Node

Any time that you start an instance of Elasticsearch, you are starting a node. A collection of connected nodes is called a cluster. If you are running a single node of Elasticsearch, then you have a cluster of one node.

Every node in the cluster can handle HTTP and Transport traffic by default. The transport layer is used exclusively for communication between nodes and the Java TransportClient; the HTTP layer is used only by external REST clients.

All nodes know about all the other nodes in the cluster and can forward client requests to the appropriate node. Besides that, each node serves one or more purpose:

By default a node is a master-eligible node and a data node, plus it can pre-process documents through ingest pipelines. This is very convenient for small clusters but, as the cluster grows, it becomes important to consider separating dedicated master-eligible nodes from dedicated data nodes.

The master node is responsible for lightweight cluster-wide actions such as creating or deleting an index, tracking which nodes are part of the cluster, and deciding which shards to allocate to which nodes. It is important for cluster health to have a stable master node.

Any master-eligible node (all nodes by default) may be elected to become the master node by the master election process.

Indexing and searching your data is CPU-, memory-, and I/O-intensive work which can put pressure on a node’s resources. To ensure that your master node is stable and not under pressure, it is a good idea in a bigger cluster to split the roles between dedicated master-eligible nodes and dedicated data nodes.

While master nodes can also behave as coordinating nodes and route search and indexing requests from clients to data nodes, it is better not to use dedicated master nodes for this purpose. It is important for the stability of the cluster that master-eligible nodes do as little work as possible.

To create a dedicated master-eligible node, set:

node.master: true 
node.data: false 
node.ingest: false 
cluster.remote.connect: false 

To prevent data loss, it is vital to configure the discovery.zen.minimum_master_nodes setting (which defaults to 1) so that each master-eligible node knows the minimum number of master-eligible nodes that must be visible in order to form a cluster.

To explain, imagine that you have a cluster consisting of two master-eligible nodes. A network failure breaks communication between these two nodes. Each node sees one master-eligible node…​ itself. With minimum_master_nodes set to the default of 1, this is sufficient to form a cluster. Each node elects itself as the new master (thinking that the other master-eligible node has died) and the result is two clusters, or a split brain. These two nodes will never rejoin until one node is restarted. Any data that has been written to the restarted node will be lost.

Now imagine that you have a cluster with three master-eligible nodes, and minimum_master_nodes set to 2. If a network split separates one node from the other two nodes, the side with one node cannot see enough master-eligible nodes and will realise that it cannot elect itself as master. The side with two nodes will elect a new master (if needed) and continue functioning correctly. As soon as the network split is resolved, the single node will rejoin the cluster and start serving requests again.

This setting should be set to a quorum of master-eligible nodes:

(master_eligible_nodes / 2) + 1

In other words, if there are three master-eligible nodes, then minimum master nodes should be set to (3 / 2) + 1 or 2:

discovery.zen.minimum_master_nodes: 2 

To be able to remain available when one of the master-eligible nodes fails, clusters should have at least three master-eligible nodes, with minimum_master_nodes set accordingly. A rolling upgrade, performed without any downtime, also requires at least three master-eligible nodes to avoid the possibility of data loss if a network split occurs while the upgrade is in progress.

This setting can also be changed dynamically on a live cluster with the cluster update settings API:

PUT _cluster/settings
{
  "transient": {
    "discovery.zen.minimum_master_nodes": 2
  }
}

Data nodes hold the shards that contain the documents you have indexed. Data nodes handle data related operations like CRUD, search, and aggregations. These operations are I/O-, memory-, and CPU-intensive. It is important to monitor these resources and to add more data nodes if they are overloaded.

The main benefit of having dedicated data nodes is the separation of the master and data roles.

To create a dedicated data node, set:

node.master: false 
node.data: true 
node.ingest: false 
cluster.remote.connect: false 

Ingest nodes can execute pre-processing pipelines, composed of one or more ingest processors. Depending on the type of operations performed by the ingest processors and the required resources, it may make sense to have dedicated ingest nodes, that will only perform this specific task.

To create a dedicated ingest node, set:

node.master: false 
node.data: false 
node.ingest: true 
cluster.remote.connect: false 

If you take away the ability to be able to handle master duties, to hold data, and pre-process documents, then you are left with a coordinating node that can only route requests, handle the search reduce phase, and distribute bulk indexing. Essentially, coordinating only nodes behave as smart load balancers.

Coordinating only nodes can benefit large clusters by offloading the coordinating node role from data and master-eligible nodes. They join the cluster and receive the full cluster state, like every other node, and they use the cluster state to route requests directly to the appropriate place(s).

To create a dedicated coordinating node, set:

node.master: false 
node.data: false 
node.ingest: false 
cluster.remote.connect: false 

Every data and master-eligible node requires access to a data directory where shards and index and cluster metadata will be stored. The path.data defaults to $ES_HOME/data but can be configured in the elasticsearch.yml config file an absolute path or a path relative to $ES_HOME as follows:

path.data:  /var/elasticsearch/data

Like all node settings, it can also be specified on the command line as:

./bin/elasticsearch -Epath.data=/var/elasticsearch/data

The data path can be shared by multiple nodes, even by nodes from different clusters. This is very useful for testing failover and different configurations on your development machine. In production, however, it is recommended to run only one node of Elasticsearch per server.

By default, Elasticsearch is configured to prevent more than one node from sharing the same data path. To allow for more than one node (e.g., on your development machine), use the setting node.max_local_storage_nodes and set this to a positive integer larger than one.

More node settings can be found in Modules. Of particular note are the cluster.name, the node.name and the network settings.

If X-Pack is installed, there is an additional node type:

If X-Pack is installed, nodes are master-eligible, data, ingest, and machine learning nodes by default. As the cluster grows and in particular if you have large machine learning jobs, consider separating dedicated master-eligible nodes from dedicated data nodes and dedicated machine learning nodes.

To create a dedicated master-eligible node when X-Pack is installed, set:

node.master: true 
node.data: false 
node.ingest: false 
node.ml: false 
xpack.ml.enabled: true 

To create a dedicated data node when X-Pack is installed, set:

node.master: false 
node.data: true 
node.ingest: false 
node.ml: false 

To create a dedicated ingest node when X-Pack is installed, set:

node.master: false 
node.data: false 
node.ingest: true 
cluster.remote.connect: false 
node.ml: false 

To create a dedicated coordinating node when X-Pack is installed, set:

node.master: false 
node.data: false 
node.ingest: false 
cluster.remote.connect: false 
node.ml: false 

The machine learning features provide machine learning nodes, which run jobs and handle machine learning API requests. If xpack.ml.enabled is set to true and node.ml is set to false, the node can service API requests but it cannot run jobs.

If you want to use machine learning features in your cluster, you must enable machine learning (set xpack.ml.enabled to true) on all master-eligible nodes. Do not use these settings if you do not have X-Pack installed.

For more information about these settings, see Machine learning settings.

To create a dedicated machine learning node, set:

node.master: false 
node.data: false 
node.ingest: false 
cluster.remote.connect: false 
node.ml: true 
xpack.ml.enabled: true