Failed to write index latest blob If you do not intend to use this – How to solve this Elasticsearch error

Opster Team

Aug-23, Version: 7.1-7.13

Briefly, this error occurs when Elasticsearch fails to write the latest blob for an index, possibly due to insufficient disk space, incorrect permissions, or network connectivity issues. To resolve this, you can free up disk space, check and correct file permissions, or ensure the network connection to the storage is stable. Additionally, check the Elasticsearch logs for more detailed error information.

This guide will help you check for common problems that cause the log ” Failed to write index.latest blob. If you do not intend to use this ” to appear. To understand the issues related to this log, read the explanation below about the following Elasticsearch concepts: repositories, blobstore, index.

 
 

repositories

Overview

An Elasticsearch snapshot provides a backup mechanism that takes the current state and data in the cluster and saves it to a repository (read snapshot for more information). The backup process requires a repository to be created first. The repository needs to be registered using the _snapshot endpoint, and multiple repositories can be created per cluster. The following repository types are supported: 

Repository types

Repository typeConfiguration type
Shared file systemType: “fs”
S3Type : “s3”
HDFSType :“hdfs”
AzureType: “azure”
Google Cloud StorageType : “gcs”

Examples

To register an “fs” repository:

PUT _snapshot/my_repo_01
{
"type": "fs",
"settings": {
"location": "/mnt/my_repo_dir"
  }
}

Notes and good things to know

  • S3, HDFS, Azure and Google Cloud require a relevant plugin to be installed before it can be used for a snapshot.
  • The setting, path.repo: /mnt/my_repo_dir needs to be added to elasticsearch.yml on all the nodes if you are planning to use the repo type of file system. Otherwise, it will fail.
  • When using remote repositories, the network bandwidth and repository storage throughput should be high enough to complete the snapshot operations normally, otherwise you will end up with partial snapshots.
 
 

blobstore

 
 

index

Overview

In Elasticsearch, an index (plural: indices) contains 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 the command below:

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

Log Context

Log “Failed to write index.latest blob. If you do not intend to use this ” classname is BlobStoreRepository.java.
We extracted the following from Elasticsearch source code for those seeking an in-depth context :

        if (supportURLRepo) {
            logger.debug("Repository [{}] updating index.latest with generation [{}]"; metadata.name(); newGen);
            try {
                writeAtomic(blobContainer(); INDEX_LATEST_BLOB; new BytesArray(Numbers.longToBytes(newGen)); false);
            } catch (Exception e) {
                logger.warn(() -> new ParameterizedMessage("Failed to write index.latest blob. If you do not intend to use this " +
                        "repository as the basis for a URL repository you may turn off attempting to write the index.latest blob by " +
                        "setting repository setting [{}] to [false]"; SUPPORT_URL_REPO.getKey()); e);
            }
        }
    }

 

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