{"work":{"id":"b6ca6a52-fd51-47d3-ac78-3cb3b6e892bd","openalex_id":null,"doi":null,"arxiv_id":"2105.09613","raw_key":null,"title":"FreshDiskANN: A Fast and Accurate Graph-Based ANN Index for Streaming Similarity Search","authors":null,"authors_text":"Aditi Singh, Suhas Jayaram Subramanya, Ravishankar Krishnaswamy, and Har- sha Vardhan Simhadri","year":2021,"venue":"cs.IR","abstract":"Approximate nearest neighbor search (ANNS) is a fundamental building block in information retrieval with graph-based indices being the current state-of-the-art and widely used in the industry. Recent advances in graph-based indices have made it possible to index and search billion-point datasets with high recall and millisecond-level latency on a single commodity machine with an SSD.\n  However, existing graph algorithms for ANNS support only static indices that cannot reflect real-time changes to the corpus required by many key real-world scenarios (e.g. index of sentences in documents, email, or a news index). To overcome this drawback, the current industry practice for manifesting updates into such indices is to periodically re-build these indices, which can be prohibitively expensive.\n  In this paper, we present the first graph-based ANNS index that reflects corpus updates into the index in real-time without compromising on search performance. Using update rules for this index, we design FreshDiskANN, a system that can index over a billion points on a workstation with an SSD and limited memory, and support thousands of concurrent real-time inserts, deletes and searches per second each, while retaining $>95\\%$ 5-recall@5. This represents a 5-10x reduction in the cost of maintaining freshness in indices when compared to existing methods.","external_url":"https://arxiv.org/abs/2105.09613","cited_by_count":null,"metadata_source":"pith","metadata_fetched_at":"2026-07-11T03:27:46.428816+00:00","pith_arxiv_id":"2105.09613","created_at":"2026-05-10T00:24:46.860155+00:00","updated_at":"2026-07-11T03:27:46.428816+00:00","title_quality_ok":true,"display_title":"FreshDiskANN: A fast and accurate graph-based ANN index for streaming similarity search.arXiv preprint arXiv:2105.09613","render_title":"FreshDiskANN: A fast and accurate graph-based ANN index for streaming similarity search.arXiv preprint arXiv:2105.09613"},"hub":{"state":{"work_id":"b6ca6a52-fd51-47d3-ac78-3cb3b6e892bd","tier":"hub","tier_reason":"10+ Pith inbound or 1,000+ external citations","pith_inbound_count":19,"external_cited_by_count":null,"distinct_field_count":7,"first_pith_cited_at":"2025-05-22T11:11:02+00:00","last_pith_cited_at":"2026-07-07T00:06:22+00:00","author_build_status":"not_needed","summary_status":"needed","contexts_status":"needed","graph_status":"needed","ask_index_status":"not_needed","reader_status":"not_needed","recognition_status":"not_needed","updated_at":"2026-08-21T17:59:52.236766+00:00","tier_text":"hub"},"tier":"hub","role_counts":[{"context_role":"background","n":3},{"context_role":"dataset","n":1}],"polarity_counts":[{"context_polarity":"background","n":3},{"context_polarity":"use_dataset","n":1}],"runs":{},"summary":{},"graph":{},"authors":[]}}