FS26
Ctrl K
Phase 25 · Lesson 06

Filtered and Paginated Caches

≈ 2 min core reading · 196 source/companion words
Learning contract

What you should be able to do after this

  • Explain Filtered and Paginated Caches without relying on framework buzzwords.
  • Connect the lesson to the phase outcome: Add optimistic UI safely by defining snapshot, optimistic state, rollback, reconciliation, temporary identity, and concurrency behavior.
  • Reproduce the relevant behavior and verify it with evidence.
  • Diagnose at least one representative failure instead of guessing.
Figure 25.6 — TaskFlow boundary map
user action
cancel/snapshot relevant queries
optimistic cache write
server mutation
rollback on error
invalidate/reconcile on settle
This lesson belongs to Phase 25. Keep the full request/data path visible even when today's code changes only one box.
Instructor workflow: predict first → implement the smallest slice → run it → inspect evidence → break one assumption → diagnose → explain it back. Do not copy the whole code block before understanding the first failing boundary.

Filtered and Paginated Caches Phase 25 - Optimistic Updates

Volume 07 - Advanced Application Architecture

The hard part is list membership One mutation can affect several cached views differently

All Tasks status=TODO status=DONE Task remains visible Task may disappear Task may appear

Perfectly recreating every filtered + paginated server result on the client is expensive.

Prefer a small local prediction, then invalidate and let the server reconcile authoritative lists.

Why “patch every list” becomes fragile A status update can remove a Task from one filtered page, add it to another filter, shift page boundaries, change counts, and interact with search/sort order. Reproducing all backend query semantics in client cache code creates a second query engine that can drift from NestJS/Prisma behavior.

TaskFlow strategy • Use UI optimism for the visible row when possible.

• Optimistically patch the Task detail cache when several observers need the same pending value.

• Avoid fabricating pagination metadata.

• Invalidate list prefixes after settlement so the server reconstructs the authoritative collection.

• Only implement filter-aware list patching for specific high-value interactions with strong tests.

Important rule If an optimistic cache algorithm starts reimplementing filtering, authorization, search, sorting, and pagination, stop. You are duplicating backend responsibility.

Executable lesson studio · query

Now turn the explanation into engineering skill.

TanStack Query is a server-state synchronization system, not merely a fetch helper. Cache identity, freshness, invalidation, and mutation semantics are the core model.

By the end, you should be able to

  • Explain Filtered and Paginated Caches in terms of concrete inputs, ownership, outputs, and evidence.
  • Implement or reproduce the smallest TaskFlow slice that demonstrates this query concept.
  • Diagnose one representative query failure using observable evidence instead of trial-and-error.
Figure 25.6B — Mental model
component
query key
cache state
fetch/mutation
reconciliation
Read left-to-right while implementing. When debugging, walk right-to-left until the first observed fact differs from your prediction.
Code-along sequence

One coherent change at a time

  1. 01

    Restate the goal of “Filtered and Paginated Caches” as one observable behavior. Write the success signal before touching code.

  2. 02

    Trace the boundary map: component → query key → cache state → fetch/mutation → reconciliation. Mark the one boundary this lesson changes.

  3. 03

    Inspect the current repository state and record the exact files, route, table, or runtime involved. Avoid creating an abstraction before you can name the existing owner.

  4. 04

    Implement the smallest vertical slice. Add one coherent change, run the narrowest verification command, and read the output before continuing.

  5. 05

    Connect the slice to TaskFlow’s phase artifact: Optimistic task update/create flow with rollback and authoritative server reconciliation.. Verify behavior from the outside as a user or API client would.

  6. 06

    Create one controlled failure that is safe to reproduce. Predict the symptom first, capture evidence, locate the earliest broken boundary, then fix only that cause.

  7. 07

    Close the reference and rebuild or explain the core change from memory. Record what you would monitor or test before trusting the same change in production.

tsCache identity should mirror resource identity
export const taskKeys = {
  all: ["tasks"] as const,
  list: (filters: TaskFilters) => ["tasks", "list", filters] as const,
  detail: (id: string) => ["tasks", "detail", id] as const,
};

Do not copy this as a finished answer. Predict what each boundary does, type the smallest useful slice, run it, then compare the result with your prediction.

Verification

Prove the happy path

  1. Run the narrowest relevant command or request and require a clean exit/status.
  2. Inspect the boundary-specific evidence: terminal output, Network panel, database row/query plan, test report, container health, or deployment logs.
  3. Repeat the behavior once after the change so a one-off cached or stale result cannot masquerade as success.
Break it on purpose

Build debugging memory

  1. Temporary IDs colliding with real IDs
  2. Change one assumption in the query example—identity, input, configuration, ordering, connectivity, or state—and predict the exact symptom before running it.
SymptomInvestigation path
Expected result does not appear

Return to the nearest observable boundary: input, network, process log, database, or rendered UI. Compare actual evidence with the predicted path.

A shortcut appears to fix it

Change one variable at a time and reproduce the original failure after the fix; otherwise you have correlation, not diagnosis.

Closed-reference challenge

Can you transfer the idea?

Without copying the final source, create a second small scenario that applies “Filtered and Paginated Caches” to a different TaskFlow resource or input. Write the acceptance criteria first, implement it, and provide the evidence that each criterion is satisfied.

Explain-back

Answer these without reopening the solution

  1. What problem does Filtered and Paginated Caches solve in TaskFlow, and what would be observably wrong without it?
  2. Which part of the query boundary owns the decision, and which neighboring layer must not own it?
  3. What evidence proves the happy path instead of merely suggesting it worked?
  4. Name one realistic failure or abuse case and the first place you would inspect.
  5. If requirements doubled tomorrow, which part of this design should stay stable and which part is allowed to change?
Lesson studio

Turn the reading into skill

01

Do it

Simulate two rapid mutations and reason about race ordering and stale responses.

Use the smallest change that demonstrates this lesson. Stop after it works and capture evidence.
02

Break it safely

Temporary IDs colliding with real IDs

Predict the symptom before causing the failure. Then locate the earliest boundary where reality diverges from expectation.
03

Explain it back

Why does this responsibility live here, what input does it trust, what output proves success, and what would go wrong if another layer owned it?

If your explanation is vague, repeat the boundary trace before moving on.
Exit ticket

Before marking complete

Close the reference. Reproduce the core idea from memory or explain the exact request/data flow aloud. Then check your diff or notes for accidental shortcuts that bypass validation, authorization, error handling, or verification earned in earlier phases.

← Server Reconciliation onSuccess onSettled and InvalidationOptimistic Create and Temporary Identity →