Briefly, this error occurs when Elasticsearch tries to access a field in a document that doesn’t exist or is not properly defined. This could be due to a typo in the field name, or the field might not exist in the document. To resolve this issue, you can check the field name for typos, ensure the field exists in the document, or handle the possibility of the field being missing in your code. If the field is not required, you can also set the “ignore_malformed” option to true to ignore such errors.
This guide will help you check for common problems that cause the log ” Missing value for field [” + field + “] ” to appear. To understand the issues related to this log, read the explanation below about the following Elasticsearch concepts: search, lucene.
Overview
Search refers to the searching of documents in an index or multiple indices. The simple search is just a GET API request to the _search endpoint. The search query can either be provided in query string or through a request body.
Examples
When looking for any documents in this index, if search parameters are not provided, every document is a hit and by default 10 hits will be returned.
GET my_documents/_search
A JSON object is returned in response to a search query. A 200 response code means the request was completed successfully.
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 2,
"successful" : 2,
"failed" : 0
},
"hits" : {
"total" : 2,
"max_score" : 1.0,
"hits" : [
...
]
}
}Notes and good things to know
- Distributed search is challenging and every shard of the index needs to be searched for hits, and then those hits are combined into a single sorted list as a final result.
- There are two phases of search: the query phase and the fetch phase.
- In the query phase, the query is executed on each shard locally and top hits are returned to the coordinating node. The coordinating node merges the results and creates a global sorted list.
- In the fetch phase, the coordinating node brings the actual documents for those hit IDs and returns them to the requesting client.
- A coordinating node needs enough memory and CPU in order to handle the fetch phase.
Overview
Lucene or Apache Lucene is an open-source Java library used as a search engine. Elasticsearch is built on top of Lucene.
Elasticsearch converts Lucene into a distributed system/search engine for scaling horizontally. Elasticsearch also provides other features like thread-pool, queues, node/cluster monitoring API, data monitoring API, Cluster management, etc. In short, Elasticsearch extends Lucene and provides additional features beyond it.
Elasticsearch hosts data on data nodes. Each data node hosts one or more indices, and each index is divided into shards with each shard holding part of the index’s data. Each shard created in Elasticsearch is a separate Lucene instance or process.
Notes and good things to know
When an index is created in Elasticsearch, it is divided into one or more primary shards for scaling the data and splitting it into multiple nodes/instances.
- As each shard is a separate instance of Lucene, creating too many shards will consume unnecessary resources and damage performance.
It takes proper planning to decide the number of primary shards for your index, taking into account the index size, max growth, and the number of data nodes.
- Previous versions of Elasticsearch defaulted to creating five shards per index. Starting with 7.0.0, the default is now one shard per index.
Log Context
Log “Missing value for field [” + field + “]” class name is FieldValueFactorFunction.java. We extracted the following from Elasticsearch source code for those seeking an in-depth context :
value = values.nextValue();
} else {
if (missing != null) {
value = missing;
} else {
throw new ElasticsearchException("Missing value for field [" + field + "]");
}
}
double val = value * boostFactor;
double result = modifier.apply(val);
if (result < 0f) {
