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Benchmarking the Availability and Fault Tolerance of Cassandra

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Big Data Benchmarking (WBDB 2015, WBDB 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10044))

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Abstract

To be able to handle big data workloads, modern NoSQL database management systems like Cassandra are designed to scale well over multiple machines. However, with each additional machine in a cluster, the likelihood for hardware failure increases. In order to still achieve high availability and fault tolerance, the data needs to be replicated within the cluster. Predictable and stable response times are required by many applications even in the case of a node failure. While Cassandra guarantees high availability, the influence of a node failure on the system performance is still unclear.

In this paper, we therefore focus on the availability and fault tolerance of Cassandra. We analyze the impact of a node outage within a Cassandra cluster on the throughput and latency for different workloads. Our results show that Cassandra is well suited to achieve high availability while preserving table response times in case of a node failure. Especially for read intensive applications that require high availability, Cassandra is a good choice.

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Notes

  1. 1.

    See http://cassandra.apache.org.

  2. 2.

    YCSB Cassandra binding based on CQL: https://github.com/jbellis/YCSB.

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Authors and Affiliations

  1. Frankfurt Big Data Lab, Goethe University Frankfurt am Main, Frankfurt, Germany

    Marten Rosselli, Raik Niemann, Todor Ivanov, Karsten Tolle & Roberto V. Zicari

  2. Accenture Germany, Frankfurt, Germany

    Marten Rosselli

  3. Institute of Information Systems, University of Applied Science Hof, Hof, Germany

    Raik Niemann

Authors
  1. Marten Rosselli
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  2. Raik Niemann
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  3. Todor Ivanov
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  4. Karsten Tolle
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  5. Roberto V. Zicari
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Corresponding author

Correspondence to Marten Rosselli .

Editor information

Editors and Affiliations

  1. Technical University of Berlin, Berlin, Germany

    Tilmann Rabl

  2. Cisco Systems, Inc., San Jose, California, USA

    Raghunath Nambiar

  3. University of California at San Diego, La Jolla, California, USA

    Chaitanya Baru

  4. Ampool, Inc., Santa Clara, California, USA

    Milind Bhandarkar

  5. Oracle Corporation, Redwood Shores, California, USA

    Meikel Poess

  6. Indian Institute of Public Health, Hyderabad, India

    Saumyadipta Pyne

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Rosselli, M., Niemann, R., Ivanov, T., Tolle, K., Zicari, R.V. (2016). Benchmarking the Availability and Fault Tolerance of Cassandra. In: Rabl, T., Nambiar, R., Baru, C., Bhandarkar, M., Poess, M., Pyne, S. (eds) Big Data Benchmarking. WBDB WBDB 2015 2015. Lecture Notes in Computer Science(), vol 10044. Springer, Cham. https://doi.org/10.1007/978-3-319-49748-8_5

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  • DOI: https://doi.org/10.1007/978-3-319-49748-8_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49747-1

  • Online ISBN: 978-3-319-49748-8

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