[Xinyu-Liao]

What changes are you trying to make? (e.g. Adding or removing code, refactoring existing code, adding reports)

adding code

What did you learn from the changes you have made?

practice different sql queries

Was there another approach you were thinking about making? If so, what approach(es) were you thinking of?

NA

Were there any challenges? If so, what issue(s) did you face? How did you overcome it?

the final question is a bit difficult

How were these changes tested?

NA

A reference to a related issue in your repository (if applicable)

NA

Checklist

• I can confirm that my changes are working as intended

[github-actions]

SQL Assignment 2 feedback

SQL checks only. ERDs, written responses, and rubric point totals require instructor marking.

02_activities/assignments/DC_Cohort/assignment2.sql

Query 1: ✅ Correct

All product descriptions and NULL replacements are correct.

SQL and output

SELECT
    product_name || ', ' ||
    COALESCE(product_size, '') ||
    ' (' || COALESCE(product_qty_type, 'unit') || ')' 
FROM product;

[
  {
    "product_name || ', ' ||\n    COALESCE(product_size, '') ||\n    ' (' || COALESCE(product_qty_type, 'unit') || ')'": "Habanero Peppers - Organic, medium (lbs)"
  },
  {
    "product_name || ', ' ||\n    COALESCE(product_size, '') ||\n    ' (' || COALESCE(product_qty_type, 'unit') || ')'": "Jalapeno Peppers - Organic, small (lbs)"
  },
  {
    "product_name || ', ' ||\n    COALESCE(product_size, '') ||\n    ' (' || COALESCE(product_qty_type, 'unit') || ')'": "Poblano Peppers - Organic, large (unit)"
  }
]

Query 2: ✅ Correct

Visit dates are numbered correctly for each customer.

SQL and output

SELECT
    customer_id,
    market_date,
	ROW_NUMBER () OVER (PARTITION BY customer_id ORDER BY market_date) AS visit_number
FROM (
				SELECT DISTINCT
									customer_id,
									market_date
				FROM customer_purchases
				WHERE market_date < '2022-04-29');

[
  {
    "customer_id": 1,
    "market_date": "2022-04-08",
    "visit_number": 1
  },
  {
    "customer_id": 1,
    "market_date": "2022-04-15",
    "visit_number": 2
  },
  {
    "customer_id": 1,
    "market_date": "2022-04-19",
    "visit_number": 3
  }
]

Query 3: ✅ Correct

Only each customer's most recent visit is returned.

SQL and output

SELECT *
FROM (
    SELECT
        customer_id,
        market_date,
        ROW_NUMBER() OVER (
            PARTITION BY customer_id
            ORDER BY market_date DESC
        ) AS visit_number
    FROM customer_purchases
) AS ranked_visits
WHERE visit_number = 1
ORDER BY customer_id;

[
  {
    "customer_id": 1,
    "market_date": "2023-10-13",
    "visit_number": 1
  },
  {
    "customer_id": 2,
    "market_date": "2023-10-13",
    "visit_number": 1
  },
  {
    "customer_id": 3,
    "market_date": "2023-10-10",
    "visit_number": 1
  }
]

Query 4: ✅ Correct

The purchase count is correct for every customer and product.

SQL and output

SELECT
    customer_id,
    product_id,
    market_date,
    COUNT(*) OVER (PARTITION BY customer_id, product_id) AS purchase_count
FROM customer_purchases
WHERE market_date < '2022-04-29'
ORDER BY customer_id, product_id;

[
  {
    "customer_id": 1,
    "product_id": 4,
    "market_date": "2022-04-19",
    "purchase_count": 2
  },
  {
    "customer_id": 1,
    "product_id": 4,
    "market_date": "2022-04-26",
    "purchase_count": 2
  },
  {
    "customer_id": 1,
    "product_id": 5,
    "market_date": "2022-04-08",
    "purchase_count": 6
  }
]

Query 5: ✅ Correct

Product descriptions are extracted and trimmed correctly.

SQL and output

SELECT
    product_name,
    TRIM(SUBSTR(
            product_name,
            NULLIF(INSTR(product_name, '-'), 0) + 1
        )
    ) AS description
FROM product;

[
  {
    "product_name": "Habanero Peppers - Organic",
    "description": "Organic"
  },
  {
    "product_name": "Jalapeno Peppers - Organic",
    "description": "Organic"
  },
  {
    "product_name": "Poblano Peppers - Organic",
    "description": "Organic"
  }
]

Query 6: ✅ Correct

Only product sizes containing a number are returned.

SQL and output

SELECT
    product_name,
    product_size
FROM product
WHERE product_size REGEXP '[0-9]';

[
  {
    "product_name": "Banana Peppers - Jar",
    "product_size": "8 oz"
  },
  {
    "product_name": "Whole Wheat Bread",
    "product_size": "1.5 lbs"
  },
  {
    "product_name": "Apple Pie",
    "product_size": "10\""
  }
]

Query 7: ✅ Correct

The highest and lowest sales dates and totals are correct.

SQL and output

WITH daily_sales AS (
    SELECT
        market_date,
        SUM(quantity * cost_per_quantity) AS total_sales
    FROM customer_purchases
    GROUP BY market_date),
ranked_sales AS (
    SELECT
        market_date,
        total_sales,
        RANK() OVER (ORDER BY total_sales DESC) AS best_rank,
        RANK() OVER (ORDER BY total_sales ASC) AS worst_rank
    FROM daily_sales
)
SELECT
    'Best Day' AS day_type,
    market_date,
    total_sales
FROM ranked_sales
WHERE best_rank = 1

UNION

SELECT
    'Worst Day' AS day_type,
    market_date,
    total_sales
FROM ranked_sales
WHERE worst_rank = 1;

[
  {
    "day_type": "Best Day",
    "market_date": "2022-12-20",
    "total_sales": 979
  },
  {
    "day_type": "Worst Day",
    "market_date": "2023-04-07",
    "total_sales": 272.5
  }
]

Query 8: ✅ Correct

Possible revenue is correct for every vendor and product.

SQL and output

WITH vendor_products AS (
    SELECT
        vendor_id,
        product_id,
        AVG(original_price) AS price
    FROM vendor_inventory
    GROUP BY vendor_id, product_id
),

potential_sales AS (
    SELECT
        vp.vendor_id,
        vp.product_id,
        5 * vp.price AS sales_per_customer
    FROM vendor_products AS vp
    CROSS JOIN customer AS c
)

SELECT
    v.vendor_name,
    p.product_name,
    ROUND(SUM(ps.sales_per_customer), 2) AS total_sales
FROM potential_sales AS ps
JOIN vendor AS v
    ON ps.vendor_id = v.vendor_id
JOIN product AS p
    ON ps.product_id = p.product_id
GROUP BY
    ps.vendor_id,
    ps.product_id,
    v.vendor_name,
    p.product_name
ORDER BY v.vendor_name, p.product_name;

[
  {
    "vendor_name": "Annie's Pies",
    "product_name": "Apple Pie",
    "total_sales": 2340
  },
  {
    "vendor_name": "Annie's Pies",
    "product_name": "Cherry Pie",
    "total_sales": 2340
  },
  {
    "vendor_name": "Annie's Pies",
    "product_name": "Whole Wheat Bread",
    "total_sales": 845
  }
]

Query 9: ✅ Correct

Unit products and current snapshot timestamps are correct.

SQL and output

CREATE TABLE product_units AS
SELECT
    *,
    CURRENT_TIMESTAMP AS snapshot_timestamp
FROM product
WHERE product_qty_type = 'unit';

Query 10: ✅ Correct

One product with a current timestamp was inserted.

SQL and output

INSERT INTO product_units
SELECT
    *,
    CURRENT_TIMESTAMP AS snapshot_timestamp
FROM product
WHERE product_name = 'Apple Pie'
LIMIT 1;

Query 11: ✅ Correct

Only the older copy was removed; the inserted copy remains.

SQL and output

DELETE FROM product_units
WHERE rowid = (
    SELECT rowid
    FROM product_units
    WHERE product_name = 'Apple Pie'
    ORDER BY snapshot_timestamp ASC, rowid ASC
    LIMIT 1
);

Query 12: ❌ Error

The SQL could not be executed or assessed.

incomplete input

ALTER TABLE product_units
ADD current_quantity INT;

UPDATE product_units