la_vector
SOSD
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| la_vector | SOSD | |
|---|---|---|
| 1 | 2 | |
| 35 | 260 | |
| - | 3.1% | |
| 0.0 | 0.0 | |
| over 1 year ago | about 2 years ago | |
| C++ | C++ | |
| Apache License 2.0 | GNU General Public License v3.0 only |
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la_vector
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PGM Indexes: Learned indexes that match B-tree performance with 83x less space
Hi Jouni!
You may find interesting these other papers of ours:
- The ALENEX21 paper "A 'learned' approach to quicken and compress rank/select dictionaries" (http://pages.di.unipi.it/vinciguerra/publication/learned-ran..., https://github.com/gvinciguerra/la_vector), where we introduce a compressed bitvector supporting efficient rank and select queries, which is competitive with several well-established implementations of succinct data structures.
- The ICML20 paper "Why are learned indexes so effective?" (http://pages.di.unipi.it/vinciguerra/publication/learned-ind...) where we prove that, under some general assumptions on the input data, the space of the PGM-index is actually O(n/B^2) whp (versus Θ(n/B) of classic B-trees).
SOSD
- SOSD: A Benchmark for Learned Indexes
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PGM Indexes: Learned indexes that match B-tree performance with 83x less space
For a detailed study of learned indexes, see this work: https://vldb.org/pvldb/vol14/p1-marcus.pdf
All code is available in open source: https://github.com/learnedsystems/SOSD
What are some alternatives?
RadixSpline - A Single-Pass Learned Index
PGM-index - 🏅State-of-the-art learned data structure that enables fast lookup, predecessor, range searches and updates in arrays of billions of items using orders of magnitude less space than traditional indexes
ALEX - A library for building an in-memory, Adaptive Learned indEX