Sorting Movies by Year in a Dataset Using SQL

SQL Filtering: Sorting by Year in a Movie Dataset

When working with datasets that contain mixed data types, such as text strings that may hold numerical values, filtering and sorting can be a challenge. In this post, we’ll explore how to extract the year from a string of text in SQL and use it to filter our movie dataset.

Understanding the Problem

The IMDb dataset contains movies with titles that include the production year, like “Toy Story (1995)”. To sort only the movies produced in 1995, we need to separate the year from the title. We’ll discuss how to achieve this using SQL queries.

SQL Functions: `LIKE`, `REGEXP`, and `SUBSTR`

LIKE Operator

The LIKE operator is used for pattern matching in SQL. It allows us to search for a specified pattern in a column. In our case, we want to find movies with the year “(1995)” at the end of the title.

SELECT * FROM Movies WHERE name LIKE '% (1995)';

The % wildcard matches any characters before and after the specified pattern. The ( ) part is escaped because it has a special meaning in SQL.

However, this approach may not work for all cases, as there might be different formats for representing years, such as “19” or “9”. To improve the query’s effectiveness, we can use other SQL functions like REGEXP or modify our LIKE clause.

REGEXP Operator

The REGEXP operator allows us to search for a pattern using regular expressions. Regular expressions provide a more flexible way of matching patterns than the LIKE operator.

SELECT * FROM Movies WHERE name REGEXP '(1995)';

This query matches any string that contains the literal characters “19” followed by exactly 2 digits, which represents the year in our dataset.

SUBSTR Function

Another approach is to use the SUBSTR function to extract a substring from the movie title. We can assume that the year is always enclosed within parentheses and has at least two characters.

SELECT * FROM Movies WHERE SUBSTR(name, INSTR(name, '(') + 1, LENGTH(name) - INSTR(name, ')')) = '1995';

This query extracts a substring from name starting from the position of the first opening parenthesis (+1) to the position just before the last closing parenthesis. It then checks if this extracted substring is equal to “19” (not “1995”, but we can modify the query as needed).

Advanced Filtering: Using SQL Functions with Complex Patterns

We’ll explore how to use more advanced SQL functions, like REGEXP or LIKE, in combination with other operators.

Regular Expression Patterns

Regular expressions offer a wide range of patterns for matching various types of data. For example:

\d{4} matches exactly 4 digits
[0-9]{4} matches any digit from 0 to 9, exactly 4 times
%$.*?$ matches any characters inside parentheses (non-greedy)

These patterns can be used in SQL queries for more accurate filtering.

SELECT * FROM Movies WHERE name REGEXP '^\d{4}(?= (\\d{4}))$';

This query uses a regular expression pattern to match the title only when followed by another 4-digit number enclosed within parentheses. The ^ symbol denotes the start of the string, \d{4} matches exactly 4 digits, (?= (\\d{4})) is a positive lookahead that checks for the pattern after the year, and $ denotes the end of the string.

Handling Missing or Inconsistent Data

In real-world datasets, you’ll encounter missing or inconsistent data. Here are some strategies to address these issues:

Missing Values: `NULL` or `''`

If there’s a possibility that a movie title might be NULL, we should handle it explicitly in our query.

SELECT * FROM Movies WHERE name IS NOT NULL AND SUBSTR(name, INSTR(name, '(') + 1, LENGTH(name) - INSTR(name, ')')) = '1995';

In this example, we’re checking if the name field is not NULL.

Inconsistent Data: Using `REGEXP` or `LIKE`

If there’s inconsistency in how the year is represented (e.g., “19” vs. “(19)”), it might be more effective to use a regular expression pattern for more precise matching.

SELECT * FROM Movies WHERE SUBSTR(name, INSTR(name, '(') + 1, LENGTH(name) - INSTR(name, ')')) REGEXP '^\d{2}(?= (\\d{4}))$';

This query uses the REGEXP operator to match a pattern where exactly two digits are followed by another four-digit number enclosed in parentheses.

Optimizing Your Query

As your dataset grows, optimize your queries for better performance. Here’s an example of how you might rewrite a query using indexing and subqueries:

-- Create an index on the year column for faster filtering
CREATE INDEX idx_year ON Movies (SUBSTR(name, INSTR(name, '(') + 1, LENGTH(name) - INSTR(name, ')')));

-- Rewrite your main query to use this index
SELECT * FROM Movies WHERE SUBSTR(name, INSTR(name, '(') + 1, LENGTH(name) - INSTR(name, ')')) = '1995';

By creating an index on the year column and rewriting your query, you can reduce the number of rows being scanned, resulting in faster execution times.

Conclusion

In this post, we’ve explored how to sort movies by year using SQL queries. We discussed various techniques for matching patterns within a string, from simple LIKE operators to more advanced REGEXP patterns. By mastering these skills and optimizing your queries, you can efficiently handle large datasets and extract valuable insights.