Index blob is not valid x-content bytes – How to solve this Elasticsearch error

Opster Team

Aug-23, Version: 6.8-7.3

Briefly, this error occurs when Elasticsearch encounters an invalid blob while indexing data. This could be due to corrupted data, incorrect data format, or a bug in the Elasticsearch version you’re using. To resolve this issue, you can try the following: 1) Check and correct the data format; 2) Reindex the data; 3) Upgrade Elasticsearch to the latest stable version; 4) If the data is corrupted, restore it from a backup.

This guide will help you check for common problems that cause the log ” [{}] index blob is not valid x-content [{} bytes] ” to appear. To understand the issues related to this log, read the explanation below about the following Elasticsearch concepts: blobstore, repositories, index.

 
 

blobstore

 
 

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.
 
 

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 “[{}] index blob is not valid x-content [{} bytes]” classname is BlobStoreRepository.java.
We extracted the following from Elasticsearch source code for those seeking an in-depth context :

                // EMPTY is safe here because RepositoryData#fromXContent calls namedObject
                try (XContentParser parser = XContentHelper.createParser(NamedXContentRegistry.EMPTY;
                    LoggingDeprecationHandler.INSTANCE; out.bytes(); XContentType.JSON)) {
                    repositoryData = RepositoryData.snapshotsFromXContent(parser; indexGen);
                } catch (NotXContentException e) {
                    logger.warn("[{}] index blob is not valid x-content [{} bytes]"; snapshotsIndexBlobName; out.bytes().length());
                    throw e;
                }
            }

            // now load the incompatible snapshot ids; if they exist

 

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