VictoriaMetrics Description
VictoriaMetrics is a cost-effective, scalable monitoring solution that can also be used as a time series database. It can also be used to store Prometheus' long-term data. VictoriaMetrics is a single executable that does not have any external dependencies. All configuration is done using explicit command-line flags and reasonable defaults. It provides global query view. Multiple Prometheus instances, or other data sources, may insert data into VictoriaMetrics. Later this data may be queried via a single query. It can handle high cardinality and high churn rates issues by using a series limiter.
VictoriaMetrics Alternatives
Prometheus
Open-source monitoring solutions are able to power your alerting and metrics. Prometheus stores all data in time series. These are streams of timestamped value belonging to the same metric with the same labeled dimensions. Prometheus can also generate temporary derived times series as a result of queries. Prometheus offers a functional query language called PromQL, which allows the user to select and aggregate time series data real-time. The expression result can be displayed as a graph or tabular data in Prometheus’s expression browser. External systems can also consume the HTTP API.
Prometheus can be configured using command-line flags or a configuration file. The command-line flags can be used to configure immutable system parameters such as storage locations and the amount of data to be kept on disk and in memory. .
Download: https://sourceforge.net/projects/prometheus.mirror/
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VictoriaMetrics Anomaly Detection
VictoriaMetrics Anomaly Detection, a service which continuously scans data stored in VictoriaMetrics to detect unexpected changes in real-time, is a service for detecting anomalies in data patterns. It does this by using user-configurable models of machine learning. VictoriaMetrics Anomaly Detection is a key tool in the dynamic and complex world system monitoring. It is part of our Enterprise offering. It empowers SREs, DevOps and other teams by automating the complex task of identifying anomalous behavior in time series data. It goes beyond threshold-based alerting by utilizing machine learning to detect anomalies, minimize false positives and reduce alert fatigue. The use of unified anomaly scores and simplified alerting mechanisms allows teams to identify and address potential issues quicker, ensuring system reliability.
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QuestDB
QuestDB is a relational database that uses column-oriented databases. It can be used for event and time series data. It uses SQL with extensions to time series to aid in real-time analytics. These pages provide information about core concepts of QuestDB. They include setup steps, usage guides, as well as reference documentation for syntax, APIs, and configuration. This section explains the architecture of QuestDB and how it stores and queries data. It also introduces new capabilities and features that are unique to the system. The core feature of QuestDB is the designated timestamp. It enables partitioning and time-oriented language capabilities. The symbol type makes it easy to store and retrieve repetitive strings. QuestDB's storage model describes how it stores records and partitions within tables. Indexes can be used to provide faster access to specific columns. Partitions can be used to provide significant performance improvements in calculations and queries. SQL extensions enable time series analysis that is efficient and concise with a concise syntax.
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Blueflood
Blueflood, a distributed metric processing system that is high throughput and low latency for multi-tenant multi-tenants, is behind Rackspace Metrics. It is currently being used in production by Rackspace Monitoring team as well as Rackspace public cloud team to store metrics created by their systems. Blueflood is also used in large-scale Rackspace deployments. Blueflood data can be used to create dashboards, reports, graphs, or any other use that involves time-series information. It is focused on near-realtime data and data that can be queryable within milliseconds of ingestion. You send metrics to ingestion service. The Query service allows you to query your metrics. Rollups are processed offline in the background so that queries with long time periods are quickly returned.
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Pricing
Pricing Starts At:
$0
Pricing Information:
Hassle-free monitoring solution
100% Free - Open Source
100% Free - Open Source
Free Version:
Yes
Integrations
Company Details
Company:
VictoriaMetrics
Year Founded:
2018
Headquarters:
United States
Website:
victoriametrics.com
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Product Details
Platforms
SaaS
Linux
Type of Training
Documentation
Live Online
Webinars
Videos
Customer Support
24/7 Live Support
Online
VictoriaMetrics Features and Options
Data Replication Software
Asynchronous Data Replication
Automated Data Retention
Continuous Replication
Cross-Platform Replication
Dashboard
Instant Failover
Orchestration
Remote Database Replication
Reporting / Analytics
Simulation / Testing
Synchronous Data Replication
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