTERVAL '1 month',
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end_offset => INTERVAL '1 day',
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schedule_interval => INTERVAL '1 hour');
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SELECT add_retention_policy('mesure_compteur_heure', INTERVAL '5 years'); -- on ne garde les données par h que 5 ans
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CREATE MATERIALIZED VIEW mesure_compteur_jour -- valeurs par jour
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WITH (timescaledb.continuous) AS
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SELECT
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id_captation,
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time_bucket(INTERVAL '1 DAY', mg."date") AS "date",
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rollup(valeur_temporelle) AS valeur_temporelle
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FROM mesure_compteur_heure mg
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GROUP BY id_captation, time_bucket(INTERVAL '1 DAY', mg."date");
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SELECT add_continuous_aggregate_policy( -- On met à jour les données tt les h en relisant les données brutes entre 1 mois et 1 jour
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'mesure_compteur_jour',
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start_offset => INTERVAL '1 month',
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end_offset => INTERVAL '1 day',
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schedule_interval => INTERVAL '1 hour'
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);
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SELECT add_retention_policy('mesure_compteur_jour', INTERVAL '20 years'); -- on ne garde les données par h que 20 ans
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-- ex. select
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SELECT
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mch.id_captation,
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time_bucket(INTERVAL '1 DAY', mch."date") AS "date", -- 1 jour et pas seulement une heure
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delta(rollup(mch.valeur_temporelle)) AS consommation,
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rate(rollup(mch.valeur_temporelle)) * 3600 AS debit_moyen,
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irate_left(rollup(mch.valeur_temporelle)) * 3600 AS debit_instantane,
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first_val(rollup(mch.valeur_temporelle)) AS index_debut,
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last_val(rollup(mch.valeur_temporelle)) AS index_fin
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FROM mesure_compteur_heure mch
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where mch.id_captation = 89 -- HP
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group by mch.id_captation, time_bucket(INTERVAL '1 DAY', mch."date")
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order by "date" desc
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7
sql/4_drop.sql
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7
sql/4_drop.sql
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@ -0,0 +1,7 @@
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DROP MATERIALIZED VIEW IF EXISTS mesure_gauge_jour;
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DROP MATERIALIZED VIEW IF EXISTS mesure_gauge_heure;
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DROP TABLE IF EXISTS mesure_gauge;
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DROP MATERIALIZED VIEW IF EXISTS mesure_compteur_jour;
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DROP MATERIALIZED VIEW IF EXISTS mesure_compteur_jour;
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DROP TABLE IF EXISTS mesure_compteur;
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