elasticsearch fuzzy search example

Full Text SearchNow that we've discussed some simple use cases for searching structured data, it's time to start exploring full-Text search-How to find the most relevant documents in a whole-word field.The two most important aspects of a full-text search are:Correlation degree (relevance)The results of the query are based on their ability to sort the relevance of the query itself, and the correlation can be

comparison and tests, it is found that the query performance of SOLR during index creation is very poor, because SOLR will produce Io blocking during index creation, resulting in a decline in search performance, but elasticsearch will not, because it first saves the index content to the memory, and persists the index to the hard disk when the memory is insufficient. At the same time, it also has a queue, t

Elasticsearch is an open source distributed real-time search and analysis engine that supports cloud services. It is based on the Apache Lucene search engine's class library and provides full-text search capabilities, multi-lingual support, a dedicated query language, support for geolocation services, context-based

[Elasticsearch] distributed searchDistributed search This article is translated from the Distributed Search Execution Chapter in the Elasticsearch official guide. Before proceeding, let's take a look at how the search is executed in a distributed environment. This process is

The configuration file is located in the%es_home%/config/elasticsearch.yml file, and you can configure it by opening it with EditPlus.All configurations can use environment variables, for example: Node.rack: ${rack_env_var} Indicates that there is a Rack_env_var variable in the environment variable.The following is a list of Elasticsearch configurable items:1. Cluster name, default is Elasticsearch:cluster.

number of parts is too large, opening more files during retrieval may also lead to communication between multiple servers. If the number of parts is too small, the retrieval speed is slow because the index of a single part is too large. Before determining the number of shards, You need to test a single service single index single shard. For example, when I created an index on a machine in the IBM-3650, the index had only one partition, and the retrie

Rest interface Now that we have a functioning node (and cluster), the next step is to understand how to communicate with it. Fortunately, Elasticsearch provides a very comprehensive and powerful rest API that allows you to interact with your cluster using this REST API. Here are a few things you can do with this API: 1, check your cluster, node and index health status and various statistical information2. Manage your clusters, nodes, index data, and

can now search for a single word in the field, which is good, but sometimes you want to match several words or phrases exactly (phrases). For example, we want to query employee records that contain both "rock" and "climbing" (and are adjacent).To do this, we simply match change the query to a match_phrase query:GET /megacorp/employee/_search{ "query" : { "match_phrase" : { "about""rock

1, first need to install good elasticsearch and Elasticsearch-analysis-ik word breaker2. Configure IK synonymsThe Elasticsearch comes with a synonym filter named synonym. In order for IK and synonym to work together, we need to define a new analyzer and use IK as the tokenizer,synonym filter. It sounds complicated, but the only thing to do is add a configuration.

Es default is the mapping of dynamically created indexes and index types. This is equivalent to not having to define the schema in SOLR, and it is convenient to index the file without specifying the index rules for each field. But sometimes the convenience represents the inflexible. For example, es default a field is to do participle, but we sometimes want to search matches the entire field but not. If ther