Pub Date : 2023-04-21DOI: 10.48550/arXiv.2304.11277
Yanli Zhao, A. Gu, R. Varma, Liangchen Luo, Chien-chin Huang, Min Xu, Less Wright, Hamid Shojanazeri, Myle Ott, Sam Shleifer, Alban Desmaison, Can Balioglu, Bernard Nguyen, Geeta Chauhan, Y. Hao, Shen Li
It is widely acknowledged that large models have the potential to deliver superior performance across a broad range of domains. Despite the remarkable progress made in the field of machine learning systems research, which has enabled the development and exploration of large models, such abilities remain confined to a small group of advanced users and industry leaders, resulting in an implicit technical barrier for the wider community to access and leverage these technologies. In this paper, we introduce PyTorch Fully Sharded Data Parallel (FSDP) as an industry-grade solution for large model training. FSDP has been closely co-designed with several key PyTorch core components including Tensor implementation, dispatcher system, and CUDA memory caching allocator, to provide non-intrusive user experiences and high training efficiency. Additionally, FSDP natively incorporates a range of techniques and settings to optimize resource utilization across a variety of hardware configurations. The experimental results demonstrate that FSDP is capable of achieving comparable performance to Distributed Data Parallel while providing support for significantly larger models with near-linear scalability in terms of TFLOPS.
{"title":"PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel","authors":"Yanli Zhao, A. Gu, R. Varma, Liangchen Luo, Chien-chin Huang, Min Xu, Less Wright, Hamid Shojanazeri, Myle Ott, Sam Shleifer, Alban Desmaison, Can Balioglu, Bernard Nguyen, Geeta Chauhan, Y. Hao, Shen Li","doi":"10.48550/arXiv.2304.11277","DOIUrl":"https://doi.org/10.48550/arXiv.2304.11277","url":null,"abstract":"It is widely acknowledged that large models have the potential to deliver superior performance across a broad range of domains. Despite the remarkable progress made in the field of machine learning systems research, which has enabled the development and exploration of large models, such abilities remain confined to a small group of advanced users and industry leaders, resulting in an implicit technical barrier for the wider community to access and leverage these technologies. In this paper, we introduce PyTorch Fully Sharded Data Parallel (FSDP) as an industry-grade solution for large model training. FSDP has been closely co-designed with several key PyTorch core components including Tensor implementation, dispatcher system, and CUDA memory caching allocator, to provide non-intrusive user experiences and high training efficiency. Additionally, FSDP natively incorporates a range of techniques and settings to optimize resource utilization across a variety of hardware configurations. The experimental results demonstrate that FSDP is capable of achieving comparable performance to Distributed Data Parallel while providing support for significantly larger models with near-linear scalability in terms of TFLOPS.","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":"40 1","pages":"3848-3860"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89610502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-07DOI: 10.48550/arXiv.2304.04759
C. Aguerrebere, Ishwar Bhati, Mark Hildebrand, Mariano Tepper, Ted L. Willke
Nowadays, data is represented by vectors. Retrieving those vectors, among millions and billions, that are similar to a given query is a ubiquitous problem, known as similarity search, of relevance for a wide range of applications. Graph-based indices are currently the best performing techniques for billion-scale similarity search. However, their random-access memory pattern presents challenges to realize their full potential. In this work, we present new techniques and systems for creating faster and smaller graph-based indices. To this end, we introduce a novel vector compression method, Locally-adaptive Vector Quantization (LVQ), that uses per-vector scaling and scalar quantization to improve search performance with fast similarity computations and a reduced effective bandwidth, while decreasing memory footprint and barely impacting accuracy. LVQ, when combined with a new high-performance computing system for graph-based similarity search, establishes the new state of the art in terms of performance and memory footprint. For billions of vectors, LVQ outcompetes the second-best alternatives: (1) in the low-memory regime, by up to 20.7x in throughput with up to a 3x memory footprint reduction, and (2) in the high-throughput regime by 5.8x with 1.4x less memory.
{"title":"Similarity search in the blink of an eye with compressed indices","authors":"C. Aguerrebere, Ishwar Bhati, Mark Hildebrand, Mariano Tepper, Ted L. Willke","doi":"10.48550/arXiv.2304.04759","DOIUrl":"https://doi.org/10.48550/arXiv.2304.04759","url":null,"abstract":"Nowadays, data is represented by vectors. Retrieving those vectors, among millions and billions, that are similar to a given query is a ubiquitous problem, known as similarity search, of relevance for a wide range of applications. Graph-based indices are currently the best performing techniques for billion-scale similarity search. However, their random-access memory pattern presents challenges to realize their full potential. In this work, we present new techniques and systems for creating faster and smaller graph-based indices. To this end, we introduce a novel vector compression method, Locally-adaptive Vector Quantization (LVQ), that uses per-vector scaling and scalar quantization to improve search performance with fast similarity computations and a reduced effective bandwidth, while decreasing memory footprint and barely impacting accuracy. LVQ, when combined with a new high-performance computing system for graph-based similarity search, establishes the new state of the art in terms of performance and memory footprint. For billions of vectors, LVQ outcompetes the second-best alternatives: (1) in the low-memory regime, by up to 20.7x in throughput with up to a 3x memory footprint reduction, and (2) in the high-throughput regime by 5.8x with 1.4x less memory.","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":"116 2 1","pages":"3433-3446"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84224984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Distributed transaction systems incur extensive cross-node communication to execute and commit serializable OLTP transactions. As a result, their performance greatly suffers. Caching data at nodes that execute transactions can cut down remote reads. Batching transactions for validation and persistence can amortize the communication cost during committing. However, caching and batching can significantly increase the likelihood of conflicts, causing expensive aborts. In this paper, we develop Hackwrench to address the challenge of caching and batching. Instead of aborting conflicted transactions, Hackwrench tries to repair them using fine-grained re-execution by tracking the dependencies of operations among a batch of transactions. Tracked dependencies allow Hackwrench to selectively invalidate and re-execute only those operations necessary to "fix" the conflict, which is cheaper than aborting and executing an entire batch of transactions. Evaluations using TPC-C and other micro-benchmarks show that Hackwrench can outperform existing commercial and research systems including FoundationDB, Calvin, COCO, and Sundial under comparable settings.
{"title":"Fine-Grained Re-Execution for Efficient Batched Commit of Distributed Transactions","authors":"Zhiyuan Dong, Zhaoguo Wang, Xiaodong Zhang, Xian Xu, Changgeng Zhao, Haibo Chen, Aurojit Panda, Jinyang Li","doi":"10.14778/3594512.3594523","DOIUrl":"https://doi.org/10.14778/3594512.3594523","url":null,"abstract":"Distributed transaction systems incur extensive cross-node communication to execute and commit serializable OLTP transactions. As a result, their performance greatly suffers. Caching data at nodes that execute transactions can cut down remote reads. Batching transactions for validation and persistence can amortize the communication cost during committing. However, caching and batching can significantly increase the likelihood of conflicts, causing expensive aborts.\u0000 \u0000 In this paper, we develop Hackwrench to address the challenge of caching and batching. Instead of aborting conflicted transactions, Hackwrench tries to repair them using\u0000 fine-grained re-execution\u0000 by tracking the dependencies of operations among a batch of transactions. Tracked dependencies allow Hackwrench to selectively invalidate and re-execute only those operations necessary to \"fix\" the conflict, which is cheaper than aborting and executing an entire batch of transactions. Evaluations using TPC-C and other micro-benchmarks show that Hackwrench can outperform existing commercial and research systems including FoundationDB, Calvin, COCO, and Sundial under comparable settings.\u0000","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":"33 1","pages":"1930-1943"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82550975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-01DOI: 10.14778/3594512.3594515
Christine Schäler, Thomas Hütter, Martin Schäler
The w -event framework is the current standard for ensuring differential privacy on continuously monitored data streams. Following the proposition of w -event differential privacy, various mechanisms to implement the framework are proposed. Their comparability in empirical studies is vital for both practitioners to choose a suitable mechanism, and researchers to identify current limitations and propose novel mechanisms. By conducting a literature survey, we observe that the results of existing studies are hardly comparable and partially intrinsically inconsistent. To this end, we formalize an empirical study of w -event mechanisms by re-occurring elements found in our survey. We introduce requirements on these elements that ensure the comparability of experimental results. Moreover, we propose a benchmark that meets all requirements and establishes a new way to evaluate existing and newly proposed mechanisms. Conducting a large-scale empirical study, we gain valuable new insights into the strengths and weaknesses of existing mechanisms. An unexpected - yet explainable - result is a baseline supremacy, i.e., using one of the two baseline mechanisms is expected to deliver good or even the best utility. Finally, we provide guidelines for practitioners to select suitable mechanisms and improvement options for researchers.
w -event框架是确保连续监控数据流上的差异隐私的当前标准。在w事件差分隐私的基础上，提出了实现该框架的各种机制。它们在实证研究中的可比性对于从业者选择合适的机制和研究人员识别当前的局限性并提出新的机制至关重要。通过进行文献调查，我们观察到现有研究的结果很难比较，部分本质上不一致。为此，我们通过重新出现我们调查中发现的元素，形式化了w事件机制的实证研究。我们介绍了对这些元素的要求，以确保实验结果的可比性。此外，我们提出了一个满足所有要求的基准，并建立了一种评估现有机制和新提议机制的新方法。通过大规模的实证研究，我们对现有机制的优缺点获得了宝贵的新见解。一个意想不到的(但可以解释的)结果是基线至上，也就是说，使用两种基线机制中的一种有望提供良好甚至最佳的效用。最后，我们为从业者提供了选择合适的机制和研究人员改进方案的指南。
{"title":"Benchmarking the Utility of w-event Differential Privacy Mechanisms - When Baselines Become Mighty Competitors","authors":"Christine Schäler, Thomas Hütter, Martin Schäler","doi":"10.14778/3594512.3594515","DOIUrl":"https://doi.org/10.14778/3594512.3594515","url":null,"abstract":"\u0000 The\u0000 w\u0000 -event framework is the current standard for ensuring differential privacy on continuously monitored data streams. Following the proposition of\u0000 w\u0000 -event differential privacy, various mechanisms to implement the framework are proposed. Their comparability in empirical studies is vital for both practitioners to choose a suitable mechanism, and researchers to identify current limitations and propose novel mechanisms. By conducting a literature survey, we observe that the results of existing studies are hardly comparable and partially intrinsically inconsistent.\u0000 \u0000 \u0000 To this end, we formalize an empirical study of\u0000 w\u0000 -event mechanisms by re-occurring elements found in our survey. We introduce requirements on these elements that ensure the comparability of experimental results. Moreover, we propose a benchmark that meets all requirements and establishes a new way to evaluate existing and newly proposed mechanisms. Conducting a large-scale empirical study, we gain valuable new insights into the strengths and weaknesses of existing mechanisms. An unexpected - yet explainable - result is a baseline supremacy, i.e., using one of the two baseline mechanisms is expected to deliver good or even the best utility. Finally, we provide guidelines for practitioners to select suitable mechanisms and improvement options for researchers.\u0000","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":"2 1","pages":"1830-1842"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78674664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Graph edit distance (GED) computation is a fundamental NP-hard problem in graph theory. Given a graph pair ( G 1 , G 2 ), GED is defined as the minimum number of primitive operations converting G 1 to G 2 . Early studies focus on search-based inexact algorithms such as A*-beam search, and greedy algorithms using bipartite matching due to its NP-hardness. They can obtain a sub-optimal solution by constructing an edit path (the sequence of operations that converts G 1 to G 2 ). Recent studies convert the GED between a given graph pair ( G 1 , G 2 ) into a similarity score in the range (0, 1) by a well designed function. Then machine learning models (mostly based on graph neural networks) are applied to predict the similarity score. They achieve a much higher numerical precision than the sub-optimal solutions found by classical algorithms. However, a major limitation is that these machine learning models cannot generate an edit path. They treat the GED computation as a pure regression task to bypass its intrinsic complexity, but ignore the essential task of converting G 1 to G 2 . This severely limits the interpretability and usability of the solution. In this paper, we propose a novel deep learning framework that solves the GED problem in a two-step manner: 1) The proposed graph neural network GEDGNN is in charge of predicting the GED value and a matching matrix; and 2) A post-processing algorithm based on k -best matching is used to derive k possible node matchings from the matching matrix generated by GEDGNN. The best matching will finally lead to a high-quality edit path. Extensive experiments are conducted on three real graph data sets and synthetic power-law graphs to demonstrate the effectiveness of our framework. Compared to the best result of existing GNN-based models, the mean absolute error (MAE) on GED value prediction decreases by 4.9% ~ 74.3%. Compared to the state-of-the-art searching algorithm Noah, the MAE on GED value based on edit path reduces by 53.6% ~ 88.1%.
{"title":"Computing Graph Edit Distance via Neural Graph Matching","authors":"Chengzhi Piao, Tingyang Xu, Xiangguo Sun, Yu Rong, Kangfei Zhao, Hongtao Cheng","doi":"10.14778/3594512.3594514","DOIUrl":"https://doi.org/10.14778/3594512.3594514","url":null,"abstract":"\u0000 Graph edit distance (GED) computation is a fundamental NP-hard problem in graph theory. Given a graph pair (\u0000 G\u0000 1\u0000 ,\u0000 G\u0000 2\u0000 ), GED is defined as the minimum number of primitive operations converting\u0000 G\u0000 1\u0000 to\u0000 G\u0000 2\u0000 . Early studies focus on search-based inexact algorithms such as A*-beam search, and greedy algorithms using bipartite matching due to its NP-hardness. They can obtain a sub-optimal solution by constructing an edit path (the sequence of operations that converts\u0000 G\u0000 1\u0000 to\u0000 G\u0000 2\u0000 ). Recent studies convert the GED between a given graph pair (\u0000 G\u0000 1\u0000 ,\u0000 G\u0000 2\u0000 ) into a similarity score in the range (0, 1) by a well designed function. Then machine learning models (mostly based on graph neural networks) are applied to predict the similarity score. They achieve a much higher numerical precision than the sub-optimal solutions found by classical algorithms. However, a major limitation is that these machine learning models cannot generate an edit path. They treat the GED computation as a pure regression task to bypass its intrinsic complexity, but ignore the essential task of converting\u0000 G\u0000 1\u0000 to\u0000 G\u0000 2\u0000 . This severely limits the interpretability and usability of the solution.\u0000 \u0000 \u0000 In this paper, we propose a novel deep learning framework that solves the GED problem in a two-step manner: 1) The proposed graph neural network GEDGNN is in charge of predicting the GED value and a matching matrix; and 2) A post-processing algorithm based on\u0000 k\u0000 -best matching is used to derive\u0000 k\u0000 possible node matchings from the matching matrix generated by GEDGNN. The best matching will finally lead to a high-quality edit path. Extensive experiments are conducted on three real graph data sets and synthetic power-law graphs to demonstrate the effectiveness of our framework. Compared to the best result of existing GNN-based models, the mean absolute error (MAE) on GED value prediction decreases by 4.9% ~ 74.3%. Compared to the state-of-the-art searching algorithm Noah, the MAE on GED value based on edit path reduces by 53.6% ~ 88.1%.\u0000","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":"21 1","pages":"1817-1829"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73153903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-01DOI: 10.14778/3594512.3594522
Eugenio Marinelli, Yiqing Yan, V. Magnone, Charlotte Dumargne, P. Barbry, T. Heinis, Raja Appuswamy
Given the growing adoption of AI, cloud data lakes are facing the need to support cost-effective "just-in-case" data archival over long time periods to meet regulatory compliance requirements. Unfortunately, current media technologies suffer from fundamental issues that will soon, if not already, make cost-effective data archival infeasible. In this paper, we present a vision for redesigning the archival tier of cloud data lakes based on a novel, obsolescence-free storage medium-synthetic DNA. In doing so, we make two contributions: (i) we highlight the challenges in using DNA for data archival and list several open research problems, (ii) we outline OligoArchive-DSM (OA-DSM)-an end-to-end DNA storage pipeline that we are developing to demonstrate the feasibility of our vision.
{"title":"Towards Migration-Free Just-In-Case Data Archival for Future Cloud Data Lakes","authors":"Eugenio Marinelli, Yiqing Yan, V. Magnone, Charlotte Dumargne, P. Barbry, T. Heinis, Raja Appuswamy","doi":"10.14778/3594512.3594522","DOIUrl":"https://doi.org/10.14778/3594512.3594522","url":null,"abstract":"Given the growing adoption of AI, cloud data lakes are facing the need to support cost-effective \"just-in-case\" data archival over long time periods to meet regulatory compliance requirements. Unfortunately, current media technologies suffer from fundamental issues that will soon, if not already, make cost-effective data archival infeasible. In this paper, we present a vision for redesigning the archival tier of cloud data lakes based on a novel, obsolescence-free storage medium-synthetic DNA. In doing so, we make two contributions: (i) we highlight the challenges in using DNA for data archival and list several open research problems, (ii) we outline OligoArchive-DSM (OA-DSM)-an end-to-end DNA storage pipeline that we are developing to demonstrate the feasibility of our vision.","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":"99 1","pages":"1923-1929"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73844961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-01DOI: 10.14778/3594512.3594530
John Paparrizos, Kaize Wu, Aaron J. Elmore, C. Faloutsos, M. Franklin
Similarity search is a core analytical task, and its performance critically depends on the choice of distance measure. For time-series querying, elastic measures achieve state-of-the-art accuracy but are computationally expensive. Thus, fast lower bounding (LB) measures prune unnecessary comparisons with elastic distances to accelerate similarity search. Despite decades of attention, there has never been a study to assess the progress in this area. In addition, the research has disproportionately focused on one popular elastic measure, while other accurate measures have received little or no attention. Therefore, there is merit in developing a framework to accumulate knowledge from previously developed LBs and eliminate the notoriously challenging task of designing separate LBs for each elastic measure. In this paper, we perform the first comprehensive study of 11 LBs spanning 5 elastic measures using 128 datasets. We identify four properties that constitute the effectiveness of LBs and propose the Generalized Lower Bounding (GLB) framework to satisfy all desirable properties. GLB creates cache-friendly summaries, adaptively exploits summaries of both query and target time series, and captures boundary distances in an unsupervised manner. GLB outperforms all LBs in speedup (e.g., up to 13.5× faster against the strongest LB in terms of pruning power), establishes new state-of-the-art results for the 5 elastic measures, and provides the first LBs for 2 elastic measures with no known LBs. Overall, GLB enables the effective development of LBs to facilitate fast similarity search.
{"title":"Accelerating Similarity Search for Elastic Measures: A Study and New Generalization of Lower Bounding Distances","authors":"John Paparrizos, Kaize Wu, Aaron J. Elmore, C. Faloutsos, M. Franklin","doi":"10.14778/3594512.3594530","DOIUrl":"https://doi.org/10.14778/3594512.3594530","url":null,"abstract":"\u0000 Similarity search is a core analytical task, and its performance critically depends on the choice of distance measure. For time-series querying, elastic measures achieve state-of-the-art accuracy but are computationally expensive. Thus, fast lower bounding (LB) measures prune unnecessary comparisons with elastic distances to accelerate similarity search. Despite decades of attention, there has never been a study to assess the progress in this area. In addition, the research has disproportionately focused on one popular elastic measure, while other accurate measures have received little or no attention. Therefore, there is merit in developing a framework to accumulate knowledge from previously developed LBs and eliminate the notoriously challenging task of designing separate LBs for each elastic measure. In this paper, we perform the first comprehensive study of 11 LBs spanning 5 elastic measures using 128 datasets. We identify four properties that constitute the effectiveness of LBs and propose the Generalized Lower Bounding (GLB) framework to satisfy all desirable properties. GLB creates cache-friendly summaries, adaptively exploits summaries of both query and target time series, and captures boundary distances in an unsupervised manner. GLB outperforms\u0000 all\u0000 LBs in speedup (e.g., up to 13.5× faster against the strongest LB in terms of pruning power), establishes new state-of-the-art results for the 5 elastic measures, and provides the first LBs for 2 elastic measures with no known LBs. Overall, GLB enables the effective development of LBs to facilitate fast similarity search.\u0000","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":"26 1","pages":"2019-2032"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75057549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-01DOI: 10.14778/3594512.3594526
Jakub Lemiesz
We study the problem of analyzing massive data streams based on concise data sketches. Recently, a number of papers have investigated how to estimate the results of set-theory operations based on sketches. In this paper we present a framework that allows to estimate the result of any sequence of set-theory operations. The starting point for our solution is the solution from 2021. Compared to this solution, the newly presented sketching algorithm is much more computationally efficient as it requires on average O (log n ) rather than O ( n ) comparisons for n stream elements. We also show that the estimator dedicated to sketches proposed in that reference solution is, in fact, a maximum likelihood estimator.
{"title":"Efficient framework for operating on data sketches","authors":"Jakub Lemiesz","doi":"10.14778/3594512.3594526","DOIUrl":"https://doi.org/10.14778/3594512.3594526","url":null,"abstract":"We study the problem of analyzing massive data streams based on concise data sketches. Recently, a number of papers have investigated how to estimate the results of set-theory operations based on sketches. In this paper we present a framework that allows to estimate the result of any sequence of set-theory operations.\u0000 \u0000 The starting point for our solution is the solution from 2021. Compared to this solution, the newly presented sketching algorithm is much more computationally efficient as it requires on average\u0000 O\u0000 (log\u0000 n\u0000 ) rather than\u0000 O\u0000 (\u0000 n\u0000 ) comparisons for\u0000 n\u0000 stream elements. We also show that the estimator dedicated to sketches proposed in that reference solution is, in fact, a maximum likelihood estimator.\u0000","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":"18 1","pages":"1967-1978"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79484598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-01DOI: 10.14778/3594512.3594528
Zhaoyan Sun, Xuanhe Zhou, Guoliang Li
Indexes can improve query-processing performance by avoiding full table scans. Although traditional indexes (e.g., B+-tree) have been widely used, learned indexes are proposed to adopt machine learning models to reduce the query latency and index size. However, existing learned indexes are (1) not thoroughly evaluated under the same experimental framework and are (2) not comprehensively compared with different settings (e.g., key lookup, key insert, concurrent operations, bulk loading). Moreover, it is hard to select appropriate learned indexes for practitioners in different settings. To address those problems, this paper detailedly reviews existing learned indexes and discusses the design choices of key components in learned indexes, including key lookup (position inference which predicts the position of a key, and position refinement which re-searches the position if the predicted position is incorrect), key insert, concurrency, and bulk loading. Moreover, we provide a testbed to facilitate the design and test of new learned indexes for researchers. We compare state-of-the-art learned indexes in the same experimental framework, and provide findings to select suitable learned indexes under various practical scenarios.
{"title":"Learned Index: A Comprehensive Experimental Evaluation","authors":"Zhaoyan Sun, Xuanhe Zhou, Guoliang Li","doi":"10.14778/3594512.3594528","DOIUrl":"https://doi.org/10.14778/3594512.3594528","url":null,"abstract":"Indexes can improve query-processing performance by avoiding full table scans. Although traditional indexes (e.g., B+-tree) have been widely used, learned indexes are proposed to adopt machine learning models to reduce the query latency and index size. However, existing learned indexes are (1) not thoroughly evaluated under the same experimental framework and are (2) not comprehensively compared with different settings (e.g., key lookup, key insert, concurrent operations, bulk loading). Moreover, it is hard to select appropriate learned indexes for practitioners in different settings. To address those problems, this paper detailedly reviews existing learned indexes and discusses the design choices of key components in learned indexes, including key lookup (position inference which predicts the position of a key, and position refinement which re-searches the position if the predicted position is incorrect), key insert, concurrency, and bulk loading. Moreover, we provide a testbed to facilitate the design and test of new learned indexes for researchers. We compare state-of-the-art learned indexes in the same experimental framework, and provide findings to select suitable learned indexes under various practical scenarios.","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":"94 1","pages":"1992-2004"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77554100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this work, we propose Longshot, a novel design for secure outsourced database systems that supports ad-hoc queries through the use of secure multi-party computation and differential privacy. By combining these two techniques, we build and maintain data structures (i.e., synopses, indexes, and stores) that improve query execution efficiency while maintaining strong privacy and security guarantees. As new data records are uploaded by data owners, these data structures are continually updated by Longshot using novel algorithms that leverage bounded information leakage to minimize the use of expensive cryptographic protocols. Furthermore, Long-shot organizes the data structures as a hierarchical tree based on when the update occurred, allowing for update strategies that provide logarithmic error over time. Through this approach, Longshot introduces a tunable three-way trade-off between privacy, accuracy, and efficiency. Our experimental results confirm that our optimizations are not only asymptotic improvements but also observable in practice. In particular, we see a 5x efficiency improvement to update our data structures even when the number of updates is less than 200. Moreover, the data structures significantly improve query runtimes over time, about ~10 3 x faster compared to the baseline after 20 updates.
{"title":"Longshot: Indexing Growing Databases using MPC and Differential Privacy","authors":"Yanping Zhang, Johes Bater, Kartik Nayak, Ashwin Machanavajjhala","doi":"10.14778/3594512.3594529","DOIUrl":"https://doi.org/10.14778/3594512.3594529","url":null,"abstract":"\u0000 In this work, we propose Longshot, a novel design for secure outsourced database systems that supports ad-hoc queries through the use of secure multi-party computation and differential privacy. By combining these two techniques, we build and maintain data structures (i.e., synopses, indexes, and stores) that improve query execution efficiency while maintaining strong privacy and security guarantees. As new data records are uploaded by data owners, these data structures are continually updated by Longshot using novel algorithms that leverage bounded information leakage to minimize the use of expensive cryptographic protocols. Furthermore, Long-shot organizes the data structures as a hierarchical tree based on when the update occurred, allowing for update strategies that provide logarithmic error over time. Through this approach, Longshot introduces a tunable three-way trade-off between privacy, accuracy, and efficiency. Our experimental results confirm that our optimizations are not only asymptotic improvements but also observable in practice. In particular, we see a 5x efficiency improvement to update our data structures even when the number of updates is less than 200. Moreover, the data structures significantly improve query runtimes over time, about ~10\u0000 3\u0000 x faster compared to the baseline after 20 updates.\u0000","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":"51 1","pages":"2005-2018"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85094874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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