What is Prometheus?

Prometheus is a powerful system monitoring and alerting toolkit that:

• Collects and stores metrics as time-series data.
• Uses a powerful query language called PromQL to aggregate and query metrics.
• Supports multiple modes of graphing and dashboarding.
• Integrates with numerous third-party tools and services.

Getting Started with Prometheus

1. Installation and Setup

Step 1: Download Prometheus

• Visit the Prometheus download page and download the latest release for your operating system.

Step 2: Install Prometheus

• Extract the downloaded archive and navigate to the directory.
• You should see binaries like prometheus and promtool.

Step 3: Configure Prometheus

• Create a configuration file named prometheus.yml. Here’s an example configuration:

global:
  scrape_interval: 15s  # Set the scrape interval to 15 seconds.
  evaluation_interval: 15s  # Evaluate rules every 15 seconds.

scrape_configs:
  - job_name: 'prometheus'
    static_configs:
      - targets: ['localhost:9090']  # The Prometheus server itself.

Step 4: Start Prometheus

• Run the Prometheus server:

./prometheus --config.file=prometheus.yml

• Access the Prometheus web UI at http://localhost:9090.

2. Collecting Metrics

Prometheus scrapes metrics from HTTP endpoints. Applications need to expose metrics in a format that Prometheus understands.

Step 1: Exporting Metrics

• Use client libraries available for various programming languages to instrument your code.
  • Go: prometheus/client_golang
  • Java: prometheus/client_java
  • Python: prometheus/client_python
  • Node.js: prometheus/client_node

Example (Python)

• Install the client library:

pip install prometheus-client

• Instrument your application:

from prometheus_client import start_http_server, Summary
import random
import time

# Create a metric to track time spent and requests made.
REQUEST_TIME = Summary('request_processing_seconds', 'Time spent processing request')

# Decorate function with metric.
@REQUEST_TIME.time()
def process_request(t):
    time.sleep(t)

if __name__ == '__main__':
    start_http_server(8000)
    while True:
        process_request(random.random())

Step 2: Configure Prometheus to Scrape Your Application

• Update your prometheus.yml configuration file:

scrape_configs:
  - job_name: 'python_app'
    static_configs:
      - targets: ['localhost:8000']

3. Querying Metrics with PromQL

PromQL is a powerful query language used to aggregate and retrieve time-series data.

Basic Queries

• Instant Vector: up
• Range Vector: up[5m]
• Aggregation: sum(rate(http_requests_total[1m]))
• Label Filtering: http_requests_total{job="python_app"}

Step 1: Access Prometheus UI

• Navigate to the Graph tab in the Prometheus web UI.

Step 2: Run a Query

• Enter a query in the query box and click “Execute”. For example:

rate(http_requests_total[5m])

• This query calculates the per-second rate of HTTP requests over the last 5 minutes.

4. Setting Up Alerts

Prometheus allows you to define alerting rules and integrates with Alertmanager for handling alerts.

Step 1: Define Alerting Rules

• Create a file named alert.rules.yml:

groups:
  - name: example
    rules:
      - alert: HighErrorRate
        expr: rate(http_requests_total{status="500"}[5m]) > 0.05
        for: 10m
        labels:
          severity: page
        annotations:
          summary: "High error rate detected"
          description: "Error rate is greater than 5% for the last 10 minutes."

Step 2: Configure Prometheus to Use the Alerting Rules

• Update your prometheus.yml:

rule_files:
  - "alert.rules.yml"

Step 3: Install and Configure Alertmanager

• Download Alertmanager from the Prometheus download page.
• Create a configuration file for Alertmanager, alertmanager.yml:

global:
  resolve_timeout: 5m

route:
  receiver: 'email'

receivers:
  - name: 'email'
    email_configs:
      - to: 'you@example.com'
        from: 'alertmanager@example.com'
        smarthost: 'smtp.example.com:587'
        auth_username: 'alertmanager@example.com'
        auth_identity: 'alertmanager@example.com'
        auth_password: 'password'

Step 4: Start Alertmanager

• Run Alertmanager:

./alertmanager --config.file=alertmanager.yml

Step 5: Configure Prometheus to Send Alerts to Alertmanager

• Update your prometheus.yml:

alerting:
  alertmanagers:
    - static_configs:
        - targets: ['localhost:9093']

5. Visualizing Metrics

Prometheus does not include advanced visualization capabilities. Instead, it integrates seamlessly with Grafana for advanced dashboarding.

Step 1: Install Grafana

• Download Grafana from the Grafana website.

Step 2: Start Grafana

• Follow the installation instructions and start the Grafana server.

Step 3: Add Prometheus as a Data Source

• Log in to Grafana (default http://localhost:3000, admin/admin).
• Go to “Configuration” > “Data Sources”.
• Click “Add data source” and select “Prometheus”.
• Configure the URL (e.g., http://localhost:9090) and save.

Step 4: Create a Dashboard

• Go to “Dashboards” > “New Dashboard”.
• Click “Add new panel” and use PromQL to query Prometheus metrics.
• Customize the panel with different visualization options and save the dashboard.

What is Kibana?

Kibana is an open-source analytics and visualization platform designed to work with Elasticsearch. It provides a user-friendly interface for exploring, visualizing, and sharing insights from your data. Whether you are analyzing logs, metrics, or any other type of structured and unstructured data, Kibana makes it easy to turn your data into actionable insights.

Getting Started with Kibana

1. Installation and Setup

To get started with Kibana, you need to have Elasticsearch installed and running. Follow these steps:

1. Download and Install Elasticsearch and Kibana:
   • Download Elasticsearch from Elastic’s website.
   • Follow the installation instructions specific to your operating system.
   • Download Kibana from Elastic’s website.
   • Follow the installation instructions for Kibana.
2. Start Elasticsearch and Kibana:
   • Start Elasticsearch by running the elasticsearch executable.
   • Start Kibana by running the kibana executable.
   • Open your web browser and navigate to http://localhost:5601 to access the Kibana interface.
3. Load Sample Data:
   • To familiarize yourself with Kibana, you can load sample data sets from the Kibana home page. These samples include data for e-commerce, logs, and more.

2. Index Your Data in Elasticsearch

Before you can visualize data in Kibana, you need to index your data in Elasticsearch. You can use various tools like Logstash, Beats, or custom scripts to send data to Elasticsearch. For example, using Logstash:

1. Install Logstash:
   • Download and install Logstash from the Elastic website.
2. Configure Logstash:
   • Create a configuration file for Logstash (e.g., logstash.conf):

input {
  file {
    path => "/path/to/your/logfile.log"
    start_position => "beginning"
  }
}
output {
  elasticsearch {
    hosts => ["localhost:9200"]
    index => "your-index-name"
  }
}

Start Logstash:

• Run Logstash with the configuration file:

bin/logstash -f logstash.conf

3. Creating Visualizations in Kibana

Once your data is indexed in Elasticsearch, you can start creating visualizations in Kibana.

1. Access Kibana:
   • Navigate to http://localhost:5601 in your web browser.
2. Configure Index Patterns:
   • Go to “Management” > “Stack Management” > “Index Patterns”.
   • Create a new index pattern that matches the name of the index where your data is stored (e.g., your-index-name*).
3. Create a Visualization:
   • Go to the “Visualize” tab.
   • Click “Create visualization” and choose the type of visualization you want to create (e.g., bar chart, pie chart, line graph).
   • Select the index pattern you created earlier.
   • Configure the visualization by selecting the fields and metrics you want to visualize. For example, for a bar chart:
     • Choose an aggregation type (e.g., count, average, sum).
     • Select the field to aggregate on (e.g., timestamp for a time-series visualization).
     • Configure any additional options such as intervals, filters, and split series.
4. Save the Visualization:
   • Once you’re satisfied with your visualization, click “Save” and give it a meaningful name.

4. Building Dashboards

Dashboards in Kibana allow you to combine multiple visualizations into a single view, providing a comprehensive overview of your data.

1. Create a Dashboard:
   • Go to the “Dashboard” tab.
   • Click “Create new dashboard”.
   • Click “Add” to add visualizations to your dashboard.
   • Select the visualizations you created earlier and arrange them as needed.
2. Customize and Save the Dashboard:
   • Customize the layout and appearance of your dashboard.
   • Add filters and controls to enable interactive data exploration.
   • Save the dashboard with a meaningful name.

Conclusion

Kibana is a versatile and powerful tool for data visualization and analysis. By following this detailed guide, you can get started with Kibana, from installation and setup to creating advanced visualizations and dashboards. Whether you are a beginner or an experienced user, Kibana offers the tools you need to turn your data into actionable insights, helping you make informed decisions and drive your projects forward.