Bigtable: a distributed storage system for structured data

By Fay W. Chang, Joseph Dean, Sanjay Ghemawat, Wilson C. Hsieh, Deborah A. Wallach, M. Burrows, Tushar Chandra, Andrew Fikes, Robert E. Gruber. USENIX Symposium on Operating Systems Design and Implementation, 2006

5,721 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/18a5f443299784479e78d9e77f175af57cb2fa2b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Closing the Loop: Fast, Interactive Semi-Supervised Annotation With Queries on Features and Instances (2011, background): “Distributed data storage (Chang et al., 2008) and parallelized learning algorithms (Chu et al., 2007) may help scale this approach into the millions.”
Structured Comparative Analysis of Systems Logs to Diagnose Performance Problems (2012, methodology): “HBase is an open-source implementation of BigTable [4], depending on the Hadoop [17] implementation of the Google File System [15].”
Big Data Service Architecture: A Survey (2020, methodology): “Column family stores are inspired by Googles Bigtable [29], and its data is stored based on the column family model.”
Transactional storage for geo-replicated systems (2011, methodology): “A Walter server confirms its role in the system by obtaining a lease from the configuration service, similar to what is done in [12, 46].”
Efficient task replication for fast response times in parallel computation (2014, unknown): “One of the typical scenarios in cloud computing is large scale computation in a data centers with a large number of computers, which is pioneered by companies like Google with the support from distributed computing frameworks such as MapReduce [9] and Percolator [15] , and distributed storage system”
Benchmarking cloud serving systems with YCSB (2010, methodology): “While both Cassandra and HBase have a data model similar to that of Google’s BigTable [16], their underlying implementations are quite diﬀerent—HBase’s architecture is similar to BigTable (using synchronous updates to multiple copies of data chunks), while Cassandra’s is similar to Dynamo [18]…”
RCFile: A fast and space-efficient data placement structure in MapReduce-based warehouse systems (2011, methodology): “Other related work includes the Google Bigtable system [26] and its open-source implementation Hbase [27] built on Hadoop.”
On the energy (in)efficiency of Hadoop clusters (2010, background): “Workloads and Applications — While this paper considered Hadoop’s MapReduce, note that HBase [2] and BigTable [ 6 ] build upon the same filesystem infrastructure as their sibling MapReduce frameworks.”
Megastore: Providing Scalable, Highly Available Storage for Interactive Services (2011, unknown): “Categories and Subject Descriptors C.2.4 [Distributed Systems]: Distributed databases; H.2.4 [Database Management]: Systems—concurrency, distributed databases General Terms Algorithms, Design, Performance, Reliability Keywords Large databases, Distributed transactions, Bigtable, Paxos”
Managing Google's data lake: an overview of the Goods system (2016, background): “For instance, bigtable column families[4] are first-class entriies in the GOODS catalog, and so are the bigtables themselves.”
The nature of data center traffic: measurements & analysis (2009, background): “As a result, distributed infrastructuresthat support query processing on peta-bytes of data using commodity servers are increasingly prevalent (e.g., GFS, BigTable [ 9 , 17], Yahoo’s Hadoop, PIG [4, 27] and Microso’s Cosmos, Scope [8, 23]).”
Site Reliability Engineering: How Google Runs Production Systems (2016, methodology): “Bigtable [Cha06] is a NoSQL database system that can handle databases that are petabytes in size.”
The Eucalyptus Open-Source Cloud-Computing System (2009, methodology): “Finally, we provide evidence that EUCALYPTUS enables users familiar with existing Grid and HPC systems to explore new cloud computing functionality while maintaining access to existing, familiar application development software and Grid middle-ware.”
Big Data: A Survey (2014, background): “Such high-level languages include Sawzall, Pig, and Hive used for MapReduce, as well as Scope and DryadLINQ used for Dryad.”