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Kibana Query Language (KQL) is a text-based language for filtering documents in Kibana. It narrows results to documents that match conditions; it does not aggregate, transform, or sort data.
What is Kibana Query Language (KQL)?
KQL lets you express search filters in a concise text box, typically by naming a field and the value or condition you want it to match. For example, http.request.method: GET filters for documents whose http.request.method field matches GET. What matches depends on the field’s mapping and the data indexed in Elasticsearch.
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KQL is a filter language, not a general-purpose analysis language. It selects documents but does not calculate aggregations, change field values, or order results. See Elastic’s KQL reference for the current syntax and behavior.
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KQL expressions are built from field-value conditions, existence checks, ranges, Boolean operators, parentheses, and supported wildcard patterns.
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Match a field or search without naming one
Use field: value to filter on a named field. For instance, http.request.method: GET is a field-value condition. A bare term without a field name searches across fields, so its results depend on the fields available in the data view.
Check whether a field has an indexed value
Use an asterisk as the value to find documents where a field has an indexed value: http.request.method: *. This can include an empty string if that string is indexed; it does not mean the field necessarily contains a non-empty value.
Set a range
Comparison operators express ranges. For example, http.response.bytes > 10000 and http.response.bytes <= 20000 finds values above 10,000 and up to and including 20,000. Range syntax can also be used with strings, IP addresses, and timestamps, subject to the field mapping.
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Combine conditions
Use AND, OR, and NOT to combine or exclude conditions. For example, http.request.method: GET AND http.response.status_code: 400 requires both conditions to match. Add parentheses when you need to make the intended grouping explicit, especially when mixing operators.
Match a pattern
KQL supports * as a wildcard for zero or more characters. For example, machine.os: win* matches values beginning with “win” on supported field types. Wildcards can be used with keyword, text, and wildcard fields, but not numeric, date, or boolean fields. KQL does not support other wildcard characters in this pattern syntax.
A leading wildcard, as in url: *elastic*, can make a search slower. Kibana’s query:allowLeadingWildcards advanced setting can disable leading wildcards. Avoid assuming that wildcard queries are fast or valid for every field.
Search nested fields
Nested fields need KQL’s nested-field syntax rather than ordinary top-level field matching. The syntax and behavior differ from a simple field-value condition; consult the KQL reference when querying nested data.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Why can the same KQL expression match differently across fields?
KQL matching follows the field’s Elasticsearch mapping. Keyword, numeric, date, and boolean values use exact matching; for those types, matching is sensitive to case and punctuation as described in Elastic’s reference. Text fields are analyzed according to their mapping settings, so a supplied value may be processed rather than compared as an unmodified string. Quotation marks can request phrase behavior for text searches.
That is why a syntax example is not a guarantee of a particular result in every index: check the field type and mapping when a filter returns unexpected documents. For multi-value fields, KQL evaluates each condition against every array value. Separate conditions may therefore match different values in the same array. If one single array value must satisfy all conditions, Elastic directs users to Query DSL.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What KQL does not do
KQL only filters documents. It does not aggregate or transform data, nor does it sort the results. Use another query language or tool when the task goes beyond selecting matching documents.
KQL vs. Lucene, ES|QL, and Query DSL
| Language | Best suited to | How it differs |
|---|---|---|
| KQL | Concise text-based document filtering in Kibana. | Filters matching documents; it does not aggregate or transform data. |
| Lucene | Kibana searches that need Lucene-specific advanced features. | Uses a different syntax and feature set, including regular expressions and fuzzy-term matching; these are not KQL operators. |
| ES|QL | Filtering, transforming, and analyzing data through a piped workflow. | Supports workflows that extend beyond a simple Kibana filter. |
| Query DSL | Complex Elasticsearch searches, filters, and aggregations, or precise control over certain query semantics. | Uses a JSON-style structure and is described by Elastic as its primary, most flexible option. |
Elastic also provides an Elasticsearch kql query that accepts a KQL expression and rewrites it into Query DSL in supported query contexts. This is distinct from using KQL as a free-standing aggregation or transformation language. See Elastic’s query-language overview and KQL query documentation.
Quick Recap
Which language should you choose?
- Choose KQL when you need to quickly narrow Kibana results by fields and conditions.
- Choose Lucene when you need Lucene features such as fuzzy matching or regular expressions.
- Choose ES|QL when the work involves a sequence of filtering, transformation, or analysis steps.
- Choose Query DSL when you need structured, flexible search and aggregation, or exact control over how multi-value conditions are applied.
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