Data processing method and device based on big data

Abstract

The embodiment of the invention provides a data processing method and device based on big data, and the method comprises the steps: carrying out the data cleaning of an original target file under a target directory, and obtaining at least one first target file; according to the file size of each first target file, dividing all the first target files into a plurality of combinations, wherein the sum of the file sizes of all the first target files in each combination does not exceed a first preset reference value, and the number of the combinations is the minimum value of all the possible numbers; performing file merging on all the first target files in each permutation and combination to obtain a second target file, the second target file being correspondingly provided with a data block ofa distributed file system. According to the method and the device, the storage efficiency of mass data is higher, the resource allocation is more reasonable, and the subsequent calculation performanceis better.

Description

technical field [0001] This application relates to the field of data processing, in particular to a data processing method and device based on big data. Background technique [0002] Hadoop is a distributed system infrastructure developed by the Apache Foundation. Users can develop distributed programs without knowing the underlying details of the distribution, and then can make full use of the integrity of the cluster for high-speed computing and storage. The framework of Hadoop The core design is: HDFS (Hadoop Distributed File System, distributed file system) and MapReduce. HDFS provides storage for massive data, while MapReduce provides calculation for massive data. [0003] In related technologies, the storage efficiency of data storage in files is low, and a file with a small amount of data also occupies a separate HDFS data block. The resource allocation is unreasonable, which makes the entire HDFS cluster have too many data blocks, which affects the Operational effi...

AI Technical Summary

Problems solved by technology

[0003] In related technologies, the storage efficiency of data storage in files is low, and a file with a small amount of data also occupies a separate HDFS data block. The resource allocation is unreasonable, which makes the entire HDFS cluster have too many data blocks, which affects the Computational Efficiency of

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