Full-text search technology provided us with the information retrieval tool according to the content of data rather than the external features based on a variety of computer data such text, sound, image as processing object. The core of information search is the full-text retrieval technology. ![]() With the rapid development of Internet and with the explosive growth of Web information, Internet users how to remove the impurities and retained the essence quickly and easily to gain the information they need in the vast ocean of information to become a hot research topic in this field. ![]() This paper introduces us the full-text search engine based on Lucene and full-text retrieval technology, including indexing and system architecture, compares the full-text search of Lucene with the String search retrieval’s response time, the experimental results show that the full text search of Lucene has faster retrieval speed. Keywords: Full Text Search Engine System Architecture Lucene Individual queries include other factors for calculating the relevance score, such as term proximity, fuzziness, and so on.1Computer Science and Technology, Changchun University of Technology, Changchun, ChinaĢChangchun Rural Commercial Bank, Changchun, ChinaģSoftware Vocational and Technical College, Changchun University of Technology, Changchun, ChinaĮmail: Augrevised Septemaccepted October 10, 2012 The values might have some (typically small) inaccuracies as it’s based on summing the samples returned from each shard. The tf, idf, and fieldNorm values are calculated and stored at index time when a document is added or updated. OpenSearch assigns a higher relevance score to a term appearing in a relatively short field. The more often the term appears the lower is the relevance score. How often the term appears within the index (across all the documents). The more times the term occurs the higher is the relevance score. How many times the term appears in a field for a given document. The value shows the result of the calculation, the description explains what type of calculation is performed, and the details shows any subcalculations performed. The explanation object has three properties: value, description, and details. Indicates if the document is a match for the query. Default is true.Ī comma-separated list of source fields to exclude in the query response.Ī comma-separated list of source fields to include in the query response. Whether to include the _source field in the response body. Value used to route the operation to a specific shard. If true, the operation retrieves document fields stored in the index rather than the document’s _source. By default, OpenSearch executes the explain operation on random shards. Available options are _local, which tells the operation to retrieve results from a locally allocated shard replica, and a custom string value assigned to a specific shard replica. Specifies a preference of which shard to retrieve results from. Specifies whether OpenSearch should ignore format-based query failures (for example, querying a text field for an integer). The default field in case a field prefix is not provided in the query string. Indicates whether the default operator for a string query should be AND or OR. ![]() Specifies whether to analyze wildcard and prefix queries. ![]() You can only specify a single index.Ī unique identifier to attach to the document. Snapshot Management in OpenSearch Dashboards.
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