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DiskANN Index
Keeps hot vectors in RAM, rest on disk · handles billion-scale with small memory
DiskANN keeps hot vectors in fast RAM and stores the rest on disk. Search scans RAM first, then reads targeted disk vectors. Near-HNSW quality with much less memory.
Flow:
📥 Query
→
🧠 Search RAM
→
💿 Read Disk
→
🏆 Top Match
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Query Vector
Editable🎚️
RAM Cache Size
1
5
3 in RAM
More disk readsFewer disk reads
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RAM Cache
Editable 3 vectors
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Disk Storage
Editable 5 vectors🔍
Click Search to see results
Adjust the RAM slider, edit vectors, then run a search to see how DiskANN works.
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Search Statistics
RAM Reads
0
Disk Reads
0
Total Checked
0
FLAT checks
8
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Ranked Results
Live💡 Key Idea
RAM as fast guide, disk for full data. 10–100× less RAM than pure HNSW.
✅ Best For
Billion-scale datasets, limited RAM, cost-sensitive production workloads.
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Tips
3 tips
Hot vectors stay in RAM, the rest live on disk, letting DiskANN handle datasets far larger than available memory.
It's built for billion-scale search, where keeping everything in RAM (like HNSW) would be too expensive.
The trade-off is slightly higher latency from disk access compared to fully in-memory indexes.
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Key Takeaway
DiskANN keeps only the most-used vectors in RAM and the rest on disk, enabling billion-scale search with a small memory footprint.