Write Service Code

When to use

Reach for this when you're writing the body of a handler/service/query/event/test and want the team's implementation conventions — readability, async, nullability, SQL performance, messaging, logging, and test shape. For where files go and what they're named, use the companion skill structure-a-backend-service.

Each rule: portable principle → ▸ Example (TS/NestJS) (neutral listing domain; <Entity>/<feature> = rename) → ▸ Other stacks. Some rules restate global policy; the global rule stays authoritative (see Related).

Steps

1. Control flow — return early, no manual loops

• Return (or throw) early; don't nest. Handle the invalid/empty case first and bail, so the happy path stays unindented.

  if (minPrice == null && maxPrice == null) return;     // guard
  if (!items?.length) return null;
  // ...happy path, never more than ~2 levels deep
  

• Transform collections with pipeline functions, not for/while. map/filter/reduce/ find/some/every/flatMap. Keep callbacks pure (no mutating the source).

  const coverIds = media.filter((m) => m.type === MediaType.COVER).map((m) => m.id);
  const labelByCode = categories.reduce((acc, c) => { acc[c.code] = c.label; return acc; }, {} as Record<string, string>);
  

• Imperative iteration only for: side effects (forEach / for...of to mutate external state, dispatch, log) and sequential async that must be ordered (for...of with await, e.g. a retry loop). Never forEach with an async callback. ▸ Other stacks: comprehensions / streams / map equivalents; the principle (declarative transform, imperative only for effects & ordered async) is universal. (Matches global Code Style — Iteration & Collections.)

2. Parallelize independent async with Promise.all

Independent awaits run together; only await in sequence when one result feeds the next.

const [detail, owner] = await Promise.all([getDetail(id), getOwner(id)]);       // independent → parallel
const files = await Promise.all(media.map(async (m) => ({ name: m.name, data: await toBase64(m) })));

A sequential await inside a map/loop for independent work is the anti-pattern — it serializes needlessly. ▸ Other stacks: asyncio.gather, goroutines + errgroup, CompletableFuture.allOf.

Cheaper than parallelizing: don't run what a branch discards. If some flag/condition zeroes out or ignores a result, compute that condition FIRST and skip the calls entirely (precedent: listings-api#719 — an estimate engine ran 2 queries for non-tillable farms whose prices were then forced to 0). And "moved/ported verbatim" is not an exemption — code you relocate must pass this section even if its old home didn't.

3. Prefer null over undefined

Use an explicit nullable type for "absent" values; reserve undefined for "not provided". This keeps DB-nullable columns, DTO fields, and return types consistent.

@Column({ nullable: true }) publishedAt!: Nullable<Date>;         // entity field
async getFile(req: FileReq): Promise<Nullable<FileDto>> { ... }   // return type

▸ Other stacks: Optional<T> (Java), *T/sql.NullString (Go), T | None (Python). The point: one agreed "absent" value, not a mix of null and undefined. (TS note: ==/!= against null intentionally matches both null and undefined.)

API response defaults: an absent array is [] (never null/undefined); an absent scalar/object is null (never undefined). undefined disappears from JSON and breaks clients that expect the key. ▸ Other stacks: same — empty collection for lists, explicit null for the rest.

4. Put private helpers below the public API

A class/file reads top-down: public methods first, then private helpers underneath. Helper names are verb-led + single responsibility (fetchOwnerProfile, shouldTriggerReview, buildPayload).

@Injectable()
export class ListingService {
  async createListing(dto: CreateListingDto) { /* ...delegates to helpers... */ }
  async getListing(id: string) { ... }
  // ── private helpers below ──────────────────────────────
  private async fetchOwnerProfile(listing: Listing) { ... }
  private shouldTriggerReview(listing: Listing): boolean { ... }
}

Keep methods to one screenful; extract a focused private method when one covers 3+ concerns. (Matches global Code Style — Function Size & Density.)

5. Querying & performance

The repository/query builder is where most performance is won or lost. Apply all of these:

• Select only the columns you need — never implicit SELECT *.

  .select(['listing.id', 'listing.status', 'listing.price', 'photo'])   // explicit, includes joined alias
  

• Push every filter into the query (status/type/date/soft-delete) and batch multi-value filters with IN (:...ids) — never fetch broadly then .filter() in code, and never query per-item in a loop (that's the N+1). (Matches global Database & Migration Rules.)

  .where('listing.isDeleted = false')
  .andWhere('listing.status IN (:...statuses)', { statuses })    // one query, not one-per-status
  

• Load relations in the query, not lazily per row, to avoid N+1: leftJoinAndSelect for optional relations you need in the result, innerJoin for a required relation you only filter on (no select), Brackets for grouped OR.

  .leftJoinAndSelect('listing.photos', 'photo', 'photo.isDeleted = false')   // eager, optional
  .innerJoin('listing.owner', 'owner')                                       // required, filter-only
  

• One round-trip when you can — fetch rows + total together (getManyAndCount) instead of two queries; combine related work rather than looping queries.

  const [rows, total] = await qb.take(take).skip(skip).orderBy(column, direction).getManyAndCount();
  

• Write-or-update in one statement with upsert instead of select-then-insert/update; it also lets you batch insert + update + soft-delete together.

  await this.photoRepo.upsert(
    [...newPhotos, ...unused.map((p) => ({ ...p, isDeleted: true }))],
    ['id'],   // conflict key
  );
  

• Index for your filters + sort, and order deterministically. Add a composite @Index matching the common WHERE + ORDER BY; drive orderBy from a typed sort-map constant (not free-text), so only indexed columns are sortable.

  @Index('listing_status_createdAt_idx', ['status', 'createdAt'])
  // sort map: enum → { column, direction } ; query: const { column, direction } = listingSortMap[sortBy];
  

• Money & dates go through standard libraries, never ad-hoc math. Precise/decimal numeric work uses a decimal library (floats drift — 0.1 + 0.2 !== 0.3); date/time uses a date library (parsing, timezones, formatting). ▸ Example: a Decimal type + a decimal(p,s) column transformer for money; a day/date lib for every date operation. ▸ Other stacks: your ecosystem's decimal + datetime libs — never hand-roll currency math or timezone arithmetic.

▸ Other stacks: the same SQL discipline applies through any ORM/query layer (Django select_related/prefetch_related, JPA fetch joins, sqlc/sqlx); upsert = INSERT ... ON CONFLICT / MERGE.

6. Events & async messaging (SQS/pub-sub)

• One event per file under the feature's events/, holding the event class (extends a shared BaseEvent<T>) and its handler; a barrel exports the handler list for batch registration.
• Emit from the use-case via the bus; never publish externally inline. The command/query handler publishes a domain event; an event handler does the outbound I/O.

  // in a command handler
  this.eventBus.publish(new ListingStatusChangedEvent(listings));
  
  // events/listing-status-changed.event.ts
  @EventsHandler(ListingStatusChangedEvent)
  export class ListingStatusChangedEventHandler implements IEventHandler<ListingStatusChangedEvent> {
    async handle(event: ListingStatusChangedEvent): Promise<void> {
      await this.publisher.dispatch({
        event: TOPIC.LISTING_STATUS_CHANGED,
        payload: event.data.map((l) => ({ listingId: l.id, status: l.status })),  // mapped subset, NOT the raw entity
      });
    }
  }
  

• Outbound payloads are an explicit mapped subset (id + the few fields subscribers need), never the whole entity — that's a contract you don't want leaking internal columns.
• Inbound consumers are wired from config + named constants, not hard-coded URLs/strings. Queue URLs are built from a centralized config block; topic/queue/pattern names are constants.

  SqsModule.registerAsync({
    useFactory: (config: ConfigService) => {
      const { region, accountNumber, sqsEndpoint } = config.get(configEvents);
      const consumers = [getQueueName(EVENT_PATTERNS.listing.published)].map((name) => ({
        name, queueUrl: `${sqsEndpoint}/${accountNumber}/${name}`, region,
      }));
      return { consumers };
    },
    inject: [ConfigService],
  });
  // @SqsMessageHandler(queueName, false) async handleMessage(message: Message) { ... }
  

• In a queue (SQS) consumer, never throw — log and return. An unhandled throw returns the message to the queue, where it's re-consumed until it expires — a poison message that can loop forever. ▸ Example: the handler extends a shared AbstractEventHandler; on failure it calls logger.error(...) then return, so the message is acked/deleted while the error still reaches alarms. Handle partial-batch failures explicitly (report which records failed). ▸ Other stacks: ack/commit the message and route the failure to a dead-letter queue — don't let an exception trigger blind redelivery.

▸ Other stacks: an in-process event bus or outbox emits; a separate consumer/worker handles the queue. Principle: use-case → event → handler → broker, payloads are explicit, names are config.

7. Logging

• Structured metadata, not string concatenation. A short message string + a context object.

  this.logger.info(`Response from <system> for "${path}"`, { data: maskPii(data), payload: maskPii(payload) });
  this.logger.error("Can't publish listing", { listingId, resError: (error as Error).message });
  

• Mask PII/secrets before logging (identity numbers, tokens) — pass values through a masker.
• Levels: info = normal milestones (external call made, status transition), warn = recovered/ fallback paths, error = exceptions & failed external calls (include the error message + ids), debug = detailed tracing. Log every critical branch, fallback, and missing-config path (global Feature Flags & Observability). Request logging + a global exception filter are wired once centrally, not per handler. ▸ Other stacks: any structured logger (zap, structlog, SLF4J + MDC) — key-value context, not interpolated strings.

8. Writing a test (integration layer)

These are integration tests — they boot the app and hit the real transport/DB boundary. For the isolated unit-test layer (mocked deps, no DB — fast tests for handlers/services/DTOs) see write-unit-tests; a repo may run one layer or both (two jest projects). See structure-a-backend-service step 8 for the harness. Per spec:

describe('ListingCmdController', () => {
  let client: ClientProxy, app: INestApplication, dataSource: DataSource;
  let factoryCtx: FactoryContext, listingFactory: ListingFactory, listingRepo: Repository<Listing>;

  beforeAll(() => {
    ({ client, app, dataSource } = global.testContext);   // app booted once in the shared setup
    listingRepo = dataSource.getRepository(Listing);
  });
  beforeEach(() => {
    factoryCtx = new FactoryContext(dataSource);
    listingFactory = new ListingFactory(factoryCtx);       // fresh factory per test
  });
  afterEach(async () => {
    jest.restoreAllMocks();
    await listingRepo.delete({});
    await factoryCtx.destroy();                            // clean state between tests
  });

  it('publishes the listing and persists it', async () => {
    // Arrange — seed via factory, mock only externals
    const listing = await listingFactory.build({ status: ListingStatus.DRAFT });
    jest.spyOn(SearchIndexService.prototype, 'index').mockResolvedValue({ ok: true });

    // Act — exactly one action under test, blank line before & after
    const { success } = await lastValueFrom(client.send(PATTERN.listing.publish, msg({ id: listing.id })));

    // Assert — response AND persisted state, with specific expected values
    expect(success).toBe(true);
    const updated = await listingRepo.findOneOrFail({ where: { id: listing.id } });
    expect(updated.status).toBe(ListingStatus.PUBLISHED);   // not toBeTruthy()
  });

  it('fails for an unknown listing', async () => {
    const { success } = await lastValueFrom(client.send(PATTERN.listing.publish, msg({ id: v4() })));
    expect(success).toBe(false);                            // cover the error path too
  });
});

• Structure every test Arrange → Act → Assert, blank line around the Act, exactly one Act (one action under test) — the comments above show the shape.
• Seed with factories + a faker lib for inputs (realistic random names/emails/numbers/ids), never hand-rolled literals; build only the fields the test asserts on. Clean up the rows created in this test afterward (and only those) so parallel specs don't collide.
• Mock only what crosses the process boundary (external services via spyOn(...prototype), outbound HTTP via an intercept lib). Use the real DB; assert the persisted row, not just the reply.
• Assert specific expected values, not just truthiness — expect(id).toBe(realId), not toBeTruthy(). Common matchers: toBe (string/number), toMatchObject (part of an object), toEqual (whole object/array), arrayContaining (array members).
• Group branch variations with it.each instead of copy-pasting near-identical tests.
• Cover the success path + the error/edge path; assert outbound side effects with spy-called-with.
• Keep specs small and run coverage. One spec per API when it has many cases (≤ ~300 lines); group several small APIs into one spec otherwise. Run the coverage report over what you wrote.
• Don't write isolated unit tests that bypass the boundary — if it can't be reached through the boundary, skip it (global HTTP-layer testing rule).

9. Robustness — transactions, event-handler safety, external clients

• Multi-repo writes go in one transaction; broadcast a compensating event before re-throwing. Wrap related mutations in dataSource.transaction(em => …) and pass em to each repo op for atomicity. In an event-driven flow, if the transaction fails after side effects were signalled, emit a compensating event (e.g. OrderCanceled releasing held inventory) before throwing, so downstream can undo:

  await dataSource.transaction(async (em) => {
    const order = await saveOrder(em, dto);
    try { await reserveInventory(em, order); await debitWallet(em, order); return order; }
    catch (e) { await events.publish(new OrderCanceledEvent(order)); throw e; }  // compensate, then roll back
  });
  

• In-process event handlers must not crash the bus. A CQRS @EventsHandler is fire-and-forget; one failing handler (missing template, third-party down) shouldn't break sibling handlers. Wrap handle so it logs + swallows instead of throwing — e.g. a shared @CatchException() decorator (log + return). (This is the in-process twin of the SQS "don't throw" rule in §6.)
• External/microservice clients own their lifecycle. A ClientProxy (TCP/microservice client) closes on OnApplicationShutdown (no zombie connections on deploy); retry config (attempts + delay) is env-driven (exponential backoff + jitter, capped); route every call through one base send() wrapper that centralizes retries + error mapping (don't scatter clientProxy.send across services).

  @Injectable() export class WalletClient implements OnApplicationShutdown {
    constructor(@Inject(walletMs) private readonly proxy: ClientProxy) {}
    onApplicationShutdown() { this.proxy.close(); }
    send<I, R>(pattern: string, data: I) { return this.base.sendAsync<I, R>(this.proxy, pattern, data); }
  }
  

• Scope every read/write to the caller's tenant/permitted set. For non-admin roles, an interceptor auto-injects the caller's allowed scope (e.g. their locationIds/orgId) into the query — or intersects a requested scope with the allowed set and drops the rest — so a handler can't return or mutate another tenant's data even when asked. Centralize this in one interceptor/guard; don't re-check in every handler. ▸ Other stacks: a query-scoping middleware or DB row-level security.
• Bulk CSV/spreadsheet import (streaming parse + per-row error report + chunked upsert) has its own skill — see import-data-from-csv.
• Background jobs & caching (Bull queues, Redis cache) have their own skill — see background-jobs-and-caching. ▸ Other stacks: a DB transaction + outbox/compensation; a global error-swallowing wrapper on async event handlers; one connection-managed client per dependency with retries centralized.

Verification

Reviewing a change, confirm:

• Control flow: no manual index for/while for transforms (grep -nE 'for ?\(' src --include='*.ts' should hit only side-effect/ordered-async spots); guard clauses up top; ≤2 nesting levels.
• Async: independent awaits are inside a Promise.all; no await inside a .map used for independent work.
• Nullability: nullable fields/returns use the nullable type, not undefined.
• Layout of logic: private helpers sit below public methods; helper names are verb-led.
• Queries: every list/detail query has an explicit .select([...]), filters are in the SQL (IN (:...) for lists), relations are joined (no per-item query in a loop), pagination uses one count+rows call, writes that can conflict use upsert, and sorted/filtered columns are indexed.
• Events: outbound payload is a mapped subset (not the raw entity); topic/queue names are constants; consumers wired from config.
• Logging: message + context-object form (no string-concatenated context); PII masked; errors log the cause + ids.
• Tests: go through the transport/HTTP boundary, seed via factories, assert persisted state, mock only externals, cover an error path.
• Gates: lint, build, and the test suite pass (e.g. pnpm lint / pnpm build / pnpm test).

Related

• structure-a-backend-service — where these files live and how a feature is wired (this skill is the how to write the code inside; that one is the where & what).
• README — how project skills are organized.
• Global assistant instructions (the authority for the do/don't this skill restates): Code Style — Iteration & Collections, Code Style — Function Size & Density, Database & Migration Rules, DRY — Parallel flows, Feature Flags & Observability, Testing Rules / HTTP-layer testing rule, Root-Cause First.