{"title":"Session details: Research 14: Approximate Query Processing","authors":"Stratos Idreos","doi":"10.1145/3258022","DOIUrl":"https://doi.org/10.1145/3258022","url":null,"abstract":"","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":"114 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89398102","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}
We study changes in metrics that are defined on a cartesian product of trees. Such metrics occur naturally in many practical applications, where a global metric (such as revenue) can be broken down along several hierarchical dimensions (such as location, gender, etc). Given a change in such a metric, our goal is to identify a small set of non-overlapping data segments that account for a majority of the change. An organization interested in improving the metric can then focus their attention on these data segments. Our key contribution is an algorithm that naturally mimics the operation of a hierarchical organization of analysts. The algorithm has been successfully applied within Google's ad platform (AdWords) to help Google's advertisers triage the performance of their advertising campaigns, and within Google Analytics to help website developers understand their traffic. We empirically analyze the runtime and quality of the algorithm by comparing it against benchmarks for a census dataset. We prove theoretical, worst-case bounds on the performance of the algorithm. For instance, we show that the algorithm is optimal for two dimensions, and has an approximation ratio log d-2 (n+1) for d ≥ 3 dimensions, where n is the number of input data segments. For the advertising application, we can show that our algorithm is a 2-approximation. To characterize the hardness of the problem, we study data patterns called conflicts These allow us to construct hard instances of the problem, and derive a lower bound of 1.144 d-2 (again d ≥3) for our algorithm, and to show that the problem is NP-hard; this justifies are focus on approximation.
{"title":"The Cascading Analysts Algorithm","authors":"M. Ruhl, Mukund Sundararajan, Qiqi Yan","doi":"10.1145/3183713.3183745","DOIUrl":"https://doi.org/10.1145/3183713.3183745","url":null,"abstract":"We study changes in metrics that are defined on a cartesian product of trees. Such metrics occur naturally in many practical applications, where a global metric (such as revenue) can be broken down along several hierarchical dimensions (such as location, gender, etc). Given a change in such a metric, our goal is to identify a small set of non-overlapping data segments that account for a majority of the change. An organization interested in improving the metric can then focus their attention on these data segments. Our key contribution is an algorithm that naturally mimics the operation of a hierarchical organization of analysts. The algorithm has been successfully applied within Google's ad platform (AdWords) to help Google's advertisers triage the performance of their advertising campaigns, and within Google Analytics to help website developers understand their traffic. We empirically analyze the runtime and quality of the algorithm by comparing it against benchmarks for a census dataset. We prove theoretical, worst-case bounds on the performance of the algorithm. For instance, we show that the algorithm is optimal for two dimensions, and has an approximation ratio log d-2 (n+1) for d ≥ 3 dimensions, where n is the number of input data segments. For the advertising application, we can show that our algorithm is a 2-approximation. To characterize the hardness of the problem, we study data patterns called conflicts These allow us to construct hard instances of the problem, and derive a lower bound of 1.144 d-2 (again d ≥3) for our algorithm, and to show that the problem is NP-hard; this justifies are focus on approximation.","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77060506","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 most parallel DBSCAN algorithms, neighboring points are assigned to the same data partition for parallel processing to facilitate calculation of the density of the neighbors. This data partitioning scheme causes a few critical problems including load imbalance between data partitions, especially in a skewed data set. To remedy these problems, we propose a cell-based data partitioning scheme, pseudo random partitioning , that randomly distributes small cells rather than the points themselves. It achieves high load balance regardless of data skewness while retaining the data contiguity required for DBSCAN. In addition, we build and broadcast a highly compact summary of the entire data set, which we call a two-level cell dictionary , to supplement random partitions. Then, we develop a novel parallel DBSCAN algorithm, Random Partitioning-DBSCAN (shortly, RP-DBSCAN), that uses pseudo random partitioning together with a two-level cell dictionary. The algorithm simultaneously finds the local clusters to each data partition and then merges these local clusters to obtain global clustering. To validate the merit of our approach, we implement RP-DBSCAN on Spark and conduct extensive experiments using various real-world data sets on 12 Microsoft Azure machines (48 cores). In RP-DBSCAN, data partitioning and cluster merging are very light, and clustering on each split is not dragged out by a specific worker. Therefore, the performance results show that RP-DBSCAN significantly outperforms the state-of-the-art algorithms by up to 180 times.
{"title":"RP-DBSCAN: A Superfast Parallel DBSCAN Algorithm Based on Random Partitioning","authors":"Hwanjun Song, Jae-Gil Lee","doi":"10.1145/3183713.3196887","DOIUrl":"https://doi.org/10.1145/3183713.3196887","url":null,"abstract":"In most parallel DBSCAN algorithms, neighboring points are assigned to the same data partition for parallel processing to facilitate calculation of the density of the neighbors. This data partitioning scheme causes a few critical problems including load imbalance between data partitions, especially in a skewed data set. To remedy these problems, we propose a cell-based data partitioning scheme, pseudo random partitioning , that randomly distributes small cells rather than the points themselves. It achieves high load balance regardless of data skewness while retaining the data contiguity required for DBSCAN. In addition, we build and broadcast a highly compact summary of the entire data set, which we call a two-level cell dictionary , to supplement random partitions. Then, we develop a novel parallel DBSCAN algorithm, Random Partitioning-DBSCAN (shortly, RP-DBSCAN), that uses pseudo random partitioning together with a two-level cell dictionary. The algorithm simultaneously finds the local clusters to each data partition and then merges these local clusters to obtain global clustering. To validate the merit of our approach, we implement RP-DBSCAN on Spark and conduct extensive experiments using various real-world data sets on 12 Microsoft Azure machines (48 cores). In RP-DBSCAN, data partitioning and cluster merging are very light, and clustering on each split is not dragged out by a specific worker. Therefore, the performance results show that RP-DBSCAN significantly outperforms the state-of-the-art algorithms by up to 180 times.","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":"147 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79590868","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}
{"title":"Session details: Research 9: Similarity Queries & Estimation","authors":"Abolfazl Asudeh","doi":"10.1145/3258016","DOIUrl":"https://doi.org/10.1145/3258016","url":null,"abstract":"","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88954938","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}
Smooth end-to-end performance of mission-critical database system is essential to the stability of applications deployed on the cloud. It's a challenge for cloud database vendors to detect any performance degradation in real-time and locate the root cause quickly in sophisticated network environment. Cloud databases vendors tend to favor a multi-tier distributed architecture to achieve multi-tenant management, scalability and high-availability, which may further complicate the problem. This paper presents TcpRT, the instrument and diagnosis infrastructure in Alibaba Cloud RDS that achieves real-time anomaly detection. We wrote a Linux kernel module to collect trace data of each SQL query, designed to be efficient with minimal overhead, it adds tracepoints in callbacks of TCP congestion control kernel module, that is totally transparent to database processes. In order to reduce the amount of data significantly before sending it to backend, raw trace data is aggregated. Aggregated trace data is then processed, grouped and analyzed in a distributed streaming computing platform. By utilizing a self-adjustable Cauchy distribution statistical model from historical performance data for each DB instance, anomalous events can be automatically detected in databases, which eliminates manually configuring thresholds by experience. A fault or hiccup occurred in any network component that is shared among multiple DB instances (e.g. hosted on the same physical machine or uplinked to the same pair of TOR switches) may cause large-scale service quality degradations. The ratio of anomalous DB instances vs networks components is being calculated, which helps pinpoint the faulty component. TcpRT has been deployed in production at Alibaba Cloud for the past 3 years, collects over 20 million raw traces per second, and processes over 10 billion locally aggregated results in the backend per day, and managed to have within 1% performance impact on DB system. We present case studies of typical scenarios where TcpRT helps to solve various problems occurred in production system.
{"title":"TcpRT: Instrument and Diagnostic Analysis System for Service Quality of Cloud Databases at Massive Scale in Real-time","authors":"Wei Cao, Yusong Gao, Bingchen Lin, Xiaojie Feng, Yu Xie, Xiao Lou, Peng Wang","doi":"10.1145/3183713.3190659","DOIUrl":"https://doi.org/10.1145/3183713.3190659","url":null,"abstract":"Smooth end-to-end performance of mission-critical database system is essential to the stability of applications deployed on the cloud. It's a challenge for cloud database vendors to detect any performance degradation in real-time and locate the root cause quickly in sophisticated network environment. Cloud databases vendors tend to favor a multi-tier distributed architecture to achieve multi-tenant management, scalability and high-availability, which may further complicate the problem. This paper presents TcpRT, the instrument and diagnosis infrastructure in Alibaba Cloud RDS that achieves real-time anomaly detection. We wrote a Linux kernel module to collect trace data of each SQL query, designed to be efficient with minimal overhead, it adds tracepoints in callbacks of TCP congestion control kernel module, that is totally transparent to database processes. In order to reduce the amount of data significantly before sending it to backend, raw trace data is aggregated. Aggregated trace data is then processed, grouped and analyzed in a distributed streaming computing platform. By utilizing a self-adjustable Cauchy distribution statistical model from historical performance data for each DB instance, anomalous events can be automatically detected in databases, which eliminates manually configuring thresholds by experience. A fault or hiccup occurred in any network component that is shared among multiple DB instances (e.g. hosted on the same physical machine or uplinked to the same pair of TOR switches) may cause large-scale service quality degradations. The ratio of anomalous DB instances vs networks components is being calculated, which helps pinpoint the faulty component. TcpRT has been deployed in production at Alibaba Cloud for the past 3 years, collects over 20 million raw traces per second, and processes over 10 billion locally aggregated results in the backend per day, and managed to have within 1% performance impact on DB system. We present case studies of typical scenarios where TcpRT helps to solve various problems occurred in production system.","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":"98 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91180198","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}
Building interactive tools to support data analysis is hard because it is not always clear what to build and how to build it. To address this problem, we present Precision Interfaces, a semi-automatic system to generate task-specific data analytics interfaces. Precision Interface can turn a log of executed programs into an interface, by identifying micro-variations between the programs and mapping them to interface components. This paper focuses on SQL query logs, but we can generalize the approach to other languages. Our system operates in two steps: it first builds an interaction graph, which describes how the queries can be transformed into each other. Then, it finds a set of UI components that covers a maximal number of transformations. To restrict the domain of changes to be detected, our system uses a domain-specific language, PILang. We describe each of Precision Interface's components, showcase an early prototype on real program logs, and discuss future research opportunities. This demonstration highlights the potential for data-driven interactive interface mining from query logs. We will first walk participants through the process that Precision Interfaces goes through to generate interactive analysis interfaces from query logs. We will then show the versatility of Precision Interfaces by letting participants choose from multiple different interface modalities, interaction designs, and query logs to generate 2D point-and-click, gestural, and even natural language analysis interfaces for commonly performed analyses.
{"title":"Precision Interfaces for Different Modalities","authors":"Haoci Zhang, Viraj Raj, Thibault Sellam, Eugene Wu","doi":"10.1145/3183713.3193570","DOIUrl":"https://doi.org/10.1145/3183713.3193570","url":null,"abstract":"Building interactive tools to support data analysis is hard because it is not always clear what to build and how to build it. To address this problem, we present Precision Interfaces, a semi-automatic system to generate task-specific data analytics interfaces. Precision Interface can turn a log of executed programs into an interface, by identifying micro-variations between the programs and mapping them to interface components. This paper focuses on SQL query logs, but we can generalize the approach to other languages. Our system operates in two steps: it first builds an interaction graph, which describes how the queries can be transformed into each other. Then, it finds a set of UI components that covers a maximal number of transformations. To restrict the domain of changes to be detected, our system uses a domain-specific language, PILang. We describe each of Precision Interface's components, showcase an early prototype on real program logs, and discuss future research opportunities. This demonstration highlights the potential for data-driven interactive interface mining from query logs. We will first walk participants through the process that Precision Interfaces goes through to generate interactive analysis interfaces from query logs. We will then show the versatility of Precision Interfaces by letting participants choose from multiple different interface modalities, interaction designs, and query logs to generate 2D point-and-click, gestural, and even natural language analysis interfaces for commonly performed analyses.","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88869549","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}
Given a directed graph G, a source node s, and a target node t, the personalized PageRank (PPR of t with respect to s is the probability that a random walk starting from s terminates at t. The average of the personalized PageRank score of t with respect to each source node υ∈ V is exactly the PageRank score π( t ) of node t , which denotes the overall importance of node t in the graph. A heavy hitter of node t is a node whose contribution to π( t ) is above a φ fraction, where φ is a value between 0 and 1. Finding heavy hitters has important applications in link spam detection, classification of web pages, and friend recommendations. In this paper, we propose BLOG, an efficient framework for three types of heavy hitter queries: the pairwise approximate heavy hitter (AHH), the reverse AHH, and the multi-source reverse AHH queries. For pairwise AHH queries, our algorithm combines the Monte-Carlo approach and the backward propagation approach to reduce the cost of both methods, and incorporates new techniques to deal with high in-degree nodes. For reverse AHH and multi-source reverse AHH queries, our algorithm extends the ideas behind the pairwise AHH algorithm with a new "logarithmic bucketing'' technique to improve the query efficiency. Extensive experiments demonstrate that our BLOG is far more efficient than alternative solutions on the three queries.
{"title":"Efficient Algorithms for Finding Approximate Heavy Hitters in Personalized PageRanks","authors":"Sibo Wang, Yufei Tao","doi":"10.1145/3183713.3196919","DOIUrl":"https://doi.org/10.1145/3183713.3196919","url":null,"abstract":"Given a directed graph G, a source node s, and a target node t, the personalized PageRank (PPR of t with respect to s is the probability that a random walk starting from s terminates at t. The average of the personalized PageRank score of t with respect to each source node υ∈ V is exactly the PageRank score π( t ) of node t , which denotes the overall importance of node t in the graph. A heavy hitter of node t is a node whose contribution to π( t ) is above a φ fraction, where φ is a value between 0 and 1. Finding heavy hitters has important applications in link spam detection, classification of web pages, and friend recommendations. In this paper, we propose BLOG, an efficient framework for three types of heavy hitter queries: the pairwise approximate heavy hitter (AHH), the reverse AHH, and the multi-source reverse AHH queries. For pairwise AHH queries, our algorithm combines the Monte-Carlo approach and the backward propagation approach to reduce the cost of both methods, and incorporates new techniques to deal with high in-degree nodes. For reverse AHH and multi-source reverse AHH queries, our algorithm extends the ideas behind the pairwise AHH algorithm with a new \"logarithmic bucketing'' technique to improve the query efficiency. Extensive experiments demonstrate that our BLOG is far more efficient than alternative solutions on the three queries.","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81825550","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}
Modern persistent key-value stores typically use a log-structured merge-tree (LSM-tree) design, which allows for high write throughput. Our observation is that the LSM-tree, however, has suboptimal performance during read-intensive workload windows with non-uniform key access distributions. To address this shortcoming, we propose and analyze a simple decision scheme that can be added to any LSM-based key-value store and dramatically reduce the number of disk I/Os for these classes of workloads. The key insight is that copying a frequently accessed key to the top of an LSM-tree ("splaying'') allows cheaper reads on that key in the near future.
{"title":"Splaying Log-Structured Merge-Trees","authors":"Thomas Lively, Luca Schroeder, Carlos Mendizábal","doi":"10.1145/3183713.3183723","DOIUrl":"https://doi.org/10.1145/3183713.3183723","url":null,"abstract":"Modern persistent key-value stores typically use a log-structured merge-tree (LSM-tree) design, which allows for high write throughput. Our observation is that the LSM-tree, however, has suboptimal performance during read-intensive workload windows with non-uniform key access distributions. To address this shortcoming, we propose and analyze a simple decision scheme that can be added to any LSM-based key-value store and dramatically reduce the number of disk I/Os for these classes of workloads. The key insight is that copying a frequently accessed key to the top of an LSM-tree (\"splaying'') allows cheaper reads on that key in the near future.","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":"71 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85006710","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}
{"title":"Session details: Research 11: Data Mining","authors":"L. Lakshmanan","doi":"10.1145/3258018","DOIUrl":"https://doi.org/10.1145/3258018","url":null,"abstract":"","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":"70 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90504153","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}
Data lineage is a fundamental type of information that describes the relationships between input and output data items in a workflow. As such, an immense amount of data-intensive applications with logic over the input-output relationships can be expressed declaratively in lineage terms. Unfortunately, many applications resort to hand-tuned implementations because either lineage systems are not fast enough to meet their requirements or due to no knowledge of the lineage capabilities. Recently, we introduced a set of implementation design principles and associated techniques to optimize lineage-enabled database engines and realized them in our prototype database engine, namely, Smoke. In this demonstration, we showcase lineage as the building block across a variety of data-intensive applications, including tooltips and details on demand; crossfilter; and data profiling. In addition, we show how Smoke outperforms alternative lineage systems to meet or improve on existing hand-tuned implementations of these applications.
{"title":"Demonstration of Smoke: A Deep Breath of Data-Intensive Lineage Applications","authors":"Fotis Psallidas, Eugene Wu","doi":"10.1145/3183713.3193537","DOIUrl":"https://doi.org/10.1145/3183713.3193537","url":null,"abstract":"Data lineage is a fundamental type of information that describes the relationships between input and output data items in a workflow. As such, an immense amount of data-intensive applications with logic over the input-output relationships can be expressed declaratively in lineage terms. Unfortunately, many applications resort to hand-tuned implementations because either lineage systems are not fast enough to meet their requirements or due to no knowledge of the lineage capabilities. Recently, we introduced a set of implementation design principles and associated techniques to optimize lineage-enabled database engines and realized them in our prototype database engine, namely, Smoke. In this demonstration, we showcase lineage as the building block across a variety of data-intensive applications, including tooltips and details on demand; crossfilter; and data profiling. In addition, we show how Smoke outperforms alternative lineage systems to meet or improve on existing hand-tuned implementations of these applications.","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88042857","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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