1. The Cost of Full Table Scans
As relational database tables scale past millions of records, sequential table scans degrade performance and consume disk I/O. Proper index architecture is the single most impactful lever for improving backend response latency.
2. Partial Indexes: Indexing Only What Matters
Standard B-Tree indexes include every row in a table. In many applications, 90% of queries target only a small subset (such as active subscriptions or unread notifications). Partial indexes use a WHERE clause to index only relevant rows, reducing index disk size and write overhead by up to 85%.
-- Partial index for active subscribers only
CREATE INDEX idx_active_users
ON users (email, last_login_at)
WHERE status = 'active';
-- Indexing pending notifications
CREATE INDEX idx_pending_notifications
ON notifications (user_id, created_at)
WHERE is_read = false;3. Materialized Views for Analytical Reporting
For expensive aggregate queries that join multiple tables, standard database views re-execute the underlying query on every request. Materialized views persist the calculated result set directly on disk, enabling instantaneous responses.
CREATE MATERIALIZED VIEW monthly_editorial_stats AS
SELECT
p.author_id,
count(p.id) as total_articles,
sum(p.views) as total_views,
avg(p.likes_count) as avg_engagement
FROM posts p
WHERE p.published_at >= NOW() - INTERVAL '30 days'
GROUP BY p.author_id;
-- Concurrent refresh without locking readers
CREATE UNIQUE INDEX idx_stats_author ON monthly_editorial_stats (author_id);
REFRESH MATERIALIZED VIEW CONCURRENTLY monthly_editorial_stats;4. Summary
Combining partial indexes on hot operational tables with concurrently refreshed materialized views allows PostgreSQL to comfortably handle tens of millions of records on modest hardware.
