Apache HBase and HDFS

Vohra, Deepak

doi:10.1007/978-1-4842-2424-3_2

Cited by 15 publications

(11 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other GDBMSs, such as JanusGraph [11] , use established NoSQL stores as their underlying graph storage. JanusGraph is able to use different storage backend as a wide-column store, including Cassandra [14] , HBase [15] , Google Cloud Bigtable [16] , and Berkeley DB [17] . This allows for flexibility, but requires a standard interface and may not allow for deep co-design.…”

Section: Related Workmentioning

confidence: 99%

Building a High-Performance Graph Storage on Top of Tree-Structured Key-Value Stores

Lin,

Wang,

et al. 2024

Big Data Min. Anal.

View full text Add to dashboard Cite

Graph databases have gained widespread adoption in various industries and have been utilized in a range of applications, including financial risk assessment, commodity recommendation, and data lineage tracking. While the principles and design of these databases have been the subject of some investigation, there remains a lack of comprehensive examination of aspects such as storage layout, query language, and deployment. The present study focuses on the design and implementation of graph storage layout, with a particular emphasis on tree-structured key-value stores. We also examine different design choices in the graph storage layer and present our findings through the development of TuGraph, a highly efficient single-machine graph database that significantly outperforms well-known Graph DataBase Management System (GDBMS).Additionally, TuGraph demonstrates superior performance in the Linked Data Benchmark Council (LDBC) Social Network Benchmark (SNB) interactive benchmark.

show abstract

Section: Related Workmentioning

confidence: 99%

Building a High-Performance Graph Storage on Top of Tree-Structured Key-Value Stores

Lin,

Wang,

et al. 2024

Big Data Min. Anal.

View full text Add to dashboard Cite

show abstract

“…The new back-end store is based on Apache HBase [6], which is compatible with the Apache Phoenix [7] SQL interface. This allows improved data structures and simplified management.…”

Section: Data Collection Systemmentioning

confidence: 99%

HBase/Phoenix-based Data Collection and Storage for the ATLAS EventIndex

García Montoro,

Sánchez,

Barberis

et al. 2024

EPJ Web of Conf.

View full text Add to dashboard Cite

The ATLAS EventIndex is the global catalogue of all ATLAS real and simulated events. During the LHC long shutdown between Run 2 (20152018) and Run 3 (2022-2025) its components were substantially revised, and a new system was deployed for the start of Run 3 in Spring 2022. The new core storage system is based on HBase tables with a Phoenix interface. It allows faster data ingestion rates and scales better than the old system. This paper describes the data collection, the technical design of the core storage, and the properties that make it fast and efficient, namely the compact and optimized design of the events table, which already holds more than 400 billion entries, and all the auxiliary tables, and the EventIndex Supervisor, in charge of orchestrating the whole data collection, now simplified thanks to the Loaders, the Spark jobs that load the data into the new core system. The extractors, in charge of preparing the pieces of data that the loaders will put into the final back-end, have been updated too. The data migration from HDFS to HBase and Phoenix is also described.

show abstract

“…Such applications are long-standing, user-facing and interactive services, working in "request-and-response" manner to serve user requests. Representative examples of LRAs include streaming processing frameworks (Storm [3], Flink [4], Kafka streams [5]), latency-sensitive database applications (HBase [6] and MongoDB [37]), and data-intensive inmemory computing frameworks (Spark [53], Tensorflow [8]).…”

Section: Microservice and Long-running Applicationsmentioning

confidence: 99%