## **Node** #### **Definition**: An individual server, part of a cluster, designated to store data and participate in the cluster's indexing and search capabilities. #### **Roles**: In OpenSearch and Elasticsearch, a cluster is composed of one or more nodes, which are instances of the software that store data and participate in the cluster's indexing and search capabilities. Nodes can have different roles, each tailored to specific functions within the cluster. Understanding these roles is crucial for designing an efficient and resilient OpenSearch cluster. Here are the primary node types and their specific functions: ##### Master-Eligible Node - **Function**: Master-eligible nodes are responsible for managing the cluster-wide operations and configuration changes. These include creating or deleting indices, keeping track of which nodes are part of the cluster, and allocating shards to nodes. - **Characteristics**: A cluster should have multiple master-eligible nodes to ensure high availability. However, only one node acts as the master at a time, with the others ready to take over if the current master fails. - **Best Practice**: It's recommended to have at least three master-eligible nodes in a production environment to avoid split-brain scenarios, where multiple nodes believe they are the master. ##### Data Node - **Function**: Data nodes store the data and execute data-related operations such as CRUD (Create, Read, Update, Delete), search, and aggregations. These nodes do the heavy lifting when it comes to handling the data and search queries. - **Characteristics**: Because they handle data and query operations, data nodes require significant disk space, CPU, and memory resources. The performance of data nodes directly impacts the cluster's performance. ##### Ingest Node - **Function**: Ingest nodes are responsible for pre-processing documents before indexing. This involves applying transformations and enrichments to the data as defined by an ingest pipeline. - **Characteristics**: Ingest nodes can reduce the load on data nodes by handling data transformation upfront. This is especially useful when data from various sources needs to be normalized or enriched before storage. ##### Coordinating Node - **Function**: Also known simply as a "coordinating node," its role is to distribute incoming client requests to the appropriate nodes (e.g., data nodes for search queries) and compile the results back to the client. Every node in an OpenSearch cluster can act as a coordinating node by default. - **Characteristics**: These nodes do not hold data or perform cluster management tasks but require sufficient CPU and memory to handle and coordinate client requests efficiently. ##### Machine Learning Node - **Function**: Machine Learning (ML) nodes are specialized nodes that perform machine learning tasks, available in Elastic's X-Pack. They can analyze data and identify anomalies, trends, and patterns. - **Characteristics**: ML nodes require sufficient CPU and memory resources to handle the computational demands of machine learning tasks. ### Multi-role Nodes - **Flexibility**: Nodes can be configured to fulfill multiple roles simultaneously. For example, in smaller clusters, a node might be both a master-eligible and data node. However, for scalability and performance, it's often better to dedicate nodes to specific roles in larger production environments. ### Design Considerations - **Cluster Design**: The choice and configuration of node types depend on the specific requirements of your cluster, including size, performance, and redundancy needs. A well-designed cluster balances these factors to ensure efficient operation and high availability. - **Resource Allocation**: Assigning specific roles to nodes can help in optimizing the allocation of resources. For example, separating heavy data-processing tasks from master-eligibility can prevent management tasks from being delayed by data processing loads.