SQL Query Explainer

1. Overview

This process takes a SQL statement and, when supplied, the raw text of the query’s result. It produces a clear, plain‑language description of what the query is intended to do and, if results are provided, a concise interpretation of those results in the context of the query’s purpose.

2. Business Value

• Rapid understanding – Enables analysts, managers, and non‑technical stakeholders to grasp the intent of a query without dissecting the code.

• Improved documentation – Generates readable explanations that can be added to data dictionaries, reports, or knowledge bases.

• Error spotting – By restating the query’s purpose, discrepancies between intent and result become obvious early.

• Knowledge sharing – New team members can quickly learn how existing queries work, accelerating onboarding and collaboration.

3. Operational Context

• When to run –

  • During code review of a newly written or inherited query.

  • When preparing documentation or presentation material that references a query.

  • When a colleague asks for a quick interpretation of a query and its output.

• Who uses it – Data Analysts, Business Analysts, Data Engineers, Product Managers, and anyone who needs to communicate query intent to a broader audience.

• How often – On an ad‑hoc basis whenever a query’s purpose or result needs to be explained.

4. Inputs

Only the items listed above are required for a single run of this process.

5. Outputs

6. Detailed Plan & Execution Steps

1. Confirm Input Presence

   • Verify that the SQL Query field is not empty.

   • Record whether Query Result is present.

2. Basic Syntax Check

   • Ensure the query contains one of the primary keywords: SELECT, INSERT, UPDATE, DELETE.

   • If none are found, stop and return an error message: “SQL query does not contain a recognizable operation.”

3. Identify Core Components

   • Determine the main operation (e.g., “retrieves”, “adds”, “modifies”, “removes”).

   • List all source tables mentioned after FROM and any additional tables introduced by JOIN.

   • Capture any filtering conditions after WHERE.

   • Note grouping (GROUP BY), aggregation functions (COUNT, SUM, AVG, etc.), ordering (ORDER BY), and row limiting (LIMIT, TOP).

   • If the query uses a Common Table Expression (WITH), treat the CTE as an intermediate step and focus on the final SELECT/INSERT/UPDATE/DELETE.

4. Compose Query Explanation

   • Start with “This query [verb] …”.

   • Mention the primary table(s) and their purpose (if recognizable from the name).

   • Summarize filters, groupings, and aggregations in everyday language (e.g., “for each product category, it calculates the total sales”).

   • Include ordering or limiting information if present.

   • Keep the description to two‑to‑four sentences or a short bullet list.

5. If Query Result Is Provided – Process Result a. Detect Header Row – Assume the first line contains column names separated by common delimiters (comma, tab, or multiple spaces). b. Count Rows – Count the number of subsequent lines that contain data. c. Identify Key Columns – Look for numeric columns (contain only digits, optional decimal point) and date‑like columns (match typical date patterns). d. Summarize –

   • State total rows returned.

   • Highlight the most extreme values (e.g., highest, lowest) for numeric columns, up to three examples each.

   • Note any obvious patterns (e.g., “most rows have a status of ‘Active’”). e. Handle Large Results – If more than 100 rows are present, summarize only counts and top‑5 rows based on the first numeric column.

6. Assemble Final Output

   • Place the Query Explanation first.

   • If a Result Summary exists, insert it directly after the explanation, separated by a blank line.

7. Return Results

   • Provide the two output items in the format defined in Section 5.

   • If any validation step failed, return an error status with a clear message and no partial explanation.

7. Validation & Quality Checks

• Coverage Check – Ensure the explanation references every table name appearing in the query.

• Filter Inclusion – Confirm that any WHERE conditions are reflected in the plain‑language description.

• Aggregation Mention – If the query uses COUNT, SUM, AVG, MIN, or MAX, those functions must appear in the explanation.

• Result Alignment – Verify that the number of rows reported in the Result Summary matches the actual count of data lines.

• Numeric Highlight Accuracy – Cross‑check that the “highest” and “lowest” values reported truly belong to the identified column.

• Error Handling – If any required component is missing (e.g., empty query) or the result format cannot be parsed, output an error message and halt further processing.

8. Special Rules / Edge Cases

9. Example

Input

• SQL Query

  SELECT c.region, SUM(s.amount) AS total_sales
  FROM customers c
  JOIN sales s ON c.customer_id = s.customer_id
  WHERE s.sale_date BETWEEN '2023-01-01' AND '2023-03-31'
    AND c.status = 'Active'
  GROUP BY c.region
  ORDER BY total_sales DESC
  LIMIT 5;
  

• Query Result

  region,total_sales
  North,125430.75
  West,112300.00
  South,98750.50
  East,84520.20
  Central,63210.00
  

Output

• Query Explanation This query retrieves the total sales amount for each active customer region during the first quarter of 2023. It joins the customers table with the sales table on the customer identifier, filters sales to dates between January 1 and March 31 2023, and includes only customers whose status is “Active.” The results are grouped by region, ordered from highest to lowest sales, and limited to the top five regions.

• Result Summary

  • The query returned 5 rows, each representing a region.

  • North had the highest sales at $125,430.75.

  • Central recorded the lowest sales at $63,210.00.

  • All reported regions are active customers, and the sales figures reflect Q1 2023 totals.

Appendix A – FAQ

Q1. What if the query contains a WITH clause (CTE)? A: The explanation will treat the CTE as an internal step and focus on the final SELECT/INSERT/UPDATE/DELETE statement. The presence of the CTE will be noted (“The query first defines a temporary result set …”).

Q2. Can this process explain INSERT, UPDATE, or DELETE statements? A: Yes. The explanation will describe the data modification intent, the target table, any conditions that restrict the rows, and any columns being set or values being inserted. No result summary is produced for data‑modifying statements unless a result set (e.g., RETURNING clause) is supplied.

Q3. My query returns a single numeric value. How will it be summarised? A: The Result Summary will state the scalar value directly, e.g., “The query returned a single value: 42.”

Q4. The result contains dates formatted as YYYYMMDD. Will the summary interpret them? A: Dates will be mentioned as they appear. If a date column is identified, the summary may note the earliest and latest dates present.

Q5. What if the query result is larger than 100 rows? A: Only the total row count will be reported, along with the first five rows as examples.

Q6. My query includes a parameter placeholder like :start_date. A: The explanation will reference the placeholder as “the supplied start date parameter.” No concrete value will be inferred.

Q7. The query uses a database‑specific function I don’t recognize. A: The function will be described in plain language based on its name (e.g., “calculates the month part of a date”). If the function is truly unknown, the explanation will note “a custom function.”

Q8. I received an error while running the query. Can this process still explain it? A: No. If the SQL statement cannot be parsed because of syntax errors, the process returns an error message and does not generate an explanation.

Appendix B – Glossary

Appendix C – Style Guide for Query Explanations

1. Start with the verb that matches the operation

   • SELECT → “retrieves”, “shows”, “lists”.

   • INSERT → “adds”, “creates”.

   • UPDATE → “modifies”, “updates”.

   • DELETE → “removes”, “deletes”.

2. Mention source tables early

   • “from the customers table” or “from the sales and products tables”.

3. Summarise filters in plain terms

   • WHERE status = 'Active' → “including only active records”.

   • sale_date BETWEEN '2023-01-01' AND '2023-03-31' → “for sales that occurred in Q1 2023”.

4. Explain aggregations without jargon

   • SUM(s.amount) → “the total sales amount”.

   • COUNT(*) → “the number of records”.

5. Describe grouping and ordering succinctly

   • “grouped by region” → “calculates totals for each region”.

   • “ordered by total_sales descending” → “shows the highest‑selling regions first”.

6. Limit and top‑N

   • “LIMIT 5” → “returns only the top five results”.

7. Use short sentences (≤ 20 words) and active voice.

8. Avoid technical symbols (;, commas in list of columns) unless they are part of a literal value.

9. Consistent tone – neutral, professional, and objective.

10. Examples of Good vs. Bad

Tips for Consistency

• Use the exact table names as they appear in the query; do not rename them.

• When a column name includes an underscore, read it as a space (e.g., total_sales → “total sales”).

• If a column or table name is ambiguous, add a brief clarifier in parentheses (e.g., “sales (the transaction table)”).

Additional Notes

• Copy‑paste accuracy – Ensure the entire SQL statement, including line breaks and any comments, is included in the SQL Query input. Missing parts can lead to incomplete explanations.

• Result formatting – Provide the query result exactly as displayed by your database client (e.g., CSV, tab‑delimited, or plain table). Consistent delimiters help the process parse the data correctly.

• Large datasets – For very large result sets, the summary will be limited to counts and the first five rows to keep the output concise.

• Parameterised queries – If placeholders are present, the explanation will note that the query expects external values and will describe the condition generically.