Spend an hour inspecting permitted database rows, not just the code and schema. The records show what the system has actually accumulated—and can reveal old values, migrations, and edge cases that help explain surprising code paths. Use read-only access where possible, preferably to a masked copy, and keep the queries light.
Why inspect rows when you already have the code?
Code and schemas describe what data is expected to look like. The rows show what is present in practice. Differences between the two can help explain why the application contains defensive checks, special cases, or behavior that is otherwise hard to understand.
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In a short article published October 1, 2026, developer Asael Shinder recommends asking a simple question: “Have you ever actually looked at the rows?” His point is not to conduct a full audit, but to make time for a focused look at real records and note what surprises you. Read the article on DEV Community.
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Work through these checks using data you are authorized to inspect. The sequence is a practical way to organize the hour, not a prescribed schedule; the source does not specify SQL syntax, tooling, sampling methods, or a statistical protocol.
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- Check the basic shape. Look at row counts and growth over time. A count is most useful when you know which table, time period, or other scope it covers.
- Look for missing values. Check null frequency in columns that application code appears to assume will be populated. Frequent nulls may help explain why code has defensive handling—or show where assumptions deserve a closer look.
- Compare old and new records. Ask: “What is the oldest record, and does it look different from the newest?” Differences may reflect changes in how the application collected or stored information.
- Review categorical values. Find the common status values, then look for less common or unexpected ones. Compare them with the values the code handles rather than assuming the current schema tells the whole story.
- Check for unusually large customer-level counts. Look for customers with far more orders than others. An outlier is a clue to investigate, not proof of a problem.
- Record surprises and discuss a few. Write down what you found and share the most useful two or three observations with your team. Someone may know the historical reason even if it is missing from the documentation.
What unexpected records can tell you
Shinder offers examples of how present-day rows can preserve a system’s history. A retired status may remain on older orders; phone numbers may appear in three formats after a 2021 migration; and production may still contain test accounts that were never removed. These are illustrative examples, not estimates of how often such conditions occur.
Use findings as clues, not conclusions. An old value may be valid for historical records, multiple formats may be an intentional consequence of a migration, and an account that looks like a test account may need confirmation. The useful next question is often what changed, when it changed, and whether current code is meant to support the older data.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Keep the inspection safe and lightweight
- Use only access you have been granted, and prefer a masked copy when one is available.
- Do not copy customer data into an unapproved document, chat, or other location.
- Keep queries light so the inspection does not slow a reporting database.
- Share observations in a way that respects your organization’s data-handling rules.
The goal is to understand the shape and history of permitted data—not to export records or run an unbounded investigation.
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