Kibana聽vs聽Nagios

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Kibana
Kibana

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Nagios

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Kibana vs Nagios: What are the differences?

Developers describe Kibana as "Explore & Visualize Your Data". 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. On the other hand, Nagios is detailed as "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.

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

Some of the features offered by Kibana are:

  • Flexible analytics and visualization platform
  • Real-time summary and charting of streaming data
  • Intuitive interface for a variety of users

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

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

"Easy to setup" is the top reason why over 76 developers like Kibana, while over 49 developers mention "It just works" as the leading cause for choosing Nagios.

Kibana and Nagios are both open source tools. It seems that Kibana with 12.2K GitHub stars and 4.72K forks on GitHub has more adoption than Nagios with 60 GitHub stars and 36 GitHub forks.

According to the StackShare community, Kibana has a broader approval, being mentioned in 889 company stacks & 453 developers stacks; compared to Nagios, which is listed in 176 company stacks and 39 developer stacks.

What is 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.

What is Nagios?

Nagios is a host/service/network monitoring program written in C and released under the GNU General Public License.
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    What are some alternatives to Kibana and Nagios?
    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.
    Loggly
    The world's most popular cloud-based log management service delivers application intelligence.
    Graylog
    Centralize and aggregate all your log files for 100% visibility. Use our powerful query language to search through terabytes of log data to discover and analyze important information.
    Splunk
    Splunk Inc. provides the leading platform for Operational Intelligence. Customers use Splunk to search, monitor, analyze and visualize machine data.
    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.
    See all alternatives
    Decisions about Kibana and Nagios
    StackShare Editors
    StackShare Editors
    Icinga
    Icinga
    Graphite
    Graphite
    Logstash
    Logstash
    Elasticsearch
    Elasticsearch
    Grafana
    Grafana
    Kibana
    Kibana

    One size definitely doesn鈥檛 fit all when it comes to open source monitoring solutions, and executing generally understood best practices in the context of unique distributed systems presents all sorts of problems. Megan Anctil, a senior engineer on the Technical Operations team at Slack gave a talk at an O鈥橰eilly Velocity Conference sharing pain points and lessons learned at wrangling known technologies such as Icinga, Graphite, Grafana, and the Elastic Stack to best fit the company鈥檚 use cases.

    At the time, Slack used a few well-known monitoring tools since it鈥檚 Technical Operations team wasn鈥檛 large enough to build an in-house solution for all of these. Nor did the team think it鈥檚 sustainable to throw money at the problem, given the volume of information processed and the not-insignificant price and rigidity of many vendor solutions. With thousands of servers across multiple regions and millions of metrics and documents being processed and indexed per second, the team had to figure out how to scale these technologies to fit Slack鈥檚 needs.

    On the backend, they experimented with multiple clusters in both Graphite and ELK, distributed Icinga nodes, and more. At the same time, they鈥檝e tried to build usability into Grafana that reflects the team鈥檚 mental models of the system and have found ways to make alerts from Icinga more insightful and actionable.

    See more
    Conor Myhrvold
    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber | 10 upvotes 561.5K 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鈥檚 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鈥檚 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
    Grafana
    Grafana
    Splunk
    Splunk
    Kibana
    Kibana

    I use Kibana because it ships with the ELK stack. I don't find it as powerful as Splunk however it is light years above grepping through log files. We previously used Grafana but found it to be annoying to maintain a separate tool outside of the ELK stack. We were able to get everything we needed from Kibana.

    See more
    Elasticsearch
    Elasticsearch
    Grafana
    Grafana
    Kibana
    Kibana

    I use both Kibana and Grafana on my workplace: Kibana for logging and Grafana for monitoring. Since you already work with Elasticsearch, I think Kibana is the safest choice in terms of ease of use and variety of messages it can manage, while Grafana has still (in my opinion) a strong link to metrics

    See more
    Kibana
    Kibana
    Grafana
    Grafana

    For our Predictive Analytics platform, we have used both Grafana and Kibana

    Kibana has predictions and ML algorithms support, so if you need them, you may be better off with Kibana . The multi-variate analysis features it provide are very unique (not available in Grafana).

    For everything else, definitely Grafana . Especially the number of supported data sources, and plugins clearly makes Grafana a winner (in just visualization and reporting sense). Creating your own plugin is also very easy. The top pros of Grafana (which it does better than Kibana ) are:

    • Creating and organizing visualization panels
    • Templating the panels on dashboards for repetetive tasks
    • Realtime monitoring, filtering of charts based on conditions and variables
    • Export / Import in JSON format (that allows you to version and save your dashboard as part of git)
    See more
    Interest over time
    Reviews of Kibana and Nagios
    No reviews found
    How developers use Kibana and Nagios
    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 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 Clarabridge Engage
    Clarabridge Engage uses KibanaKibana

    Used for graphing internal logging data; including metrics related to how fast we serve pages and execute MySQL/ElasticSearch queries.

    Avatar of Wirkn Inc.
    Wirkn Inc. uses KibanaKibana

    Our Kibana instances uses our ElasticSearch search data to help answer any complicated questions we have about our data.

    Avatar of OnlineCity
    OnlineCity uses NagiosNagios

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

    Avatar of Hevelop
    Hevelop uses KibanaKibana

    Kibana is our tools to query data in Elasticsearch clusters set up as catalog search engine.

    Avatar of Diogo Silva
    Diogo Silva uses KibanaKibana

    Perfect for exploring and visualizing the data available at ElasticSearch

    Avatar of Tongliang Liu
    Tongliang Liu uses KibanaKibana

    Log visualization. Wish it could add built-in alert functionality.

    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.

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