Data Partitioning in Trino: Best Practices

Trino, formerly known as PrestoSQL, offers powerful capabilities for distributed querying across large datasets. However, to leverage its full potential, understanding and implementing best practices for data partitioning is essential. In this article, we’ll explore the best practices for data partitioning in Trino, accompanied by detailed examples and outputs.

Understanding Data Partitioning in Trino

Data partitioning involves dividing datasets into smaller, manageable subsets based on specific criteria, such as date ranges, regions, or categories. Trino utilizes data partitioning to distribute query processing tasks across nodes efficiently.

Best Practices for Data Partitioning in Trino

1. Partitioning by Date: When dealing with time-series data, partitioning by date can significantly improve query performance, allowing for efficient filtering and aggregation based on time intervals.
2. Partitioning by Key: Partitioning data based on a unique key, such as customer ID or product ID, enables parallel processing and faster lookup operations.
3. Avoid Over-Partitioning: While partitioning is beneficial, excessive partitioning can lead to increased metadata overhead and slower query performance. Strive for a balance between granularity and efficiency.
4. Utilize Dynamic Partitioning: Trino supports dynamic partitioning, allowing partitions to be created automatically based on query predicates. This dynamic approach reduces manual partition management efforts.

Example: Partitioning by Date

Let’s consider an example where we have a dataset of sales transactions partitioned by date. We’ll create a table partitioned by date and demonstrate how querying specific date ranges improves performance.

-- Create a partitioned table
CREATE TABLE sales (
    id INT,
    product VARCHAR,
    amount DECIMAL,
    sale_date DATE
)
WITH (
    partitioned_by = ARRAY['sale_date']
);

-- Insert data into the partitioned table
INSERT INTO sales VALUES
    (1, 'Product A', 100.0, DATE '2023-01-01'),
    (2, 'Product B', 150.0, DATE '2023-01-02'),
    (3, 'Product C', 200.0, DATE '2023-01-03');

-- Query sales for a specific date range
SELECT * FROM sales WHERE sale_date BETWEEN DATE '2023-01-01' AND DATE '2023-01-02';

 id | product   | amount | sale_date 
----+-----------+--------+-----------
  1 | Product A | 100.0  | 2023-01-01
  2 | Product B | 150.0  | 2023-01-02