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Transforming Dimension Values

The following JSON fields can be used in a query to operate on dimension values.

DimensionSpec

DimensionSpecs define how dimension values get transformed prior to aggregation.

Default DimensionSpec

Returns dimension values as is and optionally renames the dimension.

{ "type" : "default", "dimension" : <dimension>, "outputName": <output_name> }

Extraction DimensionSpec

Returns dimension values transformed using the given extraction function.

{
  "type" : "extraction",
  "dimension" : <dimension>,
  "outputName" :  <output_name>,
  "extractionFn" : <extraction_function>
}

Extraction Functions

Extraction functions define the transformation applied to each dimension value.

Transformations can be applied to both regular (string) dimensions, as well as the special __time dimension, which represents the current time bucket according to the query aggregation granularity.

Note: for functions taking string values (such as regular expressions), __time dimension values will be formatted in ISO-8601 format before getting passed to the extraction function.

Regular Expression Extraction Function

Returns the first matching group for the given regular expression. If there is no match, it returns the dimension value as is.

{
  "type" : "regex", "expr" : <regular_expression>,
  "replaceMissingValue" : true,
  "replaceMissingValueWith" : "foobar"
}

For example, using "expr" : "(\\w\\w\\w).*" will transform 'Monday', 'Tuesday', 'Wednesday' into 'Mon', 'Tue', 'Wed'.

If the replaceMissingValue property is true, the extraction function will transform dimension values that do not match the regex pattern to a user-specified String. Default value is false.

The replaceMissingValueWith property sets the String that unmatched dimension values will be replaced with, if replaceMissingValue is true. If replaceMissingValueWith is not specified, unmatched dimension values will be replaced with nulls.

For example, if expr is "(a\w+)" in the example JSON above, a regex that matches words starting with the letter a, the extraction function will convert a dimension value like banana to foobar.

Partial Extraction Function

Returns the dimension value unchanged if the regular expression matches, otherwise returns null.

{ "type" : "partial", "expr" : <regular_expression> }

Search Query Extraction Function

Returns the dimension value unchanged if the given SearchQuerySpec matches, otherwise returns null.

{ "type" : "searchQuery", "query" : <search_query_spec> }

Substring Extraction Function

Returns a substring of the dimension value starting from the supplied index and of the desired length. If the desired length exceeds the length of the dimension value, the remainder of the string starting at index will be returned. If index is greater than the length of the dimension value, null will be returned.

{ "type" : "substring", "index" : 1, "length" : 4 }

The length may be omitted for substring to return the remainder of the dimension value starting from index, or null if index greater than the length of the dimension value.

{ "type" : "substring", "index" : 3 }

Time Format Extraction Function

Returns the dimension value formatted according to the given format string, time zone, and locale.

For __time dimension values, this formats the time value bucketed by the aggregation granularity

For a regular dimension, it assumes the string is formatted in ISO-8601 date and time format.

format : date time format for the resulting dimension value, in Joda Time DateTimeFormat.
locale : locale (language and country) to use, given as a IETF BCP 47 language tag, e.g. en-US, en-GB, fr-FR, fr-CA, etc.
timeZone : time zone to use in IANA tz database format, e.g. Europe/Berlin (this can possibly be different than the aggregation time-zone)

{ "type" : "timeFormat",
  "format" : <output_format>,
  "timeZone" : <time_zone> (optional),
  "locale" : <locale> (optional) }

For example, the following dimension spec returns the day of the week for Montréal in French:

{
  "type" : "extraction",
  "dimension" : "__time",
  "outputName" :  "dayOfWeek",
  "extractionFn" : {
    "type" : "timeFormat",
    "format" : "EEEE",
    "timeZone" : "America/Montreal",
    "locale" : "fr"
  }
}

Time Parsing Extraction Function

Parses dimension values as timestamps using the given input format, and returns them formatted using the given output format.

Note, if you are working with the __time dimension, you should consider using the time extraction function instead instead, which works on time value directly as opposed to string values.

Time formats are described in the SimpleDateFormat documentation

{ "type" : "time",
  "timeFormat" : <input_format>,
  "resultFormat" : <output_format> }

Javascript Extraction Function

Returns the dimension value, as transformed by the given JavaScript function.

For regular dimensions, the input value is passed as a string.

For the __time dimension, the input value is passed as a number representing the number of milliseconds since January 1, 1970 UTC.

Example for a regular dimension

{
  "type" : "javascript",
  "function" : "function(str) { return str.substr(0, 3); }"
}

{
  "type" : "javascript",
  "function" : "function(str) { return str + '!!!'; }",
  "injective" : true
}

A property of injective specifies if the javascript function preserves uniqueness. The default value is false meaning uniqueness is not preserved

Example for the __time dimension:

{
  "type" : "javascript",
  "function" : "function(t) { return 'Second ' + Math.floor((t % 60000) / 1000); }"
}

Lookup extraction function

Lookups are a concept in Druid where dimension values are (optionally) replaced with new values. For more documentation on using lookups, please see here. Explicit lookups allow you to specify a set of keys and values to use when performing the extraction.

{
  "type":"lookup",
  "lookup":{
    "type":"map",
    "map":{"foo":"bar", "baz":"bat"}
  },
  "retainMissingValue":true,
  "injective":true
}

{
  "type":"lookup",
  "lookup":{
    "type":"map",
    "map":{"foo":"bar", "baz":"bat"}
  },
  "retainMissingValue":false,
  "injective":false,
  "replaceMissingValueWith":"MISSING"
}

{
  "type":"lookup",
  "lookup":{"type":"namespace","namespace":"some_lookup"},
  "replaceMissingValueWith":"Unknown",
  "injective":false
}

{
  "type":"lookup",
  "lookup":{"type":"namespace","namespace":"some_lookup"},
  "retainMissingValue":true,
  "injective":false
}

A lookup can be of type namespace or map. A map lookup is passed as part of the query. A namespace lookup is populated on all the nodes which handle queries as per lookups

A property of retainMissingValue and replaceMissingValueWith can be specified at query time to hint how to handle missing values. Setting replaceMissingValueWith to "" has the same effect as setting it to null or omitting the property. Setting retainMissingValue to true will use the dimension's original value if it is not found in the lookup. The default values are replaceMissingValueWith = null and retainMissingValue = false which causes missing values to be treated as missing.

It is illegal to set retainMissingValue = true and also specify a replaceMissingValueWith.

A property of injective specifies if optimizations can be used which assume there is no combining of multiple names into one. For example: If ABC123 is the only key that maps to SomeCompany, that can be optimized since it is a unique lookup. But if both ABC123 and DEF456 BOTH map to SomeCompany, then that is NOT a unique lookup. Setting this value to true and setting retainMissingValue to FALSE (the default) may cause undesired behavior.

A property optimize can be supplied to allow optimization of lookup based extraction filter (by default optimize = true). The optimization layer will run on the broker and it will rewrite the extraction filter as clause of selector filters. For instance the following filter

{
    "filter": {
        "type": "selector",
        "dimension": "product",
        "value": "bar_1",
        "extractionFn": {
            "type": "lookup",
            "optimize": true,
            "lookup": {
                "type": "map",
                "map": {
                    "product_1": "bar_1",
                    "product_3": "bar_1"
                }
            }
        }
    }
}

will be rewritten as

{
   "filter":{
      "type":"or",
      "fields":[
         {
            "filter":{
               "type":"selector",
               "dimension":"product",
               "value":"product_1"
            }
         },
         {
            "filter":{
               "type":"selector",
               "dimension":"product",
               "value":"product_3"
            }
         }
      ]
   }
}

A null dimension value can be mapped to a specific value by specifying the empty string as the key. This allows distinguishing between a null dimension and a lookup resulting in a null. For example, specifying {"":"bar","bat":"baz"} with dimension values [null, "foo", "bat"] and replacing missing values with "oof" will yield results of ["bar", "oof", "baz"]. Omitting the empty string key will cause the missing value to take over. For example, specifying {"bat":"baz"} with dimension values [null, "foo", "bat"] and replacing missing values with "oof" will yield results of ["oof", "oof", "baz"].

Registered Lookup Extraction Function

While it is recommended that the lookup dimension spec be used whenever possible, any lookup that is registered for use as a lookup dimension spec can be used as a dimension extraction.

The specification for dimension extraction using dimension specification named lookups is formatted as per the following example:

{
  "type":"registeredLookup",
  "lookup":"some_lookup_name",
  "retainMissingValue":true,
  "injective":false
}

All the flags for lookup extraction function apply here as well.

In general, the dimension specification should be used. This dimension extraction implementation is made available for testing, validation, and transitioning from dimension extraction to the dimension specification style lookups. There is also a chance that a feature uses dimension extraction in such a way that it is not applied to dimension specification lookups. Such a scenario should be brought to the attention of the development mailing list.

Cascade Extraction Function

Provides chained execution of extraction functions.

A property of extractionFns contains an array of any extraction functions, which is executed in the array index order.

Example for chaining regular expression extraction function, javascript extraction function, and substring extraction function is as followings.

{
  "type" : "cascade", 
  "extractionFns": [
    { 
      "type" : "regex", 
      "expr" : "/([^/]+)/", 
      "replaceMissingValue": false,
      "replaceMissingValueWith": null
    },
    { 
      "type" : "javascript", 
      "function" : "function(str) { return \"the \".concat(str) }" 
    },
    { 
      "type" : "substring", 
      "index" : 0, "length" : 7 
    }
  ]
}

It will transform dimension values with specified extraction functions in the order named. For example, '/druid/prod/historical' is transformed to 'the dru' as regular expression extraction function first transforms it to 'druid' and then, javascript extraction function transforms it to 'the druid', and lastly, substring extraction function transforms it to 'the dru'.

String Format Extraction Function

Returns the dimension value formatted according to the given format string.

{ "type" : "stringFormat", "format" : <sprintf_expression>, "nullHandling" : <optional attribute for handling null value> }

For example if you want to concat "[" and "]" before and after the actual dimension value, you need to specify "[%s]" as format string. "nullHandling" can be one of nullString, emptyString or returnNull. With "[%s]" format, each configuration will result [null], [], null. Default is nullString.

Filtered DimensionSpecs

These are only valid for multi-value dimensions. If you have a row in druid that has a multi-value dimension with values ["v1", "v2", "v3"] and you send a groupBy/topN query grouping by that dimension with query filter for value "v1". In the response you will get 3 rows containing "v1", "v2" and "v3". This behavior might be unintuitive for some use cases.

It happens because "query filter" is internally used on the bitmaps and only used to match the row to be included in the query result processing. With multi-value dimensions, "query filter" behaves like a contains check, which will match the row with dimension value ["v1", "v2", "v3"]. Please see the section on "Multi-value columns" in segment for more details. Then groupBy/topN processing pipeline "explodes" all multi-value dimensions resulting 3 rows for "v1", "v2" and "v3" each.

In addition to "query filter" which efficiently selects the rows to be processed, you can use the filtered dimension spec to filter for specific values within the values of a multi-value dimension. These dimensionSpecs take a delegate DimensionSpec and a filtering criteria. From the "exploded" rows, only rows matching the given filtering criteria are returned in the query result.

The following filtered dimension spec acts as a whitelist or blacklist for values as per the "isWhitelist" attribute value.

{ "type" : "listFiltered", "delegate" : <dimensionSpec>, "values": <array of strings>, "isWhitelist": <optional attribute for true/false, default is true> }

Following filtered dimension spec retains only the values matching regex. Note that listFiltered is faster than this and one should use that for whitelist or blacklist usecase.

{ "type" : "regexFiltered", "delegate" : <dimensionSpec>, "pattern": <java regex pattern> }

For more details and examples, see multi-value dimensions.

Upper and Lower extraction functions.

Returns the dimension values as all upper case or lower case. Optionally user can specify the language to use in order to perform upper or lower transformation

{
  "type" : "upper",
  "locale":"fr"
}

or without setting "locale" (in this case, the current value of the default locale for this instance of the Java Virtual Machine.)

{
  "type" : "lower"
}

Bucket Extraction Function

Bucket extraction function is used to bucket numerical values in each range of the given size by converting them to the same base value. Non numeric values are converted to null.

size : the size of the buckets (optional, default 1)
offset : the offset for the buckets (optional, default 0)

The following extraction function creates buckets of 5 starting from 2. In this case, values in the range of [2, 7) will be converted to 2, values in [7, 12) will be converted to 7, etc.

{
  "type" : "bucket",
  "size" : 5,
  "offset" : 2
}

Lookup DimensionSpecs

Lookups are an experimental feature.

Lookup DimensionSpecs can be used to define directly a lookup implementation as dimension spec. Generally speaking there is two different kind of lookups implementations. The first kind is passed at the query time like map implementation.

{ 
  "type":"lookup",
  "dimension":"dimensionName",
  "outputName":"dimensionOutputName",
  "replaceMissingValueWith":"missing_value",
  "retainMissingValue":false,
  "lookup":{"type": "map", "map":{"key":"value"}, "isOneToOne":false}
}