How the analysis was done

I used camelAI with a ClickHouse database of every HN story to do all analysis. You can use it for free with no login here to explore the data interactively yourself.

18 years • 1.8 million headlines • 13 database engines

Hacker News is a real-time barometer of developer excitement. I mined every story title from February 2007 to June 2025 and asked three questions:

• How has headline volume for each database changed over time?
• Which engines are accelerating the fastest right now?
• Which ones are losing steam?

1 · Raw popularity through time

The chart below shows monthly headline counts for the eight most-talked-about engines, plus DuckDB (small base, big growth).

Click on the individual database names to toggle their visibility. Double click to isolate a single database.

PostgreSQL’s curve is a near-monotonic climb; by 2020 it dwarfs every other line. MySQL dominates the pre-2012 era, then flat-lines. MongoDB peaks around 2013, then slips as SQL engines add JSON support. ClickHouse (2016) and DuckDB (2020) rocket up in the analytics renaissance. Redis & SQLite are steady, underscoring their “invisible infrastructure” roles.

2 · Who’s gaining the fastest?

I compared the last 12 months (Jul 2024 – Jun 2025) with the previous 12 months, then fit a linear regression over the last 36 months to capture trajectory.

DuckDB tops the growth chart: half of its lifetime headlines appeared this year. ClickHouse maintains double-digit gains on a larger base, buoyed by vector search & managed services. Supabase benefits from the “Firebase, but OSS” narrative. PostgreSQL’s YoY dip merely reflects an all-time-high 2023; its 3-yr slope (+1.22) is the steepest of all. Cloud-native SaaS engines (DynamoDB, BigQuery, Redshift) are down sharply in share of conversation.

3 · Engines in decline

Headline counts aren’t everything, but they do reveal mind-share:

Why? Maturity – less novelty, fewer “Show HN” launches. OSS competition – Postgres extensions + parquet/iceberg lakehouses replace single-purpose stores. Discussion shift – Cost, lock-in, and serverless topics often omit the product name.

Takeaways

• Open‑source engines drive most new discussion.
• Analytics‑focused stores (DuckDB, ClickHouse) gain as work moves from batch to interactive.
• Slow, steady improvement (PostgreSQL) beats hype cycles.
• Proprietary cloud databases draw fewer submissions over time.

Further analysis

Questions you can run next:

• Which databases appear most in headlines containing "alternative to" or "replacing"?
• What's the ratio of positive (launched, shipped) vs negative (outage, breach) headlines?
• How do vector database mentions (Pinecone, Weaviate, Qdrant) compare to traditional databases?
• Which lesser-known databases are on exponential growth curves?

Run any query on the same dataset here, no login required.