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Before you dig into the details of this guide, have you tried asking OpsGPT? You’ll receive concise answers that will help streamline your Elasticsearch/OpenSearch operations.
Try OpsGPT now for step-by-step guidance and tailored insights into your search operation.
In addition to understanding Elasticsearch filters and being aware of pros and cons of their use, try AutoOps for Elasticsearch. It diagnoses problems by analyzing hundreds of metrics collected by a lightweight agent and offers guidance for resolving them.
Overview
A filter in Elasticsearch is all about applying some conditions inside the query that are used to narrow down the matching result set.
What it is used for
When a query is executed, Elasticsearch by default calculates the relevance score of the matching documents. But in some conditions it does not require scores to be calculated, for instance if a document falls in the range of two given timestamps. For all these Yes/No criteria, a filter clause is used.
Examples
Return all the results of a given index that falls between a date range:
GET my_index/_search
{
"query": {
"bool": {
"filter": {
"range": {
"created_at": {
"gte": "2020-01-01",
"lte": "2020-01-10"
}
}
}
}
}
}Notes
- Queries are used to find out how relevant a document is to a particular query by calculating a score for each document, whereas filters are used to match certain criteria and are cacheable to enable faster execution.
- Filters do not contribute to scoring and thus are faster to execute.
- There are major changes introduced in Elasticsearch version 2.x onward related to how query and filters are written and performed internally.
Common problems
- The most common problem with filters is incorrect use inside the query. If filters are not used correctly, query performance can be significantly affected. So filters must be used wherever there is scope of not calculating the score.
- Another problem often arises when using date range filters, if “now” is used to represent the current time. It has to be noted that “now” is continuously changing the timestamp and thus Elasticsearch cannot use caching of the response since the data set will keep changing.
Related log errors to this ES concept
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