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Jaeger vs Kibana: What are the differences?
Introduction:
Jaeger and Kibana are two widely used tools in the field of distributed tracing and log analysis respectively. While both tools serve similar purposes, there are several key differences between Jaeger and Kibana that set them apart. This article aims to outline these differences in order to help users understand their unique features and benefits.
Data Collection and Visualization: Jaeger is specifically designed for distributed tracing, collecting and visualizing trace data within a microservices architecture. It provides end-to-end visibility into requests flowing through multiple services. On the other hand, Kibana is primarily used for log analysis and visualization, aggregating log data from various sources and providing powerful search capabilities.
Data Enablement: Jaeger focuses on capturing and analyzing data related to distributed systems' performance, latency, and request flows. It helps identify bottlenecks and optimize overall system performance. Kibana, on the other hand, empowers users to gain insights and perform analysis on log data, facilitating troubleshooting and root cause analysis of issues in distributed systems.
Interface and User Experience: Jaeger provides a specialized, easy-to-use interface for distributed tracing. It allows users to drill down into traces, examine span details, and visualize dependency graphs. Kibana, on the other hand, features a more comprehensive interface that supports logs, metrics, and other analytical visualizations, providing a broader range of capabilities beyond distributed tracing.
Query and Search Capabilities: Jaeger allows users to search and filter traces based on specific criteria such as service name, operation name, and duration. It enables users to identify and analyze traces matching certain conditions. Kibana, on the other hand, offers advanced search capabilities on log data, including filters, aggregations, and complex queries. It facilitates searching, filtering, and extracting insights from large volumes of log events.
Integration with Ecosystem: Jaeger is specifically designed to work with systems adopting the OpenTracing standard, making it easily integrable with various programming languages and frameworks. On the other hand, Kibana belongs to the Elastic Stack, which includes Elasticsearch and Logstash, enabling seamless integration with the broader Elastic ecosystem for log analysis and management.
Alerting and Anomaly Detection: Jaeger does not provide built-in alerting or anomaly detection capabilities, as its primary focus is on distributed tracing and performance analysis. However, Kibana offers alerting functionalities that can trigger notifications based on predefined conditions, allowing proactive monitoring and timely response to critical events in log data.
In summary, Jaeger and Kibana serve different purposes in the field of distributed systems analysis. Jaeger excels in distributed tracing, visualizing end-to-end request flows, and performance optimization. On the other hand, Kibana specializes in log analysis, providing powerful search capabilities and a comprehensive interface for troubleshooting and root cause analysis.
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."
For our Predictive Analytics platform, we have used both Grafana and Kibana
- Grafana based demo video: https://www.youtube.com/watch?v=tdTB2AcU4Sg
- Kibana based reporting screenshot: https://imgur.com/vuVvZKN
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)
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
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 .
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.
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).
@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.
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.
Pros of Jaeger
- Easy to install6
- Open Source6
- Feature Rich UI5
- CNCF Project4
Pros of Kibana
- Easy to setup88
- Free64
- Can search text45
- Has pie chart21
- X-axis is not restricted to timestamp13
- Easy queries and is a good way to view logs9
- Supports Plugins6
- Dev Tools4
- Can build dashboards3
- More "user-friendly"3
- Out-of-Box Dashboards/Analytics for Metrics/Heartbeat2
- Easy to drill-down2
- Up and running1
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Cons of Jaeger
Cons of Kibana
- Unintuituve6
- Elasticsearch is huge4
- Hardweight UI3
- Works on top of elastic only3
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