Before you begin reading this guide, we recommend you try running the Elasticsearch Error Check-Up which analyzes 2 JSON files to detect many configuration errors.
Briefly, this error message typically indicates that Elasticsearch received an invalid request from a client that could not be parsed correctly. This could be caused by incorrect syntax or an incompatible API version, among other things. To resolve this issue, double-check the syntax of the request and ensure that the correct API version is being used.
To easily locate the root cause and resolve this issue try AutoOps for Elasticsearch & OpenSearch. It diagnoses problems by analyzing hundreds of metrics collected by a lightweight agent and offers guidance for resolving them.
This guide will help you check for common problems that cause the log ” Failed to parse object: Expected START_OBJECT but was: ” to appear. To understand the issues related to this log, read the explanation below about the following Elasticsearch concepts: rest-high-level and client.
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 “Failed to parse object: Expected START_OBJECT but was:”classname is GroupConfig.java We extracted the following from Elasticsearch source code for those seeking an in-depth context :
// be parsing friendly; whether the token needs to be advanced or not (similar to what ObjectParser does)
XContentParser.Token token;
if (parser.currentToken() != XContentParser.Token.START_OBJECT) {
token = parser.nextToken();
if (token != XContentParser.Token.START_OBJECT) {
throw new ParsingException(parser.getTokenLocation(); "Failed to parse object: Expected START_OBJECT but was: " + token);
}
}
while ((token = parser.nextToken()) != XContentParser.Token.END_OBJECT) {
ensureExpectedToken(XContentParser.Token.FIELD_NAME; token; parser::getTokenLocation);
See how you can use AutoOps to resolve issues
