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‎src/main/kotlin/g3701_3800/s3705_find_golden_hour_customers/readme.md‎

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3705\. Find Golden Hour Customers
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Medium
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Table: `restaurant_orders`
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+------------------+----------+
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| Column Name | Type |
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+------------------+----------+
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| order_id | int |
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| customer_id | int |
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| order_timestamp | datetime |
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| order_amount | decimal |
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| payment_method | varchar |
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| order_rating | int |
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+------------------+----------+
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order_id is the unique identifier for this table.
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payment_method can be cash, card, or app.
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order_rating is between 1 and 5, where 5 is the best (NULL if not rated).
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order_timestamp contains both date and time information.
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Write a solution to find **golden hour customers** - customers who consistently order during peak hours and provide high satisfaction. A customer is a **golden hour customer** if they meet ALL the following criteria:
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* Made **at least** `3` orders.
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* **At least** `60%` of their orders are during **peak hours **(`11:00`\-`14:00` or `18:00`\-`21:00`).
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* Their **average rating** for rated orders is at least `4.0,` round it to `2` decimal places.
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* Have rated **at least** `50%` of their orders.
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Return _the result table ordered by_ `average_rating` _in **descending** order, then by_ `customer_id` _in **descending** order_.
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The result format is in the following example.
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**Example:**
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**Input:**
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restaurant\_orders table:
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+----------+-------------+---------------------+--------------+----------------+--------------+
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| order_id | customer_id | order_timestamp | order_amount | payment_method | order_rating |
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+----------+-------------+---------------------+--------------+----------------+--------------+
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| 1 | 101 | 2024-03-01 12:30:00 | 25.50 | card | 5 |
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| 2 | 101 | 2024-03-02 19:15:00 | 32.00 | app | 4 |
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| 3 | 101 | 2024-03-03 13:45:00 | 28.75 | card | 5 |
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| 4 | 101 | 2024-03-04 20:30:00 | 41.00 | app | NULL |
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| 5 | 102 | 2024-03-01 11:30:00 | 18.50 | cash | 4 |
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| 6 | 102 | 2024-03-02 12:00:00 | 22.00 | card | 3 |
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| 7 | 102 | 2024-03-03 15:30:00 | 19.75 | cash | NULL |
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| 8 | 103 | 2024-03-01 19:00:00 | 55.00 | app | 5 |
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| 9 | 103 | 2024-03-02 20:45:00 | 48.50 | app | 4 |
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| 10 | 103 | 2024-03-03 18:30:00 | 62.00 | card | 5 |
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| 11 | 104 | 2024-03-01 10:00:00 | 15.00 | cash | 3 |
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| 12 | 104 | 2024-03-02 09:30:00 | 18.00 | cash | 2 |
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| 13 | 104 | 2024-03-03 16:00:00 | 20.00 | card | 3 |
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| 14 | 105 | 2024-03-01 12:15:00 | 30.00 | app | 4 |
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| 15 | 105 | 2024-03-02 13:00:00 | 35.50 | app | 5 |
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| 16 | 105 | 2024-03-03 11:45:00 | 28.00 | card | 4 |
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+----------+-------------+---------------------+--------------+----------------+--------------+
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**Output:**
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+-------------+--------------+----------------------+----------------+
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| customer_id | total_orders | peak_hour_percentage | average_rating |
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+-------------+--------------+----------------------+----------------+
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| 103 | 3 | 100 | 4.67 |
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| 101 | 4 | 75 | 4.67 |
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| 105 | 3 | 100 | 4.33 |
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+-------------+--------------+----------------------+----------------+
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**Explanation:**
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* **Customer 101**:
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* Total orders: 4 (at least 3)
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* Peak hour orders: 3 out of 4 (12:30, 19:15, 13:45, and 20:30 are in peak hours)
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* Peak hour percentage: 3/4 = 75% (at least 60%)
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* Rated orders: 3 out of 4 (75% rating completion)
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* Average rating: (5+4+5)/3 = 4.67 (at least 4.0)
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* Result: **Golden hour customer**
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* **Customer 102**:
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* Total orders: 3 (at least 3)
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* Peak hour orders: 2 out of 3 (11:30, 12:00 are in peak hours; 15:30 is not)
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* Peak hour percentage: 2/3 = 66.67% (at least 60%)
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* Rated orders: 2 out of 3 (66.67% rating completion)
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* Average rating: (4+3)/2 = 3.5 (less than 4.0)
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* Result: **Not a golden hour customer** (average rating too low)
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* **Customer 103**:
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* Total orders: 3 (at least 3)
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* Peak hour orders: 3 out of 3 (19:00, 20:45, 18:30 all in evening peak)
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* Peak hour percentage: 3/3 = 100% (at least 60%)
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* Rated orders: 3 out of 3 (100% rating completion)
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* Average rating: (5+4+5)/3 = 4.67 (at least 4.0)
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* Result: **Golden hour customer**
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* **Customer 104**:
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* Total orders: 3 (at least 3)
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* Peak hour orders: 0 out of 3 (10:00, 09:30, 16:00 all outside peak hours)
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* Peak hour percentage: 0/3 = 0% (less than 60%)
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* Result: **Not a golden hour customer** (insufficient peak hour orders)
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* **Customer 105**:
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* Total orders: 3 (at least 3)
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* Peak hour orders: 3 out of 3 (12:15, 13:00, 11:45 all in lunch peak)
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* Peak hour percentage: 3/3 = 100% (at least 60%)
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* Rated orders: 3 out of 3 (100% rating completion)
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* Average rating: (4+5+4)/3 = 4.33 (at least 4.0)
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* Result: **Golden hour customer**
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The results table is ordered by average\_rating DESC, then customer\_id DESC.

‎src/main/kotlin/g3701_3800/s3705_find_golden_hour_customers/script.sql‎

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# Write your MySQL query statement below
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# #Medium #Database #2025_10_10_Time_281_ms_(71.26%)_Space_0.0_MB_(100.00%)
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SELECT
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customer_id,
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COUNT(order_id) AS total_orders,
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ROUND(
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(
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SUM(
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CASE
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WHEN HOUR(order_timestamp) BETWEEN 11 AND 13
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OR HOUR(order_timestamp) BETWEEN 18 AND 20
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THEN 1 ELSE 0
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END
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) * 100.0
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) / COUNT(order_id)
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) AS peak_hour_percentage,
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ROUND(AVG(order_rating), 2) AS average_rating
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FROM restaurant_orders
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GROUP BY customer_id
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HAVING
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(SUM(CASE WHEN order_rating IS NOT NULL THEN 1 ELSE 0 END) * 1.0 / COUNT(order_id)) >= 0.5
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AND COUNT(order_id) >= 3
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AND (
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(
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SUM(
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CASE
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WHEN HOUR(order_timestamp) BETWEEN 11 AND 13
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OR HOUR(order_timestamp) BETWEEN 18 AND 20
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THEN 1 ELSE 0
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END
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) * 100.0
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) / COUNT(order_id)
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) >= 60
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AND AVG(order_rating) >= 4.0
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ORDER BY AVG(order_rating) DESC, customer_id DESC;

‎src/test/kotlin/g3701_3800/s3705_find_golden_hour_customers/MysqlTest.kt‎

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package g3701_3800.s3705_find_golden_hour_customers
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import org.hamcrest.CoreMatchers
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import org.hamcrest.MatcherAssert
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import org.junit.jupiter.api.Test
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import org.zapodot.junit.db.annotations.EmbeddedDatabase
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import org.zapodot.junit.db.annotations.EmbeddedDatabaseTest
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import org.zapodot.junit.db.common.CompatibilityMode
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import java.io.BufferedReader
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import java.io.FileNotFoundException
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import java.io.FileReader
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import java.sql.SQLException
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import java.util.stream.Collectors
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import javax.sql.DataSource
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@EmbeddedDatabaseTest(
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compatibilityMode = CompatibilityMode.MySQL,
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initialSqls = [
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(
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"CREATE TABLE restaurant_orders (" +
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" order_id INTEGER," +
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" customer_id INTEGER NOT NULL," +
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" order_timestamp DATETIME NOT NULL," +
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" order_amount DECIMAL(10,2) NOT NULL," +
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" payment_method VARCHAR(20) NOT NULL," +
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" order_rating INTEGER" +
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");" +
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"INSERT INTO restaurant_orders (order_id, customer_id, " +
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"order_timestamp, order_amount, payment_method, order_rating) VALUES" +
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"(1, 101, '2024-03-01 12:30:00', 25.50, 'card', 5)," +
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"(2, 101, '2024-03-02 19:15:00', 32.00, 'app', 4)," +
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"(3, 101, '2024-03-03 13:45:00', 28.75, 'card', 5)," +
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"(4, 101, '2024-03-04 20:30:00', 41.00, 'app', NULL)," +
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"(5, 102, '2024-03-01 11:30:00', 18.50, 'cash', 4)," +
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"(6, 102, '2024-03-02 12:00:00', 22.00, 'card', 3)," +
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"(7, 102, '2024-03-03 15:30:00', 19.75, 'cash', NULL)," +
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"(8, 103, '2024-03-01 19:00:00', 55.00, 'app', 5)," +
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"(9, 103, '2024-03-02 20:45:00', 48.50, 'app', 4)," +
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"(10, 103, '2024-03-03 18:30:00', 62.00, 'card', 5)," +
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"(11, 104, '2024-03-01 10:00:00', 15.00, 'cash', 3)," +
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"(12, 104, '2024-03-02 09:30:00', 18.00, 'cash', 2)," +
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"(13, 104, '2024-03-03 16:00:00', 20.00, 'card', 3)," +
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"(14, 105, '2024-03-01 12:15:00', 30.00, 'app', 4)," +
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"(15, 105, '2024-03-02 13:00:00', 35.50, 'app', 5)," +
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"(16, 105, '2024-03-03 11:45:00', 28.00, 'card', 4);"
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),
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],
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)
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internal class MysqlTest {
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@Test
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@Throws(SQLException::class, FileNotFoundException::class)
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fun testScript(@EmbeddedDatabase dataSource: DataSource) {
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dataSource.connection.use { connection ->
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connection.createStatement().use { statement ->
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statement.executeQuery(
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BufferedReader(
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FileReader(
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(
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"src/main/kotlin/g3701_3800/" +
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"s3705_find_golden_hour_customers/" +
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"script.sql"
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),
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),
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)
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.lines()
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.collect(Collectors.joining("\n"))
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.replace("#.*?\\r?\\n".toRegex(), ""),
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).use { resultSet ->
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MatcherAssert.assertThat<Boolean>(resultSet.next(), CoreMatchers.equalTo<Boolean>(true))
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MatcherAssert.assertThat<String>(resultSet.getString(1), CoreMatchers.equalTo<String>("103"))
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MatcherAssert.assertThat<String>(resultSet.getString(2), CoreMatchers.equalTo<String>("3"))
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MatcherAssert.assertThat<String>(resultSet.getString(3), CoreMatchers.equalTo<String>("100"))
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MatcherAssert.assertThat<String>(resultSet.getString(4), CoreMatchers.equalTo<String>("4.67"))
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MatcherAssert.assertThat<Boolean>(resultSet.next(), CoreMatchers.equalTo<Boolean>(true))
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MatcherAssert.assertThat<String>(resultSet.getString(1), CoreMatchers.equalTo<String>("101"))
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MatcherAssert.assertThat<String>(resultSet.getString(2), CoreMatchers.equalTo<String>("4"))
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MatcherAssert.assertThat<String>(resultSet.getString(3), CoreMatchers.equalTo<String>("100"))
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MatcherAssert.assertThat<String>(resultSet.getString(4), CoreMatchers.equalTo<String>("4.67"))
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MatcherAssert.assertThat<Boolean>(resultSet.next(), CoreMatchers.equalTo<Boolean>(true))
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MatcherAssert.assertThat<String>(resultSet.getString(1), CoreMatchers.equalTo<String>("105"))
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MatcherAssert.assertThat<String>(resultSet.getString(2), CoreMatchers.equalTo<String>("3"))
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MatcherAssert.assertThat<String>(resultSet.getString(3), CoreMatchers.equalTo<String>("100"))
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MatcherAssert.assertThat<String>(resultSet.getString(4), CoreMatchers.equalTo<String>("4.33"))
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MatcherAssert.assertThat<Boolean>(resultSet.next(), CoreMatchers.equalTo<Boolean>(false))
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}
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}
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}
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}
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}

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