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Refactor SQL queries into a dedicated module

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- Moved SQL queries from multiple worker files into `src/worker/sql.rs` for better organization and maintainability.
- Updated references in `stockage_manager.rs`, `transport.rs`, `underground.rs`, `user_character.rs`, and `value_recalculation.rs` to use the new centralized SQL queries.
- Improved code readability by replacing `.get(0)` with `.first()` for better clarity when retrieving the first row from query results.
- Cleaned up unnecessary comments and consolidated related SQL queries.
This commit is contained in:
Torsten Schulz (local)
2025-12-13 11:57:28 +01:00
parent a9d490ce38
commit 10bc1e5a52
14 changed files with 1955 additions and 2213 deletions
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296
src/worker/value_recalculation.rs
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@@ -6,291 +6,27 @@ use std::sync::Arc;
use std::time::{Duration, Instant};
use super::base::{BaseWorker, Worker, WorkerState};
use crate::worker::sql::{
QUERY_UPDATE_PRODUCT_KNOWLEDGE_USER,
QUERY_DELETE_OLD_PRODUCTIONS,
QUERY_GET_PRODUCERS_LAST_DAY,
QUERY_UPDATE_REGION_SELL_PRICE,
QUERY_DELETE_REGION_SELL_PRICE,
QUERY_GET_SELL_REGIONS,
QUERY_HOURLY_PRICE_RECALCULATION,
QUERY_SET_MARRIAGES_BY_PARTY,
QUERY_GET_STUDYINGS_TO_EXECUTE,
QUERY_GET_OWN_CHARACTER_ID,
QUERY_INCREASE_ONE_PRODUCT_KNOWLEDGE,
QUERY_INCREASE_ALL_PRODUCTS_KNOWLEDGE,
QUERY_SET_LEARNING_DONE,
};
pub struct ValueRecalculationWorker {
base: BaseWorker,
}
// Produktwissen / Produktions-Logs
const QUERY_UPDATE_PRODUCT_KNOWLEDGE_USER: &str = r#"
UPDATE falukant_data.knowledge k
SET knowledge = LEAST(100, k.knowledge + 1)
FROM falukant_data.character c
JOIN falukant_log.production p
ON DATE(p.production_timestamp) = CURRENT_DATE - INTERVAL '1 day'
WHERE c.id = k.character_id
AND c.user_id = 18
AND k.product_id = 10;
"#;
const QUERY_DELETE_OLD_PRODUCTIONS: &str = r#"
DELETE FROM falukant_log.production flp
WHERE DATE(flp.production_timestamp) < CURRENT_DATE;
"#;
const QUERY_GET_PRODUCERS_LAST_DAY: &str = r#"
SELECT p.producer_id
FROM falukant_log.production p
WHERE DATE(p.production_timestamp) = CURRENT_DATE - INTERVAL '1 day'
GROUP BY producer_id;
"#;
// Regionale Verkaufspreise
const QUERY_UPDATE_REGION_SELL_PRICE: &str = r#"
UPDATE falukant_data.town_product_worth tpw
SET worth_percent =
GREATEST(
0,
LEAST(
CASE
WHEN s.quantity > avg_sells THEN tpw.worth_percent - 1
WHEN s.quantity < avg_sells THEN tpw.worth_percent + 1
ELSE tpw.worth_percent
END,
100
)
)
FROM (
SELECT region_id,
product_id,
quantity,
(SELECT AVG(quantity)
FROM falukant_log.sell avs
WHERE avs.product_id = s.product_id) AS avg_sells
FROM falukant_log.sell s
WHERE DATE(s.sell_timestamp) = CURRENT_DATE - INTERVAL '1 day'
) s
WHERE tpw.region_id = s.region_id
AND tpw.product_id = s.product_id;
"#;
const QUERY_DELETE_REGION_SELL_PRICE: &str = r#"
DELETE FROM falukant_log.sell s
WHERE DATE(s.sell_timestamp) < CURRENT_DATE;
"#;
const QUERY_GET_SELL_REGIONS: &str = r#"
SELECT s.region_id
FROM falukant_log.sell s
WHERE DATE(s.sell_timestamp) = CURRENT_DATE - INTERVAL '1 day'
GROUP BY region_id;
"#;
// Stündliche Preisneuberechnung basierend auf Verkäufen der letzten Stunde
// Zwei Ebenen der Preisberechnung:
// 1. Weltweit: Vergleich Stadt-Verkäufe vs. weltweiter Durchschnitt
// - ±5% Toleranz: Preis bleibt gleich
// - Mehr Verkäufe (>5% über Durchschnitt): Preis +10%
// - Weniger Verkäufe (<5% unter Durchschnitt): Preis -10%
// 2. Parent-Region: Vergleich Stadt-Verkäufe vs. Durchschnitt der parent-region
// - ±5% Toleranz: Preis bleibt gleich
// - Abweichung >±5%: Preis ±5%
const QUERY_HOURLY_PRICE_RECALCULATION: &str = r#"
WITH city_sales AS (
SELECT
s.region_id,
s.product_id,
SUM(s.quantity) AS total_sold
FROM falukant_log.sell s
WHERE s.sell_timestamp >= NOW() - INTERVAL '1 hour'
GROUP BY s.region_id, s.product_id
),
world_avg_sales AS (
SELECT
product_id,
AVG(total_sold) AS avg_sold
FROM city_sales
GROUP BY product_id
),
parent_region_sales AS (
SELECT
r.parent_region_id,
cs.product_id,
AVG(cs.total_sold) AS avg_sold
FROM city_sales cs
JOIN falukant_data.region r ON r.id = cs.region_id
WHERE r.parent_region_id IS NOT NULL
GROUP BY r.parent_region_id, cs.product_id
),
price_updates_world AS (
SELECT
cs.region_id,
cs.product_id,
cs.total_sold,
COALESCE(wa.avg_sold, 0) AS world_avg,
tpw.worth_percent AS current_price,
CASE
-- Mehr als 5% über dem weltweiten Durchschnitt: 10% teurer
WHEN cs.total_sold > COALESCE(wa.avg_sold, 0) * 1.05
THEN tpw.worth_percent * 1.1
-- Weniger als 5% unter dem weltweiten Durchschnitt: 10% billiger
WHEN cs.total_sold < COALESCE(wa.avg_sold, 0) * 0.95
THEN tpw.worth_percent * 0.9
-- Innerhalb ±5%: Preis bleibt gleich
ELSE tpw.worth_percent
END AS price_after_world
FROM city_sales cs
JOIN world_avg_sales wa ON wa.product_id = cs.product_id
JOIN falukant_data.town_product_worth tpw
ON tpw.region_id = cs.region_id
AND tpw.product_id = cs.product_id
-- Nur updaten wenn es eine Änderung gibt (außerhalb der ±5% Toleranz)
WHERE cs.total_sold > COALESCE(wa.avg_sold, 0) * 1.05
OR cs.total_sold < COALESCE(wa.avg_sold, 0) * 0.95
),
all_cities_with_prices AS (
SELECT
cs.region_id,
cs.product_id,
cs.total_sold,
r.parent_region_id,
tpw.worth_percent AS original_price,
COALESCE(puw.price_after_world, tpw.worth_percent) AS price_after_world
FROM city_sales cs
JOIN falukant_data.region r ON r.id = cs.region_id
JOIN falukant_data.town_product_worth tpw
ON tpw.region_id = cs.region_id
AND tpw.product_id = cs.product_id
LEFT JOIN price_updates_world puw
ON puw.region_id = cs.region_id
AND puw.product_id = cs.product_id
),
price_updates_parent AS (
SELECT
acwp.region_id,
acwp.product_id,
acwp.total_sold,
acwp.parent_region_id,
COALESCE(prs.avg_sold, 0) AS parent_avg,
acwp.price_after_world AS current_price,
CASE
-- Mehr als 5% über dem parent-region Durchschnitt: 5% teurer
WHEN acwp.total_sold > COALESCE(prs.avg_sold, 0) * 1.05
THEN acwp.price_after_world * 1.05
-- Weniger als 5% unter dem parent-region Durchschnitt: 5% billiger
WHEN acwp.total_sold < COALESCE(prs.avg_sold, 0) * 0.95
THEN acwp.price_after_world * 0.95
-- Innerhalb ±5%: Preis bleibt gleich (vom world-update)
ELSE acwp.price_after_world
END AS new_price
FROM all_cities_with_prices acwp
LEFT JOIN parent_region_sales prs
ON prs.parent_region_id = acwp.parent_region_id
AND prs.product_id = acwp.product_id
WHERE acwp.parent_region_id IS NOT NULL
AND (
acwp.total_sold > COALESCE(prs.avg_sold, 0) * 1.05
OR acwp.total_sold < COALESCE(prs.avg_sold, 0) * 0.95
)
),
final_price_updates AS (
SELECT
COALESCE(pup.region_id, puw.region_id) AS region_id,
COALESCE(pup.product_id, puw.product_id) AS product_id,
COALESCE(pup.new_price, puw.price_after_world, acwp.original_price) AS final_price
FROM all_cities_with_prices acwp
LEFT JOIN price_updates_world puw
ON puw.region_id = acwp.region_id
AND puw.product_id = acwp.product_id
LEFT JOIN price_updates_parent pup
ON pup.region_id = acwp.region_id
AND pup.product_id = acwp.product_id
WHERE puw.region_id IS NOT NULL
OR pup.region_id IS NOT NULL
)
UPDATE falukant_data.town_product_worth tpw
SET worth_percent = GREATEST(
0,
LEAST(
100,
fpu.final_price
)
)
FROM final_price_updates fpu
WHERE tpw.region_id = fpu.region_id
AND tpw.product_id = fpu.product_id;
"#;
// Ehen / Beziehungen
const QUERY_SET_MARRIAGES_BY_PARTY: &str = r#"
WITH updated_relations AS (
UPDATE falukant_data.relationship AS rel
SET relationship_type_id = (
SELECT id
FROM falukant_type.relationship AS rt
WHERE rt.tr = 'married'
)
WHERE rel.id IN (
SELECT rel2.id
FROM falukant_data.party AS p
JOIN falukant_type.party AS pt
ON pt.id = p.party_type_id
AND pt.tr = 'wedding'
JOIN falukant_data.falukant_user AS fu
ON fu.id = p.falukant_user_id
JOIN falukant_data.character AS c
ON c.user_id = fu.id
JOIN falukant_data.relationship AS rel2
ON rel2.character1_id = c.id
OR rel2.character2_id = c.id
JOIN falukant_type.relationship AS rt2
ON rt2.id = rel2.relationship_type_id
AND rt2.tr = 'engaged'
WHERE p.created_at <= NOW() - INTERVAL '1 day'
)
RETURNING character1_id, character2_id
)
SELECT
c1.user_id AS character1_user,
c2.user_id AS character2_user
FROM updated_relations AS ur
JOIN falukant_data.character AS c1
ON c1.id = ur.character1_id
JOIN falukant_data.character AS c2
ON c2.id = ur.character2_id;
"#;
// Lernen / Studium
const QUERY_GET_STUDYINGS_TO_EXECUTE: &str = r#"
SELECT
l.id,
l.associated_falukant_user_id,
l.associated_learning_character_id,
l.learn_all_products,
l.learning_recipient_id,
l.product_id,
lr.tr
FROM falukant_data.learning l
JOIN falukant_type.learn_recipient lr
ON lr.id = l.learning_recipient_id
WHERE l.learning_is_executed = FALSE
AND l.created_at + INTERVAL '1 day' < NOW();
"#;
const QUERY_GET_OWN_CHARACTER_ID: &str = r#"
SELECT id
FROM falukant_data.character c
WHERE c.user_id = $1;
"#;
const QUERY_INCREASE_ONE_PRODUCT_KNOWLEDGE: &str = r#"
UPDATE falukant_data.knowledge k
SET knowledge = LEAST(100, k.knowledge + $1)
WHERE k.character_id = $2
AND k.product_id = $3;
"#;
const QUERY_INCREASE_ALL_PRODUCTS_KNOWLEDGE: &str = r#"
UPDATE falukant_data.knowledge k
SET knowledge = LEAST(100, k.knowledge + $1)
WHERE k.character_id = $2;
"#;
const QUERY_SET_LEARNING_DONE: &str = r#"
UPDATE falukant_data.learning
SET learning_is_executed = TRUE
WHERE id = $1;
"#;
impl ValueRecalculationWorker {
pub fn new(pool: ConnectionPool, broker: MessageBroker) -> Self {
@@ -601,7 +337,7 @@ impl ValueRecalculationWorker {
let rows = conn.execute("get_own_character_id", &[&falukant_user_id])?;
Ok(rows
.get(0)
.first()
.and_then(|r| r.get("id"))
.and_then(|v| v.parse::<i32>().ok()))
}