bcquality/microsoft/knowledge/performance/singleton-setup-tables-need-no-access-optimization.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

1.5 KiB

bc-version domain keywords technologies countries application-area
all
performance
singleton
setup-table
sales-receivables-setup
general-ledger-setup
setloadfields
bounded
al
w1
all

Singleton setup tables hold one row; access-pattern optimization is wasted

Description

Business Central setup tables — Sales & Receivables Setup, General Ledger Setup, FA Setup, Purchases & Payables Setup, and the broader pattern of any *Setup table — hold at most one record per company. Per the upstream guidance, "any access pattern is fine, no SetLoadFields needed" on these tables. The same applies to other small bounded tables (enum mappings, permission objects, Role IDs) and system metadata tables (TableMetadata, Field, AllObjWithCaption) where iteration is safe.

Best Practice

Skip access-pattern optimization on singleton-setup-style tables. SalesReceivablesSetup.Get() does not need SetLoadFields (see use-setloadfields-for-partial-records.md); a repeat ... until over a permission-object table does not need bulk operations. Spend the review attention on the production-scale tables instead (see production-scale-tables-warrant-extra-analysis.md).

Anti Pattern

Mechanically applying the rules in this domain to every Record variable in the codebase. Flagging "missing SetLoadFields" on GeneralLedgerSetup or "use IsEmpty instead of FindSet" on a setup table adds noise without payoff — the optimization saves nothing measurable on a one-row table — and trains readers to ignore the review channel.