Efficient In-memory Data Management: An Analysis

Summary: Comparative analysis of Memcached, Redis, and Spark RDD for in-memory data management across analytics and key-value workloads. TCP/IP bottlenecks in Memcached/Redis; RDD startup hinders random gets; defines features for efficient in-memory systems. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 11147
Venue: VLDB
Year: 2014
Pagerank: 5.7008974e-05
Overall Rank: 7,105 | 51.26%
DOI: 10.14778/2733004.2733009

Incoming Non-self Citations Over Time

Authors

1. Hao Zhang (National University of Singapore)
2. Bogdan Marius Tudor (National University of Singapore)
3. Gang Chen (Zhejiang University)
4. Beng Chin Ooi (National University of Singapore)

BibTeX Citation

@article{zhang_vldb14,
        title = {{Efficient In-memory Data Management: An Analysis}},
        author = {Zhang, Hao and Tudor, Bogdan Marius and Chen, Gang and Ooi, Beng Chin},
        journal = {PVLDB},
        series = {{VLDB} '14},
        volume = {7},
        number = {10},
        pages = {833--844},
        doi = {10.14778/2733004.2733009},
        url = {https://doi.org/10.14778/2733004.2733009},
        year = {2014}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
2,498	Mega-KV: A Case for GPUs to Maximize the Throughput of In-Memory Key-Value Stores	2015	VLDB	8.5016595e-05

Outgoing Citations (Sorted by Pagerank)

Showing 0 of 0 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank

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