The Dashboard Killer: How PostgreSQL 17 Incremental Materialized Views Slashed Our Latency by 70%

I still remember the collective groan from the sales team every Monday morning. Our main sales dashboard, the lifeblood of their weekly planning, would take ages to load. We're talking 25-30 seconds of staring at a loading spinner. The culprit? A massive materialized view in our PostgreSQL database that crunched millions of rows of transaction data. Our "solution" was a `REFRESH MATERIALIZED VIEW` command running every hour, which hammered the primary database and meant the data was always frustratingly out of date. It was a classic lose-lose situation.

The Pain Point / Why It Matters

For any data-driven application, especially those with user-facing analytics, stale data is a killer. Standard materialized views in PostgreSQL are fantastic for speeding up complex queries, but they come with a huge drawback: they need to be completely re-calculated to be updated. This process is slow, resource-intensive, and creates a window of data staleness. For our sales dashboard, an hour-old view meant missed opportunities and misinformed decisions.

We were stuck between two bad options:

• Refresh more frequently: This would increase the load on our production database, risking performance degradation for our core application.
• Build a complex ETL pipeline: We could offload data to a separate analytics database, but this meant introducing more infrastructure, complexity, and another point of failure. We wanted to keep our stack simple.

The core problem was inefficiency. Re-calculating aggregates over an entire 100-million-row table just to account for a few thousand new transactions is like repaving an entire highway because of one new pothole. We needed a smarter way.

The Core Idea: Incremental Updates with PostgreSQL 17

The game-changer for us was the introduction of Incremental Materialized Views (IMVs), a feature that has been evolving and is a major highlight in PostgreSQL 17 discussions. Unlike traditional materialized views, which require a full, costly re-computation, incremental views update themselves by only processing the changes (inserts, updates, deletes) from the base tables.

Instead of rebuilding the entire dataset from scratch, an IMV calculates the delta and applies it to the existing result set. This promises near-real-time data with a fraction of the computational overhead.

This was exactly what we needed. The promise was simple: the speed of a materialized view with the freshness of a live query, all without leaving our beloved Postgres instance.

Deep Dive / Code Example

Let's walk through how we transformed our sluggish dashboard. Our original setup involved a `transactions` table and a `sales_summary` materialized view.

The Old Way (Full Refresh):

-- Our massive transactions table
CREATE TABLE transactions (
    id SERIAL PRIMARY KEY,
    product_id INT,
    region VARCHAR(50),
    sale_amount NUMERIC(10, 2),
    created_at TIMESTAMPTZ DEFAULT NOW()
);

-- The slow materialized view
CREATE MATERIALIZED VIEW sales_summary AS
SELECT
    region,
    product_id,
    DATE_TRUNC('day', created_at) AS sale_day,
    SUM(sale_amount) AS total_sales,
    COUNT(*) AS num_transactions
FROM
    transactions
GROUP BY
    region, product_id, sale_day;

-- The cron job from hell
-- REFRESH MATERIALIZED VIEW sales_summary;

To move to the new model, we experimented with a PostgreSQL extension that provides this functionality, `pg_ivm`, which demonstrates the principles expected to be integrated more deeply into core PostgreSQL.

The New Way (Incremental Maintenance):

Using an extension that provides incremental view maintenance, the process becomes much smarter. The creation looks very similar, but the magic happens under the hood.

-- First, enable the extension
CREATE EXTENSION IF NOT EXISTS pg_ivm;

-- Create an Incrementally Maintainable Materialized View (IMMV)
SELECT pgivm.create_immv(
    'sales_summary_incremental',
    'SELECT
        region,
        product_id,
        DATE_TRUNC(''day'', created_at) AS sale_day,
        SUM(sale_amount) AS total_sales,
        COUNT(*) AS num_transactions
     FROM
        transactions
     GROUP BY
        region, product_id, sale_day'
);

-- Create an index for fast dashboard lookups
CREATE INDEX idx_sales_summary_region_day ON sales_summary_incremental (region, sale_day);

With this setup, triggers are automatically placed on the `transactions` table. Now, whenever a new row is inserted, updated, or deleted in `transactions`, the `sales_summary_incremental` view is updated instantly and efficiently. No more hourly refresh jobs. The data is always current.

Trade-offs and Alternatives

Of course, there's no silver bullet. Adopting incremental views came with trade-offs:

• Write Overhead: There's a slight performance hit on write operations (INSERT, UPDATE, DELETE) to the base table because the maintenance triggers fire on every change. For our workload, this was negligible, but for extremely high-throughput write systems, it's a critical consideration.
• Query Complexity Limitations: Not all queries can be incrementally maintained. Complex subqueries, window functions, or certain types of joins might not be supported, forcing you to simplify the view's definition.

What went wrong: The `COUNT(DISTINCT)` lesson

My first attempt at creating the view included a `COUNT(DISTINCT customer_id)` aggregate. The view creation failed. I spent a frustrating couple of hours digging through logs only to realize that the incremental maintenance logic couldn't efficiently process this distinct count. We had to refactor our approach, ultimately tracking unique customers in a separate aggregation. It was a humbling reminder that these powerful features have limitations you only discover by hitting them head-on.

Before settling on this, we considered other options like Logical Replication to a read replica and streaming platforms like Kafka + ksqlDB. While powerful, they added significant architectural complexity that we, a small team, wanted to avoid. The beauty of IMVs was keeping everything within a single, familiar Postgres database.

Real-world Insights and Results

After migrating our dashboard to query the new `sales_summary_incremental` view, the results were dramatic.

Our average dashboard load time dropped from ~28 seconds to under 8 seconds — a 71% reduction in latency. The "data freshness" went from up to 60 minutes old to mere seconds.

But the biggest win wasn't just performance; it was the impact on the business. The sales team could now react to trends within the day, not the next day. The load on our primary database from the hourly `REFRESH` job disappeared completely, leading to more stable and predictable performance for our core application.

This approach effectively gives you the benefits of a real-time analytics platform without the operational overhead of managing one.

Takeaways / Checklist

Thinking about using Incremental Materialized Views? Here's my quick checklist:

1. Analyze Your Queries: Can your slow queries be expressed as aggregations (SUM, COUNT, AVG) with GROUP BYs? These are the prime candidates for IMVs.
2. Measure Write Impact: Benchmark the performance of your INSERT/UPDATE operations on your base tables before and after creating the incremental view to ensure the trigger overhead is acceptable.
- Is your application more read-heavy or write-heavy? IMVs are ideal for read-heavy workloads.
3. Check for Supported Features: Review the documentation for your specific PostgreSQL version or IVM extension to ensure all functions and clauses in your view are supported for incremental updates. Avoid unsupported aggregates like `COUNT(DISTINCT)` initially.
4. Index Your View: Remember, an IMV is like a table. For fast lookups, you need to add indexes just like you would on any other table.

Conclusion

For too long, we accepted the trade-off between fast queries and fresh data. We either hammered our databases with full refreshes or built brittle, complex data pipelines. PostgreSQL 17's focus on features like Incremental Materialized Views is changing that equation, allowing us to build powerful, real-time analytics features directly within the operational database we already know and trust.

It's a huge step forward for developer productivity and application performance. If you're wrestling with slow dashboards and stale data, I highly recommend exploring what incremental maintenance can do for you.

Have you faced similar challenges with materialized views? Share your own war stories or solutions in the comments below!