kibana

Your window into the Elastic Stack

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Quick Overview

Kibana is an open-source data visualization and exploration tool for Elasticsearch. It provides a user-friendly interface for searching, analyzing, and visualizing large volumes of data stored in Elasticsearch indices. Kibana is a key component of the Elastic Stack, working seamlessly with Elasticsearch to provide powerful data insights and monitoring capabilities.

Pros

User-friendly interface for data exploration and visualization
Extensive set of built-in visualizations and dashboards
Highly customizable and extensible through plugins
Seamless integration with Elasticsearch and other Elastic Stack components

Cons

Steep learning curve for advanced features and customizations
Resource-intensive for large datasets or complex visualizations
Limited support for non-Elasticsearch data sources
Some features require a paid license for full functionality

Getting Started

To get started with Kibana, follow these steps:

Install and configure Elasticsearch
Download and install Kibana from the official website
Configure Kibana to connect to your Elasticsearch instance
Start Kibana and access the web interface

# Download and install Kibana
wget https://artifacts.elastic.co/downloads/kibana/kibana-8.8.1-linux-x86_64.tar.gz
tar -xzf kibana-8.8.1-linux-x86_64.tar.gz
cd kibana-8.8.1-linux-x86_64

# Configure Kibana (edit kibana.yml)
nano config/kibana.yml
# Set elasticsearch.hosts: ["http://localhost:9200"]

# Start Kibana
./bin/kibana

Once Kibana is running, access the web interface at http://localhost:5601 and start exploring your Elasticsearch data.

Competitor Comparisons

grafana

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Pros of Grafana

More flexible data source support, allowing integration with various databases and time-series stores
Easier to set up and configure for basic use cases
Stronger community-driven plugin ecosystem

Cons of Grafana

Less powerful when working with Elasticsearch data compared to Kibana
Limited built-in alerting capabilities
Fewer advanced analytics features out of the box

Code Comparison

Grafana (React component):

export const PanelChrome: React.FC<Props> = ({ children, width, height, padding = 'md' }) => {
  const theme = useTheme2();
  const styles = getPanelChromeStyles(theme, padding);

  return (
    <div className={styles.wrapper} style={{ width, height }}>
      {children}
    </div>
  );
};

Kibana (React component):

export function EuiPageTemplate({
  children,
  pageSideBar,
  pageHeader,
  pageBodyContent,
  minHeight,
  ...rest
}) {
  return (
    <EuiPage style={{ minHeight }} {...rest}>
      {pageSideBar && <EuiPageSideBar>{pageSideBar}</EuiPageSideBar>}
      <EuiPageBody>
        {pageHeader}
        <EuiPageContent>{pageBodyContent || children}</EuiPageContent>
      </EuiPageBody>
    </EuiPage>
  );
}

Both projects use React for their frontend components, but Kibana tends to rely more heavily on the Elastic UI framework, while Grafana has a more custom approach to UI components.

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Pros of Superset

More lightweight and easier to set up compared to Kibana
Supports a wider range of databases and data sources out of the box
Offers more customizable and interactive visualizations

Cons of Superset

Less robust when it comes to log analysis and time-series data exploration
Smaller community and ecosystem compared to Kibana
Fewer built-in security features and less granular access control

Code Comparison

Superset (Python):

from superset import db
from superset.models import Slice

slice = Slice(
    slice_name="My Chart",
    datasource_type="table",
    datasource_id=1,
    viz_type="bar"
)
db.session.add(slice)
db.session.commit()

Kibana (JavaScript):

const visualization = {
  title: 'My Chart',
  type: 'histogram',
  params: {
    addLegend: true,
    addTooltip: true
  },
  aggs: [
    { id: '1', type: 'count', schema: 'metric' },
    { id: '2', type: 'date_histogram', schema: 'segment', params: { field: 'timestamp', interval: 'auto' } }
  ]
};

Both Superset and Kibana offer powerful data visualization capabilities, but they cater to different use cases and have distinct strengths. Superset excels in its flexibility and ease of use, while Kibana shines in log analysis and integration with the Elastic Stack.

metabase

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Pros of Metabase

Easier setup and configuration for non-technical users
More intuitive UI for creating visualizations and dashboards
Supports a wider range of databases out-of-the-box

Cons of Metabase

Less powerful for complex data analysis and aggregations
Limited customization options compared to Kibana's advanced features
Smaller ecosystem and fewer third-party integrations

Code Comparison

Metabase (JavaScript):

const query = {
  "type": "query",
  "query": {
    "source-table": 1,
    "aggregation": [["count"]],
    "breakout": [["field", 2, {"temporal-unit": "month"}]]
  }
};

Kibana (JSON):

{
  "aggs": {
    "2": {
      "date_histogram": {
        "field": "timestamp",
        "interval": "1M",
        "time_zone": "UTC"
      }
    }
  },
  "size": 0,
  "query": {"match_all": {}}
}

Both examples show how to create a monthly aggregation query, but Metabase uses a more abstracted approach, while Kibana's query is more detailed and flexible.

redash

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Pros of Redash

More lightweight and easier to set up compared to Kibana
Supports a wider range of data sources out of the box
Offers a simpler, more intuitive user interface for creating visualizations

Cons of Redash

Less powerful for real-time data analysis and log management
Fewer advanced features and customization options
Smaller community and ecosystem compared to Kibana

Code Comparison

Redash query example:

SELECT date_trunc('day', created_at) AS day, COUNT(*) AS count
FROM users
GROUP BY day
ORDER BY day

Kibana query example (using Kibana Query Language):

GET my_index/_search
{
  "query": {
    "range": {
      "@timestamp": {
        "gte": "now-7d/d",
        "lte": "now/d"
      }
    }
  },
  "aggs": {
    "daily_count": {
      "date_histogram": {
        "field": "@timestamp",
        "calendar_interval": "day"
      }
    }
  }
}

Both examples demonstrate querying and aggregating data, but Redash uses SQL-like syntax, while Kibana uses Elasticsearch Query DSL, reflecting their different approaches to data analysis.

echarts

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Apache ECharts is a powerful, interactive charting and data visualization library for browser

Pros of ECharts

Lightweight and fast rendering, suitable for various devices and platforms
Extensive chart types and customization options
Strong community support and frequent updates

Cons of ECharts

Limited data analysis capabilities compared to Kibana
Less integrated with other data processing and visualization tools
Steeper learning curve for advanced customizations

Code Comparison

ECharts:

option = {
  xAxis: {
    type: 'category',
    data: ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
  },
  yAxis: {
    type: 'value'
  },
  series: [{
    data: [120, 200, 150, 80, 70, 110, 130],
    type: 'bar'
  }]
};

Kibana:

{
  "aggs": {
    "2": {
      "date_histogram": {
        "field": "@timestamp",
        "interval": "auto",
        "min_doc_count": 1
      }
    }
  },
  "size": 0,
  "query": {
    "match_all": {}
  }
}

ECharts focuses on chart configuration, while Kibana's code typically involves query and aggregation definitions for data visualization. ECharts provides more direct control over chart appearance, whereas Kibana integrates closely with Elasticsearch for data retrieval and processing.

chronograf

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Open source monitoring and visualization UI for the TICK stack

Pros of Chronograf

Lightweight and faster to set up compared to Kibana
Tighter integration with InfluxDB time-series database
More straightforward UI for time-series data visualization

Cons of Chronograf

Less extensive plugin ecosystem than Kibana
Narrower focus on time-series data, less versatile for general log analysis
Smaller community and fewer third-party resources available

Code Comparison

Chronograf (React component):

export const Page: FC<PageProps> = ({children}) => (
  <div className="chronograf-page">
    <Header />
    <Sidebar />
    <main>{children}</main>
  </div>
)

Kibana (React component):

export function KibanaLayout({
  children,
  showTopNav = true,
}: Props) {
  return (
    <EuiPage className="kbnPage">
      {showTopNav && <TopNavMenu />}
      <EuiPageBody>{children}</EuiPageBody>
    </EuiPage>
  );
}

Both projects use React for their frontend, with similar component structures. Chronograf's code tends to be more concise, while Kibana's often includes more extensive prop types and uses Elastic UI components.

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README

Kibana

Kibana is your window into the Elastic Stack. Specifically, it's a browser-based analytics and search dashboard for Elasticsearch.

Getting Started
- Using a Kibana Release
- Building and Running Kibana, and/or Contributing Code
Documentation
Version Compatibility with Elasticsearch
Questions? Problems? Suggestions?

Getting Started

If you just want to try Kibana out, check out the Elastic Stack Getting Started Page to give it a whirl.

If you're interested in diving a bit deeper and getting a taste of Kibana's capabilities, head over to the Kibana Getting Started Page.

Using a Kibana Release

If you want to use a Kibana release in production, give it a test run, or just play around:

Download the latest version on the Kibana Download Page.
Learn more about Kibana's features and capabilities on the Kibana Product Page.
We also offer a hosted version of Kibana on our Cloud Service.

Building and Running Kibana, and/or Contributing Code

You might want to build Kibana locally to contribute some code, test out the latest features, or try out an open PR:

CONTRIBUTING.md will help you get Kibana up and running.
If you would like to contribute code, please follow our STYLEGUIDE.mdx.
For all other questions, check out the FAQ.md and wiki.

Documentation

Visit Elastic.co for the full Kibana documentation.

For information about building the documentation, see the README in elastic/docs.

Version Compatibility with Elasticsearch

Ideally, you should be running Elasticsearch and Kibana with matching version numbers. If your Elasticsearch has an older version number or a newer major number than Kibana, then Kibana will fail to run. If Elasticsearch has a newer minor or patch number than Kibana, then the Kibana Server will log a warning.

Note: The version numbers below are only examples, meant to illustrate the relationships between different types of version numbers.

Situation	Example Kibana version	Example ES version	Outcome
Versions are the same.	7.15.1	7.15.1	ð OK
ES patch number is newer.	7.15.0	7.15.1	â ï¸ Logged warning
ES minor number is newer.	7.14.2	7.15.0	â ï¸ Logged warning
ES major number is newer.	7.15.1	8.0.0	ð« Fatal error
ES patch number is older.	7.15.1	7.15.0	â ï¸ Logged warning
ES minor number is older.	7.15.1	7.14.2	ð« Fatal error
ES major number is older.	8.0.0	7.15.1	ð« Fatal error

Questions? Problems? Suggestions?

If you've found a bug or want to request a feature, please create a GitHub Issue. Please check to make sure someone else hasn't already created an issue for the same topic.
Need help using Kibana? Ask away on our Kibana Discuss Forum and a fellow community member or Elastic engineer will be glad to help you out.

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