This study experimentally compares bitmap and inverted list compression techniques for accelerating analytical queries on relational databases. Researchers evaluated a range of established and novel compression methods, including Roaring, WAH, Concise, and COMPAX, across diverse datasets and query workloads. The results demonstrate that bitmap compression, specifically Roaring, consistently outperforms inverted lists in terms of query processing time and storage space for most workloads, particularly those with high selectivity or involving multiple attributes. While inverted lists demonstrate some advantages for low-selectivity queries and updates, Roaring bitmaps generally offer a superior balance of performance and efficiency for analytical workloads. The study concludes that careful selection of the compression method based on data characteristics and query patterns is crucial for optimizing analytical query performance.
This paper introduces a new benchmark, OCR-Bench, specifically designed to evaluate the performance of vision-language models (VLMs) on Optical Character Recognition (OCR) within dynamic video environments. Existing OCR benchmarks primarily focus on static images, overlooking the challenges posed by video, such as motion blur, varying lighting, and camera angles. OCR-Bench comprises diverse video clips with text overlaid or embedded within the scene, encompassing various fonts, languages, and complexities. The benchmark provides a comprehensive evaluation across three core tasks: text detection, recognition, and grounding. By assessing VLMs on these tasks within a dynamic video context, OCR-Bench aims to drive the development of more robust and accurate VLMs for real-world video understanding.
HN users discuss the challenges of OCR in video, particularly dynamic environments. Several commenters highlight the difficulty of evaluating OCR accuracy due to the subjective nature of "correctness" and the lack of standardized benchmarks. The impact of video compression, motion blur, and varying fonts/styles is also mentioned as complicating factors. One commenter suggests the need for a benchmark focused on specific use cases, like recognizing text in sporting events, rather than generic datasets. Another questions the value of focusing on vision-language models (VLMs) for this task, suggesting specialized OCR models might be more efficient. There's also a discussion about the limited real-world applications for this type of OCR beyond content moderation and surveillance, with some questioning the ethics of the latter.
Summary of Comments ( 5 )
https://news.ycombinator.com/item?id=43206385
HN users discussed the trade-offs between bitmap and inverted list compression, focusing on performance in different scenarios. Some highlighted the importance of data characteristics like cardinality and query patterns in determining the optimal choice. Bitmap indexing was noted for its speed with simple queries on high-cardinality attributes but suffers from performance degradation with increasing updates or complex queries. Inverted lists, while generally slower for simple queries, were favored for their efficiency with updates and range queries. Several comments pointed out the paper's age (2017) and questioned the relevance of its findings given advancements in hardware and newer techniques like Roaring bitmaps. There was also discussion of the practical implications for database design and the need for careful benchmarking based on specific use cases.
The Hacker News post "An Experimental Study of Bitmap Compression vs. Inverted List Compression" generated several comments discussing the nuances and implications of the linked research paper.
One commenter highlights the paper's focus on cache efficiency as a primary driver for performance differences, more so than the raw compression ratios. They point out that bitmap compression, while sometimes larger on disk, can be significantly faster due to better cache utilization, especially with SIMD instructions. This performance advantage is attributed to the contiguous nature of bitmaps, which facilitates sequential access and predictable memory patterns, benefiting CPU caching mechanisms.
Another commenter notes the historical context of bitmap indexes, mentioning their prevalence in older database systems before the rise of more sophisticated techniques like B-trees. They suggest the paper's findings reaffirm the value proposition of bitmaps, particularly in scenarios involving frequent analytical queries or data warehousing applications. This revisits the trade-offs between space efficiency and query speed, demonstrating that sometimes larger indexes can lead to faster results.
Further discussion delves into specific compression methods for inverted lists, like Frame-of-Reference (FOR) and Variable Byte (VB) encoding. Commenters explore how these techniques impact both storage size and query performance, acknowledging the complex interplay of factors at play. One comment specifically contrasts FOR and VB, suggesting VB's advantages in compressing highly skewed distributions.
The practicality of using bitmap indexes in real-world systems is also questioned. A commenter raises concerns about the performance overhead when dealing with high-cardinality data, where bitmaps can become excessively large. They advocate for considering alternatives like B-trees or other tree-based structures for such scenarios.
One insightful comment analyzes the paper's experimental methodology. They emphasize the importance of the chosen dataset and workload in influencing the results. The comment suggests that the findings might not generalize to all situations, urging readers to carefully consider their own specific requirements and data characteristics before opting for either bitmap or inverted list compression.
Finally, there's discussion about the relevance of the research in modern contexts. While acknowledging the increasing prevalence of columnar databases, a commenter argues that the insights from the paper remain applicable, particularly for specialized applications or custom-built systems. They point out that understanding the fundamental trade-offs between different indexing strategies is crucial for optimizing performance, regardless of the overall database architecture.