perf: replace /dataEntry/metadata with flat per-type requests [DHIS2-…#577
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…21757] The bespoke GET /api/dataEntry/metadata endpoint walks the whole metadata entity graph via lazy Hibernate associations, causing an N+1 query explosion (10,202 SQL statements / 245s / HTTP 500 on the Uganda instance). Its cost scales with the size of the user's writable metadata. Rebuild the same metadata client-side from flat, bulk, per-type requests to the standard metadata endpoints instead. useMetadata()'s return shape is unchanged (id-keyed dicts via hashArraysInObject), so every selector and consumer is untouched. Verified byte-equivalent against the real endpoint on the Sierra Leone demo (757 entities across 7 types match exactly), and ~6-8x fewer SQL statements (~230-300 vs ~1,880), well-batched per type. Also marks the query meta.persist so the preloaded metadata survives reloads and is available offline (the endpoint's original purpose). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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The bespoke GET /api/dataEntry/metadata endpoint walks the whole metadata entity graph via lazy Hibernate associations, causing an N+1 query explosion (10,202 SQL statements / 245s / HTTP 500 on the Uganda instance). Its cost scales with the size of the user's writable metadata.
Rebuild the same metadata client-side from flat, bulk, per-type requests to the standard metadata endpoints instead. useMetadata()'s return shape is unchanged (id-keyed dicts via hashArraysInObject), so every selector and consumer is untouched. Verified byte-equivalent against the real endpoint on the Sierra Leone demo (757 entities across 7 types match exactly), and ~6-8x fewer SQL statements (~230-300 vs ~1,880), well-batched per type.
Also marks the query meta.persist so the preloaded metadata survives reloads and is available offline (the endpoint's original purpose).