grafana

Pros of Chronograf

• Tightly integrated with InfluxDB, offering seamless compatibility
• User-friendly interface for creating dashboards and alerts
• Built-in support for Kapacitor, enabling advanced alerting capabilities

Cons of Chronograf

• Limited plugin ecosystem compared to Grafana
• Fewer data source options, primarily focused on InfluxDB
• Less extensive customization options for visualizations

Code Comparison

Chronograf (React-based UI):

import React from 'react'
import {Page} from 'src/reusable_ui'
import {ErrorHandling} from 'src/shared/decorators/errors'

@ErrorHandling
class HostsPage extends React.Component {
  render() {
    return <Page>...</Page>
  }
}

Grafana (Angular-based UI):

import { Component } from '@angular/core';
import { NavModelSrv } from 'app/core/core';

@Component({
  selector: 'grafana-app',
  template: '<grafana-root></grafana-root>',
})
export class GrafanaCtrl {
  constructor(private navModelSrv: NavModelSrv) {}
}

Both projects use modern JavaScript frameworks for their user interfaces, with Chronograf opting for React and Grafana using Angular. Grafana's codebase tends to be more extensive due to its broader feature set and plugin system.

Pros of Netdata

• Lightweight and efficient, with minimal system resource usage
• Real-time monitoring with per-second granularity
• Zero-configuration setup, auto-detects most metrics

Cons of Netdata

• Less flexible for custom dashboards and visualizations
• Limited long-term data storage capabilities
• Fewer integrations with external data sources

Code Comparison

Netdata configuration (netdata.conf):

[global]
  update every = 1
  memory mode = ram

Grafana dashboard JSON snippet:

{
  "datasource": "Prometheus",
  "fieldConfig": {
    "defaults": {
      "color": {"mode": "palette-classic"},
      "custom": {"axisLabel": "", "axisPlacement": "auto"}
    }
  }
}

Netdata focuses on simplicity and automatic configuration, while Grafana offers more customization options but requires more setup. Netdata excels at real-time monitoring with minimal overhead, making it ideal for resource-constrained environments. Grafana, on the other hand, provides a more powerful platform for creating complex dashboards and visualizations, with better support for long-term data analysis and a wider range of data source integrations.

Pros of Prometheus

• Highly efficient time-series database optimized for metrics collection and storage
• Built-in alerting system with flexible rule configuration
• Supports service discovery for dynamic environments

Cons of Prometheus

• Less versatile in terms of data visualization compared to Grafana
• Limited long-term storage capabilities without additional components
• Steeper learning curve for query language (PromQL)

Code Comparison

Prometheus (Go):

func (h *Handler) serveMetrics(w http.ResponseWriter, r *http.Request) {
    metrics.IncrementCounter(metrics.HttpRequestsTotal)
    promhttp.Handler().ServeHTTP(w, r)
}

Grafana (TypeScript):

export function MetricsPanel({ metrics }: Props) {
  return (
    <div>
      {metrics.map((metric) => (
        <MetricDisplay key={metric.id} value={metric.value} />
      ))}
    </div>
  );
}

Summary

Prometheus excels in metrics collection and alerting, while Grafana offers superior visualization capabilities. Prometheus uses Go and focuses on efficient time-series data handling, whereas Grafana, built with TypeScript, emphasizes creating rich, interactive dashboards. Both projects complement each other well in monitoring ecosystems, with Grafana often used to visualize Prometheus data.

Pros of Kibana

• Tightly integrated with Elasticsearch, offering seamless data exploration and visualization for ELK stack users
• Powerful machine learning and anomaly detection capabilities built-in
• Advanced geospatial analysis and mapping features

Cons of Kibana

• Less flexible for integrating with diverse data sources compared to Grafana
• Steeper learning curve for users not familiar with Elasticsearch
• More resource-intensive, especially for large-scale deployments

Code Comparison

Kibana (TypeScript):

import { CoreStart, Plugin } from '@kbn/core/public';
import { KibanaPluginSetup, KibanaPluginStart } from './types';

export class KibanaPlugin implements Plugin<KibanaPluginSetup, KibanaPluginStart> {
  public setup(core: CoreSetup): KibanaPluginSetup {
    // Setup logic
  }
}

Grafana (Go):

package main

import (
    "github.com/grafana/grafana/pkg/api/dtos"
    "github.com/grafana/grafana/pkg/models"
)

func (hs *HTTPServer) GetDashboard(c *models.ReqContext) response.Response {
    // Dashboard retrieval logic
}

Both projects use different programming languages and architectures, reflecting their distinct design philosophies and integration approaches.

Pros of Superset

• More extensive SQL support and database connectivity options
• Built-in data exploration and visualization features
• Stronger focus on business intelligence and data analysis workflows

Cons of Superset

• Steeper learning curve for non-technical users
• Less extensive plugin ecosystem compared to Grafana
• Fewer out-of-the-box integrations with monitoring and observability tools

Code Comparison

Grafana (TypeScript):

const panel = new GraphPanel({
  title: 'CPU Usage',
  datasource: 'Prometheus',
  targets: [
    { expr: 'avg(node_cpu_utilization)' }
  ]
});

Superset (Python):

from superset import viz

viz_obj = viz.viz_types['line'](
    datasource=cpu_usage_ds,
    form_data={
        'metrics': ['avg_cpu_utilization'],
        'granularity': 'hour'
    }
)

Both examples demonstrate creating a simple visualization, but Grafana's approach is more focused on time-series data and monitoring, while Superset's code reflects its broader data analysis capabilities.

Pros of Metabase

• More user-friendly interface, suitable for non-technical users
• Built-in SQL editor for ad-hoc queries
• Easier setup and configuration process

Cons of Metabase

• Less extensive data source support compared to Grafana
• Limited customization options for visualizations
• Fewer advanced features for time-series data analysis

Code Comparison

Metabase (Clojure):

(defn format-number
  [n]
  (if (number? n)
    (let [formatted (format-number-with-period n)]
      (if (re-find #"[.,]\d+$" formatted)
        (str/replace formatted #"0+$" "")
        formatted))
    n))

Grafana (TypeScript):

export function formatNumber(value: number, decimals: number): string {
  if (value === null || value === undefined) {
    return '';
  }
  const factor = decimals ? Math.pow(10, Math.max(0, decimals)) : 1;
  const formatted = Math.round(value * factor) / factor;
  return formatted.toLocaleString();
}

Both projects aim to provide data visualization and analytics capabilities, but they cater to different user groups and use cases. Metabase focuses on simplicity and ease of use, making it ideal for business users and small teams. Grafana, on the other hand, offers more advanced features and customization options, making it better suited for technical users and complex monitoring scenarios.