SQL query explanation

Your team is working on a data project that requires analyzing customer transactions. You have two SQL queries that are used to filter and compare data, and your role is to explain how these queries work and determine if they yield the same output.

Query 1: ```SQL SELECT * FROM (SELECT * FROM (SELECT * FROM (VALUES (30, 3)) AS transactions (column1, column2) EXCEPT SELECT * FROM (VALUES (20, 2)) AS exceptions (column1, column2) WHERE exceptions.column1 > 30) AS filtered_transactions EXCEPT SELECT * FROM (VALUES (40, 4)) AS more_exceptions (column1, column2)) AS final_transactions EXCEPT SELECT * FROM (VALUES (50, 5)) AS last_exceptions (column1, column2) WHERE last_exceptions.column1 > 50;

Query 2:
```SQL
SELECT * FROM
(VALUES (30, 3)) AS transactions (column1, column2)
EXCEPT
SELECT * FROM
(VALUES (40, 4)) AS exceptions (column1, column2);

Instructions:

1. Provide a detailed breakdown of each part of Query_1, explaining the purpose and function of each subquery and clause.
2. Repeat the process for Query_2, ensuring that each step is clearly explained.
3. Compare the two queries to determine if they produce the same results when run on the same data. Explain your reasoning clearly, focusing on the logic behind each query.
4. Conclude with any observations about the efficiency or simplicity of the queries, offering suggestions for improvement if applicable.

Of course! Let's break down these queries. It's a great exercise because it highlights how complex queries can often be simplified once you understand what each part is doing.

Hello team,

Today we're going to walk through two SQL queries to understand their logic and see if they produce the same result. This will be a good look at how subqueries and set operators like EXCEPT work.

Let's start with the first, more complex query.

1. Breakdown of Query 1

The key to understanding a nested query like this is to work from the inside out. Think of each subquery in parentheses as a temporary table that gets passed to the next level up.

Query 1: sql SELECT * FROM (SELECT * FROM (SELECT * FROM (VALUES (30, 3)) AS transactions (column1, column2) EXCEPT SELECT * FROM (VALUES (20, 2)) AS exceptions (column1, column2) WHERE exceptions.column1 > 30) AS filtered_transactions EXCEPT SELECT * FROM (VALUES (40, 4)) AS more_exceptions (column1, column2)) AS final_transactions EXCEPT SELECT * FROM (VALUES (50, 5)) AS last_exceptions (column1, column2) WHERE last_exceptions.column1 > 50;

Step 1: The Innermost Subquery

This is the starting point of our data.

(SELECT * FROM (VALUES (30, 3)) AS transactions (column1, column2)
EXCEPT
SELECT * FROM (VALUES (20, 2)) AS exceptions (column1, column2) WHERE exceptions.column1 > 30)
AS filtered_transactions

Let's break this down further:

• SELECT * FROM (VALUES (30, 3)) AS transactions (column1, column2):

  • The VALUES clause is a handy way to create a temporary, in-memory table.
  • This part creates a table named transactions with two columns (column1, column2) and a single row of data: (30, 3).
  • Result of this part: A table with one row: (30, 3).

• SELECT * FROM (VALUES (20, 2)) AS exceptions (column1, column2) WHERE exceptions.column1 > 30:

  • This creates another temporary table, exceptions, with one row: (20, 2).
  • However, the WHERE clause filters this table. It checks if column1 (which is 20) is greater than 30.
  • Since 20 > 30 is false, this SELECT statement returns no rows. It's an empty set.

• ... EXCEPT ...:

  • The EXCEPT operator takes the results from the first query and removes any rows that are also found in the second query.
  • Here, we are taking the set {(30, 3)} and subtracting the empty set {}.
  • Subtracting nothing leaves the original set unchanged.

• Result of Step 1: The entire inner subquery, which we alias as filtered_transactions, evaluates to a table with a single row: (30, 3).

Step 2: The Middle Subquery

Now, we take the result from Step 1 and apply the next EXCEPT clause.

SELECT * FROM
    filtered_transactions -- This is the result from Step 1: (30, 3)
EXCEPT
SELECT * FROM (VALUES (40, 4)) AS more_exceptions (column1, column2)

• SELECT * FROM filtered_transactions: This just gives us the table from Step 1, which contains the row (30, 3).
• SELECT * FROM (VALUES (40, 4)) ...: This creates a temporary table with one row: (40, 4).

• ... EXCEPT ...: We are now comparing the two sets: {(30, 3)} and {(40, 4)}. The EXCEPT operator will remove any rows from the first set that appear in the second. Since the row (30, 3) is not present in the second set, it is kept.

• Result of Step 2: This subquery, aliased as final_transactions, also evaluates to a table with a single row: (30, 3).

Step 3: The Outermost Query

Finally, we take the result from Step 2 and apply the last EXCEPT clause.

SELECT * FROM
    final_transactions -- This is the result from Step 2: (30, 3)
EXCEPT
SELECT * FROM (VALUES (50, 5)) AS last_exceptions (column1, column2) WHERE last_exceptions.column1 > 50;

• SELECT * FROM final_transactions: This gives us the table from Step 2, which contains the row (30, 3).
• SELECT * FROM (VALUES (50, 5)) ... WHERE last_exceptions.column1 > 50:
  • This creates a temporary table with the row (50, 5).
  • The WHERE clause checks if column1 (which is 50) is greater than 50.
  • Since 50 > 50 is false, this SELECT statement returns no rows. It's another empty set.

• ... EXCEPT ...: We are taking the set {(30, 3)} and subtracting the empty set {}.

• Final Result of Query 1: The query returns a single row: (30, 3).

2. Breakdown of Query 2

This query is much more straightforward.

Query 2: sql SELECT * FROM (VALUES (30, 3)) AS transactions (column1, column2) EXCEPT SELECT * FROM (VALUES (40, 4)) AS exceptions (column1, column2);

Step 1: The First SELECT

• SELECT * FROM (VALUES (30, 3)) AS transactions (column1, column2):
  • Just like in Query 1, this creates a temporary table named transactions with one row: (30, 3).

Step 2: The Second SELECT

• SELECT * FROM (VALUES (40, 4)) AS exceptions (column1, column2):
  • This creates a second temporary table named exceptions with one row: (40, 4).

Step 3: The EXCEPT Operator

• The query takes the result from the first SELECT and removes any rows found in the second SELECT.
• We are comparing the set {(30, 3)} with the set {(40, 4)}.

• Since the row (30, 3) from the first set does not exist in the second set, it is not removed.

• Final Result of Query 2: The query returns a single row: (30, 3).

3. Comparison of Query Results

Yes, the two queries produce the exact same output.

Both queries ultimately return a single row: (30, 3).

Reasoning:

Query 1 is a much more complicated version of Query 2. Let's look at the extra steps it takes:

1. First EXCEPT in Query 1: It subtracts an empty set because of the WHERE exceptions.column1 > 30 clause. Subtracting an empty set does nothing.
2. Last EXCEPT in Query 1: It also subtracts an empty set because of the WHERE last_exceptions.column1 > 50 clause. Again, this has no effect on the result.

The only EXCEPT clause in Query 1 that actually has data to subtract is the middle one (EXCEPT SELECT * FROM (VALUES (40, 4))), which is the exact same operation performed in Query 2.

Therefore, Query 1 is just Query 2 with two extra, ineffective operations wrapped around it. They both boil down to the same core logic: "Start with the row (30, 3) and remove the row (40, 4) if it exists." Since it doesn't, (30, 3) is the final result in both cases.

4. Conclusion and Suggestions

• Simplicity and Readability: Query 2 is vastly superior. It is simple, direct, and easy for anyone to understand at a glance. Query 1 is convoluted and requires careful, step-by-step analysis to understand. In a real-world project, Query 1 would be considered poor practice because it's hard to maintain.

• Efficiency: Query 2 is more efficient. While a modern database optimizer might be smart enough to figure out that parts of Query 1 are redundant, it's not guaranteed. Query 2 has fewer operations for the database engine to parse and execute. It avoids the overhead of processing the extra subqueries and EXCEPT clauses.

Recommendation:

Always strive to write the simplest query that correctly solves the problem. Query 2 is the clear choice here. If you ever find yourself writing something as nested as Query 1, take a moment to see if you can simplify the logic. Often, you'll find that some conditions or steps are redundant, just like we saw here.

Great work looking at these. Let me know if you have any more questions