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  1. Stackups
  2. DevOps
  3. Monitoring
  4. Monitoring Tools
  5. Shinken vs Thanos

Shinken vs Thanos

OverviewComparisonAlternatives

Overview

Shinken
Shinken
Stacks17
Followers39
Votes0
Thanos
Thanos
Stacks100
Followers126
Votes0

Shinken vs Thanos: What are the differences?

  1. Architecture: Shinken is a monitoring framework written in Python that allows users to monitor their infrastructure via plugins and extensions, whereas Thanos is a monitoring tool designed for Prometheus metrics that provides a scalable, highly available solution for storing and querying Prometheus metrics.

  2. Data Storage: Shinken stores monitoring data in its own backend database, which can be flexible but requires additional setup and configuration, while Thanos integrates directly with Prometheus and utilizes its storage system, simplifying the data storage process and ensuring compatibility with Prometheus metrics.

  3. Scalability: Shinken's scalability depends on the resources of the server it is installed on and requires manual adjustments and configurations for scaling out, whereas Thanos is inherently highly scalable, utilizing distributed systems principles to handle large amounts of data without the need for extensive manual intervention.

  4. Query Language: Shinken does not have a built-in query language for analyzing and visualizing monitoring data, requiring users to utilize external tools or plugins for this functionality, whereas Thanos comes with its own powerful query language that enables users to perform complex queries and analysis on Prometheus metrics data.

  5. Alerting: Shinken offers basic alerting capabilities through its integration with Nagios plugins, allowing users to set up alerts based on threshold values, while Thanos lacks a native alerting system, relying on Prometheus Alertmanager for setting up and managing alerts based on monitoring data.

  6. Community Support: Shinken has a smaller community compared to Thanos, which has a large and active user base contributing to its development and providing support through forums, documentation, and community resources.

In Summary, Shinken and Thanos differ in their architecture, data storage, scalability, query language, alerting capabilities, and community support in the monitoring and metrics space.

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Detailed Comparison

Shinken
Shinken
Thanos
Thanos

Shinken's main goal is to give users a flexible architecture for their monitoring system that is designed to scale to large environments. Shinken is backwards-compatible with the Nagios configuration standard and plugins. It works on any operating system and architecture that supports Python, which includes Windows, GNU/Linux and FreeBSD.

Thanos is a set of components that can be composed into a highly available metric system with unlimited storage capacity. It can be added seamlessly on top of existing Prometheus deployments and leverages the Prometheus 2.0 storage format to cost-efficiently store historical metric data in any object storage while retaining fast query latencies. Additionally, it provides a global query view across all Prometheus installations and can merge data from Prometheus HA pairs on the fly.

Easy to install : install is mainly done with pip but some packages are available (deb / rpm) and we are planning to provide nightly build; Easy for new users : once installed, Shinken provide a simple command line interface to install new module and packs; Easy to migrate from Nagios : we want Nagios configuration and plugins to work in Shinken so that it is a “in place” replacement; Plugins provide great flexibility and are a big legacy codebase to use. It would be a shame not to use all this community work Multi-platform : python is available in a lot of OS. We try to write generic code to keep this possible; Utf8 compliant : python is here to do that. For now Shinken is compatible with 2.6-2.7 version but python 3.X is even more character encoding friendly; Independent from other monitoring solution : our goal is to provide a modular tool that can integrate with others through standard interfaces). Flexibility first; Flexible : in an architecture point view. It is very close to our scalability wish. Cloud computing is make architecture moving a lot, we have to fit to it; Fun to code : python ensure good code readability. Adding code should not be a pain when developing;
Global querying view across all connected Prometheus servers; Deduplication and merging of metrics collected from Prometheus HA pairs; Seamless integration with existing Prometheus setups; Any object storage as its only, optional dependency; Downsampling historical data for massive query speedup; Cross-cluster federation; Fault-tolerant query routing; Simple gRPC "Store API" for unified data access across all metric data; Easy integration points for custom metric providers
Statistics
Stacks
17
Stacks
100
Followers
39
Followers
126
Votes
0
Votes
0
Integrations
Nagios
Nagios
Prometheus
Prometheus

What are some alternatives to Shinken, Thanos?

Grafana

Grafana

Grafana is a general purpose dashboard and graph composer. It's focused on providing rich ways to visualize time series metrics, mainly though graphs but supports other ways to visualize data through a pluggable panel architecture. It currently has rich support for for Graphite, InfluxDB and OpenTSDB. But supports other data sources via plugins.

Kibana

Kibana

Kibana is an open source (Apache Licensed), browser based analytics and search dashboard for Elasticsearch. Kibana is a snap to setup and start using. Kibana strives to be easy to get started with, while also being flexible and powerful, just like Elasticsearch.

Prometheus

Prometheus

Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true.

Nagios

Nagios

Nagios is a host/service/network monitoring program written in C and released under the GNU General Public License.

Netdata

Netdata

Netdata collects metrics per second & presents them in low-latency dashboards. It's designed to run on all of your physical & virtual servers, cloud deployments, Kubernetes clusters & edge/IoT devices, to monitor systems, containers & apps

Zabbix

Zabbix

Zabbix is a mature and effortless enterprise-class open source monitoring solution for network monitoring and application monitoring of millions of metrics.

Sensu

Sensu

Sensu is the future-proof solution for multi-cloud monitoring at scale. The Sensu monitoring event pipeline empowers businesses to automate their monitoring workflows and gain deep visibility into their multi-cloud environments.

Graphite

Graphite

Graphite does two things: 1) Store numeric time-series data and 2) Render graphs of this data on demand

Lumigo

Lumigo

Lumigo is an observability platform built for developers, unifying distributed tracing with payload data, log management, and real-time metrics to help you deeply understand and troubleshoot your systems.

StatsD

StatsD

It is a network daemon that runs on the Node.js platform and listens for statistics, like counters and timers, sent over UDP or TCP and sends aggregates to one or more pluggable backend services (e.g., Graphite).

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