Nagios vs StatsD

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Nagios
Nagios

571
398
+ 1
93
StatsD
StatsD

192
126
+ 1
27
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Nagios vs StatsD: What are the differences?

What is Nagios? Complete monitoring and alerting for servers, switches, applications, and services. Nagios is a host/service/network monitoring program written in C and released under the GNU General Public License.

What is StatsD? Simple daemon for easy stats aggregation. StatsD is a front-end proxy for the Graphite/Carbon metrics server, originally written by Etsy's Erik Kastner. StatsD is a network daemon that runs on the Node.js platform and listens for statistics, like counters and timers, sent over UDP and sends aggregates to one or more pluggable backend services (e.g., Graphite).

Nagios and StatsD belong to "Monitoring Tools" category of the tech stack.

Some of the features offered by Nagios are:

  • Monitor your entire IT infrastructure
  • Spot problems before they occur
  • Know immediately when problems arise

On the other hand, StatsD provides the following key features:

  • buckets: Each stat is in its own "bucket". They are not predefined anywhere. Buckets can be named anything that will translate to Graphite (periods make folders, etc)
  • values: Each stat will have a value. How it is interpreted depends on modifiers. In general values should be integer.
  • flush: After the flush interval timeout (defined by config.flushInterval, default 10 seconds), stats are aggregated and sent to an upstream backend service.

"It just works" is the primary reason why developers consider Nagios over the competitors, whereas "Single responsibility" was stated as the key factor in picking StatsD.

Nagios and StatsD are both open source tools. StatsD with 14.2K GitHub stars and 1.83K forks on GitHub appears to be more popular than Nagios with 60 GitHub stars and 36 GitHub forks.

According to the StackShare community, Nagios has a broader approval, being mentioned in 177 company stacks & 40 developers stacks; compared to StatsD, which is listed in 72 company stacks and 16 developer stacks.

What is Nagios?

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

What is StatsD?

StatsD is a front-end proxy for the Graphite/Carbon metrics server, originally written by Etsy's Erik Kastner. StatsD is a network daemon that runs on the Node.js platform and listens for statistics, like counters and timers, sent over UDP and sends aggregates to one or more pluggable backend services (e.g., Graphite).
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    What are some alternatives to Nagios and StatsD?
    Zabbix
    Zabbix is a mature and effortless enterprise-class open source monitoring solution for network monitoring and application monitoring of millions of metrics.
    Splunk
    Splunk Inc. provides the leading platform for Operational Intelligence. Customers use Splunk to search, monitor, analyze and visualize machine data.
    Icinga
    It monitors availability and performance, gives you simple access to relevant data and raises alerts to keep you in the loop. It was originally created as a fork of the Nagios system monitoring application.
    Solarwinds
    Developed by network and systems engineers who know what it takes to manage today's dynamic IT environments, SolarWinds has a deep connection to the IT community.
    AppDynamics
    AppDynamics develops application performance management (APM) solutions that deliver problem resolution for highly distributed applications through transaction flow monitoring and deep diagnostics.
    See all alternatives
    Decisions about Nagios and StatsD
    Conor Myhrvold
    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 10 upvotes · 568.3K views
    atUber TechnologiesUber Technologies
    Nagios
    Nagios
    Grafana
    Grafana
    Graphite
    Graphite
    Prometheus
    Prometheus

    Why we spent several years building an open source, large-scale metrics alerting system, M3, built for Prometheus:

    By late 2014, all services, infrastructure, and servers at Uber emitted metrics to a Graphite stack that stored them using the Whisper file format in a sharded Carbon cluster. We used Grafana for dashboarding and Nagios for alerting, issuing Graphite threshold checks via source-controlled scripts. While this worked for a while, expanding the Carbon cluster required a manual resharding process and, due to lack of replication, any single node’s disk failure caused permanent loss of its associated metrics. In short, this solution was not able to meet our needs as the company continued to grow.

    To ensure the scalability of Uber’s metrics backend, we decided to build out a system that provided fault tolerant metrics ingestion, storage, and querying as a managed platform...

    https://eng.uber.com/m3/

    (GitHub : https://github.com/m3db/m3)

    See more
    StackShare Editors
    StackShare Editors
    Flask
    Flask
    AWS EC2
    AWS EC2
    Celery
    Celery
    Datadog
    Datadog
    PagerDuty
    PagerDuty
    Airflow
    Airflow
    StatsD
    StatsD
    Grafana
    Grafana

    Data science and engineering teams at Lyft maintain several big data pipelines that serve as the foundation for various types of analysis throughout the business.

    Apache Airflow sits at the center of this big data infrastructure, allowing users to “programmatically author, schedule, and monitor data pipelines.” Airflow is an open source tool, and “Lyft is the very first Airflow adopter in production since the project was open sourced around three years ago.”

    There are several key components of the architecture. A web UI allows users to view the status of their queries, along with an audit trail of any modifications the query. A metadata database stores things like job status and task instance status. A multi-process scheduler handles job requests, and triggers the executor to execute those tasks.

    Airflow supports several executors, though Lyft uses CeleryExecutor to scale task execution in production. Airflow is deployed to three Amazon Auto Scaling Groups, with each associated with a celery queue.

    Audit logs supplied to the web UI are powered by the existing Airflow audit logs as well as Flask signal.

    Datadog, Statsd, Grafana, and PagerDuty are all used to monitor the Airflow system.

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    Łukasz Korecki
    Łukasz Korecki
    CTO & Co-founder at EnjoyHQ · | 6 upvotes · 56.2K views
    atEnjoyHQEnjoyHQ
    Stackdriver
    Stackdriver
    Clojure
    Clojure
    StatsD
    StatsD
    Google Compute Engine
    Google Compute Engine
    collectd
    collectd

    We use collectd because of it's low footprint and great capabilities. We use it to monitor our Google Compute Engine machines. More interestingly we setup collectd as StatsD replacement - all our Clojure services push application-level metrics using our own metrics library and collectd pushes them to Stackdriver

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    Amazon CloudWatch
    Amazon CloudWatch
    PagerDuty
    PagerDuty
    Grafana
    Grafana
    Graphite
    Graphite
    StatsD
    StatsD
    Sentry
    Sentry

    A huge part of our continuous deployment practices is to have granular alerting and monitoring across the platform. To do this, we run Sentry on-premise, inside our VPCs, for our event alerting, and we run an awesome observability and monitoring system consisting of StatsD, Graphite and Grafana. We have dashboards using this system to monitor our core subsystems so that we can know the health of any given subsystem at any moment. This system ties into our PagerDuty rotation, as well as alerts from some of our Amazon CloudWatch alarms (we’re looking to migrate all of these to our internal monitoring system soon).

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    Interest over time
    Reviews of Nagios and StatsD
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    How developers use Nagios and StatsD
    Avatar of ShadowICT
    ShadowICT uses NagiosNagios

    We use Nagios to monitor our stack and alert us when problems arise. Nagios allows us to monitor every aspect of each of our servers such as running processes, CPU usage, disk usage, and more. This means that as soon as problems arise, we can detect them and call out an engineer to resolve the issues as soon as possible.

    Avatar of Stream
    Stream uses StatsDStatsD

    StatsD is used to track the number of messages we're publishing and the type of realtime subscribers. So it shows the number of longpoll connections, the number of websocket connections etc. It also tracks how Redis is performing.

    Avatar of Analytical Informatics
    Analytical Informatics uses NagiosNagios

    We use Nagios to monitor customer instances of Bridge and proactively alert us about issues like queue sizes, downed services, errors in logs, etc.

    Avatar of OnlineCity
    OnlineCity uses NagiosNagios

    We use nagios based OpsView to monitor our server farm and keep everything running smoothly.

    Avatar of Chris Hartwig
    Chris Hartwig uses StatsDStatsD

    Business and system counters go through StatsD and are pushed to InfluxDB

    Avatar of Tongliang Liu
    Tongliang Liu uses StatsDStatsD

    Arm yourself with sensor all over your application

    Avatar of Peter Degen-Portnoy
    Peter Degen-Portnoy uses NagiosNagios

    Monitor web servers, databases, utility servers

    Avatar of Veggie Sailor
    Veggie Sailor uses NagiosNagios

    For the monitoring full stack of the platform.

    How much does Nagios cost?
    How much does StatsD cost?
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