Indexes in PostgreSQL enhance database performance by allowing faster retrieval of specific rows. They work like an index in a book, providing quick references to relevant data. Here are the main index types:

​
B-Tree Index

B-Tree indexes play a crucial role in enhancing database performance by allowing faster retrieval of specific rows. Imagine them as the index pages in a book, providing quick references to relevant data.

1. Structure of B-Tree Indexes:

  • B-tree indexes are organized as balanced tree structures.
  • Each level of the tree acts like a doubly-linked list of pages.
  • The index starts with a metapage at the beginning of the first segment file.
  • All other pages are either leaf pages (the lowest level) or internal pages.

2. Behavior and Use Cases:

  • B-trees are versatile and widely applicable:
    • Equality and Range Queries: They excel in handling equality and range queries. Common operators include =, <, >, BETWEEN, and IN.
    • NULL Conditions: B-trees can handle IS NULL or IS NOT NULL conditions.
    • Pattern Matching: When anchored to the beginning of a string, they efficiently support pattern matching using LIKE or ~.

3. Practical Examples: Let’s create an example employees table and demonstrate B-tree index usage:

CREATE TABLE employees (
    emp_id SERIAL PRIMARY KEY,
    emp_name VARCHAR(255) NOT NULL,
    emp_salary NUMERIC(10, 2) NOT NULL
);

-- Insert some data
INSERT INTO employees (emp_name, emp_salary) VALUES
    ('Alice', 60000.00),
    ('Bob', 75000.00),
    ('Charlie', 90000.00);

-- Create an index on emp_name
CREATE INDEX employees_name ON employees(emp_name);

-- Query using the index
SELECT * FROM employees WHERE emp_name = 'Bob';

The output will be:

 emp_id | emp_name | emp_salary
--------+----------+------------
      2 | Bob      |   75000.00

​
Hash Index

Hash indexes use a hash function to map indexed column values to 32-bit hash codes. These indexes are optimized for simple equality comparisons (using the = operator). Here’s how they work:

  1. Structure:
    • Hash indexes store only the hash value of the data being indexed.
    • No restrictions on the size of the indexed column.
    • Support only single-column indexes.
    • Do not allow uniqueness checking.
  2. Use Cases:
    • Ideal for scenarios where exact matches are common.
    • Not suitable for range queries or pattern matching.
  3. Performance Considerations:
    • Fast for equality lookups.
    • Minimal overhead during data insertion.
    • Not automatically maintained (unlike B-tree indexes).

​
Example: Employees Table

Let’s create an example employees table and demonstrate Hash index usage:

CREATE TABLE employees (
    emp_id SERIAL PRIMARY KEY,
    emp_name VARCHAR(255) NOT NULL,
    emp_salary NUMERIC(10, 2) NOT NULL
);

-- Insert some data
INSERT INTO employees (emp_name, emp_salary) VALUES
    ('Alice', 60000.00),
    ('Bob', 75000.00),
    ('Charlie', 90000.00);

-- Create a Hash index on emp_name
CREATE INDEX employees_name_hash ON employees USING HASH (emp_name);

-- Query using the index
SELECT * FROM employees WHERE emp_name = 'Bob';

Output:

 emp_id | emp_name | emp_salary
--------+----------+------------
      2 | Bob      |   75000.00

​
GiST Index

GiST indexes are a versatile type of index that can handle complex data types, such as geometric shapes, full-text search, and network addresses. They are implemented using a custom data structure optimized for searching large amounts of data1. Here are some key points:

​
Examples of GiST Indexes

Let’s explore some examples using tables related to employees. We’ll create a sample table, insert data, and demonstrate how GiST indexes work.

​
Example 1: Geometric Shapes

Suppose we have an employees table with a location column representing the employees’ office locations (stored as geometric points). We want to efficiently query employees based on their proximity to a specific location.

  1. Table Creation:

    CREATE TABLE employees (
    id SERIAL PRIMARY KEY,
    name VARCHAR(100),
    location POINT
);

  2. Insert Data:

    INSERT INTO employees (name, location)
VALUES
    ('Alice', POINT(1, 2)),
    ('Bob', POINT(3, 4)),
    -- ... (more data)
;

  3. Create GiST Index:

    CREATE INDEX idx_location ON employees USING GIST (location);

  4. Query Using Index:

    SELECT name
FROM employees
WHERE location <-> POINT(2, 3) < 1;

    This query retrieves employees within 1 unit of distance from the point (2, 3).

​
Performance Considerations

  • GiST indexes are powerful but may have higher insertion and maintenance costs compared to B-tree indexes.
  • Choose the appropriate operator class and indexing strategy based on your data type and query requirements.

​
SP-GiST

SP-GiST (Spatial Generalized Search Tree) indexes are a versatile index type offered by PostgreSQL. They are designed for complex, non-rectangular data types and work especially well with geometrical and network-based data. Here are some key points:

  1. Infrastructure: SP-GiST indexes support various kinds of searches, similar to GiST indexes. They permit the implementation of a wide range of different non-balanced disk-based data structures, such as quadtrees, k-d trees, and radix trees (tries) 1.
  2. Use Cases:
  3. Performance Considerations:

​
Example: Employee Table

Let’s create an example employee table and demonstrate how to use SP-GiST indexes.

​
1. Create the Employee Table

CREATE TABLE employees (
    emp_id SERIAL PRIMARY KEY,
    emp_name VARCHAR(100),
    emp_location POINT
);

​
2. Insert Sample Data

INSERT INTO employees (emp_name, emp_location)
VALUES
    ('Alice', POINT(10, 20)),
    ('Bob', POINT(15, 25)),
    ('Charlie', POINT(30, 40));

​
3. Create an SP-GiST Index on emp_location

CREATE INDEX idx_emp_location ON employees USING spgist(emp_location);

​
4. Query Using the Index

​
Find Employees Near a Given Point

SELECT emp_name
FROM employees
WHERE emp_location <-> POINT(12, 22) < 5;

This query finds employees whose location is within 5 units of the point (12, 22).

​
Output:

emp_name

Alice

Bob

Suppose we have an IP address range column:

CREATE TABLE network_devices (
    device_id SERIAL PRIMARY KEY,
    device_name VARCHAR(100),
    ip_range inet
);

INSERT INTO network_devices (device_name, ip_range)
VALUES
    ('Router A', '192.168.1.0/24'),
    ('Switch B', '10.0.0.0/16'),
    ('Firewall C', '172.16.0.0/20');

CREATE INDEX idx_ip_range ON network_devices USING spgist (ip_range);

SELECT device_name
FROM network_devices
WHERE ip_range >> '192.168.1.42';

This query finds devices whose IP range includes the address ‘192.168.1.42’.

​
Output:

device_name

Router A

​
GIN Index

A GIN index is designed for efficiently handling composite data values, such as arrays or JSON objects. Here are the key points:

  1. What is a GIN Index?
  2. Use Cases for GIN Indexes:
  3. Example 1: Basic Query Using GIN Index

  • Suppose we have an employees table with a column skills (an array of skills). Let’s create an employees table:

    CREATE TABLE employees (
    id SERIAL PRIMARY KEY,
    name VARCHAR(100),
    skills TEXT[] -- Array of skills
);

  • Insert some data:

    INSERT INTO employees (name, skills)
VALUES
    ('Alice', ARRAY['Java', 'SQL']),
    ('Bob', ARRAY['Python', 'JavaScript']);

  • To create a GIN index on the skills column:

    CREATE INDEX idx_gin_skills ON employees USING gin(skills);

  • Query using the GIN index:

    SELECT name FROM employees WHERE skills @> ARRAY['Java'];

  • Output: Alice

  1. Example 2: Searching for Multiple Skills

    • Query to find employees with both Java and SQL skills:

      SELECT name FROM employees WHERE skills @> ARRAY['Java', 'SQL'];

    • Output: Alice

  2. Example 3: Partial Match

    • Query to find employees with any of the specified skills:

      SELECT name FROM employees WHERE skills && ARRAY['Python', 'JavaScript'];

    • Output: Bob

  3. Performance Considerations:

    • GIN indexes are efficient for array-based queries but may have overhead during updates.
    • Consider the trade-off between query performance and update cost.
    • Regularly vacuum the GIN index to maintain performance.

​
BRIN Index

  • BRIN stands for Block Range Index.
  • Designed for handling very large tables with columns that have natural correlation to their physical location within the table.
  • Works in terms of block ranges (or “page ranges”).
  • Each block range groups physically adjacent pages in the table.
  • Summary information is stored by the index for each block range.
  • Lossy: BRIN indexes can satisfy queries via regular bitmap index scans but are lossy, meaning the query executor rechecks tuples and discards those not matching query conditions.
  • Size of block range determined at index creation time by pages_per_range storage parameter.

​
Use Cases for BRIN Indexes

  1. Time-Series Data: Ideal for tables with a timestamp column (e.g., sales orders, logs).
  2. Geospatial Data: Useful for tables with spatial data (e.g., ZIP codes, geographical coordinates).

​
Example: Employee Table

Let’s create an employee table and demonstrate BRIN index usage.

​
1. Create Employee Table

CREATE TABLE employees (
    emp_id SERIAL PRIMARY KEY,
    emp_name VARCHAR(100),
    hire_date DATE
);

​
2. Insert Sample Data

INSERT INTO employees (emp_name, hire_date)
VALUES
    ('Alice', '2022-01-15'),
    ('Bob', '2021-03-10'),
    ('Charlie', '2020-11-20');

​
3. Create BRIN Index on hire_date

CREATE INDEX idx_employees_hire_date_brin
ON employees USING brin (hire_date);

​
4. Query Using BRIN Index

-- Find employees hired after 2021-01-01
SELECT emp_name
FROM employees
WHERE hire_date >= '2021-01-01';

​
Output

 emp_name
----------
 Alice
 Bob
(2 rows)

Remember that BRIN indexes are most effective when dealing with large tables and specific column types.

For more details, refer to the official PostgreSQL documentation.

Was this page helpful?

ArraysJoins