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This document describes the native
language. For information about aggregators available in SQL, refer to the
SQL documentation.
A filter is a JSON object indicating which rows of data should be included in the computation for a query. It’s essentially the equivalent of the WHERE clause in SQL. Apache Druid supports the following types of filters.
Note
Filters are commonly applied on dimensions, but can be applied on aggregated metrics, for example, see filtered-aggregator and having-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 grammar for a SELECTOR filter is as follows:
"filter": { "type": "selector", "dimension": dimension_string , "value": dimension_value_string }
This is the equivalent of WHERE dimension_string = dimension_value_string .
The selector filter supports the use of extraction functions, see Filtering with Extraction Functions for details.
Column Comparison filter
The column comparison filter is similar to the selector filter, but instead compares dimensions to each other. For example:
"filter": { "type": "columnComparison", "dimensions": [ dimension_a , dimension_b ] }
This is the equivalent of WHERE dimension_a = dimension_b .
dimensions is list of DimensionSpecs, 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.
"filter": { "type": "regex", "dimension": dimension_string , "pattern": pattern_string }
The regex filter supports the use of extraction functions, see Filtering with Extraction Functions for details.
Logical expression filters
AND
The grammar for an AND filter is as follows:
"filter": { "type": "and", "fields": [ filter , filter , ...] }
The filters in fields can be any other filter defined on this page.
OR
The grammar for an OR filter is as follows:
"filter": { "type": "or", "fields": [ filter , filter , ...] }
The filters in fields can be any other filter defined on this page.
NOT
The grammar for a NOT filter is as follows:
"filter": { "type": "not", "field": filter }
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.
{
"type" : "javascript",
"dimension" : dimension_string ,
"function" : "function(value) { ... }"
Example
The following matches any dimension values for the dimension name between bar and foo
{
"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 for details.
JavaScript-based functionality is disabled by default. Please refer to the Druid JavaScript programming guide for guidelines about using Druids 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.
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
{
"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.
{
"filter": {
"type": "search",
"dimension": "product",
"query": {
"type": "insensitive_contains",
"value": "foo"
typeThis String should always be search .yes
dimensionThe dimension to perform the search over.yes
queryA JSON object for the type of search. See below for more information.yes
extractionFnExtraction function to apply to the dimensionno
typeThis String should always be contains .yes
valueA String value to run the search over.yes
caseSensitiveWhether two string should be compared as case sensitive or notno (default == false)
typeThis String should always be insensitive_contains .yes
valueA 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
typeThis String should always be fragment .yes
valuesA JSON array of String values to run the search over.yes
caseSensitiveWhether strings should be compared as case sensitive or not. Default: false(insensitive)no
In filter
In filter can be used to express the following SQL query:
SELECT COUNT(*) AS Count FROM `table` WHERE `outlaw` IN (Good, Bad, Ugly)
The grammar for a IN filter is as follows:
{
"type": "in",
"dimension": "outlaw",
"values": ["Good", "Bad", "Ugly"]
The IN filter supports the use of extraction functions, see 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.
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).
dimensionStringThe dimension to filter onyes
patternStringLIKE pattern, such as foo% or ___bar .yes
escapeStringAn escape character that can be used to escape special characters.no
extractionFnExtraction functionExtraction function to apply to the dimensionno
Like filters support the use of extraction functions, see Filtering with Extraction Functions for details.
This Like filter expresses the condition last_name LIKE D% (i.e. last_name starts with D ).
{
"type": "like",
"dimension": "last_name",
"pattern": "D%"
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).
dimensionStringThe dimension to filter onyes
lowerStringThe lower bound for the filterno
upperStringThe upper bound for the filterno
lowerStrictBooleanPerform strict comparison on the lower bound ( instead of = )no, default: false
upperStrictBooleanPerform strict comparison on the upper bound ( instead of = )no, default: false
orderingStringSpecifies 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 for more details.no, default: lexicographic
extractionFnExtraction functionExtraction function to apply to the dimensionno
Bound filters support the use of extraction functions, see Filtering with Extraction Functions for details.
The following bound filter expresses the condition 21 = age = 31:
{
"type": "bound",
"dimension": "age",
"lower": "21",
"upper": "31" ,
"ordering": "numeric"
This filter expresses the condition foo = name = hoo, using the default lexicographic sorting order.
{
"type": "bound",
"dimension": "name",
"lower": "foo",
"upper": "hoo"
Using strict bounds, this filter expresses the condition 21 age 31
{
"type": "bound",
"dimension": "age",
"lower": "21",
"lowerStrict": true,
"upper": "31" ,
"upperStrict": true,
"ordering": "numeric"
The user can also specify a one-sided bound by omitting upper or lower . This filter expresses age 31.
{
"type": "bound",
"dimension": "age",
"upper": "31" ,
"upperStrict": true,
"ordering": "numeric"
Likewise, this filter expresses age = 18
{
"type": "bound",
"dimension": "age",
"lower": "18" ,
"ordering": "numeric"
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).
typeStringThis should always be interval .yes
dimensionStringThe dimension to filter onyes
intervalsArrayA JSON array containing ISO-8601 interval strings. This defines the time ranges to filter on.yes
extractionFnExtraction functionExtraction function to apply to the dimensionno
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