High-Availability

vminsert acts as a gateway for incoming monitoring data. It receives data in different formats, processes it by parsing and adjusting labels, then uses memory buffers to send this data to storage nodes. It’s smart enough to always send the same type of data to the same storage node and can redirect data if a node isn’t working properly.

This article walks through how data flows from collection to storage, explaining how vmstorage processes incoming metrics, assigns unique IDs to time series, and organizes everything into different types of storage parts. The whole system is pretty clever - it uses in-memory buffers for speed, smart compression to save space, and has various watchdogs keeping an eye on things like disk space and data retention.

VictoriaMetrics agent, or vmagent, is a lightweight tool designed to gather metrics from a number of different sources. Once it pulls in all those metrics, vmagent lets you ‘design’ them (through ‘relabeling’) or filter them down (doing things like reducing cardinality, stream aggregation, deduplication, and so on) before shipping them off to wherever you want to store them.

When looking for a most scalable open source time series database, what are the criteria to care about? Read this blog to get our recommendations.

Three examples of vmagent high-availability setup for pull and push models