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document new filters and stuff (#14760)

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2
docs/ingestion/ingestion-spec.md
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@ -186,7 +186,7 @@ Treat `__time` as a millisecond timestamp: the number of milliseconds since Jan
### `dimensionsSpec` ### `dimensionsSpec`
The `dimensionsSpec` is located in `dataSchema` → `dimensionsSpec` and is responsible for The `dimensionsSpec` is located in `dataSchema` → `dimensionsSpec` and is responsible for
configuring [dimensions](./schema-model.md#dimensions). An example `dimensionsSpec` is: configuring [dimensions](./schema-model.md#dimensions).
You can either manually specify the dimensions or take advantage of schema auto-discovery where you allow Druid to infer all or some of the schema for your data. This means that you don't have to explicitly specify your dimensions and their type. You can either manually specify the dimensions or take advantage of schema auto-discovery where you allow Druid to infer all or some of the schema for your data. This means that you don't have to explicitly specify your dimensions and their type.

2
docs/ingestion/schema-design.md
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@ -261,6 +261,8 @@ native boolean types, Druid ingests these values as strings if `druid.expression
the [array functions](../querying/sql-array-functions.md) or [UNNEST](../querying/sql-functions.md#unnest). Nested the [array functions](../querying/sql-array-functions.md) or [UNNEST](../querying/sql-functions.md#unnest). Nested
columns can be queried with the [JSON functions](../querying/sql-json-functions.md). columns can be queried with the [JSON functions](../querying/sql-json-functions.md).
We also highly recommend setting `druid.generic.useDefaultValueForNull=false` when using these columns since it also enables out of the box `ARRAY` type filtering. If not set to `false`, setting `sqlUseBoundsAndSelectors` to `false` on the [SQL query context](../querying/sql-query-context.md) can enable `ARRAY` filtering instead.
Mixed type columns are stored in the _least_ restrictive type that can represent all values in the column. For example: Mixed type columns are stored in the _least_ restrictive type that can represent all values in the column. For example:
- Mixed numeric columns are `DOUBLE` - Mixed numeric columns are `DOUBLE`

784
docs/querying/filters.md
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@ -35,199 +35,187 @@ Apache Druid supports the following types of filters.
## Selector filter ## Selector filter
The simplest filter is a selector filter. The selector filter will match a specific dimension with a specific value. Selector filters can be used as the base filters for more complex Boolean expressions of filters. The simplest filter is a selector filter. The selector filter matches a specific dimension with a specific value. Selector filters can be used as the base filters for more complex Boolean expressions of filters.
The grammar for a SELECTOR filter is as follows: | Property | Description | Required |
| -------- | ----------- | -------- |
| `type` | Must be "selector".| Yes |
| `dimension` | Input column or virtual column name to filter. | Yes |
| `value` | String value to match. | No. If not specified the filter matches NULL values. |
| `extractionFn` | [Extraction function](./dimensionspecs.md#extraction-functions) to apply to `dimension` prior to value matching. See [filtering with extraction functions](#filtering-with-extraction-functions) for details. | No |
The selector filter can only match against `STRING` (single and multi-valued), `LONG`, `FLOAT`, `DOUBLE` types. Use the newer null and equality filters to match against `ARRAY` or `COMPLEX` types.
When the selector filter matches against numeric inputs, the string `value` will be best-effort coerced into a numeric value.
### Example: equivalent of `WHERE someColumn = 'hello'`
``` json ``` json
"filter": { "type": "selector", "dimension": <dimension_string>, "value": <dimension_value_string> } { "type": "selector", "dimension": "someColumn", "value": "hello" }
``` ```
This is the equivalent of `WHERE <dimension_string> = '<dimension_value_string>'` or `WHERE <dimension_string> IS NULL`
(if the `value` is `null`).
The selector filter supports the use of extraction functions, see [Filtering with Extraction Functions](#filtering-with-extraction-functions) for details. ### Example: equivalent of `WHERE someColumn IS NULL`
``` json
{ "type": "selector", "dimension": "someColumn", "value": null }
```
## Equality Filter
The equality filter is a replacement for the selector filter with the ability to match against any type of column. The equality filter is designed to have more SQL compatible behavior than the selector filter and so can not match null values. To match null values use the null filter.
Druid's SQL planner uses the equality filter by default instead of selector filter whenever `druid.generic.useDefaultValueForNull=false`, or if `sqlUseBoundAndSelectors` is set to false on the [SQL query context](./sql-query-context.md).
| Property | Description | Required |
| -------- | ----------- | -------- |
| `type` | Must be "equality".| Yes |
| `column` | Input column or virtual column name to filter. | Yes |
| `matchValueType` | String specifying the type of value to match. For example `STRING`, `LONG`, `DOUBLE`, `FLOAT`, `ARRAY<STRING>`, `ARRAY<LONG>`, or any other Druid type. The `matchValueType` determines how Druid interprets the `matchValue` to assist in converting to the type of the matched `column`. | Yes |
| `matchValue` | Value to match, must not be null. | Yes |
### Example: equivalent of `WHERE someColumn = 'hello'`
```json
{ "type": "equality", "column": "someColumn", "matchValueType": "STRING", "matchValue": "hello" }
```
### Example: equivalent of `WHERE someNumericColumn = 1.23`
```json
{ "type": "equality", "column": "someNumericColumn", "matchValueType": "DOUBLE", "matchValue": 1.23 }
```
### Example: equivalent of `WHERE someArrayColumn = ARRAY[1, 2, 3]`
```json
{ "type": "equality", "column": "someArrayColumn", "matchValueType": "ARRAY<LONG>", "matchValue": [1, 2, 3] }
```
## Null Filter
The null filter is a partial replacement for the selector filter. It is dedicated to matching NULL values.
Druid's SQL planner uses the null filter by default instead of selector filter whenever `druid.generic.useDefaultValueForNull=false`, or if `sqlUseBoundAndSelectors` is set to false on the [SQL query context](./sql-query-context.md).
| Property | Description | Required |
| -------- | ----------- | -------- |
| `type` | Must be "null".| Yes |
| `column` | Input column or virtual column name to filter. | Yes |
### Example: equivalent of `WHERE someColumn IS NULL`
```json
{ "type": "null", "column": "someColumn" }
```
## Column comparison filter ## Column comparison filter
The column comparison filter is similar to the selector filter, but instead compares dimensions to each other. For example: The column comparison filter is similar to the selector filter, but compares dimensions to each other. For example:
``` json | Property | Description | Required |
"filter": { "type": "columnComparison", "dimensions": [<dimension_a>, <dimension_b>] } | -------- | ----------- | -------- |
``` | `type` | Must be "selector".| Yes |
| `dimensions` | List of [`DimensionSpec`](./dimensionspecs.md) to compare. | Yes |
This is the equivalent of `WHERE <dimension_a> = <dimension_b>`.
`dimensions` is list of [DimensionSpecs](./dimensionspecs.md), making it possible to apply an extraction function if needed. `dimensions` is list of [DimensionSpecs](./dimensionspecs.md), making it possible to apply an extraction function if needed.
## Regular expression filter Note that the column comparison filter converts all values to strings prior to comparison. This allows differently-typed input columns to match without a cast operation.
The regular expression filter is similar to the selector filter, but using regular expressions. It matches the specified dimension with the given pattern. The pattern can be any standard [Java regular expression](http://docs.oracle.com/javase/6/docs/api/java/util/regex/Pattern.html). ### Example: equivalent of `WHERE someColumn = someLongColumn`
``` json ``` json
"filter": { "type": "regex", "dimension": <dimension_string>, "pattern": <pattern_string> } {
"type": "columnComparison",
"dimensions": [
"someColumn",
{
"type" : "default",
"dimension" : someLongColumn,
"outputType": "LONG"
}
]
}
``` ```
The regex filter supports the use of extraction functions, see [Filtering with Extraction Functions](#filtering-with-extraction-functions) for details.
## Logical expression filters ## Logical expression filters
### AND ### AND
The grammar for an AND filter is as follows: | Property | Description | Required |
| -------- | ----------- | -------- |
| `type` | Must be "and".| Yes |
| `fields` | List of filter JSON objects, such as any other filter defined on this page or provided by extensions. | Yes |
#### Example: equivalent of `WHERE someColumn = 'a' AND otherColumn = 1234 AND anotherColumn IS NULL`
``` json ``` json
"filter": { "type": "and", "fields": [<filter>, <filter>, ...] } {
"type": "and",
"fields": [
{ "type": "equality", "column": "someColumn", "matchValue": "a", "matchValueType": "STRING" },
{ "type": "equality", "column": "otherColumn", "matchValue": 1234, "matchValueType": "LONG" },
{ "type": "null", "column": "anotherColumn" }
]
}
``` ```
The filters in fields can be any other filter defined on this page.
### OR ### OR
The grammar for an OR filter is as follows: | Property | Description | Required |
| -------- | ----------- | -------- |
| `type` | Must be "or".| Yes |
| `fields` | List of filter JSON objects, such as any other filter defined on this page or provided by extensions. | Yes |
#### Example: equivalent of `WHERE someColumn = 'a' OR otherColumn = 1234 OR anotherColumn IS NULL`
``` json ``` json
"filter": { "type": "or", "fields": [<filter>, <filter>, ...] } {
"type": "or",
"fields": [
{ "type": "equality", "column": "someColumn", "matchValue": "a", "matchValueType": "STRING" },
{ "type": "equality", "column": "otherColumn", "matchValue": 1234, "matchValueType": "LONG" },
{ "type": "null", "column": "anotherColumn" }
]
}
``` ```
The filters in fields can be any other filter defined on this page.
### NOT ### NOT
The grammar for a NOT filter is as follows: | Property | Description | Required |
| -------- | ----------- | -------- |
| `type` | Must be "not".| Yes |
| `field` | Filter JSON objects, such as any other filter defined on this page or provided by extensions. | Yes |
#### Example: equivalent of `WHERE someColumn IS NOT NULL`
```json ```json
"filter": { "type": "not", "field": <filter> } { "type": "not", "field": { "type": "null", "column": "someColumn" }}
``` ```
The filter specified at field can be any other filter defined on this page.
## JavaScript filter
The JavaScript filter matches a dimension against the specified JavaScript function predicate. The filter matches values for which the function returns true.
The function takes a single argument, the dimension value, and returns either true or false.
```json
{
"type" : "javascript",
"dimension" : <dimension_string>,
"function" : "function(value) { <...> }"
}
```
**Example**
The following matches any dimension values for the dimension `name` between `'bar'` and `'foo'`
```json
{
"type" : "javascript",
"dimension" : "name",
"function" : "function(x) { return(x >= 'bar' && x <= 'foo') }"
}
```
The JavaScript filter supports the use of extraction functions, see [Filtering with Extraction Functions](#filtering-with-extraction-functions) for details.
> JavaScript-based functionality is disabled by default. Please refer to the Druid [JavaScript programming guide](../development/javascript.md) for guidelines about using Druid's JavaScript functionality, including instructions on how to enable it.
## Extraction filter
> The extraction filter is now deprecated. The selector filter with an extraction function specified
> provides identical functionality and should be used instead.
Extraction filter matches a dimension using some specific [Extraction function](./dimensionspecs.md#extraction-functions).
The following filter matches the values for which the extraction function has transformation entry `input_key=output_value` where
`output_value` is equal to the filter `value` and `input_key` is present as dimension.
**Example**
The following matches dimension values in `[product_1, product_3, product_5]` for the column `product`
```json
{
"filter": {
"type": "extraction",
"dimension": "product",
"value": "bar_1",
"extractionFn": {
"type": "lookup",
"lookup": {
"type": "map",
"map": {
"product_1": "bar_1",
"product_5": "bar_1",
"product_3": "bar_1"
}
}
}
}
}
```
## Search filter
Search filters can be used to filter on partial string matches.
```json
{
"filter": {
"type": "search",
"dimension": "product",
"query": {
"type": "insensitive_contains",
"value": "foo"
}
}
}
```
|property|description|required?|
|--------|-----------|---------|
|type|This String should always be "search".|yes|
|dimension|The dimension to perform the search over.|yes|
|query|A JSON object for the type of search. See [search query spec](#search-query-spec) for more information.|yes|
|extractionFn|[Extraction function](#filtering-with-extraction-functions) to apply to the dimension|no|
The search filter supports the use of extraction functions, see [Filtering with Extraction Functions](#filtering-with-extraction-functions) for details.
### Search query spec
#### Contains
|property|description|required?|
|--------|-----------|---------|
|type|This String should always be "contains".|yes|
|value|A String value to run the search over.|yes|
|caseSensitive|Whether two string should be compared as case sensitive or not|no (default == false)|
#### Insensitive Contains
|property|description|required?|
|--------|-----------|---------|
|type|This String should always be "insensitive_contains".|yes|
|value|A String value to run the search over.|yes|
Note that an "insensitive_contains" search is equivalent to a "contains" search with "caseSensitive": false (or not
provided).
#### Fragment
|property|description|required?|
|--------|-----------|---------|
|type|This String should always be "fragment".|yes|
|values|A JSON array of String values to run the search over.|yes|
|caseSensitive|Whether strings should be compared as case sensitive or not. Default: false(insensitive)|no|
## In filter ## In filter
The in filter can match input rows against a set of values, where a match occurs if the value is contained in the set.
In filter can be used to express the following SQL query: | Property | Description | Required |
| -------- | ----------- | -------- |
| `type` | Must be "in".| Yes |
| `dimension` | Input column or virtual column name to filter. | Yes |
| `values` | List of string value to match. | Yes |
| `extractionFn` | [Extraction function](./dimensionspecs.md#extraction-functions) to apply to `dimension` prior to value matching. See [filtering with extraction functions](#filtering-with-extraction-functions) for details. | No |
```sql
SELECT COUNT(*) AS 'Count' FROM `table` WHERE `outlaw` IN ('Good', 'Bad', 'Ugly')
```
The grammar for a "in" filter is as follows: If an empty `values` array is passed to the "in" filter, it will simply return an empty result.
If the `values` array contains `null`, the "in" filter matches null values. This differs from the SQL IN filter, which
does not match NULL values.
### Example: equivalent of `WHERE `outlaw` IN ('Good', 'Bad', 'Ugly')`
```json ```json
{ {
@ -237,40 +225,6 @@ The grammar for a "in" filter is as follows:
} }
``` ```
The "in" filter supports the use of extraction functions, see [Filtering with Extraction Functions](#filtering-with-extraction-functions) for details.
If an empty `values` array is passed to the "in" filter, it will simply return an empty result.
If the `dimension` is a multi-valued dimension, the "in" filter will return true if one of the dimension values is
in the `values` array.
If the `values` array contains `null`, the "in" filter matches null values. This differs from the SQL IN filter, which
does not match NULL values.
## Like filter
Like filters can be used for basic wildcard searches. They are equivalent to the SQL LIKE operator. Special characters
supported are "%" (matches any number of characters) and "\_" (matches any one character).
|property|type|description|required?|
|--------|-----------|---------|---------|
|type|String|This should always be "like".|yes|
|dimension|String|The dimension to filter on|yes|
|pattern|String|LIKE pattern, such as "foo%" or "___bar".|yes|
|escape|String|An escape character that can be used to escape special characters.|no|
|extractionFn|[Extraction function](#filtering-with-extraction-functions)| Extraction function to apply to the dimension|no|
Like filters support the use of extraction functions, see [Filtering with Extraction Functions](#filtering-with-extraction-functions) for details.
This Like filter expresses the condition `last_name LIKE "D%"` (i.e. last_name starts with "D").
```json
{
"type": "like",
"dimension": "last_name",
"pattern": "D%"
}
```
## Bound filter ## Bound filter
@ -278,20 +232,24 @@ Bound filters can be used to filter on ranges of dimension values. It can be use
greater than, less than, greater than or equal to, less than or equal to, and "between" (if both "lower" and greater than, less than, greater than or equal to, less than or equal to, and "between" (if both "lower" and
"upper" are set). "upper" are set).
|property|type|description|required?| | Property | Description | Required |
|--------|-----------|---------|---------| | -------- | ----------- | -------- |
|type|String|This should always be "bound".|yes| | `type` | Must be "bound". | Yes |
|dimension|String|The dimension to filter on|yes| | `dimension` | Input column or virtual column name to filter. | Yes |
|lower|String|The lower bound for the filter|no| | `lower` | The lower bound string match value for the filter. | No |
|upper|String|The upper bound for the filter|no| | `upper`| The upper bound string match value for the filter. | No |
|lowerStrict|Boolean|Perform strict comparison on the lower bound (">" instead of ">=")|no, default: false| | `lowerStrict` | Boolean indicating whether to perform strict comparison on the `lower` bound (">" instead of ">="). | No, default: `false` |
|upperStrict|Boolean|Perform strict comparison on the upper bound ("<" instead of "<=")|no, default: false| | `upperStrict` | Boolean indicating whether to perform strict comparison on the upper bound ("<" instead of "<="). | No, default: `false`|
|ordering|String|Specifies the sorting order to use when comparing values against the bound. Can be one of the following values: "lexicographic", "alphanumeric", "numeric", "strlen", "version". See [Sorting Orders](./sorting-orders.md) for more details.|no, default: "lexicographic"| | `ordering` | String that specifies the sorting order to use when comparing values against the bound. Can be one of the following values: `"lexicographic"`, `"alphanumeric"`, `"numeric"`, `"strlen"`, `"version"`. See [Sorting Orders](./sorting-orders.md) for more details. | No, default: `"lexicographic"`|
|extractionFn|[Extraction function](#filtering-with-extraction-functions)| Extraction function to apply to the dimension|no| | `extractionFn` | [Extraction function](./dimensionspecs.md#extraction-functions) to apply to `dimension` prior to value matching. See [filtering with extraction functions](#filtering-with-extraction-functions) for details. | No |
Bound filters support the use of extraction functions, see [Filtering with Extraction Functions](#filtering-with-extraction-functions) for details. When the bound filter matches against numeric inputs, the string `lower` and `upper` bound values are best-effort coerced into a numeric value when using the `"numeric"` mode of ordering.
The following bound filter expresses the condition `21 <= age <= 31`: The bound filter can only match against `STRING` (single and multi-valued), `LONG`, `FLOAT`, `DOUBLE` types. Use the newer range to match against `ARRAY` or `COMPLEX` types.
Note that the bound filter matches null values if you don't specify a lower bound. Use the range filter if SQL-compatible behavior.
### Example: equivalent to `WHERE 21 <= age <= 31`
```json ```json
{ {
@ -303,7 +261,7 @@ The following bound filter expresses the condition `21 <= age <= 31`:
} }
``` ```
This filter expresses the condition `foo <= name <= hoo`, using the default lexicographic sorting order. ### Example: equivalent to `WHERE 'foo' <= name <= 'hoo'`, using the default lexicographic sorting order
```json ```json
{ {
@ -314,7 +272,7 @@ This filter expresses the condition `foo <= name <= hoo`, using the default lexi
} }
``` ```
Using strict bounds, this filter expresses the condition `21 < age < 31` ### Example: equivalent to `WHERE 21 < age < 31`
```json ```json
{ {
@ -328,7 +286,7 @@ Using strict bounds, this filter expresses the condition `21 < age < 31`
} }
``` ```
The user can also specify a one-sided bound by omitting "upper" or "lower". This filter expresses `age < 31`. ### Example: equivalent to `WHERE age < 31`
```json ```json
{ {
@ -340,7 +298,7 @@ The user can also specify a one-sided bound by omitting "upper" or "lower". This
} }
``` ```
Likewise, this filter expresses `age >= 18` ### Example: equivalent to `WHERE age >= 18`
```json ```json
{ {
@ -352,18 +310,154 @@ Likewise, this filter expresses `age >= 18`
``` ```
## Range filter
The range filter is a replacement for the bound filter. It compares against any type of column and is designed to have has more SQL compliant behavior than the bound filter. It won't match null values, even if you don't specify a lower bound.
Druid's SQL planner uses the range filter by default instead of bound filter whenever `druid.generic.useDefaultValueForNull=false`, or if `sqlUseBoundAndSelectors` is set to false on the [SQL query context](./sql-query-context.md).
| Property | Description | Required |
| -------- | ----------- | -------- |
| `type` | Must be "range".| Yes |
| `column` | Input column or virtual column name to filter. | Yes |
| `matchValueType` | String specifying the type of bounds to match. For example `STRING`, `LONG`, `DOUBLE`, `FLOAT`, `ARRAY<STRING>`, `ARRAY<LONG>`, or any other Druid type. The `matchValueType` determines how Druid interprets the `matchValue` to assist in converting to the type of the matched `column` and also defines the type of comparison used when matching values. | Yes |
| `lower` | Lower bound value to match. | No. At least one of `lower` or `upper` must not be null. |
| `upper` | Upper bound value to match. | No. At least one of `lower` or `upper` must not be null. |
| `lowerOpen` | Boolean indicating if lower bound is open in the interval of values defined by the range (">" instead of ">="). | No |
| `upperOpen` | Boolean indicating if upper bound is open on the interval of values defined by range ("<" instead of "<="). | No |
### Example: equivalent to `WHERE 21 <= age <= 31`
```json
{
"type": "range",
"column": "age",
"matchValueType": "LONG",
"lower": 21,
"upper": 31
}
```
### Example: equivalent to `WHERE 'foo' <= name <= 'hoo'`, using STRING comparison
```json
{
"type": "range",
"column": "name",
"matchValueType": "STRING",
"lower": "foo",
"upper": "hoo"
}
```
### Example: equivalent to `WHERE 21 < age < 31`
```json
{
"type": "range",
"column": "age",
"matchValueType": "LONG",
"lower": "21",
"lowerOpen": true,
"upper": "31" ,
"upperOpen": true
}
```
### Example: equivalent to `WHERE age < 31`
```json
{
"type": "range",
"column": "age",
"matchValueType": "LONG",
"upper": "31" ,
"upperOpen": true
}
```
### Example: equivalent to `WHERE age >= 18`
```json
{
"type": "range",
"column": "age",
"matchValueType": "LONG",
"lower": 18
}
```
### Example: equivalent to `WHERE ARRAY['a','b','c'] < arrayColumn < ARRAY['d','e','f']`, using ARRAY comparison
```json
{
"type": "range",
"column": "name",
"matchValueType": "ARRAY<STRING>",
"lower": ["a","b","c"],
"lowerOpen": true,
"upper": ["d","e","f"],
"upperOpen": true
}
```
## Like filter
Like filters can be used for basic wildcard searches. They are equivalent to the SQL LIKE operator. Special characters
supported are "%" (matches any number of characters) and "\_" (matches any one character).
| Property | Description | Required |
| -------- | ----------- | -------- |
| `type` | Must be "like".| Yes |
| `dimension` | Input column or virtual column name to filter. | Yes |
| `pattern` | String LIKE pattern, such as "foo%" or "___bar".| Yes |
| `escape`| A string escape character that can be used to escape special characters. | No |
| `extractionFn` | [Extraction function](./dimensionspecs.md#extraction-functions) to apply to `dimension` prior to value matching. See [filtering with extraction functions](#filtering-with-extraction-functions) for details. | No |
Like filters support the use of extraction functions, see [Filtering with Extraction Functions](#filtering-with-extraction-functions) for details.
### Example: equivalent of `WHERE last_name LIKE "D%"` (last_name starts with "D")
```json
{
"type": "like",
"dimension": "last_name",
"pattern": "D%"
}
```
## Regular expression filter
The regular expression filter is similar to the selector filter, but using regular expressions. It matches the specified dimension with the given pattern.
| Property | Description | Required |
| -------- | ----------- | -------- |
| `type` | Must be "regex".| Yes |
| `dimension` | Input column or virtual column name to filter. | Yes |
| `pattern` | String pattern to match - any standard [Java regular expression](http://docs.oracle.com/javase/6/docs/api/java/util/regex/Pattern.html). | Yes |
| `extractionFn` | [Extraction function](./dimensionspecs.md#extraction-functions) to apply to `dimension` prior to value matching. See [filtering with extraction functions](#filtering-with-extraction-functions) for details. | No |
Note that it is often more optimal to use a like filter instead of a regex for simple matching of prefixes.
### Example: matches values that start with "50."
``` json
{ "type": "regex", "dimension": "someColumn", "pattern": ^50.* }
```
## Interval filter ## Interval filter
The Interval filter enables range filtering on columns that contain long millisecond values, with the boundaries specified as ISO 8601 time intervals. It is suitable for the `__time` column, long metric columns, and dimensions with values that can be parsed as long milliseconds. The Interval filter enables range filtering on columns that contain long millisecond values, with the boundaries specified as ISO 8601 time intervals. It is suitable for the `__time` column, long metric columns, and dimensions with values that can be parsed as long milliseconds.
This filter converts the ISO 8601 intervals to long millisecond start/end ranges and translates to an OR of Bound filters on those millisecond ranges, with numeric comparison. The Bound filters will have left-closed and right-open matching (i.e., start <= time < end). This filter converts the ISO 8601 intervals to long millisecond start/end ranges and translates to an OR of Bound filters on those millisecond ranges, with numeric comparison. The Bound filters will have left-closed and right-open matching (i.e., start <= time < end).
|property|type|description|required?| | Property | Description | Required |
|--------|-----------|---------|---------| | -------- | ----------- | -------- |
|type|String|This should always be "interval".|yes| | `type` | Must be "interval". | Yes |
|dimension|String|The dimension to filter on|yes| | `dimension` | Input column or virtual column name to filter. | Yes |
|intervals|Array|A JSON array containing ISO-8601 interval strings. This defines the time ranges to filter on.|yes| | `intervals` | A JSON array containing ISO-8601 interval strings that defines the time ranges to filter on. | Yes |
|extractionFn|[Extraction function](#filtering-with-extraction-functions)| Extraction function to apply to the dimension|no| | `extractionFn` | [Extraction function](./dimensionspecs.md#extraction-functions) to apply to `dimension` prior to value matching. See [filtering with extraction functions](#filtering-with-extraction-functions) for details. | No |
The interval filter supports the use of extraction functions, see [Filtering with Extraction Functions](#filtering-with-extraction-functions) for details. The interval filter supports the use of extraction functions, see [Filtering with Extraction Functions](#filtering-with-extraction-functions) for details.
@ -410,6 +504,157 @@ The filter above is equivalent to the following OR of Bound filters:
} }
``` ```
## True filter
A filter which matches all values. You can use it to temporarily disable other filters without removing them.
```json
{ "type" : "true" }
```
## False filter
A filter matches no values. You can use it to force a query to match no values.
```json
{"type": "false" }
```
## Search filter
You can use search filters to filter on partial string matches.
```json
{
"filter": {
"type": "search",
"dimension": "product",
"query": {
"type": "insensitive_contains",
"value": "foo"
}
}
}
```
| Property | Description | Required |
| -------- | ----------- | -------- |
| `type` | Must be "search". | Yes |
| `dimension` | Input column or virtual column name to filter. | Yes |
| `query`| A JSON object for the type of search. See [search query spec](#search-query-spec) for more information. | Yes |
| `extractionFn` | [Extraction function](./dimensionspecs.md#extraction-functions) to apply to `dimension` prior to value matching. See [filtering with extraction functions](#filtering-with-extraction-functions) for details. | No |
### Search query spec
#### Contains
| Property | Description | Required |
| -------- | ----------- | -------- |
| `type` | Must be "contains". | Yes |
| `value` | A String value to search. | Yes |
| `caseSensitive` | Whether the string comparison is case-sensitive or not. | No, default is false (insensitive) |
#### Insensitive contains
| Property | Description | Required |
| -------- | ----------- | -------- |
| `type` | Must be "insensitive_contains". | Yes |
| `value` | A String value to search. | Yes |
Note that an "insensitive_contains" search is equivalent to a "contains" search with "caseSensitive": false (or not
provided).
#### Fragment
| Property | Description | Required |
| -------- | ----------- | -------- |
| `type` | Must be "fragment". | Yes |
| `values` | A JSON array of string values to search. | Yes |
| `caseSensitive` | Whether the string comparison is case-sensitive or not. | No, default is false (insensitive) |
## Expression filter
The expression filter allows for the implementation of arbitrary conditions, leveraging the Druid expression system. This filter allows for complete flexibility, but it might be less performant than a combination of the other filters on this page because it can't always use the same optimizations available to other filters.
| Property | Description | Required |
| -------- | ----------- | -------- |
| `type` | Must be "expression" | Yes |
| `expression` | Expression string to evaluate into true or false. See the [Druid expression system](math-expr.md) for more details. | Yes |
### Example: expression based matching
```json
{
"type" : "expression" ,
"expression" : "((product_type == 42) && (!is_deleted))"
}
```
## JavaScript filter
The JavaScript filter matches a dimension against the specified JavaScript function predicate. The filter matches values for which the function returns true.
| Property | Description | Required |
| -------- | ----------- | -------- |
| `type` | Must be "javascript" | Yes |
| `dimension` | Input column or virtual column name to filter. | Yes |
| `function` | JavaScript function which accepts the dimension value as a single argument, and returns either true or false. | Yes |
| `extractionFn` | [Extraction function](./dimensionspecs.md#extraction-functions) to apply to `dimension` prior to value matching. See [filtering with extraction functions](#filtering-with-extraction-functions) for details. | No |
### Example: matching any dimension values for the dimension `name` between `'bar'` and `'foo'`
```json
{
"type" : "javascript",
"dimension" : "name",
"function" : "function(x) { return(x >= 'bar' && x <= 'foo') }"
}
```
> JavaScript-based functionality is disabled by default. Refer to the Druid [JavaScript programming guide](../development/javascript.md) for guidelines about using Druid's JavaScript functionality, including instructions on how to enable it.
## Extraction filter
> The extraction filter is now deprecated. Use the selector filter with an extraction function instead.
Extraction filter matches a dimension using a specific [extraction function](./dimensionspecs.md#extraction-functions).
The following filter matches the values for which the extraction function has a transformation entry `input_key=output_value` where
`output_value` is equal to the filter `value` and `input_key` is present as a dimension.
| Property | Description | Required |
| -------- | ----------- | -------- |
| `type` | Must be "extraction" | Yes |
| `dimension` | Input column or virtual column name to filter. | Yes |
| `value` | String value to match. | No. If not specified the filter will match NULL values. |
| `extractionFn` | [Extraction function](./dimensionspecs.md#extraction-functions) to apply to `dimension` prior to value matching. See [filtering with extraction functions](#filtering-with-extraction-functions) for details. | No |
### Example: matching dimension values in `[product_1, product_3, product_5]` for the column `product`
```json
{
"filter": {
"type": "extraction",
"dimension": "product",
"value": "bar_1",
"extractionFn": {
"type": "lookup",
"lookup": {
"type": "map",
"map": {
"product_1": "bar_1",
"product_5": "bar_1",
"product_3": "bar_1"
}
}
}
}
}
```
## Filtering with extraction functions ## Filtering with extraction functions
All filters except the "spatial" filter support extraction functions. All filters except the "spatial" filter support extraction functions.
@ -420,9 +665,7 @@ If specified, the extraction function will be used to transform input values bef
The example below shows a selector filter combined with an extraction function. This filter will transform input values The example below shows a selector filter combined with an extraction function. This filter will transform input values
according to the values defined in the lookup map; transformed values will then be matched with the string "bar_1". according to the values defined in the lookup map; transformed values will then be matched with the string "bar_1".
### Example: matches dimension values in `[product_1, product_3, product_5]` for the column `product`
**Example**
The following matches dimension values in `[product_1, product_3, product_5]` for the column `product`
```json ```json
{ {
@ -449,29 +692,97 @@ The following matches dimension values in `[product_1, product_3, product_5]` fo
Druid supports filtering on timestamp, string, long, and float columns. Druid supports filtering on timestamp, string, long, and float columns.
Note that only string columns have bitmap indexes. Therefore, queries that filter on other column types will need to Note that only string columns and columns produced with the ['auto' ingestion spec](../ingestion/ingestion-spec.md#dimension-objects) also used by [type aware schema discovery](../ingestion/schema-design.md#type-aware-schema-discovery) have bitmap indexes. Queries that filter on other column types must
scan those columns. scan those columns.
### Filtering on multi-value string columns
All filters return true if any one of the dimension values is satisfies the filter.
#### Example: multi-value match behavior
Given a multi-value STRING row with values `['a', 'b', 'c']`, a filter such as
```json
{ "type": "equality", "column": "someMultiValueColumn", "matchValueType": "STRING", "matchValue": "b" }
```
will successfully match the entire row. This can produce sometimes unintuitive behavior when coupled with the implicit UNNEST functionality of Druid [GroupBy](./groupbyquery.md) and [TopN](./topnquery.md) queries.
Additionally, contradictory filters may be defined and perfectly legal in native queries which will not work in SQL.
#### Example: SQL "contradiction"
This query is impossible to express as is in SQL since it is a contradiction that the SQL planner will optimize to false and match nothing.
Given a multi-value STRING row with values `['a', 'b', 'c']`, and filter such as
```json
{
"type": "and",
"fields": [
{
"type": "equality",
"column": "someMultiValueColumn",
"matchValueType": "STRING",
"matchValue": "a"
},
{
"type": "equality",
"column": "someMultiValueColumn",
"matchValueType": "STRING",
"matchValue": "b"
}
]
}
```
will successfully match the entire row, but not match a row with value `['a', 'c']`.
To express this filter in SQL, use [SQL multi-value string functions](./sql-multivalue-string-functions.md) such as `MV_CONTAINS`, which can be optimized by the planner to the same native filters.
### Filtering on numeric columns ### Filtering on numeric columns
When filtering on numeric columns, you can write filters as if they were strings. In most cases, your filter will be Some filters, such as equality and range filters allow accepting numeric match values directly since they include a secondary `matchValueType` parameter.
When filtering on numeric columns using string based filters such as the selector, in, and bounds filters, you can write filter match values as if they were strings. In most cases, your filter will be
converted into a numeric predicate and will be applied to the numeric column values directly. In some cases (such as converted into a numeric predicate and will be applied to the numeric column values directly. In some cases (such as
the "regex" filter) the numeric column values will be converted to strings during the scan. the "regex" filter) the numeric column values will be converted to strings during the scan.
For example, filtering on a specific value, `myFloatColumn = 10.1`: #### Example: filtering on a specific value, `myFloatColumn = 10.1`
```json ```json
"filter": { {
"type": "equality",
"dimension": "myFloatColumn",
"matchValueType": "FLOAT",
"value": 10.1
}
```
or with a selector filter:
```json
{
"type": "selector", "type": "selector",
"dimension": "myFloatColumn", "dimension": "myFloatColumn",
"value": "10.1" "value": "10.1"
} }
``` ```
Filtering on a range of values, `10 <= myFloatColumn < 20`: #### Example: filtering on a range of values, `10 <= myFloatColumn < 20`
```json ```json
"filter": { {
"type": "range",
"column": "myFloatColumn",
"matchvalueType": "FLOAT",
"lower": 10.1,
"lowerOpen": false,
"upper": 20.9,
"upperOpen": true
}
```
or with a bound filter:
```json
{
"type": "bound", "type": "bound",
"dimension": "myFloatColumn", "dimension": "myFloatColumn",
"ordering": "numeric", "ordering": "numeric",
@ -488,22 +799,33 @@ Query filters can also be applied to the timestamp column. The timestamp column
to the timestamp column, use the string `__time` as the dimension name. Like numeric dimensions, timestamp filters to the timestamp column, use the string `__time` as the dimension name. Like numeric dimensions, timestamp filters
should be specified as if the timestamp values were strings. should be specified as if the timestamp values were strings.
If the user wishes to interpret the timestamp with a specific format, timezone, or locale, the [Time Format Extraction Function](./dimensionspecs.md#time-format-extraction-function) is useful. If you want to interpret the timestamp with a specific format, timezone, or locale, the [Time Format Extraction Function](./dimensionspecs.md#time-format-extraction-function) is useful.
For example, filtering on a long timestamp value: #### Example: filtering on a long timestamp value
```json ```json
"filter": { {
"type": "equality",
"dimension": "__time",
"matchValueType": "LONG",
"value": 124457387532
}
```
or with a selector filter:
```json
{
"type": "selector", "type": "selector",
"dimension": "__time", "dimension": "__time",
"value": "124457387532" "value": "124457387532"
} }
``` ```
Filtering on day of week: #### Example: filtering on day of week using an extraction function
```json ```json
"filter": { {
"type": "selector", "type": "selector",
"dimension": "__time", "dimension": "__time",
"value": "Friday", "value": "Friday",
@ -516,7 +838,7 @@ Filtering on day of week:
} }
``` ```
Filtering on a set of ISO 8601 intervals: #### Example: filtering on a set of ISO 8601 intervals
```json ```json
{ {
@ -529,25 +851,3 @@ Filtering on a set of ISO 8601 intervals:
} }
``` ```
### True filter
The true filter is a filter which matches all values. It can be used to temporarily disable other filters without removing the filter.
```json
{ "type" : "true" }
```
### Expression filter
The expression filter allows for the implementation of arbitrary conditions, leveraging the Druid expression system.
This filter allows for more flexibility, but it might be less performant than a combination of the other filters on this page due to the fact that not all filter optimizations are in place yet.
```json
{
"type" : "expression" ,
"expression" : "((product_type == 42) && (!is_deleted))"
}
```
See the [Druid expression system](math-expr.md) for more details.

1
docs/querying/sql-query-context.md
View File

@ -44,6 +44,7 @@ Configure Druid SQL query planning using the parameters in the table below.
|`enableTimeBoundaryPlanning`|If true, SQL queries will get converted to TimeBoundary queries wherever possible. TimeBoundary queries are very efficient for min-max calculation on `__time` column in a datasource |`druid.query.default.context.enableTimeBoundaryPlanning` on the Broker (default: false)| |`enableTimeBoundaryPlanning`|If true, SQL queries will get converted to TimeBoundary queries wherever possible. TimeBoundary queries are very efficient for min-max calculation on `__time` column in a datasource |`druid.query.default.context.enableTimeBoundaryPlanning` on the Broker (default: false)|
|`useNativeQueryExplain`|If true, `EXPLAIN PLAN FOR` will return the explain plan as a JSON representation of equivalent native query(s), else it will return the original version of explain plan generated by Calcite.<br /><br />This property is provided for backwards compatibility. It is not recommended to use this parameter unless you were depending on the older behavior.|`druid.sql.planner.useNativeQueryExplain` on the Broker (default: true)| |`useNativeQueryExplain`|If true, `EXPLAIN PLAN FOR` will return the explain plan as a JSON representation of equivalent native query(s), else it will return the original version of explain plan generated by Calcite.<br /><br />This property is provided for backwards compatibility. It is not recommended to use this parameter unless you were depending on the older behavior.|`druid.sql.planner.useNativeQueryExplain` on the Broker (default: true)|
|`sqlFinalizeOuterSketches`|If false (default behavior in Druid 25.0.0 and later), `DS_HLL`, `DS_THETA`, and `DS_QUANTILES_SKETCH` return sketches in query results, as documented. If true (default behavior in Druid 24.0.1 and earlier), sketches from these functions are finalized when they appear in query results.<br /><br />This property is provided for backwards compatibility with behavior in Druid 24.0.1 and earlier. It is not recommended to use this parameter unless you were depending on the older behavior. Instead, use a function that does not return a sketch, such as `APPROX_COUNT_DISTINCT_DS_HLL`, `APPROX_COUNT_DISTINCT_DS_THETA`, `APPROX_QUANTILE_DS`, `DS_THETA_ESTIMATE`, or `DS_GET_QUANTILE`.|`druid.query.default.context.sqlFinalizeOuterSketches` on the Broker (default: false)| |`sqlFinalizeOuterSketches`|If false (default behavior in Druid 25.0.0 and later), `DS_HLL`, `DS_THETA`, and `DS_QUANTILES_SKETCH` return sketches in query results, as documented. If true (default behavior in Druid 24.0.1 and earlier), sketches from these functions are finalized when they appear in query results.<br /><br />This property is provided for backwards compatibility with behavior in Druid 24.0.1 and earlier. It is not recommended to use this parameter unless you were depending on the older behavior. Instead, use a function that does not return a sketch, such as `APPROX_COUNT_DISTINCT_DS_HLL`, `APPROX_COUNT_DISTINCT_DS_THETA`, `APPROX_QUANTILE_DS`, `DS_THETA_ESTIMATE`, or `DS_GET_QUANTILE`.|`druid.query.default.context.sqlFinalizeOuterSketches` on the Broker (default: false)|
|`sqlUseBoundAndSelectors`|If false (default behavior if `druid.generic.useDefaultValueForNull=false` in Druid 27.0.0 and later), the SQL planner will use [equality](./filters.md#equality-filter), [null](./filters.md#null-filter), and [range](./filters.md#range-filter) filters instead of [selector](./filters.md#selector-filter) and [bounds](./filters.md#bound-filter). This value must be set to `false` for correct behavior for filtering `ARRAY` typed values. | Defaults to same value as `druid.generic.useDefaultValueForNull` |
## Setting the query context ## Setting the query context
The query context parameters can be specified as a "context" object in the [JSON API](../api-reference/sql-api.md) or as a [JDBC connection properties object](../api-reference/sql-jdbc.md). The query context parameters can be specified as a "context" object in the [JSON API](../api-reference/sql-api.md) or as a [JDBC connection properties object](../api-reference/sql-jdbc.md).