Elasticsearch Client

Average Read Time

2 Mins

Elasticsearch Client

Opster Team

October 2021

Average Read Time

2 Mins


In addition to reading this guide, we recommend you run the Elasticsearch Health Check-Up. It will detect issues and improve your Elasticsearch performance by analyzing your shard sizes, threadpools, memory, snapshots, disk watermarks and more.

The Elasticsearch Check-Up is free and requires no installation.

Aside from reading this guide about Elasticsearch clients, we recommend you run the Elasticsearch Health Check-Up. It will detect issues and improve your Elasticsearch performance by analyzing your shard sizes, threadpools, memory, snapshots, disk watermarks and more.

The Elasticsearch Check-Up is free and requires no installation.

Run the Elasticsearch check-up to receive recommendations like this:

checklist Run Check-Up
error

The following configuration error was detected on node 123...

error-img

Description

This error can have a severe impact on your system. It's important to understand that it was caused by...

error-img

Recommendation

In order to resolve this issue and prevent it from occurring again, we recommend that you begin by changing the configuration to...

1

X-PUT curl -H "Content-Type: application/json" [customized recommendation]

Overview

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

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

Examples

There are many open-source client applications for monitoring, alerting and visualization, such as ElasticHQ, Elastalerts, and Grafana to name a few. On top of Elastic client applications such as filebeat, metricbeat, logstash and kibana that have all been designed to integrate with Elasticsearch.

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

from datetime import datetime
from elasticsearch import Elasticsearch
es = Elasticsearch()

doc = {
    'author': 'Testing',
    'text': 'Elasticsearch: 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 Elasticsearch clients follow a similar structure, working as light wrappers around the Elasticsearch rest API, so if you are familiar with Elasticsearch query structure they are usually quite straightforward to implement.

Notes and Good Things to Know

Use official Elasticsearch libraries.

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

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

Keep your Elasticsearch version and client versions in sync.

To avoid surprises, always keep your client versions in line with the Elasticsearch version you are using. Always test clients with Elasticsearch 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 Elasticsearch application properly handles exceptions.

In the case of Elasticsearch 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.


Related log errors to this ES concept


Use of the low-level REST client on JDK 7 is deprecated and will be removed in version 7.0.0 of the client
Http client did not trust this servers certificate; closing connection
Using default proxy for http input and slack/hipchat/pagerduty/webhook actions :
Giving up on search because it failed with a non-retryable exception
Error while closing Azure client
Error while sniffing nodes
Failed to sample
Failed to send error message back to client for action
No nodes to set; nodes will be updated at the next sniffing round
Unknown role
Failed to get node info for ; disconnecting.
An error occurred while closing the internal client sniffer

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Run the Check-Up to get a customized report like this:

Analyze your cluster