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Elasticsearch cluster expansion method supporting resource leveling

An extension method and clustering technology, applied in the direction of resource allocation, special data processing applications, instruments, etc., can solve the problem that the sharding mechanism cannot meet the requirements, cannot fix the number of main shards of the Hash sharding mechanism, etc., and avoid local hot spots. Effect

Active Publication Date: 2018-01-09
XIAMEN MEIYA PICO INFORMATION
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  • Claims
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AI Technical Summary

Problems solved by technology

Therefore, the default sharding mechanism of Elasticsearch cannot meet the requirements, and the number of primary shards in the Hash sharding mechanism cannot be fixed.

Method used

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  • Elasticsearch cluster expansion method supporting resource leveling
  • Elasticsearch cluster expansion method supporting resource leveling
  • Elasticsearch cluster expansion method supporting resource leveling

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Embodiment Construction

[0026] To further illustrate the various embodiments, the present invention is provided with accompanying drawings. These drawings are a part of the disclosure of the present invention, which are mainly used to illustrate the embodiments, and can be combined with related descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, those skilled in the art should understand other possible implementations and advantages of the present invention. Components in the figures are not drawn to scale, and similar component symbols are generally used to denote similar components.

[0027] The present invention will be further described in conjunction with the accompanying drawings and specific embodiments.

[0028] The present invention is realized based on an Elasticsearch cluster and an alias mechanism. Elasticsearch provides an alias mechanism for libraries (index), and libraries (index) with the same alias will be logi...

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Abstract

The invention relates to an Elasticsearch cluster expansion method supporting resource leveling. The method includes steps of S1, mounting Elasticsearch nodes according to server hardwire resource; S2, setting basic parameters of a dynamic cluster and recording some state values of the dynamic cluster, and performing data persistence on the parameters and the dynamic values in format of database or XML into a magnetic disk; S3, creating a thread, detecting file number and space occupancy regularly, and creating zones and adjusting storage strategy dynamically according to system parameters ifa threshold value is reached. According to the invention, data volume of corresponding proportions can be stored according to performance load leveling of a server in an Elasticsearch cluster and automatic creating of bases and zones are supported according a target data volume and the magnetic disk space threshold value, and the cluster is expanded dynamically. Therefore, problems of excessive data volume of one zone and local hot spots during new data insertion are avoided. At the same time, automatic recognition and load leveling on bases and zones of the Elasticsearch cluster can be realized after a new server is added into the cluster.

Description

technical field [0001] The invention relates to an Elasticsearch cluster expansion method supporting balanced resources. Background technique [0002] With the development and progress of society, it has entered the era of big data, and the storage and retrieval of massive data has been applied to various fields. Among them, full-text search is one of the commonly used functions, which is similar to the query effect of Baidu and Taobao. Elasticsearch is the most used enterprise-level search application server in full-text search, and has provided solutions for large enterprises such as Acerson, Blizzard Entertainment, Dell, and eBay. It has the characteristics of rich functions, near real-time retrieval, cluster support, etc., and is free and open source. [0003] For the storage of massive data, Elasticsearch supports cluster mode, and multiple data nodes can be deployed on each server. Each library can be divided into multiple primary shards, and each primary shard is s...

Claims

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Application Information

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IPC IPC(8): H04L29/08G06F9/50G06F17/30
Inventor 曾超温若辉江汉祥吴金汉
Owner XIAMEN MEIYA PICO INFORMATION
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