Source-linked AI summary
From Theory to Practice: Plug and Play with Succinct Data Structures
Simon Gog, Timo Beller, Alistair Moffat, Matthias Petri
TL;DR
Efficient experimentation with succinct data structures is difficult because implementations are costly and existing baselines may use different components. The paper presents SDSL version 2, a modular framework with configurable components, tests, benchmarks, and resource-analysis tools, and demonstrates it by recomposing document-retrieval solutions. The framework supports consistent exploration of implementation alternatives, while the recomposed solutions reduce space usage and produce differing runtime trade-offs across query settings.
Problem
Implementing optimized, composable succinct data structures and comparing theoretical proposals against consistent baselines remains time-consuming and difficult.
Method
The paper introduces SDSL version 2, a modular library of configurable succinct-data-structure components with instrumentation, visualization, construction, serialization, tests, and benchmarks.
Results
The framework enabled recomposition and re-evaluation of document-retrieval structures, yielding lower space usage and runtime trade-offs that vary with query ranges and pattern lengths.
Takeaways & Limitations
Composable implementations make it practical to explore alternatives and perform more consistent, reproducible evaluations of succinct structures.
Takeaways & Limitations
The evaluation excludes the Wall Street Journal file because licensing restrictions prevented its public distribution.
Abstract
from arXiv · showhide
Engineering efficient implementations of compact and succinct structures is a time-consuming and challenging task, since there is no standard library of easy-to- use, highly optimized, and composable components. One consequence is that measuring the practical impact of new theoretical proposals is a difficult task, since older base- line implementations may not rely on the same basic components, and reimplementing from scratch can be very time-consuming. In this paper we present a framework for experimentation with succinct data structures, providing a large set of configurable components, together with tests, benchmarks, and tools to analyze resource requirements. We demonstrate the functionality of the framework by recomposing succinct solutions for document retrieval.
1 Introduction
Succinct data structures offer near-information-theoretic space while preserving functionality, but their layered components and implementation choices make practical experimentation difficult. The paper introduces SDSL version 2 as a modular framework for composing, measuring, and evaluating these structures.
- Succinct data structures provide uncompressed functionality using space asymptotically close to the information-theoretic lower bound.
- Bitvector operations underpin wavelet trees, which in turn support compressed suffix arrays and more complex structures such as compressed suffix trees.The hierarchy offers multiple alternatives at each level, creating varied time-space trade-offs.
- Missing implementations and non-optimized or non-composable substructures hinder empirical evaluation and impartial comparison of complex structures.Implementation costs also make it harder for new researchers to enter the field.
- SDSL version 2 provides flexible, efficient, modular implementations with instrumentation, visualization, construction, serialization, and hardware-aware support.Available features include popcount operations and optional hugepages.
- The library enables complex structures to be composed, measured, and compared more consistently across alternatives and applications.The authors demonstrate this functionality through document-retrieval recomposition and indexing-construction case studies.
2 Document Retrieval Recomposed
The study recomposes succinct document-retrieval structures from configurable components and evaluates their space and query-time behavior. SADA and GREEDY expose different trade-offs against the SORT baseline across collection types and query ranges.
- Problem and setup: Top-k retrieval is studied for single-term frequency and tf-idf queries using concatenated documents indexed by succinct structures.SADA and GREEDY translate patterns into suffix-array ranges over the concatenated text.
- SADA: SADA combines a CSA, border bitvector with rank/select support, document-array emulation, and RMQ structures to enumerate distinct documents.Its RMQ recursion identifies document occurrences within the suffix-array range.
- GREEDY: GREEDY stores the document array in a wavelet tree and expands range nodes with a priority queue until k frequent documents emerge.The method avoids listing every document but performs more work per document because the wavelet tree has height log N.
- Space: On PROTEINS, recomposed SADA is 6.4 times smaller and recomposed GREEDY 1.3 times smaller than the earlier implementations.The reductions mainly come from better-engineered components, with bit-compressed inverse suffix arrays as the algorithmic change.
- Space: For ENWIKI-BIG, the document array occupies around 92.2% of GREEDY’s total space, while rrr_vector compression reduces the overall size to 25,320 MB but slows queries by 2–4×.The character-based SADA visualization also shows doc_isa taking over half the space and CSA sampling contributing 2.4 of 5.3 bits per character.
- Runtime: Replacing GREEDY’s compressed rrr_vector with an uncompressed bitvector would speed pattern matching but make its space much greater than SADA.Pattern matching becomes the dominating cost, with different CSA designs producing different rank-operation costs.
- Runtime: SORT is always faster than SADA, while GREEDY only dominates SORT for very wide intervals; for long word queries, SADA can outperform GREEDY.Larger collections and ranges increase runtime, especially for methods that process many matching documents.
3 Efficient Construction of Complex Structures
Constructing complex succinct structures requires careful resource management because construction can dominate memory and time, especially at large scale. The SDSL tools expose these costs and support engineering choices that reduce them.
- Construction costs: 5,250 seconds and 13 GB were required to construct word-based SADA over the 4.6 GB ENWIKI-BIG collection.The measured throughput was 0.88 MB per second.
- Construction costs: 65% of construction time was spent building the CSA, while its plain suffix-array phase accounted for the 13 GB peak.The plain suffix array uses twice the memory required by the resulting bit-compressed suffix array.
- Resource management: Semi-external construction limits resource consumption by writing intermediate representations to disk instead of retaining all components in memory.This design is especially important during later construction stages, when keeping completed components in memory would increase overhead.
- Resource management: Visualization exposed that retaining the CSA in memory caused unnecessary resource consumption, enabling the implementation to serialize it to disk.The same monitoring approach showed that optimizing wavelet-tree construction would have limited overall impact because that phase represented around 4% of total cost.
- Hardware effects: 35% faster construction was achieved for the 5 GB csa_wt index with hugepages, although the improvement decreased for the full collection.The reduced benefit was attributed to the translation lookaside buffer maintaining only a finite number of hugepage entries.
4 Related Work
SDSL version 2 differs from earlier succinct-data-structure libraries through broad composability, large-scale input support, visualization, and automated evaluation tools.
- Existing resources: Earlier libraries and corpora provide implementations of selected succinct structures, including bitvectors, wavelet trees, tries, RMQ structures, and text indexes.Examples include PIZZA&CHILI, LIBCDS, SUX, and SUCCINCT.
- Distinctive features: SDSL version 2 supports both character and word inputs and is optimized for large-scale data, including hugepage support.These capabilities distinguish it from the other implementations discussed.
- Distinctive features: Its components cover a wide range of alternative structures that can be composed and substituted in different configurations.The library is designed to support experimentation across implementation choices.
- Distinctive features: Dynamic visualizations enable detailed space evaluations, while fully automated tests and benchmarks support systematic experimentation.These tools complement the library's implementations and common experimental infrastructure.
5 Conclusion
The paper presents SDSL as an open-source, modular library that supports flexible prototyping and exploration of succinct data-structure alternatives. It also reports practical gains from low-level optimization, hugepages, and visualization tools, with code and experiments publicly available.
- SDSL provides efficient implementations of many succinct data structures through precisely defined interfaces and modular building blocks.Its design facilitates flexible prototyping of new high-level structures and rapid exploration of implementation alternatives.
- The library handles input sequences of arbitrary length over arbitrary alphabets.
- Hugepages can have a notable effect on execution times for large-scale succinct data structures.
- SDSL’s visualization features provide insights into the time and space requirements of succinct data structures.
- The library code, test suite, benchmarks, and tutorial are publicly available.The paper directs readers to the SDSL-Lite GitHub repository.