Quick Links
- Overview
- Understanding Inline Scripts
- Using Inline Scripts in Aggregations
- Using Inline Scripts in Script Fields
- Security Considerations
- Performance Considerations
- Conclusion
Overview
Elasticsearch inline scripts are a versatile tool that can be used to perform complex search operations, manipulate document fields, and even compute new fields on the fly. They are written in Painless, Elasticsearch’s scripting language, which is designed to be safe, fast, and easy to use.
Understanding Inline Scripts
Inline scripts are defined directly in the request body, making them ideal for one-time use cases. They can be used in various parts of Elasticsearch, including search queries, aggregations, and script fields.
For instance, in a search query, an inline script can be used to filter documents based on a computed value. Here’s an example:
GET /_search
{
"query": {
"bool": {
"filter": {
"script": {
"script": {
"source": "doc['price'].value * params.conversion_rate <= params.max_price",
"params": {
"conversion_rate": 0.88,
"max_price": 100
}
}
}
}
}
}
}
In this example, the inline script is used to filter products based on their price after conversion to a different currency. The `params` object is used to pass variables to the script.
Using Inline Scripts in Aggregations
Inline scripts can also be used in aggregations to compute new bucketing criteria or metrics. For example, you can use an inline script to aggregate documents based on the length of a text field:
GET /_search
{
"aggs": {
"title_length": {
"histogram": {
"script": {
"source": "doc['title.keyword'].value.length()",
"lang": "painless"
},
"interval": 10
}
}
}
}In this example, the inline script is used to compute the length of the `title` field, and a histogram aggregation is used to group documents based on this computed value.
Using Inline Scripts in Script Fields
Script fields are computed fields that are returned with the search results. They can be used to compute new fields based on the existing ones. Here’s an example:
GET /_search
{
"script_fields": {
"full_name": {
"script": {
"source": "doc['first_name'].value + ' ' + doc['last_name'].value",
"lang": "painless"
}
}
}
}In this example, the inline script is used to compute a `full_name` field based on the `first_name` and `last_name` fields.
Security Considerations
While inline scripts are powerful, they can also be a security risk if not properly managed. By default, Elasticsearch allows dynamic scripting, which includes both inline and stored scripts.
To disable either inline or stored scripts, you need to modify the `elasticsearch.yml` configuration file:
# Valid values are “inline” and “stored”, set to “none” to disable dynamic scripting
script.allowed_types: inline
Similarly, Elasticsearch allows dynamic scripting in all available contexts. You can find all the available contexts by running `GET _script_context`. If you want to restrict dynamic scripting to only a specific set of contexts, you also need to modify the `elasticsearch.yml` configuration file:
script.allowed_contexts: search, aggs
In this example, inline scripts are enabled for the search and aggs contexts. It’s recommended to only enable the contexts that you need.
Performance Considerations
Inline scripts can have a significant impact on performance, especially when used in large-scale operations. They are executed for each document, which can be resource-intensive.
To mitigate this, Elasticsearch caches compiled scripts by default. However, this only applies to scripts that are identical. If a script uses different parameters, it will be compiled separately.
To improve performance, try to minimize the use of inline scripts, especially in hot paths. Use them judiciously and consider other options, such as indexed fields or pre-computed fields, whenever possible. Whenever possible, try to add a query to reduce the number of documents on which scripts are executed.
Conclusion
In conclusion, Elasticsearch inline scripts are a powerful tool for advanced search operations. They offer flexibility and versatility, but also come with security and performance considerations. Use them wisely to get the most out of your Elasticsearch deployment.
