Bigtable

Chang, Fay W.; Dean, J. Michael; Ghemawat, Sanjay; Hsieh, Wilson C.; Wallach, Deborah A.; Burrows, Mike; Chandra, Tushar; Fikes, Andrew; Gruber, R.

doi:10.1145/1365815.1365816

Cited by 2,435 publications

(295 citation statements)

References 19 publications

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“…Apache HBase is a non-relational, distributed data management system modeled after Google's BigTable [13]. HBase is developed as a part of the Apache Hadoop project and runs on top of Hadoop Distributed File System (HDFS).…”

Section: A Hbase and Coprocessorsmentioning

confidence: 99%

Graph Aware Caching Policy for Distributed Graph Stores

Aksu

Canim

Chang

et al. 2015

2015 IEEE International Conference on Cloud Engineering

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Abstract-Graph stores are becoming increasingly popular among NOSQL applications seeking flexibility and heterogeneity in managing linked data. Conceptually and in practice, applications ranging from social networks, knowledge representations to Internet of things benefit from graph data stores built on a combination of relational and non-relational technologies aimed at desired performance characteristics. The most common data access pattern in querying graph stores is to traverse from a node to its neighboring nodes. This paper studies the impact of such traversal pattern to common data caching policies in a partitioned data environment where a big graph is distributed across servers in a cluster. We propose and evaluate a new graph aware caching policy designed to keep and evict nodes, edges and their metadata optimized for query traversal pattern. The algorithm distinguishes the topology of the graph as well as the latency of access to the graph nodes and neighbors. We implemented graph aware caching on a distributed data store Apache HBase in the Hadoop family. Performance evaluations showed up to 15x speedup on the benchmark datasets preferring our new graph aware policy over non-aware policies. We also show how to improve the performance of existing caching algorithms for distributed graphs by exploiting the topology information.

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Section: A Hbase and Coprocessorsmentioning

confidence: 99%

Graph Aware Caching Policy for Distributed Graph Stores

Aksu

Canim

Chang

et al. 2015

2015 IEEE International Conference on Cloud Engineering

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“…Third, we require the key-value store to utilize SSTable (Sorted String Table) to persist the entries permanently. An SSTable is essentially an immutable sorted list of entries stored on disk [4]. Fourth, each write operation accompanies a timestamp or a version number which can be used to resolve a conflict.…”

Section: System Modelmentioning

confidence: 99%

“…Dynamo [1] and Apache Cassandra [2] rely on the use of a "virtual ring" to place servers as well as keys; keys are assigned to servers whose segment they fall into. On the other hand, MongoDB [3] and BigTable [4] rely on a fully flexible assignment of shards across servers.…”

mentioning

confidence: 99%

Parqua: Online Reconfigurations in Virtual Ring-Based NoSQL Systems

Shin

Ghosh

Gupta

2015

2015 International Conference on Cloud and Autonomic Computing

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Abstract-The performance of key-value/NoSQL storage systems is highly tied to the choice of (primary) key for the database table. As application requirements change over time, system administrators need to change the primary key of the table to improve performance. The primary key change is a specific example of a broader class of reconfiguration operations that affect a lot of data all at once. In this paper we propose a system called Parqua, which imbues ring-based keyvalue/NoSQL stores with the ability to perform reconfiguration operations in an online and efficient manner. We present the design and implementation of Parqua. Experiments based on our cluster deployments show that during reconfiguration Parqua maintains high availability, and with a small impact on read and write latencies.

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“…AppEngine's data storage solution, BigTable, when accessed from a Java program, features an interface similar to the Java Persistence API (JPA). It also supports a query language similar to SQL but without some of the complex queries, deemed google query language (GQL) [27,28].…”

Section: Google's Appenginementioning

confidence: 99%

The Building Blocks of a PaaS

et al. 2013

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Traditional cloud computing providers enable developers to program and deploy applications in the cloud by using platform as a service (PaaS) providers. Despite the benefits of such an approach, this service model usually comes with a high level of the lock in effect into the service provider. The lock in effect occurs when a software developer needs to change its cloud provider. When this happens, it usually means a major application rewrite, specific for the new PaaS. This paper details the initial results of a project whose goal is to build a PaaS where vendor lock in does not occur. With this PaaS, developers wishing to deploy their applications into the cloud may continue to program using their usual development environments. There are no necessary changes required to make the application PaaS compatible. As a proof of concept, we developed an open source PaaS management application as a regular web application and then deployed it on the cloud.

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Bigtable

Cited by 2,435 publications

References 19 publications

Graph Aware Caching Policy for Distributed Graph Stores

Graph Aware Caching Policy for Distributed Graph Stores

Parqua: Online Reconfigurations in Virtual Ring-Based NoSQL Systems

The Building Blocks of a PaaS

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