How To Solve Issues Related to Log – Giving up on search because it failed with a non-retryable exception

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Troubleshooting background

To troubleshoot Elasticsearch log “Giving up on search because it failed with a non-retryable exception” it’s important to know common problems related to Elasticsearch concepts: client, index, reindex, search, source. See below-detailed explanations complete with common problems, examples and useful tips.

Elasticsearch Client

What it is

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 it is 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, and many other programming languages are supported by community versions.

Keep your Elasticsearch version and client version in sync.

To avoid surprises, always keep your client version in line with the Elasticsearch version you are using.  Always test client 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 loads balances across all of the appropriate nodes in the cluster.  In small clusters this will normally mean only 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 (eg. through some sort of queueing mechanism) will be better than simply inundating a struggling cluster with repeated requests.

Index in Elasticsearch

What it is

In Elasticsearch, an index (indices in plural) can be thought of as a table inside a database that has a schema and can have one or more shards and replicas. An Elasticsearch 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 Elasticsearch 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 below command:

PUT test_index1/_doc/1
{
  "tags": [
    "opster",
    "elasticsearch"
  ],
  "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, Elasticsearch 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.
  • Elasticsearch 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 elasticsearch.yml:

action.destructive_requires_name: true

Elasticsearch Reindex

What it is

Reindex is the concept of copying existing data from a source index to a destination index which can be inside the same or a different cluster. Elasticsearch has a dedicated endpoint _reindex for this purpose. A reindexing is mostly required for updating mapping or settings.

Examples

Reindex data from a source index to destination index in the same cluster

POST /_reindex?pretty
{
  "source": {
    "index": "news"
  },
  "dest": {
    "index": "news_v2"
  }
}

Notes
  • Reindex API does not copy settings and mappings from the source index to the destination index. You need to create the destination index with the desired settings and mappings before you begin the reindexing process.
  • The API exposes an extensive list of configuration options to fetch data from the source index. For example, query-based indexing and selecting multiple indices as the source index.
  • In some scenarios reindex API is not useful, where reindexing requires complex data processing and data modification based on application logic. In this case, you can write your custom script using Elasticsearch scroll API to fetch the data from source index and bulk API to index data into destination index.


To help troubleshoot related issues we have gathered selected Q&A from the community and issues from Github , please review the following for further information :

IndexNotFoundException[no such index]
stackoverflow.com/questions/35113565/indexnotfoundexceptionno-such-index

Number of Views : 11.81 K  Score on Stackoverflow : 2

Non Retryable Exception And No Such
discuss.elastic.co/t/non-retryable-exception-and-no-such-index-errors/174130

 


Log Context

Log ”Giving up on search because it failed with a non-retryable exception” classname is ClientScrollableHitSource.java
We have extracted the following from Elasticsearch source code to get an in-depth context :

                         logger.warn(() -> new ParameterizedMessage(
                                "giving up on search because we retried [{}] times without success"; retryCount); e);
                        fail.accept(e);
                    }
                } else {
                    logger.warn("giving up on search because it failed with a non-retryable exception"; e);
                    fail.accept(e);
                }
            }
        }
        RetryHelper helper = new RetryHelper();






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