1. Overview
In Elasticsearch, when working with multiple indices or data sources with varying structures, we may encounter situations where certain fields are present in some documents but absent in others. This can often lead to unexpected query results.
In this tutorial, we’ll explore effective techniques to ignore indices where a specific field doesn’t exist, ensuring more accurate and efficient Elasticsearch queries.
2. Problem Statement
Data as part of Elasticsearch often evolves over time. For instance, new fields may be added, old ones deprecated, or different sources might have varying schemas. Consequently, this can lead to a situation where some indices or documents lack fields that exist in others.
2.1. Missing Fields
Let’s take an e-commerce platform as an example. Perhaps this platform recently started tracking the featured_product status for certain items. Newer indices would include this boolean field, while older ones would lack it entirely:
// Document in a newer index
{
"product_id": "ABC123",
"name": "Wireless Earbuds",
"price": 99.99,
"featured_product": true
}
// Document in an older index
{
"product_id": "XYZ789",
"name": "Wired Headphones",
"price": 49.99
}
This inconsistency in field presence across indices may present challenges when querying or sorting based on the featured_product field.
2.2. Impact on Query Results
The consequences of not properly handling missing fields can be significant:
- incomplete query results: queries filtering on a non-existent field may unintentionally exclude relevant documents from older indices
- sorting errors: attempting to sort on a field that doesn’t exist in all indices can lead to runtime errors or unexpected ordering of results
- performance degradation: Elasticsearch may waste resources attempting to process non-existent fields across all indices, leading to slower query execution
In the following sections, we explore Elasticsearch’s query and mapping concepts relevant to this problem.
3. Elasticsearch Query DSL and Mapping Concepts
Now, let’s look into Elasticsearch’s Query DSL and mapping concepts.
3.1. Query DSL Structure
Elasticsearch’s Query Domain Specific Language (DSL) is a flexible, JSON-based language for defining queries.
In particular, it has a basic structure:
{
"query": {
"<query_type>": {
"<field_name>": "<value>"
}
}
}
We have some notable definitions in this basic structure:
- query_type: type of query we want to perform, such as match, term*, range*, bool, and others
- field_name: name of the field in the documents we want to query
- value: value we’re searching for within that field
We can also combine and nest queries to create complex search criteria:
{
"query": {
"bool": {
"must": [
{ "match": { "name": "Wireless Earbuds" } }
],
"should": [
{ "term": { "featured_product": true } }
]
}
}
}
This query returns documents where the name field matches Wireless Earbuds. Among those documents, the ones where featured_product is true were given a higher relevance score, pushing them higher in the search results.
3.2. Field Mappings and Their Importance
Field mapping in Elasticsearch defines how documents and their fields are stored and indexed:
{
"mappings": {
"properties": {
"name": { "type": "text" },
"price": { "type": "float" },
"featured_product": { "type": "boolean" }
}
}
}
Notably, understanding mappings is essential because they affect how Elasticsearch handles missing fields and how we can query and sort the data effectively.
4. Techniques for Handling Missing Fields
Now that we’ve covered the basics, let’s explore practical techniques for handling queries and sorts when fields may not exist across all indices.
4.1. Using the index Query
The index query targets specific indices based on field existence:
GET /products_*/_search
{
"query": {
"bool": {
"must": [
{
"index": {
"value": ["products_new", "products_updated"]
}
},
{
"term": { "featured_product": true }
}
]
}
}
}
Thus, the query efficiently filters results to only include documents from products_new and products_updated where we know the featured_product field exists. Moreover, it uses the index query within a bool query to specifically target these two indices.
4.2. Leveraging Index Patterns and the exists Query
We can combine index patterns with the exists query to dynamically target indices where the field exists:
GET /products_*/_search
{
"query": {
"bool": {
"filter": [
{ "exists": { "field": "featured_product" } }
],
"must": [
{ "term": { "featured_product": true } }
]
}
}
}
Firstly, we use the filter clause to narrow down the results without affecting the relevance score of the documents. Then, the exists query inside the filter checks for the presence of the featured_product field in the documents. Only documents that contain this field pass through the filter.
4.3. Using the _all Meta-Field
The _all meta-field enables searching across all indices and all fields. While it’s not recommended for production use due to performance implications, it can be useful for exploratory queries or when we’re unsure about field existence across indices:
GET /_all/_search
{
"query": {
"query_string": {
"query": "featured_product:true"
}
}
}
This query searches for featured_product:true across all indices and fields. However, it only matches documents where the field actually exists, effectively ignoring indices without the field.
4.4. Using Multi-index Aliases
Another approach is to create an alias that only includes indices with the desired field.
Firstly, we use the mapping API to identify indices with the required field:
GET /products_*/_mapping/field/featured_product
In this case, the _mapping API is used to retrieve the mapping of an index, which includes the fields and their types.
Then, we create an alias including only those indices:
POST /_aliases
{
"actions": [
{
"add": {
"index": "products_new",
"alias": "products_with_featured"
}
},
{
"add": {
"index": "products_updated",
"alias": "products_with_featured"
}
}
]
}
Now, we can query this alias instead of using wildcards:
GET /products_with_featured/_search
{
"query": {
"term": { "featured_product": true }
}
}
This approach ensures we’re only querying indices where the featured_product field exists, effectively ignoring all others.
5. Using Index Templates for Consistent Mapping
To prevent issues with missing fields in future indices, we can use index templates to ensure consistent mapping across new indices:
PUT _template/products_template
{
"index_patterns": ["products_*"],
"mappings": {
"properties": {
"featured_product": { "type": "boolean" }
}
}
}
This template automatically applies the specified mapping to any new index created that matches the products_* pattern. Thus, this ensures the featured_product field is consistently present and correctly typed.
6. Conclusion
In this article, we explored various techniques to ignore indices where a field doesn’t exist in Elasticsearch. We started by understanding the challenges posed by evolving data structures and inconsistent field presence across indices. From there, we delved into solutions to create more robust queries.
Initially, we learned how to use the index query to target specific indices, leverage the exists query for dynamic field checks, and implement multi-index querying strategies. These techniques ensure effective handling when fields may be absent in some indices without compromising the accuracy or efficiency of the searches.