An optimized method of HDFS for massive small files storage

Jing, Weipeng; Tong, Danyu; Chen, Guangsheng; Zhao, Chuanyu; Zhu, Liangkuan

doi:10.2298/csis171015021j

Cited by 14 publications

(5 citation statements)

References 18 publications

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“…Hadoop's dynamic hashing and order-preserving operations can efficiently handle thousands of small files. Weipeng Jing et al [14] suggested a dynamic queue method (DQSF) based on the analytical hierarchical process, in which small files are classified and processed according to their size. Jian-Feng Peng et al [15] tackle the HDFS small file issue by merging related small files and caching frequently accessed data to reduce small file access time.…”

Section: Related Workmentioning

confidence: 99%

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A Dynamic Repository Approach for Small File Management With Fast Access Time on Hadoop Cluster: Hash Based Extended Hadoop Archive

Sharma

Barwar

Afthanorhan³

et al. 2022

IEEE Access

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Small file processing in Hadoop is one of the challenging task. The performance of the Hadoop is quite good when dealing with large files, because they require lesser meta data and consume less memory. But while dealing with enormous amount of small files, meta data grows linearly and Name Node memory got overloaded hence overall performance of the Hadoop degrades. This paper presents a dual merge technique HB-EHA (Hash Based-Extended Hadoop Archive) that will resolve the small file issue of Hadoop and provide an excellent solution for massive small files that are generated in the health care management applications. The proposed technique merges the small files using two level compaction, therefore size of meta data at name node get reduced and less memory will be used. The indexing will be carried out over the archives and files can be accessed after merging in real time. Index files in the proposed approach can read partially that improves the name node memory usage and also offers the file appending capability in the existing archive. The proposed technique first creates Hadoop archive from the small files and then uses two special hash functions i.e. SSHF (Scalable-Splittable Hash Function) and HT-MMPHF (Hollow Trie Monotone Minimal Perfect Hash Function), SSHF is used to dynamically distribute the archives meta-data to the associated slave index files, and these slave index files will be further written to the final index files, the order of the meta-data in final index file will be preserved by the HT-MMPHF. The evaluation outcome exhibit that proposed technique is 13% & 17% faster than HDFS with caching enabled and disabled respectively, and 38% & 47% faster than the HAR with caching and without caching respectively. While comparing with map file, proposed technique is 28 & 35 time faster with caching and without caching respectively. HB-EHA is maximum 40% & 28% faster than the HBAF with and without caching respectively.

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Section: Related Workmentioning

confidence: 99%

“…Technique to handle Massive Small Files [14] -DQFS: Dynamic queues are used for the variable sized small files.…”

Section: Fast Access Containermentioning

confidence: 99%

A Dynamic Repository Approach for Small File Management With Fast Access Time on Hadoop Cluster: Hash Based Extended Hadoop Archive

Sharma

Barwar

Afthanorhan³

et al. 2022

IEEE Access

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“…Their solution includes a file merging algorithm based on temporal continuity, an index structure to retrieve small files, and a prefetching mechanism to improve file reading and writing. Some of these archiving systems, such as LHF [34], DQSF [21], [19], [9], before merging the files classify them or rely on some distributions criteria that help to optimize the storage or access efficiency. In case the proposed solution is built on top HDFS, it is easy to migrate to the latest version of HDFS.…”

Section: Combining Small Files Into Large Filesmentioning

confidence: 99%

Hadoop Perfect File: A fast and memory-efficient metadata access archive file to face small files problem in HDFS

Zhai

Tchaye-Kondi²,

Lin³

et al. 2021

Journal of Parallel and Distributed Computing

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“…Z. Gao proposed an effective merge strategy based hierarchy for improving small file problem on HDFS [19], which makes a radix sort on files set and merges these files orderly. Weipeng Jing [20] and his team aim to correct the problems of IoT and CPS because of small files problem.…”

Section: Related Workmentioning

confidence: 99%

LHF: A New Archive based Approach to Accelerate Massive Small Files Access Performance in HDFS

Tao

Zhai

Tchaye-Kondi

2019

EasyChair Preprints

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As one of the most popular open source projects, Hadoop is considered nowadays as the de-facto framework for managing and analyzing huge amounts of data. HDFS (Hadoop Distributed File System) is one of the core components in Hadoop framework to store big data, especially semi-structured and unstructured data. HDFS provides high scalability and reliability when handling large files across thousands of machines. But the performance will be severely degraded while dealing with massive small files. Although some effort was spent to investigate this well-known issue, existing approaches, such as HAR, SequenceFile, and MapFile, are limited in their ability to reduce the memory consumption of the NameNode and optimize the access performance in the meantime. In this paper, we presented LHF, a solution to handle massive small files in HDFS by merging small files into big files and building a linear hashing based extendable index to speed up the process of locating a small file. The advantages of our approach are (1) it significantly reduces the size of the metadata, (2) it does not require sorting the files at the client side, (3) it supports appending more small files to the merged file afterwards and (4) it achieves good access performance.  A series of experiments were performed to demonstrate the effectiveness and efficiency of LHF as well, which takes less time while accessing files compared with other methods.

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An optimized method of HDFS for massive small files storage

Cited by 14 publications

References 18 publications

A Dynamic Repository Approach for Small File Management With Fast Access Time on Hadoop Cluster: Hash Based Extended Hadoop Archive

A Dynamic Repository Approach for Small File Management With Fast Access Time on Hadoop Cluster: Hash Based Extended Hadoop Archive

Hadoop Perfect File: A fast and memory-efficient metadata access archive file to face small files problem in HDFS

LHF: A New Archive based Approach to Accelerate Massive Small Files Access Performance in HDFS

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