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new e57f880020 document new filters and stuff (#14760)
e57f880020 is described below
commit e57f8800206ec34c0b21382eec65b620a1885e53
Author: Clint Wylie <[email protected]>
AuthorDate: Tue Aug 8 16:01:06 2023 -0700
document new filters and stuff (#14760)
---
docs/ingestion/ingestion-spec.md | 2 +-
docs/ingestion/schema-design.md | 2 +
docs/querying/filters.md | 738 ++++++++++++++++++++++++++-----------
docs/querying/sql-query-context.md | 1 +
4 files changed, 523 insertions(+), 220 deletions(-)
diff --git a/docs/ingestion/ingestion-spec.md b/docs/ingestion/ingestion-spec.md
index 43baf601bd..6ed2f0a838 100644
--- a/docs/ingestion/ingestion-spec.md
+++ b/docs/ingestion/ingestion-spec.md
@@ -186,7 +186,7 @@ Treat `__time` as a millisecond timestamp: the number of
milliseconds since Jan
### `dimensionsSpec`
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.
diff --git a/docs/ingestion/schema-design.md b/docs/ingestion/schema-design.md
index 060a9dc7ab..7fd29c1d0e 100644
--- a/docs/ingestion/schema-design.md
+++ b/docs/ingestion/schema-design.md
@@ -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
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 numeric columns are `DOUBLE`
diff --git a/docs/querying/filters.md b/docs/querying/filters.md
index 82fdb81168..74ae3f406a 100644
--- a/docs/querying/filters.md
+++ b/docs/querying/filters.md
@@ -35,263 +35,221 @@ Apache Druid supports the following types of filters.
## 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 |
-``` json
-"filter": { "type": "selector", "dimension": <dimension_string>, "value":
<dimension_value_string> }
-```
-
-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 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.
-The selector filter supports the use of extraction functions, see [Filtering
with Extraction Functions](#filtering-with-extraction-functions) for details.
+When the selector filter matches against numeric inputs, the string `value`
will be best-effort coerced into a numeric value.
-## Column comparison filter
-
-The column comparison filter is similar to the selector filter, but instead
compares dimensions to each other. For example:
+### Example: equivalent of `WHERE someColumn = 'hello'`
``` json
-"filter": { "type": "columnComparison", "dimensions": [<dimension_a>,
<dimension_b>] }
+{ "type": "selector", "dimension": "someColumn", "value": "hello" }
```
-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.
-
-## 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.
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 IS NULL`
``` json
-"filter": { "type": "regex", "dimension": <dimension_string>, "pattern":
<pattern_string> }
+{ "type": "selector", "dimension": "someColumn", "value": null }
```
-The regex filter supports the use of extraction functions, see [Filtering with
Extraction Functions](#filtering-with-extraction-functions) for details.
-
-
-## Logical expression filters
-
-### AND
-The grammar for an AND filter is as follows:
+## Equality Filter
-``` json
-"filter": { "type": "and", "fields": [<filter>, <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.
-The filters in fields can be any other filter defined on this page.
+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).
-### OR
+| 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 |
-The grammar for an OR filter is as follows:
+### Example: equivalent of `WHERE someColumn = 'hello'`
-``` json
-"filter": { "type": "or", "fields": [<filter>, <filter>, ...] }
+```json
+{ "type": "equality", "column": "someColumn", "matchValueType": "STRING",
"matchValue": "hello" }
```
-The filters in fields can be any other filter defined on this page.
+### Example: equivalent of `WHERE someNumericColumn = 1.23`
-### NOT
+```json
+{ "type": "equality", "column": "someNumericColumn", "matchValueType":
"DOUBLE", "matchValue": 1.23 }
+```
-The grammar for a NOT filter is as follows:
+### Example: equivalent of `WHERE someArrayColumn = ARRAY[1, 2, 3]`
```json
-"filter": { "type": "not", "field": <filter> }
+{ "type": "equality", "column": "someArrayColumn", "matchValueType":
"ARRAY<LONG>", "matchValue": [1, 2, 3] }
```
-The filter specified at field can be any other filter defined on this page.
-## JavaScript filter
+## Null Filter
-The JavaScript filter matches a dimension against the specified JavaScript
function predicate. The filter matches values for which the function returns
true.
+The null filter is a partial replacement for the selector filter. It is
dedicated to matching NULL values.
-The function takes a single argument, the dimension value, and returns either
true or false.
+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).
-```json
-{
- "type" : "javascript",
- "dimension" : <dimension_string>,
- "function" : "function(value) { <...> }"
-}
-```
+| Property | Description | Required |
+| -------- | ----------- | -------- |
+| `type` | Must be "null".| Yes |
+| `column` | Input column or virtual column name to filter. | Yes |
-**Example**
-The following matches any dimension values for the dimension `name` between
`'bar'` and `'foo'`
+### Example: equivalent of `WHERE someColumn IS NULL`
```json
-{
- "type" : "javascript",
- "dimension" : "name",
- "function" : "function(x) { return(x >= 'bar' && x <= 'foo') }"
-}
+{ "type": "null", "column": "someColumn" }
```
-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.
+## Column comparison filter
-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.
+The column comparison filter is similar to the selector filter, but compares
dimensions to each other. For example:
-**Example**
-The following matches dimension values in `[product_1, product_3, product_5]`
for the column `product`
+| Property | Description | Required |
+| -------- | ----------- | -------- |
+| `type` | Must be "selector".| Yes |
+| `dimensions` | List of [`DimensionSpec`](./dimensionspecs.md) to compare. |
Yes |
-```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"
- }
- }
- }
- }
-}
-```
+`dimensions` is list of [DimensionSpecs](./dimensionspecs.md), making it
possible to apply an extraction function if needed.
-## Search 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.
-Search filters can be used to filter on partial string matches.
+### Example: equivalent of `WHERE someColumn = someLongColumn`
-```json
+``` json
{
- "filter": {
- "type": "search",
- "dimension": "product",
- "query": {
- "type": "insensitive_contains",
- "value": "foo"
- }
+ "type": "columnComparison",
+ "dimensions": [
+ "someColumn",
+ {
+ "type" : "default",
+ "dimension" : someLongColumn,
+ "outputType": "LONG"
}
+ ]
}
```
-|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.
+## Logical expression filters
-### Search query spec
+### AND
-#### Contains
+| 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 |
-|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
+#### Example: equivalent of `WHERE someColumn = 'a' AND otherColumn = 1234 AND
anotherColumn IS NULL`
-|property|description|required?|
-|--------|-----------|---------|
-|type|This String should always be "insensitive_contains".|yes|
-|value|A String value to run the search over.|yes|
+``` json
+{
+ "type": "and",
+ "fields": [
+ { "type": "equality", "column": "someColumn", "matchValue": "a",
"matchValueType": "STRING" },
+ { "type": "equality", "column": "otherColumn", "matchValue": 1234,
"matchValueType": "LONG" },
+ { "type": "null", "column": "anotherColumn" }
+ ]
+}
+```
-Note that an "insensitive_contains" search is equivalent to a "contains"
search with "caseSensitive": false (or not
-provided).
+### OR
-#### Fragment
+| 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 |
-|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|
+#### Example: equivalent of `WHERE someColumn = 'a' OR otherColumn = 1234 OR
anotherColumn IS NULL`
-## In filter
+``` json
+{
+ "type": "or",
+ "fields": [
+ { "type": "equality", "column": "someColumn", "matchValue": "a",
"matchValueType": "STRING" },
+ { "type": "equality", "column": "otherColumn", "matchValue": 1234,
"matchValueType": "LONG" },
+ { "type": "null", "column": "anotherColumn" }
+ ]
+}
+```
-In filter can be used to express the following SQL query:
+### NOT
-```sql
- SELECT COUNT(*) AS 'Count' FROM `table` WHERE `outlaw` IN ('Good', 'Bad',
'Ugly')
-```
+| 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 |
-The grammar for a "in" filter is as follows:
+#### Example: equivalent of `WHERE someColumn IS NOT NULL`
```json
-{
- "type": "in",
- "dimension": "outlaw",
- "values": ["Good", "Bad", "Ugly"]
-}
+{ "type": "not", "field": { "type": "null", "column": "someColumn" }}
```
-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.
+## 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.
-## Like filter
+| 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 |
-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|
+If an empty `values` array is passed to the "in" filter, it will simply return
an empty result.
-Like filters support the use of extraction functions, see [Filtering with
Extraction Functions](#filtering-with-extraction-functions) for details.
+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.
-This Like filter expresses the condition `last_name LIKE "D%"` (i.e. last_name
starts with "D").
+### Example: equivalent of `WHERE `outlaw` IN ('Good', 'Bad', 'Ugly')`
```json
{
- "type": "like",
- "dimension": "last_name",
- "pattern": "D%"
+ "type": "in",
+ "dimension": "outlaw",
+ "values": ["Good", "Bad", "Ugly"]
}
```
+
## Bound filter
Bound filters can be used to filter on ranges of dimension values. It can be
used for comparison filtering like
greater than, less than, greater than or equal to, less than or equal to, and
"between" (if both "lower" and
"upper" are set).
-|property|type|description|required?|
-|--------|-----------|---------|---------|
-|type|String|This should always be "bound".|yes|
-|dimension|String|The dimension to filter on|yes|
-|lower|String|The lower bound for the filter|no|
-|upper|String|The upper bound for the filter|no|
-|lowerStrict|Boolean|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|
-|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"|
-|extractionFn|[Extraction function](#filtering-with-extraction-functions)|
Extraction function to apply to the dimension|no|
+| Property | Description | Required |
+| -------- | ----------- | -------- |
+| `type` | Must be "bound". | Yes |
+| `dimension` | Input column or virtual column name to filter. | Yes |
+| `lower` | The lower bound string match value for the filter. | No |
+| `upper`| The upper bound string match value for the filter. | No |
+| `lowerStrict` | Boolean indicating whether to perform strict comparison on
the `lower` 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 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](./dimensionspecs.md#extraction-functions) to apply to `dimension`
prior to value matching. See [filtering with extraction
functions](#filtering-with-extraction-functions) for details. | No |
+
+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.
-Bound filters support the use of extraction functions, see [Filtering with
Extraction Functions](#filtering-with-extraction-functions) for details.
+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.
-The following bound filter expresses the condition `21 <= age <= 31`:
+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
{
@@ -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
{
@@ -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
{
@@ -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
{
@@ -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
{
@@ -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
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).
-|property|type|description|required?|
-|--------|-----------|---------|---------|
-|type|String|This should always be "interval".|yes|
-|dimension|String|The dimension to filter on|yes|
-|intervals|Array|A JSON array containing ISO-8601 interval strings. This
defines the time ranges to filter on.|yes|
-|extractionFn|[Extraction function](#filtering-with-extraction-functions)|
Extraction function to apply to the dimension|no|
+| Property | Description | Required |
+| -------- | ----------- | -------- |
+| `type` | Must be "interval". | Yes |
+| `dimension` | Input column or virtual column name to filter. | Yes |
+| `intervals` | A JSON array containing ISO-8601 interval strings that defines
the time ranges to filter on. | 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 |
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
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
according to the values defined in the lookup map; transformed values will
then be matched with the string "bar_1".
-
-**Example**
-The following matches dimension values in `[product_1, product_3, product_5]`
for the column `product`
+### Example: matches dimension values in `[product_1, product_3, product_5]`
for the column `product`
```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.
-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.
+### 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
-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
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
-"filter": {
+{
+ "type": "equality",
+ "dimension": "myFloatColumn",
+ "matchValueType": "FLOAT",
+ "value": 10.1
+}
+```
+
+or with a selector filter:
+
+```json
+{
"type": "selector",
"dimension": "myFloatColumn",
"value": "10.1"
}
```
-Filtering on a range of values, `10 <= myFloatColumn < 20`:
+#### Example: filtering on a range of values, `10 <= myFloatColumn < 20`
```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",
"dimension": "myFloatColumn",
"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
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.
+
+#### Example: filtering on a long timestamp value
+
+```json
+{
+ "type": "equality",
+ "dimension": "__time",
+ "matchValueType": "LONG",
+ "value": 124457387532
+}
+```
-For example, filtering on a long timestamp value:
+or with a selector filter:
```json
-"filter": {
+{
"type": "selector",
"dimension": "__time",
"value": "124457387532"
}
```
-Filtering on day of week:
+#### Example: filtering on day of week using an extraction function
```json
-"filter": {
+{
"type": "selector",
"dimension": "__time",
"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
{
@@ -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.
diff --git a/docs/querying/sql-query-context.md
b/docs/querying/sql-query-context.md
index e469fa390a..7798fbf34c 100644
--- a/docs/querying/sql-query-context.md
+++ b/docs/querying/sql-query-context.md
@@ -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)|
|`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 [...]
+|`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 `d [...]
## 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).
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