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Kibana vs Laravel Telescope: What are the differences?
Key differences between Kibana and Laravel Telescope
Kibana and Laravel Telescope are two popular tools used for monitoring and debugging applications. While both serve similar purposes, there are some key differences between them.
Data Visualization Capabilities: Kibana is primarily used as a data visualization tool, allowing users to create interactive dashboards and visualizations based on data stored in Elasticsearch. It provides a wide range of visualizations options such as line charts, bar charts, and maps. Laravel Telescope, on the other hand, focuses on providing real-time insights into application performance and errors, rather than data visualization.
Integration with Framework: Laravel Telescope is specifically designed for Laravel applications, making it easy to integrate and use within Laravel projects. It provides detailed information about the current requests, database queries, and exceptions, allowing developers to quickly identify and debug issues specific to Laravel. Kibana, on the other hand, is a standalone tool that can be used with any application that utilizes Elasticsearch as its data store.
Monitoring Capabilities: Kibana offers a wide range of monitoring capabilities, allowing users to monitor the health and performance of their Elasticsearch cluster, as well as other data sources. It provides various built-in visualizations and monitors that enable users to track metrics such as CPU usage, memory usage, and network traffic. Laravel Telescope primarily focuses on monitoring Laravel applications, providing insights into the performance and errors occurring within the application.
Alerting and Notification: Kibana offers robust alerting and notification features, allowing users to set up alerts based on predefined conditions and receive notifications when those conditions are met. Laravel Telescope, on the other hand, does not directly provide alerting and notification capabilities. However, developers can integrate Telescope with other alerting systems or use custom solutions to achieve similar functionality.
Scalability: Kibana is designed to handle large volumes of data and can scale horizontally by adding more nodes to the Elasticsearch cluster. It can handle the visualization of data from multiple sources and allows for the creation of complex dashboards. Laravel Telescope, on the other hand, is more focused on providing real-time insights and debugging capabilities for individual Laravel applications, rather than handling large-scale data visualization.
Deployment and Configuration: Kibana requires a separate deployment and configuration process, as it needs to be installed and configured alongside Elasticsearch. It provides additional configuration options for indexing and visualizing data. Laravel Telescope, on the other hand, can be easily installed and configured within Laravel applications using Composer, making it a more straightforward option for Laravel developers.
In summary, Kibana is a powerful data visualization and monitoring tool that can be used with various applications, while Laravel Telescope is specifically designed for Laravel applications, providing real-time insights and debugging capabilities.
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 Kibana
- Easy to setup88
- Free65
- 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
- More "user-friendly"3
- Can build dashboards3
- Out-of-Box Dashboards/Analytics for Metrics/Heartbeat2
- Easy to drill-down2
- Up and running1
Pros of Laravel Telescope
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Cons of Kibana
- Unintuituve7
- Works on top of elastic only4
- Elasticsearch is huge4
- Hardweight UI3
Cons of Laravel Telescope
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