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**Alias**: `match_field_ignore_case(field, 'value')`(Available in OpenObserve version 0.15.0 and later)<br>
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**Description**: <br>
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- Filters logs where the specified field contains the string value.
@@ -37,11 +38,11 @@ This query filters logs from the `default` stream where the `k8s_pod_name` field
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### `match_all('value')`
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**Description**: <br>
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- Filters logs by searching for the keyword across all fields that have the Index Type set to Full Text Search in the [stream settings](../docs/user-guide/streams/schema-settings.md).
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- Filters logs by searching for the keyword across all fields that have the Index Type set to Full Text Search in the [stream settings](../user-guide/streams/schema-settings/).
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- This function is case-insensitive and returns matches regardless of the keyword's casing.
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!!! Note
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To enable support for fields indexed using the Inverted Index method, set the environment variable `ZO_ENABLE_INVERTED_INDEX` to true. Once enabled, you can configure the fields to use the Inverted Index by updating the [stream settings](../docs/user-guide/streams/schema-settings.md) in the user interface or through the [setting API](../docs/api/stream/setting.md).
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To enable support for fields indexed using the Inverted Index method, set the environment variable `ZO_ENABLE_INVERTED_INDEX` to true. Once enabled, you can configure the fields to use the Inverted Index by updating the [stream settings](../user-guide/streams/schema-settings/) in the user interface or through the [setting API](../api/stream/setting/).
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The `match_all` function searches through inverted indexed terms, which are internally converted to lowercase. Therefore, keyword searches using `match_all` are always case-insensitive.
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@@ -261,7 +262,7 @@ Aggregate functions compute a single result from a set of input values. For usag
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### `histogram(field, 'duration')`
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**Description:** <br>
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Use the `histogram` function to divide your time-based log data into time buckets of a fixed duration and then apply aggregate functions such as COUNT() or SUM() to those intervals.
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Use the `histogram` function to divide your time-based log data into time buckets of a fixed duration and then apply aggregate functions such as `COUNT()` or `SUM()` to those intervals.
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This helps in visualizing time-series trends and performing meaningful comparisons over time. <br><br>
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