5 Pro Tips for Fast PostgreSQL Computed Timestamps (2025)

CREATE TABLE products (
  id uuid PRIMARY KEY DEFAULT gen_random_uuid(),
  name text NOT NULL,
  -- Let PostgreSQL handle the creation timestamp
  created_at timestamptz NOT NULL DEFAULT NOW(),
  -- A trigger can handle the updated_at timestamp
  updated_at timestamptz NOT NULL DEFAULT NOW()
);

Tip 3: Leverage Generated Columns for Derived Values

What if you have a timestamp that depends on another? For example, an `expires_at` field that should always be 30 days after `created_at`. The old way involved application logic or complex triggers. The modern, elegant solution is a generated column.

Introduced in PostgreSQL 12 and now a mature feature, generated columns compute their values from other columns in the same row. They are declarative, efficient, and enforce data integrity at the database level.

CREATE TABLE user_sessions (
  id uuid PRIMARY KEY DEFAULT gen_random_uuid(),
  user_id uuid NOT NULL REFERENCES users(id),
  created_at timestamptz NOT NULL DEFAULT NOW(),
  -- This column is automatically computed and stored!
  expires_at timestamptz NOT NULL GENERATED ALWAYS AS (created_at + INTERVAL '30 days') STORED
);

The magic keyword here is STORED. This tells Postgres to compute the value once upon insert (or update of `created_at`) and save it to disk like a regular column. This means reading the `expires_at` value is instantaneous—there's no computation penalty on `SELECT`. Furthermore, you can directly index a `STORED` generated column, making queries like `WHERE expires_at < NOW()` incredibly fast.

Tip 4: Indexing for Time-Traveling Queries

Storing timestamps is one thing; querying them effectively is another. If you're running queries that filter on a function of a timestamp column, a standard index might not be used at all.

Functional Indexes to the Rescue

Consider this common query to find all orders from today:

-- This query cannot use a simple index on `created_at`
SELECT * FROM orders WHERE date_trunc('day', created_at) = date_trunc('day', NOW());

Because the index is on the raw `created_at` values, PostgreSQL can't use it to look up the result of `date_trunc`. It has to scan the table and compute the function for every row. The fix is a functional index.

-- Create an index on the *result* of the function
CREATE INDEX idx_orders_created_day ON orders (date_trunc('day', created_at));

With this index in place, PostgreSQL pre-computes the truncated date for each row and stores it. Now, the query becomes blazingly fast because it can directly seek to the matching values in the index.

Consider BRIN Indexes for Massive Tables

For truly enormous, append-only tables (think event logs, IoT data), a standard B-Tree index can become bloated and slow to maintain. This is where BRIN (Block Range Index) shines.

A BRIN index doesn't store a pointer for every row. Instead, it stores the minimum and maximum value for a large range of table blocks (e.g., "in these 128 pages, the `created_at` value is between Monday and Wednesday"). It's incredibly small and fast to update. It works best when the indexed values are naturally correlated with their physical storage order, which is almost always true for timestamps in append-only tables.

-- A tiny, fast index for a huge, ordered table
CREATE INDEX idx_events_created_at_brin ON events USING BRIN (created_at);

If your `SELECT` queries typically target large date ranges on huge tables, a BRIN index can provide massive performance gains with a fraction of the overhead of a B-Tree.

Tip 5: Tame Complexity with Pre-Calculated CTEs

In complex reports or analytical queries, you often need to reference a specific point in time or a time interval in multiple places. Repeating the calculation logic—like `NOW() - INTERVAL '90 days'`—across your query is not only messy but can also confuse the query planner.

A Common Table Expression (CTE) is the perfect tool to simplify and optimize this. Define your time-based constants once at the beginning of your query.

WITH report_params AS (
  SELECT
    NOW() AS report_generated_ts,
    date_trunc('month', NOW()) AS start_of_month,
    NOW() - INTERVAL '7 days' AS last_week_ts
)
SELECT
  c.name,
  COUNT(o.id) AS recent_orders
FROM customers c
JOIN orders o ON c.id = o.customer_id
CROSS JOIN report_params -- Make params available to the query
WHERE o.created_at >= report_params.last_week_ts
GROUP BY c.name;

This approach offers several advantages:

• Readability and Maintainability: All your time logic is in one place. Need to change the report from 7 days to 14? You only edit one line.
• Planner Optimization: The planner sees that `last_week_ts` is a stable value throughout the query, which can lead to better execution plans.
• Clarity of Intent: It makes the query self-documenting. Anyone reading it immediately understands the time boundaries being used.

Bringing It All Together

Timestamps are more than just data; they're the pulse of your application. Treating them with the respect they deserve pays huge dividends in performance, stability, and code clarity. As you build and scale your systems in 2025, keep these pro tips in your back pocket:

1. Choose Wisely: Stick to `NOW()` for data consistency and performance.
2. Delegate to the DB: Use `DEFAULT NOW()` to simplify code and improve efficiency.
3. Generate, Don't Calculate: Use `GENERATED ... STORED` columns for fast, reliable derived timestamps.
4. Index Smartly: Use functional or BRIN indexes to supercharge your time-based queries.
5. Stay DRY with CTEs: Pre-calculate time constants in complex queries for clarity and speed.

By moving beyond the basics and embracing these powerful PostgreSQL features, you'll ensure your database's handling of time is as fast and reliable as possible. Happy querying!