Before you begin reading this guide, we recommend you run Elasticsearch Error Check-Up which analyzes 2 JSON files to detect many errors.
To easily locate the root cause and resolve this issue try AutoOps for Elasticsearch & OpenSearch. It diagnoses problems by analyzing hundreds of metrics collected by a lightweight agent and offers guidance for resolving them. Take a self-guided product tour to see for yourself (no registration required).
This guide will help you check for common problems that cause the log ” Failed to update the original token document after all retries. the update result was . ” to appear. To understand the issues related to this log, read the explanation below about the following Elasticsearch concepts: document and plugin.
Document in Elasticsearch
What is an Elasticsearch document?
While an SQL database has rows of data stored in tables, Elasticsearch stores data as multiple documents inside an index. This is where the analogy must end however, since the way that Elasticsearch treats documents and indices differs significantly from a relational database.
For example, documents could be:
- Products in an e-commerce index
- Log lines in a data logging application
- Invoice lines in an invoicing system
Document fields
Each document is essentially a JSON structure, which is ultimately considered to be a series of key:value pairs. These pairs are then indexed in a way that is determined by the document mapping. The mapping defines the field data type as text, keyword, float, time, geo point or various other data types.
Elasticsearch documents are described as schema-less because Elasticsearch does not require us to pre-define the index field structure, nor does it require all documents in an index to have the same structure. However, once a field is mapped to a given data type, then all documents in the index must maintain that same mapping type.
Each field can also be mapped in more than one way in the index. This can be useful because we may want a keyword structure for aggregations, and at the same time be able to keep an analysed data structure which enables us to carry out full text searches for individual words in the field.
For a full discussion on mapping please see here.
Document source
An Elasticsearch document _source consists of the original JSON source data before it is indexed. This data is retrieved when fetched by a search query.
Document metadata
Each document is also associated with metadata, the most important items being:
_index – The index where the document is stored
_id – The unique ID which identifies the document in the index
Documents and index architecture
Note that different applications could consider a “document” to be a different thing. For example, in an invoicing system, we could have an architecture which stores invoices as documents (1 document per invoice), or we could have an index structure which stores multiple documents as “invoice lines” for each invoice. The choice would depend on how we want to store, map and query the data.
Examples:
Creating a document in the user’s index:
POST /users/_doc
{
"name" : "Petey",
"lastname" : "Cruiser",
"email" : "petey@gmail.com"
}In the above request, we haven’t mentioned an ID for the document so the index operation generates a unique ID for the document. Here _doc is the type of document.
POST /users/_doc/1
{
"name" : "Petey",
"lastname" : "Cruiser",
"email" : "petey@gmail.com"
}In the above query, the document will be created with ID 1.
You can use the below ‘GET’ query to get a document from the index using ID:
GET /users/_doc/1
Below is the result, which contains the document (in _source field) as metadata:
{
"_index": "users",
"_type": "_doc",
"_id": "1",
"_version": 1, "_seq_no": 1, "_primary_term": 1,
"found": true,
"_source": {
"name": "Petey",
"lastname": "Cruiser",
"email": "petey@gmail.com"
}
}Notes
Starting version 7.0 types are deprecated, so for backward compatibility on version 7.x all docs are under type ‘_doc’, starting 8.x type will be completely removed from ES APIs.
Overview
A plugin is used to enhance the core functionalities of Elasticsearch. Elasticsearch provides some core plugins as a part of their release installation. In addition to those core plugins, it is possible to write your own custom plugins as well. There are several community plugins available on GitHub for various use cases.
Examples
Get all of the instructions for the plugin:
sudo bin/elasticsearch-plugin -h
Installing the S3 plugin for storing Elasticsearch snapshots on S3:
sudo bin/elasticsearch-plugin install repository-s3
Removing a plugin:
sudo bin/elasticsearch-plugin remove repository-s3
Installing a plugin using the file’s path:
sudo bin/elasticsearch-plugin install file:///path/to/plugin.zip
Notes and good things to know
- Plugins are installed and removed using the elasticsearch-plugin script, which ships as a part of the Elasticsearch installation and can be found inside the bin/ directory of the Elasticsearch installation path.
- A plugin has to be installed on every node of the cluster and each of the nodes has to be restarted to make the plugin visible.
- You can also download the plugin manually and then install it using the elasticsearch-plugin install command, providing the file name/path of the plugin’s source file.
- When a plugin is removed, you will need to restart every Elasticsearch node in order to complete the removal process.
Common issues
- Managing permission issues during and after plugin installation is the most common problem. If Elasticsearch was installed using the DEB or RPM packages then the plugin has to be installed using the root user. Otherwise you can install the plugin as the user that owns all of the Elasticsearch files.
- In the case of DEB or RPM package installation, it is important to check the permissions of the plugins directory after you install it. You can update the permission if it has been modified using the following command:
chown -R elasticsearch:elasticsearch path_to_plugin_directory
- If your Elasticsearch nodes are running in a private subnet without internet access, you cannot install a plugin directly. In this case, you can simply download the plugins and copy the files inside the plugins directory of the Elasticsearch installation path on every node. The node has to be restarted in this case as well.
Log Context
Log “failed to update the original token document [{}] after all retries; the update result was [{}].” classname is TokenService.java.
We extracted the following from Elasticsearch source code for those seeking an in-depth context :
final Runnable retryWithContextRunnable = client.threadPool().getThreadContext()
.preserveContext(() -> innerRefresh(refreshToken; tokenDocId; source; seqNo; primaryTerm; clientAuth;
backoff; refreshRequested; listener));
client.threadPool().schedule(retryWithContextRunnable; backoff.next(); GENERIC);
} else {
logger.info("failed to update the original token document [{}] after all retries; the update result was [{}]. ";
tokenDocId; updateResponse.getResult());
listener.onFailure(invalidGrantException("could not refresh the requested token"));
}
}; e -> {
Throwable cause = ExceptionsHelper.unwrapCause(e);
Find & fix Elasticsearch problems
