Boosting React Response Speeds by 30% with TanStack Query

Introduction#

In modern client-side architectures, excessive API roundtrips and stale client states degrade user experience. At Approveage Inc., we faced a similar challenge: our client-side administrative portals frequently polled server states, triggering excessive database re-queries. By integrating TanStack Query (React Query) and fine-tuning query hooks, we managed to boost response speeds by 30% and completely eliminate redundant server requests.

The Problem: Redundant Client Polls#

Before moving to TanStack Query, our frontend relied on standard React useEffect hooks coupled with Axios fetches. Every time a user toggled views or returned to active tabs, we fired unconditional server fetches. This led to:

• Race conditions: Delayed API responses overwriting fresher state variables.
• Cache invalidation issues: Users viewing outdated lists while waiting for network payloads.
• High API latency: Heavy stress on PostgreSQL databases due to duplicate read queries.

The Solution: Strategic Stale & Cache Management#

Our first major optimization was configuring robust staleTime and gcTime values. Instead of treating all data as immediately stale, we divided our data endpoints into three major categories:

• 1Immutable Configuration Data: staleTime: Infinity (e.g. system constants, user profiles).
• 2Semi-Mutable Portal States: staleTime: 5 * 60 * 1000 (5 minutes). This kept list items accessible with zero fetch delays.
• 3Highly Volatile Realtime Elements: staleTime: 10 * 1000 (10 seconds) with Socket.IO fallbacks.

const { data, isLoading } = useQuery({
  queryKey: ['portal-users', projectId],
  queryFn: () => fetchUsers(projectId),
  staleTime: 5 * 60 * 1000, // Keep cached data fresh for 5 mins
  gcTime: 10 * 60 * 1000,    // Retain in garbage collection for 10 mins
  refetchOnWindowFocus: false // Prevent re-fetching on tab toggles
});

Key Takeaway: Localized Mutation Optimizations#

Additionally, we implemented Optimistic Updates for actions like adding or editing list items. Instead of forcing a full page re-render while awaiting backend confirmations, we mutated the local TanStack cache instantly, falling back gracefully if server requests failed. This gave users a near-instantaneous 0ms perceived latency.