Elastic search cluster , use External load balancer or internal self managed - unix1998/technical_notes GitHub Wiki

Elasticsearch can handle load balancing internally without the need for an external load balancer like Nginx, Apache, or F5. However, using an external load balancer can provide additional features and benefits such as SSL termination, advanced routing, and better failover mechanisms. Here are the details for both approaches:

Internal Load Balancing in Elasticsearch:

Elasticsearch has built-in capabilities for load balancing and node discovery. When you configure a multi-node cluster, clients (Elasticsearch clients or applications) can send requests to any node in the cluster, and Elasticsearch will automatically distribute the requests among the nodes. This is managed through the discovery.seed_hosts and cluster.initial_master_nodes settings.

Advantages:

• Simplicity: No additional components are required.
• Built-in: Elasticsearch handles the distribution and failover internally.

Disadvantages:

• Limited Features: Lacks advanced load balancing features such as SSL termination or complex routing rules.

External Load Balancer:

Using an external load balancer like Nginx, Apache, or F5 can provide additional control and features for handling traffic to your Elasticsearch cluster.

Advantages:

• Advanced Features: SSL termination, URL rewriting, caching, etc.
• Centralized Control: Easier to manage and monitor traffic.
• Better Failover: More robust failover mechanisms and health checks.

Disadvantages:

• Added Complexity: Requires configuration and management of the external load balancer.
• Potential Bottleneck: The load balancer itself can become a single point of failure if not properly managed.

Example of Using Nginx as a Load Balancer for Elasticsearch:

1. Install Nginx:

   sudo apt-get update
   sudo apt-get install nginx
   

2. Configure Nginx:

   • Edit the Nginx configuration file (usually located at /etc/nginx/nginx.conf or create a new file in /etc/nginx/conf.d/).

   upstream elasticsearch {
       server node1_ip:9200;
       server node2_ip:9200;
       server node3_ip:9200;
   }
   
   server {
       listen 80;
   
       location / {
           proxy_pass http://elasticsearch;
           proxy_set_header Host $host;
           proxy_set_header X-Real-IP $remote_addr;
           proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
           proxy_set_header X-Forwarded-Proto $scheme;
       }
   }
   

3. Restart Nginx:

   sudo systemctl restart nginx
   

Example of Using F5 Load Balancer:

1. Configure Pool Members:

   • Add your Elasticsearch nodes (node1_ip:9200, node2_ip:9200, node3_ip:9200) as pool members in the F5 configuration.

2. Create a Virtual Server:

   • Create a virtual server that listens on the desired port (e.g., port 80 or 443) and forwards traffic to the Elasticsearch pool.

3. Health Monitoring:

   • Set up health monitors to check the health of each Elasticsearch node and ensure traffic is only sent to healthy nodes.

Internal Elasticsearch Configuration:

If you decide to rely on Elasticsearch's internal mechanisms without an external load balancer, ensure that your client applications are configured to connect to multiple nodes. For example, in a Java application using the Elasticsearch REST client:

RestClientBuilder builder = RestClient.builder(
    new HttpHost("node1_ip", 9200, "http"),
    new HttpHost("node2_ip", 9200, "http"),
    new HttpHost("node3_ip", 9200, "http")
);
RestHighLevelClient client = new RestHighLevelClient(builder);

Conclusion:

While Elasticsearch can handle load balancing internally, using an external load balancer provides additional features and can enhance the robustness of your deployment. The choice depends on your specific requirements and the complexity you're willing to manage.