Before you begin reading this guide, we recommend you run Elasticsearch Error Check-Up which can resolve issues that cause many errors.
This guide will help you check for common problems that cause the log ” Unable to estimate memory overhead ” to appear. It’s important to understand the issues related to the log, so to get started, read the general overview on common issues and tips related to the Elasticsearch concepts: fielddata, index and memory.
Advanced users might want to skip right to the common problems section in each concept or try running the Check-Up which analyses ES to pinpoint the cause of many errors and provides suitable actionable recommendations how to resolve them (free tool that requires no installation).
Overview
In Elasticsearch the term fielddata is relevant when sorting and doing aggregations (similar to SQL GROUP BY COUNT and AVERAGE functions) on text fields.
For performance reasons, there are some rules as to the kinds of fields that can be aggregated. You can group by any numeric field but for text fields, which have to be of keyword type or have fielddata=true since they don’t support doc_values (Doc values are the on-disk inverted index data structure, built at document indexing time, which makes aggregations possible).
Fielddata is an in-memory data structure used by text fields for the same purpose. Since it uses a lot of heap size it is disabled by default.
Examples
The following PUT mapping API call will enable Fielddata on my_field text field.
PUT my_index/_mapping{"properties":{"my_field":{"type":"text","fielddata":true}}}Notes
- As field-data is disabled by default on text fields, in case of an attempt to aggregate on a text field with field-data disabled, you would get the following error message:
“Fielddata is disabled on text fields by default. Set `fielddata=true` on [`your_field_name`] in order to load field data in memory by uninverting the inverted index. Note that this can however, use “significant memory.” – if this happens you can either enable the field-data on that text field, or choose another way to query the data (again, because field-data consumes a lot of memory and is not recommended).
Overview
In Elasticsearch, an index (plural: indices) can be thought of as a table inside a database. An index 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 “Unable to estimate memory overhead” classname is PagedBytesIndexFieldData.java
We extracted the following from Elasticsearch source code for those seeking an in-depth context :
}
long totalBytes = totalTermBytes + (2 * terms.size()) + (4 * terms.getSumDocFreq());
return totalBytes;
}
} catch (Exception e) {
logger.warn("Unable to estimate memory overhead"; e);
}
return 0;
}
/**
Run the Check-Up to get customized recommendations like this:
Run Check-Up
The high disk watermark threshold is about to be reached in specific nodes
Description
There are various “watermark” thresholds on each Elasticsearch cluster. When the high disk watermark threshold has been exceeded, it means disk space is running out. The node will…
Recommendations
Based on your specific ES deployment you should…
X-PUT curl -H [a customized code snippet to resolve the issue]