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Filter: High-Availability
Blog posts
vmagent: Key Features Explained in Under 15 Minutes
vmagent is a lightweight agent for collecting and forwarding metrics to remote storage. It supports relabeling, sharding, replication, aggregation, deduplication, and disk buffering to ensure reliable and efficient metric delivery.
VictoriaMetrics Components: Getting Started
VictoriaMetrics is a fast, scalable monitoring system made of modular components like vminsert, vmstorage, and vmselect. It supports both single-node and clustered setups, along with tools for backup, restore, alerting, access control, and data migration. Data can be ingested, stored, queried, backed up, and restored with high performance and minimal resource use.
Prometheus Alerting 101: Rules, Recording Rules, and Alertmanager
Recording rules can transform complex PromQL queries into pre-computed metrics, eliminating repeated calculation overhead. When organized in groups, these alerting and recording rules can be precisely scheduled to run at specific times like 01:30, 02:30, and even execute in parallel for maximum efficiency.
Inside vmselect: The Query Processing Engine of VictoriaMetrics
The article explains vmselect’s core functionalities: concurrent request processing, query parsing and validation, data fetching and merging from vmstorage nodes, and memory-efficient result caching.
How vmstorage Handles Query Requests From vmselect
When vmselect asks vmstorage for data, there’s actually quite a bit happening behind the scenes. This article breaks down the process of how vmstorage locates and retrieves your metrics, from finding the right TSIDs to sending back the actual data points.
How vmstorage's IndexDB Works
IndexDB acts as vmstorage’s memory - it remembers which numbers (TSIDs) belong to which metrics, making sure your queries get answered fast. This article walks through how this system works, from the way it organizes data to how it keeps track of millions of timeseries.
How vmstorage Processes Data: Retention, Merging, Deduplication,...
vmstorage takes data from vminsert through a concurrency limiter, creates TSIDs for each row, and puts them in memory buffers. Every few seconds, it moves data to storage parts on disk. The system merges parts, removes duplicates, and cleans old data. This turns raw metrics into data users can search.
How vmstorage Handles Data Ingestion From vminsert
This article explains how vmstorage processes incoming metrics, assigns unique IDs to timeseries, 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.
When Metrics Meet vminsert: A Data-Delivery Story
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.