Performance

Performance — Keep Loads Fast

Rule

Treat every list, every read, and every page load as a latency budget. Two things dominate it: how much data crosses the wire, and how many round-trips and table scans it takes. On a hosted/serverless SQL backend each query is a network round-trip, and an unindexed filter scans the whole — often shared and growing — table. So default to projected columns, indexed hot-path queries, and parallel/batched fetches. These rules hold on local PGlite or hosted Postgres.

This skill is about the data and load path. See the storing-data skill for the schema and migration mechanics it references, and the real-time-sync skill for how updates already reach the UI without polling.

1. Project columns — never SELECT * on a list

A list/index query should select only the columns the list actually renders.

• Never return heavy columns in a list: large JSON/text blobs such as document bodies, rendered HTML, config/layout/spec/data/tracks, tool results, or base64 attachments. Pulling them for every row is the single most common cause of a slow list.

• Heavy/full columns belong on the single-item GET/detail path only.

• Need a preview from a big column? Select a truncated substring at the DB, not the whole column — and it stays portable:

  // Drizzle — project, and truncate the heavy column for the preview
  const rows = await db
    .select({
      id: docs.id,
      title: docs.title,
      updatedAt: docs.updatedAt,
      preview: sql<string>`substr(${docs.content}, 1, 400)`,
    })
    .from(docs)
    .where(accessFilter(docs, docShares))
    .orderBy(desc(docs.updatedAt));
  

• After narrowing the projection, update the row mapper and its return type so a dropped column is provably unused on the list path. If the list genuinely renders a heavy column (a thumbnail, an inline preview the UI shows), keep it — don't break behavior to chase a payload win.

1b. Never put a heavy column in a WHERE

Projecting a blob out of the SELECT is only half the job. A predicate on a large text/JSON column is worse, because Postgres must fetch and detoast that value for every row the scan touches — before LIMIT applies. The column does not even have to be selected.

Measured in production on the agent chat sidebar list (~20 rows of title + timestamp), from one predicate on the message-history blob:

limit=5 costing more than limit=20 is the fingerprint. If asking for less data costs more, something in the WHERE is scanning what LIMIT cannot bound. Diagnose it from the browser console on the live page — fetch the endpoint with and without the suspect filter — rather than reading the plan.

A marker you match with a hardcoded string belongs in its own indexed column: add it, backfill once in a migration, then filter on the column. A legacy compensator on a read path is a backfill you have not done yet, and you pay for it on every request until you do.

Searching a blob against a user-supplied term is different and legitimate — full-text search over message history has no cheaper form. guard:no-blob-column-predicate draws exactly that line: it flags a hardcoded literal and ignores a bound parameter.

Related: a LOWER(col) = ? access predicate cannot use a plain btree on col. Add the matching expression index — see org/migrations.ts for the pattern — or the list scans the whole shared table.

2. Index the hot paths

Indexes are added through the versioned migration array in server/plugins/db.ts as CREATE INDEX IF NOT EXISTS … — not through a schema-level index() helper (the framework applies indexes via migrations; see the storing-data skill). Add an index for any column a hot query filters or sorts on. The recurring ones:

• Ownable tables → (owner_email, org_id, <the list's ORDER BY column>). Access scoping filters by owner/org and lists sort by updated_at/created_at.
• Shares tables ({resource}_shares) → (resource_id, principal_type, principal_id). Access checks run correlated EXISTS subqueries against these on every list.
• Child / foreign-key columns used to load children (e.g. responses.form_id, comments.parent_id, an events log's *_id) → index the FK, plus its sort column when the children are ordered. An unindexed FK means a full scan of the child table on every parent open. A foreign-key reference does not create an index automatically — add it explicitly.
• Status-filtered lists → match the real WHERE, e.g. (owner_email, status) or (status, <sort>).

Keep index DDL PostgreSQL-compatible and idempotent:

CREATE INDEX IF NOT EXISTS forms_owner_org_updated_idx ON forms (owner_email, org_id, updated_at)

No DESC or partial WHERE; keep the index DDL idempotent and apply it through the migration path. Indexes mostly bite as data grows and on unbounded child tables (a seq-scan of 10 rows is instant; of a shared, ever-growing log it is not), so index the growing tables first.

3. Don't fan out queries — batch and parallelize

• No N+1. Never loop issuing one query per item. Load children for many parents in one inArray(child.parentId, ids) query, then group in memory.
• Count in SQL (count()), never "select all rows then .length".
• Parallelize independent queries with Promise.all rather than sequential awaits — each await is another round-trip.
• Prefer one composed endpoint over several dependent calls.

For provider wrappers, inspect the upstream API before building a list-then- enrich flow. Prefer the richest endpoint that can apply the real filters and return the needed associations or participants in one paginated operation. Cursor pagination is already serial; adding a serial detail/enrichment request to every page doubles its critical path. Exhaustive records belong in corpus recipes or data programs with explicit coverage, not one agent tool call per page or item.

First-class provider actions should represent one stable conceptual operation. Keep arbitrary endpoint, filter, and pagination access in the provider API substrate; do not turn a convenience action into a capability ceiling or duplicate the provider transport, auth, quota, and cache implementation.

4. Avoid client-side waterfalls

• Don't gate query B on query A's result unless B truly needs it. Fire independent useActionQuery / useQuery hooks in parallel; never make the loading skeleton wait on a serial chain.
• Load the visible page from one read where possible, and lazy-load secondary / below-the-fold data after first paint.

5. Poll cheaply; compute once

• Updates already reach the UI through the real-time-sync skill (useDbSync / SSE). Don't add an aggressive refetchInterval that re-runs a heavy list/read every couple of seconds. If you must poll, use a wide interval and a cheap endpoint.
• Never do expensive per-request work on a read that runs on every load/poll: re-rendering HTML/markdown, pretty-printing, re-parsing / migrating / normalizing / sanitizing stored JSON. Do that work at write time (store the result) or compute it lazily only for the caller that needs it. Reads on the hot path must be cheap.
• Data the UI doesn't display (export formats, alternate renderings) belongs in a separate on-demand action, not baked into the hot read.

6. SSR shell caching — load-bearing, do not undo

Every SSR HTML page and React Router .data response is one impersonal, public shell, hard-cached at the CDN and served identically to every visitor — logged in or not. This is the single biggest lever on first-response latency: one shared cache entry serves the whole site instead of a per-user render on every request. Adding private, no-store, Vary: Cookie, a session read, or an auth branch to the SSR path defeats the cache for every visitor, not just one.

If you're debugging a slow first response, check whether something re-personalized the shell before concluding the render itself is slow — the fix is client-side data loading after the shell paints, never per-user SSR. If the shell is clean and a cold miss is still seconds long, the cost is upstream of the render: see §9. See the authentication skill for the full model and guard:ssr-cache-shell plus ssr-handler.spec.ts (packages/core/src/server/ssr-handler.ts) for the enforced contract.

Netlify prerendered HTML/.data bypasses the SSR handler, so its build must emit the same public SWR policy in _headers; run guard:ssr-cache-artifact against Netlify-mode output. Styling-only work must not alter this cache, prerender, or deploy seam without explicit scope expansion.

Never route mutation-fresh reads through SSR loader data. Data that changes when a user acts belongs in an action, read from the client with useActionQuery / useActionMutation and kept live by useDbSync() polling — that path never touches the SSR shell cache. A useRevalidator() after a mutation re-fetches .data with a plain GET and can legitimately be served the cached copy. SSR loaders render the public shell; the client resolves anything that must be fresh.

The one supported knob is the deployment-wide AGENT_NATIVE_SSR_CACHE env var: unset/on keeps the default, off sends no-store, and a duration such as 30s / 5m shortens freshness. It is for deployments whose host does not purge its CDN on deploy, or whose loaders genuinely serve mutable public data. It changes cache duration only — cookies are still stripped before render, so turning it off does not make SSR personalized. There is deliberately no per-route or per-request override; that is how one visitor's payload lands in another visitor's shared CDN entry.

7. Big payloads and long lists

• Paginate or window unbounded lists (messages, responses, events, activity). Don't load the entire history on open; load a recent window and fetch older on demand.
• Don't store unbounded blobs inline in a row that a list/load pulls. Reference large content separately so opening the parent stays cheap.
• Never inline binary payloads in columns a list, poll, or view-screen summary reads. Images, PDFs, audio/video, archives, screenshots, and base64 attachments belong in file/blob storage; SQL rows should hold URLs, asset ids, storage keys, or opaque blob refs.
• Virtualize very long rendered lists on the client so off-screen rows aren't parsed/rendered every update.

8. Don't do data work at startup

A server plugin's body is not "once per deploy." These apps run as serverless functions, so it runs once per cold start — on the critical path of whichever user's request woke the process, and again on the next cold start. An in-process let done = false memo does not help: the new isolate starts with false.

This has already cost real outages and sustained slowness here, not hypothetical ones — Slides startup slowness, Analytics paying startup cost on API calls, and a production incident. The shape that did it:

// templates/<app>/server/plugins/db.ts — every cold start pays all of this
export default async (nitroApp) => {
  await migrations(nitroApp);
  await retypeBooleanColumnsOnPostgres();   // rewrites tables on Postgres
  await backfillLegacyTables();
  await syncWorkspacesToOrganizations();
  await backfillRecordingOrgId();
};

Schema DDL is not exempt, though it reads like it should be. Measured on a 180-table production database: the migration "fast path" (SELECT MAX(version)) took 5.5s and the information_schema probe 8.3s — paid on every cold start, until health checks timed out and the app was down. Bounded is not the same as fast, and "it short-circuits cheaply" is an assumption until someone measures it on the largest database you have.

The same applies doubly to work whose cost grows with the data — backfills, retypes, aggregations, recomputes, re-syncs, sweeps, cache warming, index rebuilds. Those have three better homes, all of which already exist:

• a scheduled job (recurring-jobs, automations skills),
• a one-off CLI or release-time script, run deliberately, once,
• lazily behind the first caller that needs it, memoized — accepting that the memo is per-isolate, so the work must be small enough to repeat.

If it truly must complete before the app can serve a correct response, it is a migration, not a backfill — say so on the line and keep it bounded:

await backfillOneRow(); // guard:allow-boot-data-work — single row, bounded

guard:no-boot-data-work fails on new boot-time data work, scoped to lines this branch adds. It cannot see everything — a helper that hides the work one call deeper reads as innocent — so the rule matters more than the check.

9. Cold start is the artifact, not just the work it does

§8 covers what the process does at boot. This covers how much there is to boot. Measured in production: a cold cache miss on www.agent-native.com returned in 4.5–6.0s while the in-handler server-timing: app;dur was only ~2100ms — the other ~2900ms is platform init, spent before any of our code evaluates. A different app with a healthy database measured 13.4s TTFB on its first cold request with app;dur=1338, so ~12s of init. Platform init scales with the size of the deployed artifact. Every app pays it, and no query tuning can reach it.

• The /* page function is the one every visitor's cache miss wakes. Nothing belongs in it that a page render cannot call. Headless browsers, ffmpeg, image rasterizers, and other heavy runtimes belong in the function that actually invokes them, or behind a job — not in the default handler. PR #2684, titled "Harden auth and cold-start data paths", put 78MB of headless Chromium into every page function; nothing in the diff looked like a performance change.
• Each extra emitted function is a full second copy of the bundle. Netlify copies the whole server directory per function, so splitting out a -background or per-route function multiplies existing weight rather than dividing it. Trim the artifact before you split it.
• Already-compressed binaries do not shrink again in the deploy zip. A Brotli-packed browser or a static ffmpeg build costs close to its full size in upload and in cold-start extraction. Budget from bytes on disk, never from an assumption that compression will absorb it.
• Never resolve a copied dependency by walking ancestor node_modules. In a monorepo that walk does not fail — it finds a sibling app's copy and ships that. Resolve from the app's own dependency root and throw when it is missing; a silently-found wrong package is precisely the indistinguishable-from-success failure this repo bans.

packages/core/src/deploy/build.ts is what decides all of this: the platform/arch filter at :2384-2410 and the per-preset copy list at :4499-4504. Adding a package there adds it to every page function.

Measure a built bundle by timing the import and forcing exit. Never measure by waiting for the process to exit — module scope starts timers and opens handles, so process lifetime measures those, not boot cost. That exact mistake produced a wrong number during the investigation behind this section.

node -e 'const t=Date.now();import(process.argv[1]).then(()=>{console.log(`${Date.now()-t}ms`);process.exit(0)})' \
  ./.netlify/functions-internal/server/main.mjs

Checklist — run before shipping a list/read or a new table

• List selects only displayed columns; heavy blobs excluded or substr-truncated.
• Every hot-path WHERE / ORDER BY column is indexed (owner/org/sort, shares resource_id, child FKs, status filters) via a db.ts migration.
• No N+1; independent queries parallelized; counts via SQL count().
• Client fires independent queries in parallel, not a waterfall.
• No heavy recompute on every read; no aggressive polling of heavy endpoints.
• Unbounded lists are paginated/windowed; large blobs aren't inlined on the hot path.
• SSR HTML/.data path stays session-blind and cacheable — no private, no-store, Vary: Cookie, or auth branch added to it.
• No data work added to a server plugin body / module scope — backfills, aggregations and re-syncs run on every cold start there (see §8).
• Mutation-fresh reads go through actions + useActionQuery, not SSR loader data.
• No heavy runtime (browser, ffmpeg, rasterizer) added to what the /* page function ships, and no new copied dependency resolved by walking ancestor node_modules (see §9).