Jaeger vs Kibana vs Prometheus

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Jaeger

326
453
+ 1
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Kibana

20.1K
16K
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Prometheus

4.1K
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Jaeger vs Kibana vs Prometheus: What are the differences?

Introduction

In the world of observability and monitoring, there are several tools available to help monitor and troubleshoot systems. Two popular tools in this space are Jaeger and Kibana, both commonly used for log analysis and visualization. Another popular tool is Prometheus, which is primarily used for metric monitoring and alerting. While they may have some overlap in functionality, there are key differences between these tools that make them suitable for different use cases.

1. Distributed Tracing vs. Log Analysis: One of the key differences between Jaeger and Kibana is their primary focus. Jaeger is a distributed tracing system, used for end-to-end monitoring and tracking of requests as they traverse through complex microservices architectures. It helps identify bottlenecks and latency issues by providing detailed insights into the flow of requests. On the other hand, Kibana is primarily used for log analysis and visualization, helping analyze and search through logs to identify patterns or troubleshoot issues.

2. Visualization Capabilities: Kibana is known for its powerful visualization capabilities. It provides a wide range of visualizations, including charts, graphs, and maps, to help analyze and understand log data effectively. Kibana also supports real-time streaming of logs, enabling users to visualize the data as it comes in. In contrast, Jaeger is focused on providing detailed traces and spans to understand the flow of requests rather than visualizing log data.

3. Query Language: Another difference is the query language used by these tools. Kibana uses Elasticsearch Query Language (EQL), which is an expressive and powerful language for querying and filtering log data. It allows users to perform complex queries and aggregations on log data to extract meaningful insights. Jaeger, on the other hand, uses its own query language called Jaeger Query Language (JQL) specifically designed for distributed tracing data. It allows users to filter and aggregate trace data based on specific attributes or conditions.

4. Alerting and Monitoring: While both Jaeger and Kibana provide monitoring capabilities, they differ in their alerting capabilities. Kibana provides a flexible alerting framework that allows users to create custom alerts based on log data conditions. It can trigger notifications or perform specific actions when certain log events occur. Jaeger, on the other hand, focuses more on providing insights into the performance and latency of requests rather than built-in alerting capabilities. However, Jaeger can be integrated with external monitoring and alerting systems to achieve similar functionality.

5. Data Collection and Storage: Prometheus differs from Jaeger and Kibana in terms of data collection and storage. Prometheus is a pull-based monitoring system, where it periodically scrapes metrics from the configured targets or endpoints. It stores these metrics locally and provides powerful querying and alerting capabilities on the collected data. In contrast, Jaeger and Kibana rely on centralized logging and tracing data sources. They collect and store log and tracing data from various sources, allowing users to analyze and visualize the data at a central location.

6. Use Case Focus: Finally, the key difference lies in the primary use case focus of these tools. Jaeger is built specifically for distributed tracing, making it ideal for monitoring and troubleshooting complex microservices architectures. Kibana, on the other hand, is more suitable for log analysis and visualization, helping identify patterns or troubleshoot issues in log data. Prometheus focuses on metric monitoring and alerting, making it well-suited for monitoring the health and performance of systems and services based on metrics.

In summary, Jaeger and Kibana have different focuses, with Jaeger being a distributed tracing system for end-to-end monitoring and troubleshooting, while Kibana is primarily used for log analysis and visualization. Prometheus, on the other hand, is focused on metric monitoring and alerting. Their key differences lie in their primary use case focus, visualization capabilities, query language, alerting and monitoring capabilities, data collection and storage methods, and their specialties in distributed tracing, log analysis, or metric monitoring.

Advice on Jaeger, Kibana, and Prometheus
Susmita Meher
Senior SRE at African Bank · | 4 upvotes · 785.3K views
Needs advice
on
GrafanaGrafanaGraphiteGraphite
and
PrometheusPrometheus

Looking for a tool which can be used for mainly dashboard purposes, but here are the main requirements:

  • Must be able to get custom data from AS400,
  • Able to display automation test results,
  • System monitoring / Nginx API,
  • Able to get data from 3rd parties DB.

Grafana is almost solving all the problems, except AS400 and no database to get automation test results.

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Replies (1)
Sakti Behera
Technical Specialist, Software Engineering at AT&T · | 3 upvotes · 570.7K views
Recommends
on
GrafanaGrafanaPrometheusPrometheus

You can look out for Prometheus Instrumentation (https://prometheus.io/docs/practices/instrumentation/) Client Library available in various languages https://prometheus.io/docs/instrumenting/clientlibs/ to create the custom metric you need for AS4000 and then Grafana can query the newly instrumented metric to show on the dashboard.

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Sunil Chaudhari
Needs advice
on
MetricbeatMetricbeat
and
PrometheusPrometheus

Hi, We have a situation, where we are using Prometheus to get system metrics from PCF (Pivotal Cloud Foundry) platform. We send that as time-series data to Cortex via a Prometheus server and built a dashboard using Grafana. There is another pipeline where we need to read metrics from a Linux server using Metricbeat, CPU, memory, and Disk. That will be sent to Elasticsearch and Grafana will pull and show the data in a dashboard.

Is it OK to use Metricbeat for Linux server or can we use Prometheus?

What is the difference in system metrics sent by Metricbeat and Prometheus node exporters?

Regards, Sunil.

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Replies (2)
Matthew Rothstein
Recommends
on
PrometheusPrometheus

If you're already using Prometheus for your system metrics, then it seems like standing up Elasticsearch just for Linux host monitoring is excessive. The node_exporter is probably sufficient if you'e looking for standard system metrics.

Another thing to consider is that Metricbeat / ELK use a push model for metrics delivery, whereas Prometheus pulls metrics from each node it is monitoring. Depending on how you manage your network security, opting for one solution over two may make things simpler.

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Recommends
on
InstanaInstana

Hi Sunil! Unfortunately, I don´t have much experience with Metricbeat so I can´t advise on the diffs with Prometheus...for Linux server, I encourage you to use Prometheus node exporter and for PCF, I would recommend using the instana tile (https://www.instana.com/supported-technologies/pivotal-cloud-foundry/). Let me know if you have further questions! Regards Jose

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Mat Jovanovic
Head of Cloud at Mats Cloud · | 3 upvotes · 714.6K views
Needs advice
on
DatadogDatadogGrafanaGrafana
and
PrometheusPrometheus

We're looking for a Monitoring and Logging tool. It has to support AWS (mostly 100% serverless, Lambdas, SNS, SQS, API GW, CloudFront, Autora, etc.), as well as Azure and GCP (for now mostly used as pure IaaS, with a lot of cognitive services, and mostly managed DB). Hopefully, something not as expensive as Datadog or New relic, as our SRE team could support the tool inhouse. At the moment, we primarily use CloudWatch for AWS and Pandora for most on-prem.

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Replies (2)
Lucas Rincon
Recommends
on
InstanaInstana

this is quite affordable and provides what you seem to be looking for. you can see a whole thing about the APM space here https://www.apmexperts.com/observability/ranking-the-observability-offerings/

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Recommends
on
DatadogDatadog

I worked with Datadog at least one year and my position is that commercial tools like Datadog are the best option to consolidate and analyze your metrics. Obviously, if you can't pay the tool, the best free options are the mix of Prometheus with their Alert Manager and Grafana to visualize (that are complementary not substitutable). But I think that no use a good tool it's finally more expensive that use a not really good implementation of free tools and you will pay also to maintain its.

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Needs advice
on
GrafanaGrafana
and
KibanaKibana

From a StackShare Community member: “We need better analytics & insights into our Elasticsearch cluster. Grafana, which ships with advanced support for Elasticsearch, looks great but isn’t officially supported/endorsed by Elastic. Kibana, on the other hand, is made and supported by Elastic. I’m wondering what people suggest in this situation."

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Replies (7)
Recommends
on
GrafanaGrafana
at

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)
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Recommends
on
KibanaKibana

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

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Bram Verdonck
Recommends
on
GrafanaGrafana
at

After looking for a way to monitor or at least get a better overview of our infrastructure, we found out that Grafana (which I previously only used in ELK stacks) has a plugin available to fully integrate with Amazon CloudWatch . Which makes it way better for our use-case than the offer of the different competitors (most of them are even paid). There is also a CloudFlare plugin available, the platform we use to serve our DNS requests. Although we are a big fan of https://smashing.github.io/ (previously dashing), for now we are starting with Grafana .

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Recommends
on
KibanaKibana

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.

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Recommends
on
KibanaKibana

Kibana should be sufficient in this architecture for decent analytics, if stronger metrics is needed then combine with Grafana. Datadog also offers nice overview but there's no need for it in this case unless you need more monitoring and alerting (and more technicalities).

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Recommends
on
GrafanaGrafana

I use Grafana because it is without a doubt the best way to visualize metrics

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Povilas Brilius
PHP Web Developer at GroundIn Software · | 0 upvotes · 594.8K views
Recommends
on
KibanaKibana
at

@Kibana, of course, because @Grafana looks like amateur sort of solution, crammed with query builder grouping aggregates, but in essence, as recommended by CERN - KIbana is the corporate (startup vectored) decision.

Furthermore, @Kibana comes with complexity adhering ELK stack, whereas @InfluxDB + @Grafana & co. recently have become sophisticated development conglomerate instead of advancing towards a understandable installation step by step inheritance.

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Decisions about Jaeger, Kibana, and Prometheus
Leonardo Henrique da Paixão
Junior QA Tester at SolarMarket · | 15 upvotes · 354.6K views

The objective of this work was to develop a system to monitor the materials of a production line using IoT technology. Currently, the process of monitoring and replacing parts depends on manual services. For this, load cells, microcontroller, Broker MQTT, Telegraf, InfluxDB, and Grafana were used. It was implemented in a workflow that had the function of collecting sensor data, storing it in a database, and visualizing it in the form of weight and quantity. With these developed solutions, he hopes to contribute to the logistics area, in the replacement and control of materials.

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Pros of Jaeger
Pros of Kibana
Pros of Prometheus
  • 6
    Easy to install
  • 6
    Open Source
  • 5
    Feature Rich UI
  • 4
    CNCF Project
  • 88
    Easy to setup
  • 64
    Free
  • 45
    Can search text
  • 21
    Has pie chart
  • 13
    X-axis is not restricted to timestamp
  • 9
    Easy queries and is a good way to view logs
  • 6
    Supports Plugins
  • 4
    Dev Tools
  • 3
    Can build dashboards
  • 3
    More "user-friendly"
  • 2
    Out-of-Box Dashboards/Analytics for Metrics/Heartbeat
  • 2
    Easy to drill-down
  • 1
    Up and running
  • 47
    Powerful easy to use monitoring
  • 38
    Flexible query language
  • 32
    Dimensional data model
  • 27
    Alerts
  • 23
    Active and responsive community
  • 22
    Extensive integrations
  • 19
    Easy to setup
  • 12
    Beautiful Model and Query language
  • 7
    Easy to extend
  • 6
    Nice
  • 3
    Written in Go
  • 2
    Good for experimentation
  • 1
    Easy for monitoring

Sign up to add or upvote prosMake informed product decisions

Cons of Jaeger
Cons of Kibana
Cons of Prometheus
    Be the first to leave a con
    • 6
      Unintuituve
    • 4
      Elasticsearch is huge
    • 3
      Hardweight UI
    • 3
      Works on top of elastic only
    • 12
      Just for metrics
    • 6
      Bad UI
    • 6
      Needs monitoring to access metrics endpoints
    • 4
      Not easy to configure and use
    • 3
      Supports only active agents
    • 2
      Written in Go
    • 2
      TLS is quite difficult to understand
    • 2
      Requires multiple applications and tools
    • 1
      Single point of failure

    Sign up to add or upvote consMake informed product decisions

    What is Jaeger?

    Jaeger, a Distributed Tracing System

    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 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.

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    Blog Posts

    Dec 8 2020 at 5:50PM

    DigitalOcean

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    May 21 2020 at 12:02AM

    Rancher Labs

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    Elastic

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    What are some alternatives to Jaeger, Kibana, and Prometheus?
    Zipkin
    It helps gather timing data needed to troubleshoot latency problems in service architectures. Features include both the collection and lookup of this data.
    AppDynamics
    AppDynamics develops application performance management (APM) solutions that deliver problem resolution for highly distributed applications through transaction flow monitoring and deep diagnostics.
    OpenTracing
    Consistent, expressive, vendor-neutral APIs for distributed tracing and context propagation.
    Datadog
    Datadog is the leading service for cloud-scale monitoring. It is used by IT, operations, and development teams who build and operate applications that run on dynamic or hybrid cloud infrastructure. Start monitoring in minutes with Datadog!
    Splunk
    It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.
    See all alternatives