Why does my React Firebase Firestore app slow down and freeze in multiple Chrome tabs?

Below is a list of potential causes and debugging steps to narrow down the issue:

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Potential Causes

• Multiple Simultaneous Snapshot Listeners
  Even though a single tab works fine, if each tab creates its own set of listeners (especially on large or high‑churn collections), the overall number of listeners can become overwhelming for that user’s session. Cloud Firestore is designed to scale, but if your schema returns a large amount of data (or many documents are changing frequently), each tab gets its own fan‑out from the changelog—the combined load might slow down processing or cause freezes.

• Heavy Write / High Churn Data
  Since your largest user has the biggest Firestore database, they might have a high write rate to some collections. Running identical queries in multiple tabs against such a dataset can magnify the cost of re‑evaluations. Even if a snapshot listener’s query is eventually filtered with a small result set, the initial data load or the split among many servers for high‑churn collections may introduce latency, especially when rocked by multiple concurrent listeners.

• Recreating Listeners or Listeners Not Getting Cleaned Up Properly
  If your app duplicates or reinitializes listeners each time a new tab or duplicate is opened, shared state might not be maintained, and cleanup may be insufficient. This could lead to redundant work or conflicts when switching between tabs.

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Debugging & Reproduction Steps

1. Local Replication with Multiple Tabs

   • Open several Chrome tabs with your app in development mode.
   • If necessary, simulate similar database load by either connecting to a large dataset or artificially increasing write churn (e.g., using a script to update documents).
   • Use Chrome’s CPU Throttling to mimic user conditions.

2. Monitoring Firestore Listeners

   • In each tab, open Chrome DevTools to inspect network activity. Check how many Firestore connections are made and monitor the data payloads.
   • Log when snapshot listeners are created and ensure that listeners are being cleaned up correctly when the tab is closed or the component unmounts.

3. Profiling Application Performance

   • Use performance profiling tools in Chrome DevTools to pinpoint whether the delay comes from the Firestore SDK initialization, query execution, or React re‑rendering.
   • Check if the app’s rendering logic is triggering expensive calculations repeatedly. Although not directly related to Firestore listeners, optimizing with hooks like useMemo (with proper dependency arrays) may help reduce unnecessary processing.

4. Review Query Patterns and Indexes

   • If your queries, even with filter conditions, are still broad due to how your data is structured, consider breaking data into smaller subcollections. This helps the system serve snapshot listeners from fewer servers.

5. Examine Browser Limitations

   • Some issues might stem from how browsers handle multiple active connections or tabs. Investigate whether duplicating a tab overburdens the client’s local resources, even if the backend handles these requests efficiently.

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Conclusion

The problem likely stems from the increased load of maintaining multiple active snapshot listeners on your largest user’s high‑churn Firestore data. Each duplicate tab creates its own set of connections and listeners, which may cause delays and freezing if the client’s machine or network is strained. By replicating the scenario locally (with CPU throttling or simulated data churn), confirming proper cleanup of listeners, and ensuring queries are well‑optimized (by possibly breaking up large collections), you should be able to pinpoint the issue.

Try these steps in your debugging workflow to gather more insights into whether the delay is due to Firebase’s real‑time query scaling or some client‑side inefficiency.