How We Optimized Top K in Postgres
By Ming Ying on February 13, 2026
In databases, Top K means “give me the K best rows, ordered by some column or value.” Commonly that means “the most recent rows,” “the highest scores,” or “the largest values.”
It feels like a basic problem that Postgres should solve. After all, can’t we just create an index? Yet in many production Postgres deployments, Top K is deceptively hard. This post examines where Postgres’ Top K optimizations shine, where they falter, and why search libraries like Lucene/Tantivy or databases like ParadeDB that specialize in Top K take a fundamentally different approach.
Postgres' Solution: A Sorted B-Tree
Let’s begin with a single table containing 100M rows.
CREATE TABLE benchmark_logs ( id SERIAL PRIMARY KEY , message TEXT, country VARCHAR ( 255 ), severity INTEGER , timestamp TIMESTAMP , metadata JSONB );
We want to return the top 10 most recent rows by timestamp :
SELECT * FROM benchmark_logs ORDER BY timestamp DESC LIMIT 10 ;
Without an index, this query takes 15 seconds. To accelerate it, we can create a B-Tree index on timestamp .
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