# Querying collections in SQL-like style

version 4.0.2

Groovy’s `groovy-ginq` module provides a higher-level abstraction over collections. It could perform queries against in-memory collections of objects in SQL-like style. Also, querying XML, JSON, YAML, etc. could also be supported because they can be parsed into collections. As GORM and jOOQ are powerful enough to support querying DB, we will cover collections first.

## 1. GINQ a.k.a. Groovy-Integrated Query

GINQ is a DSL for querying with SQL-like syntax, which consists of the following structure:

    GQ, i.e. abbreviation for GINQ
    |__ from
    |   |__ <data_source_alias> in <data_source>
    |__ [join/innerjoin/leftjoin/rightjoin/fulljoin/crossjoin]*
    |   |__ <data_source_alias> in <data_source>
    |   |__ on <condition> ((&& | ||) <condition>)* (NOTE: `crossjoin` does not need `on` clause)
    |__ [where]
    |   |__ <condition> ((&& | ||) <condition>)*
    |__ [groupby]
    |   |__ <expression> [as <alias>] (, <expression> [as <alias>])*
    |   |__ [having]
    |       |__ <condition> ((&& | ||) <condition>)*
    |__ [orderby]
    |   |__ <expression> [in (asc|desc)] (, <expression> [in (asc|desc)])*
    |__ [limit]
    |   |__ [<offset>,] <size>
    |__ select
        |__ <expression> [as <alias>] (, <expression> [as <alias>])*

| `[]` means the related clause is optional, `*` means zero or more times, and `+` means one or more times. Also, the clauses of GINQ are order sensitive, so the order of clauses should be kept as the above structure

As we could see, the simplest GINQ consists of a `from` clause and a `select` clause, which looks like:

    from n in [0, 1, 2]
    select n

| ONLY ONE `from` clause is required in GINQ. Also, GINQ supports multiple data sources through `from` and the related joins.

As a DSL, GINQ should be wrapped with the following block to be executed:

    GQ { /* GINQ CODE */ }

For example,

    def numbers = [0, 1, 2]
    assert [0, 1, 2] == GQ {
        from n in numbers
        select n
    }.toList()

    import java.util.stream.Collectors

    def numbers = [0, 1, 2]
    assert '0#1#2' == GQ {
        from n in numbers
        select n
    }.stream()
        .map(e -> String.valueOf(e))
        .collect(Collectors.joining('#'))

And it is strongly recommended to use `def` to define the variable for the result of GINQ execution, which is a `Queryable` instance that is lazy.

    def result = GQ {
        /* GINQ CODE */
    }
    def stream = result.stream() // get the stream from GINQ result
    def list = result.toList() // get the list from GINQ result

| Currently GINQ can not work well when STC is enabled.

Also, GINQ could be written in a method marked with `@GQ`:

    @GQ
    def someGinqMethod() {
        /* GINQ CODE */
    }

For example,

- Mark the `ginq` method as a GINQ method with `@GQ` annotation:

    @groovy.ginq.transform.GQ
    def ginq(list, b, e) {
        from n in list
        where b < n && n < e
        select n
    }

    assert [3, 4] == ginq([1, 2, 3, 4, 5, 6], 2, 5).toList()

- Specify the result type as `List`:

    import groovy.ginq.transform.GQ

    @GQ(List)
    def ginq(b, e) {
        from n in [1, 2, 3, 4, 5, 6]
        where b < n && n < e
        select n
    }

    assert [3, 4] == ginq(2, 5)

| GINQ supports many result types, e.g. `List`, `Set`, `Collection`, `Iterable`, `Iterator`, `java.util.stream.Stream` and array types.

- Enable parallel querying:

    import groovy.ginq.transform.GQ

    @GQ(parallel=true)
    def ginq(x) {
        from n in [1, 2, 3]
        where n < x
        select n
    }

    assert [1] == ginq(2).toList()

### 1.1. GINQ Syntax

#### 1.1.1. Data Source

The data source for GINQ could be specified by `from` clause, which is equivalent to SQL’s `FROM`. Currently GINQ supports `Iterable`, `Stream`, array and GINQ result set as its data source:

##### `Iterable` Data Source

    from n in [1, 2, 3] select n

##### `Stream` Data Source

    from n in [1, 2, 3].stream() select n

##### Array Data Source

    from n in new int[] {1, 2, 3} select n

##### GINQ Result Set Data Source

    def vt = GQ {from m in [1, 2, 3] select m}
    assert [1, 2, 3] == GQ {
        from n in vt select n
    }.toList()

#### 1.1.2. Projection

The column names could be renamed with `as` clause:

    def result = GQ {
        from n in [1, 2, 3]
        select Math.pow(n, 2) as powerOfN
    }
    assert [[1, 1], [4, 4], [9, 9]] == result.stream().map(r -> [r[0], r.powerOfN]).toList()

| The renamed column could be referenced by its new name, e.g. `r.powerOfN`. Also, it could be referenced by its index, e.g. `r[0]`

    assert [[1, 1], [2, 4], [3, 9]] == GQ {
        from v in (
            from n in [1, 2, 3]
            select n, Math.pow(n, 2) as powerOfN
        )
        select v.n, v.powerOfN
    }.toList()

| `select P1, P2, …​, Pn` is a simplifed syntax of `select new NamedRecord(P1, P2, …​, Pn)` when and only when `n` >= 2. Also, `NamedRecord` instance will be created if `as` clause is used. The values stored in the `NamedRecord` could be referenced by their names.

Construct new objects as column values:

    @groovy.transform.EqualsAndHashCode
    class Person {
        String name
        Person(String name) {
            this.name = name
        }
    }
    def persons = [new Person('Daniel'), new Person('Paul'), new Person('Eric')]
    assert persons == GQ {
        from n in ['Daniel', 'Paul', 'Eric']
        select new Person(n)
    }.toList()

##### Distinct

`distinct` is equivalent to SQL’s `DISTINCT`

    def result = GQ {
        from n in [1, 2, 2, 3, 3, 3]
        select distinct(n)
    }
    assert [1, 2, 3] == result.toList()

    def result = GQ {
        from n in [1, 2, 2, 3, 3, 3]
        select distinct(n, n + 1)
    }
    assert [[1, 2], [2, 3], [3, 4]] == result.toList()

#### 1.1.3. Filtering

`where` is equivalent to SQL’s `WHERE`

    from n in [0, 1, 2, 3, 4, 5]
    where n > 0 && n <= 3
    select n * 2

##### In

    from n in [0, 1, 2]
    where n in [1, 2]
    select n

    from n in [0, 1, 2]
    where n in (
        from m in [1, 2]
        select m
    )
    select n

    import static groovy.lang.Tuple.tuple
    assert [0, 1] == GQ {
        from n in [0, 1, 2]
        where tuple(n, n + 1) in (
            from m in [1, 2]
            select m - 1, m
        )
        select n
    }.toList()

##### Not In

    from n in [0, 1, 2]
    where n !in [1, 2]
    select n

    from n in [0, 1, 2]
    where n !in (
        from m in [1, 2]
        select m
    )
    select n

    import static groovy.lang.Tuple.tuple
    assert [2] == GQ {
        from n in [0, 1, 2]
        where tuple(n, n + 1) !in (
            from m in [1, 2]
            select m - 1, m
        )
        select n
    }.toList()

##### Exists

    from n in [1, 2, 3]
    where (
        from m in [2, 3]
        where m == n
        select m
    ).exists()
    select n

##### Not Exists

    from n in [1, 2, 3]
    where !(
        from m in [2, 3]
        where m == n
        select m
    ).exists()
    select n

#### 1.1.4. Joining

More data sources for GINQ could be specified by join clauses.

    from n1 in [1, 2, 3]
    join n2 in [1, 3] on n1 == n2
    select n1, n2

| `join` is preferred over `innerjoin` and `innerhashjoin` as it has better readability, and it is smart enough to choose the correct concrete join(i.e. `innerjoin` or `innerhashjoin`) by its `on` clause.

    from n1 in [1, 2, 3]
    innerjoin n2 in [1, 3] on n1 == n2
    select n1, n2

    from n1 in [1, 2, 3]
    leftjoin n2 in [2, 3, 4] on n1 == n2
    select n1, n2

    from n1 in [2, 3, 4]
    rightjoin n2 in [1, 2, 3] on n1 == n2
    select n1, n2

    from n1 in [1, 2, 3]
    fulljoin n2 in [2, 3, 4] on n1 == n2
    select n1, n2

    from n1 in [1, 2, 3]
    crossjoin n2 in [3, 4, 5]
    select n1, n2

hash join is especially efficient when data sources contain lots of objects

    from n1 in [1, 2, 3]
    innerhashjoin n2 in [1, 3] on n1 == n2
    select n1, n2

    from n1 in [1, 2, 3]
    lefthashjoin n2 in [2, 3, 4] on n1 == n2
    select n1, n2

    from n1 in [2, 3, 4]
    righthashjoin n2 in [1, 2, 3] on n1 == n2
    select n1, n2

    from n1 in [1, 2, 3]
    fullhashjoin n2 in [2, 3, 4] on n1 == n2
    select n1, n2

| Only binary expressions(`==`, `&&`) are allowed in the `on` clause of hash join

#### 1.1.5. Grouping

`groupby` is equivalent to SQL’s `GROUP BY`, and `having` is equivalent to SQL’s `HAVING`

    from n in [1, 1, 3, 3, 6, 6, 6]
    groupby n
    select n, count(n)

    from n in [1, 1, 3, 3, 6, 6, 6]
    groupby n
    having n >= 3
    select n, count(n)

    from n in [1, 1, 3, 3, 6, 6, 6]
    groupby n
    having count() < 3
    select n, count()

The group columns could be renamed with `as` clause:

    from s in ['ab', 'ac', 'bd', 'acd', 'bcd', 'bef']
    groupby s.size() as length, s[0] as firstChar
    select length, firstChar, max(s)

    from s in ['ab', 'ac', 'bd', 'acd', 'bcd', 'bef']
    groupby s.size() as length, s[0] as firstChar
    having length == 3 && firstChar == 'b'
    select length, firstChar, max(s)

##### Aggregate Functions

GINQ provides some built-in aggregate functions:

Function | Argument Type(s) | Return Type | Description
--- | --- | --- | ---
count() |  | java.lang.Long | number of rows, similar to `count(*)` in SQL
count(expression) | any | java.lang.Long | number of rows for which the value of expression is not `null`
min(expression) | java.lang.Comparable | same as argument type | minimum value of expression across all non-null values
max(expression) | java.lang.Comparable | same as argument type | maximum value of expression across all non-null values
sum(expression) | java.lang.Number | java.math.BigDecimal | sum of expression across all non-null values
avg(expression) | java.lang.Number | java.math.BigDecimal | the average (arithmetic mean) of all non-null values
median(expression) | java.lang.Number | java.math.BigDecimal | value such that the number of non-null values above and below it is the same ("middle" value, not necessarily same as average or mean)
stdev(expression) | java.lang.Number | java.math.BigDecimal | the statistical standard deviation of all non-null values
stdevp(expression) | java.lang.Number | java.math.BigDecimal | the statistical standard deviation for the population for all non-null values
var(expression) | java.lang.Number | java.math.BigDecimal | the statistical variance of all non-null values
varp(expression) | java.lang.Number | java.math.BigDecimal | the statistical variance for the population for all non-null values
agg(expression) | any | any | customizes the aggregation logic in expression and returns single value

    from n in [1, 1, 3, 3, 6, 6, 6]
    groupby n
    select n, count()

    from s in ['a', 'b', 'cd', 'ef']
    groupby s.size() as length
    select length, min(s)

    from s in ['a', 'b', 'cd', 'ef']
    groupby s.size() as length
    select length, max(s)

    from n in [1, 1, 3, 3, 6, 6, 6]
    groupby n
    select n, sum(n)

    from n in [1, 1, 3, 3, 6, 6, 6]
    groupby n
    select n, avg(n)

    from n in [1, 1, 3, 3, 6, 6, 6]
    groupby n
    select n, median(n)

    from n in [1, 1, 3, 3, 6, 6, 6]
    groupby n
    select n, agg(_g.stream().map(r -> r.n).reduce(BigDecimal.ZERO, BigDecimal::add))

| `_g` is an implicit variable for `agg` aggregate function, it represents the grouped `Queryable` object and its record(e.g. `r`) could reference the data source by alias(e.g. `n`)

    from fruit in ['Apple', 'Apricot', 'Banana', 'Cantaloupe']
    groupby fruit.substring(0, 1) as firstChar
    select firstChar, agg(_g.stream().map(r -> r.fruit).toList()) as fruit_list

Also, we could apply the aggregate functions for the whole GINQ result, i.e. no `groupby` clause is needed:

    assert [3] == GQ {
        from n in [1, 2, 3]
        select max(n)
    }.toList()

    assert [[1, 3, 2, 2, 6, 3, 3, 6]] == GQ {
        from n in [1, 2, 3]
        select min(n), max(n), avg(n), median(n), sum(n), count(n), count(),
                agg(_g.stream().map(r -> r.n).reduce(BigDecimal.ZERO, BigDecimal::add))
    }.toList()

    assert [0.816496580927726] == GQ {
        from n in [1, 2, 3]
        select stdev(n)
    }.toList()

    assert [1] == GQ {
        from n in [1, 2, 3]
        select stdevp(n)
    }.toList()

    assert [0.6666666666666667] == GQ {
        from n in [1, 2, 3]
        select var(n)
    }.toList()

    assert [1] == GQ {
        from n in [1, 2, 3]
        select varp(n)
    }.toList()

#### 1.1.6. Sorting

`orderby` is equivalent to SQL’s `ORDER BY`

    from n in [1, 5, 2, 6]
    orderby n
    select n

| `in asc` is optional when sorting `in` ascending order

    from n in [1, 5, 2, 6]
    orderby n in asc
    select n

    from n in [1, 5, 2, 6]
    orderby n in desc
    select n

    from s in ['a', 'b', 'ef', 'cd']
    orderby s.length() in desc, s in asc
    select s

    from s in ['a', 'b', 'ef', 'cd']
    orderby s.length() in desc, s
    select s

    from n in [1, null, 5, null, 2, 6]
    orderby n in asc(nullslast)
    select n

| `nullslast` is equivalent to SQL’s `NULLS LAST` and applied by default. `nullsfirst` is equivalent to SQL’s `NULLS FIRST`.

    from n in [1, null, 5, null, 2, 6]
    orderby n in asc(nullsfirst)
    select n

    from n in [1, null, 5, null, 2, 6]
    orderby n in desc(nullslast)
    select n

    from n in [1, null, 5, null, 2, 6]
    orderby n in desc(nullsfirst)
    select n

#### 1.1.7. Pagination

`limit` is similar to the `limit` clause of MySQL, which could specify the `offset`(first argument) and `size`(second argument) for paginating, or just specify the only one argument as `size`

    from n in [1, 2, 3, 4, 5]
    limit 3
    select n

    from n in [1, 2, 3, 4, 5]
    limit 1, 3
    select n

#### 1.1.8. Nested GINQ

##### Nested GINQ in `from` clause

    from v in (
        from n in [1, 2, 3]
        select n
    )
    select v

##### Nested GINQ in `where` clause

    from n in [0, 1, 2]
    where n in (
        from m in [1, 2]
        select m
    )
    select n

    from n in [0, 1, 2]
    where (
        from m in [1, 2]
        where m == n
        select m
    ).exists()
    select n

##### Nested GINQ in `select` clause

    assert [null, 2, 3] == GQ {
        from n in [1, 2, 3]
        select (
            from m in [2, 3, 4]
            where m == n
            limit 1
            select m
        )
    }.toList()

| It’s recommended to use `limit 1` to restrict the count of sub-query result because `TooManyValuesException` will be thrown if more than one values returned

We could use `as` clause to name the sub-query result

    assert [[1, null], [2, 2], [3, 3]] == GQ {
        from n in [1, 2, 3]
        select n, (
            from m in [2, 3, 4]
            where m == n
            select m
        ) as sqr
    }.toList()

#### 1.1.9. Window Functions

Window can be defined by `partitionby`, `orderby`, `rows` and `range`:

    over(
        [partitionby <expression> (, <expression>)*]
        [orderby <expression> (, <expression>)*
           [rows <lower>, <upper> | range <lower>, <upper>]]
    )

- `0` used as bound of `rows` and `range` clause is equivalent to SQL’s `CURRENT ROW`, and negative means `PRECEDING`, positive means `FOLLOWING`

- `null` used as the lower bound of `rows` and `range` clause is equivalent to SQL’s `UNBOUNDED PRECEDING`

- `null` used as the upper bound of `rows` and `range` clause is equivalent to SQL’s `UNBOUNDED FOLLOWING`

Also, GINQ provides some built-in window functions:

Function | Argument Type(s) | Return Type | Description
--- | --- | --- | ---
rowNumber() |  | java.lang.Long | number of the current row within its partition, counting from `0`
rank() |  | java.lang.Long | rank of the current row with gaps
denseRank() |  | java.lang.Long | rank of the current row without gaps
percentRank() |  | java.math.BigDecimal | relative rank of the current row: (rank - 1) / (total rows - 1)
cumeDist() |  | java.math.BigDecimal | relative rank of the current row: (number of rows preceding or peer with current row) / (total rows)
ntile(expression) | java.lang.Long | java.lang.Long | bucket index ranging from `0` to `expression - 1`, dividing the partition as equally as possible
lead(expression [, offset [, default]]) | any [, java.lang.Long [, same as expression type]] | same as expression type | returns expression evaluated at the row that is offset rows after the current row within the partition; if there is no such row, instead return default (which must be of the same type as expression). Both offset and default are evaluated with respect to the current row. If omitted, offset defaults to `1` and default to `null`
lag(expression [, offset [, default]]) | any [, java.lang.Long [, same as expression type]] | same as expression type | returns expression evaluated at the row that is offset rows before the current row within the partition; if there is no such row, instead return default (which must be of the same type as expression). Both offset and default are evaluated with respect to the current row. If omitted, offset defaults to `1` and default to `null`
firstValue(expression) | any | same type as expression | returns expression evaluated at the row that is the first row of the window frame
lastValue(expression) | any | same type as expression | returns expression evaluated at the row that is the last row of the window frame
nthValue(expression, n) | any, java.lang.Long | same type as expression | returns expression evaluated at the row that is the nth row of the window frame
count() |  | java.lang.Long | number of rows, similar to `count(*)` in SQL
count(expression) | any | java.lang.Long | number of rows for which the value of expression is not `null`
min(expression) | java.lang.Comparable | same as argument type | minimum value of expression across all non-null values
max(expression) | java.lang.Comparable | same as argument type | maximum value of expression across all non-null values
sum(expression) | java.lang.Number | java.math.BigDecimal | sum of expression across all non-null values
avg(expression) | java.lang.Number | java.math.BigDecimal | the average (arithmetic mean) of all non-null values
median(expression) | java.lang.Number | java.math.BigDecimal | value such that the number of non-null values above and below it is the same ("middle" value, not necessarily same as average or mean)
stdev(expression) | java.lang.Number | java.math.BigDecimal | the statistical standard deviation of all non-null values
stdevp(expression) | java.lang.Number | java.math.BigDecimal | the statistical standard deviation for the population for all non-null values
var(expression) | java.lang.Number | java.math.BigDecimal | the statistical variance of all non-null values
varp(expression) | java.lang.Number | java.math.BigDecimal | the statistical variance for the population for all non-null values
agg(expression) | any | any | INCUBATING: customizes the aggregation logic in expression and returns single value

##### `rowNumber`

    assert [[2, 1, 1, 1], [1, 0, 0, 2], [null, 3, 3, 3], [3, 2, 2, 0]] == GQ {
        from n in [2, 1, null, 3]
        select n, (rowNumber() over(orderby n)),
                  (rowNumber() over(orderby n in asc)),
                  (rowNumber() over(orderby n in desc))
    }.toList()

    assert [[1, 0, 1, 2, 3], [2, 1, 2, 1, 2], [null, 3, 0, 3, 0], [3, 2, 3, 0, 1]] == GQ {
        from n in [1, 2, null, 3]
        select n, (rowNumber() over(orderby n in asc(nullslast))),
                  (rowNumber() over(orderby n in asc(nullsfirst))),
                  (rowNumber() over(orderby n in desc(nullslast))),
                  (rowNumber() over(orderby n in desc(nullsfirst)))
    }.toList()

| The parentheses around the window function is required.

##### `rank`, `denseRank`, `percentRank`, `cumeDist` and `ntile`

    assert [['a', 1, 1], ['b', 2, 2], ['b', 2, 2],
            ['c', 4, 3], ['c', 4, 3], ['d', 6, 4],
            ['e', 7, 5]] == GQ {
        from s in ['a', 'b', 'b', 'c', 'c', 'd', 'e']
        select s,
            (rank() over(orderby s)),
            (denseRank() over(orderby s))
    }.toList()

    assert [[60, 0, 0.4], [60, 0, 0.4], [80, 0.5, 0.8], [80, 0.5, 0.8], [100, 1, 1]] == GQ {
        from n in [60, 60, 80, 80, 100]
        select n,
            (percentRank() over(orderby n)),
            (cumeDist() over(orderby n))
    }.toList()

    assert [[1, 0], [2, 0], [3, 0],
            [4, 1], [5, 1],
            [6, 2], [7, 2],[8, 2],
            [9, 3], [10, 3]] == GQ {
        from n in 1..10
        select n, (ntile(4) over(orderby n))
    }.toList()

##### `lead` and `lag`

    assert [[2, 3], [1, 2], [3, null]] == GQ {
        from n in [2, 1, 3]
        select n, (lead(n) over(orderby n))
    }.toList()

    assert [[2, 3], [1, 2], [3, null]] == GQ {
        from n in [2, 1, 3]
        select n, (lead(n) over(orderby n in asc))
    }.toList()

    assert [['a', 'bc'], ['ab', null], ['b', 'a'], ['bc', 'ab']] == GQ {
        from s in ['a', 'ab', 'b', 'bc']
        select s, (lead(s) over(orderby s.length(), s in desc))
    }.toList()

    assert [['a', null], ['ab', null], ['b', 'a'], ['bc', 'ab']] == GQ {
        from s in ['a', 'ab', 'b', 'bc']
        select s, (lead(s) over(partitionby s.length() orderby s.length(), s in desc))
    }.toList()

    assert [[2, 1], [1, null], [3, 2]] == GQ {
        from n in [2, 1, 3]
        select n, (lag(n) over(orderby n))
    }.toList()

    assert [[2, 3], [1, 2], [3, null]] == GQ {
        from n in [2, 1, 3]
        select n, (lag(n) over(orderby n in desc))
    }.toList()

    assert [['a', null], ['b', 'a'], ['aa', null], ['bb', 'aa']] == GQ {
        from s in ['a', 'b', 'aa', 'bb']
        select s, (lag(s) over(partitionby s.length() orderby s))
    }.toList()

    assert [[2, 3, 1], [1, 2, null], [3, null, 2]] == GQ {
        from n in [2, 1, 3]
        select n, (lead(n) over(orderby n)), (lag(n) over(orderby n))
    }.toList()

The offset can be specified other than the default offset `1`:

    assert [[2, null, null], [1, 3, null], [3, null, 1]] == GQ {
        from n in [2, 1, 3]
        select n, (lead(n, 2) over(orderby n)), (lag(n, 2) over(orderby n))
    }.toList()

The default value can be returned when the index specified by offset is out of window, e.g. `'NONE'`:

    assert [[2, 'NONE', 'NONE'], [1, 3, 'NONE'], [3, 'NONE', 1]] == GQ {
        from n in [2, 1, 3]
        select n, (lead(n, 2, 'NONE') over(orderby n)), (lag(n, 2, 'NONE') over(orderby n))
    }.toList()

##### `firstValue`, `lastValue` and `nthValue`

    assert [[2, 1], [1, 1], [3, 2]] == GQ {
        from n in [2, 1, 3]
        select n, (firstValue(n) over(orderby n rows -1, 1))
    }.toList()

    assert [[2, 3], [1, 2], [3, 3]] == GQ {
        from n in [2, 1, 3]
        select n, (lastValue(n) over(orderby n rows -1, 1))
    }.toList()

    assert [[2, 2], [1, 1], [3, 3]] == GQ {
        from n in [2, 1, 3]
        select n, (firstValue(n) over(orderby n rows 0, 1))
    }.toList()

    assert [[2, 1], [1, null], [3, 1]] == GQ {
        from n in [2, 1, 3]
        select n, (firstValue(n) over(orderby n rows -2, -1))
    }.toList()

    assert [[2, 1], [1, null], [3, 2]] == GQ {
        from n in [2, 1, 3]
        select n, (lastValue(n) over(orderby n rows -2, -1))
    }.toList()

    assert [[2, 3], [1, 3], [3, null]] == GQ {
        from n in [2, 1, 3]
        select n, (lastValue(n) over(orderby n rows 1, 2))
    }.toList()

    assert [[2, 3], [1, 2], [3, null]] == GQ {
        from n in [2, 1, 3]
        select n, (firstValue(n) over(orderby n rows 1, 2))
    }.toList()

    assert [[2, 2], [1, 1], [3, 3]] == GQ {
        from n in [2, 1, 3]
        select n, (lastValue(n) over(orderby n rows -1, 0))
    }.toList()

    assert [[2, 1], [1, 1], [3, 1]] == GQ {
        from n in [2, 1, 3]
        select n, (firstValue(n) over(orderby n rows null, 1))
    }.toList()

    assert [[2, 3], [1, 3], [3, 3]] == GQ {
        from n in [2, 1, 3]
        select n, (lastValue(n) over(orderby n rows -1, null))
    }.toList()

    assert [['a', 'a', 'b'], ['aa', 'aa', 'bb'], ['b', 'a', 'b'], ['bb', 'aa', 'bb']] == GQ {
        from s in ['a', 'aa', 'b', 'bb']
        select s, (firstValue(s) over(partitionby s.length() orderby s)),
                (lastValue(s) over(partitionby s.length() orderby s))
    }.toList()

    assert [[1, 1, 2, 3, null], [2, 1, 2, 3, null], [3, 1, 2, 3, null]] == GQ {
        from n in 1..3
        select n, (nthValue(n, 0) over(orderby n)),
                  (nthValue(n, 1) over(orderby n)),
                  (nthValue(n, 2) over(orderby n)),
                  (nthValue(n, 3) over(orderby n))
    }.toList()

##### `min`, `max`, `count`, `sum`, `avg`, `median`, `stdev`, `stdevp`, `var` ,`varp` and `agg`

    assert [['a', 'a', 'b'], ['b', 'a', 'b'], ['aa', 'aa', 'bb'], ['bb', 'aa', 'bb']] == GQ {
        from s in ['a', 'b', 'aa', 'bb']
        select s, (min(s) over(partitionby s.length())), (max(s) over(partitionby s.length()))
    }.toList()

    assert [[1, 2, 2, 2, 1, 1], [1, 2, 2, 2, 1, 1],
            [2, 2, 2, 4, 2, 2], [2, 2, 2, 4, 2, 2],
            [3, 2, 2, 6, 3, 3], [3, 2, 2, 6, 3, 3]] == GQ {
        from n in [1, 1, 2, 2, 3, 3]
        select n, (count() over(partitionby n)),
                  (count(n) over(partitionby n)),
                  (sum(n) over(partitionby n)),
                  (avg(n) over(partitionby n)),
                  (median(n) over(partitionby n))
    }.toList()

    assert [[2, 6, 3, 1, 3, 4], [1, 6, 3, 1, 3, 4],
            [3, 6, 3, 1, 3, 4], [null, 6, 3, 1, 3, 4]] == GQ {
        from n in [2, 1, 3, null]
        select n, (sum(n) over()),
                  (max(n) over()),
                  (min(n) over()),
                  (count(n) over()),
                  (count() over())
    }.toList()

    assert [[1, 1, 1], [2, 2, 3], [5, 2, 10], [5, 2, 10]] == GQ {
        from n in [1, 2, 5, 5]
        select n, (count() over(orderby n range -2, 0)),
                  (sum(n) over(orderby n range -2, 0))
    }.toList()

    assert [[1, 2, 3], [2, 1, 2], [5, 2, 10], [5, 2, 10]] == GQ {
        from n in [1, 2, 5, 5]
        select n, (count() over(orderby n range 0, 1)),
                  (sum(n) over(orderby n range 0, 1))
    }.toList()

    assert [[1, 2, 3], [2, 2, 3], [5, 2, 10], [5, 2, 10]] == GQ {
        from n in [1, 2, 5, 5]
        select n, (count() over(orderby n range -1, 1)),
                  (sum(n) over(orderby n range -1, 1))
    }.toList()

    assert [[1, 1, 2], [2, 0, 0], [5, 0, 0], [5, 0, 0]] == GQ {
        from n in [1, 2, 5, 5]
        select n, (count() over(orderby n in desc range 1, 2)),
                  (sum(n) over(orderby n in desc range 1, 2))
    }.toList()

    assert [[1, 0, 0], [2, 1, 1], [5, 0, 0], [5, 0, 0]] == GQ {
        from n in [1, 2, 5, 5]
        select n, (count() over(orderby n in desc range -2, -1)),
                  (sum(n) over(orderby n in desc range -2, -1))
    }.toList()

    assert [[1, 3, 12], [2, 2, 10], [5, 0, 0], [5, 0, 0]] == GQ {
        from n in [1, 2, 5, 5]
        select n, (count() over(orderby n range 1, null)),
                  (sum(n) over(orderby n range 1, null))
    }.toList()

    assert [[1, 2, 3], [2, 2, 3], [5, 4, 13], [5, 4, 13]] == GQ {
        from n in [1, 2, 5, 5]
        select n, (count() over(orderby n range null, 1)),
                  (sum(n) over(orderby n range null, 1))
    }.toList()

    assert [[1, 0.816496580927726],
            [2, 0.816496580927726],
            [3, 0.816496580927726]] == GQ {
        from n in [1, 2, 3]
        select n, (stdev(n) over())
    }.toList()

    assert [[1, 1], [2, 1], [3, 1]] == GQ {
        from n in [1, 2, 3]
        select n, (stdevp(n) over())
    }.toList()

    assert [[1, 0.6666666666666667],
            [2, 0.6666666666666667],
            [3, 0.6666666666666667]] == GQ {
        from n in [1, 2, 3]
        select n, (var(n) over())
    }.toList()

    assert [[1, 1], [2, 1], [3, 1]] == GQ {
        from n in [1, 2, 3]
        select n, (varp(n) over())
    }.toList()

    assert [[1, 4], [2, 2], [3, 4]] == GQ {
        from n in [1, 2, 3]
        select n,
               (agg(_g.stream().map(r -> r.n).reduce(BigDecimal.ZERO, BigDecimal::add)) over(partitionby n % 2))
    }.toList()

### 1.2. GINQ Tips

#### 1.2.1. Row Number

`_rn` is the implicit variable representing row number for each record in the result set. It starts with `0`

    from n in [1, 2, 3]
    select _rn, n

#### 1.2.2. List Comprehension

List comprehension is an elegant way to define and create lists based on existing lists:

    assert [4, 16, 36, 64, 100] == GQ {from n in 1..<11 where n % 2 == 0 select n ** 2}.toList()

    assert [4, 16, 36, 64, 100] == GQ {from n in 1..<11 where n % 2 == 0 select n ** 2} as List

    assert [4, 16, 36, 64, 100] == GQL {from n in 1..<11 where n % 2 == 0 select n ** 2}

| `GQL {…​}` is the abbreviation of `GQ {…​}.toList()`

GINQ could be used as list comprehension in the loops directly:

    def result = []
    for (def x : GQ {from n in 1..<11 where n % 2 == 0 select n ** 2}) {
        result << x
    }
    assert [4, 16, 36, 64, 100] == result

#### 1.2.3. Query & Update

This is like `update` statement in SQL

    import groovy.transform.*
    @TupleConstructor
    @EqualsAndHashCode
    @ToString
    class Person {
        String name
        String nickname
    }

    def linda = new Person('Linda', null)
    def david = new Person('David', null)
    def persons = [new Person('Daniel', 'ShanFengXiaoZi'), linda, david]
    def result = GQ {
        from p in persons
        where p.nickname == null
        select p
    }.stream()
        .peek(p -> { p.nickname = 'Unknown' }) // update `nickname`
        .toList()

    def expected = [new Person('Linda', 'Unknown'), new Person('David', 'Unknown')]
    assert expected == result
    assert ['Unknown', 'Unknown'] == [linda, david]*.nickname // ensure the original objects are updated

#### 1.2.4. Alternative for `with` clause

GINQ does not support `with` clause for now, but we could define a temporary variable to workaround:

    def v = GQ { from n in [1, 2, 3] where n < 3 select n }
    def result = GQ {
        from n in v
        where n > 1
        select n
    }
    assert [2] == result.toList()

#### 1.2.5. Alternative for `case-when`

`case-when` of SQL could be replaced with switch expression:

    assert ['a', 'b', 'c', 'c'] == GQ {
        from n in [1, 2, 3, 4]
        select switch (n) {
            case 1 -> 'a'
            case 2 -> 'b'
            default -> 'c'
        }
    }.toList()

#### 1.2.6. Query JSON

    import groovy.json.JsonSlurper
    def json = new JsonSlurper().parseText('''
        {
            "fruits": [
                {"name": "Orange", "price": 11},
                {"name": "Apple", "price": 6},
                {"name": "Banana", "price": 4},
                {"name": "Mongo", "price": 29},
                {"name": "Durian", "price": 32}
            ]
        }
    ''')

    def expected = [['Mongo', 29], ['Orange', 11], ['Apple', 6], ['Banana', 4]]
    assert expected == GQ {
        from f in json.fruits
        where f.price < 32
        orderby f.price in desc
        select f.name, f.price
    }.toList()

#### 1.2.7. Parallel Querying

Parallel querying is especially efficient when querying big data sources. It is disabled by default, but we could enable it by hand:

    assert [[1, 1], [2, 2], [3, 3]] == GQ(parallel: true) {
        from n1 in 1..1000
        join n2 in 1..10000 on n2 == n1
        where n1 <= 3 && n2 <= 5
        select n1, n2
    }.toList()

As parallel querying will use a shared thread pool, the following code can release resources after all GINQ statements execution are completed, and it will wait util all tasks of threads are completed.

    GQ {
        shutdown
    }

| Once `shutdown` is issued, parallel querying can not work anymore.

The following code is equivalent to the above code, in other words, `immediate` is optional:

    GQ {
        shutdown immediate
    }

Shutdown without waiting tasks to complete:

    GQ {
        shutdown abort
    }

#### 1.2.8. Customize GINQ

For advanced users, you could customize GINQ behaviour by specifying your own target code generator. For example, we could specify the qualified class name `org.apache.groovy.ginq.provider.collection.GinqAstWalker` as the target code generator to generate GINQ method calls for querying collections, which is the default behaviour of GINQ:

    assert [0, 1, 2] == GQ(astWalker: 'org.apache.groovy.ginq.provider.collection.GinqAstWalker') {
        from n in [0, 1, 2]
        select n
    }.toList()

#### 1.2.9. Optimize GINQ

GINQ optimizer is enabled by default for better performance. It will transform the GINQ AST to achieve better execution plan. We could disable it by hand:

    assert [[2, 2]] == GQ(optimize: false) {
        from n1 in [1, 2, 3]
        join n2 in [1, 2, 3] on n1 == n2
        where n1 > 1 &&  n2 < 3
        select n1, n2
    }.toList()

### 1.3. GINQ Examples

#### 1.3.1. Generate Multiplication Table

    from v in (
        from a in 1..9
        join b in 1..9 on a <= b
        select a as f, b as s, "$a * $b = ${a * b}".toString() as r
    )
    groupby v.s
    select max(v.f == 1 ? v.r : '') as v1,
           max(v.f == 2 ? v.r : '') as v2,
           max(v.f == 3 ? v.r : '') as v3,
           max(v.f == 4 ? v.r : '') as v4,
           max(v.f == 5 ? v.r : '') as v5,
           max(v.f == 6 ? v.r : '') as v6,
           max(v.f == 7 ? v.r : '') as v7,
           max(v.f == 8 ? v.r : '') as v8,
           max(v.f == 9 ? v.r : '') as v9

#### 1.3.2. More examples

link: the latest GINQ examples

| Some examples in the above link require the latest SNAPSHOT version of Groovy to run.