In this blog, I am using a Dockerized ELK Stack that results in: three Docker containers running in parallel, for Elasticsearch, Logstash, and Kibana, port forwarding set up, and a data volume for persisting Elasticsearch data.

The ELK Stack (Elasticsearch, Logstash, and Kibana) can be installed on a variety of different operating systems and in various different setups. While the most common installation setup is Linux and other Unix-based systems, a less-discussed scenario is using Docker.

To get the default distributions of Elasticsearch and Kibana up and running in Docker, you can use Docker Compose.

Create a docker-compose.yml file for the single node Elastic with Logstash & Kibana. The following example brings up three containers so you can see how things work. 

version: '3.2'

services:
  elasticsearch:
    build:
      context: elasticsearch/
      args:
        ELK_VERSION: $ELK_VERSION
    volumes:
      - type: bind
        source: ./elasticsearch/config/elasticsearch.yml
        target: /usr/share/elasticsearch/config/elasticsearch.yml
        read_only: true
      - type: volume
        source: elasticsearch
        target: /usr/share/elasticsearch/data
    ports:
      - "9200:9200"
      - "9300:9300"
    environment:
      ES_JAVA_OPTS: "-Xmx256m -Xms256m"
      ELASTIC_PASSWORD: changeme
      # Use single node discovery in order to disable production mode and avoid bootstrap checks.
      # see: https://www.elastic.co/guide/en/elasticsearch/reference/current/bootstrap-checks.html
      discovery.type: single-node
    networks:
      - elk

  logstash:
    build:
      context: logstash/
      args:
        ELK_VERSION: $ELK_VERSION
    volumes:
      - type: bind
        source: ./logstash/config/logstash.yml
        target: /usr/share/logstash/config/logstash.yml
        read_only: true
      - type: bind
        source: ./logstash/pipeline
        target: /usr/share/logstash/pipeline
        read_only: true
    ports:
      - "5044:5044"
      - "5000:5000/tcp"
      - "5000:5000/udp"
      - "9600:9600"
    environment:
      LS_JAVA_OPTS: "-Xmx256m -Xms256m"
    networks:
      - elk
    depends_on:
      - elasticsearch

  kibana:
    build:
      context: kibana/
      args:
        ELK_VERSION: $ELK_VERSION
    volumes:
      - type: bind
        source: ./kibana/config/kibana.yml
        target: /usr/share/kibana/config/kibana.yml
        read_only: true
    ports:
      - "5601:5601"
    networks:
      - elk
    depends_on:
      - elasticsearch

networks:
  elk:
    driver: bridge

volumes:
  elasticsearch:

Make sure Docker Engine is allotted at least 6-8 GiB of memory. In Docker Desktop.

Run docker-compose to bring up the three docker container Elasticsearch, Logstasg and Kibana:

Submit a _cat/nodes request to see that the nodes are up and running:

curl -X GET "localhost:9200/_cat/nodes?v&pretty"

Open Kibana to load sample data and interact with the cluster: 

When you’re done experimenting, you can tear down the containers and volumes by running 

Installing the ElasticSearch Logstash & Kibana (ELK) Stack on Docker with Centos Base system

Installing the ELK Stack on Docker Container

ELK requires Java, so first, you need to install Java on your Ubuntu system. You can do this by running the following command:

Next, you need to install Elasticsearch. You can do this by following these steps:

a. Download Elasticsearch using the following command:

wget -qO - https://artifacts.elastic.co/GPG-KEY-elasticsearch | sudo apt-key add -

b. Add the Elasticsearch repository to apt:

echo "deb https://artifacts.elastic.co/packages/7.x/apt stable main" | sudo tee -a /etc/apt/sources.list.d/elastic-7.x.list

d. Start and enable the Elasticsearch service:

Next, you need to install Logstash. You can do this by following these steps:

Finally, you need to install Kibana. You can do this by following these steps:

That's it! You have successfully installed ELK on Ubuntu. You can access Kibana by visiting http://localhost:5601 in your web browser.

ELK stands for Elasticsearch, Logstash, and Kibana, which are three open-source software tools commonly used together for log management and analysis. Here are the steps to set up ELK on Ubuntu:

In Ubuntu, how to install and configure Elasticsearch, Logstash, and Kibana (ELK)!

Kibana is an open-source data visualization and exploration platform designed to work with Elasticsearch. It allows users to visualize and analyze data stored in Elasticsearch using a variety of tools, including charts, tables, maps, and graphs.

Kibana provides a user-friendly interface for visualizing and exploring data, making it easier for users to gain insights from their data. It also includes a wide range of features for creating custom dashboards, exploring and analyzing data, and sharing insights with others.

Some of the key features of Kibana include:

1. Data visualization: Kibana allows users to create visualizations of their data using a variety of charts, graphs, and tables.

2. Dashboard creation: Users can create custom dashboards to monitor and analyze data from multiple sources.

3. Data exploration: Kibana provides tools for exploring and analyzing data, including filtering, sorting, and aggregating data.

4. Data sharing: Users can share dashboards and visualizations with others, making it easier to collaborate on data analysis projects.

Kibana is a part of the Elastic Stack, which also includes Elasticsearch, Logstash, and Beats. Elasticsearch is a distributed search and analytics engine that stores and indexes data, while Logstash and Beats are data processing tools that allow users to collect, parse, and send data to Elasticsearch.

Kibana can be used to analyze and visualize data from a wide range of sources, including logs, metrics, and transactional data. It supports a variety of data formats, including JSON, CSV, and text.

Some of the common use cases for Kibana include:

1. Log analysis: Kibana can be used to analyze logs from various sources, including servers, applications, and network devices.

2. Metrics monitoring: Kibana can be used to monitor and analyze metrics such as CPU usage, memory usage, and network traffic.

3. Security analytics: Kibana can be used to analyze security-related data, such as firewall logs and intrusion detection logs.

4. Business analytics: Kibana can be used to analyze business-related data, such as sales data and customer feedback.

Kibana has a variety of features and tools that make it a powerful platform for data visualization and exploration. Some of the key features include:

1. Visualizations: Kibana provides a wide range of visualization options, including bar charts, line charts, pie charts, heat maps, and more. Users can customize the visualizations with various settings and options to best display their data.

2. Dashboards: Kibana allows users to create custom dashboards to display multiple visualizations and data sources in a single view. Dashboards can be shared with others, and users can create and save multiple dashboards for different purposes.

3. Querying and filtering: Kibana allows users to query and filter data in real-time, making it easy to explore and analyze large datasets. Users can create complex queries using a simple query language or filter data using various criteria.

4. Data aggregation and analysis: Kibana provides a variety of tools for aggregating and analyzing data, including histograms, date histograms, percentiles, and more. Users can create custom aggregations to suit their needs.

5. Geo-mapping: Kibana includes tools for visualizing and analyzing geographic data, including support for mapping and geocoding.

Overall, Kibana is a powerful tool for visualizing and exploring data stored in Elasticsearch, and is widely used by data analysts, developers, and other professionals in a variety of industries. Its flexible and customizable interface, combined with its advanced data visualization and analysis capabilities, make it a popular choice for data-driven organizations.

In this blog, I will discuss what is Kibana and what are their advantages

Kibana : A complete overview

I configured elastic seek, kibana and log stash cluster. Elastic search is working excellent, however kibana is showing an errors mentioning kibana server now not equipped.

Jan 13 12:50:05 kibana-csi kibana[28007]: {"type":"log","@timestamp":"2020-01-13T12:50:05Z","tags":["info","migrations"],"pid":28007,"message":"Creating index .kibana_1."}

Jan 13 12:50:05 kibana-csi kibana[28007]: {"type":"log","@timestamp":"2020-01-13T12:50:05Z","tags":["warning","migrations"],"pid":28007,"message":"Unable to connect to Elasticsearch. Error: [resource_already_exists_exception] index [.kibana_task_manager_1/4qj7_j5URN6TW2Pvr5qk7w] already exists, with { index_uuid=\"4qj7_j5URN6TW2Pvr5qk7w\" & index=\".kibana_task_manager_1\" }"}

Jan 13 12:50:05 kibana-csi kibana[28007]: {"type":"log","@timestamp":"2020-01-13T12:50:05Z","tags":["warning","migrations"],"pid":28007,"message":"Another Kibana instance appears to be migrating the index. Waiting for that migration to complete. If no other Kibana instance is attempting migrations, you can get past this message by deleting index .kibana_task_manager_1 and restarting Kibana."}

Jan 13 12:50:35 kibana-csi kibana[28007]: {"type":"log","@timestamp":"2020-01-13T12:50:35Z","tags":["warning","migrations"],"pid":28007,"message":"Unable to connect to Elasticsearch. Error: Request Timeout after 30000ms"}

Jan 13 12:50:38 kibana-csi kibana[28007]: {"type":"log","@timestamp":"2020-01-13T12:50:38Z","tags":["warning","migrations"],"pid":28007,"message":"Unable to connect to Elasticsearch. Error: [resource_already_exists_exception] index [.kibana_1/LDkaYE3JQH2hhq4Xb4uDZw] already exists, with { index_uuid=\"LDkaYE3JQH2hhq4Xb4uDZw\" & index=\".kibana_1\" }"}

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Installing the ELK Stack on Docker Container

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Kibana : A complete overview

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Installing the ELK Stack on Docker Container

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Kibana : A complete overview

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