Category Archives: database

Elasticsearch – Advance Query [DSL Query Context]

DSL Query Context

The exists checks if the field email exists under the professor not that if there is any value exists or not.

Under the “must”…

Elasticsearch – Full Text Queries

In the previous section, we went through a number of term level queries, which are used for exact matches. While we can use term level queries for searching for long…

Elasticsearch – Term Level Queries

These are most commonly used to query structured data such as dates and numbers. We can use term level queries to query text fields too. But searching a description field…

Elasticsearch – Searching and Querying

Searching with Request URI The query DSL is the most common approach. The way you issue a search request is to send a get request to the search API, then…

Elasticsearch – Analyzers in Mapping and Adding Analyzers to existing Indices

Let’s see how we can use analyzers in field mappings. All it takes is an analyzer parameter within a field mapping with the name of an analyzer.

As we…

Elasticsearch – Analyzer Part 2

Configure the built-in tokenizer We can configure the built-in analyzers, some of them can be configured through parameters. In the following example, we will configure the standard analyzer to remove…

Elasticsearch – Analysis Process

All this process does is tokenizing and normalizing a block of text. This is done to make the text easier to search. We have full control over the analysis process. The analyzed term is stored within the inverted index. That means whenever we perform search queries we are searching through the results of the analysis process and not the document.

Elasticsearch – Configuring and Managing Documents in Kibana

After kibana is up and running if you access http://localhost:5601, we will see the system is asking us to configure the index pattern. Here you can enter a pattern for…