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In a 2026 migration account, Munchable developer Daniel Pertu says his team removed 45% of stored ingredient tags by deleting only tags already implied by an earlier tag—and then checking every candidate row against the application’s existing engine. Rows were written only when the engine’s fit, allergen, and health outputs remained byte-for-byte identical. The account reports saving 38 MB from a 148 MB footprint, but those figures are the author’s measurements, not an independently audited benchmark.
Why ingredient tags were duplicated
Munchable scans packaged-food barcodes and checks products against gut-related conditions. Pertu says its PostgreSQL catalogue contained roughly a million product rows, with ingredient tags stored in the order they appeared on printed labels.
The app’s ingredient engine already expanded specific ingredients into broader categories. For example, cheddar could imply cheese and dairy. Yet a database row might store all three tags. That duplicated information made rows larger and increased the data sent to mobile clients. Pertu reports that 45% of stored tags could be derived by the engine, and that the relevant data occupied 38 MB out of 148 MB. These are figures from his 2026 account, not independent measurements. Pertu’s account
Why tag order constrained the cleanup
The cleanup did not simply remove every tag that had a relationship to another tag. It preserved the sequence of the printed ingredients: a tag could be removed 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.
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Allergen tags were protected, including generic tags. Allergen checks rely on the printed ingredients, and the migration’s rule was not allowed to discard information needed for warnings. The graph walk used the seed and hand-curated layers; it excluded an AI-proposed overlay that might differ between phone builds. That kept the migration’s definition of “implied” consistent with the layers it chose to trust.
How the existing engine served as the oracle
For each original row and its proposed compacted version, Pertu ran the application’s existing engine. The test profile enabled every condition, allergen, and health preference. The migration compared the outputs for fit, allergens, and health after serializing them as JSON.
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A row qualified for writing only when the before-and-after outputs matched byte for byte. If they differed, the script left the row unchanged and surfaced it in the run report. This is a differential check against the app’s own behavior: it tests whether the representation change alters the outputs the application produces, rather than assuming the cleanup rule is harmless. As Pertu put it, “The interesting part of the job is not the delete, it is proving the delete changes nothing.”
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How the migration limited operational risk
Paginate and batch updates
The script used keyset pagination, reading 2,000 rows per page, and applied updates in batches. Each update was guarded by the original tag array. If a row had changed since it was read, the guard prevented the migration from overwriting that newer value.
Keep the run non-destructive by default
The script defaulted to dry-run mode and required --apply to write changes. It targeted seed rows. The author also says it deliberately left updated_at unchanged because that field participates in device cache behavior.
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Why reclaiming quota space required more than UPDATE
Updating rows can leave dead tuples behind. As Pertu explains the deployment, ordinary VACUUM makes that space reusable inside the table but does not return it to the hosting quota. The reported approach used VACUUM FULL to rewrite the table and reclaim quota space.
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That rewrite has costs: it needs free space for the compacted copy and takes an exclusive lock. Pertu reports that the described run held the lock for about a minute; that duration is specific to this deployment and is not a general estimate or guarantee. Plan such a rewrite around the database’s available space and the application’s tolerance for a lock.
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What this case study does—and does not—show
The account illustrates a useful pattern when stored data duplicates information the application can derive: constrain removal rules by order and protected categories, compare old and proposed behavior using the real engine, and guard writes against concurrent changes. It reports a substantial reduction in stored tags, but it does not independently verify the measurements or establish that the change remains in production.
The same discipline applies beyond ingredient graphs: a smaller representation is valuable only when it preserves the behavior users depend on. In Munchable’s example, public answer-page questions included “Is cheddar high in lactose?”, “Is cheese high in lactose?”, and “Is onion low FODMAP?” These illustrate the kinds of user-facing distinctions the system serves; they are not evidence of search volume.
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