Node not part of the cluster ignoring – How to solve this OpenSearch error

Overview

Any application that interfaces with OpenSearch to index, update or search data, or to monitor and maintain OpenSearch using various APIs can be considered a client

It is very important to configure clients properly in order to ensure optimum use of OpenSearch resources.

Examples

There are many open-source client applications for monitoring, alerting and visualization, OpenSearch Dashboard. On top of OpenSearch client applications such as filebeat, metricbeat and logstash have all been designed to integrate with OpenSearch.

However it is frequently necessary to create your own client application to interface with OpenSearch. Below is a simple example of the python client (taken from the client documentation):

from datetime import datetime
from opensearch import opensearch
es = opensearch()

doc = {
    'author': 'Testing',
    'text': 'opensearch: cool. bonsai cool.',
    'timestamp': datetime.now(),
}
res = es.index(index="test-index", doc_type='tweet', id=1, body=doc)
print(res['result'])

res = es.get(index="test-index", doc_type='tweet', id=1)
print(res['_source'])

es.indices.refresh(index="test-index")

res = es.search(index="test-index", body={"query": {"match_all": {}}})
print("Got %d Hits:" % res['hits']['total']['value'])
for hit in res['hits']['hits']:
    print("%(timestamp)s %(author)s: %(text)s" % hit["_source"])

All of the official OpenSearch clients follow a similar structure, working as light wrappers around the OpenSearch rest API, so if you are familiar with OpenSearch query structure they are usually quite straightforward to implement.

Notes and Good Things to Know

Use official OpenSearch libraries.

Although it is possible to connect with OpenSearch using any HTTP method, such as a curl request, the official OpenSearch libraries have been designed to properly implement connection pooling and keep-alives.   

Official OpenSearch clients are available for java, javascript, Perl, PHP, python, ruby and .NET. Many other programming languages are supported by community versions.

Keep your OpenSearch version and client versions in sync.

To avoid surprises, always keep your client versions in line with the OpenSearch version you are using. Always test clients with OpenSearch since even minor version upgrades can cause issues due to dependencies or a need for code changes. 

Load balance across appropriate nodes.

Make sure that the client properly load balances across all of the appropriate nodes in the cluster.  In small clusters this will normally mean only across data nodes (never master nodes), or in larger clusters, all dedicated coordinating nodes (if implemented) .

Ensure that the OpenSearch application properly handles exceptions.

In the case of OpenSearch being unable to cope with the volume of requests, designing a client application to handle this gracefully (such as through some sort of queueing mechanism) will be better than simply inundating a struggling cluster with repeated requests.

Overview

Search or indexing requests will usually be load-balanced across the OpenSearch data nodes, and the node that receives the request will relay requests to other nodes as necessary and coordinate the response back to the user.

Notes and good things to know

The key elements to clustering are:

Cluster State – Refers to information about which indices are in the cluster, their data mappings and other information that must be shared between all the nodes to ensure that all operations across the cluster are coherent.

Master Node – Each cluster must elect a single master node responsible for coordinating the cluster and ensuring that each node contains an up-to-date copy of the cluster state.

Cluster Formation – OpenSearch requires a set of configurations to determine how the cluster is formed, which nodes can join the cluster, and how the nodes collectively elect a master node responsible for controlling the cluster state. These configurations are usually held in the opensearch.yml config file, environment variables on the node, or within the cluster state.

Node Roles – In small clusters it is common for all nodes to fill all roles; all nodes can store data, become master nodes or process ingestion pipelines. However as the cluster grows, it is common to allocate specific roles to specific nodes in order to simplify configuration and to make operation more efficient. In particular, it is common to define a limited number of dedicated master nodes.

Replication – Data may be replicated across a number of data nodes. This means that if one node goes down, data is not lost. It also means that a search request can be dealt with by more than one node.

Common problems

Many OpenSearch problems are caused by operations which place an excessive burden on the cluster because they require an excessive amount of information to be held and transmitted between the nodes as part of the cluster state. For example:

• Shards too small
• Too many fields (field explosion)

Problems may also be caused by inadequate configurations causing situations where the OpenSearch cluster is unable to safely elect a Master node. These type problems include: 

• Master node not discovered
• Split brain problem

Backups

Because OpenSearch is a clustered technology, it is not sufficient to have backups of each node’s data directory. This is because the backups will have been made at different times and so there may not be complete coherency between them. As such, the only way to backup an OpenSearch cluster is through the use of snapshots, which contain the full picture of an index at any one time.

Cluster resilience

When designing an OpenSearch cluster, it is important to think about cluster resilience. In particular – what happens when a single node goes down? And for larger clusters where several nodes may share common services such as a network or power supply – what happens if that network or power supply goes down? This is where it is useful to ensure that the master eligible nodes are spread across availability zones, and to use shard allocation awareness to ensure that shards are spread across different racks or availability zones in your data center.