MATCH

Operators

The AGAINST expression consists of a mix of text with zero or more of the following operators.

Stopwords

Certain words are ignored by full-text search due to their commonality resulting in less relevant results. These are called stopwords. SingleStore’s default list of stopwords is as follows:

a, an, and, are, as, at, be, but, by, for, if, in, into, is, it, no, not, 
of, on, or, such, that, the, their, then, there, these, they, this, to, was, will, with

Wildcard Support

Single and multiple character wildcard searches within single terms are supported (not within phrase queries).

To perform a single character wildcard search use the ? symbol and to perform a multiple character wildcard search use the * symbol.

The single character wildcard search looks for terms that match that with the single character replaced. For example, to search for text or test you can use the search: te?t

Multiple character wildcard searches looks for zero or more characters. For example, to search for test, tests or tester, you can use the search: test* You can also use the wildcard searches in the middle of a term. te*t

Special Characters

Depending on the use case, FULLTEXT matching may not always be compatible with match expressions involving special characters. This is because the default tokenizer for SingleStore’s FULLTEXT removes special characters from the search. The current list of special characters is:

+ - && || ! ( ) { } [ ] ^ " ~ * ? : \

Relevancy Score

The relevancy score of an expression in a MATCH statement denotes the ranking of the expression based on the following factors:

• Number of times an expression appears in a column. More occurrences of an expression in the matched column(s) increases its relevancy score.

• Rarity of the expression. Rare words have a higher relevancy score than commonly used words.

• The length of the column containing the expression. A column with a short expression has a higher relevancy score than a column with a long expression.

Create a Table with a Full-Text Index

CREATE TABLE articles (
    id INT UNSIGNED,
    year int UNSIGNED,
    title VARCHAR(200),
    body TEXT,
    SORT KEY (id),
    FULLTEXT (title, body));

Use a SQL Predicate

SELECT count(*)
    FROM articles
    WHERE year = 2017 AND MATCH (body) AGAINST ('memsql');

Search for the Word database across Two Columns

SELECT * FROM articles
    WHERE MATCH (title,body)
    AGAINST ('database');

Use a Single Operator

SELECT title
    FROM articles  
    WHERE MATCH (title) AGAINST ('Database OR "Business Intelligence"');

Use Multiple Operators

The following example returns the title of an article that contains either database or Business Intelligence and the string real-time analytics in the body. As '-' is a special character, results for both real time and real-time will be included.

SELECT title
    FROM articles  
    WHERE MATCH (title) AGAINST ('Database OR "Business Intelligence"')
    AND MATCH(body) AGAINST ("real-time analytics");

Use a Wildcard

This example uses the wildcard ’*’ to return all articles with titles starting with the word Journal such as Journalism, Journalist, Journals, and so on.

SELECT *
    FROM articles  
    WHERE MATCH(title) AGAINST ('Journal*');

Create a Relevance Score as an Output Column

SELECT id, title, MATCH(body) AGAINST ('database') relevance
    FROM articles
    WHERE MATCH(body) AGAINST ('database');

Search with a Specific Relevance Score

SELECT id, title, MATCH(body) AGAINST ('database')
    FROM articles
    WHERE MATCH(body) AGAINST ('database') > .12;

UPDATE or DELETE Queries

UPDATE articles set name = concat(name,".DATABASE") where MATCH(body) AGAINST ('database');

DELETE from articles where MATCH(body) AGAINST ('database');