Parallel In-Memory Evaluation of Spatial Joins

The spatial join is a popular operation in spatial database systems and its evaluation is a well-studied problem. As main memories become bigger and faster and commodity hardware supports parallel processing, there is a need to revamp classic join algorithms which have been designed for I/O-bound processing. In view of this, we study the in-memory and parallel evaluation of spatial joins, by re-designing a classic partitioning-based algorithm to consider alternative approaches for space partitioning. Our study shows that, compared to a straightforward implementation of the algorithm, our tuning can improve performance significantly. We also show how to select appropriate partitioning parameters based on data statistics, in order to tune the algorithm for the given join inputs. Our parallel implementation scales gracefully with the number of threads reducing the cost of the join to at most one second even for join inputs with tens of millions of rectangles.Comment: Extended version of the SIGSPATIAL'19 paper under the same titl

Accelerating Spatial Data Processing with MapReduce

Abstract—MapReduce is a key-value based programming model and an associated implementation for processing large data sets. It has been adopted in various scenarios and seems promising. However, when spatial computation is expressed straightforward by this key-value based model, difficulties arise due to unfit features and performance degradation. In this paper, we present methods as follows: 1) a splitting method for balancing workload, 2) pending file structure and redundant data partition dealing with relation between spatial objects, 3) a strip-based two-direction plane sweep-ing algorithm for computation accelerating. Based on these methods, ANN(All nearest neighbors) query and astronomical cross-certification are developed. Performance evaluation shows that the MapReduce-based spatial applications outperform the traditional one on DBMS

Efficient Parallel and Adaptive Partitioning for Load-balancing in Spatial Join

Due to the developments of topographic techniques, clear satellite imagery, and various means for collecting information, geospatial datasets are growing in volume, complexity, and heterogeneity. For efficient execution of spatial computations and analytics on large spatial data sets, parallel processing is required. To exploit fine-grained parallel processing in large scale compute clusters, partitioning in a load-balanced way is necessary for skewed datasets. In this work, we focus on spatial join operation where the inputs are two layers of geospatial data. Our partitioning method for spatial join uses Adaptive Partitioning (ADP) technique, which is based on Quadtree partitioning. Unlike existing partitioning techniques, ADP partitions the spatial join workload instead of partitioning the individual datasets separately to provide better load-balancing. Based on our experimental evaluation, ADP partitions spatial data in a more balanced way than Quadtree partitioning and Uniform grid partitioning. ADP uses an output-sensitive duplication avoidance technique which minimizes duplication of geometries that are not part of spatial join output. In a distributed memory environment, this technique can reduce data communication and storage requirements compared to traditional methods.To improve the performance of ADP, an MPI+Threads based parallelization is presented. With ParADP, a pair of real world datasets, one with 717 million polylines and another with 10 million polygons, is partitioned into 65,536 grid cells within 7 seconds. ParADP performs well with both good weak scaling up to 4,032 CPU cores and good strong scaling up to 4,032 CPU cores

Load Balancing Algorithms for Parallel Spatial Join on HPC Platforms

Geospatial datasets are growing in volume, complexity, and heterogeneity. For efficient execution of geospatial computations and analytics on large scale datasets, parallel processing is necessary. To exploit fine-grained parallel processing on large scale compute clusters, partitioning of skewed datasets in a load-balanced way is challenging. The workload in spatial join is data dependent and highly irregular. Moreover, wide variation in the size and density of geometries from one region of the map to another, further exacerbates the load imbalance. This dissertation focuses on spatial join operation used in Geographic Information Systems (GIS) and spatial databases, where the inputs are two layers of geospatial data, and the output is a combination of the two layers according to join predicate.This dissertation introduces a novel spatial data partitioning algorithm geared towards load balancing the parallel spatial join processing. Unlike existing partitioning techniques, the proposed partitioning algorithm divides the spatial join workload instead of partitioning the individual datasets separately to provide better load-balancing. This workload partitioning algorithm has been evaluated on a high-performance computing system using real-world datasets. An intermediate output-sensitive duplication avoidance technique is proposed that decreases the external memory space requirement for storing spatial join candidates across the partitions. GPU acceleration is used to further reduce the spatial partitioning runtime. For dynamic load balancing in spatial join, a novel framework for fine-grained work stealing is presented. This framework is efficient and NUMA-aware. Performance improvements are demonstrated on shared and distributed memory architectures using threads and message passing. Experimental results show effective mitigation of data skew. The framework supports a variety of spatial join predicates and spatial overlay using partitioned and un-partitioned datasets

A scalable analysis framework for large-scale RDF data

With the growth of the Semantic Web, the availability of RDF datasets from multiple domains as Linked Data has taken the corpora of this web to a terabyte-scale, and challenges modern knowledge storage and discovery techniques. Research and engineering on RDF data management systems is a very active area with many standalone systems being introduced. However, as the size of RDF data increases, such single-machine approaches meet performance bottlenecks, in terms of both data loading and querying, due to the limited parallelism inherent to symmetric multi-threaded systems and the limited available system I/O and system memory. Although several approaches for distributed RDF data processing have been proposed, along with clustered versions of more traditional approaches, their techniques are limited by the trade-off they exploit between loading complexity and query efficiency in the presence of big RDF data. This thesis then, introduces a scalable analysis framework for processing large-scale RDF data, which focuses on various techniques to reduce inter-machine communication, computation and load-imbalancing so as to achieve fast data loading and querying on distributed infrastructures. The first part of this thesis focuses on the study of RDF store implementation and parallel hashing on big data processing. (1) A system-level investigation of RDF store implementation has been conducted on the basis of a comparative analysis of runtime characteristics of a representative set of RDF stores. The detailed time cost and system consumption is measured for data loading and querying so as to provide insight into different triple store implementation as well as an understanding of performance differences between different platforms. (2) A high-level structured parallel hashing approach over distributed memory is proposed and theoretically analyzed. The detailed performance of hashing implementations using different lock-free strategies has been characterized through extensive experiments, thereby allowing system developers to make a more informed choice for the implementation of their high-performance analytical data processing systems. The second part of this thesis proposes three main techniques for fast processing of large RDF data within the proposed framework. (1) A very efficient parallel dictionary encoding algorithm, to avoid unnecessary disk-space consumption and reduce computational complexity of query execution. The presented implementation has achieved notable speedups compared to the state-of-art method and also has achieved excellent scalability. (2) Several novel parallel join algorithms, to efficiently handle skew over large data during query processing. The approaches have achieved good load balancing and have been demonstrated to be faster than the state-of-art techniques in both theoretical and experimental comparisons. (3) A two-tier dynamic indexing approach for processing SPARQL queries has been devised which keeps loading times low and decreases or in some instances removes intermachine data movement for subsequent queries that contain the same graph patterns. The results demonstrate that this design can load data at least an order of magnitude faster than a clustered store operating in RAM while remaining within an interactive range for query processing and even outperforms current systems for various queries

Efficient processing of similarity queries with applications

Today, a myriad of data sources, from the Internet to business operations to scientific instruments, produce large and different types of data. Many application scenarios, e.g., marketing analysis, sensor networks, and medical and biological applications, call for identifying and processing similarities in big data. As a result, it is imperative to develop new similarity query processing approaches and systems that scale from low dimensional data to high dimensional data, from single machine to clusters of hundreds of machines, and from disk-based to memory-based processing. This dissertation introduces and studies several similarity-aware query operators, analyzes and optimizes their performance. The first contribution of this dissertation is an SQL-based Similarity Group-by operator (SGB, for short) that extends the semantics of the standard SQL Group-by operator to group data with similar but not necessarily equal values. We realize these SGB operators by extending the Standard SQL Group-by and introduce two new SGB operators for multi-dimensional data. We implement and test the new SGB operators and their algorithms inside an open-source centralized database server (PostgreSQL). In the second contribution of this dissertation, we study how to efficiently process Hamming-distance-based similarity queries (Hamming-distance select and Hamming-distance join) that are crucial to many applications. We introduce a new index, termed the HA-Index, that speeds up distance comparisons and eliminates redundancies when performing the two flavors of Hamming distance range queries (namely, the selects and joins). In the third and last contribution of this dissertation, we develop a system for similarity query processing and optimization in an in-memory and distributed setup for big spatial data. We propose a query scheduler and a distributed query optimizer that use a new cost model to optimize the cost of similarity query processing in this in-memory distributed setup. The scheduler and query optimizer generates query execution plans that minimize the effect of query skew. The query scheduler employs new spatial indexing techniques based on bloom filters to forward queries to the appropriate local sites. The proposed query processing and optimization techniques are prototyped inside Spark, a distributed main-memory computation system

Incremental elasticity for array databases

Relational databases benefit significantly from elasticity, whereby they execute on a set of changing hardware resources provisioned to match their storage and processing requirements. Such flexibility is especially attractive for scientific databases because their users often have a no-overwrite storage model, in which they delete data only when their available space is exhausted. This results in a database that is regularly growing and expanding its hardware proportionally. Also, scientific databases frequently store their data as multidimensional arrays optimized for spatial querying. This brings about several novel challenges in clustered, skew-aware data placement on an elastic shared-nothing database. In this work, we design and implement elasticity for an array database. We address this challenge on two fronts: determining when to expand a database cluster and how to partition the data within it. In both steps we propose incremental approaches, affecting a minimum set of data and nodes, while maintaining high performance. We introduce an algorithm for gradually augmenting an array database's hardware using a closed-loop control system. After the cluster adds nodes, we optimize data placement for n-dimensional arrays. Many of our elastic partitioners incrementally reorganize an array, redistributing data only to new nodes. By combining these two tools, the scientific database efficiently and seamlessly manages its monotonically increasing hardware resources.Intel Corporation (Science and Technology Center for Big Data

Towards a Learned Cost Model for Distributed Spatial Join: Data, Code & Models

Geospatial data comprise around 60% of all the publicly available data. One of the essential and most complex operations that brings together multiple geospatial datasets is the spatial join operation. Due to its complexity, there is a lot of partitioning techniques and parallel algorithms for the spatial join problem. This leads to a complex query optimization problem: which algorithm to use for a given pair of input datasets that we want to join? With the rise of machine learning, there is a promise in addressing this problem with the use of various learned models. However, one of the concerns is the lack of a standard and publicly available data to train and test on, as well as the lack of accessible baseline models. This resource paper helps the research community to solve this problem by providing synthetic and real datasets for spatial join, source code for constructing more datasets, and several baseline solutions that researchers can further extend and compare to