Analyzing Database Trends Through 1.8 Million Hacker News Headlines

July 02, 2025

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.

Engine	Headlines 2024-25	2023-24	YoY Δ %	3-yr slope (mentions / mo)
DuckDB	225	149	+50.7 %	+0.44
ClickHouse	211	170	+24.1 %	+0.39
Supabase	105	84	+25.0 %	+0.12
PostgreSQL	1 229	1 315	−6.6 %	+1.22
Snowflake	106	156	−32.1 %	+0.01
Redis	180	236	−23.7 %	+0.07
MySQL	164	185	−11.4 %	+0.03
MongoDB	96	78	+23.1 %	+0.03
DynamoDB	25	38	−34.2 %	−0.08
BigQuery	20	35	−42.9 %	−0.05
Redshift	10	18	−44.4 %	−0.05
TimescaleDB	3	4	−25.0 %	−0.01

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:

Engine	Peak year	Peak headlines	2024-25 headlines	% off peak
MongoDB	2013	242	96	−60 %
MySQL	2009	267	164	−39 %
DynamoDB	2019	59	25	−58 %
BigQuery	2020	62	20	−68 %
Redshift	2016	61	10	−84 %

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.