To understand why Elasticsearch is unable to parse the responce body, we recommend you run the Elasticsearch Error Check-Up. It will analyse your cluster and help you resolve and prevent this error from occuring again.The tool is free and require no installation.
This guide will review common problems related to shards and searches that might cause shard failure.
Overview
Rest-high-level is built on top of low-level rest-client and is a method of communicating with Elasticsearch based on HTTP REST endpoints. This concept is majorly popular in the context of a Java-based Elasticsearch client. From day one, Elasticsearch supports transport clients for Java to communicate with Elasticsearch. In version 5.0, a low-level rest-client was released with lots of advantages over the existing transport client such as version independencies, increased stability, and lightweight JAR file libraries.
What it is used for
It is used for communicating with Elasticsearch HTTP REST endpoints in which marshalling and unmarshalling of response objects are handled by the Elasticsearch server itself.
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.
Log Context
Log”Unable to parse response body”classname is RestHighLevelClient.java We extracted the following from Elasticsearch source code for those seeking an in-depth context :
} else { try { elasticsearchException = parseEntity(entity; BytesRestResponse::errorFromXContent); elasticsearchException.addSuppressed(responseException); } catch (Exception e) { elasticsearchException = new ElasticsearchStatusException("Unable to parse response body"; restStatus; responseException); elasticsearchException.addSuppressed(e); } } return elasticsearchException; }
Run the Check-Up to get customized recommendations like this:

Heavy merges detected in specific nodes

Description
A large number of small shards can slow down searches and cause cluster instability. Some indices have shards that are too small…

Recommendations Based on your specific ES deployment you should…
Based on your specific ES deployment you should…
X-PUT curl -H [a customized recommendation]