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Briefly, this error occurs when OpenSearch fails to terminate a client thread correctly. This could be due to a variety of reasons such as system resource constraints, software bugs, or improper shutdown procedures. To resolve this issue, you could try the following: 1) Ensure that your system has sufficient resources (CPU, memory, disk space) to handle the workload. 2) Update OpenSearch to the latest version to fix any potential bugs. 3) Make sure to follow the correct shutdown procedures when stopping OpenSearch. 4) Check the OpenSearch logs for more detailed error messages that could help identify the problem.
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This guide will help you check for common problems that cause the log ” Failed to properly stop client thread [{}] ” to appear. To understand the issues related to this log, read the explanation below about the following OpenSearch concepts: client, index, thread, reindex.
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.
Quick links
Overview
In OpenSearch, an index (plural: indices) contains a schema and can have one or more shards and replicas. An OpenSearch index is divided into shards and each shard is an instance of a Lucene index.
Indices are used to store the documents in dedicated data structures corresponding to the data type of fields. For example, text fields are stored inside an inverted index whereas numeric and geo fields are stored inside BKD trees.
Examples
Create index
The following example is based on OpenSearch version 5.x onwards. An index with two shards, each having one replica will be created with the name test_index1
PUT /test_index1?pretty
{
"settings" : {
"number_of_shards" : 2,
"number_of_replicas" : 1
},
"mappings" : {
"properties" : {
"tags" : { "type" : "keyword" },
"updated_at" : { "type" : "date" }
}
}
}List indices
All the index names and their basic information can be retrieved using the following command:
GET _cat/indices?v
Index a document
Let’s add a document in the index with the command below:
PUT test_index1/_doc/1
{
"tags": [
"opster",
"OpenSearch"
],
"date": "01-01-2020"
}Query an index
GET test_index1/_search
{
"query": {
"match_all": {}
}
}Query multiple indices
It is possible to search multiple indices with a single request. If it is a raw HTTP request, index names should be sent in comma-separated format, as shown in the example below, and in the case of a query via a programming language client such as python or Java, index names are to be sent in a list format.
GET test_index1,test_index2/_search
Delete indices
DELETE test_index1
Common problems
- It is good practice to define the settings and mapping of an Index wherever possible because if this is not done, OpenSearch tries to automatically guess the data type of fields at the time of indexing. This automatic process may have disadvantages, such as mapping conflicts, duplicate data and incorrect data types being set in the index. If the fields are not known in advance, it’s better to use dynamic index templates.
- OpenSearch supports wildcard patterns in Index names, which sometimes aids with querying multiple indices, but can also be very destructive too. For example, It is possible to delete all the indices in a single command using the following commands:
DELETE /*
To disable this, you can add the following lines in the OpenSearch.yml:
action.destructive_requires_name: true
Log Context
Log “Failed to properly stop client thread [{}]” classname is Reindexer.java.
We extracted the following from OpenSearch source code for those seeking an in-depth context :
super.finishHim(failure; indexingFailures; searchFailures; timedOut);
// A little extra paranoia so we log something if we leave any threads running
for (Thread thread : createdThreads) {
if (thread.isAlive()) {
assert false : "Failed to properly stop client thread [" + thread.getName() + "]";
logger.error("Failed to properly stop client thread [{}]"; thread.getName());
}
}
}
@Override
