选择数据库来存储地理时空数据

Anton Shykhmat, Z. Veres
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引用次数: 0

摘要

在传感器平台和物联网广泛应用的推动下,地理空间时空数据激增,对有效数据管理解决方案的需求也随之升级。GeoMesa 是一个开源工具包,旨在实现分布式计算系统中的综合地理空间查询和分析。GeoMesa 将地理空间时空索引功能与 Accumulo、HBase、Google Bigtable 和 Cassandra 等数据库无缝集成,促进了大量地理空间数据集的存储和管理。本文旨在满足对 Accumulo 和 Cassandra 作为 GeoMesa 底层数据存储时的性能进行基准测试和比较的迫切需求。通过进行性能测试,我们旨在就这些数据库系统的相对优缺点提供有价值的见解,从而帮助决策者选择最适合其特定应用需求的解决方案。评估包括对吞吐量和延迟等性能指标的深入分析,以及对系统参数、查询密度和数据访问分布的考虑。结果表明,Accumulo 几乎在所有方面都优于 Cassandra,包括大负载下的读取延迟和资源使用,以及任何负载下的写入延迟。反过来,Cassandra 在低负载情况下的读取延迟和大负载情况下的 CPU 占用率更低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SELECTION OF DATABASES TO STORE GEOSPATIAL-TEMPORAL DATA
The proliferation of geospatial-temporal data, driven by the widespread adoption of sensor platforms and the Internet of Things, has escalated the demand for effective data management solutions. In this context, GeoMesa, an open-source toolkit designed to enable comprehensive geospatial querying and analytics in distributed computing systems, plays a pivotal role. GeoMesa seamlessly integrates geospatial-temporal indexing capabilities with databases like Accumulo, HBase, Google Bigtable, and Cassandra, facilitating the storage and management of extensive geospatial datasets. This article addresses the critical need to benchmark and compare the performance of Accumulo and Cassandra when employed as underlying data stores for GeoMesa. By conducting performance tests, we aim to provide valuable insights into the relative strengths and weaknesses of these database systems, thereby aiding decision-makers in selecting the most suitable solution for their specific application requirements. The evaluation includes an in-depth analysis of performance metrics, such as throughput and latency, as well as consideration of system parameters, query density, and data access distribution. It was identified that Accumulo outperforms Cassandra almost in all areas – read latency and resource usage under heavy load and write latency under any load. In turn, Cassandra has lower read latency under low load and CPU usage under heavy load.
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