November 26, 2025
When pixels meet pitchforks
Downsampling: Largest-Triangle-Three-Buckets and the Fourier Transform
Slimmer charts, louder arguments: is the picture worth the noise
TLDR: The post shows that a chart-thinning trick (LTTB) keeps graphs pretty but can mangle what the signal “sounds” like, making a 16-beat look like 32. Commenters split between pragmatists using it for dashboards (even in ClickHouse) and purists warning “keep the phase,” with jokers pledging undying love to Fourier.
A blog demo shows a trick for making huge wiggly charts fit your screen by keeping only a few “important” points using the LTTB method. It looks fine to the eye—until you peek under the hood with a Fourier transform, which reads signals like musical notes. Suddenly a clean 16‑beat rhythm looks like 32, plus stray “harmonics.” Translation: the picture stays pretty, but the “sound” gets weird.
That’s where the comments went full battle mode. One camp waved the practicality flag: “real problem,” “we use this in IoT,” dashboards gotta load. Another camp yelled “phase!”—as in, don’t throw away the timing info of the waves unless you enjoy nonsense. A drive‑by alternative popped up with Ramer–Douglas–Peucker for visuals, cheekily warned to “rain havoc” on frequency analysis. Somewhere in between, a comedian declared eternal love: if stranded, “I’d wish for the Fourier”—the math gets the rose. Meanwhile, the toolbelt crowd flexed: ClickHouse has LTTB built in, so ship it.
The vibe: screen‑friendly vs signal‑faithful. Fans say LTTB is great for charts and bandwidth; purists call out illusions and off‑key math when you downsample too hard. It’s pixels vs. physics—with jokes, nerd romance, and links for days (LTTB)
Key Points
- •LTTB downsampling reduces time series points by selecting one significant point per bucket to preserve visual shape.
- •Applying LTTB to a 16 Hz sine wave shifts the dominant frequency in the FFT from 16 Hz to 32 Hz, indicating distortion.
- •More aggressive downsampling (e.g., to 256 samples) increases spectral distortion and alters apparent base frequency.
- •Nyquist–Shannon theorem requires sampling at least twice the highest frequency; for 16 Hz, at least 32 Hz is needed.
- •At the minimum sampling threshold, the original frequency can be inferred; below it, the signal becomes completely distorted.