HadoopDB: An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads

By Azza Abouzeid, Kamil Bajda-Pawlikowski, Daniel J. Abadi, A. Rasin, A. Silberschatz. Proceedings of the VLDB Endowment, 2009

1,028 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Management Information Systems

View Paper: https://www.semanticscholar.org/paper/9f991c9b481050213d471d4e1cc5243807d9a992

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Citations in the corpus

A Survey of Large Scale Data Management Approaches in Cloud Environments (2011, background): “wish list [25], [26]: • Availability: They must be always accessible even on the occasions where there is a network failure or a whole datacenter has gone offline.”
An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics (2010, background): “HadoopDB [37,38] is a hybrid of MapReduce and a standard relational db system.”
Analytics over large-scale multidimensional data: the big data revolution! (2011, background): “Several studies, like [9,12], have provided recommendations for further improving the computational capabilities of Hadoop, whereas [1] proposes HadoopDB, a novel hybrid architecture that combines MapReduce and traditional DBMS technologies for supporting advanced analytics over large-scale data rep”
The rise of "big data" on cloud computing: Review and open research issues (2015, methodology): “…data analysis ” Jaql Declarative query language designed for JavaScript Object Notation [90] “ Tenzing an SQL implementation in the MapReduce framework ” Tenzing An SQL query execution engine [91] “ HadoopDB: an architectural hybrid of MapReduce and DBMS technologies for analytical workloads ””
A Fundamental Tradeoff Between Computation and Communication in Distributed Computing (2016, background): “Examples of these applications include machine learning algorithms [42], SQL queries for databases [43], [44], and scientific analytics [45].”
RCFile: A fast and space-efficient data placement structure in MapReduce-based warehouse systems (2011, methodology): “This benchmark has been widely-used to evaluate different large-scale data processing platforms [23].”
Shark: SQL and rich analytics at scale (2012, methodology): “Shark presents a much simpler single-system architecture that supports all of the properties of this third class of systems, as well as statistical learning capabilities that HadoopDB and Osprey lack.”
Starfish: A Self-tuning System for Big Data Analytics (2011, background): “While Hadoop currently trails existing sys tems in peak query performance, a number of research efforts are add ssing this issue [1, 7, 10].”
Scalable SPARQL querying of large RDF graphs (2011, methodology): “We leverage recent work on HadoopDB [5] to handle the splitting of query execution across high performance single-node database systems and the Hadoop data processing framework.”
Trends in big data analytics (2014, background): “Pig [67] offers a high-level SQL like language for easier analysis of large-scale data, while HadoopDB [2] builds a distributed database by using Hadoop for (storage and analysis) distribution over single-node databases.”
Shark: fast data analysis using coarse-grained distributed memory (2012, background): “HadoopDB and split execution [6, 10] explore on the architectural level exploiting hybrid MapReduce and relational database systems.”
Hadoop++ (2010, methodology): “For HadoopDB, we created databases exactly as in [3].”
Book Reviews: Data-Intensive Text Processing with MapReduce by Jimmy Lin and Chris Dyer (2010, background): “However, see Dean and Ghemawat’s counterarguments [47] and recent attempts at hybrid architectures [1].”
Spanner: Google's Globally-Distributed Database (2012, neutral): “They point to several other efforts that have been made to explore database functionality layered on distributed key-value stores [1, 4, 7, 41] as evidence that the two worlds are converging.”
HaLoop (2010, methodology): “MapReduce has been followed by a series of related systems including Dryad [10], Hive [9], Pig [14], and HadoopDB [2].”
Hyracks: A flexible and extensible foundation for data-intensive computing (2011, neutral): “One other project that improves the performance of relational queries using Hadoop, essentially using it as a distributed job dispatcher, is HadoopDB [32].”
Hadoop-GIS: A High Performance Spatial Data Warehousing System over MapReduce (2013, background): “Tight integration of DBMS and MapReduce is discussed in [10, 37].”