Two-Way Refinement Approach For Extra Corrupted Shard Removal In Elastic Search With Lucene And Translog
Main Article Content
Abstract
Elasticsearch is most popular search engine which is based on Apache Lucene. Many advantages are identified with the Elasticsearch. For every inserted document or record the Elasticsearch’s auto-generated id values are created. But this may leads to increasing the duplicate values. To overcome this various duplicate methods are introduced by various researchers. Indexing is very important for the elastic search removing duplicates in elastic is based on indexing. For this Lucene Index and Translog are used. This can be used for all types of data in Elastic search. Many researchers working on removing duplicates and shards from the data. But still there is lot of corrupted shards is present in output. To overcome this, A Two way Refining Algorithm (TWRA) is introduced to remove the extra corrupted shards for extra refinement of data. The TWRA consists of two refinements of data such as Advanced Advanced Data Cleaning and Advanced Data Filtering Algorithm. Experimental results show the performance of the proposed methodology.
Downloads
Metrics
Article Details
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
