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.
This guide will help you check for common problems that cause the log ” Failed add ILM history item to queue for index ” to appear. To understand the issues related to this log, read the explanation below about the following Elasticsearch concepts: index, plugin and queue.
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
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.
Overview
The queue term in Elasticsearch is used in the context of thread pools. Each node of the Elasticsearch cluster holds various thread pools to manage the memory consumption on that node for different types of requests. The queues come up with initial default limits as per node size but can be modified dynamically using _settings REST endpoint.
What it is used for
Queues are used to hold the pending requests for the corresponding thread pool instead of requests being rejected. For example, if there are too many search requests coming on the node which can not be processed at the same time, the requests are sent to the search thread pool queue.
Examples
Monitoring the thread pools using _cat API:
GET /_cat/thread_pool?v
Get details about each thread pool, including current size:
GET /_nodes/thread_pool
Notes
- Thread pool queues are one of the most important stats to monitor in Elasticsearch as they have a direct impact on the cluster performance and may halt the indexing and search requests.
- The specific thread pool queue size can be changed using its type-specific parameters.
- It is possible to update thread pool queue size dynamically using cluster setting API in version 2.x.
- From Elasticsearch version 5.x onward, it is not possible to update the thread pool settings dynamically via the cluster setting API. Rather, it is a node level setting and it must be configured inside elasticsearch.yml on each node and a node restart is required after the updates.
Common problems
- The most common problem that arises in Elasticsearch related to queues is EsRejectedExecutionException that occurs when queues are full and Elasticsearch nodes cannot keep up with the speed of the requests. This may lead to nodes not responding as well. To deal with this issue, thread pools need continuous monitoring and based on thread pool queue utilization, you may need to review and control the indexing/search requests or increase the resources of the cluster.
- In case of bulk indexing queue rejection, increasing the size of the queue may cause the node to keep more data in memory, which may cause requests taking longer to complete and more heap space to be consumed. As a result you may face impact on cluster performance and stability.
Log Context
Log “failed add ILM history item to queue for index [{}]: [{}]” classname is ILMHistoryStore.java.
We extracted the following from Elasticsearch source code for those seeking an in-depth context :
// see: https://github.com/elastic/elasticsearch/issues/50440
threadPool.executor(ThreadPool.Names.GENERIC).execute(() -> {
try {
processor.add(request);
} catch (Exception e) {
logger.error(new ParameterizedMessage("failed add ILM history item to queue for index [{}]: [{}]";
ILM_HISTORY_ALIAS; item); e);
}
});
} catch (IOException exception) {
logger.error(new ParameterizedMessage("failed to queue ILM history item in index [{}]: [{}]";
