Briefly, this error occurs when Elasticsearch fails to read the generation index of a repository, which is crucial for tracking changes. This could be due to issues like corruption, network problems, or insufficient permissions. To resolve this, you can try the following: 1) Check the health of your network and ensure stable connectivity. 2) Verify that Elasticsearch has the necessary permissions to access the repository. 3) If corruption is suspected, consider restoring from a backup or reindexing your data. Always ensure your data is regularly backed up to prevent loss.
This guide will help you check for common problems that cause the log ” Resetting repository generation tracker because we failed to read generation [” + indexGen + “] ” to appear. To understand the issues related to this log, read the explanation below about the following Elasticsearch concepts: repositories, index, blobstore, repository.
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 type | Configuration type |
|---|---|
| Shared file system | Type: “fs” |
| S3 | Type : “s3” |
| HDFS | Type :“hdfs” |
| Azure | Type: “azure” |
| Google Cloud Storage | Type : “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.
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 “Resetting repository generation tracker because we failed to read generation [” + indexGen + “]” classname is BlobStoreRepository.java.
We extracted the following from Elasticsearch source code for those seeking an in-depth context :
if (bestEffortConsistency) {
// If we fail to load the generation we tracked in latestKnownRepoGen we reset it.
// This is done as a fail-safe in case a user manually deletes the contents of the repository in which case subsequent
// operations must start from the EMPTY_REPO_GEN again
if (latestKnownRepoGen.compareAndSet(indexGen; RepositoryData.EMPTY_REPO_GEN)) {
logger.warn("Resetting repository generation tracker because we failed to read generation [" + indexGen + "]"; ioe);
}
}
throw new RepositoryException(metadata.name(); "could not read repository data from index blob"; ioe);
}
}
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