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Distributed search system based on Elasticsearch framework

A distributed search and framework technology, applied in database management systems, network data retrieval, other database retrieval, etc., can solve the problems of difficult development, inability to apply multiple databases, and high learning costs, so as to improve development efficiency and improve data The effect of introducing performance and reducing learning costs

Pending Publication Date: 2019-12-10
中电万维信息技术有限责任公司
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] However, Elasticsearch only provides an interface for data import and search, and does not solve a series of problems from data source to application end. Moreover, the original search interface of Elasticsearch is relatively complicated, and the learning cost is relatively high. It is difficult for developers to master it. High difficulty
Existing search data only supports a single database and cannot be applied to multiple databases

Method used

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  • Distributed search system based on Elasticsearch framework

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Effect test

Embodiment 1

[0017] The system includes the following modules:

[0018] Hive data sources provide data sources and support for upper-layer services.

[0019] The data is imported into the Java database, which is responsible for importing the data from the data source into the Elasticsearch cluster. Connect to the Hive data source and call the API interface to import the data into a specific index.

[0020] The Elasticsearch cluster provides basic retrieval services.

[0021] The web application is responsible for invoking the search service interface to realize the visualized system business interface.

Embodiment 2

[0023] The system includes the following modules:

[0024] Oracle data sources provide data sources and support for upper-layer services.

[0025] The data is imported into the Java database, which is responsible for importing the data from the data source into the Elasticsearch cluster. Connect to the Oracle data source and call the API interface to import data into a specific index.

[0026] The Elasticsearch cluster provides basic retrieval services.

[0027] The web application is responsible for invoking the search service interface to realize the visualized system business interface.

Embodiment 3

[0029] The system includes the following modules:

[0030] Hadoop, Hive, Oracle, and Mysql databases provide data sources and support for upper-layer services.

[0031] The data is imported into the Java database, which is responsible for importing the data from the data source into the Elasticsearch cluster. Connect to hadoop, Hive, Oracle and Mysql databases at the same time and call the API interface to import data into specific indexes.

[0032] The Elasticsearch cluster provides basic retrieval services.

[0033] The web application is responsible for invoking the search service interface to realize the visualized system business interface.

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Abstract

The invention belongs to the field of big data infrastructure, and relates to a distributed search system, in particular to a distributed search system based on an Elasticsearch framework. According to the distributed search system based on the Elasticsearch framework, provided by the invention, the problem of real-time search query under the condition of large data volume is solved. Data is imported into a Java database, an API interface and an external configuration file, a data source, a target cluster and an index are flexibly matched, and universality and maintainability are achieved. Thesource data layer is connected with the support connection layer through the data aggregation layer, the support connection layer is connected with the data application layer, the data import performance is greatly improved through the connection, and the data import speed reaches about 25,000,000 pieces per hour. Using the API interface, the search interface is closer to a specific service, webend development is decoupled from Elasticsearch, the learning cost is reduced, and the development efficiency is greatly improved.

Description

technical field [0001] The invention belongs to the field of big data infrastructure, and relates to a distributed search system, in particular to a distributed search system based on an Elasticsearch framework. Background technique [0002] Elasticsearch is currently the mainstream full-text search framework, which solves the problem of real-time full-text search under the condition of large amounts of data, and supports the json parameter interface based on the get / post method, as well as a variety of visual plug-ins and tokenizers, making search technology easier to be applied in IT application systems. [0003] However, Elasticsearch only provides an interface for data import and search, and does not solve a series of problems from data source to application end. Moreover, the original search interface of Elasticsearch is relatively complicated, and the learning cost is relatively high. It is difficult for developers to master it. High difficulty. Existing search data ...

Claims

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Application Information

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IPC IPC(8): G06F16/953G06F16/25G06F16/2458
Inventor 王志鹏吕伟
Owner 中电万维信息技术有限责任公司
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