bcquality/microsoft/knowledge/performance/prefer-dictionary-over-temporary-table-for-lookups.md
Jesper Schulz-Wedde a9f3c50863 Regenerate microsoft/knowledge from upstream BCApps instructions
The previous LLM-generated knowledge files contained factual
hallucinations. The most visible was the claim that `FindFirst` /
`FindLast` "forces a full-table scan" on an unfiltered record - it does
not; those APIs return a single row via the current key.

Other inaccuracies the audit found and fixed:

* `FindSet(true)` was described as "taking a LockTable". The correct
  upstream phrasing is that `FindSet(true)` sets
  `ReadIsolation::UpdLock` on the read. UpdLock and LockTable are
  related but distinct mechanisms.
* The list of production-scale tables had been invented beyond the
  upstream source (e.g. "Detailed Cust. Ledg. Entry") without a
  citation. The regenerated list matches the ten tables upstream lists
  with their P95 row counts.
* `SetLoadFields` guidance had been augmented with an extra mechanism
  claim ("the database resolves the filter using the index without
  hydrating the value") not present in upstream.

Approach: full regeneration of `microsoft/knowledge/` from the six
upstream BCApps Code Review instruction files, with Microsoft Learn /
the AL language reference as a secondary source. Every claim in every
regenerated file is anchored to a verbatim upstream quote (or a Learn
URL); the audit trail lives in artifacts/trace-<domain>.json on the
session workspace.

The PR #11 transaction/error-handling cluster is preserved verbatim:

* performance/understand-implicit-transaction-boundary.md
* performance/codeunit-run-as-atomic-sub-operation.{md,good.al,bad.al}
* performance/codeunit-run-requires-prior-commit-inside-transaction.{md,good.al,bad.al}
* performance/use-tryfunction-for-error-catching-not-rollback.{md,good.al,bad.al}
* performance/avoid-commit-inside-loops.{md,good.al,bad.al}
* security/commitbehavior-attribute-scopes-explicit-commits.{md,good.al,bad.al}
* testing/transactionmodel-attribute-governs-test-transactions.{md,good.al,bad.al}

These articles already cite Microsoft Learn and were carefully
cross-referenced; the regeneration skips their topics rather than
duplicating them.

File counts after regeneration:

  performance   35 .md  (5 preserved + 30 new)
  privacy       17 .md
  security      18 .md  (1 preserved + 17 new)
  style         33 .md
  testing        1 .md  (preserved)
  ui            19 .md
  upgrade       18 .md

Total 141 atomic knowledge files, each strictly one rule. All pass
.github/scripts/validate_frontmatter.py with 0 errors and 0 warnings.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-05-21 09:53:09 +02:00

22 lines
1.5 KiB
Markdown

---
bc-version: [all]
domain: performance
keywords: [dictionary, temporary-table, lookup, o-of-1, key-lookup]
technologies: [al]
countries: [w1]
application-area: [all]
---
# Prefer a Dictionary over a temporary table for pure lookups
## Description
A temporary table supports a full record API — filters, iteration, multi-field keys — but a pure key→value lookup pays for plumbing it does not use. Per the upstream guidance, "if a temporary table record is ONLY used as a lookup table, it is faster to use a dictionary which supports O(1) lookups instead of O(lg n) for temporary tables." The Dictionary type has no record machinery to traverse; the key hash answers the lookup directly.
## Best Practice
When the use of a temp record is "set a key, see if the row exists, read a single value", switch to `Dictionary of [Key, Value]`. Use the temp-table form when the use genuinely needs filtering, iteration in a specific order, or a multi-field key. Compatibility with code that expects a `Record` parameter is a real reason to keep the temp table; performance alone, on a pure lookup, is not.
## Anti Pattern
A temp `Record` declared, populated row by row, then queried with `SetRange(KeyField, X); if Find('=') then Value := Rec.ValueField;`. The lookup hashes the key behind the scenes and does the same work a `Dictionary` would, plus the per-row record overhead. The pattern often appears because the author originally needed iteration and the iteration was later removed without revisiting the data structure.