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A cleanup of Munchable’s ingredient catalogue removed tags that its app could already derive, while preserving the app’s existing answers. In an October 1, 2026 account, developer Daniel Pertu reported that 45% of stored tags were redundant under the app’s normalization rules. The key safeguard was not simply checking whether a tag could be removed: the team ran the existing engine on both versions of each row and wrote a change only when its outputs matched exactly.
Why remove tags the app could already derive?
Munchable scans packaged-food barcodes and checks products against gut-related conditions. Its PostgreSQL catalogue held roughly a million product rows, with ingredient tags stored in the order they appeared on printed labels. Pertu reported that 45% of those stored tags could be derived by the app’s ingredient engine.
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The duplication came from keeping both an ingredient and its ancestors. A row for cheddar, for example, could also contain cheese and dairy, even though the engine expands cheddar to those tags during normalization. That redundant representation added database data and increased the payload sent to mobile devices. Pertu reported that the tags accounted for 38 MB of a 148 MB figure; the account does not establish an independent measurement or clarify a broader production outcome. Daniel Pertu’s October 1, 2026 account
How did the cleanup decide what was redundant?
Keep the sequence meaningful
The rule was order-sensitive: remove a tag only when an earlier tag already implied it. A later, more specific ingredient could not make an earlier generic tag redundant, because position can affect ranking and reporting. This preserves information about the order in which ingredients were listed rather than treating the row as an unordered set.
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Protect allergen tags
Allergen tags were excluded from removal, including generic tags. Allergen checks rely on printed ingredients, and the cleanup was designed not to discard information needed for warnings.
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How did the app’s own engine act as the oracle?
For each original row and its candidate compacted version, Pertu says the team ran the existing application engine using a profile with every condition, allergen, and health preference enabled. They serialized the resulting fit, allergen, and health outputs as JSON and compared them. A row was eligible for writing only if the before-and-after outputs matched byte for byte; rows with differences were left unchanged and surfaced in the run report.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsThis is differential testing against the behavior the application already exposes: rather than assuming the cleanup rule is correct, compare what the app actually returns before and after it. As Pertu put it, “The interesting part of the job is not the delete, it is proving the delete changes nothing.” The account describes the method, but does not provide an independent audit or a reproducible result artifact.
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How were writes made safer?
The migration targeted seed rows and used keyset pagination in pages of 2,000. Updates were batched and guarded by the original tag array, so a row changed after it was read would not be overwritten with a stale candidate. The script defaulted to dry-run mode; writing required the explicit --apply flag.
The update deliberately left updated_at unchanged because, according to Pertu, that field participates in device cache behavior. This is an important migration detail: a cleanup can affect consumers even when the logical ingredient result remains the same, so fields tied to cache invalidation need to be considered separately.
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Why did the database need a table rewrite?
Deleting values with an UPDATE does not necessarily shrink a PostgreSQL table’s on-disk footprint. Pertu says the update left dead tuples; ordinary VACUUM would make that space reusable inside the table, but would not return it to the hosting quota. The account reports using VACUUM FULL to rewrite the table and reclaim quota space.
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That choice has operational costs. The rewrite needs enough free space to create the compacted version and takes an exclusive lock while it runs. Pertu reports about a minute of exclusive locking in this deployment; that duration is specific to the described run, not a general expectation or guarantee. Teams considering a similar operation need to plan for the lock and temporary space as well as the data change itself.
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What the case study establishes—and what it does not
The account offers a concrete pattern for safely removing data that can be derived at runtime: make the rule respect ordering and protected categories, test candidates against actual application behavior, and guard writes against concurrent changes. It also distinguishes logical cleanup from reclaiming storage at the hosting-quota level.
The figures—45% of tags, 38 MB of a reported 148 MB, roughly a million product rows, and 2,000 rows per page—are Pertu’s reported measurements and configuration. They are not independently audited. The account does not establish the current production schema or verify that the change remains deployed, so these numbers describe the reported migration rather than a guaranteed or ongoing result.
Quick Recap
When this approach is useful
- Use behavior-based checks when removing stored data could alter application output in ways that are difficult to capture with a simple schema-level test.
- Preserve ordering or protected classifications whenever they influence ranking, warnings, or downstream interpretation.
- Use guarded, restartable batches for long-running migrations where rows may change during processing.
- Plan separately for logical correctness and physical storage reclamation; a successful update may not reduce the space charged by a hosting provider.
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