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SOLR-11144: Add Analytics Component docs to the Ref Guide

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= Analytics Expression Sources
:page-tocclass: right
// Licensed to the Apache Software Foundation (ASF) under one
// or more contributor license agreements. See the NOTICE file
// distributed with this work for additional information
// regarding copyright ownership. The ASF licenses this file
// to you under the Apache License, Version 2.0 (the
// "License"); you may not use this file except in compliance
// with the License. You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing,
// software distributed under the License is distributed on an
// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, either express or implied. See the License for the
// specific language governing permissions and limitations
// under the License.
Expression sources are the source of the data being aggregated in <<analytics.adoc#expressions,analytics expressions>>.
These sources can be either Solr fields indexed with docValues, or constants.
== Supported Field Types
The following <<field-types-included-with-solr.adoc#field-types-included-with-solr, Solr field types>> are supported.
Fields of these types can be either multi-valued and single-valued.
All fields used in analytics expressions *must* have <<docvalues.adoc#docvalues,docValues>> enabled.
// Since Trie* fields are deprecated as of 7.0, we should consider removing Trie* fields from this list...
[horizontal]
String::
StrField
Boolean::
BoolField
Integer::
TrieIntField +
IntPointField
Long::
TrieLongField +
LongPointField
Float::
TrieFloatField +
FloatPointField
Double::
TrieDoubleField +
DoublePointField
Date::
TrieDateField +
DatePointField
.Multi-valued Field De-duplication
[WARNING]
====
All multi-valued field types, except for PointFields, are de-duplicated, meaning duplicate values for the same field are removed during indexing.
In order to save duplicates, you must use PointField types.
====
== Constants
Constants can be included in expressions to use along side fields and functions. The available constants are shown below.
Constants do not need to be surrounded by any function to define them, they can be used exactly like fields in an expression.
=== Strings
There are two possible ways of specifying constant strings, as shown below.
* Surrounded by double quotes, inside the quotes both `"` and `\` must be escaped with a `\` character.
+
`"Inside of 'double' \\ \"quotes\""` \=> `Inside of 'double' \ "quotes"`
* Surrounded by single quotes, inside the quotes both `'` and `\` must be escaped with a `\` character.
+
`'Inside of "single" \\ \'quotes\''` \=> `Inside of "double" \ 'quotes'`
=== Dates
Dates can be specified in the same way as they are in Solr queries. Just use ISO-8601 format.
For more information, refer to the <<working-with-dates.adoc#working-with-dates,Working with Dates>> section.
* `2017-07-17T19:35:08Z`
=== Numeric
Any non-decimal number will be read as an integer, or as a long if it is too large for an integer. All decimal numbers will be read as doubles.
* `-123421`: Integer
* `800000000000`: Long
* `230.34`: Double

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= Analytics Mapping Functions
:page-tocclass: right
// Licensed to the Apache Software Foundation (ASF) under one
// or more contributor license agreements. See the NOTICE file
// distributed with this work for additional information
// regarding copyright ownership. The ASF licenses this file
// to you under the Apache License, Version 2.0 (the
// "License"); you may not use this file except in compliance
// with the License. You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing,
// software distributed under the License is distributed on an
// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, either express or implied. See the License for the
// specific language governing permissions and limitations
// under the License.
Mapping functions map values for each Solr Document or Reduction.
Below is a list of all mapping functions provided by the Analytics Component.
These mappings can be chained together to implement more complex functionality.
== Numeric Functions
=== Negation
Negates the result of a numeric expression.
`neg(<_Numeric_ T>)` \=> `<T>`::
* `neg(10.53)` \=> `-10.53`
* `neg([1, -4])` \=> `[-1, 4]`
=== Absolute Value
Returns the absolute value of the numeric expression.
`abs(< _Numeric_ T >)` \=> `< T >`::
* `abs(-10.53)` \=> `10.53`
* `abs([1, -4])` \=> `[1, 4]`
[[analytics-round]]
=== Round
Rounds the numeric expression to the nearest `Integer` or `Long` value.
`round(< _Float_ >)` \=> `< _Int_ >`::
`round(< _Double_ >)` \=> `< _Long_ >`::
* `round(-1.5)` \=> `-1`
* `round([1.75, 100.34])` \=> `[2, 100]`
=== Ceiling
Rounds the numeric expression to the nearest `Integer` or `Long` value that is greater than or equal to the original value.
`ceil(< _Float_ >)` \=> `< _Int_ >`::
`ceil(< _Double_ >)` \=> `< _Long_ >`::
* `ceil(5.01)` \=> `5`
* `ceil([-4.999, 6.99])` \=> `[-4, 7]`
[[analytics-floor]]
=== Floor
Rounds the numeric expression to the nearest `Integer` or `Long` value that is less than or equal to the original value.
`floor(< _Float_ >)` \=> `< _Int_ >`::
`floor(< _Double_ >)` \=> `< _Long_ >`::
* `floor(5.75)` \=> `5`
* `floor([-4.001, 6.01])` \=> `[-5, 6]`
=== Addition
Adds the values of the numeric expressions.
`add(< _Multi Double_ >)` \=> `< _Single Double_ >`::
* `add([1, -4])` \=> `-3.0`
`add(< _Single Double_ >, < _Multi Double_ >)` \=> `< _Multi Double_ >`::
* `add(3.5, [1, -4])` \=> `[4.5, -0.5]`
`add(< _Multi Double_ >, < _Single Double_ >)` \=> `< _Multi Double_ >`::
* `add([1, -4], 3.5)` \=> `[4.5, -0.5]`
`add(< _Single Double_ >, ...)` \=> `< _Single Double_ >`::
* `add(3.5, 100, -27.6)` \=> `75.9`
=== Subtraction
Subtracts the values of the numeric expressions.
`sub(< _Single Double_ >, < _Single Double_ >)` \=> `< _Single Double_ >`::
* `sub(3.5, 100)` \=> `-76.5`
`sub(< _Single Double_ >, < _Multi Double_ >)` \=> `< _Multi Double_ >`::
* `sub(3.5, [1, -4])` \=> `[2.5, 7.5]`
`sub(< _Multi Double_ >, < _Single Double_ >)` \=> `< _Multi Double_ >`::
* `sub([1, -4], 3.5)` \=> `[-2.5, -7.5]`
=== Multiplication
Multiplies the values of the numeric expressions.
`mult(< _Multi Double_ >)` \=> `< _Single Double_ >`::
* `mult([1, -4])` \=> `-4.0`
`mult(< _Single Double_ >, < _Multi Double_ >)` \=> `< _Multi Double_ >`::
* `mult(3.5, [1, -4])` \=> `[3.5, -16.0]`
`mult(< _Multi Double_ >, < _Single Double_ >)` \=> `< _Multi Double_ >`::
* `mult([1, -4], 3.5)` \=> `[3.5, 16.0]`
`mult(< _Single Double_ >, ...)` \=> `< _Single Double_ >`::
* `mult(3.5, 100, -27.6)` \=> `-9660`
=== Division
Divides the values of the numeric expressions.
`div(< _Single Double_ >, < _Single Double_ >)` \=> `< _Single Double_ >`::
* `div(3.5, 100)` \=> `.035`
`div(< _Single Double_ >, < _Multi Double_ >)` \=> `< _Multi Double_ >`::
* `div(3.5, [1, -4])` \=> `[3.5, -0.875]`
`div(< _Multi Double_ >, < _Single Double_ >)` \=> `< _Multi Double_ >`::
* `div([1, -4], 25)` \=> `[0.04, -0.16]`
=== Power
Takes one numeric expression to the power of another.
*NOTE:* The square root function `sqrt(< _Double_ >)` can be used as shorthand for `pow(< _Double_ >, .5)`
`pow(< _Single Double_ >, < _Single Double_ >)` \=> `< _Single Double_ >`::
* `pow(2, 4)` \=> `16.0`
`pow(< _Single Double_ >, < _Multi Double_ >)` \=> `< _Multi Double_ >`::
* `pow(16, [-1, 0])` \=> `[0.0625, 1]`
`pow(< _Multi Double_ >, < _Single Double_ >)` \=> `< _Multi Double_ >`::
* `pow([1, 16], .25)` \=> `[1.0, 2.0]`
=== Logarithm
Takes one logarithm of numeric expressions, with an optional second numeric expression as the base.
If only one expression is given, the natural log is used.
`log(< _Double_ >)` \=> `< _Double_ >`::
* `log(5)` \=> `1.6094...`
* `log([1.0, 100.34])` \=> `[0.0, 4.6085...]`
`log(< _Single Double_ >, < _Single Double_ >)` \=> `< _Single Double_ >`::
* `log(2, 4)` \=> `0.5`
`log(< _Single Double_ >, < _Multi Double_ >)` \=> `< _Multi Double_ >`::
* `log(16, [2, 4])` \=> `[4, 2]`
`log(< _Multi Double_ >, < _Single Double_ >)` \=> `< _Multi Double_ >`::
* `log([81, 3], 9)` \=> `[2.0, 0.5]`
== Logic
[[analytics-logic-neg]]
=== Negation
Negates the result of a boolean expression.
`neg(< _Bool_ >)` \=> `< _Bool_>`::
* `neg(F)` \=> `T`
* `neg([F, T])` \=> `[T, F]`
[[analytics-and]]
=== And
ANDs the values of the boolean expressions.
`and(< _Multi Bool_ >)` \=> `< _Single Bool_ >`::
* `and([T, F, T])` \=> `F`
`and(< _Single Bool_ >, < _Multi Bool_ >)` \=> `< _Multi Bool_ >`::
* `and(F, [T, T])` \=> `[F, F]`
`and(< _Multi Bool_ >, < _Single Bool_ >)` \=> `< _Multi Bool_ >`::
* `and([F, T], T)` \=> `[F, T]`
`and(< _Single Bool_ >, ...)` \=> `< _Single Bool_ >`::
* `and(T, T, T)` \=> `T`
[[analytics-or]]
=== Or
ORs the values of the boolean expressions.
`or(< _Multi Bool_ >)` \=> `< _Single Bool_ >`::
* `or([T, F, T])` \=> `T`
`or(< _Single Bool_ >, < _Multi Bool_ >)` \=> `< _Multi Bool_ >`::
* `or(F, [F, T])` \=> `[F, T]`
`or(< _Multi Bool_ >, < _Single Bool_ >)` \=> `< _Multi Bool_ >`::
* `or([F, T], T)` \=> `[T, T]`
`or(< _Single Bool_ >, ...)` \=> `< _Single Bool_ >`::
* `or(F, F, F)` \=> `F`
==== Exists
Checks whether any value(s) exist for the expression.
`exists( T )` \=> `< _Single Bool_ >`::
* `exists([1, 2, 3])` \=> `T`
* `exists([])` \=> `F`
* `exists(_empty_)` \=> `F`
* `exists('abc')` \=> `T`
== Comparison
=== Equality
Checks whether two expressions' values are equal. The parameters must be the same type, after implicit casting.
`equal(< _Single_ T >, < _Single_ T >)` \=> `< _Single Bool_ >`::
* `equal(F, F)` \=> `T`
`equal(< _Single_ T >, < _Multi_ T >)` \=> `< _Multi Bool_ >`::
* `equal("a", ["a", "ab"])` \=> `[T, F]`
`equal(< _Multi_ T >, < _Single_ T >)` \=> `< _Multi Bool_ >`::
* `equal([1.5, -3.0], -3)` \=> `[F, T]`
=== Greater Than
Checks whether a numeric or `Date` expression's values are greater than another expression's values.
The parameters must be the same type, after implicit casting.
`gt(< _Single Numeric/Date_ T >, < _Single_ T >)` \=> `< _Single Bool_ >`::
* `gt(1800-01-02, 1799-12-20)` \=> `F`
`gt(< _Single Numeric/Date_ T >, < _Multi_ T >)` \=> `< _Multi Bool_ >`::
* `gt(30.756, [30, 100])` \=> `[F, T]`
`gt(< _Multi Numeric/Date_ T >, < _Single_ T >)` \=> `< _Multi Bool_ >`::
* `gt([30, 75.6], 30)` \=> `[F, T]`
=== Greater Than or Equals
Checks whether a numeric or `Date` expression's values are greater than or equal to another expression's values.
The parameters must be the same type, after implicit casting.
`gte(< _Single Numeric/Date_ T >, < _Single_ T >)` \=> `< _Single Bool_ >`::
* `gte(1800-01-02, 1799-12-20)` \=> `F`
`gte(< _Single Numeric/Date_ T >, < _Multi_ T >)` \=> `< _Multi Bool_ >`::
* `gte(30.756, [30, 100])` \=> `[F, T]`
`gte(< _Multi Numeric/Date_ T >, < _Single_ T >)` \=> `< _Multi Bool_ >`::
* `gte([30, 75.6], 30)` \=> `[T, T]`
=== Less Than
Checks whether a numeric or `Date` expression's values are less than another expression's values.
The parameters must be the same type, after implicit casting.
`lt(< _Single Numeric/Date_ T >, < _Single_ T >)` \=> `< _Single Bool_ >`::
* `lt(1800-01-02, 1799-12-20)` \=> `T`
`lt(< _Single Numeric/Date_ T >, < _Multi_ T >)` \=> `< _Multi Bool_ >`::
* `lt(30.756, [30, 100])` \=> `[T, F]`
`lt(< _Multi Numeric/Date_ T >, < _Single_ T >)` \=> `< _Multi Bool_ >`::
* `lt([30, 75.6], 30)` \=> `[F, F]`
=== Less Than or Equals
Checks whether a numeric or `Date` expression's values are less than or equal to another expression's values.
The parameters must be the same type, after implicit casting.
`lte(< _Single Numeric/Date_ T >, < _Single_ T >)` \=> `< _Single Bool_ >`::
* `lte(1800-01-02, 1799-12-20)` \=> `T`
`lte(< _Single Numeric/Date_ T >, < _Multi_ T >)` \=> `< _Multi Bool_ >`::
* `lte(30.756, [30, 100])` \=> `[T, F]`
`lte(< _Multi Numeric/Date_ T >, < _Single_ T >)` \=> `< _Multi Bool_ >`::
* `lte([30, 75.6], 30)` \=> `[T, F]`
[[analytics-top]]
=== Top
Returns the maximum of the numeric, `Date` or `String` expression(s)' values.
The parameters must be the same type, after implicit casting.
(Currently the only type not compatible is `Boolean`, which will be converted to a `String` implicitly in order to compile the expression)
`top(< _Multi_ T >)` \=> `< _Single_ T >`::
* `top([30, 400, -10, 0])` \=> `400`
`top(< _Single_ T >, ...)` \=> `< _Single_ T >`::
* `top("a", 1, "d")` \=> `"d"`
=== Bottom
Returns the minimum of the numeric, `Date` or `String` expression(s)' values.
The parameters must be the same type, after implicit casting.
(Currently the only type not compatible is `Boolean`, which will be converted to a `String` implicitly in order to compile the expression)
`bottom(< _Multi_ T >)` \=> `< _Single_ T >`::
* `bottom([30, 400, -10, 0])` \=> `-10`
`bottom(< _Single_ T >, ...)` \=> `< _Single_ T >`::
* `bottom("a", 1, "d")` \=> `"1"`
== Conditional
[[analytics-if]]
=== If
Returns the value(s) of the `THEN` or `ELSE` expressions depending on whether the boolean conditional expression's value is `true` or `false`.
The `THEN` and `ELSE` expressions must be of the same type and cardinality after implicit casting is done.
`if(< _Single Bool_>, < T >, < T >)` \=> `< T >`::
* `if(true, "abc", [1,2])` \=> `["abc"]`
* `if(false, "abc", 123)` \=> `"123"`
=== Replace
Replace all values from the 1^st^ expression that are equal to the value of the 2^nd^ expression with the value of the 3^rd^ expression.
All parameters must be the same type after implicit casting is done.
`replace(< T >, < _Single_ T >, < _Single_ T >)` \=> `< T >`::
* `replace([1,3], 3, "4")` \=> `["1", "4"]`
* `replace("abc", "abc", 18)` \=> `"18"`
* `replace("abc", 1, "def")` \=> `"abc"`
=== Fill Missing
If the 1^st^ expression does not have values, fill it with the values for the 2^nd^ expression.
Both expressions must be of the same type and cardinality after implicit casting is done
`fill_missing(< T >, < T >)` \=> `< T >`::
* `fill_missing([], 3)` \=> `[3]`
* `fill_missing(_empty_, "abc")` \=> `"abc"`
* `fill_missing("abc", [1])` \=> `["abc"]`
=== Remove
Remove all occurrences of the 2^nd^ expression's value from the values of the 1^st^ expression.
Both expressions must be of the same type after implicit casting is done
`remove(< T >, < _Single_ T >)` \=> `< T >`::
* `remove([1,2,3,2], 2)` \=> `[1, 3]`
* `remove("1", 1)` \=> `_empty_`
* `remove(1, "abc")` \=> `"1"`
=== Filter
Return the values of the 1^st^ expression if the value of the 2^nd^ expression is `true`, otherwise return no values.
`filter(< T >, < _Single Boolean_ >)` \=> `< T >`::
* `filter([1,2,3], true)` \=> `[1,2,3]`
* `filter([1,2,3], false)` \=> `[]`
* `filter("abc", false)` \=> `_empty_`
* `filter("abc", true)` \=> `1`
== Date
=== Date Parse
Explicitly converts the values of a `String` or `Long` expression into `Dates`.
`date(< _String_ >)` \=> `< _Date_ >`::
* `date('1800-01-02')` \=> `1800-01-02T&#8203;00:00:00Z`
* `date(['1800-01-02', '2016-05-23'])` \=> `[1800-01-02T..., 2016-05-23T...]`
`date(< _Long_ >)` \=> `< _Date_ >`::
* `date(1232343246648)` \=> `2009-01-19T&#8203;05:34:06Z`
* `date([1232343246648, 223234324664])` \=> `[2009-01-19T..., 1977-01-27T...]`
[[analytics-date-math]]
=== Date Math
Compute the given date math strings for the values of a `Date` expression. The date math strings *must* be <<analytics-expression-sources.adoc#strings, constant>>.
`date_math(< _Date_ >, < _Constant String_ >...)` \=> `< _Date_ >`::
* `date_math(1800-04-15, '+1DAY', '-1MONTH')` \=> `1800-03-16`
* `date_math([1800-04-15,2016-05-24], '+1DAY', '-1MONTH')` \=> `[1800-03-16, 2016-04-25]`
== String
=== Explicit Casting
Explicitly casts the expression to a `String` expression.
`string(< _String_ >)` \=> `< _String_ >`::
* `string(1)` \=> `'1'`
* `string([1.5, -2.0])` \=> `['1.5', '-2.0']`
=== Concatenation
Concatenations the values of the `String` expression(s) together.
`concat(< _Multi String_ >)` \=> `< _Single String_ >`::
* `concat(['a','b','c'])` \=> `'abc'`
`concat(< _Single String_ >, < _Multi String_ >)` \=> `< _Multi String_ >`::
* `concat(1, ['a','b','c'])` \=> `['1a','1b','1c']`
`concat(< _Multi String_ >, < _Single String_ >)` \=> `< _Multi String_ >`::
* `concat(['a','b','c'], 1)` \=> `['a1','b1','c1']`
`concat(< _Single String_ >...)` \=> `< _Single String_ >`::
* `concat('a','b','c')` \=> `'abc'`
* `concat('a',_empty_,'c')` \=> `'ac'` +
_Empty values are ignored_
=== Separated Concatenation
Concatenations the values of the `String` expression(s) together using the given <<analytics-expression-sources.adoc#strings, constant string>> value as a separator.
`concat_sep(< _Constant String_ >, < _Multi String_ >)` \=> `< _Single String_ >`::
* `concat_sep('-', ['a','b'])` \=> `'a-b'`
`concat_sep(< _Constant String_ >, < _Single String_ >, < _Multi String_ >)` \=> `< _Multi String_ >`::
* `concat_sep(2,1,['a','b'])` \=> `['12a','12b']`
`concat_sep(< _Constant String_ >, < _Multi String_ >, < _Single String_ >)` \=> `< _Multi String_ >`::
* `concat_sep(2,['a','b'],1)` \=> `['a21','b21']`
* `concat_sep('-','a',2,3)` \=> `'a-2-3'`
* `concat_sep(';','a',_empty_,'c')` \=> `'a;c'` +
_Empty values are ignored_

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= Analytics Reduction Functions
:page-tocclass: right
:page-toclevels: 2
// Licensed to the Apache Software Foundation (ASF) under one
// or more contributor license agreements. See the NOTICE file
// distributed with this work for additional information
// regarding copyright ownership. The ASF licenses this file
// to you under the Apache License, Version 2.0 (the
// "License"); you may not use this file except in compliance
// with the License. You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing,
// software distributed under the License is distributed on an
// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, either express or implied. See the License for the
// specific language governing permissions and limitations
// under the License.
Reduction functions reduce the values of <<analytics-expression-sources.adoc#analytics-expression-sources,sources>>
and/or unreduced <<analytics-mapping-functions.adoc#analytics-mapping-functions,mapping functions>>
for every Solr Document to a single value.
Below is a list of all reduction functions provided by the Analytics Component.
These can be combined using mapping functions to implement more complex functionality.
== Counting Reductions
=== Count
The number of existing values for an expression. For single-valued expressions, this is equivalent to `docCount`.
If no expression is given, the number of matching documents is returned.
`count()` \=> `< _Single Long_ >`
`count(< T >)` \=> `< _Single Long_ >`
=== Doc Count
The number of documents for which an expression has existing values. For single-valued expressions, this is equivalent to `count`.
If no expression is given, the number of matching documents is returned.
`doc_count()` \=> `< _Single Long_ >`
`doc_count(< T >)` \=> `< _Single Long_ >`
=== Missing
The number of documents for which an expression has no existing value.
`missing(< T >)` \=> `< _Single Long_ >`
[[analytics-unique]]
=== Unique
The number of unique values for an expression. This function accepts `Numeric`, `Date` and `String` expressions.
`unique(< T >)` \=> `< _Single Long_ >`
== Math Reductions
=== Sum
Returns the sum of all values for the expression.
`sum(< _Double_ >)` \=> `< _Single Double_ >`
=== Variance
Returns the variance of all values for the expression.
`variance(< _Double_ >)` \=> `< _Single Double_ >`
=== Standard Deviation
Returns the standard deviation of all values for the expression.
`stddev(< _Double_ >)` \=> `< _Single Double_ >`
=== Mean
Returns the arithmetic mean of all values for the expression.
`mean(< _Double_ >)` \=> `< _Single Double_ >`
=== Weighted Mean
Returns the arithmetic mean of all values for the second expression weighted by the values of the first expression.
`wmean(< _Double_ >, < _Double_ >)` \=> `< _Single Double_ >`
NOTE: The expressions must satisfy the rules for `mult` function parameters.
== Ordering Reductions
=== Minimum
Returns the minimum value for the expression. This function accepts `Numeric`, `Date` and `String` expressions.
`min(< T >)` \=> `< _Single_ T >`
=== Maximum
Returns the maximum value for the expression. This function accepts `Numeric`, `Date` and `String` expressions.
`max(< T >)` \=> `< _Single_ T >`
=== Median
Returns the median of all values for the expression. This function accepts `Numeric` and `Date` expressions.
`median(< T >)` \=> `< _Single_ T >`
=== Percentile
Calculates the given percentile of all values for the expression.
This function accepts `Numeric`, `Date` and `String` expressions for the 2^nd^ parameter.
The percentile, given as the 1^st^ parameter, must be a <<analytics-expression-sources.adoc#numeric,constant double>> between [0, 100).
`percentile(<Constant Double>, < T >)` \=> `< _Single_ T >`
=== Ordinal
Calculates the given ordinal of all values for the expression.
This function accepts `Numeric`, `Date` and `String` expressions for the 2^nd^ parameter.
The ordinal, given as the 1^st^ parameter, must be a <<analytics-expression-sources.adoc#numeric,constant integer>>.
*0 is not accepted as an ordinal value.*
If the ordinal is positive, the returned value will be the _n_^th^ smallest value.
If the ordinal is negative, the returned value will be the _n_^th^ largest value.
`ordinal(<Constant Int>, < T >)` \=> `< _Single_ T >`

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= Analytics Component
:page-children: analytics-expression-sources, analytics-mapping-functions, analytics-reduction-functions
:page-tocclass: right
:page-toclevel: 2
// Licensed to the Apache Software Foundation (ASF) under one
// or more contributor license agreements. See the NOTICE file
// distributed with this work for additional information
// regarding copyright ownership. The ASF licenses this file
// to you under the Apache License, Version 2.0 (the
// "License"); you may not use this file except in compliance
// with the License. You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing,
// software distributed under the License is distributed on an
// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, either express or implied. See the License for the
// specific language governing permissions and limitations
// under the License.
The Analytics Component allows users to calculate complex statistical aggregations over result sets.
The component enables interacting with data in a variety of ways, both through a diverse set of analytics functions as well as powerful faceting functionality.
The standard facets are supported within the analytics component with additions that leverage its analytical capabilities.
== Analytics Configuration
The Analytics component is in a contrib module, therefore it will need to be enabled in the `solrconfig.xml` for each collection where you would like to use it.
Since the Analytics framework is a _search component_, it must be declared as such and added to the search handler.
For distributed analytics requests over cloud collections, the component uses the `AnalyticsHandler` strictly for inter-shard communication.
The Analytics Handler should not be used by users to submit analytics requests.
To configure Solr to use the Analytics Component, the first step is to add a `lib` directive so Solr loads the Analytic Component classes (for more about the `lib` directive, see <<lib-directives-in-solrconfig.adoc#lib-directives-in-solrconfig, Lib Directives in SolrConfig>>). In the section of `solrconfig.xml` where the default `lib` directive are, add a line:
[source,xml]
<lib dir="${solr.install.dir:../../../..}/dist/" regex="solr-analytics-\d.*\.jar" />
Next you need to enable the request handler and search component. Add the following lines to `solrconfig.xml`, near the defintions for other request handlers:
[source,xml]
.solrconfig.xml
----
<!-- To handle user requests -->
<searchComponent name="analytics" class="org.apache.solr.handler.component.AnalyticsComponent" />
<requestHandler name="/select" class="solr.SearchHandler">
<arr name="last_components">
<str>analytics</str>
</arr>
</requestHandler>
<!-- For inter-shard communication during distributed requests -->
<requestHandler name="/analytics" class="org.apache.solr.handler.AnalyticsHandler" />
----
For these changes to take effect, restart Solr or reload the core or collection.
== Request Syntax
An Analytics request is passed to Solr with the parameter `analytics` in a request sent to the
<<requesthandlers-and-searchcomponents-in-solrconfig.adoc#searchhandlers,Search Handler>>.
Since the analytics request is sent inside of a search handler request, it will compute results based on the result set determined by the search handler.
For example, this curl command encodes and POSTs a simple analytics request to the the search handler:
[source,bash]
----
curl --data-urlencode 'analytics={
"expressions" : {
"revenue" : "sum(mult(price,quantity))"
}
}'
http://localhost:8983/solr/sales/select?q=*:*&wt=json&rows=0
----
There are 3 main parts of any analytics request:
Expressions::
A list of calculations to perform over the entire result set. Expressions aggregate the search results into a single value to return.
This list is entirely independent of the expressions defined in each of the groupings. Find out more about them in the section <<Expressions>>.
Functions::
One or more <<variable-functions, Variable Functions>> to be used throughout the rest of the request. These are essentially lambda functions and can be combined in a number of ways.
These functions for the expressions defined in `expressions` as well as `groupings`.
Groupings::
The list of <<groupings-and-facets, Groupings>> to calculate in addition to the expressions.
Groupings hold a set of facets and a list of expressions to compute over those facets.
The expressions defined in a grouping are only calculated over the facets defined in that grouping.
[NOTE]
.Optional Parameters
Either the `expressions` or the `groupings` parameter must be present in the request, or else there will be no analytics to compute.
The `functions` parameter is always optional.
[source,json]
.Example Analytics Request
----
{
"functions": {
"sale()": "mult(price,quantity)"
},
"expressions" : {
"max_sale" : "max(sale())",
"med_sale" : "median(sale())"
},
"groupings" : {
"sales" : {
"expressions" : {
"stddev_sale" : "stddev(sale())",
"min_price" : "min(price)",
"max_quantity" : "max(quantity)"
},
"facets" : {
"category" : {
"type" : "value",
"expression" : "fill_missing(category, 'No Category')",
"sort" : {
"criteria" : [
{
"type" : "expression",
"expression" : "min_price",
"direction" : "ascending"
},
{
"type" : "facetvalue",
"direction" : "descending"
}
],
"limit" : 10
}
},
"temps" : {
"type" : "query",
"queries" : {
"hot" : "temp:[90 TO *]",
"cold" : "temp:[* TO 50]"
}
}
}
}
}
}
----
== Expressions
Expressions are the way to request pieces of information from the analytics component. These are the statistical expressions that you want computed and returned in your response.
=== Constructing an Expression
==== Expression Components
An expression is built using fields, constants, mapping functions and reduction functions. The ways that these can be defined are described below.
Sources::
* Constants: The values defined in the expression.
The supported constant types are described in the <<analytics-expression-sources.adoc#constants, Analytics Expression Source Reference>>.
* Fields: Solr fields that are read from the index.
The supported fields are listed in the <<analytics-expression-sources.adoc#supported-field-types, Analytics Expression Source Reference>>.
Mapping Functions::
Mapping functions map values for each Solr Document or Reduction.
The provided mapping functions are detailed in the <<analytics-mapping-functions.adoc#analytics-mapping-functions,Analytics Mapping Function Reference>>.
* Unreduced Mapping: Mapping a Field with another Field or Constant returns a value for every Solr Document.
Unreduced mapping functions can take fields, constants as well as other unreduced mapping functions as input.
* Reduced Mapping: Mapping a Reduction Function with another Reduction Function or Constant returns a single value.
Reduction Functions::
Functions that reduce the values of sources and/or unreduced mapping functions for every Solr Document to a single value.
The provided reduction functions are detailed in the <<analytics-reduction-functions.adoc#analytics-reduction-functions,Analytics Reduction Function Reference>>.
==== Component Ordering
The expression components must be used in the following order to create valid expressions.
. Reduced Mapping Function
.. Constants
.. Reduction Function
... Sources
... Unreduced Mapping Function
.... Sources
.... Unreduced Mapping Function
.. Reduced Mapping Function
. Reduction Function
This ordering is based on the following rules:
* No reduction function can be an argument of another reduction function.
Since all reduction is done together in one step, one reduction function cannot rely on the result of another.
* No fields can be left unreduced, since the analytics component cannot return a list of values for an expression (one for every document).
Every expression must be reduced to a single value.
* Mapping functions are not necessary when creating functions, however as many nested mappings as needed can be used.
* Nested mapping functions must be the same type, so either both must be unreduced or both must be reduced.
A reduced mapping function cannot take an unreduced mapping function as a parameter and vice versa.
==== Example Construction
With the above definitions and ordering, an example expression can be broken up into its components:
[source,bash]
div(sum(a,fill_missing(b,0)),add(10.5,count(mult(a,c)))))
As a whole, this is a reduced mapping function. The `div` function is a reduced mapping function since it is a <<analytics-mapping-functions.adoc#division,provided mapping function>> and has reduced arguments.
If we break down the expression further:
* `sum(a,fill_missing(b,0))`: Reduction Function +
`sum` is a <<analytics-reduction-functions.adoc#sum,provided reduction function>>.
** `a`: Field
** `fill_missing(b,0)`: Unreduced Mapping Function +
`fill_missing` is an unreduced mapping function since it is a <<analytics-mapping-functions.adoc#fill-missing,provided mapping function>> and has a field argument.
*** `b`: Field
*** `0`: Constant
* `add(10.5,count(mult(a,c)))`: Reduced Mapping Function +
`add` is a reduced mapping function since it is a <<analytics-mapping-functions.adoc#addition,provided mapping function>> and has a reduction function argument.
** `10.5`: Constant
** `count(mult(a,c))`: Reduction Function +
`count` is a <<analytics-reduction-functions.adoc#count,provided reduction function>>
*** `mult(a,c)`: Unreduced Mapping Function +
`mult` is an unreduced mapping function since it is a <<analytics-mapping-functions.adoc#multiplication,provided mapping function>> and has two field arguments.
**** `a`: Field
**** `c`: Field
=== Expression Cardinality (Multi-Valued and Single-Valued)
The root of all multi-valued expressions are multi-valued fields. Single-valued expressions can be started with constants or single-valued fields.
All single-valued expressions can be treated as multi-valued expressions that contain one value.
Single-valued expressions and multi-valued expressions can be used together in many mapping functions, as well as multi-valued expressions being used alone, and many single-valued expressions being used together. For example:
`add(<single-valued double>, <single-valued double>, ...)`::
Returns a single-valued double expression where the value of the values of each expression are added.
`add(<single-valued double>, <multi-valued double>)`::
Returns a multi-valued double expression where each value of the second expression is added to the single value of the first expression.
`add(<multi-valued double>, <single-valued double>)`::
Acts the same as the above function.
`add(<multi-valued double>)`::
Returns a single-valued double expression which is the sum of the multiple values of the parameter expression.
=== Types and Implicit Casting
The new analytics component currently supports the types listed in the below table.
These types have one-way implicit casting enabled for the following relationships:
[cols="20s,80",options="header"]
|===
| Type | Implicitly Casts To
| Boolean | String
| Date | Long, String
| Integer | Long, Float, Double, String
| Long | Double, String
| Float | Double, String
| Double | String
| String | _none_
|===
An implicit cast means that if a function requires a certain type of value as a parameter, arguments will be automatically converted to that type if it is possible.
For example, `concat()` only accepts string parameters and since all types can be implicitly cast to strings, any type is accepted as an argument.
This also goes for dynamically typed functions. `fill_missing()` requires two arguments of the same type. However, two types that implicitly cast to the same type can also be used.
For example, `fill_missing(<long>,<float>)` will be cast to `fill_missing(<double>,<double>)` since long cannot be cast to float and float cannot be cast to long implicitly.
There is an ordering to implicit casts, where the more specialized type is ordered ahead of the more general type.
Therefore even though both long and float can be implicitly cast to double and string, they will be cast to double.
This is because double is a more specialized type than string, which every type can be cast to.
The ordering is the same as their order in the above table.
Cardinality can also be implicitly cast.
Single-valued expressions can always be implicitly cast to multi-valued expressions, since all single-valued expressions are multi-valued expressions with one value.
Implicit casting will only occur when an expression will not "compile" without it.
If an expression follows all typing rules initially, no implicit casting will occur.
Certain functions such as `string()`, `date()`, `round()`, `floor()`, and `ceil()` act as explicit casts, declaring the type that is desired.
However `round()`, `floor()` and `cell()` can return either int or long, depending on the argument type.
== Variable Functions
Variable functions are a way to shorten your expressions and make writing analytics queries easier. They are essentially lambda functions defined in a request.
[source,json]
.Example Basic Function
----
{
"functions" : {
"sale()" : "mult(price,quantity)"
},
"expressions" : {
"max_sale" : "max(sale())",
"med_sale" : "median(sale())"
}
}
----
In the above request, instead of writing `mult(price,quantity)` twice, a function `sale()` was defined to abstract this idea. Then that function was used in the multiple expressions.
Suppose that we want to look at the sales of specific categories:
[source,json]
----
{
"functions" : {
"clothing_sale()" : "filter(mult(price,quantity),equal(category,'Clothing'))",
"kitchen_sale()" : "filter(mult(price,quantity),equal(category,\"Kitchen\"))"
},
"expressions" : {
"max_clothing_sale" : "max(clothing_sale())"
, "med_clothing_sale" : "median(clothing_sale())"
, "max_kitchen_sale" : "max(kitchen_sale())"
, "med_kitchen_sale" : "median(kitchen_sale())"
}
}
----
=== Arguments
Instead of making a function for each category, it would be much easier to use `category` as an input to the `sale()` function.
An example of this functionality is shown below:
[source,json]
.Example Function with Arguments
----
{
"functions" : {
"sale(cat)" : "filter(mult(price,quantity),equal(category,cat))"
},
"expressions" : {
"max_clothing_sale" : "max(sale(\"Clothing\"))"
, "med_clothing_sale" : "median(sale('Clothing'))"
, "max_kitchen_sale" : "max(sale(\"Kitchen\"))"
, "med_kitchen_sale" : "median(sale('Kitchen'))"
}
}
----
Variable Functions can take any number of arguments and use them in the function expression as if they were a field or constant.
=== Variable Length Arguments
There are analytics functions that take a variable amount of parameters.
Therefore there are use cases where variable functions would need to take a variable amount of parameters.
For example, maybe there are multiple, yet undetermined, number of components to the price of a product.
Functions can take a variable length of parameters if the last parameter is followed by `..`
[source,json]
.Example Function with a Variable Length Argument
----
{
"functions" : {
"sale(cat, costs..)" : "filter(mult(add(costs),quantity),equal(category,cat))"
},
"expressions" : {
"max_clothing_sale" : "max(sale('Clothing', material, tariff, tax))"
, "med_clothing_sale" : "median(sale('Clothing', material, tariff, tax))"
, "max_kitchen_sale" : "max(sale('Kitchen', material, construction))"
, "med_kitchen_sale" : "median(sale('Kitchen', material, construction))"
}
}
----
In the above example a variable length argument is used to encapsulate all of the costs to use for a product.
There is no definite number of arguments requested for the variable length parameter, therefore the clothing expressions can use 3 and the kitchen expressions can use 2.
When the `sale()` function is called, `costs` is expanded to the arguments given.
Therefore in the above request, inside of the `sale` function:
* `add(costs)`
is expanded to both of the following:
* `add(material, tariff, tax)`
* `add(material, construction)`
=== For-Each Functions
[CAUTION]
.Advanced Functionality
====
The following function details are for advanced requests.
====
Although the above functionality allows for an undefined number of arguments to be passed to a function, it does not allow for interacting with those arguments.
Many times we might want to wrap each argument in additional functions.
For example maybe we want to be able to look at multiple categories at the same time.
So we want to see if `category EQUALS x *OR* category EQUALS y` and so on.
In order to do this we need to use for-each lambda functions, which transform each value of the variable length parameter.
The for-each is started with the `:` character after the variable length parameter.
[source,json]
.Example Function with a For-Each
----
{
"functions" : {
"sale(cats..)" : "filter(mult(price,quantity),or(cats:equal(category,_)))"
},
"expressions" : {
"max_sale_1" : "max(sale('Clothing', 'Kitchen'))"
, "med_sale_1" : "median(sale('Clothing', 'Kitchen'))"
, "max_sale_2" : "max(sale('Electronics', 'Entertainment', 'Travel'))"
, "med_sale_2" : "median(sale('Electronics', 'Entertainment', 'Travel'))"
}
}
----
In this example, `cats:` is the syntax that starts a for-each lambda function over every parameter `cats`, and the `\_` character is used to refer to the value of `cats` in each iteration in the for-each.
When `sale("Clothing", "Kitchen")` is called, the lambda function `equal(category,_)` is applied to both Clothing and Kitchen inside of the `or()` function.
Using all of these rules, the expression:
[source,text]
`sale("Clothing","Kitchen")`
is expanded to:
[source,text]
`filter(mult(price,quantity),or(equal(category,"Kitchen"),equal(category,"Clothing")))`
by the expression parser.
== Groupings And Facets
Facets, much like in other parts of Solr, allow analytics results to be broken up and grouped by attributes of the data that the expressions are being calculated over.
The currently available facets for use in the analytics component are Value Facets, Pivot Facets, Range Facets and Query Facets.
Each facet is required to have a unique name within the grouping it is defined in, and no facet can be defined outside of a grouping.
Groupings allow users to calculate the same grouping of expressions over a set of facets.
Groupings must have both `expressions` and `facets` given.
[source,json]
.Example Base Facet Request
----
{
"functions" : {
"sale()" : "mult(price,quantity)"
},
"groupings" : {
"sales_numbers" : {
"expressions" : {
"max_sale" : "max(sale())",
"med_sale" : "median(sale())"
},
"facets" : {
"<name>" : "< facet request >"
}
}
}
}
----
[source,json]
.Example Base Facet Response
----
{
"analytics_response" : {
"groupings" : {
"sales_numbers" : {
"<name>" : "< facet response >"
}
}
}
}
----
=== Facet Sorting
Some Analytics facets allow for complex sorting of their results.
The two current sortable facets are <<value-facets, Analytic Value Facets>> and <<analytic-pivot-facets, Analytic Pivot Facets>>.
==== Parameters
`criteria`::
The list of criteria to sort the facet by.
+
It takes the following parameters:
`type`::: The type of sort. There are two possible values:
* `expression`: Sort by the value of an expression defined in the same grouping.
* `facetvalue`: Sort by the string-representation of the facet value.
`Direction`:::
_(Optional)_ The direction to sort.
* `ascending` _(Default)_
* `descending`
`expression`:::
When `type = expression`, the name of an expression defined in the same grouping.
`limit`::
Limit the number of returned facet values to the top _N_. _(Optional)_
`offset`::
When a limit is set, skip the top _N_ facet values. _(Optional)_
[source,json]
.Example Sort Request
----
{
"criteria" : [
{
"type" : "expression",
"expression" : "max_sale",
"direction" : "ascending"
},
{
"type" : "facetvalue",
"direction" : "descending"
}
],
"limit" : 10,
"offset" : 5
}
----
=== Value Facets
Value Facets are used to group documents by the value of a mapping expression applied to each document.
Mapping expressions are expressions that do not include a reduction function.
For more information, refer to the <<expression-components, Expressions section>>.
* `mult(quantity, sum(price, tax))`: breakup documents by the revenue generated
* `fillmissing(state, "N/A")`: breakup documents by state, where N/A is used when the document doesn't contain a state
Value Facets can be sorted.
==== Parameters
`expression`:: The expression to choose a facet bucket for each document.
`sort`:: A <<Facet Sorting,sort>> for the results of the pivot.
[NOTE]
.Optional Parameters
The `sort` parameter is optional.
[source,json]
.Example Value Facet Request
----
{
"type" : "value",
"expression" : "fillmissing(category,'No Category')",
"sort" : {}
}
----
[source,json]
.Example Value Facet Response
----
[
{ "..." : "..." },
{
"value" : "Electronics",
"results" : {
"max_sale" : 103.75,
"med_sale" : 15.5
}
},
{
"value" : "Kitchen",
"results" : {
"max_sale" : 88.25,
"med_sale" : 11.37
}
},
{ "..." : "..." }
]
----
[NOTE]
.Field Facets
This is a replacement for Field Facets in the original Analytics Component.
Field Facet functionality is maintained in Value Facets by using the name of a field as the expression.
=== Analytic Pivot Facets
Pivot Facets are used to group documents by the value of multiple mapping expressions applied to each document.
Pivot Facets work much like layers of <<value-facets,Analytic Value Facets>>.
A list of pivots is required, and the order of the list directly impacts the results returned.
The first pivot given will be treated like a normal value facet.
The second pivot given will be treated like one value facet for each value of the first pivot.
Each of these second-level value facets will be limited to the documents in their first-level facet bucket.
This continues for however many pivots are provided.
Sorting is enabled on a per-pivot basis. This means that if your top pivot has a sort with `limit:1`, then only that first value of the facet will be drilled down into. Sorting in each pivot is independent of the other pivots.
==== Parameters
`pivots`:: The list of pivots to calculate a drill-down facet for. The list is ordered by top-most to bottom-most level.
`name`::: The name of the pivot.
`expression`::: The expression to choose a facet bucket for each document.
`sort`::: A <<Facet Sorting,sort>> for the results of the pivot.
[NOTE]
.Optional Parameters
The `sort` parameter within the pivot object is optional, and can be given in any, none or all of the provided pivots.
[source,json]
.Example Pivot Facet Request
----
{
"type" : "pivot",
"pivots" : [
{
"name" : "country",
"expression" : "country",
"sort" : {}
},
{
"name" : "state",
"expression" : "fillmissing(state, fillmissing(providence, territory))"
},
{
"name" : "city",
"expression" : "fillmissing(city, 'N/A')",
"sort" : {}
}
]
}
----
[source,json]
.Example Pivot Facet Response
----
[
{ "..." : "..." },
{
"pivot" : "Country",
"value" : "USA",
"results" : {
"max_sale" : 103.75,
"med_sale" : 15.5
},
"children" : [
{ "..." : "..." },
{
"pivot" : "State",
"value" : "Texas",
"results" : {
"max_sale" : 99.2,
"med_sale" : 20.35
},
"children" : [
{ "..." : "..." },
{
"pivot" : "City",
"value" : "Austin",
"results" : {
"max_sale" : 94.34,
"med_sale" : 17.60
}
},
{ "..." : "..." }
]
},
{ "..." : "..." }
]
},
{ "..." : "..." }
]
----
=== Analytics Range Facets
Range Facets are used to group documents by the value of a field into a given set of ranges.
The inputs for analytics range facets are identical to those used for Solr range facets.
Refer to the <<faceting.adoc#range-faceting,Range Facet documentation>> for additional questions regarding use.
==== Parameters
`field`:: Field to be faceted over
`start`:: The bottom end of the range
`end`:: The top end of the range
`gap`:: A list of range gaps to generate facet buckets. If the buckets do not add up to fit the `start` to `end` range,
then the last `gap` value will repeated as many times as needed to fill any unused range.
`hardend`:: Whether to cutoff the last facet bucket range at the `end` value if it spills over. Defaults to `false`.
`include`:: The boundaries to include in the facet buckets. Defaults to `lower`.
* `lower` - All gap-based ranges include their lower bound.
* `upper` - All gap-based ranges include their upper bound.
* `edge` - The first and last gap ranges include their edge bounds (lower for the first one, upper for the last one) even if the corresponding upper/lower option is not specified.
* `outer` - The `before` and `after` ranges will be inclusive of their bounds, even if the first or last ranges already include those boundaries.
* `all` - Includes all options: `lower`, `upper`, `edge`, and `outer`
`others`:: Additional ranges to include in the facet. Defaults to `none`.
* `before` - All records with field values lower then lower bound of the first range.
* `after` - All records with field values greater then the upper bound of the last range.
* `between` - All records with field values between the lower bound of the first range and the upper bound of the last range.
* `none` - Include facet buckets for none of the above.
* `all` - Include facet buckets for `before`, `after` and `between`.
[NOTE]
.Optional Parameters
The `hardend`, `include` and `others` parameters are all optional.
[source,json]
.Example Range Facet Request
----
{
"type" : "range",
"field" : "price",
"start" : "0",
"end" : "100",
"gap" : [
"5",
"10",
"10",
"25"
],
"hardend" : true,
"include" : [
"lower",
"upper"
],
"others" : [
"after",
"between"
]
}
----
[source,json]
.Example Range Facet Response
----
[
{
"value" : "[0 TO 5]",
"results" : {
"max_sale" : 4.75,
"med_sale" : 3.45
}
},
{
"value" : "[5 TO 15]",
"results" : {
"max_sale" : 13.25,
"med_sale" : 10.20
}
},
{
"value" : "[15 TO 25]",
"results" : {
"max_sale" : 22.75,
"med_sale" : 18.50
}
},
{
"value" : "[25 TO 50]",
"results" : {
"max_sale" : 47.55,
"med_sale" : 30.33
}
},
{
"value" : "[50 TO 75]",
"results" : {
"max_sale" : 70.25,
"med_sale" : 64.54
}
},
{ "..." : "..." }
]
----
=== Query Facets
Query Facets are used to group documents by given set of queries.
==== Parameters
`queries`:: The list of queries to facet by.
[source,json]
.Example Query Facet Request
----
{
"type" : "query",
"queries" : {
"high_quantity" : "quantity:[ 5 TO 14 ] AND price:[ 100 TO * ]",
"low_quantity" : "quantity:[ 1 TO 4 ] AND price:[ 100 TO * ]"
}
}
----
[source,json]
.Example Query Facet Response
----
[
{
"value" : "high_quantity",
"results" : {
"max_sale" : 4.75,
"med_sale" : 3.45
}
},
{
"value" : "low_quantity",
"results" : {
"max_sale" : 13.25,
"med_sale" : 10.20
}
}
]
----

3
solr/solr-ref-guide/src/searching.adoc
View File

@ -1,5 +1,5 @@
= Searching = Searching
:page-children: overview-of-searching-in-solr, velocity-search-ui, relevance, query-syntax-and-parsing, json-request-api, faceting, highlighting, spell-checking, query-re-ranking, transforming-result-documents, suggester, morelikethis, pagination-of-results, collapse-and-expand-results, result-grouping, result-clustering, spatial-search, the-terms-component, the-term-vector-component, the-stats-component, the-query-elevation-component, response-writers, near-real-time-searching, realtime-get, exporting-result-sets, streaming-expressions, parallel-sql-interface :page-children: overview-of-searching-in-solr, velocity-search-ui, relevance, query-syntax-and-parsing, json-request-api, faceting, highlighting, spell-checking, query-re-ranking, transforming-result-documents, suggester, morelikethis, pagination-of-results, collapse-and-expand-results, result-grouping, result-clustering, spatial-search, the-terms-component, the-term-vector-component, the-stats-component, the-query-elevation-component, response-writers, near-real-time-searching, realtime-get, exporting-result-sets, streaming-expressions, parallel-sql-interface, analytics
// Licensed to the Apache Software Foundation (ASF) under one // Licensed to the Apache Software Foundation (ASF) under one
// or more contributor license agreements. See the NOTICE file // or more contributor license agreements. See the NOTICE file
// distributed with this work for additional information // distributed with this work for additional information
@ -55,3 +55,4 @@ This section describes how Solr works with search requests. It covers the follow
* <<exporting-result-sets.adoc#exporting-result-sets,Exporting Result Sets>>: Functionality to export large result sets out of Solr. * <<exporting-result-sets.adoc#exporting-result-sets,Exporting Result Sets>>: Functionality to export large result sets out of Solr.
* <<streaming-expressions.adoc#streaming-expressions,Streaming Expressions>>: A stream processing language for Solr, with a suite of functions to perform many types of queries and parallel execution tasks. * <<streaming-expressions.adoc#streaming-expressions,Streaming Expressions>>: A stream processing language for Solr, with a suite of functions to perform many types of queries and parallel execution tasks.
* <<parallel-sql-interface.adoc#parallel-sql-interface,Parallel SQL Interface>>: An interface for sending SQL statements to Solr, and using advanced parallel query processing and relational algebra for complex data analysis. * <<parallel-sql-interface.adoc#parallel-sql-interface,Parallel SQL Interface>>: An interface for sending SQL statements to Solr, and using advanced parallel query processing and relational algebra for complex data analysis.
* <<analytics.adoc#analytics,The Analytics Component>>: A framework to compute complex analytics over a result set.