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Abstract: In this Exa byte scale era, data increases at an exponential rate. This is in turn generating a massive amount of metadata in the file system. hadoop is the most widely used framework to deal with big data. Due to this growth of huge amount of metadata, however, the efficiency of hadoop is questioned numerous times by many researchers. Therefore, it is essential to create an efficient and scalable metadata management for hadoop. Hash-based mapping and subtree partitioning are suitable in distributed metadata management schemes. Subtree partitioning does not uniformly distribute workload among the metadata servers, and metadata needs to be migrated to keep the load roughly balanced. Hash-based mapping suffers from a constraint on the locality of metadata, though it uniformly distributes the load among nameNodes, which are the metadata servers of hadoop. In this paper, we present a circular metadata management mechanism named dynamic circular metadata splitting (DCMS). DCMS preserves metadata locality using consistent hashing and locality-preserving hashing, keeps replicated metadata for excellent reliability, and dynamically distributes metadata among the nameNodes to keep load balancing. nameNode is a centralized heart of the hadoop. Keeping the directory tree of all files, failure of which causes the single point of failure (SPOF). DCMS removes hadoop’s SPOF and provides an efficient and scalable metadata management. The new framework is named ‘Dr. hadoop’ after the name of the authors.

This paper aims to address the bottlenecks (limited scalability, single point of failure) of the centralized metadata management in the HDFS of Hadoop framework by proposing distributed peer-to-peer metadata cluster management. The metadata is distributed across multiple metadata servers (namenodes) through the consistent hashing technique to achieve load balancing, and the locality-preserving hashing to achieve data-locality. The proposed work is called Dr. Hadoop, which is compared with the vanilla Hadoop using two data sets from Yahoo and Microsoft, and Dr. Hadoop shows considerable performance improvement. This work is definitely meaningful, as the centralized metadata management of HDFS has been an inherent scalability and reliability problem. This work tries to address the problems, and will be beneficial to the data processing community in the Internet domains in the era of big data.

Dr. Hadoop: Hadoop的一种无限可扩展元数据管理机制—小象如何不老？

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