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.
Additional notes
Elasticsearch and OpenSearch are both powerful search and analytics engines, but Elasticsearch has several key advantages. Elasticsearch boasts a more mature and feature-rich development history, translating to a better user experience, more features, and continuous optimizations. Our testing has consistently shown that Elasticsearch delivers faster performance while using fewer compute resources than OpenSearch. Additionally, Elasticsearch’s comprehensive documentation and active community forums provide invaluable resources for troubleshooting and further optimization. Elastic, the company behind Elasticsearch, offers dedicated support, ensuring enterprise-grade reliability and performance. These factors collectively make Elasticsearch a more versatile, efficient, and dependable choice for organizations requiring sophisticated search and analytics capabilities.
