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Creating a cluster

Before diving into OpenSearch and searching and aggregating data, you first need to create an OpenSearch cluster.

OpenSearch can operate as a single-node or multi-node cluster. The steps to configure both are, in general, quite similar. This page demonstrates how to create and configure a multi-node cluster, but with only a few minor adjustments, you can follow the same steps to create a single-node cluster.

To create and deploy an OpenSearch cluster according to your requirements, it’s important to understand how node discovery and cluster formation work and what settings govern them.

There are many ways to design a cluster. The following illustration shows a basic architecture that includes a four-node cluster that has one dedicated cluster manager node, one dedicated coordinating node, and two data nodes that are cluster manager eligible and also used for ingesting data.

The nomenclature for the master node is now referred to as the cluster manager node.

multi-node cluster architecture diagram

Nodes

The following table provides brief descriptions of the node types:

Node type Description Best practices for production
Cluster manager Manages the overall operation of a cluster and keeps track of the cluster state. This includes creating and deleting indexes, keeping track of the nodes that join and leave the cluster, checking the health of each node in the cluster (by running ping requests), and allocating shards to nodes. Three dedicated cluster manager nodes in three different zones is the right approach for almost all production use cases. This configuration ensures your cluster never loses quorum. Two nodes will be idle for most of the time except when one node goes down or needs some maintenance.
Cluster manager eligible Elects one node among them as the cluster manager node through a voting process. For production clusters, make sure you have dedicated cluster manager nodes. The way to achieve a dedicated node type is to mark all other node types as false. In this case, you have to mark all the other nodes as not cluster manager eligible.
Data Stores and searches data. Performs all data-related operations (indexing, searching, aggregating) on local shards. These are the worker nodes of your cluster and need more disk space than any other node type. As you add data nodes, keep them balanced between zones. For example, if you have three zones, add data nodes in multiples of three, one for each zone. We recommend using storage and RAM-heavy nodes.
Ingest Pre-processes data before storing it in the cluster. Runs an ingest pipeline that transforms your data before adding it to an index. If you plan to ingest a lot of data and run complex ingest pipelines, we recommend you use dedicated ingest nodes. You can also optionally offload your indexing from the data nodes so that your data nodes are used exclusively for searching and aggregating.
Coordinating Delegates client requests to the shards on the data nodes, collects and aggregates the results into one final result, and sends this result back to the client. A couple of dedicated coordinating-only nodes is appropriate to prevent bottlenecks for search-heavy workloads. We recommend using CPUs with as many cores as you can.
Dynamic Delegates a specific node for custom work, such as machine learning (ML) tasks, preventing the consumption of resources from data nodes and therefore not affecting any OpenSearch functionality.  
Search Provides access to searchable snapshots. Incorporates techniques like frequently caching used segments and removing the least used data segments in order to access the searchable snapshot index (stored in a remote long-term storage source, for example, Amazon S3 or Google Cloud Storage). Search nodes contain an index allocated as a snapshot cache. Thus, we recommend dedicated nodes with a setup with more compute (CPU and memory) than storage capacity (hard disk).

By default, each node is a cluster-manager-eligible, data, ingest, and coordinating node. Deciding on the number of nodes, assigning node types, and choosing the hardware for each node type depends on your use case. You must take into account factors like the amount of time you want to hold on to your data, the average size of your documents, your typical workload (indexing, searches, aggregations), your expected price-performance ratio, your risk tolerance, and so on.

After you assess all these requirements, we recommend you use a benchmark testing tool like OpenSearch Benchmark to provision a small sample cluster and run tests with varying workloads and configurations. Compare and analyze the system and query metrics for these tests to design an optimum architecture.

This page demonstrates how to work with the different node types. It assumes that you have a four-node cluster similar to the preceding illustration.

Prerequisites

Before you get started, you must install and configure OpenSearch on all of your nodes. For information about the available options, see Install and configure OpenSearch.

After you’re done, use SSH to connect to each node, then open the config/opensearch.yml file. You can set all configurations for your cluster in this file.

Step 1: Name a cluster

Specify a unique name for the cluster. If you don’t specify a cluster name, it’s set to opensearch by default. Setting a descriptive cluster name is important, especially if you want to run multiple clusters inside a single network.

To specify the cluster name, change the following line:

#cluster.name: my-application

to

cluster.name: opensearch-cluster

Make the same change on all the nodes to make sure that they’ll join to form a cluster.

Step 2: Set node attributes for each node in a cluster

After you name the cluster, set node attributes for each node in your cluster.

Cluster manager node

Give your cluster manager node a name. If you don’t specify a name, OpenSearch assigns a machine-generated name that makes the node difficult to monitor and troubleshoot.

node.name: opensearch-cluster_manager

You can also explicitly specify that this node is a cluster manager node, even though it is already set to true by default. Set the node role to cluster_manager to make it easier to identify the cluster manager node.

node.roles: [ cluster_manager ]

Data nodes

Change the name of two nodes to opensearch-d1 and opensearch-d2, respectively:

node.name: opensearch-d1

node.name: opensearch-d2

You can make them cluster-manager-eligible data nodes that will also be used for ingesting data:

node.roles: [ data, ingest ]

You can also specify any other attributes that you’d like to set for the data nodes.

Coordinating node

Change the name of the coordinating node to opensearch-c1:

node.name: opensearch-c1

Every node is a coordinating node by default, so to make this node a dedicated coordinating node, set node.roles to an empty list:

node.roles: []

Step 3: Bind a cluster to specific IP addresses

network.bind_host defines the IP address used to bind the node. By default, OpenSearch listens on a local host, which limits the cluster to a single node. You can also use _local_ and _site_ to bind to any loopback or site-local address, whether IPv4 or IPv6:

network.bind_host: [_local_, _site_]

To form a multi-node cluster, specify the IP address of the node:

network.bind_host: <IP address of the node>

Make sure to configure these settings on all of your nodes.

Step 4: Configure discovery hosts and initial cluster manager nodes for a cluster

Now that you’ve configured the network hosts, you need to configure the discovery hosts and specify the cluster manager nodes for the initial cluster election.

For example, the setting looks like the following:

cluster.initial_cluster_manager_nodes: ["opensearch-cluster_manager"]

Zen Discovery is the built-in, default mechanism that uses unicast to find other nodes in the cluster.

You can generally add all of your cluster-manager-eligible nodes to the discovery.seed_hosts array. When a node starts up, it finds the other cluster-manager-eligible nodes, determines which one is the cluster manager, and asks to join the cluster.

For example, for opensearch-cluster_manager the line looks something like this:

discovery.seed_hosts: ["<private IP of opensearch-d1>", "<private IP of opensearch-d2>", "<private IP of opensearch-c1>"]

Step 5: Start the cluster

After you set the configurations, start OpenSearch on all nodes:

sudo systemctl start opensearch.service

Installing OpenSearch from a tar archive will not automatically create a service with systemd. See Run OpenSearch as a service with systemd for instructions on how to create and start the service if you receive an error like Failed to start opensearch.service: Unit not found.

Then go to the logs file to see the formation of the cluster:

less /var/log/opensearch/opensearch-cluster.log

Perform the following _cat query on any node to see all the nodes formed as a cluster:

curl -XGET https://<private-ip>:9200/_cat/nodes?v -u 'admin:<custom-admin-password>' --insecure

ip             heap.percent ram.percent cpu load_1m load_5m load_15m node.role cluster_manager name
x.x.x.x           13          61   0    0.02    0.04     0.05 mi        *      opensearch-cluster_manager
x.x.x.x           16          60   0    0.06    0.05     0.05 md        -      opensearch-d1
x.x.x.x           34          38   0    0.12    0.07     0.06 md        -      opensearch-d2
x.x.x.x           23          38   0    0.12    0.07     0.06 md        -      opensearch-c1

To better understand and monitor your cluster, use the CAT API.

(Advanced) Step 6: Configure shard allocation awareness or forced awareness

Shard allocation awareness

If your nodes are spread across several geographical zones, you can configure shard allocation awareness to allocate all replica shards to a zone that’s different from their primary shard.

With shard allocation awareness, if the nodes in one of your zones fail, you can be assured that your replica shards are spread across your other zones. It adds a layer of fault tolerance to ensure your data survives a zone failure beyond just individual node failures.

To configure shard allocation awareness, add zone attributes to opensearch-d1 and opensearch-d2, respectively:

node.attr.zone: zoneA

node.attr.zone: zoneB

Update the cluster settings:

PUT _cluster/settings
{
  "persistent": {
    "cluster.routing.allocation.awareness.attributes": "zone"
  }
}

You can either use persistent or transient settings. We recommend the persistent setting because it persists through a cluster reboot. Transient settings don’t persist through a cluster reboot.

Shard allocation awareness attempts to separate primary and replica shards across multiple zones. However, if only one zone is available (such as after a zone failure), OpenSearch allocates replica shards to the only remaining zone.

Forced awareness

Another option is to require that primary and replica shards are never allocated to the same zone. This is called forced awareness.

To configure forced awareness, specify all the possible values for your zone attributes:

PUT _cluster/settings
{
  "persistent": {
    "cluster.routing.allocation.awareness.attributes": "zone",
    "cluster.routing.allocation.awareness.force.zone.values":["zoneA", "zoneB"]
  }
}

Now, if a data node fails, forced awareness doesn’t allocate the replicas to a node in the same zone. Instead, the cluster enters a yellow state and only allocates the replicas when nodes in another zone come online.

In our two-zone architecture, we can use allocation awareness if opensearch-d1 and opensearch-d2 are less than 50% utilized, so that each of them have the storage capacity to allocate replicas in the same zone. If that is not the case, and opensearch-d1 and opensearch-d2 do not have the capacity to contain all primary and replica shards, we can use forced awareness. This approach helps to make sure that, in the event of a failure, OpenSearch doesn’t overload your last remaining zone and lock up your cluster due to lack of storage.

Choosing allocation awareness or forced awareness depends on how much space you might need in each zone to balance your primary and replica shards.

Replica count enforcement

To enforce an even distribution of shards across all zones and avoid hotspots, you can set the routing.allocation.awareness.balance attribute to true. This setting can be configured in the opensearch.yml file and dynamically updated using the cluster update settings API:

PUT _cluster/settings
{
  "persistent": {
    "cluster": {
      "routing.allocation.awareness.balance": "true"
    }
  }
}

The routing.allocation.awareness.balance setting is false by default. When it is set to true, the total number of shards for the index must be a multiple of the highest count for any awareness attribute. For example, consider a configuration with two awareness attributes—zones and rack IDs. Let’s say there are two zones and three rack IDs. The highest count of either the number of zones or the number of rack IDs is three. Therefore, the number of shards must be a multiple of three. If it is not, OpenSearch throws a validation exception.

routing.allocation.awareness.balance takes effect only if cluster.routing.allocation.awareness.attributes and cluster.routing.allocation.awareness.force.zone.values are set.

routing.allocation.awareness.balance applies to all operations that create or update indexes. For example, let’s say you’re running a cluster with three nodes and three zones in a zone-aware setting. If you try to create an index with one replica or update an index’s settings to one replica, the attempt will fail with a validation exception because the number of shards must be a multiple of three. Similarly, if you try to create an index template with one shard and no replicas, the attempt will fail for the same reason. However, in all of those operations, if you set the number of shards to one and the number of replicas to two, the total number of shards is three and the attempt will succeed.

(Advanced) Step 7: Set up a hot-warm architecture

You can design a hot-warm architecture where you first index your data to hot nodes—fast and expensive—and after a certain period of time move them to warm nodes—slow and cheap.

If you analyze time-series data that you rarely update and want the older data to go onto cheaper storage, this architecture can be a good fit.

This architecture helps save money on storage costs. Rather than increasing the number of hot nodes and using fast, expensive storage, you can add warm nodes for data that you don’t access as frequently.

To configure a hot-warm storage architecture, add temp attributes to opensearch-d1 and opensearch-d2, respectively:

node.attr.temp: hot

node.attr.temp: warm

You can set the attribute name and value to whatever you want as long as it’s consistent for all your hot and warm nodes.

To add an index newindex to the hot node:

PUT newindex
{
  "settings": {
    "index.routing.allocation.require.temp": "hot"
  }
}

Take a look at the following shard allocation for newindex:

GET _cat/shards/newindex?v
index     shard prirep state      docs store ip         node
new_index 2     p      STARTED       0  230b 10.0.0.225 opensearch-d1
new_index 2     r      UNASSIGNED
new_index 3     p      STARTED       0  230b 10.0.0.225 opensearch-d1
new_index 3     r      UNASSIGNED
new_index 4     p      STARTED       0  230b 10.0.0.225 opensearch-d1
new_index 4     r      UNASSIGNED
new_index 1     p      STARTED       0  230b 10.0.0.225 opensearch-d1
new_index 1     r      UNASSIGNED
new_index 0     p      STARTED       0  230b 10.0.0.225 opensearch-d1
new_index 0     r      UNASSIGNED

In this example, all primary shards are allocated to opensearch-d1, which is our hot node. All replica shards are unassigned, because we’re forcing this index to allocate only to hot nodes.

To add an index oldindex to the warm node:

PUT oldindex
{
  "settings": {
    "index.routing.allocation.require.temp": "warm"
  }
}

The shard allocation for oldindex:

GET _cat/shards/oldindex?v
index     shard prirep state      docs store ip        node
old_index 2     p      STARTED       0  230b 10.0.0.74 opensearch-d2
old_index 2     r      UNASSIGNED
old_index 3     p      STARTED       0  230b 10.0.0.74 opensearch-d2
old_index 3     r      UNASSIGNED
old_index 4     p      STARTED       0  230b 10.0.0.74 opensearch-d2
old_index 4     r      UNASSIGNED
old_index 1     p      STARTED       0  230b 10.0.0.74 opensearch-d2
old_index 1     r      UNASSIGNED
old_index 0     p      STARTED       0  230b 10.0.0.74 opensearch-d2
old_index 0     r      UNASSIGNED

In this case, all primary shards are allocated to opensearch-d2. Again, all replica shards are unassigned because we only have one warm node.

A popular approach is to configure your index templates to set the index.routing.allocation.require.temp value to hot. This way, OpenSearch stores your most recent data on your hot nodes.

You can then use the Index State Management (ISM) plugin to periodically check the age of an index and specify actions to take on it. For example, when the index reaches a specific age, change the index.routing.allocation.require.temp setting to warm to automatically move your data from hot nodes to warm nodes.

Next steps

If you are using the Security plugin, the previous request to _cat/nodes?v might have failed with an initialization error. For full guidance around using the Security plugin, see Security configuration.

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