PostgreSQL Full-Text Search Examples

Any database should have an effective and versatile search capability. Whenever it refers to databases, PostgreSQL is a master of all crafts. It combines all of the things you’ve grown to love with SQL with a slew of non-SQL database functionalities. Any of these Non-SQL functions, such as the JSONB information sort, are fantastic, and you wouldn’t even have to try a different database. Full-Text Search is among the newest Non-SQL features incorporated into PostgreSQL. Is PostgreSQL’s complete-text search completely functional, or would you want a distinct search index? If you can somehow develop a complete text search deprived of adding one more cover of code, it would be a fantastic idea. You’re already acquainted with pattern search in the MySQL database. So, let’s have a look at them first. Open the PostgreSQL command-line shell in your computer system. Write the server title, database name, port number, username, and password for the specific user other than default options. If you need to slog with default considerations, leave all choices blank and hit Enter each option. At the moment, your command-line shell is equipped to work on.

To understand the concept of full-text search, you have to recollect pattern search knowledge via the LIKE keyword. So, let’s assume a table ‘person’ in the database ‘test’ with the following records in it.

>> SELECT * FROM person;

Let’s suppose you want to fetch the records of this table, where the column’ name’ has a character’ i’ in any of its values. Try the below SELECT query while using the LIKE clause in the command-shell. From the output below, you can see that we have only 5 records for this particular character’ i’ in the column ‘name’.

>> SELECT * FROM person WHERE name LIKE '%i%';

Use of Tvsector:

Sometimes it is of no use to use the LIKE Keyword to do a quick pattern search, although the word is there. Maybe you’d be considering using standard expressions, and although this is a feasible alternative, regular expressions are both strong and sluggish. Having a procedural vector for entire words in a text, a vernacular description of those words, is a much more efficient way to address this issue. The concept of complete text search and the data type tsvector was created to respond to it. There are two methods in PostgreSQL that do just what we want:

• To_tvsector: Used to make a list of tokens (ts means for “text search”).
• To_tsquery: Used to search the vector for incidences of specific terms or phrases.

Example 01:

Let’s start with a simple illustration of creating a vector. Suppose you want to make a vector for the string: “Some people have curly brown hair through proper brushing.”. So you have to write a to_tvsector() function along with this sentence in the brackets of a SELECT query as appended below. From the output below, you can see it would yield a vector of references (file positions) for each token, and also where terms with little context, like articles (the) and conjunctions (and, or), are deliberately ignored.

>> SELECT to_tsvector('Some people have curly brown hairs through proper brushing');

Example 02:

Assume you have two documents with some data in both of them. To store this data, now we will be using a real example of generating tokens. Assume you have created a table ‘Data’ in your database ‘test’ with some columns in it using the below CREATE TABLE query. Don’t forget to create a TVSECTOR type column named ‘token’ in it. From the output below, you can have a look at the table that has been created.

>> CREATE TABLE Data (Id SERIAL PRIMARY KEY, info TEXT, token TSVECTOR);

Now, it turns for us to add the overall data of both the documents in this table. So try the below INSERT command in your command-line shell to do so. Finally, the records from both the documents have been successfully added into the table ‘Data’.

>> INSERT INTO Data (info) VALUES ('Two wrongs can never make one right.'), ('He is the one who can play football.'), ('Can I play a part in this?'), ('The pain inside of one cannot be understood'), ('Bring peach in your life);

Now you have to colonize the token column of both the documents with their specific vector. Ultimately, a simple UPDATE query will fill the tokens column by their corresponding vector for each file. So, you have to execute the stated below query in the command-shell to do so. The output is showing that the update has been finally made.

>> UPDATE Data f1 SET token = to_tsvector(f1.info) FROM Data f2;

Now that we have it all in place let’s return to our illustration of “can one” with a scan. To to_tsquery with AND operator, as previously said, makes no difference between the files’ locations in the files as shown from the output stated below.

>> SELECT Id, info FROM Data WHERE token @@ to_tsquery('can & one');

Example 04:

To find words that are “next to” one another, we will try the very same query with the ‘<->’ operator. The change is displayed in the output below.

>> SELECT Id, info FROM Data WHERE token @@ to_tsquery('can <-> one');

Here is an example of no immediate word next to another.

>> SELECT Id, info FROM Data WHERE token @@ to_tsquery('one <-> pain');

Example 05:

We will find the words that aren’t immediately next to one another by using a number in the distance operator to reference distance. The proximity between ‘bring’ and ‘life is 4 words apart from the displayed image.

>> SELECT * FROM Data WHERE token @@ to_tsquery('bring <4> life');

To check proximity between the words for almost 5 words is appended below.

>> SELECT * FROM Data WHERE token @@ to_tsquery('wrong <5> right');

Conclusion:

Finally, you have done all the simple and complicated examples of Full-text search using the To_tvsector and to_tsquery operators and functions.

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