Quick links
- Introduction
- Using Wildcards and Aliases for Multi-Index Queries
- Handling Field. Mapping and- Conflicts
Introduction
Elasticsearch provides a flexible and efficient way to search and analyze data across multiple indices. Multi-index queries allow users to search for documents across several indices simultaneously, which can be useful in various use cases, such as searching for data across different time periods (in the case where you have time series data spread across several indices) or aggregating results from different data sources. In this article, we will explore advanced techniques and best practices for performing multi-index queries in Elasticsearch.
You can easily query multiple indices by simply listing them in your request URL. Each should be separated by a comma, as shown below.
GET /logs-2021-04,logs-2021-05/_search
{
"query": {
"match": {
"message": "error"
}
}
}Although this approach works, it can get tedious to list all of your indices in the request URL, not only that, but the list can get long quickly.
Fortunately there are other approaches to create a multi-index query, as we’re going to see next.
Using Wildcards and Aliases for Multi-Index Queries
1. Wildcards
When querying multiple indices, you can use wildcards to match index names. For example, if you have indices named “logs-2021-01”, “logs-2021-02”, and “logs-2021-03”, you can use a wildcard (*) to search across all these indices:
GET /logs-2021-*/_search
{
"query": {
"match": {
"message": "error"
}
}
}This query will search for documents containing the word “error” in the “message” field across all indices with names starting with “logs-2021-“.
2. Aliases
Index aliases can be used to group multiple indices under a single name, making it easier to query them together. For example, you can create an alias named “logs-2021” that points to the indices “logs-2021-01”, “logs-2021-02”, and “logs-2021-03”:
POST /_aliases
{
"actions": [
{
"add": {
"indices": ["logs-2021-01", "logs-2021-02", "logs-2021-03"],
"alias": "logs-2021"
}
}
]
}Now, you can query the alias “logs-2021” to search across all the indices it points to, which are the time series data for the year of 2021 (the actual data will be spread in other 12 indices, one for each month of the year):
GET /logs-2021/_search
{
"query": {
"match": {
"message": "error"
}
}
}Handling Field Mappings and Conflicts
When querying multiple indices, it’s essential to ensure that the field mappings are consistent across the indices. If the mappings are different, Elasticsearch may not be able to merge the results correctly, leading to inaccurate or incomplete results.
One way to get around this is to manage your indices’ settings and mapping with the help of index templates.
1. Using index templates
Index templates can help maintain consistent mappings across multiple indices. You can create an index template that defines the mappings for fields that are common across your indices:
PUT /_index_template/logs_template
{
"index_patterns": ["logs-*"],
"template": {
"mappings": {
"properties": {
"message": {
"type": "text"
},
"timestamp": {
"type": "date"
}
}
}
}
}This template will be applied to any new index with a name that matches the pattern “logs-*”, ensuring that the “message” and “timestamp” fields have consistent mappings.
2. Handling mapping conflicts
If you encounter mapping conflicts when querying multiple indices, you can use the “ignore_unmapped” option in the “sort” or “aggregations” sections of your query to ignore fields that are not mapped in some indices:
GET /logs-2021-*/_search
{
"query": {
"match": {
"message": "error"
}
},
"sort": [
{
"timestamp": {
"order": "desc",
"unmapped_type": "date"
}
}
]
}This query will sort the results by the “timestamp” field, ignoring any documents in indices where the “timestamp” field is not mapped. Elasticsearch will treat the index as if it had the field mapped to the specified data type and also will behave like it normally would with documents with null values for that field. It’s just a workaround to the problem of some of the indices in your multi-index search not complying to an expected structure. A better approach would definitely be to use index templates, as described before.
If you want to learn more about multi-index queries you can take a look at this guide. You will also find there guides to other concepts mentioned in this article, such as aliasing and index templates.
