This article was originally published on The New Stack and is reposted here with permission. By taking advantage of monitoring data, companies can ensure their infrastructure is performing optimally while reducing costs. While building new features and launching new products is fun, none of it matters if your software isn’t reliable. One key part of making sure your apps run smoothly is having robust infrastructure monitoring in place.
Time series data is foundational in almost all applications and services. Even if time series isn’t the focus, like in an IoT sensor data centered application, it appears in monitoring data as metrics, logs, and traces. Because of time series data’s unique characteristics, it’s best served in a time series database. InfluxDB is purpose-built to handle the high volume and velocity of time series ingestion, and perform real-time analytics, alerting, and anomaly detection at scale.
This article was originally published on The New Stack and is reposted here with permission. Here is a brief case study that explores the logistics and motivations that would lead a successful company to spend time and resources completely rewriting the core of their flagship product in Rust. Calling a programming language Rust almost seems like a misnomer. Rust is the brittle byproduct of corrosion — not something that would typically inspire confidence.
A few weeks ago, we published some benchmarking that showed performance gains in InfluxDB 3.0 that are orders of magnitude better than previous versions of InfluxDB – and by extension, other databases as well. There are two key factors that influence these gains: 1. Data ingest, and 2. Data compression. This begs the question, just how did we achieve such drastic improvements in our core database? This post sets out to explain how we accomplished these improvements for anyone interested.
