performance（性能）

Performance Fix

Systematically find and fix performance issues in $ARGUMENTS (or the whole app if no argument is given). Always measure first — never optimize blindly.

Step 1 — Measure baseline before touching anything

Run the production build and capture metrics before making any changes:

pnpm run build
pnpm run preview

Open the app in Chrome and capture:

• Lighthouse score (Performance tab → Run audit)
• React Profiler (React DevTools → Profiler → Record an interaction)

- Note the components with the longest render times and highest render counts

Record baseline numbers. Every fix must be measured against these.

Step 2 — Find and fix unnecessary re-renders

Read the component tree (start from src/App.tsx) and search for these patterns:

grep -rn --include="*.tsx" \
  -E "value=\{\{|onClick=\{\(\)" src/

For each instance found, apply the fix directly:

Inline object/array creation in JSX → wrap with useMemo:

// BAD — new object on every render causes children to re-render
<Chart options={{ color: "red" }} />

// FIX — wrap with useMemo
const chartOptions = useMemo(() => ({ color: "red" }), []);
<Chart options={chartOptions} />

Event handlers recreated on every render → wrap with useCallback:

// BAD
<Button onClick={() => doSomething(id)} />

// FIX — wrap with useCallback
const handleClick = useCallback(() => doSomething(id), [id]);
<Button onClick={handleClick} />

Context that changes on every render → memoize the context value:

// BAD — new object reference every render
<MyContext.Provider value={{ user, sdk }}>

// FIX — memoize the context value
const ctxValue = useMemo(() => ({ user, sdk }), [user, sdk]);
<MyContext.Provider value={ctxValue}>

Apply React.memo to pure presentational components that receive stable props. Do NOT wrap every component — only those confirmed to re-render unnecessarily via the Profiler.

Step 3 — Find and fix DMS query patterns

For read-heavy workloads, prefer APIs that hit the search/Elasticsearch path (query or search on instances) rather than list paths that stress Postgres.

# Find all DMS instance API calls
grep -rn --include="*.ts" --include="*.tsx" -E "instances\.(list|search|query|aggregate|retrieve)" src/

# Find direct SDK calls to other CDF resources
grep -rn --include="*.ts" --include="*.tsx" -E "\.(assets|timeseries|events|files|sequences|relationships)\.(list|search|retrieve)" src/

For each instances.list call in a read-heavy path (e.g. populating a table, dropdown, or search results), rewrite it to use instances.query with the equivalent filter. Preserve the existing filter logic but express it in the query API format:

// BAD — instances.list hits Postgres, expensive for read-heavy UI
const result = await client.instances.list({
  instanceType: "node",
  filter: { equals: { property: ["node", "space"], value: "my-space" } },
  limit: 100,
});

// FIX — rewrite to instances.query which hits Elasticsearch
const result = await client.instances.query({
  with: {
    nodes: {
      nodes: {
        filter: { equals: { property: ["node", "space"], value: "my-space" } },
      },
      limit: 100,
    },
  },
  select: {
    nodes: {},
  },
});

For deeper rationale on search vs relational paths, cardinality, and materialization tradeoffs, consult the semantic-knowledge/ directory if available in the workspace.

Step 4 — Find and fix client-side filtering (move to server-side)

Filters, limits, and projections must be applied in the API request — not by downloading large result sets and filtering in the browser.

# Find client-side filtering after data fetch (common anti-pattern)
grep -rn --include="*.ts" --include="*.tsx" -B 5 "\.filter(" src/ | grep -B 5 "data\|items\|result\|response\|nodes"

# Find .map() or .reduce() on full datasets that suggest client-side processing
grep -rn --include="*.ts" --include="*.tsx" -E "\.(map|reduce|find|some|every)\(" src/hooks/ src/services/ src/api/

For each client-side filter pattern, move the filter logic into the SDK call's filter parameter and remove the .filter() call:

// BAD — fetches all nodes then filters client-side
const result = await client.instances.query({ ... });
const activeNodes = result.items.nodes.filter(n => n.properties.status === "active");

// FIX — move filter into the API request, remove client-side .filter()
const result = await client.instances.query({
  with: {
    nodes: {
      nodes: {
        filter: {
          and: [
            existingFilters,
            { equals: { property: ["mySpace", "myView/v1", "status"], value: "active" } },
          ],
        },
      },
      limit: 100,
    },
  },
  select: { nodes: {} },
});
const activeNodes = result.items.nodes; // no client-side filter needed

Hard rule: If the API supports a filter for the criterion being applied client-side, move it server-side now. Client-side filtering is acceptable only for trivial local state (e.g. filtering a cached list of 10 user preferences). If the API does not support the exact filter, add a code comment explaining why client-side filtering is necessary.

Step 5 — Find and fix CDF data fetching and pagination

Read all CDF SDK calls (search for sdk., client., useQuery, useCogniteClient).

# Find pagination patterns
grep -rn --include="*.ts" --include="*.tsx" -E "(nextCursor|cursor|hasNextPage|fetchNextPage|offset|skip|page)" src/

# Find "fetch all" loops
grep -rn --include="*.ts" --include="*.tsx" -B 3 -A 3 "while.*cursor\|while.*hasMore\|while.*nextPage" src/

For each call, find the issue and apply the fix:

Fixing fetch-all loops — replace the while loop with useInfiniteQuery:

// BAD — fetches ALL pages before rendering
let allItems = [];
let cursor = undefined;
while (true) {
  const result = await client.instances.list({ limit: 1000, cursor });
  allItems.push(...result.items);
  if (!result.nextCursor) break;
  cursor = result.nextCursor;
}

// FIX — paginate on demand with useInfiniteQuery
const { data, fetchNextPage, hasNextPage } = useInfiniteQuery({
  queryKey: ["instances", filters],
  queryFn: ({ pageParam }) =>
    client.instances.list({ limit: 100, cursor: pageParam, ...filters }),
  getNextPageParam: (lastPage) => lastPage.nextCursor ?? undefined,
  staleTime: 30_000,
});

Fixing offset-based pagination — switch to cursor-based:

// BAD — offset pagination degrades at scale
const result = await client.instances.list({ limit: 100, offset: page * 100 });

// FIX — cursor-based pagination
const result = await client.instances.list({ limit: 100, cursor: nextCursor });

Step 6 — Find and fix excessive API call rates

# Find search/filter inputs that trigger queries
grep -rn --include="*.tsx" --include="*.ts" -E "onChange|onInput|onSearch|onFilter" src/ | grep -i "search\|filter\|query"

# Find debounce usage
grep -rn --include="*.ts" --include="*.tsx" -i -E "debounce|useDebouncedValue|useDebounce" src/

# Find polling/interval patterns
grep -rn --include="*.ts" --include="*.tsx" -E "setInterval|refetchInterval|pollingInterval|refetchOnWindowFocus" src/

# Find useQuery options that control refetch behavior
grep -rn --include="*.ts" --include="*.tsx" -E "staleTime|cacheTime|gcTime|refetchOnMount|refetchOnWindowFocus" src/

For each issue found, apply the fix:

Search inputs that fire on every keystroke → add debounce with 300ms delay:

// BAD — fires API call on every keystroke
const [search, setSearch] = useState("");
const { data } = useQuery({ queryKey: ["search", search], queryFn: () => api.search(search) });

// FIX — create or use a useDebouncedValue hook with 300ms delay
function useDebouncedValue<T>(value: T, delay = 300): T {
  const [debounced, setDebounced] = useState(value);
  useEffect(() => {
    const timer = setTimeout(() => setDebounced(value), delay);
    return () => clearTimeout(timer);
  }, [value, delay]);
  return debounced;
}

const [search, setSearch] = useState("");
const debouncedSearch = useDebouncedValue(search, 300);
const { data } = useQuery({
  queryKey: ["search", debouncedSearch],
  queryFn: () => api.search(debouncedSearch),
  enabled: debouncedSearch.length > 0,
});

useQuery calls without staleTime → add appropriate staleTime:

// BAD — refetches on every mount/focus
useQuery({ queryKey: ["data"], queryFn: fetchData });

// FIX — add staleTime to prevent unnecessary refetches
useQuery({ queryKey: ["data"], queryFn: fetchData, staleTime: 30_000 });

Duplicate parallel identical requests → lift the query to a shared hook:

// BAD — multiple components each call the same query independently
// ComponentA.tsx: useQuery({ queryKey: ["assets"], queryFn: fetchAssets });
// ComponentB.tsx: useQuery({ queryKey: ["assets"], queryFn: fetchAssets });

// FIX — create a shared hook, import it from both components
// hooks/useAssets.ts
export function useAssets() {
  return useQuery({ queryKey: ["assets"], queryFn: fetchAssets, staleTime: 30_000 });
}

Step 7 — Find and fix large un-virtualized lists

Search for lists that render more than ~50 items:

grep -rn --include="*.tsx" -E "\.(map|forEach)\(" src/

For any list where the data source could exceed 50 items, replace the plain .map() render with a virtualized list. Install @tanstack/react-virtual if not present:

pnpm add @tanstack/react-virtual

Apply the virtualizer pattern directly:

// BAD — renders all items in the DOM
<div>
  {items.map((item) => (
    <div key={item.id}>{item.name}</div>
  ))}
</div>

// FIX — replace with virtualized list
import { useVirtualizer } from "@tanstack/react-virtual";

const parentRef = useRef<HTMLDivElement>(null);
const rowVirtualizer = useVirtualizer({
  count: items.length,
  getScrollElement: () => parentRef.current,
  estimateSize: () => 48,
});

return (
  <div ref={parentRef} style={{ height: "600px", overflow: "auto" }}>
    <div style={{ height: rowVirtualizer.getTotalSize(), position: "relative" }}>
      {rowVirtualizer.getVirtualItems().map((virtualRow) => (
        <div
          key={virtualRow.key}
          style={{
            position: "absolute",
            top: 0,
            left: 0,
            width: "100%",
            height: `${virtualRow.size}px`,
            transform: `translateY(${virtualRow.start}px)`,
          }}
        >
          {items[virtualRow.index].name}
        </div>
      ))}
    </div>
  </div>
);

Step 8 — Find and fix missing code splitting

Read the router setup and identify routes that are imported statically but not shown on the landing page.

For each statically imported heavy page, convert to lazy import with React.lazy() and Suspense:

// BAD — statically imported, loaded in initial bundle
import { ReportPage } from "./pages/ReportPage";

// FIX — convert to lazy import
import { lazy, Suspense } from "react";
const ReportPage = lazy(() => import("./pages/ReportPage"));

// In the route — wrap with Suspense
<Suspense fallback={<PageSkeleton />}>
  <ReportPage />
</Suspense>

Similarly, large third-party components (chart libraries, PDF viewers, map renderers) should be dynamically imported inside the component that needs them, not at the module level. Apply the transformation directly to each heavy import found.

Step 9 — Analyse and fix bundle size

# Install if not already present, then run
pnpm add -D rollup-plugin-visualizer

Add to vite.config.ts temporarily:

import { visualizer } from "rollup-plugin-visualizer";

export default defineConfig({
  plugins: [
    react(),
    visualizer({ open: true, gzipSize: true, brotliSize: true }),
  ],
});

Run pnpm run build and inspect the treemap. For any chunk > 100 KB (gzipped) that is not a necessary initial dependency, apply the fix:

// BAD
import _ from "lodash";
const sorted = _.sortBy(items, "name");

// FIX — use lodash-es or native
import sortBy from "lodash-es/sortBy";
const sorted = sortBy(items, "name");
// OR native:
const sorted = [...items].sort((a, b) => a.name.localeCompare(b.name));

// BAD
import moment from "moment";
const formatted = moment(date).format("YYYY-MM-DD");

// FIX — use date-fns
import { format } from "date-fns";
const formatted = format(date, "yyyy-MM-dd");

After analysis, remove the visualizer plugin from vite.config.ts and uninstall it:

pnpm remove rollup-plugin-visualizer

Step 10 — Find and fix memory leaks

Search for useEffect hooks that set up subscriptions, timers, or event listeners without cleanup:

grep -rn --include="*.tsx" --include="*.ts" -A 10 "useEffect" src/

For every useEffect that calls addEventListener, setInterval, setTimeout, subscribe, or sets up a CDF streaming connection, add the missing cleanup function:

Fetch without abort → add AbortController:

// BAD — no cleanup, fetch continues after unmount
useEffect(() => {
  fetchData(id);
}, [id]);

// FIX — add AbortController for cleanup
useEffect(() => {
  const controller = new AbortController();
  fetchData(id, controller.signal);
  return () => controller.abort();
}, [id]);

Timer without cleanup → add clearInterval/clearTimeout:

// BAD — interval keeps running after unmount
useEffect(() => {
  const id = setInterval(() => poll(), 5000);
}, []);

// FIX — add clearInterval cleanup
useEffect(() => {
  const id = setInterval(() => poll(), 5000);
  return () => clearInterval(id);
}, []);

Event listener without cleanup → add removeEventListener:

// BAD — listener accumulates on each render
useEffect(() => {
  window.addEventListener("resize", handleResize);
}, []);

// FIX — add removeEventListener cleanup
useEffect(() => {
  window.addEventListener("resize", handleResize);
  return () => window.removeEventListener("resize", handleResize);
}, []);

Step 11 — Measure after and report the delta

Re-run the same Lighthouse audit and React Profiler session from Step 1. Report the delta and list every file changed:

If a step produced no improvement, state that explicitly. Do not fabricate numbers.

Done

List every file changed with the absolute path and a one-line explanation of what was fixed. If further gains require server-side or infrastructure changes (e.g., CDF response caching, CDN configuration), note them separately as out-of-scope recommendations.