What’s the performance impact of using indexes vs linear scans in IndexedDB?

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Using indexes vs linear scans in IndexedDB has massive performance implications, especially as your dataset grows. Here’s a breakdown: Indexed Lookup vs Linear Scan Factor Indexed Lookup Linear Scan Time Complexity O(log n) (B-tree traversal) O(n) (must inspect all records) Performance (10K items) ~1–5ms 100ms–1000ms+ (or much worse) Memory Usage Minimal (returns only matches) High (may load all into memory) Scalability Excellent Degrades rapidly Browser Optimization Fully optimized path Least optimized Real-World Example Suppose you have 10,000 users and want to find the one with email = x@example.com. 1. With Index (email indexed) ✅ Indexed B-tree lookup → Fast (~milliseconds) 2. Without Index ❌ Loads all 10,000 users into memory → Slow + memory-heavy Benchmarks (Approximate) Dataset Size Indexed Query Linear Scan 1,000 items ~1–2ms ~20–50ms 10,000 items ~2–5ms ~100–300ms 100,000 items ~5–10ms 1–3 seconds (Browser- and hardware-dependent; rough estimates) Why Indexed Queries Are Faster IndexedDB uses B-trees internally for indexes.B-trees allow fast traversal to matching keys without scanning the full store.Index-based queries go directly to matching entries or a narrow range. Linear Scan Pitfalls .toArray() retrieves all records..filter()/.find() works in memory, not on the IndexedDB level.These operations block the main thread longer and consume more memory. Best Practices Always index fields that are: Frequently filtered (where(), equals(), between())Used for sorting (orderBy())Use compound indexes for multi-field queries.Avoid toArray().filter() unless the dataset is tiny. Summary Indexed queries in IndexedDB are 10x–1000x faster than linear scans on large datasets.Always use .where() or .orderBy() on indexed fields for scalable, memory-efficient apps.

What’s the performance impact of using indexes vs linear scans in IndexedDB?

1 Answers

Indexed Lookup vs Linear Scan

Real-World Example

1. With Index (`email` indexed)

2. Without Index

Benchmarks (Approximate)

Why Indexed Queries Are Faster

Linear Scan Pitfalls

Best Practices

Summary

Factor	Indexed Lookup	Linear Scan
Time Complexity	O(log n) (B-tree traversal)	O(n) (must inspect all records)
Performance (10K items)	~1–5ms	100ms–1000ms+ (or much worse)
Memory Usage	Minimal (returns only matches)	High (may load all into memory)
Scalability	Excellent	Degrades rapidly
Browser Optimization	Fully optimized path	Least optimized

Dataset Size	Indexed Query	Linear Scan
1,000 items	~1–2ms	~20–50ms
10,000 items	~2–5ms	~100–300ms
100,000 items	~5–10ms	1–3 seconds

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What’s the performance impact of using indexes vs linear scans in IndexedDB?

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1 Answers

Indexed Lookup vs Linear Scan

Real-World Example

1. With Index (email indexed)

2. Without Index

Benchmarks (Approximate)

Why Indexed Queries Are Faster

Linear Scan Pitfalls

Best Practices

Summary

1. With Index (`email` indexed)