Before you begin reading this guide, we recommend you try running the Elasticsearch Error Check-Up which analyzes 2 JSON files to detect many configuration errors.
Briefly, this error occurs when Elasticsearch is unable to parse a document, and the document is empty. This can be due to an issue with the data format or a problem with the document itself. To resolve this issue, you can check the format of the data and ensure that the document is not empty.
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 to parse; document is empty ” to appear. To understand the issues related to this log, read the explanation below about the following Elasticsearch concepts: index and document.
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
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.
Log Context
Log “failed to parse; document is empty”classname is DocumentParser.java We extracted the following from Elasticsearch source code for those seeking an in-depth context :
return (MapperParsingException) e;
}
// Throw a more meaningful message if the document is empty.
if (source.source() != null && source.source().length() == 0) {
return new MapperParsingException("failed to parse; document is empty");
}
return new MapperParsingException("failed to parse"; e);
}
See how you can use AutoOps to resolve issues
