Builtin Functions

Numeric Functions
String Functions
Binary Functions
Spatial Functions
Similarity Functions
Tokenizing Functions
Temporal Functions
Object Functions
Aggregate Functions (Array Functions)
Comparison Functions
Type Functions
Conditional Functions
Miscellaneous Functions
Bitwise Functions
Window Functions

The system provides various classes of functions to support operations on numeric, string, spatial, and temporal data. This document explains how to use these functions.

Numeric Functions

abs

Syntax:
```
abs(numeric_value)
```
Computes the absolute value of the argument.
Arguments:
- numeric_value: a tinyint/smallint/integer/bigint/float/double value.
Return Value:
- The absolute value of the argument with the same type as the input argument,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-numeric input value will cause a type error.

Example:

{ "v1": abs(2013), "v2": abs(-4036), "v3": abs(0), "v4": abs(float("-2013.5")), "v5": abs(double("-2013.593823748327284")) };

The expected result is:

{ "v1": 2013, "v2": 4036, "v3": 0, "v4": 2013.5, "v5": 2013.5938237483274 }

acos

Syntax:
```
acos(numeric_value)
```
Computes the arc cosine value of the argument.
Arguments:
- numeric_value: a tinyint/smallint/integer/bigint/float/double value.
Return Value:
- the double arc cosine in radians for the argument, if the argument is in the range of -1 (inclusive) to 1 (inclusive),
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-numeric input value will cause a type error,
- “NaN” for other legitimate numeric values.

Example:

{ "v1": acos(1), "v2": acos(2), "v3": acos(0), "v4": acos(float("0.5")), "v5": acos(double("-0.5")) };

The expected result is:

{ "v1": 0.0, "v2": "NaN", "v3": 1.5707963267948966, "v4": 1.0471975511965979, "v5": 2.0943951023931957 }

asin

Syntax:
```
asin(numeric_value)
```
Computes the arc sine value of the argument.
Arguments:
- numeric_value: a tinyint/smallint/integer/bigint/float/double value.
Return Value:
- the double arc sin in radians for the argument, if the argument is in the range of -1 (inclusive) to 1 (inclusive),
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-numeric input value will cause a type error,
- “NaN” for other legitimate numeric values.

Example:

{ "v1": asin(1), "v2": asin(2), "v3": asin(0), "v4": asin(float("0.5")), "v5": asin(double("-0.5")) };

The expected result is:

{ "v1": 1.5707963267948966, "v2": "NaN", "v3": 0.0, "v4": 0.5235987755982989, "v5": -0.5235987755982989 }

atan

Syntax:
```
atan(numeric_value)
```
Computes the arc tangent value of the argument.
Arguments:
- numeric_value: a tinyint/smallint/integer/bigint/float/double value.
Return Value:
- the double arc tangent in radians for the argument,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-numeric input value will cause a type error.

Example:

{ "v1": atan(1), "v2": atan(2), "v3": atan(0), "v4": atan(float("0.5")), "v5": atan(double("1000")) };

The expected result is:

{ "v1": 0.7853981633974483, "v2": 1.1071487177940904, "v3": 0.0, "v4": 0.4636476090008061, "v5": 1.5697963271282298 }

atan2

Syntax:
```
atan2(numeric_value1, numeric_value2)
```
Computes the arc tangent value of numeric_value2/numeric_value1.
Arguments:
- numeric_value1: a tinyint/smallint/integer/bigint/float/double value,
- numeric_value2: a tinyint/smallint/integer/bigint/float/double value.
Return Value:
- the double arc tangent in radians for numeric_value1 and numeric_value2,
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- any other non-numeric input value will cause a type error.

Example:

{ "v1": atan2(1, 2), "v2": atan2(0, 4), "v3": atan2(float("0.5"), double("-0.5")) };

The expected result is:

{ "v1": 0.4636476090008061, "v2": 0.0, "v3": 2.356194490192345 }

ceil

Syntax:
```
ceil(numeric_value)
```
Computes the smallest (closest to negative infinity) number with no fractional part that is not less than the value of the argument. If the argument is already equal to mathematical integer, then the result is the same as the argument.
Arguments:
- numeric_value: a tinyint/smallint/integer/bigint/float/double value.
Return Value:
- The ceiling value for the given number in the same type as the input argument,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-numeric input value will cause a type error.

Example:

{
  "v1": ceil(2013),
  "v2": ceil(-4036),
  "v3": ceil(0.3),
  "v4": ceil(float("-2013.2")),
  "v5": ceil(double("-2013.893823748327284"))
};

The expected result is:

{ "v1": 2013, "v2": -4036, "v3": 1.0, "v4": -2013.0, "v5": -2013.0 }

cos

Syntax:
```
cos(numeric_value)
```
Computes the cosine value of the argument.
Arguments:
- numeric_value: a tinyint/smallint/integer/bigint/float/double value.
Return Value:
- the double cosine value for the argument,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-numeric input value will cause a type error.

Example:

{ "v1": cos(1), "v2": cos(2), "v3": cos(0), "v4": cos(float("0.5")), "v5": cos(double("1000")) };

The expected result is:

{ "v1": 0.5403023058681398, "v2": -0.4161468365471424, "v3": 1.0, "v4": 0.8775825618903728, "v5": 0.562379076290703 }

cosh

Syntax:
```
cosh(numeric_value)
```
Computes the hyperbolic cosine value of the argument.
Arguments:
- numeric_value: a tinyint/smallint/integer/bigint/float/double value.
Return Value:
- the double hyperbolic cosine value for the argument,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-numeric input value will cause a type error.

Example:

{ "v1": cosh(1), "v2": cosh(2), "v3": cosh(0), "v4": cosh(float("0.5")), "v5": cosh(double("8")) };

The expected result is:

{ "v1": 1.5430806348152437, "v2": 3.7621956910836314, "v3": 1.0, "v4": 1.1276259652063807, "v5": 1490.479161252178 }

degrees

Syntax:
```
degrees(numeric_value)
```
Converts radians to degrees
Arguments:
- numeric_value: a tinyint/smallint/integer/bigint/float/double value.
Return Value:
- The degrees value for the given radians value. The returned value has type double,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-numeric input value will cause a type error.
Example:
```
{ "v1": degrees(pi()) };
```
The expected result is:
```
{ "v1": 180.0 }
```

e

Syntax:
```
e()
```
Return Value:
- e (base of the natural logarithm)
Example:
```
{ "v1": e() };
```
The expected result is:
```
{ "v1": 2.718281828459045 }
```

exp

Syntax:
```
exp(numeric_value)
```
Computes e^{numeric_value}.
Arguments:
- numeric_value: a tinyint/smallint/integer/bigint/float/double value.
Return Value:
- e^{numeric_value},
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-numeric input value will cause a type error.

Example:

{ "v1": exp(1), "v2": exp(2), "v3": exp(0), "v4": exp(float("0.5")), "v5": exp(double("1000")) };

The expected result is:

{ "v1": 2.718281828459045, "v2": 7.38905609893065, "v3": 1.0, "v4": 1.6487212707001282, "v5": "Infinity" }

floor

Syntax:
```
floor(numeric_value)
```
Computes the largest (closest to positive infinity) number with no fractional part that is not greater than the value. If the argument is already equal to mathematical integer, then the result is the same as the argument.
Arguments:
- numeric_value: a tinyint/smallint/integer/bigint/float/double value.
Return Value:
- The floor value for the given number in the same type as the input argument,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-numeric input value will cause a type error.

Example:

{
  "v1": floor(2013),
  "v2": floor(-4036),
  "v3": floor(0.8),
  "v4": floor(float("-2013.2")),
  "v5": floor(double("-2013.893823748327284"))
};

The expected result is:

{ "v1": 2013, "v2": -4036, "v3": 0.0, "v4": -2014.0, "v5": -2014.0 }

ln

Syntax:
```
ln(numeric_value)
```
Computes log_enumeric_value.
Arguments:
- numeric_value: a tinyint/smallint/integer/bigint/float/double value.
Return Value:
- log_enumeric_value,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-numeric input value will cause a type error.

Example:

{ "v1": ln(1), "v2": ln(2), "v3": ln(0), "v4": ln(float("0.5")), "v5": ln(double("1000")) };

The expected result is:

{ "v1": 0.0, "v2": 0.6931471805599453, "v3": "-Infinity", "v4": -0.6931471805599453, "v5": 6.907755278982137 }

log

Syntax:
```
log(numeric_value)
```
Computes log₁₀numeric_value.
Arguments:
- numeric_value: a tinyint/smallint/integer/bigint/float/double value.
Return Value:
- log₁₀numeric_value,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-numeric input value will cause a type error.

Example:

{ "v1": log(1), "v2": log(2), "v3": log(0), "v4": log(float("0.5")), "v5": log(double("1000")) };

The expected result is:

{ "v1": 0.0, "v2": 0.3010299956639812, "v3": "-Infinity", "v4": -0.3010299956639812, "v5": 3.0 }

pi

Syntax:
```
pi()
```
Return Value:
- Pi
Example:
```
{ "v1": pi() };
```
The expected result is:
```
{ "v1": 3.141592653589793 }
```

power

Syntax:
```
power(numeric_value1, numeric_value2)
```
Computes numeric_value1^{numeric_value2}.
Arguments:
- numeric_value1: a tinyint/smallint/integer/bigint/float/double value,
- numeric_value2: a tinyint/smallint/integer/bigint/float/double value.
Return Value:
- numeric_value1^{numeric_value2},
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- any other non-numeric input value will cause a type error.

Example:

{ "v1": power(1, 2), "v3": power(0, 4), "v4": power(float("0.5"), double("-0.5")) };

The expected result is:

{ "v1": 1, "v3": 0, "v4": 1.4142135623730951 }

radians

Syntax:
```
radians(numeric_value)
```
Converts degrees to radians
Arguments:
- numeric_value: a tinyint/smallint/integer/bigint/float/double value.
Return Value:
- The radians value for the given degrees value. The returned value has type double,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-numeric input value will cause a type error.
Example:
```
{ "v1": radians(180) };
```
The expected result is:
```
{ "v1": 3.141592653589793 }
```

round

Syntax:
```
round(numeric_value[, round_digit])
```
Rounds the value to the given number of integer digits to the right of the decimal point, or to the left of the decimal point if the number of digits is negative.
Arguments:
- numeric_value: a tinyint/smallint/integer/bigint/float/double value that represents the numeric value to be rounded.
- round_digit: (Optional) a tinyint/smallint/integer/bigint/float/double value that specifies the digit to round to. This argument may be positive or negative; positive indicating that rounding needs to be to the right of the decimal point, and negative indicating that rounding needs to be to the left of the decimal point. Values such as 1.0 and 2.0 are acceptable, but values such as 1.3 and 1.5 result in a null. If omitted, the default is 0.
Return Value:
- The rounded value for the given number. The returned value has the following type:
  - bigint if the input value has type tinyint, smallint, integer or bigint,
  - float if the input value has type float,
  - double if the input value has type double;
- missing if the input value is a missing value,
- null if the input value is a null value,
- any other non-numeric input value will return a null value.

Example:

{
  "v1": round(2013),
  "v2": round(-4036),
  "v3": round(0.8),
  "v4": round(float("-2013.256")),
  "v5": round(double("-2013.893823748327284"))
  "v6": round(123456, -1),
  "v7": round(456.456, 2),
  "v8": round(456.456, -1),
  "v9": round(-456.456, -2)
};

The expected result is:

{ "v1": 2013, "v2": -4036, "v3": 1.0, "v4": -2013.0, "v5": -2014.0, "v6": 123460, "v7": 456.46, "v8": 460, "v9": -500 }

sign

Syntax:
```
sign(numeric_value)
```
Computes the sign of the argument.
Arguments:
- numeric_value: a tinyint/smallint/integer/bigint/float/double value.
Return Value:
- the sign (a tinyint) of the argument, -1 for negative values, 0 for 0, and 1 for positive values,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-numeric input value will cause a type error.

Example:

{ "v1": sign(1), "v2": sign(2), "v3": sign(0), "v4": sign(float("0.5")), "v5": sign(double("-1000")) };

The expected result is:

{ "v1": 1, "v2": 1, "v3": 0, "v4": 1, "v5": -1 }

sin

Syntax:
```
sin(numeric_value)
```
Computes the sine value of the argument.
Arguments:
- numeric_value: a tinyint/smallint/integer/bigint/float/double value.
Return Value:
- the double sine value for the argument,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-numeric input value will cause a type error.

Example:

{ "v1": sin(1), "v2": sin(2), "v3": sin(0), "v4": sin(float("0.5")), "v5": sin(double("1000")) };

The expected result is:

{ "v1": 0.8414709848078965, "v2": 0.9092974268256817, "v3": 0.0, "v4": 0.479425538604203, "v5": 0.8268795405320025 }

sinh

Syntax:
```
sinh(numeric_value)
```
Computes the hyperbolic sine value of the argument.
Arguments:
- numeric_value: a tinyint/smallint/integer/bigint/float/double value.
Return Value:
- the double hyperbolic sine value for the argument,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-numeric input value will cause a type error.

Example:

{ "v1": sinh(1), "v2": sinh(2), "v3": sinh(0), "v4": sinh(float("0.5")), "v5": sinh(double("8")) };

The expected result is:

{ "v1": 1.1752011936438014, "v2": 3.626860407847019, "v3": 0.0, "v4": 0.5210953054937474, "v5": 1490.4788257895502 }

sqrt

Syntax:
```
sqrt(numeric_value)
```
Computes the square root of the argument.
Arguments:
- numeric_value: a tinyint/smallint/integer/bigint/float/double value.
Return Value:
- the double square root value for the argument,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-numeric input value will cause a type error.

Example:

{ "v1": sqrt(1), "v2": sqrt(2), "v3": sqrt(0), "v4": sqrt(float("0.5")), "v5": sqrt(double("1000")) };

The expected result is:

{ "v1": 1.0, "v2": 1.4142135623730951, "v3": 0.0, "v4": 0.7071067811865476, "v5": 31.622776601683793 }

tan

Syntax:
```
tan(numeric_value)
```
Computes the tangent value of the argument.
Arguments:
- numeric_value: a tinyint/smallint/integer/bigint/float/double value.
Return Value:
- the double tangent value for the argument,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-numeric input value will cause a type error.

Example:

{ "v1": tan(1), "v2": tan(2), "v3": tan(0), "v4": tan(float("0.5")), "v5": tan(double("1000")) };

The expected result is:

{ "v1": 1.5574077246549023, "v2": -2.185039863261519, "v3": 0.0, "v4": 0.5463024898437905, "v5": 1.4703241557027185 }

tanh

Syntax:
```
tanh(numeric_value)
```
Computes the hyperbolic tangent value of the argument.
Arguments:
- numeric_value: a tinyint/smallint/integer/bigint/float/double value.
Return Value:
- the double hyperbolic tangent value for the argument,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-numeric input value will cause a type error.

Example:

{ "v1": tanh(1), "v2": tanh(2), "v3": tanh(0), "v4": tanh(float("0.5")), "v5": tanh(double("8")) };

The expected result is:

{ "v1": 0.7615941559557649, "v2": 0.964027580075817, "v3": 0.0, "v4": 0.4621171572600098, "v5": 0.999999774929676 }

trunc

Syntax:
```
trunc(numeric_value, number_digits)
```
Truncates the number to the given number of integer digits to the right of the decimal point (left if digits is negative). Digits is 0 if not given.
Arguments:
- numeric_value: a tinyint/smallint/integer/bigint/float/double value,
- number_digits: a tinyint/smallint/integer/bigint value.
Return Value:
- the double tangent value for the argument,
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is missing,
- a type error will be raised if:
  - the first argument is any other non-numeric value,
  - the second argument is any other non-tinyint, non-smallint, non-integer, and non-bigint value.

Example:

{ "v1": trunc(1, 1), "v2": trunc(2, -2), "v3": trunc(0.122, 2), "v4": trunc(float("11.52"), -1), "v5": trunc(double("1000.5252"), 3) };

The expected result is:

{ "v1": 1, "v2": 2, "v3": 0.12, "v4": 10.0, "v5": 1000.525 }

round_half_to_even

Syntax:

round_half_to_even(numeric_value, [precision])

Computes the closest numeric value to numeric_value that is a multiple of ten to the power of minus precision. precision is optional and by default value 0 is used.
Arguments:
- numeric_value: a tinyint/smallint/integer/bigint/float/double value.
- precision: an optional tinyint/smallint/integer/bigint field representing the number of digits in the fraction of the the result
Return Value:
- The rounded value for the given number in the same type as the input argument,
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- a type error will be raised if:
  - the first argument is any other non-numeric value,
  - or, the second argument is any other non-tinyint, non-smallint, non-integer, or non-bigint value.

Example:

{
  "v1": round_half_to_even(2013),
  "v2": round_half_to_even(-4036),
  "v3": round_half_to_even(0.8),
  "v4": round_half_to_even(float("-2013.256")),
  "v5": round_half_to_even(double("-2013.893823748327284")),
  "v6": round_half_to_even(double("-2013.893823748327284"), 2),
  "v7": round_half_to_even(2013, 4),
  "v8": round_half_to_even(float("-2013.256"), 5)
};

The expected result is:

{ "v1": 2013, "v2": -4036, "v3": 1.0, "v4": -2013.0, "v5": -2014.0, "v6": -2013.89, "v7": 2013, "v8": -2013.256 }

Binary Functions

parse_binary

Syntax:

parse_binary(string, encoding)
Creates a binary from an string encoded in encoding format.
Arguments:
- string : an encoded string,
- encoding : a string notation specifies the encoding type of the given string. Currently we support hex and base64 format.
Return Value:
- a binary that is decoded from the given string,
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- any other non-string input value will cause a type error.
Example:

[ parse_binary(“ABCDEF0123456789”,“hex”), parse_binary(“abcdef0123456789”,“HEX”), parse_binary(‘QXN0ZXJpeAE=’,“base64”) ];
The expected result is:

[ hex(“ABCDEF0123456789”), hex(“ABCDEF0123456789”), hex(“4173746572697801”) ]

print_binary

Syntax:

print_binary(binary, encoding)
Prints a binary to the required encoding string format.
Arguments:
- binary : a binary data need to be printed.
- encoding : a string notation specifies the expected encoding type. Currently we support hex and base64 format.
Return Value:
- a string that represents the encoded format of a binary,
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- any other non-string input value will cause a type error.

Example:

[ print_binary(hex("ABCDEF0123456789"), "base64"), print_binary(base64("q83vASNFZ4k="), "hex") ]

The expected result are:
```
[ "q83vASNFZ4k=", "ABCDEF0123456789" ]
```

binary_length

Syntax:

binary_length(binary)
Returns the number of bytes storing the binary data.
Arguments:
- binary : a binary value to be checked.
Return Value:
- an bigint that represents the number of bytes,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-binary input value will cause a type error.
Example:
```
binary_length(hex("00AA"))
```
The expected result is:

2

sub_binary

Syntax:

sub_binary(binary, offset[, length])
Returns the sub binary from the given binary based on the given start offset with the optional length.
Arguments:
- binary : a binary to be extracted,
- offset : a tinyint, smallint, integer, or bigint value as the starting offset of the sub binary in binary (starting at 0),
- length : (Optional) a tinyint, smallint, integer, or bigint value as the length of the sub binary.
Return Value:
- a binary that represents the sub binary,
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- a type error will be raised if:
  - the first argument is any other non-binary value,
  - or, the second argument is any other non-integer value,
  - or, the third argument is any other non-integer value, if it is present.
Example:
```
sub_binary(hex("AABBCCDD"), 4);
```
The expected result is
```
hex("DD")
```

binary_concat

Syntax:

binary_concat(array)
Concatenates a binary array or multiset into a single binary.
Arguments:
- array : an array or multiset of binaries (could be null or missing) to be concatenated.
Return Value :
- the concatenated binary value,
- missing if the argument is a missing value,
- null if the argument is a null value,
- missing if any element in the input array is missing,
- null if any element in the input array is null but no element in the input array is missing,
- any other non-array input value or non-binary element in the input array will cause a type error.
Example:

binary_concat([hex(“42”), hex(""), hex(‘42’)]);
The expected result is

hex(“4242”)

Spatial Functions

create_point

Syntax:
```
create_point(x, y)
```
Creates the primitive type point using an x and y value.
Arguments:
x : a double that represents the x-coordinate,
y : a double that represents the y-coordinate.
Return Value:
a point representing the ordered pair (x, y),
missing if any argument is a missing value,
null if any argument is a null value but no argument is a missing value,
any other non-double input value will cause a type error.
Example:
```
{ "point": create_point(30.0,70.0) };
```
The expected result is:
```
{ "point": point("30.0,70.0") }
```

create_line

Syntax:
```
create_line(point1, point2)
```
Creates the primitive type line using point1 and point2.
Arguments:
- point1 : a point that represents the start point of the line.
- point2 : a point that represents the end point of the line.
Return Value:
- a spatial line created using the points provided in point1 and point2,
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- any other non-point input value will cause a type error.

Example:

{ "line": create_line(create_point(30.0,70.0), create_point(50.0,90.0)) };

The expected result is:

{ "line": line("30.0,70.0 50.0,90.0") }

create_rectangle

Syntax:
```
create_rectangle(point1, point2)
```
Creates the primitive type rectangle using point1 and point2.
Arguments:
- point1 : a point that represents the lower_left point of the rectangle.
- point2 : a point that represents the upper_right point of the rectangle.
Return Value:
- a spatial rectangle created using the points provided in point1 and point2,
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- any other non-point input value will cause a type error.

Example:

{ "rectangle": create_rectangle(create_point(30.0,70.0), create_point(50.0,90.0)) };

The expected result is:

{ "rectangle": rectangle("30.0,70.0 50.0,90.0") }

create_circle

Syntax:
```
create_circle(point, radius)
```
Creates the primitive type circle using point and radius.
Arguments:
- point : a point that represents the center of the circle.
- radius : a double that represents the radius of the circle.
Return Value:
- a spatial circle created using the center point and the radius provided in point and radius.
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- a type error will be raised if:
  - the first argument is any other non-point value,
  - or, the second argument is any other non-double value.

Example:

{ "circle": create_circle(create_point(30.0,70.0), 5.0) }

The expected result is:
```
{ "circle": circle("30.0,70.0 5.0") }
```

create_polygon

Syntax:
```
create_polygon(array)
```
Creates the primitive type polygon using the double values provided in the argument array. Each two consecutive double values represent a point starting from the first double value in the array. Note that at least six double values should be specified, meaning a total of three points.
Arguments:
- array : an array of doubles representing the points of the polygon.
Return Value:
- a polygon, represents a spatial simple polygon created using the points provided in array.
- missing if the argument is a missing value,
- null if the argument is a null value,
- missing if any element in the input array is missing,
- null if any element in the input array is null but no element in the input array is missing,
- any other non-array input value or non-double element in the input array will cause a type error.

Example:

{ "polygon": create_polygon([1.0,1.0,2.0,2.0,3.0,3.0,4.0,4.0]) };

The expected result is:

{ "polygon": polygon("1.0,1.0 2.0,2.0 3.0,3.0 4.0,4.0") }

get_x/get_y

Syntax:
```
get_x(point) or get_y(point)
```
Returns the x or y coordinates of a point point.
Arguments:
- point : a point.
Return Value:
- a double representing the x or y coordinates of the point point,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-point input value will cause a type error.

Example:

{ "x_coordinate": get_x(create_point(2.3,5.0)), "y_coordinate": get_y(create_point(2.3,5.0)) };

The expected result is:

{ "x_coordinate": 2.3, "y_coordinate": 5.0 }

get_points

Syntax:
```
get_points(spatial_object)
```
Returns an ordered array of the points forming the spatial object spatial_object.
Arguments:
- spatial_object : a point, line, rectangle, circle, or polygon.
Return Value:
- an array of the points forming the spatial object spatial_object,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-spatial-object input value will cause a type error.

Example:

get_points(create_polygon([1.0,1.0,2.0,2.0,3.0,3.0,4.0,4.0]))

The expected result is:

[ point("1.0,1.0"), point("2.0,2.0"), point("3.0,3.0"), point("4.0,4.0") ]

get_center/get_radius

Syntax:

get_center(circle_expression) or get_radius(circle_expression)

Returns the center and the radius of a circle circle_expression, respectively.
Arguments:
- circle_expression : a circle.
Return Value:
- a point or double, represent the center or radius of the circle circle_expression.
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-circle input value will cause a type error.

Example:

{
  "circle_radius": get_radius(create_circle(create_point(6.0,3.0), 1.0)),
  "circle_center": get_center(create_circle(create_point(6.0,3.0), 1.0))
};

The expected result is:

{ "circle_radius": 1.0, "circle_center": point("6.0,3.0") }

spatial_distance

Syntax:
```
spatial_distance(point1, point2)
```
Returns the Euclidean distance between point1 and point2.
Arguments:
- point1 : a point.
- point2 : a point.
Return Value:
- a double as the Euclidean distance between point1 and point2.
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- any other non-point input value will cause a type error.

Example:

spatial_distance(point("47.44,80.65"), create_point(30.0,70.0));

The expected result is:
```
20.434678857275934
```

spatial_area

Syntax:
```
spatial_area(spatial_2d_expression)
```
Returns the spatial area of spatial_2d_expression.
Arguments:
- spatial_2d_expression : a rectangle, circle, or polygon.
Return Value:
- a double representing the area of spatial_2d_expression.
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-2d-spatial-object will cause a type error.

Example:

spatial_area(create_circle(create_point(0.0,0.0), 5.0));

The expected result is:
```
78.53981625
```

spatial_intersect

Syntax:

spatial_intersect(spatial_object1, spatial_object2)

Checks whether @arg1 and @arg2 spatially intersect each other.
Arguments:
- spatial_object1 : a point, line, rectangle, circle, or polygon.
- spatial_object2 : a point, line, rectangle, circle, or polygon.
Return Value:
- a boolean representing whether spatial_object1 and spatial_object2 spatially overlap with each other,
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- any other non-spatial-object input value will cause a type error.

Example:

spatial_intersect(point("39.28,70.48"), create_rectangle(create_point(30.0,70.0), create_point(40.0,80.0)));

The expected result is:
```
true
```

spatial_cell

Syntax:

spatial_cell(point1, point2, x_increment, y_increment)

Returns the grid cell that point1 belongs to.
Arguments:
- point1 : a point representing the point of interest that its grid cell will be returned.
- point2 : a point representing the origin of the grid.
- x_increment : a double, represents X increments.
- y_increment : a double, represents Y increments.
Return Value:
- a rectangle representing the grid cell that point1 belongs to,
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- a type error will be raised if:
  - the first or second argument is any other non-point value,
  - or, the second or third argument is any other non-double value.

Example:

spatial_cell(point("39.28,70.48"), create_point(20.0,50.0), 5.5, 6.0);

The expected result is:
```
rectangle("36.5,68.0 42.0,74.0");
```

Similarity Functions

AsterixDB supports queries with different similarity functions, including edit distance and Jaccard.

edit_distance

Syntax:

edit_distance(expression1, expression2)

Returns the edit distance of expression1 and expression2.
Arguments:
- expression1 : a string or a homogeneous array of a comparable item type.
- expression2 : The same type as expression1.
Return Value:
- an bigint that represents the edit distance between expression1 and expression2,
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- any other non-string input value will cause a type error.
Note: an n_gram index can be utilized for this function.

Example:

edit_distance("SuzannaTillson", "Suzanna Tilson");

The expected result is:
```
2
```

edit_distance_check

Syntax:

edit_distance_check(expression1, expression2, threshold)

Checks whether the edit distance of expression1 and expression2 is within a given threshold.
Arguments:
- expression1 : a string or a homogeneous array of a comparable item type.
- expression2 : The same type as expression1.
- threshold : a bigint that represents the distance threshold.
Return Value:
- an array with two items:
  - The first item contains a boolean value representing whether the edit distance of expression1 and expression2 is within the given threshold.
  - The second item contains an integer that represents the edit distance of expression1 and expression2 if the first item is true.
  - If the first item is false, then the second item is set to 2147483647.
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- a type error will be raised if:
  - the first or second argument is any other non-string value,
  - or, the third argument is any other non-bigint value.
Note: an n_gram index can be utilized for this function.
Example:
```
edit_distance_check("happy","hapr",2);
```
The expected result is:
```
[ true, 2 ]
```

edit_distance_contains

Syntax:

edit_distance_contains(expression1, expression2, threshold)

Checks whether expression1 contains expression2 with an edit distance within a given threshold.
Arguments:
- expression1 : a string or a homogeneous array of a comparable item type.
- expression2 : The same type as expression1.
- threshold : a bigint that represents the distance threshold.
Return Value:
- an array with two items:
  - The first item contains a boolean value representing whether expression1 can contain expression2.
  - The second item contains an integer that represents the required edit distance for expression1 to contain expression2 if the first item is true.
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- a type error will be raised if:
  - the first or second argument is any other non-string value,
  - or, the third argument is any other non-bigint value.
Note: an n_gram index can be utilized for this function.

Example:

edit_distance_contains("happy","hapr",2);

The expected result is:
```
[ true, 1 ]
```

similarity_jaccard

Syntax:
```
similarity_jaccard(array1, array2)
```
Returns the Jaccard similarity of array1 and array2.
Arguments:
- array1 : an array or multiset.
- array2 : an array or multiset.
Return Value:
- a float that represents the Jaccard similarity of array1 and array2,
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- missing if any element in any input array is missing,
- null if any element in any input array is null but no element in the input array is missing,
- any other non-array input value or non-integer element in any input array will cause a type error.
Note: a keyword index can be utilized for this function.

Example:

similarity_jaccard([1,5,8,9], [1,5,9,10]);

The expected result is:
```
0.6
```

similarity_jaccard_check

Syntax:

similarity_jaccard_check(array1, array2, threshold)

Checks whether array1 and array2 have a Jaccard similarity greater than or equal to threshold. Again, the “check” version of Jaccard is faster than the “non_check” version.
Arguments:
- array1 : an array or multiset.
- array2 : an array or multiset.
- threshold : a double that represents the similarity threshold.
Return Value:
- an array with two items:
  - The first item contains a boolean value representing whether array1 and array2 are similar.
  - The second item contains a float that represents the Jaccard similarity of array1 and array2 if it is greater than or equal to the threshold, or 0 otherwise.
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- missing if any element in any input array is missing,
- null if any element in any input array is null but no element in the input array is missing,
- a type error will be raised if:
  - the first or second argument is any other non-array value,
    - or, the third argument is any other non-double value.
Note: a keyword index can be utilized for this function.

Example:

similarity_jaccard_check([1,5,8,9], [1,5,9,10], 0.6);

The expected result is:
```
[ false, 0.0 ]
```

Tokenizing Functions

word_tokens

Syntax:
```
word_tokens(string)
```
Returns an array of word tokens of string using non_alphanumeric characters as delimiters.
Arguments:
- string : a string that will be tokenized.
Return Value:
- an array of string word tokens,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-string input value will cause a type error.

Example:

word_tokens("I like the phone, awesome!");

The expected result is:

[ "i", "like", "the", "phone", "awesome" ]

Temporal Functions

get_year/get_month/get_day/get_hour/get_minute/get_second/get_millisecond

Syntax:

get_year/get_month/get_day/get_hour/get_minute/get_second/get_millisecond(temporal_value)

Accessors for accessing fields in a temporal value
Arguments:
- temporal_value : a temporal value represented as one of the following types: date, datetime, time, and duration.
Return Value:
- an bigint value representing the field to be extracted,
- missing if the argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- any other non-interval input value will cause a type error.

Example:

{
  "year": get_year(date("2010-10-30")),
  "month": get_month(datetime("1987-11-19T23:49:23.938")),
  "day": get_day(date("2010-10-30")),
  "hour": get_hour(time("12:23:34.930")),
  "min": get_minute(duration("P3Y73M632DT49H743M3948.94S")),
  "second": get_second(datetime("1987-11-19T23:49:23.938")),
  "ms": get_millisecond(duration("P3Y73M632DT49H743M3948.94S"))
};

The expected result is:

{ "year": 2010, "month": 11, "day": 30, "hour": 12, "min": 28, "second": 23, "ms": 94 }

adjust_datetime_for_timezone

Syntax:

adjust_datetime_for_timezone(datetime, string)

Adjusts the given datetime datetime by applying the timezone information string.
Arguments:
- datetime : a datetime value to be adjusted.
- string : a string representing the timezone information.
Return Value:
- a string value representing the new datetime after being adjusted by the timezone information,
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- a type error will be raised if:
  - the first argument is any other non-datetime value,
  - or, the second argument is any other non-string value.

Example:

adjust_datetime_for_timezone(datetime("2008-04-26T10:10:00"), "+08:00");

The expected result is:
```
"2008-04-26T18:10:00.000+08:00"
```

adjust_time_for_timezone

Syntax:
```
adjust_time_for_timezone(time, string)
```
Adjusts the given time time by applying the timezone information string.
Arguments:
- time : a time value to be adjusted.
- string : a string representing the timezone information.
Return Value:
- a string value representing the new time after being adjusted by the timezone information,
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- a type error will be raised if:
  - the first argument is any other non-time value,
  - or, the second argument is any other non-string value.

Example:

adjust_time_for_timezone(get_time_from_datetime(datetime("2008-04-26T10:10:00")), "+08:00");

The expected result is:
```
"18:10:00.000+08:00"
```

calendar_duration_from_datetime

Syntax:

calendar_duration_from_datetime(datetime, duration_value)

Gets a user_friendly representation of the duration duration_value based on the given datetime datetime.
Arguments:
- datetime : a datetime value to be used as the reference time point.
- duration_value : a duration value to be converted.
Return Value:
- a duration value with the duration as duration_value but with a user_friendly representation,
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- a type error will be raised if:
  - the first argument is any other non-datetime value,
  - or, the second argument is any other non-duration input value.

Example:

calendar_duration_from_datetime(
      datetime("2016-03-26T10:10:00"),
      datetime("2016-03-26T10:10:00") - datetime("2011-01-01T00:00:00")
);

The expected result is:
```
duration("P5Y2M24DT10H10M")
```

get_year_month_duration/get_day_time_duration

Syntax:

get_year_month_duration/get_day_time_duration(duration_value)

Extracts the correct duration subtype from duration_value.
Arguments:
- duration_value : a duration value to be converted.
Return Value:
- a year_month_duration value or a day_time_duration value,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-duration input value will cause a type error.

Example:

get_year_month_duration(duration("P12M50DT10H"));

The expected result is:
```
year_month_duration("P1Y")
```

months_from_year_month_duration/ms_from_day_time_duration

Syntax:

months_from_year_month_duration/ms_from_day_time_duration(duration_value)

Extracts the number of months or the number of milliseconds from the duration subtype.
Arguments:
- duration_value : a duration of the correct subtype.
Return Value:
- a bigint representing the number of months/milliseconds,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-duration input value will cause a type error.

Example:

{
    "months": months_from_year_month_duration(get_year_month_duration(duration("P5Y7MT50M"))),
    "milliseconds": ms_from_day_time_duration(get_day_time_duration(duration("P5Y7MT50M")))
};

The expected result is:

{"months": 67, "milliseconds": 3000000}

duration_from_months/duration_from_ms

Syntax:

duration_from_months/duration_from_ms(number_value)

Creates a duration from number_value.
Arguments:
- number_value : a bigint representing the number of months/milliseconds
Return Value:
- a duration containing number_value value for months/milliseconds,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-duration input value will cause a type error.
Example:
```
duration_from_months(8);
```
The expected result is:
```
duration("P8M")
```

duration_from_interval

Syntax:
```
duration_from_interval(interval_value)
```
Creates a duration from interval_value.
Arguments:
- interval_value : an interval value
Return Value:
- a duration representing the time in the interval_value
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-duration input value will cause a type error.

Example:

{
  "dr1" : duration_from_interval(interval(date("2010-10-30"), date("2010-12-21"))),
  "dr2" : duration_from_interval(interval(datetime("2012-06-26T01:01:01.111"), datetime("2012-07-27T02:02:02.222"))),
  "dr3" : duration_from_interval(interval(time("12:32:38"), time("20:29:20"))),
  "dr4" : duration_from_interval(null)
};

The expected result is:

{
  "dr1": day_time_duration("P52D"),
  "dr2": day_time_duration("P31DT1H1M1.111S"),
  "dr3": day_time_duration("PT7H56M42S"),
  "dr4": null
}

current_date

Syntax:
```
current_date()
```
Gets the current date.
Arguments: None
Return Value:
- a date value of the date when the function is called.

current_time

Syntax:
```
current_time()
```
Get the current time
Arguments: None
Return Value:
- a time value of the time when the function is called.

current_datetime

Syntax:
```
current_datetime()
```
Get the current datetime
Arguments: None
Return Value:
- a datetime value of the datetime when the function is called.

get_date_from_datetime

Syntax:
```
get_date_from_datetime(datetime)
```
Gets the date value from the given datetime value datetime.
Arguments:
- datetime: a datetime value to be extracted from.
Return Value:
- a date value from the datetime,
- any other non-datetime input value will cause a type error.
Example:

get_date_from_datetime(datetime(“2016-03-26T10:10:00”));
The expected result is:

date(“2016-03-26”)

get_time_from_datetime

Syntax:
```
get_time_from_datetime(datetime)
```
Get the time value from the given datetime value datetime
Arguments:
- datetime: a datetime value to be extracted from.
Return Value:
- a time value from the datetime.
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-datetime input value will cause a type error.

Example:

get_time_from_datetime(datetime("2016-03-26T10:10:00"));

The expected result is:
```
time("10:10:00.000")
```

day_of_week

Syntax:
```
day_of_week(date[, week_start_day])
```
Finds the day of the week for a given date (1_7)
Arguments:
- date: a date or a datetime value
- week_start_day: (Optional) an integer or a string value (case-insensitive) specifying the day of the week to start counting from: 1=Sun[day], 2=Mon[day], …, 7=Sat[urday]. If omitted, the default is 1 (Sunday).
Return Value:
- an bigint representing the day of the week (1_7),
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-date input value will cause a type error.

Example:

{
  "day_1": day_of_week(datetime("2012-12-30T12:12:12.039")),
  "day_2": day_of_week(datetime("2012-12-30T12:12:12.039"), 2),
  "day_3": day_of_week(datetime("2012-12-30T12:12:12.039"), "Monday"),
  "day_4": day_of_week(datetime("2012-12-30T12:12:12.039"), "MON")
};

The expected result is:

{ "day_1": 1, "day_2": 7, "day_3": 7, "day_4": 7 }

day_of_year

Syntax:
```
day_of_year(date)
```
Finds the day of the year for a given date
Arguments:
- date: a date or a datetime value
Return Value:
- an bigint representing the day of the year,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-date input value will cause a type error.
Example:
```
day_of_year(date("2011-12-31"));
```
The expected result is:
```
365
```

week_of_year

Syntax:
```
week_of_year(date[, week_start_day])
```
Finds the week of the year for a given date
Arguments:
- date: a date or a datetime value
- week_start_day: (Optional) an integer or a string value (case-insensitive) specifying the day of the week to start counting from: 1=Sun[day], 2=Mon[day], …, 7=Sat[urday]. If omitted, the default is 1 (Sunday).
Return Value:
- an bigint representing the week of the year,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-date input value will cause a type error.

Example:

{
  "week_1": week_of_year(date("2012-12-01")),
  "week_2": week_of_year(date("2012-12-01"), 2),
  "week_3": week_of_year(date("2012-12-01"), "Monday"),
  "week_4": week_of_year(date("2012-12-01"), "MON")
};

The expected result is:

{ "week_1": 48, "week_2": 49, "week_3": 49, "week_4": 49 }

quarter_of_year

Syntax:
```
quarter_of_year(date)
```
Finds the quarter of the year for a given date
Arguments:
- date: a date or a datetime value
Return Value:
- an bigint representing the quarter of the year (1_4),
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-date input value will cause a type error.
Example:
```
quarter_of_year(date("2011-12-31"));
```
The expected result is:
```
4
```

datetime_from_date_time

Syntax:

datetime_from_date_time(date,time)

Gets a datetime representing the combination of date and time
- Arguments:
- date: a date value
- time a time value
Return Value:
- a datetime value by combining date and time,
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- a type error will be raised if
  - the first argument is any other non-date value,
  - or, the second argument is any other non-time value.

date_from_unix_time_in_days

Syntax:

date_from_unix_time_in_days(numeric_value)

Gets a date representing the time after numeric_value days since 1970-01-01.
Arguments:
- numeric_value: a tinyint/smallint/integer/bigint value representing the number of days.
Return Value:
- a date value as the time after numeric_value days since 1970-01-01,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-numeric input value will cause a type error.
Example:
```
date_from_unix_time_in_days(15800);
```
The expected result is:

date(“2013-04-05”)

datetime_from_unix_time_in_ms

Syntax:

datetime_from_unix_time_in_ms(numeric_value[, string])

Gets a datetime representing the time after numeric_value milliseconds since 1970-01-01T00:00:00Z.
Arguments:
- numeric_value: a tinyint/smallint/integer/bigint value representing the number of milliseconds.
- string : (Optional) a string representing the target timezone as defined by IANA Time Zone Database. If omitted, the default is UTC.
Return Value:
- a datetime value as the time in the target time zone after numeric_value milliseconds since 1970-01-01T00:00:00Z,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-numeric input value will cause a type error.

Example:

 {
   "datetime_1": datetime_from_unix_time_in_ms(1365139700000),
   "datetime_2": datetime_from_unix_time_in_ms(1365139700000, "America/Los_Angeles")
 };

The expected result is:

{ “datetime_1”: datetime(“2013-04-05T05:28:20.000”), “datetime_2”: datetime(“2013-04-04T22:28:20.000”) }

datetime_from_unix_time_in_secs

Syntax:

datetime_from_unix_time_in_secs(numeric_value[, string])

Gets a datetime representing the time after numeric_value seconds since 1970-01-01T00:00:00Z.
Arguments:
- numeric_value: a tinyint/smallint/integer/bigint value representing the number of seconds.
- string : (Optional) a string representing the target timezone as defined by IANA Time Zone Database. If omitted, the default is UTC.
Return Value:
- a datetime value as the time in the target time zone after numeric_value seconds since 1970-01-01T00:00:00Z,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-numeric input value will cause a type error.

Example:

{
  "datetime_1": datetime_from_unix_time_in_secs(1365139700),
  "datetime_2": datetime_from_unix_time_in_secs(1365139700, "America/Los_Angeles")
};

The expected result is:

{ “datetime_1”: datetime(“2013-04-05T05:28:20.000”), “datetime_2”: datetime(“2013-04-04T22:28:20.000”) }

time_from_unix_time_in_ms

Syntax:

time_from_unix_time_in_ms(numeric_value)

Gets a time representing the time after numeric_value milliseconds since 00:00:00.000.
Arguments:
- numeric_value: a tinyint/smallint/integer/bigint value representing the number of milliseconds.
Return Value:
- a time value as the time after numeric_value milliseconds since 00:00:00.000,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-numeric input value will cause a type error.
Example:
```
time_from_unix_time_in_ms(3748);
```
The expected result is:
```
time("00:00:03.748")
```

unix_time_from_date_in_days

Syntax:

unix_time_from_date_in_days(date_value)

Gets an integer value representing the number of days since 1970-01-01 for date_value.
Arguments:
- date_value: a date value.
Return Value:
- a bigint value representing the number of days,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-date input value will cause a type error.
Example:

unix_time_from_date_in_days(date(“2013-04-05”));
The expected result is:

15800

unix_time_from_datetime_in_ms

Syntax:

unix_time_from_datetime_in_ms(datetime_value[, string])

Gets an integer value representing the time in milliseconds since 1970-01-01T00:00:00Z for datetime_value.
Arguments:
- datetime_value : a datetime value.
- string : (Optional) a string representing the source timezone as defined by IANA Time Zone Database. If omitted, the default is UTC.
Return Value:
- a bigint value representing the number of milliseconds,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-datetime input value will cause a type error.
Example:

{ “unix_time_1”: unix_time_from_datetime_in_ms(datetime(“2013-04-05T05:28:20.000”)), “unix_time_2”: unix_time_from_datetime_in_ms(datetime(“2013-04-04T22:28:20.000”), “America/Los_Angeles”) };
The expected result is:

{ “unix_time_1”: 1365139700000, “unix_time_2”: 1365139700000 }

unix_time_from_datetime_in_secs

Syntax:

unix_time_from_datetime_in_secs(datetime_value[, string])

Gets an integer value representing the time in seconds since 1970-01-01T00:00:00Z for datetime_value.
Arguments:
- datetime_value : a datetime value.
string : (Optional) a string representing the source timezone as defined by IANA Time Zone Database. If omitted, the default is UTC.
Return Value:
- a bigint value representing the number of seconds,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-datetime input value will cause a type error.
Example:

{ “unix_time_1”: unix_time_from_datetime_in_secs(datetime(“2013-04-05T05:28:20.000”)), “unix_time_2”: unix_time_from_datetime_in_secs(datetime(“2013-04-04T22:28:20.000”), “America/Los_Angeles”) };
The expected result is:

{ “unix_time_1”: 1365139700, “unix_time_2”: 1365139700 }

unix_time_from_time_in_ms

Syntax:
```
unix_time_from_time_in_ms(time_value)
```
Gets an integer value representing the time the milliseconds since 00:00:00.000 for time_value.
Arguments:
- time_value : a time value.
Return Value:
- a bigint value representing the number of milliseconds,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-datetime input value will cause a type error.

Example:

unix_time_from_time_in_ms(time("00:00:03.748"));

The expected result is:
```
3748
```

parse_date/parse_time/parse_datetime

Syntax:

parse_date/parse_time/parse_datetime(date,formatting_expression)

Creates a date/time/date_time value by treating date with formatting formatting_expression
Arguments:
- date: a string value representing the date/time/datetime.
- formatting_expression a string value providing the formatting for date_expression.Characters used to create date expression:
- h hours
- m minutes
- s seconds
- n (or S) milliseconds
- a am/pm
- z timezone (parsed and ignored)
- Y year
- Q quarter of year (1-4)
- QQ quarter of year (01-04)
- M month
- D day
- EEE weekday (abbreviated name, parsed and ignored)
- EEEE weekday (full name, parsed and ignored)
- _, ', /, ., ,, T seperators for both time and date
Return Value:
- a date/time/date_time value corresponding to date,
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- a type error will be raised if:
- the first argument is any other non-date value,
- the second argument is any other non-string value.
Example:
```
parse_time("30:30","m:s");
```
The expected result is:
```
time("00:30:30.000")
```

print_date/print_time/print_datetime

Syntax:

print_date/print_time/print_datetime(date,formatting_expression)

Creates a string representing a date/time/date_time value of the date using the formatting formatting_expression
Arguments:
- date: a date/time/datetime value.
- formatting_expression a string value providing the formatting for date_expression. Characters used to create date expression:
- h hours
- m minutes
- s seconds
- n (or S) milliseconds
- a am/pm
- Y year
- Q quarter of year (1-4)
- QQ quarter of year (01-04)
- M month
- MMM month (abbreviated name)
- MMMM month (full name)
- D day
- DDD day of year
- EEE weekday (abbreviated name)
- EEEE weekday (full name)
- _, ', /, ., ,, T seperators for both time and date
Return Value:
- a string value corresponding to date,
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- a type error will be raised if:
  - the first argument is any other non-date value,
  - the second argument is any other non-string value.

Example:

print_time(time("00:30:30.000"),"m:s");

The expected result is:
```
"30:30"
```

get_interval_start, get_interval_end

Syntax:

get_interval_start/get_interval_end(interval)

Gets the start/end of the given interval.
Arguments:
- interval: the interval to be accessed.
Return Value:
- a time, date, or datetime (depending on the time instances of the interval) representing the starting or ending time,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-interval value will cause a type error.

Example:

{
  "start": get_interval_start(interval_start_from_date("1984-01-01", "P1Y")),
  "end": get_interval_end(interval_start_from_date("1984-01-01", "P1Y"))
};

The expected result is:

{ "start": date("1984-01-01"), "end": date("1985-01-01") }

get_interval_start_date/get_interval_start_datetimeget_interval_start_time, get_interval_end_date/get_interval_end_datetime/get_interval_end_time

Syntax:

get_interval_start_date/get_interval_start_datetime/get_interval_start_time/get_interval_end_date/get_interval_end_datetime/get_interval_end_time(interval)

Gets the start/end of the given interval for the specific date/datetime/time type.
Arguments:
- interval: the interval to be accessed.
Return Value:
- a time, date, or datetime (depending on the function) representing the starting or ending time,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-interval value will cause a type error.

Example:

{
  "start1": get_interval_start_date(interval_start_from_date("1984-01-01", "P1Y")),
  "end1": get_interval_end_date(interval_start_from_date("1984-01-01", "P1Y")),
  "start2": get_interval_start_datetime(interval_start_from_datetime("1984-01-01T08:30:00.000", "P1Y1H")),
  "end2": get_interval_end_datetime(interval_start_from_datetime("1984-01-01T08:30:00.000", "P1Y1H")),
  "start3": get_interval_start_time(interval_start_from_time("08:30:00.000", "P1H")),
  "end3": get_interval_end_time(interval_start_from_time("08:30:00.000", "P1H"))
};

The expected result is:

{
  "start1": date("1984-01-01"),
  "end1": date("1985-01-01"),
  "start2": datetime("1984-01-01T08:30:00.000"),
  "end2": datetime("1985-01-01T09:30:00.000"),
  "start3": time("08:30:00.000"),
  "end3": time("09:30:00.000")
}

get_overlapping_interval

Syntax:

get_overlapping_interval(interval1, interval2)

Gets the start/end of the given interval for the specific date/datetime/time type.
Arguments:
- interval1: an interval value
- interval2: an interval value
Return Value:
- an interval that is overlapping interval1 and interval2. If interval1 and interval2 do not overlap null is returned. Note each interval must be of the same type.
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- any other non-interval input value will cause a type error.

Example:

{
  "overlap1": get_overlapping_interval(interval(time("11:23:39"), time("18:27:19")), interval(time("12:23:39"), time("23:18:00"))),
  "overlap2": get_overlapping_interval(interval(time("12:23:39"), time("18:27:19")), interval(time("07:19:39"), time("09:18:00"))),
  "overlap3": get_overlapping_interval(interval(date("1980-11-30"), date("1999-09-09")), interval(date("2013-01-01"), date("2014-01-01"))),
  "overlap4": get_overlapping_interval(interval(date("1980-11-30"), date("2099-09-09")), interval(date("2013-01-01"), date("2014-01-01"))),
  "overlap5": get_overlapping_interval(interval(datetime("1844-03-03T11:19:39"), datetime("2000-10-30T18:27:19")), interval(datetime("1989-03-04T12:23:39"), datetime("2009-10-10T23:18:00"))),
  "overlap6": get_overlapping_interval(interval(datetime("1989-03-04T12:23:39"), datetime("2000-10-30T18:27:19")), interval(datetime("1844-03-03T11:19:39"), datetime("1888-10-10T23:18:00")))
};

The expected result is:

{
  "overlap1": interval(time("12:23:39.000"), time("18:27:19.000")),
  "overlap2": null,
  "overlap3": null,
  "overlap4": interval(date("2013-01-01"), date("2014-01-01")),
  "overlap5": interval(datetime("1989-03-04T12:23:39.000"), datetime("2000-10-30T18:27:19.000")),
  "overlap6": null
}

interval_bin

Syntax:

interval_bin(time_to_bin, time_bin_anchor, duration_bin_size)

Returns the interval value representing the bin containing the time_to_bin value.
Arguments:
- time_to_bin: a date/time/datetime value representing the time to be binned.
- time_bin_anchor: a date/time/datetime value representing an anchor of a bin starts. The type of this argument should be the same as the first time_to_bin argument.
- duration_bin_size: the duration value representing the size of the bin, in the type of year_month_duration or day_time_duration. The type of this duration should be compatible with the type of time_to_bin, so that the arithmetic operation between time_to_bin and duration_bin_size is well_defined. Currently AsterixDB supports the following arithmetic operations:
  - datetime +|_ year_month_duration
  - datetime +|_ day_time_duration
  - date +|_ year_month_duration
  - date +|_ day_time_duration
  - time +|_ day_time_duration
Return Value:
a interval value representing the bin containing the time_to_bin value. Note that the internal type of this interval value should be the same as the time_to_bin type,
missing if any argument is a missing value,
null if any argument is a null value but no argument is a missing value,
a type error will be raised if:
- the first argument or the second argument is any other non-date/non-time/non-datetime value,
- or, the second argument is any other non-year_month_duration/non-day_time_duration value.
Example:

{ “bin1”: interval_bin(date(“2010-10-30”), date(“1990-01-01”), year_month_duration(“P1Y”)), “bin2”: interval_bin(datetime(“1987-11-19T23:49:23.938”), datetime(“1990-01-01T00:00:00.000”), year_month_duration(“P6M”)), “bin3”: interval_bin(time(“12:23:34.930+07:00”), time(“00:00:00”), day_time_duration(“PT1M”)), “bin4”: interval_bin(datetime(“1987-11-19T23:49:23.938”), datetime(“2013-01-01T00:00:00.000”), day_time_duration(“PT24H”)) };
The expected result is:

{ “bin1”: interval(date(“2010-01-01”), date(“2011-01-01”)), “bin2”: interval(datetime(“1987-07-01T00:00:00.000”), datetime(“1988-01-01T00:00:00.000”)), “bin3”: interval(time(“12:23:00.000”), time(“12:24:00.000”)), “bin4”: interval(datetime(“1987-11-19T00:00:00.000”), datetime(“1987-11-20T00:00:00.000”)) }

interval_start_from_date/time/datetime

Syntax:

interval_start_from_date/time/datetime(date/time/datetime, duration)

Construct an interval value by the given starting date/time/datetime and the duration that the interval lasts.
Arguments:
- date/time/datetime: a string representing a date, time or datetime, or a date/time/datetime value, representing the starting time point.
- duration: a string or duration value representing the duration of the interval. Note that duration cannot be negative value.
Return Value:
- an interval value representing the interval starting from the given time point with the length of duration,
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- a type error will be raised if:
  - the first argument or the second argument is any other non-date/non-time/non-datetime value,
  - or, the second argument is any other non-duration value.

Example:

{
  "interval1": interval_start_from_date("1984-01-01", "P1Y"),
  "interval2": interval_start_from_time(time("02:23:28.394"), "PT3H24M"),
  "interval3": interval_start_from_datetime("1999-09-09T09:09:09.999", duration("P2M30D"))
};

The expected result is:

{
  "interval1": interval(date("1984-01-01"), date("1985-01-01")),
  "interval2": interval(time("02:23:28.394"), time("05:47:28.394")),
  "interval3": interval(datetime("1999-09-09T09:09:09.999"), datetime("1999-12-09T09:09:09.999"))
}

overlap_bins

Return Value:
- a interval value representing the bin containing the time_to_bin value. Note that the internal type of this interval value should be the same as the time_to_bin type.

Syntax:

 overlap_bins(interval, time_bin_anchor, duration_bin_size)

Returns an ordered list of interval values representing each bin that is overlapping the interval.
Arguments:
- interval: an interval value
- time_bin_anchor: a date/time/datetime value representing an anchor of a bin starts. The type of this argument should be the same as the first time_to_bin argument.
- duration_bin_size: the duration value representing the size of the bin, in the type of year_month_duration or day_time_duration. The type of this duration should be compatible with the type of time_to_bin, so that the arithmetic operation between time_to_bin and duration_bin_size is well_defined. Currently AsterixDB supports the following arithmetic operations:
  - datetime +|_ year_month_duration
  - datetime +|_ day_time_duration
  - date +|_ year_month_duration
  - date +|_ day_time_duration
  - time +|_ day_time_duration
Return Value:
- a ordered list of interval values representing each bin that is overlapping the interval. Note that the internal type as time_to_bin and duration_bin_size.
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- a type error will be raised if:
  - the first arugment is any other non-interval value,
  - or, the second argument is any other non-date/non-time/non-datetime value,
  - or, the second argument is any other non-year_month_duration/non-day_time_duration value.

Example:

{
  "timebins": overlap_bins(interval(time("17:23:37"), time("18:30:21")), time("00:00:00"), day_time_duration("PT30M")),
  "datebins": overlap_bins(interval(date("1984-03-17"), date("2013-08-22")), date("1990-01-01"), year_month_duration("P10Y")),
  "datetimebins": overlap_bins(interval(datetime("1800-01-01T23:59:48.938"), datetime("2015-07-26T13:28:30.218")),
                              datetime("1900-01-01T00:00:00.000"), year_month_duration("P100Y"))
};

The expected result is:

{ “timebins”: [ interval(time(“17:00:00.000”), time(“17:30:00.000”)), interval(time(“17:30:00.000”), time(“18:00:00.000”)), interval(time(“18:00:00.000”), time(“18:30:00.000”)), interval(time(“18:30:00.000”), time(“19:00:00.000”)) ], “datebins”: [ interval(date(“1980-01-01”), date(“1990-01-01”)), interval(date(“1990-01-01”), date(“2000-01-01”)), interval(date(“2000-01-01”), date(“2010-01-01”)), interval(date(“2010-01-01”), date(“2020-01-01”)) ], “datetimebins”: [ interval(datetime(“1800-01-01T00:00:00.000”), datetime(“1900-01-01T00:00:00.000”)), interval(datetime(“1900-01-01T00:00:00.000”), datetime(“2000-01-01T00:00:00.000”)), interval(datetime(“2000-01-01T00:00:00.000”), datetime(“2100-01-01T00:00:00.000”)) ] };

interval_before, interval_after

Syntax:

interval_before(interval1, interval2)
interval_after(interval1, interval2)

These two functions check whether an interval happens before/after another interval.
Arguments:
- interval1, interval2: two intervals to be compared
Return Value:
- a boolean value. Specifically, interval_before(interval1, interval2) is true if and only if interval1.end < interval2.start, and interval_after(interval1, interval2) is true if and only if interval1.start > interval2.end.
- missing if the argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- any other non-interval input value will cause a type error.

Examples:

{
  "interval_before": interval_before(interval(date("2000-01-01"), date("2005-01-01")),
                                     interval(date("2005-05-01"), date("2012-09-09"))),
  "interval_after": interval_after(interval(date("2005-05-01"), date("2012-09-09")),
                                   interval(date("2000-01-01"), date("2005-01-01")))
};

The expected result is:

{ "interval_before": true, "interval_after": true }

interval_covers, interval_covered_by

Syntax:

interval_covers(interval1, interval2)
interval_covered_by(interval1, interval2)

These two functions check whether one interval covers the other interval.
Arguments:
- interval1, interval2: two intervals to be compared
Return Value:
- a boolean value. Specifically, interval_covers(interval1, interval2) is true if and only if
  
  interval1.start <= interval2.start AND interval1.end >= interval2.end
  
  interval_covered_by(interval1, interval2) is true if and only if
  
  interval2.start <= interval1.start AND interval2.end >= interval1.end
- missing if the argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- any other non-interval input value will cause a type error.

Examples:

{
  "interval_covers": interval_covers(interval(date("2000-01-01"), date("2005-01-01")),
                                     interval(date("2000-03-01"), date("2004-09-09"))),
  "interval_covered_by": interval_covered_by(interval(date("2006-08-01"), date("2007-03-01")),
                                             interval(date("2004-09-10"), date("2012-08-01")))
};

The expected result is:

{ "interval_covers": true, "interval_covered_by": true }

interval_overlaps, interval_overlapped_by

Syntax:

interval_overlaps(interval1, interval2)
interval_overlapped_by(interval1, interval2)

These functions check whether two intervals overlap with each other.
Arguments:
- interval1, interval2: two intervals to be compared
Return Value:
- a boolean value. Specifically, interval_overlaps(interval1, interval2) is true if and only if
  interval1.start < interval2.start AND interval2.end > interval1.end AND interval1.end > interval2.start
interval_overlapped_by(interval1, interval2) is true if and only if
```
interval2.start < interval1.start
AND interval1.end > interval2.end
AND interval2.end > interval1.start
```
- missing if the argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- any other non-interval input value will cause a type error.
Note that interval_overlaps and interval_overlapped_by are following the Allen’s relations on the definition of overlap.

Examples:

{
  "overlaps": interval_overlaps(interval(date("2000-01-01"), date("2005-01-01")),
                                interval(date("2004-05-01"), date("2012-09-09"))),
  "overlapped_by": interval_overlapped_by(interval(date("2006-08-01"), date("2007-03-01")),
                                          interval(date("2004-05-01"), date("2012-09-09"))))
};

The expected result is:

{ "overlaps": true, "overlapped_by": true }

interval_overlapping

Note that interval_overlapping is not an Allen’s Relation, but syntactic sugar we added for the case that the intersect of two intervals is not empty. Basically this function returns true if any of these functions return true: interval_overlaps, interval_overlapped_by, interval_covers, or interval_covered_by.

Syntax:

interval_overlapping(interval1, interval2)

This functions check whether two intervals share any points with each other.
Arguments:
- interval1, interval2: two intervals to be compared
Return Value:
- a boolean value. Specifically, interval_overlapping(interval1, interval2) is true if
  
  interval1.start < interval2.end AND interval1.end > interval2.start
- missing if the argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- any other non-interval input value will cause a type error.

Examples:

{
  "overlapping1": interval_overlapping(interval(date("2000-01-01"), date("2005-01-01")),
                                       interval(date("2004-05-01"), date("2012-09-09"))),
  "overlapping2": interval_overlapping(interval(date("2006-08-01"), date("2007-03-01")),
                                       interval(date("2004-09-10"), date("2006-12-31")))
};

The expected result is:

{ "overlapping1": true, "overlapping2": true }

interval_meets, interval_met_by

Syntax:

interval_meets(interval1, interval2)
interval_met_by(interval1, interval2)

These two functions check whether an interval meets with another interval.
Arguments:
- interval1, interval2: two intervals to be compared
Return Value:
- a boolean value. Specifically, interval_meets(interval1, interval2) is true if and only if interval1.end = interval2.start, and interval_met_by(interval1, interval2) is true if and only if interval1.start = interval2.end. If any of the two inputs is null, null is returned.
- missing if the argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- any other non-interval input value will cause a type error.

Examples:

{
  "meets": interval_meets(interval(date("2000-01-01"), date("2005-01-01")),
                          interval(date("2005-01-01"), date("2012-09-09"))),
  "metby": interval_met_by(interval(date("2006-08-01"), date("2007-03-01")),
                           interval(date("2004-09-10"), date("2006-08-01")))
};

The expected result is:
```
{ "meets": true, "metby": true }
```

interval_starts, interval_started_by

Syntax:

interval_starts(interval1, interval2)
interval_started_by(interval1, interval2)

These two functions check whether one interval starts with the other interval.
Arguments:
- interval1, interval2: two intervals to be compared
Return Value:
- a boolean value. Specifically, interval_starts(interval1, interval2) returns true if and only if
  interval1.start = interval2.start AND interval1.end <= interval2.end
interval_started_by(interval1, interval2) returns true if and only if
```
interval1.start = interval2.start
AND interval2.end <= interval1.end
```
- missing if the argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- any other non-interval input value will cause a type error.

Examples:

{
  "interval_starts": interval_starts(interval(date("2000-01-01"), date("2005-01-01")),
                                     interval(date("2000-01-01"), date("2012-09-09"))),
  "interval_started_by": interval_started_by(interval(date("2006-08-01"), date("2007-03-01")),
                                             interval(date("2006-08-01"), date("2006-08-02")))
};

The expected result is:

{ "interval_starts": true, "interval_started_by": true }

interval_ends, interval_ended_by

Syntax:

interval_ends(interval1, interval2)
interval_ended_by(interval1, interval2)

These two functions check whether one interval ends with the other interval.
Arguments:
- interval1, interval2: two intervals to be compared
Return Value:
- a boolean value. Specifically, interval_ends(interval1, interval2) returns true if and only if
  
  interval1.end = interval2.end AND interval1.start >= interval2.start
  
  interval_ended_by(interval1, interval2) returns true if and only if
  
  interval2.end = interval1.end AND interval2.start >= interval1.start
- missing if the argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- any other non-interval input value will cause a type error.

Examples:

{
  "interval_ends": interval_ends(interval(date("2000-01-01"), date("2005-01-01")),
                                 interval(date("1998-01-01"), date("2005-01-01"))),
  "interval_ended_by": interval_ended_by(interval(date("2006-08-01"), date("2007-03-01")),
                                         interval(date("2006-09-10"), date("2007-03-01")))
};

The expected result is:

{ "interval_ends": true, "interval_ended_by": true }

Object Functions

get_object_fields

Syntax:
```
get_object_fields(input_object)
```
Access the object field names, type and open status for a given object.
Arguments:
- input_object : a object value.
Return Value:
- an array of object values that include the field_name string, field_type string, is_open boolean (used for debug purposes only: true if field is open and false otherwise), and optional nested orderedList for the values of a nested object,
- missing if the argument is a missing value,
- null if the argument is a null value,
- any other non-object input value will cause a type error.

Example:

get_object_fields(
                  {
                    "id": 1,
                    "project": "AsterixDB",
                    "address": {"city": "Irvine", "state": "CA"},
                    "related": ["Hivestrix", "Preglix", "Apache VXQuery"]
                  }
                 );

The expected result is:

[
  { "field-name": "id", "field-type": "INT64", "is-open": false },
  { "field-name": "project", "field-type": "STRING", "is-open": false },
  { "field-name": "address", "field-type": "RECORD", "is-open": false,
    "nested": [
                { "field-name": "city", "field-type": "STRING", "is-open": false },
                { "field-name": "state", "field-type": "STRING", "is-open": false }
              ]
  },
  { "field-name":
        "related",
        "field-type": "ORDEREDLIST",
        "is-open": false,
        "list": [
                  { "field-type": "STRING" },
                  { "field-type": "STRING" },
                  { "field-type": "STRING" }
                ]
  }
]

]

get_object_field_value

Syntax:

get_object_field_value(input_object, string)

Access the field name given in the string_expression from the object_expression.
Arguments:
- input_object : a object value.
- string : a string representing the top level field name.
Return Value:
- an any value saved in the designated field of the object,
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- a type error will be raised if:
  - the first argument is any other non-object value,
  - or, the second argument is any other non-string value.

Example:

get_object_field_value({
                         "id": 1,
                         "project": "AsterixDB",
                         "address": {"city": "Irvine", "state": "CA"},
                         "related": ["Hivestrix", "Preglix", "Apache VXQuery"]
                        },
                        "project"
                       );

The expected result is:
```
"AsterixDB"
```

object_remove_fields

Syntax:

object_remove_fields(input_object, field_names)

Remove indicated fields from a object given a list of field names.
Arguments:
- input_object: a object value.
- field_names: an array of strings and/or array of array of strings.
Return Value:
- a new object value without the fields listed in the second argument,
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- a type error will be raised if:
  - the first argument is any other non-object value,
  - or, the second argument is any other non-array value or recursively contains non-string items.

Example:

object_remove_fields(
                       {
                         "id":1,
                         "project":"AsterixDB",
                         "address":{"city":"Irvine", "state":"CA"},
                         "related":["Hivestrix", "Preglix", "Apache VXQuery"]
                       },
                       [["address", "city"], "related"]
                     );

The expected result is:

{
  "id":1,
  "project":"AsterixDB",
  "address":{ "state": "CA" }
}

object_add_fields

Syntax:

object_add_fields(input_object, fields)

Add fields to a object given a list of field names.
Arguments:
- input_object : a object value.
- fields: an array of field descriptor objects where each object has field_name and field_value.
Return Value:
- a new object value with the new fields included,
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- a type error will be raised if:
  - the first argument is any other non-object value,
  - the second argument is any other non-array value, or contains non-object items.

Example:

object_add_fields(
                   {
                     "id":1,
                     "project":"AsterixDB",
                     "address":{"city":"Irvine", "state":"CA"},
                     "related":["Hivestrix", "Preglix", "Apache VXQuery"]
                    },
                    [{"field-name":"employment_location", "field-value":create_point(30.0,70.0)}]
                  );

The expected result is:

{
   "id":1,
   "project":"AsterixDB",
   "address":{"city":"Irvine", "state":"CA"},
   "related":["Hivestrix", "Preglix", "Apache VXQuery"]
   "employment_location": point("30.0,70.0")
 }

object_merge

Syntax:
```
object_merge(object1, object2)
```
Merge two different objects into a new object.
Arguments:
- object1 : a object value.
- object2 : a object value.
Return Value:
- a new object value with fields from both input objects. If a field’s names in both objects are the same, an exception is issued,
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- any other non-object input value will cause a type error.

Example:

object_merge(
              {
                "id":1,
                "project":"AsterixDB",
                "address":{"city":"Irvine", "state":"CA"},
                "related":["Hivestrix", "Preglix", "Apache VXQuery"]
              },
              {
                "user_id": 22,
                "employer": "UC Irvine",
                "employment_type": "visitor"
              }
            );

The expected result is:

{
  "employment_type": "visitor",
  "address": {
    "city": "Irvine",
    "state": "CA"
  },
  "related": [
    "Hivestrix",
    "Preglix",
    "Apache VXQuery"
  ],
  "user_id": 22,
  "project": "AsterixDB",
  "employer": "UC Irvine",
  "id": 1
}

object_length

Syntax:
```
object_length(input_object)
```
Returns number of top-level fields in the given object
Arguments:
- input_object : an object value.
Return Value:
- an integer that represents the number of top-level fields in the given object,
- missing if the argument is a missing value,
- null if the argument is a null value or any other non-object value

Example:

object_length(
               {
                 "id": 1,
                 "project": "AsterixDB",
                 "address": {"city": "Irvine", "state": "CA"},
               }
             );

The expected result is:
```
3
```

object_names

Syntax:
```
object_names(input_object)
```
Returns names of top-level fields in the given object
Arguments:
- input_object : an object value.
Return Value:
- an array with top-level field names of the given object,
- missing if the argument is a missing value,
- null if the argument is a null value or any other non-object value

Example:

object_names(
               {
                 "id": 1,
                 "project": "AsterixDB",
                 "address": {"city": "Irvine", "state": "CA"},
               }
             );

The expected result is:
```
[ "id", "project", "address" ]
```

object_remove

Syntax:

object_remove(input_object, field_name)

Returns a new object that has the same fields as the input object except the field to be removed
Arguments:
- input_object : an object value.
- field_name : a string field name.
Return Value:
- A new object that has the same fields as input_object except the field field_name,
- missing if the argument input_object or field_name is missing,
- null if the argument input_object is null or any other non-object value, or the argument field_name is null or any other non-string value.

Example:

object_remove(
               {
                 "id": 1,
                 "project": "AsterixDB",
                 "address": {"city": "Irvine", "state": "CA"}
               }
               , "address"
             );

The expected result is:

{
  "id": 1,
  "project": "AsterixDB",
}

object_rename

Syntax:

object_rename(input_object, old_field, new_field)

Returns a new object that has the same fields as input_object with field old_field replaced by new_field
Arguments:
- input_object : an object value.
- old_field : a string representing the old (original) field name inside the object input_object.
- new_field : a string representing the new field name to replace old_field inside the object input_object.
Return Value:
- A new object that has the same fields as input_object with field old_field replaced by new_field,
- missing if any argument is a missing value,
- null if any argument is null or input_object is non-object value, or old_field is non-string value, or new_field is any non-string value.

Example:

object_rename(
               {
                 "id": 1,
                 "project": "AsterixDB",
                 "address": {"city": "Irvine", "state": "CA"}
               }
               , "address"
               , "location"
             );

The expected result is:

{
  "id": 1,
  "project": "AsterixDB",
  "location": {"city": "Irvine", "state": "CA"}
}

object_unwrap

Syntax:
```
object_unwrap(input_object)
```
Returns the value of the single name-value pair that appears in input_object.
Arguments:
- input_object : an object value that consists of exactly one name-value pair.
Return Value:
- The value of the single name-value pair that appears in input_object,
- missing if input_object is missing,
- null if input_object is null, or an empty object, or there is more than one name-value pair in input_object, or any non-object value.

Example:

object_unwrap(
             {
               "id": 1
             }
           );

The expected result is:
```
{
  1
}
```

object_replace

Syntax:

object_replace(input_object, old_value, new_value)

Returns a new object that has the same fields as input_object with all occurrences of value old_value replaced by new_value
Arguments:
- input_object : an object value.
- old_value : a primitive type value to be replaced by new_value.
- new_value : a value to replace old_value.
Return Value:
- A new object that has the same fields as input_object with all occurrences of value old_value replaced by new_value,
- missing if any argument is a missing value,
- null if input_object or old_value is null,
- a type error will be raised if:
  - old_value is not a primitive type value.

Example:

object_replace(
               {
                 "id": 1,
                 "project": "AsterixDB",
                 "address": {"city": "Irvine", "state": "CA"}
               }
               , "AsterixDB"
               , "Apache AsterixDB"
             );

The expected result is:

{
  "id": 1,
  "project": "Apache AsterixDB",
  "location": {"city": "Irvine", "state": "CA"}
}

object_add

Syntax:

object_add(input_object, field_name, field_value)

Returns a new object that has the same fields as input_object as well as the new field field_name.
Arguments:
- input_object : an object value.
- field_name : a string representing a field name to be added.
- field_value : a value to be assigned to the new field field_name.
Return Value:
- A new object that has the same fields as input_object as well as the new field field_name,
- missing if input_object or field_name is missing,
- null if input_object or field_name is null, or input_object is not an object, or field_name is not a string,
- input_object if field_namealready exists in input_object or field_value is missing.

Example:

object_add(
               {
                 "id": 1,
                 "project": "AsterixDB",
                 "address": {"city": "Irvine", "state": "CA"}
               }
               , "company"
               , "Apache"
             );

The expected result is:

{
  "id": 1,
  "project": "AsterixDB",
  "location": {"city": "Irvine", "state": "CA"},
  "company": "Apache"
}

object_put

Syntax:

object_put(input_object, field_name, field_value)

Adds, modifies, or removes a field of an object.
Arguments:
- input_object : an object value.
- field_name : a string representing a field name to be added.
- field_value : a value to be assigned to the new field field_name.
Return Value:
- a new object that has the same fields as input_object as well as the new field field_name, or with updated field_name value to field_value if field_name already exists in input_object, or with field_nameremoved if field_name already exists in input_object and field_value is missing,
- missing if input_object or field_name is missing,
- null if input_object or field_name is null, or input_object is not an object, or field_name is not not a string.

Example:

object_put(
               {
                 "id": 1,
                 "project": "AsterixDB",
                 "address": {"city": "Irvine", "state": "CA"}
               }
               , "project"
               , "Apache AsterixDB"
             );

The expected result is:

{
  "id": 1,
  "project": "Apache AsterixDB",
  "location": {"city": "Irvine", "state": "CA"}
}

object_values

Syntax:
```
object_values(input_object)
```
Returns an array of the values of the fields in input_object.
Arguments:
- input_object : an object value.
Return Value:
- An array of the values of the fields in input_object,
- missing if input_object is missing,
- null if input_object is null or any non-object value.

Example:

object_values(
               {
                 "id": 1,
                 "project": "AsterixDB",
                 "address": {"city": "Irvine", "state": "CA"}
               }
             );

The expected result is:

[
  1,
  "AsterixDB",
  {"city": "Irvine", "state": "CA"}
]

object_pairs

Syntax:
```
object_pairs(input_object)
```
Returns an array of objects describing fields of input_object. For each field of the input_object the returned array contains an object with two fields name and value which are set to the input_object’s field name and value.
Arguments:
- input_object : an object value.
Return Value:
- An array of the name/value pairs of the fields in input_object,
- missing if input_object is missing,
- null if input_object is null or any non-object value.

Example:

object_pairs(
              {
                "id": 1,
                "project": "AsterixDB",
                "address": {"city": "Irvine", "state": "CA"}
              }
            );

The expected result is:

[
  { "name": "id", "value": 1 },
  { "name": "project", "value": "AsterixDB" },
  { "name": "address", "value": {"city": "Irvine", "state": "CA"} }
]

pairs

Syntax:
```
pairs(input_object)
```
Returns an array of arrays describing fields of input_object, including nested fields. For each field of the input_object the returned array contains an array with two elements. The first element is the name and the second one is the value of the input_object’s field. The input object is introspected recursively, so all fields of its nested objects are returned. Nested objects contained in arrays and multisets are also processed by this function.
Arguments:
- input_object : an object value (or an array or a multiset)
Return Value:
- An array of arrays with name, value pairs of the fields in input_object, including nested fields. Each inner array has exactly two items: name and value of the input_object’s field.
- missing if input_object is missing,
- null if input_object is null or a value of a primitive data type.

Example:

pairs(
       {
         "id": 1,
         "project": "AsterixDB",
         "address": {"city": "Irvine", "state": "CA"}
       }
     );

The expected result is:

[
  [ "id", 1 ],
  [ "project", "AsterixDB" ],
  [ "address", { "city": "Irvine", "state": "CA" } ],
  [ "city", "Irvine" ],
  [ "state", "CA" ]
]

Aggregate Functions (Array Functions)

This section contains detailed descriptions of the built-in aggregate functions in the query language.

The query language also supports standard SQL aggregate functions (e.g., MIN, MAX, SUM, COUNT, and AVG). Note that these are not real functions in the query language, but just syntactic sugars over corresponding builtin aggregate functions (e.g., ARRAY_MIN, ARRAY_MAX, ARRAY_SUM, ARRAY_COUNT, and ARRAY_AVG). Refer to Aggregation Pseudo-Functions for details.

The DISTINCT keyword may be used with built-in aggregate functions and standard SQL aggregate functions. It may also be used with aggregate functions used as window functions. It determines whether the function aggregates all values in the group, or distinct values only. Refer to Function Calls for details.

Aggregate functions may be used as window functions when they are used with an OVER clause. Refer to OVER Clauses for details.

array_count

Syntax:
```
array_count(collection)
```
Gets the number of non-null and non-missing items in the given collection.
Arguments:
- collection could be:
  - an array or multiset to be counted,
  - or, a null value,
  - or, a missing value.
Return Value:
- a bigint value representing the number of non-null and non-missing items in the given collection,
- null is returned if the input is null or missing,
- any other non-array and non-multiset input value will cause an error.

Example:

array_count( ['hello', 'world', 1, 2, 3, null, missing] );

The expected result is:
```
5
```

array_avg

Syntax:
```
array_avg(num_collection)
```
Gets the average value of the non-null and non-missing numeric items in the given collection.
Arguments:
- num_collection could be:
  - an array or multiset containing numeric values, nulls or missings,
  - or, a null value,
  - or, a missing value.
Return Value:
- a double value representing the average of the non-null and non-missing numbers in the given collection,
- null is returned if the input is null or missing,
- null is returned if the given collection does not contain any non-null and non-missing items,
- any other non-array and non-multiset input value will cause a type error,
- any other non-numeric value in the input collection will cause a type error.
Example:
```
array_avg( [1.2, 2.3, 3.4, 0, null] );
```
The expected result is:
```
1.725
```

array_sum

Syntax:
```
array_sum(num_collection)
```
Gets the sum of non-null and non-missing items in the given collection.
Arguments:
- num_collection could be:
  - an array or multiset containing numeric values, nulls or missings,
  - or, a null value,
  - or, a missing value.
Return Value:
- the sum of the non-null and non-missing numbers in the given collection. The returning type is decided by the item type with the highest order in the numeric type promotion order (tinyint-> smallint->integer->bigint->float->double) among items.
- null is returned if the input is null or missing,
- null is returned if the given collection does not contain any non-null and non-missing items,
- any other non-array and non-multiset input value will cause a type error,
- any other non-numeric value in the input collection will cause a type error.

Example:

array_sum( [1.2, 2.3, 3.4, 0, null, missing] );

The expected result is:
```
6.9
```

array_min

Syntax:
```
array_min(num_collection)
```
Gets the min value of non-null and non-missing comparable items in the given collection.
Arguments:
- num_collection could be:
  - an array or multiset,
  - or, a null value,
  - or, a missing value.
Return Value:
- the min value of non-null and non-missing values in the given collection. The returning type is decided by the item type with the highest order in the type promotion order (tinyint-> smallint->integer->bigint->float->double) among numeric items.
- null is returned if the input is null or missing,
- null is returned if the given collection does not contain any non-null and non-missing items,
- multiple incomparable items in the input array or multiset will cause a type error,
- any other non-array and non-multiset input value will cause a type error.

Example:

array_min( [1.2, 2.3, 3.4, 0, null, missing] );

The expected result is:
```
0.0
```

array_max

Syntax:
```
array_max(num_collection)
```
Gets the max value of the non-null and non-missing comparable items in the given collection.
Arguments:
- num_collection could be:
  - an array or multiset,
  - or, a null value,
  - or, a missing value.
Return Value:
- the max value of non-null and non-missing numbers in the given collection. The returning type is decided by the item type with the highest order in the type promotion order (tinyint-> smallint->integer->bigint->float->double) among numeric items.
- null is returned if the input is null or missing,
- null is returned if the given collection does not contain any non-null and non-missing items,
- multiple incomparable items in the input array or multiset will cause a type error,
- any other non-array and non-multiset input value will cause a type error.

Example:

array_max( [1.2, 2.3, 3.4, 0, null, missing] );

The expected result is:
```
3.4
```

array_stddev_samp

Syntax:
```
array_stddev_samp(num_collection)
```
Gets the sample standard deviation value of the non-null and non-missing numeric items in the given collection.
Arguments:
- num_collection could be:
  - an array or multiset containing numeric values, nulls or missings,
  - or, a null value,
  - or, a missing value.
Return Value:
- a double value representing the sample standard deviation of the non-null and non-missing numbers in the given collection,
- null is returned if the input is null or missing,
- null is returned if the given collection does not contain any non-null and non-missing items,
- any other non-array and non-multiset input value will cause a type error,
- any other non-numeric value in the input collection will cause a type error.

Example:

array_stddev_samp( [1.2, 2.3, 3.4, 0, null] );

The expected result is:
```
1.4591664287073858
```

array_stddev_pop

Syntax:
```
array_stddev_pop(num_collection)
```
Gets the population standard deviation value of the non-null and non-missing numeric items in the given collection.
Arguments:
- num_collection could be:
  - an array or multiset containing numeric values, nulls or missings,
  - or, a null value,
  - or, a missing value.
Return Value:
- a double value representing the population standard deviation of the non-null and non-missing numbers in the given collection,
- null is returned if the input is null or missing,
- null is returned if the given collection does not contain any non-null and non-missing items,
- any other non-array and non-multiset input value will cause a type error,
- any other non-numeric value in the input collection will cause a type error.

Example:

array_stddev_pop( [1.2, 2.3, 3.4, 0, null] );

The expected result is:
```
1.2636751956100112
```

array_var_samp

Syntax:
```
array_var_samp(num_collection)
```
Gets the sample variance value of the non-null and non-missing numeric items in the given collection.
Arguments:
- num_collection could be:
  - an array or multiset containing numeric values, nulls or missings,
  - or, a null value,
  - or, a missing value.
Return Value:
- a double value representing the sample variance of the non-null and non-missing numbers in the given collection,
- null is returned if the input is null or missing,
- null is returned if the given collection does not contain any non-null and non-missing items,
- any other non-array and non-multiset input value will cause a type error,
- any other non-numeric value in the input collection will cause a type error.

Example:

array_var_samp( [1.2, 2.3, 3.4, 0, null] );

The expected result is:
```
2.1291666666666664
```

array_var_pop

Syntax:
```
array_var_pop(num_collection)
```
Gets the population variance value of the non-null and non-missing numeric items in the given collection.
Arguments:
- num_collection could be:
  - an array or multiset containing numeric values, nulls or missings,
  - or, a null value,
  - or, a missing value.
Return Value:
- a double value representing the population variance of the non-null and non-missing numbers in the given collection,
- null is returned if the input is null or missing,
- null is returned if the given collection does not contain any non-null and non-missing items,
- any other non-array and non-multiset input value will cause a type error,
- any other non-numeric value in the input collection will cause a type error.

Example:

array_var_pop( [1.2, 2.3, 3.4, 0, null] );

The expected result is:
```
1.5968749999999998
```

array_skewness

Syntax:
```
array_skewness(num_collection)
```
Gets the skewness value of the non-null and non-missing numeric items in the given collection.
Arguments:
- num_collection could be:
  - an array or multiset containing numeric values, nulls or missings,
  - or, a null value,
  - or, a missing value.
Return Value:
- a double value representing the skewness of the non-null and non-missing numbers in the given collection,
- null is returned if the input is null or missing,
- null is returned if the given collection does not contain any non-null and non-missing items,
- any other non-array and non-multiset input value will cause a type error,
- any other non-numeric value in the input collection will cause a type error.

Example:

array_skewness( [1.2, 2.3, 3.4, 0, null] );

The expected result is:
```
-0.04808451539164242
```

array_kurtosis

Syntax:
```
array_kurtosis(num_collection)
```
Gets the kurtosis value from the normal distribution of the non-null and non-missing numeric items in the given collection.
Arguments:
- num_collection could be:
  - an array or multiset containing numeric values, nulls or missings,
  - or, a null value,
  - or, a missing value.
Return Value:
- a double value representing the kurtosis from a normal distribution of the non-null and non-missing numbers in the given collection,
- null is returned if the input is null or missing,
- null is returned if the given collection does not contain any non-null and non-missing items,
- any other non-array and non-multiset input value will cause a type error,
- any other non-numeric value in the input collection will cause a type error.

Example:

array_kurtosis( [1.2, 2.3, 3.4, 0, null] );

The expected result is:
```
-1.342049701096427
```

strict_count

Syntax:
```
strict_count(collection)
```
Gets the number of items in the given collection.
Arguments:
- collection could be:
  - an array or multiset containing the items to be counted,
  - or a null value,
  - or a missing value.
Return Value:
- a bigint value representing the number of items in the given collection,
- null is returned if the input is null or missing.
Example:
```
strict_count( [1, 2, null, missing] );
```
The expected result is:
```
4
```

strict_avg

Syntax:
```
strict_avg(num_collection)
```
Gets the average value of the numeric items in the given collection.
Arguments:
- num_collection could be:
  - an array or multiset containing numeric values, nulls or missings,
  - or, a null value,
  - or, a missing value.
Return Value:
- a double value representing the average of the numbers in the given collection,
- null is returned if the input is null or missing,
- null is returned if there is a null or missing in the input collection,
- any other non-numeric value in the input collection will cause a type error.
Example:
```
strict_avg( [100, 200, 300] );
```
The expected result is:
```
200.0
```

strict_sum

Syntax:
```
strict_sum(num_collection)
```
Gets the sum of the items in the given collection.
Arguments:
- num_collection could be:
  - an array or multiset containing numeric values, nulls or missings,
  - or, a null value,
  - or, a missing value.
Return Value:
- the sum of the numbers in the given collection. The returning type is decided by the item type with the highest order in the numeric type promotion order (tinyint-> smallint->integer->bigint->float->double) among items.
- null is returned if the input is null or missing,
- null is returned if there is a null or missing in the input collection,
- any other non-numeric value in the input collection will cause a type error.
Example:
```
strict_sum( [100, 200, 300] );
```
The expected result is:
```
600
```

strict_min

Syntax:
```
strict_min(num_collection)
```
Gets the min value of comparable items in the given collection.
Arguments:
- num_collection could be:
  - an array or multiset,
  - or, a null value,
  - or, a missing value.
Return Value:
- the min value of the given collection. The returning type is decided by the item type with the highest order in the type promotion order (tinyint-> smallint->integer->bigint->float->double) among numeric items.
- null is returned if the input is null or missing,
- null is returned if there is a null or missing in the input collection,
- multiple incomparable items in the input array or multiset will cause a type error,
- any other non-array and non-multiset input value will cause a type error.
Example:
```
strict_min( [10.2, 100, 5] );
```
The expected result is:
```
5.0
```

strict_max

Syntax:
```
strict_max(num_collection)
```
Gets the max value of numeric items in the given collection.
Arguments:
- num_collection could be:
  - an array or multiset,
  - or, a null value,
  - or, a missing value.
Return Value:
- The max value of the given collection. The returning type is decided by the item type with the highest order in the type promotion order (tinyint-> smallint->integer->bigint->float->double) among numeric items.
- null is returned if the input is null or missing,
- null is returned if there is a null or missing in the input collection,
- multiple incomparable items in the input array or multiset will cause a type error,
- any other non-array and non-multiset input value will cause a type error.
Example:
```
strict_max( [10.2, 100, 5] );
```
The expected result is:
```
100.0
```

strict_stddev_samp

Syntax:
```
strict_stddev_samp(num_collection)
```
Gets the sample standard deviation value of the numeric items in the given collection.
Arguments:
- num_collection could be:
  - an array or multiset containing numeric values, nulls or missings,
  - or, a null value,
  - or, a missing value.
Return Value:
- a double value representing the sample standard deviation of the numbers in the given collection,
- null is returned if the input is null or missing,
- null is returned if there is a null or missing in the input collection,
- any other non-numeric value in the input collection will cause a type error.
Example:
```
strict_stddev_samp( [100, 200, 300] );
```
The expected result is:
```
100.0
```

strict_stddev_pop

Syntax:
```
strict_stddev_pop(num_collection)
```
Gets the population standard deviation value of the numeric items in the given collection.
Arguments:
- num_collection could be:
  - an array or multiset containing numeric values, nulls or missings,
  - or, a null value,
  - or, a missing value.
Return Value:
- a double value representing the population standard deviation of the numbers in the given collection,
- null is returned if the input is null or missing,
- null is returned if there is a null or missing in the input collection,
- any other non-numeric value in the input collection will cause a type error.
Example:
```
strict_stddev_pop( [100, 200, 300] );
```
The expected result is:
```
81.64965809277261
```

strict_var_samp

Syntax:
```
strict_var_samp(num_collection)
```
Gets the sample variance value of the numeric items in the given collection.
Arguments:
- num_collection could be:
  - an array or multiset containing numeric values, nulls or missings,
  - or, a null value,
  - or, a missing value.
Return Value:
- a double value representing the sample variance of the numbers in the given collection,
- null is returned if the input is null or missing,
- null is returned if there is a null or missing in the input collection,
- any other non-numeric value in the input collection will cause a type error.
Example:
```
strict_var_samp( [100, 200, 300] );
```
The expected result is:
```
10000.0
```

strict_var_pop

Syntax:
```
strict_var_pop(num_collection)
```
Gets the population variance value of the numeric items in the given collection.
Arguments:
- num_collection could be:
  - an array or multiset containing numeric values, nulls or missings,
  - or, a null value,
  - or, a missing value.
Return Value:
- a double value representing the population variance of the numbers in the given collection,
- null is returned if the input is null or missing,
- null is returned if there is a null or missing in the input collection,
- any other non-numeric value in the input collection will cause a type error.
Example:
```
strict_var_pop( [100, 200, 300] );
```
The expected result is:
```
6666.666666666667
```

strict_skewness

Syntax:
```
strict_skewness(num_collection)
```
Gets the skewness value of the numeric items in the given collection.
Arguments:
- num_collection could be:
  - an array or multiset containing numeric values, nulls or missings,
  - or, a null value,
  - or, a missing value.
Return Value:
- a double value representing the skewness of the numbers in the given collection,
- null is returned if the input is null or missing,
- null is returned if there is a null or missing in the input collection,
- any other non-numeric value in the input collection will cause a type error.
Example:
```
strict_skewness( [100, 200, 300] );
```
The expected result is:
```
0.0
```

strict_kurtosis

Syntax:
```
strict_kurtosis(num_collection)
```
Gets the kurtosis value from the normal distribution of the numeric items in the given collection.
Arguments:
- num_collection could be:
  - an array or multiset containing numeric values, nulls or missings,
  - or, a null value,
  - or, a missing value.
Return Value:
- a double value representing the kurtosis from a normal distribution of the numbers in the given collection,
- null is returned if the input is null or missing,
- null is returned if there is a null or missing in the input collection,
- any other non-numeric value in the input collection will cause a type error.
Example:
```
strict_kurtosis( [100, 200, 300] );
```
The expected result is:
```
-1.5
```

Comparison Functions

greatest

Syntax:

greatest(numeric_value1, numeric_value2, ...)

Computes the greatest value among arguments.
Arguments:
- numeric_value1: a tinyint/smallint/integer/bigint/float/double value,
- numeric_value2: a tinyint/smallint/integer/bigint/float/double value,
- ….
Return Value:
- the greatest values among arguments. The returning type is decided by the item type with the highest order in the numeric type promotion order (tinyint-> smallint->integer->bigint->float->double) among items.
- null if any argument is a missing value or null value,
- any other non-numeric input value will cause a type error.

Example:

{ "v1": greatest(1, 2, 3), "v2": greatest(float("0.5"), double("-0.5"), 5000) };

The expected result is:
```
{ "v1": 3, "v2": 5000.0 }
```

least

Syntax:

least(numeric_value1, numeric_value2, ...)

Computes the least value among arguments.
Arguments:
- numeric_value1: a tinyint/smallint/integer/bigint/float/double value,
- numeric_value2: a tinyint/smallint/integer/bigint/float/double value,
- ….
Return Value:
- the least values among arguments. The returning type is decided by the item type with the highest order in the numeric type promotion order (tinyint-> smallint->integer->bigint->float->double) among items.
- null if any argument is a missing value or null value,
- any other non-numeric input value will cause a type error.

Example:

{ "v1": least(1, 2, 3), "v2": least(float("0.5"), double("-0.5"), 5000) };

The expected result is:
```
{ "v1": 1, "v2": -0.5 }
```

Type Functions

is_array

Syntax:
```
is_array(expr)
```
Checks whether the given expression is evaluated to be an array value.
Arguments:
- expr : an expression (any type is allowed).
Return Value:
- a boolean on whether the argument is an array value or not,
- a missing if the argument is a missing value,
- a null if the argument is a null value.

Example:

{
  "a": is_array(true),
  "b": is_array(false),
  "c": isarray(null),
  "d": isarray(missing),
  "e": isarray("d"),
  "f": isarray(4.0),
  "g": isarray(5),
  "h": isarray(["1", 2]),
  "i": isarray({"a":1})
};

The expected result is:

{ "a": false, "b": false, "c": null, "e": false, "f": false, "g": false, "h": true, "i": false }

The function has an alias isarray.

is_multiset

Syntax:
```
is_multiset(expr)
```
Checks whether the given expression is evaluated to be an multiset value.
Arguments:
- expr : an expression (any type is allowed).
Return Value:
- a boolean on whether the argument is an multiset value or not,
- a missing if the argument is a missing value,
- a null if the argument is a null value.

Example:

{
  "a": is_multiset(true),
  "b": is_multiset(false),
  "c": is_multiset(null),
  "d": is_multiset(missing),
  "e": is_multiset("d"),
  "f": ismultiset(4.0),
  "g": ismultiset(["1", 2]),
  "h": ismultiset({"a":1}),
  "i": ismultiset({{"hello", 9328, "world", [1, 2, null]}})
};

The expected result is:

{ "a": false, "b": false, "c": null, "e": false, "f": false, "g": false, "h": false, "i": true }

The function has an alias ismultiset.

is_atomic (is_atom)

Syntax:
```
is_atomic(expr)
```
Checks whether the given expression is evaluated to be a value of a primitive type.
Arguments:
- expr : an expression (any type is allowed).
Return Value:
- a boolean on whether the argument is a primitive type or not,
- a missing if the argument is a missing value,
- a null if the argument is a null value.

Example:

{
  "a": is_atomic(true),
  "b": is_atomic(false),
  "c": isatomic(null),
  "d": isatomic(missing),
  "e": isatomic("d"),
  "f": isatom(4.0),
  "g": isatom(5),
  "h": isatom(["1", 2]),
  "i": isatom({"a":1})
};

The expected result is:

{ "a": true, "b": true, "c": null, "e": true, "f": true, "g": true, "h": false, "i": false }

The function has three aliases: isatomic, is_atom, and isatom.

is_boolean (is_bool)

Syntax:
```
is_boolean(expr)
```
Checks whether the given expression is evaluated to be a boolean value.
Arguments:
- expr : an expression (any type is allowed).
Return Value:
- a boolean on whether the argument is a boolean value or not,
- a missing if the argument is a missing value,
- a null if the argument is a null value.

Example:

{
  "a": isboolean(true),
  "b": isboolean(false),
  "c": is_boolean(null),
  "d": is_boolean(missing),
  "e": isbool("d"),
  "f": isbool(4.0),
  "g": isbool(5),
  "h": isbool(["1", 2]),
  "i": isbool({"a":1})
};

The expected result is:

{ "a": true, "b": true, "c": null, "e": false, "f": false, "g": false, "h": false, "i": false }

The function has three aliases: isboolean, is_bool, and isbool.

is_number (is_num)

Syntax:
```
is_number(expr)
```
Checks whether the given expression is evaluated to be a numeric value.
Arguments:
- expr : an expression (any type is allowed).
Return Value:
- a boolean on whether the argument is a smallint/tinyint/integer/bigint/float/double value or not,
- a missing if the argument is a missing value,
- a null if the argument is a null value.

Example:

{
  "a": is_number(true),
  "b": is_number(false),
  "c": isnumber(null),
  "d": isnumber(missing),
  "e": isnumber("d"),
  "f": isnum(4.0),
  "g": isnum(5),
  "h": isnum(["1", 2]),
  "i": isnum({"a":1})
};

The expected result is:

{ "a": false, "b": false, "c": null, "e": false, "f": true, "g": true, "h": false, "i": false }

The function has three aliases: isnumber, is_num, and isnum.

is_object (is_obj)

Syntax:
```
is_object(expr)
```
Checks whether the given expression is evaluated to be a object value.
Arguments:
- expr : an expression (any type is allowed).
Return Value:
- a boolean on whether the argument is a object value or not,
- a missing if the argument is a missing value,
- a null if the argument is a null value.

Example:

{
  "a": is_object(true),
  "b": is_object(false),
  "c": isobject(null),
  "d": isobject(missing),
  "e": isobj("d"),
  "f": isobj(4.0),
  "g": isobj(5),
  "h": isobj(["1", 2]),
  "i": isobj({"a":1})
};

The expected result is:

{ “a”: false, “b”: false, “c”: null, “e”: false, “f”: false, “g”: false, “h”: false, “i”: true }

The function has three aliases: isobject, is_obj, and isobj.

is_string (is_str)

Syntax:
```
is_string(expr)
```
Checks whether the given expression is evaluated to be a string value.
Arguments:
- expr : an expression (any type is allowed).
Return Value:
- a boolean on whether the argument is a string value or not,
- a missing if the argument is a missing value,
- a null if the argument is a null value.

Example:

{
  "a": is_string(true),
  "b": isstring(false),
  "c": isstring(null),
  "d": isstr(missing),
  "e": isstr("d"),
  "f": isstr(4.0),
  "g": isstr(5),
  "h": isstr(["1", 2]),
  "i": isstr({"a":1})
};

The expected result is:

{ "a": false, "b": false, "c": null, "e": true, "f": false, "g": false, "h": false, "i": false }

The function has three aliases: isstring, is_str, and isstr.

is_null

Syntax:
```
is_null(expr)
```
Checks whether the given expression is evaluated to be a null value.
Arguments:
- expr : an expression (any type is allowed).
Return Value:
- a boolean on whether the variable is a null or not,
- a missing if the input is missing.

Example:

{ "v1": is_null(null), "v2": is_null(1), "v3": is_null(missing) };

The expected result is:
```
{ "v1": true, "v2": false }
```

The function has an alias isnull.

is_missing

Syntax:
```
is_missing(expr)
```
Checks whether the given expression is evaluated to be a missing value.
Arguments:
- expr : an expression (any type is allowed).
Return Value:
- a boolean on whether the variable is a missing or not.

Example:

{ "v1": is_missing(null), "v2": is_missing(1), "v3": is_missing(missing) };

The expected result is:

{ "v1": false, "v2": false, "v3": true }

The function has an alias ismissing.

is_unknown

Syntax:
```
is_unknown(expr)
```
Checks whether the given variable is a null value or a missing value.
Arguments:
- expr : an expression (any type is allowed).
Return Value:
- a boolean on whether the variable is a null/``missingvalue (true) or not (false`).

Example:

{ "v1": is_unknown(null), "v2": is_unknown(1), "v3": is_unknown(missing) };

The expected result is:

{ "v1": true, "v2": false, "v3": true }

The function has an alias isunknown.

is_binary (is_bin)

Syntax:
```
is_binary(expr)
```
Checks whether the given expression is evaluated to be a binary value.
Arguments:
- expr : an expression (any type is allowed).
Return Value:
- a boolean on whether the argument is a binary value or not,
- a missing if the argument is a missing value,
- a null if the argument is a null value.

Example:

{
  "a": is_binary(true),
  "b": is_binary(false),
  "c": isbinary(null),
  "d": isbinary(missing),
  "e": isbin(point("1,2")),
  "f": isbin(hex("ABCDEF0123456789")),
  "g": is_bin(sub_binary(hex("AABBCCDD"), 4)),
  "h": is_bin(2),
  "i": is_bin({"a":1})
};

The expected result is:

{ "a": false, "b": false, "c": null, "e": false, "f": true, "g": true, "h": false, "i": false }

The function has three aliases: isbinary, is_bin, and isbin.

is_uuid

Syntax:
```
is_uuid(expr)
```
Checks whether the given expression is evaluated to be a uuid value.
Arguments:
- expr : an expression (any type is allowed).
Return Value:
- a boolean on whether the argument is a uuid value or not,
- a missing if the argument is a missing value,
- a null if the argument is a null value.

Example:

 {
  "a": is_uuid(true),
  "b": is_uuid(false),
  "c": is_uuid(null),
  "d": is_uuid(missing),
  "e": isuuid(4.0),
  "f": isuuid(date("2013-01-01")),
  "g": isuuid(uuid("5c848e5c-6b6a-498f-8452-8847a2957421"))
};

The expected result is:

{ "a": false, "b": false, "c": null, "e": false, "f": false, "g": true }

The function has an alias isuuid.

is_point

Syntax:
```
is_point(expr)
```
Checks whether the given expression is evaluated to be a point value.
Arguments:
- expr : an expression (any type is allowed).
Return Value:
- a boolean on whether the argument is a point value or not,
- a missing if the argument is a missing value,
- a null if the argument is a null value.

Example:

{
  "a": is_point(true),
  "b": is_point(false),
  "c": is_point(null),
  "d": is_point(missing),
  "e": is_point(point("1,2")),
  "f": ispoint(line("30.0,70.0 50.0,90.0")),
  "g": ispoint(rectangle("30.0,70.0 50.0,90.0")),
  "h": ispoint(circle("30.0,70.0 5.0")),
  "i": ispoint(polygon("1.0,1.0 2.0,2.0 3.0,3.0 4.0,4.0")),
  "j": ispoint(3)
};

The expected result is:

{ "a": false, "b": false, "c": null, "e": true, "f": false, "g": false, "h": false, "i": false, "j": false }

The function has an alias ispoint.

is_line

Syntax:
```
is_line(expr)
```
Checks whether the given expression is evaluated to be a line value.
Arguments:
- expr : an expression (any type is allowed).
Return Value:
- a boolean on whether the argument is a line value or not,
- a missing if the argument is a missing value,
- a null if the argument is a null value.

Example:

{
  "a": is_line(true),
  "b": is_line(false),
  "c": is_line(null),
  "d": is_line(missing),
  "e": is_line(point("1,2")),
  "f": isline(line("30.0,70.0 50.0,90.0")),
  "g": isline(rectangle("30.0,70.0 50.0,90.0")),
  "h": isline(circle("30.0,70.0 5.0")),
  "i": isline(polygon("1.0,1.0 2.0,2.0 3.0,3.0 4.0,4.0")),
  "j": isline(3)
};

The expected result is:

{ "a": false, "b": false, "c": null, "e": false, "f": true, "g": false, "h": false, "i": false, "j": false }

The function has an alias isline.

is_rectangle

Syntax:
```
is_rectangle(expr)
```
Checks whether the given expression is evaluated to be a rectangle value.
Arguments:
- expr : an expression (any type is allowed).
Return Value:
- a boolean on whether the argument is a rectangle value or not,
- a missing if the argument is a missing value,
- a null if the argument is a null value.

Example:

{
  "a": is_rectangle(true),
  "b": is_rectangle(false),
  "c": is_rectangle(null),
  "d": is_rectangle(missing),
  "e": is_rectangle(point("1,2")),
  "f": isrectangle(line("30.0,70.0 50.0,90.0")),
  "g": isrectangle(rectangle("30.0,70.0 50.0,90.0")),
  "h": isrectangle(circle("30.0,70.0 5.0")),
  "i": isrectangle(polygon("1.0,1.0 2.0,2.0 3.0,3.0 4.0,4.0")),
  "j": isrectangle(3)
};

The expected result is:

{ "a": false, "b": false, "c": null, "e": false, "f": false, "g": true, "h": false, "i": false, "j": false }

The function has an alias isrectangle.

is_circle

Syntax:
```
is_circle(expr)
```
Checks whether the given expression is evaluated to be a circle value.
Arguments:
- expr : an expression (any type is allowed).
Return Value:
- a boolean on whether the argument is a circle value or not,
- a missing if the argument is a missing value,
- a null if the argument is a null value.

Example:

{
  "a": is_circle(true),
  "b": is_circle(false),
  "c": is_circle(null),
  "d": is_circle(missing),
  "e": is_circle(point("1,2")),
  "f": iscircle(line("30.0,70.0 50.0,90.0")),
  "g": iscircle(rectangle("30.0,70.0 50.0,90.0")),
  "h": iscircle(circle("30.0,70.0 5.0")),
  "i": iscircle(polygon("1.0,1.0 2.0,2.0 3.0,3.0 4.0,4.0")),
  "j": iscircle(3)
};

The expected result is:

{ "a": false, "b": false, "c": null, "e": false, "f": false, "g": false, "h": true, "i": false, "j": false }

The function has an alias iscircle.

is_polygon

Syntax:
```
is_polygon(expr)
```
Checks whether the given expression is evaluated to be a polygon value.
Arguments:
- expr : an expression (any type is allowed).
Return Value:
- a boolean on whether the argument is a polygon value or not,
- a missing if the argument is a missing value,
- a null if the argument is a null value.

Example:

{
  "a": is_polygon(true),
  "b": is_polygon(false),
  "c": is_polygon(null),
  "d": is_polygon(missing),
  "e": is_polygon(point("1,2")),
  "f": ispolygon(line("30.0,70.0 50.0,90.0")),
  "g": ispolygon(rectangle("30.0,70.0 50.0,90.0")),
  "h": ispolygon(circle("30.0,70.0 5.0")),
  "i": ispolygon(polygon("1.0,1.0 2.0,2.0 3.0,3.0 4.0,4.0")),
  "j": ispolygon(3)
};

The expected result is:

{ "a": false, "b": false, "c": null, "e": false, "f": false, "g": false, "h": false, "i": true, "j": false }

The function has an alias ispolygon.

is_spatial

Syntax:
```
is_spatial(expr)
```
Checks whether the given expression is evaluated to be a spatial value.
Arguments:
- expr : an expression (any type is allowed).
Return Value:
- a boolean on whether the argument is a point/line/rectangle/circle/polygon value or not,
- a missing if the argument is a missing value,
- a null if the argument is a null value.

Example:

{
  "a": is_spatial(true),
  "b": is_spatial(false),
  "c": is_spatial(null),
  "d": is_spatial(missing),
  "e": is_spatial(point("1,2")),
  "f": isspatial(line("30.0,70.0 50.0,90.0")),
  "g": isspatial(rectangle("30.0,70.0 50.0,90.0")),
  "h": isspatial(circle("30.0,70.0 5.0")),
  "i": isspatial(polygon("1.0,1.0 2.0,2.0 3.0,3.0 4.0,4.0")),
  "j": isspatial(3)
};

The expected result is:

{ "a": false, "b": false, "c": null, "e": true, "f": true, "g": true, "h": true, "i": true, "j": false }

The function has an alias isspatial.

is_date

Syntax:
```
is_date(expr)
```
Checks whether the given expression is evaluated to be a date value.
Arguments:
- expr : an expression (any type is allowed).
Return Value:
- a boolean on whether the argument is a date value or not,
- a missing if the argument is a missing value,
- a null if the argument is a null value.

Example:

{
  "a": is_date(true),
  "b": is_date(false),
  "c": is_date(null),
  "d": is_date(missing),
  "e": is_date(date("-19700101")),
  "f": isdate(date("2013-01-01")),
  "g": isdate(time("12:12:12.039Z")),
  "h": isdate(datetime("2013-01-01T12:12:12.039Z")),
  "i": isdate(duration("P100Y12MT12M")),
  "j": isdate(interval(date("2013-01-01"), date("20130505"))),
  "k": isdate(3)
};

The expected result is:

{ "a": false, "b": false, "c": null, "e": true, "f": true, "g": false, "h": false, "i": false, "j": false, "k": false }

The function has an alias isdate.

is_datetime (is_timestamp)

Syntax:
```
is_datetime(expr)
```
Checks whether the given expression is evaluated to be a datetime value.
Arguments:
- expr : an expression (any type is allowed).
Return Value:
- a boolean on whether the argument is a datetime value or not,
- a missing if the argument is a missing value,
- a null if the argument is a null value.

Example:

{
  "a": is_datetime(true),
  "b": is_datetime(false),
  "c": is_datetime(null),
  "d": is_datetime(missing),
  "e": is_datetime(datetime("2016-02-02T12:09:22.023Z")),
  "f": isdatetime(datetime("2011-03-03T12:10:42.011Z")),
  "g": isdatetime(time("12:12:12.039Z")),
  "h": is_timestamp(datetime("2013-01-01T12:12:12.039Z")),
  "i": is_timestamp(duration("P100Y12MT12M")),
  "j": istimestamp(interval(date("2013-01-01"), date("20130505"))),
  "k": istimestamp(3)
};

The expected result is:

{ "a": false, "b": false, "c": null, "e": true, "f": true, "g": false, "h": true, "i": false, "j": false, "k": false }

The function has three aliases: isdatetime, is_timestamp, and istimestamp.

is_time

Syntax:
```
is_time(expr)
```
Checks whether the given expression is evaluated to be a time value.
Arguments:
- expr : an expression (any type is allowed).
Return Value:
- a boolean on whether the argument is a time value or not,
- a missing if the argument is a missing value,
- a null if the argument is a null value.

Example:

 {
  "a": is_time(true),
  "b": is_time(false),
  "c": is_time(null),
  "d": is_time(missing),
  "e": is_time(time("08:00:00.000Z")),
  "f": istime(date("2013-01-01")),
  "g": istime(time("12:12:12.039Z")),
  "h": istime(datetime("2013-01-01T12:12:12.039Z")),
  "i": istime(duration("P100Y12MT12M")),
  "j": istime(interval(date("2013-01-01"), date("20130505"))),
  "k": istime(3)
};

The expected result is:

{ "a": false, "b": false, "c": null, "e": true, "f": false, "g": true, "h": false, "i": false, "j": false, "k": false }

The function has an alias istime.

is_duration

Syntax:
```
is_duration(expr)
```
Checks whether the given expression is evaluated to be a duration value.
Arguments:
- expr : an expression (any type is allowed).
Return Value:
- a boolean on whether the argument is a duration/year_month_duration/day_time_duration value or not,
- a missing if the argument is a missing value,
- a null if the argument is a null value.

Example:

 {
  "a": is_duration(true),
  "b": is_duration(false),
  "c": is_duration(null),
  "d": is_duration(missing),
  "e": is_duration(duration("-PT20.943S")),
  "f": isduration(date("2013-01-01")),
  "g": isduration(time("12:12:12.039Z")),
  "h": isduration(datetime("2013-01-01T12:12:12.039Z")),
  "i": isduration(duration("P100Y12MT12M")),
  "j": isduration(interval(date("2013-01-01"), date("20130505"))),
  "k": isduration(3)
};

The expected result is:

{ "a": false, "b": false, "c": null, "e": true, "f": false, "g": false, "h": false, "i": true, "j": false, "k": false }

The function has an alias isduration.

is_interval

Syntax:
```
is_interval(expr)
```
Checks whether the given expression is evaluated to be a interval value.
Arguments:
- expr : an expression (any type is allowed).
Return Value:
- a boolean on whether the argument is a interval value or not,
- a missing if the argument is a missing value,
- a null if the argument is a null value.

Example:

 {
  "a": is_interval(true),
  "b": is_interval(false),
  "c": is_interval(null),
  "d": is_interval(missing),
  "e": is_interval(interval(datetime("2013-01-01T00:01:01.000Z"), datetime("2013-05-05T13:39:01.049Z"))),
  "f": isinterval(date("2013-01-01")),
  "g": isinterval(time("12:12:12.039Z")),
  "h": isinterval(datetime("2013-01-01T12:12:12.039Z")),
  "i": isinterval(duration("P100Y12MT12M")),
  "j": isinterval(interval(date("2013-01-01"), date("20130505"))),
  "k": isinterval(3)
};

The expected result is:

{ "a": false, "b": false, "c": null, "e": true, "f": false, "g": false, "h": false, "i": false, "j": true, "k": false }

The function has an alias isinterval.

is_temporal

Syntax:
```
is_temporal(expr)
```
Checks whether the given expression is evaluated to be a temporal value.
Arguments:
- expr : an expression (any type is allowed).
Return Value:
- a boolean on whether the argument is a date/datetime/time/duration/year_month_duration/day_time_duration/interval value or not,
- a missing if the argument is a missing value,
- a null if the argument is a null value.

Example:

 {
  "a": is_temporal(true),
  "b": is_temporal(false),
  "c": is_temporal(null),
  "d": is_temporal(missing),
  "e": is_temporal(duration("-PT20.943S")),
  "f": istemporal(date("2013-01-01")),
  "g": istemporal(time("12:12:12.039Z")),
  "h": istemporal(datetime("2013-01-01T12:12:12.039Z")),
  "i": istemporal(duration("P100Y12MT12M")),
  "j": istemporal(interval(date("2013-01-01"), date("20130505"))),
  "k": istemporal(3)
};

The expected result is:

{ "a": false, "b": false, "c": null, "e": true, "f": true, "g": true, "h": true, "i": true, "j": true, "k": false }

The function has an alias istemporal.

get_type

Syntax:
```
get_type(expr)
```
Returns a string describing the type of the given expr. This includes incomplete information types (i.e. missing and null).
Arguments:
- expr : an expression (any type is allowed).

Example:

{
  "a": get_type(true),
  "b": get_type(false),
  "c": get_type(null),
  "d": get_type(missing),
  "e": get_type("d"),
  "f": gettype(4.0),
  "g": gettype(5),
  "h": gettype(["1", 2]),
  "i": gettype({"a":1})
};

The expected result is:

{ "a": "boolean", "b": "boolean", "c": "null", "d": "missing", "e": "string", "f": "double", "g": "bigint", "h": "array", "i": "object" }

The function has an alias gettype.

to_array

Syntax:
```
to_array(expr)
```
Converts input value to an array value
Arguments:
- expr : an expression
Return Value:
- if the argument is missing then missing is returned
- if the argument is null then null is returned
- if the argument is of array type then it is returned as is
- if the argument is of multiset type then it is returned as an array with elements in an undefined order
- otherwise an array containing the input expression as its single item is returned

Example:

{
  "v1": to_array("asterix"),
  "v2": to_array(["asterix"]),
};

The expected result is:

{ "v1": ["asterix"], "v2": ["asterix"] }

The function has an alias toarray.

to_atomic (to_atom)

Syntax:
```
to_atomic(expr)
```
Converts input value to a primitive value
Arguments:
- expr : an expression
Return Value:
- if the argument is missing then missing is returned
- if the argument is null then null is returned
- if the argument is of primitive type then it is returned as is
- if the argument is of array or multiset type and has only one element then the result of invoking to_atomic() on that element is returned
- if the argument is of object type and has only one field then the result of invoking to_atomic() on the value of that field is returned
- otherwise null is returned

Example:

{
  "v1": to_atomic("asterix"),
  "v2": to_atomic(["asterix"]),
  "v3": to_atomic([0, 1]),
  "v4": to_atomic({"value": "asterix"}),
  "v5": to_number({"x": 1, "y": 2})
};

The expected result is:

{ "v1": "asterix", "v2": "asterix", "v3": null, "v4": "asterix", "v5": null }

The function has three aliases: toatomic, to_atom, and toatom.

to_boolean (to_bool)

Syntax:
```
to_boolean(expr)
```
Converts input value to a boolean value
Arguments:
- expr : an expression
Return Value:
- if the argument is missing then missing is returned
- if the argument is null then null is returned
- if the argument is of boolean type then it is returned as is
- if the argument is of numeric type then false is returned if it is 0 or NaN, otherwise true
- if the argument is of string type then false is returned if it’s empty, otherwise true
- if the argument is of array or multiset type then false is returned if it’s size is 0, otherwise true
- if the argument is of object type then false is returned if it has no fields, otherwise true
- type error is raised for all other input types

Example:

{
  "v1": to_boolean(0),
  "v2": to_boolean(1),
  "v3": to_boolean(""),
  "v4": to_boolean("asterix")
};

The expected result is:

{ "v1": false, "v2": true, "v3": false, "v4": true }

The function has three aliases: toboolean, to_bool, and tobool.

to_bigint

Syntax:
```
to_bigint(expr)
```
Converts input value to an integer value
Arguments:
- expr : an expression
Return Value:
- if the argument is missing then missing is returned
- if the argument is null then null is returned
- if the argument is of boolean type then 1 is returned if it is true, 0 if it is false
- if the argument is of numeric integer type then it is returned as the same value of bigint type
- if the argument is of numeric float/double type then it is converted to bigint type
- if the argument is of string type and can be parsed as integer then that integer value is returned, otherwise null is returned
- if the argument is of array/multiset/object type then null is returned
- type error is raised for all other input types

Example:

{
  "v1": to_bigint(false),
  "v2": to_bigint(true),
  "v3": to_bigint(10),
  "v4": to_bigint(float("1e100")),
  "v5": to_bigint(double("1e1000")),
  "v6": to_bigint("20")
};

The expected result is:

{ "v1": 0, "v2": 1, "v3": 10, "v4": 9223372036854775807, "v5": 9223372036854775807, "v6": 20 }

The function has an alias tobigint.

to_double

Syntax:
```
to_double(expr)
```
Converts input value to a double value
Arguments:
- expr : an expression
Return Value:
- if the argument is missing then missing is returned
- if the argument is null then null is returned
- if the argument is of boolean type then 1.0 is returned if it is true, 0.0 if it is false
- if the argument is of numeric type then it is returned as the value of double type
- if the argument is of string type and can be parsed as double then that double value is returned, otherwise null is returned
- if the argument is of array/multiset/object type then null is returned
- type error is raised for all other input types

Example:

{
  "v1": to_double(false),
  "v2": to_double(true),
  "v3": to_double(10),
  "v4": to_double(11.5),
  "v5": to_double("12.5")
};

The expected result is:

{ "v1": 0.0, "v2": 1.0, "v3": 10.0, "v4": 11.5, "v5": 12.5 }

The function has an alias todouble.

to_number (to_num)

Syntax:
```
to_number(expr)
```
Converts input value to a numeric value
Arguments:
- expr : an expression
Return Value:
- if the argument is missing then missing is returned
- if the argument is null then null is returned
- if the argument is of numeric type then it is returned as is
- if the argument is of boolean type then 1 is returned if it is true, 0 if it is false
- if the argument is of string type and can be parsed as bigint then that bigint value is returned, otherwise if it can be parsed as double then that double value is returned, otherwise null is returned
- if the argument is of array/multiset/object type then null is returned
- type error is raised for all other input types

Example:

{
  "v1": to_number(false),
  "v2": to_number(true),
  "v3": to_number(10),
  "v4": to_number(11.5),
  "v5": to_number("12.5")
};

The expected result is:

{ "v1": 0, "v2": 1, "v3": 10, "v4": 11.5, "v5": 12.5 }

The function has three aliases: tonumber, to_num, and tonum.

to_object (to_obj)

Syntax:
```
to_object(expr)
```
Converts input value to an object value
Arguments:
- expr : an expression
Return Value:
- if the argument is missing then missing is returned
- if the argument is null then null is returned
- if the argument is of object type then it is returned as is
- otherwise an empty object is returned

Example:

{
  "v1": to_object({"value": "asterix"}),
  "v2": to_object("asterix")
};

The expected result is:

{ "v1": {"value": "asterix"}, "v2": {} }

The function has three aliases: toobject, to_obj, and toobj.

to_string (to_str)

Syntax:
```
to_string(expr)
```
Converts input value to a string value
Arguments:
- expr : an expression
Return Value:
- if the argument is missing then missing is returned
- if the argument is null then null is returned
- if the argument is of boolean type then "true" is returned if it is true, "false" if it is false
- if the argument is of numeric type then its string representation is returned
- if the argument is of string type then it is returned as is
- if the argument is of array/multiset/object type then null is returned
- type error is raised for all other input types

Example:

{
  "v1": to_string(false),
  "v2": to_string(true),
  "v3": to_string(10),
  "v4": to_string(11.5),
  "v5": to_string("asterix")
};

The expected result is:

{ "v1": "false", "v2": "true", "v3": "10", "v4": "11.5", "v5": "asterix" }

The function has three aliases: tostring, to_str, and tostr.

Conditional Functions

if_null (ifnull)

Syntax:

if_null(expression1, expression2, ... expressionN)

Finds first argument which value is not null and returns that value
Arguments:
- expressionI : an expression (any type is allowed).
Return Value:
- a null if all arguments evaluate to null or no arguments specified
- a value of the first non-null argument otherwise

Example:

{
    "a": if_null(),
    "b": if_null(null),
    "c": if_null(null, "asterixdb"),
    "d": is_missing(if_null(missing))
};

The expected result is:

{ "a": null, "b": null, "c": "asterixdb", "d": true }

The function has an alias ifnull.

if_missing (ifmissing)

Syntax:

if_missing(expression1, expression2, ... expressionN)

Finds first argument which value is not missing and returns that value
Arguments:
- expressionI : an expression (any type is allowed).
Return Value:
- a null if all arguments evaluate to missing or no arguments specified
- a value of the first non-missing argument otherwise

Example:

{
    "a": if_missing(),
    "b": if_missing(missing),
    "c": if_missing(missing, "asterixdb"),
    "d": if_missing(null, "asterixdb")
};

The expected result is:

{ "a": null, "b": null, "c": "asterixdb", "d": null }

The function has an alias ifmissing.

if_missing_or_null (ifmissingornull, coalesce)

Syntax:

if_missing_or_null(expression1, expression2, ... expressionN)

Finds first argument which value is not null or missing and returns that value
Arguments:
- expressionI : an expression (any type is allowed).
Return Value:
- a null if all arguments evaluate to either null or missing, or no arguments specified
- a value of the first non-null, non-missing argument otherwise

Example:

{
    "a": if_missing_or_null(),
    "b": if_missing_or_null(null, missing),
    "c": if_missing_or_null(null, missing, "asterixdb")
};

The expected result is:

{ "a": null, "b": null, "c": "asterixdb" }

The function has two aliases: ifmissingornull and coalesce.

if_inf (ifinf)

Syntax:

if_inf(expression1, expression2, ... expressionN)

Finds first argument which is a non-infinite (INF or-INF) number
Arguments:
- expressionI : an expression (any type is allowed).
Return Value:
- a missing if missing argument was encountered before the first non-infinite number argument
- a null if null argument or any other non-number argument was encountered before the first non-infinite number argument
- the first non-infinite number argument otherwise

Example:

{
    "a": is_null(if_inf(null)),
    "b": is_missing(if_inf(missing)),
    "c": is_null(if_inf(double("INF"))),
    "d": if_inf(1, null, missing) ],
    "e": is_null(if_inf(null, missing, 1)) ],
    "f": is_missing(if_inf(missing, null, 1)) ],
    "g": if_inf(float("INF"), 1) ],
    "h": to_string(if_inf(float("INF"), double("NaN"), 1)) ]
};

The expected result is:

{ "a": true, "b": true, "c": true, "d": 1, "e": true, "f": true, "g": 1, "h": "NaN" }

The function has an alias ifinf.

if_nan (ifnan)

Syntax:

if_nan(expression1, expression2, ... expressionN)

Finds first argument which is a non-NaN number
Arguments:
- expressionI : an expression (any type is allowed).
Return Value:
- a missing if missing argument was encountered before the first non-NaN number argument
- a null if null argument or any other non-number argument was encountered before the first non-NaN number argument
- the first non-NaN number argument otherwise

Example:

{
    "a": is_null(if_nan(null)),
    "b": is_missing(if_nan(missing)),
    "c": is_null(if_nan(double("NaN"))),
    "d": if_nan(1, null, missing) ],
    "e": is_null(if_nan(null, missing, 1)) ],
    "f": is_missing(if_nan(missing, null, 1)) ],
    "g": if_nan(float("NaN"), 1) ],
    "h": to_string(if_nan(float("NaN"), double("INF"), 1)) ]
};

The expected result is:

{ "a": true, "b": true, "c": true, "d": 1, "e": true, "f": true, "g": 1, "h": "INF" }

The function has an alias ifnan.

if_nan_or_inf (ifnanorinf)

Syntax:

if_nan_or_inf(expression1, expression2, ... expressionN)

Finds first argument which is a non-infinite (INF or-INF) and non-NaN number
Arguments:
- expressionI : an expression (any type is allowed).
Return Value:
- a missing if missing argument was encountered before the first non-infinite and non-NaN number argument
- a null if null argument or any other non-number argument was encountered before the first non-infinite and non-NaN number argument
- the first non-infinite and non-NaN number argument otherwise

Example:

{
    "a": is_null(if_nan_or_inf(null)),
    "b": is_missing(if_nan_or_inf(missing)),
    "c": is_null(if_nan_or_inf(double("NaN"), double("INF"))),
    "d": if_nan_or_inf(1, null, missing) ],
    "e": is_null(if_nan_or_inf(null, missing, 1)) ],
    "f": is_missing(if_nan_or_inf(missing, null, 1)) ],
    "g": if_nan_or_inf(float("NaN"), float("INF"), 1) ],
};

The expected result is:

{ "a": true, "b": true, "c": true, "d": 1, "e": true, "f": true, "g": 1 }

The function has an alias ifnanorinf.

null_if (nullif)

Syntax:
```
null_if(expression1, expression2)
```
Compares two arguments and returns null if they are equal, otherwise returns the first argument.
Arguments:
- expressionI : an expression (any type is allowed).
Return Value:
- missing if any argument is a missing value,
- null if
  - any argument is a null value but no argument is a missing value, or
  - argument1 = argument2
- a value of the first argument otherwise

Example:

{
    "a": null_if("asterixdb", "asterixdb"),
    "b": null_if(1, 2)
};

The expected result is:
```
{ "a": null, "b": 1 }
```

The function has an alias nullif.

missing_if (missingif)

Syntax:
```
missing_if(expression1, expression2)
```
Compares two arguments and returns missing if they are equal, otherwise returns the first argument.
Arguments:
- expressionI : an expression (any type is allowed).
Return Value:
- missing if
  - any argument is a missing value, or
  - no argument is a null value and argument1 = argument2
- null if any argument is a null value but no argument is a missing value
- a value of the first argument otherwise

Example:

{
    "a": missing_if("asterixdb", "asterixdb")
    "b": missing_if(1, 2),
};

The expected result is:
```
{ "b": 1 }
```

The function has an alias missingif.

nan_if (nanif)

Syntax:
```
nan_if(expression1, expression2)
```
Compares two arguments and returns NaN value if they are equal, otherwise returns the first argument.
Arguments:
- expressionI : an expression (any type is allowed).
Return Value:
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value
- NaN value of type double if argument1 = argument2
- a value of the first argument otherwise

Example:

{
    "a": to_string(nan_if("asterixdb", "asterixdb")),
    "b": nan_if(1, 2)
};

The expected result is:
```
{ "a": "NaN", "b": 1 }
```

The function has an alias nanif.

posinf_if (posinfif)

Syntax:
```
posinf_if(expression1, expression2)
```
Compares two arguments and returns +INF value if they are equal, otherwise returns the first argument.
Arguments:
- expressionI : an expression (any type is allowed).
Return Value:
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value
- +INF value of type double if argument1 = argument2
- a value of the first argument otherwise

Example:

{
    "a": to_string(posinf_if("asterixdb", "asterixdb")),
    "b": posinf_if(1, 2)
};

The expected result is:
```
{ "a": "+INF", "b": 1 }
```

The function has an alias posinfif.

neginf_if (neginfif)

Syntax:
```
neginf_if(expression1, expression2)
```
Compares two arguments and returns -INF value if they are equal, otherwise returns the first argument.
Arguments:
- expressionI : an expression (any type is allowed).
Return Value:
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value
- -INF value of type double if argument1 = argument2
- a value of the first argument otherwise

Example:

{
    "a": to_string(neginf_if("asterixdb", "asterixdb")),
    "b": neginf_if(1, 2)
};

The expected result is:
```
{ "a": "-INF", "b": 1 }
```

The function has an alias neginfif.

Miscellaneous Functions

uuid

Syntax:
```
uuid()
```
Generates a uuid.
Arguments:
- none
Return Value:
- a generated, random uuid.

len

Syntax:

len(array)
Returns the length of the array array.
Arguments:
- array : an array, multiset, null, or missing, represents the collection that needs to be checked.
Return Value:
- an integer that represents the length of input array or the size of the input multiset,
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value.
Example:
```
len(["Hello", "World"])
```
The expected result is:
```
2
```

not

Syntax:
```
not(expr)
```
Inverts a boolean value
Arguments:
- expr : an expression
Return Value:
- a boolean, the inverse of expr,
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value,
- other non-boolean argument value will cause a type error.

Example:

{ "v1": `not`(true), "v2": `not`(false), "v3": `not`(null), "v4": `not`(missing) };

The expected result is:

{ "v1": false, "v2": true, "v3": null }

random

Syntax:
```
random( [seed_value] )
```
Returns a random number, accepting an optional seed value
Arguments:
- seed_value: an optional tinyint/smallint/integer/bigint/float/double value representing the seed number.
Return Value:
- A random number of type double between 0 and 1,
- missing if the argument is a missing value,
- null if the argument is a null value or a non-numeric value.

Example:

{
  "v1": random(),
  "v2": random(unix_time_from_datetime_in_ms(current_datetime()))
};

range

Syntax:

range(start_numeric_value, end_numeric_value)

Generates a series of bigint values based start the start_numeric_value until the end_numeric_value.
Arguments:
start_numeric_value: a tinyint/smallint/integer/bigint value representing the start value.
end_numeric_value: a tinyint/smallint/integer/bigint value representing the max final value.
Return Value:
- an array that starts with the integer value of start_numeric_value and ends with the integer value of end_numeric_value, where the value of each entry in the array is the integer successor of the value in the preceding entry.
Example:
```
range(0, 3);
```
The expected result is:
```
[ 0, 1, 2, 3 ]
```

switch_case

Syntax:

switch_case(
    condition,
    case1, case1_result,
    case2, case2_result,
    ...,
    default, default_result
)

Switches amongst a sequence of cases and returns the result of the first matching case. If no match is found, the result of the default case is returned.
Arguments:
- condition: a variable (any type is allowed).
- caseI/default: a variable (any type is allowed).
- caseI/default_result: a variable (any type is allowed).
Return Value:
- caseI_result if condition matches caseI, otherwise default_result.

Example 1:

switch_case(
    "a",
    "a", 0,
    "x", 1,
    "y", 2,
    "z", 3
);

The expected result is:
```
0
```

Example 2:

switch_case(
    "a",
    "x", 1,
    "y", 2,
    "z", 3
);

The expected result is:
```
3
```

deep_equal

Syntax:
```
deep_equal(expr1, expr2)
```
Assess the equality between two expressions of any type (e.g., object, arrays, or multiset). Two objects are deeply equal iff both their types and values are equal.
Arguments:
- expr1 : an expression,
- expr2 : an expression.
Return Value:
- true or false depending on the data equality,
- missing if any argument is a missing value,
- null if any argument is a null value but no argument is a missing value.

Example:

deep_equal(
           {
             "id":1,
             "project":"AsterixDB",
             "address":{"city":"Irvine", "state":"CA"},
             "related":["Hivestrix", "Preglix", "Apache VXQuery"]
           },
           {
             "id":1,
             "project":"AsterixDB",
             "address":{"city":"San Diego", "state":"CA"},
             "related":["Hivestrix", "Preglix", "Apache VXQuery"]
           }
);

The expected result is:
```
false
```

Bitwise Functions

All Bit/Binary functions can only operate on 64-bit signed integers.

Note: All non-integer numbers and other data types result in null.

Note: The query language uses two’s complement representation.

When looking at the value in binary form, bit 1 is the Least Significant Bit (LSB) and bit 32 is the Most Significant Bit (MSB).

(MSB) Bit 32 → 0000 0000 0000 0000 0000 0000 0000 0000 ← Bit 1 (LSB)

bitand

Syntax:

BITAND(int_value1, int_value2, ... , int_valueN)

Returns the result of a bitwise AND operation performed on all input integer values.

The bitwise AND operation compares each bit of int_value1 to the corresponding bit of every other int_value. If all bits are 1, then the corresponding result bit is set to 1; otherwise it is set to 0 (zero).
Arguments:
- int_valueI: Integers, or any valid expressions which evaluate to integers, that are used to compare.
Return Value:
- An integer, representing the bitwise AND between all of the input integers.
Limitations:
- Input values must be integers (such as 1 or 1.0) and cannot contain decimals (such as 1.2).
Example 1:

Compare 3 (0011 in binary) and 6 (0110 in binary).
```
{ "BitAND": BITAND(3,6) };
```
The expected result is:
```
{ "BitAND": 2 }
```
This results in 2 (0010 in binary) because only bit 2 is set in both 3 (0011) and 6 (0110).
Example 2:

Compare 4.5 and 3 (0011 in binary).
```
{ "BitAND": BITAND(4.5,3) };
```
The expected result is:
```
{ "BitAND": null }
```
The result is null because 4.5 is not an integer.
Example 3:

Compare 4.0 (0100 in binary) and 3 (0011 in binary).
```
{ "BitAND": BITAND(4.0,3) };
```
The expected result is:
```
{ "BitAND": 0 }
```
This results in 0 (zero) because 4.0 (0100) and 3 (0011) do not share any bits that are both 1.
Example 4:

Compare 3 (0011 in binary) and 6 (0110 in binary) and 15 (1111 in binary).
```
{ "BitAND": BITAND(3,6,15) };
```
The expected result is:
```
{ "BitAND": 2 }
```
This results in 2 (0010 in binary) because only the 2nd bit from the right is 1 in all three numbers.

bitclear

Syntax:
```
BITCLEAR(int_value, positions)
```
Returns the result after clearing the specified bit, or array of bits in int_value using the given positions.

Note: Specifying a negative or zero bit position makes the function return a null.
Arguments:
- int_value: An integer, or any valid expression which evaluates to an integer, that contains the target bit or bits to clear.
- positions: An integer or an array of integers specifying the position or positions to be cleared.
Return Value:
- An integer, representing the result after clearing the bit or bits specified.
Limitations:
- Input values must be integers (such as 1 or 1.0) and cannot contain decimals (such as 1.2).
Example 1:

Clear bit 1 from 6 (0110 in binary).
```
{ "BitCLEAR": BITCLEAR(6,1) };
```
The expected result is:
```
{ "BitCLEAR": 6 }
```
This results in 6 (0110 in binary) because bit 1 was already zero.
Example 2:

Clear bits 1 and 2 from 6 (0110 in binary).
```
{ "BitCLEAR": BITCLEAR(6,[1,2]) };
```
The expected result is:
```
{ "BitCLEAR": 4 }
```
This results in 4 (0100 in binary) because bit 2 changed to zero.
Example 3:

Clear bits 1, 2, 4, and 5 from 31 (011111 in binary).
```
{ "BitCLEAR": BITCLEAR(31,[1,2,4,5]) };
```
The expected result is:
```
{ "BitCLEAR": 4 }
```
This results in 4 (000100) because bits 1, 2, 4, and 5 changed to zero.

bitnot

Syntax:
```
BITNOT(int_value)
```
Returns the results of a bitwise logical NOT operation performed on an integer value.

The bitwise logical NOT operation reverses the bits in the value. For each value bit that is 1, the corresponding result bit will be set to 0 (zero); and for each value bit that is 0 (zero), the corresponding result bit will be set to 1.

Note: All bits of the integer will be altered by this operation.
Arguments:
- int_value: An integer, or any valid expression which evaluates to an integer, that contains the target bits to reverse.
Return Value:
- An integer, representing the result after performing the logical NOT operation.
Limitations:
- Input values must be integers (such as 1 or 1.0) and cannot contain decimals (such as 1.2).
Example 1:

Perform the NOT operation on 3 (0000 0000 0000 0000 0000 0000 0000 0011 in binary).
```
{ "BitNOT": BITNOT(3) };
```
The expected result is:
```
{ "BitNOT": -4 }
```
This results in -4 (1111 1111 1111 1111 1111 1111 1111 1100 in binary) because all bits changed.

bitor

Syntax:

BITOR(int_value1, int_value2, ... , int_valueN)

Returns the result of a bitwise inclusive OR operation performed on all input integer values.

The bitwise inclusive OR operation compares each bit of int_value1 to the corresponding bit of every other int_value. If any bit is 1, the corresponding result bit is set to 1; otherwise, it is set to 0 (zero).
Arguments:
- int_valueI: Integers, or any valid expressions which evaluate to integers, that are used to compare.
Return Value:
- An integer, representing the bitwise OR between all of the input integers.
Limitations:
- Input values must be integers (such as 1 or 1.0) and cannot contain decimals (such as 1.2).
Example 1:

Perform OR on 3 (0011 in binary) and 6 (0110 in binary).
```
{ "BitOR": BITOR(3,6) };
```
The expected result is:
```
{ "BitOR": 7 }
```
This results in 7 (0111 in binary) because at least 1 bit of each (0011 and 0110) is 1 in bits 1, 2, and 3.
Example 2:

Perform OR on 3 (0011 in binary) and -4 (1000 0000 0000 … 0000 1100 in binary).
```
{ "BitOR": BITOR(3,-4) };
```
The expected result is:
```
{ "BitOR": -1 }
```
This results in -1 (1111 1111 1111 … 1111 1111 in binary) because the two 1 bits in 3 fill in the two 0 bits in -4 to turn on all the bits.
Example 3:

Perform OR on 3 (0011 in binary) and 6 (0110 in binary) and 15 (1111 in binary).
```
{ "BitOR": BITOR(3,6,15) };
```
The expected result is:
```
{ "BitOR": 15 }
```
This results in 15 (1111 in binary) because there is at least one 1 in each of the four rightmost bits.

bitset

Syntax:
```
BITSET(int_value, positions)
```
Returns the result after setting the specified bit position, or array of bit positions, to 1 in the given int_value.

Note: Specifying a negative or zero position makes the function return a null.
Arguments:
- int_value: An integer, or any valid expression which evaluates to an integer, that contains the target bit or bits to set.
- positions: An integer or an array of integers specifying the position or positions to be set.
Return Value:
- An integer, representing the result after setting the bit or bits specified. If the bit is already set, then it stays set.
Limitations:
- Input values must be integers (such as 1 or 1.0) and cannot contain decimals (such as 1.2).
Example 1:

Set bit 1 in the value 6 (0110 in binary).
```
{ "BitSET": BITSET(6,1) };
```
The expected result is:
```
{ "BitSET": 7 }
```
This results in 7 (0111 in binary) because bit 1 changed to 1.
Example 2:

Set bits 1 and 2 in the value 6 (0110 in binary).
```
{ "BitSET": BITSET(6,[1,2]) };
```
The expected result is:
```
{ "BitSET": 7 }
```
This also results in 7 (0111 in binary) because bit 1 changed while bit 2 remained the same.
Example 3:

Set bits 1 and 4 in the value 6 (0110 in binary).
```
{ "BitSET": BITSET(6,[1,4]) };
```
The expected result is:
```
{ "BitSET": 15 }
```
This results in 15 (1111 in binary) because bit 1 and 4 changed to ones.

bitshift

Syntax:

BITSHIFT(int_value, shift_amount[, rotate])

Returns the result of a bit shift operation performed on the integer value int_value. The shift_amount supports left and right shifts. These are logical shifts. The third parameter rotate supports circular shift. This is similar to the BitROTATE function in Oracle.

Arguments:

int_value: An integer, or any valid expression which evaluates to an integer, that contains the target bit or bits to shift.
shift_amount: An integer, or any valid expression which evaluates to an integer, that contains the number of bits to shift.
- A positive (+) number means this is a LEFT shift.
- A negative (-) number means this is a RIGHT shift.
rotate: (Optional) A boolean, or any valid expression which evaluates to a boolean, where:
- FALSE means this is a LOGICAL shift, where bits shifted off the end of a value are considered lost.
- TRUE means this is a CIRCULAR shift (shift-and-rotate operation), where bits shifted off the end of a value are rotated back onto the value at the other end. In other words, the bits rotate in what might be thought of as a circular pattern; therefore, these bits are not lost.
If omitted, the default is FALSE.

For comparison, see the below table.

Input	Shift	Result of Logical Shift (Rotate FALSE)	Result of Circular Shift (Rotate TRUE)
6 (0000 0110)	4	96 (0110 0000)	96 (0110 0000)
6 (0000 0110)	3	48 (0011 0000)	48 (0011 0000)
6 (0000 0110)	2	24 (0001 1000)	24 (0001 1000)
6 (0000 0110)	1	12 (0000 1100)	12 (0000 1100)
6 (0000 0110)	0	6 (0000 0110)	6 (0000 0110)
6 (0000 0110)	-1	3 (0000 0011)	3 (0000 0011)
6 (0000 0110)	-2	1 (0000 0001)	-9223372036854775807 (1000 0000 … 0000 0001)
6 (0000 0110)	-3	0 (0000 0000)	-4611686018427387904 (1100 0000 … 0000 0000)
6 (0000 0110)	-4	0 (0000 0000)	6917529027641081856 (0110 0000 … 0000 0000)

Return Value:
- An integer, representing the result of either a logical or circular shift of the given integer.
Limitations:
- Input values must be integers (such as 1 or 1.0) and cannot contain decimals (such as 1.2).
Example 1:

Logical left shift of the number 6 (0110 in binary) by one bit.
```
{ "BitSHIFT": BITSHIFT(6,1,FALSE) };
```
The expected result is:
```
{ "BitSHIFT": 12 }
```
This results in 12 (1100 in binary) because the 1-bits moved from positions 2 and 3 to positions 3 and 4.
Example 2:

Logical right shift of the number 6 (0110 in binary) by two bits.
```
{ "BitSHIFT": BITSHIFT(6,-2) };
```
The expected result is:
```
{ "BitSHIFT": 1 }
```
This results in 1 (0001 in binary) because the 1-bit in position 3 moved to position 1 and the 1-bit in position 2 was dropped.
Example 2b:

Circular right shift of the number 6 (0110 in binary) by two bits.
```
{ "BitSHIFT": BITSHIFT(6,-2,TRUE) };
```
The expected result is:
```
{ "BitSHIFT": -9223372036854775807 }
```
This results in -9223372036854775807 (1100 0000 0000 0000 0000 0000 0000 0000 in binary) because the two 1-bits wrapped right, around to the Most Significant Digit position and changed the integer’s sign to negative.
Example 3:

Circular left shift of the number 524288 (1000 0000 0000 0000 0000 in binary) by 45 bits.
```
{ "BitSHIFT": BITSHIFT(524288,45,TRUE) };
```
The expected result is:
```
{ "BitSHIFT": 1 }
```
This results in 1 because the 1-bit wrapped left, around to the Least Significant Digit position.

bittest

Syntax:

BITTEST(int_value, positions [, all_set])

Returns TRUE if the specified bit, or bits, is a 1; otherwise, returns FALSE if the specified bit, or bits, is a 0 (zero).

Note: Specifying a negative or zero bit position will result in null being returned.
Arguments:
- int_value: An integer, or any valid expression which evaluates to an integer, that contains the target bit or bits to test.
- positions: An integer or an array of integers specifying the position or positions to be tested.
- all_set: (Optional) A boolean, or any valid expression which evaluates to a boolean.
  - When all_set is FALSE, then it returns TRUE even if one bit in one of the positions is set.
  - When all_set is TRUE, then it returns TRUE only if all input positions are set.
  If omitted, the default is FALSE.

Return Value:

A boolean, that follows the below table:

`int_value`	`all_set`	Return Value
all specified bits are TRUE	FALSE	TRUE
all specified bits are TRUE	TRUE	TRUE
some specified bits are TRUE	FALSE	TRUE
some specified bits are TRUE	TRUE	FALSE

Limitations:
- Input values must be integers (such as 1 or 1.0) and cannot contain decimals (such as 1.2).
Example 1:

In the number 6 (0110 in binary), is bit 1 set?
```
{ "IsBitSET": ISBITSET(6,1) };
```
The expected result is:
```
{ "IsBitSET": false }
```
This returns FALSE because bit 1 of 6 (0110 in binary) is not set to 1.
Example 2:

In the number 1, is either bit 1 or bit 2 set?
```
{ "BitTEST": BITTEST(1,[1,2],FALSE) };
```
The expected result is:
```
{ "BitTEST": true }
```
This returns TRUE because bit 1 of the number 1 (0001 in binary) is set to 1.
Example 3:

In the number 6 (0110 in binary), are both bits 2 and 3 set?
```
{ "IsBitSET": ISBITSET(6,[2,3],TRUE) };
```
The expected result is:
```
{ "IsBitSET": true }
```
This returns TRUE because both bits 2 and 3 in the number 6 (0110 in binary) are set to 1.
Example 4:

In the number 6 (0110 in binary), are all the bits in positions 1 through 3 set?
```
{ "BitTEST": BITTEST(6,[1,3],TRUE) };
```
The expected result is:
```
{ "BitTEST": false }
```
This returns FALSE because bit 1 in the number 6 (0110 in binary) is set to 0 (zero).

The function has an alias isbitset.

bitxor

Syntax:

BITXOR(int_value1, int_value2, ... , int_valueN)

Returns the result of a bitwise Exclusive OR operation performed on two or more integer values.

The bitwise Exclusive OR operation compares each bit of int_value1 to the corresponding bit of int_value2.

If there are more than two input values, the first two are compared; then their result is compared to the next input value; and so on.

When the compared bits do not match, the result bit is 1; otherwise, the compared bits do match, and the result bit is 0 (zero), as summarized:

Bit 1 Bit 2 XOR Result Bit

0 0 0

0 1 1

1 0 1

1 1 0
Arguments:
- int_valueI: Integers, or any valid expressions which evaluate to integers, that are used to compare.
Return Value:
- An integer, representing the bitwise XOR between the input integers.
Limitations:
- Input values must be integers (such as 1 or 1.0) and cannot contain decimals (such as 1.2).
Example 1:

Perform the XOR operation on 3 (0011 in binary) and 6 (0110 in binary).
```
{ "BitXOR": BITXOR(3,6) };
```
The expected result is:
```
{ "BitXOR": 5 }
```
This returns 5 (0101 in binary) because the 1st bit pair and 3rd bit pair are different (resulting in 1) while the 2nd bit pair and 4th bit pair are the same (resulting in 0):
```
0011 (3)
0110 (6)
====
0101 (5)
```
Example 2:

Perform the XOR operation on 3 (0011 in binary) and 6 (0110 in binary) and 15 (1111 in binary).
```
{ "BitXOR": BITXOR(3,6,15) };
```
The expected result is:
```
{ "BitXOR": 10 }
```
This returns 10 (1010 in binary) because 3 XOR 6 equals 5 (0101 in binary), and then 5 XOR 15 equals 10 (1010 in binary).

Bit 1	Bit 2	XOR Result Bit
0	0	0
0	1	1
1	0	1
1	1	0

Window Functions

Window functions are used to compute an aggregate or cumulative value, based on a portion of the tuples selected by a query. For each input tuple, a movable window of tuples is defined. The window determines the tuples to be used by the window function.

The tuples are not grouped into a single output tuple — each tuple remains separate in the query output.

All window functions must be used with an OVER clause. Refer to Window Queries for details.

Window functions cannot appear in the FROM clause clause or LIMIT clause.

cume_dist

Syntax:

CUME_DIST() OVER ([window-partition-clause] [window-order-clause])

Returns the percentile rank of the current tuple as part of the cumulative distribution – that is, the number of tuples ranked lower than or equal to the current tuple, including the current tuple, divided by the total number of tuples in the window partition.

The window order clause determines the sort order of the tuples. If the window order clause is omitted, the function returns the same result (1.0) for each tuple.
Arguments:
- None.
Clauses:
- (Optional) Window Partition Clause.
- (Optional) Window Order Clause.
Return Value:
- A number greater than 0 and less than or equal to 1. The higher the value, the higher the ranking.

Example:

For each customer, find the cumulative distribution of all orders by order number.

FROM orders AS o
SELECT o.custid, o.orderno, CUME_DIST() OVER (
  PARTITION BY o.custid
  ORDER BY o.orderno
) AS `rank`
ORDER BY o.custid, o.orderno;

The expected result is:

[
  {
    "rank": 0.25,
    "custid": "C13",
    "orderno": 1002
  },
  {
    "rank": 0.5,
    "custid": "C13",
    "orderno": 1007
  },
  {
    "rank": 0.75,
    "custid": "C13",
    "orderno": 1008
  },
  {
    "rank": 1,
    "custid": "C13",
    "orderno": 1009
  },
  {
    "rank": 1,
    "custid": "C31",
    "orderno": 1003
  },
  {
    "rank": 1,
    "custid": "C35",
    "orderno": 1004
  },
  {
    "rank": 1,
    "custid": "C37",
    "orderno": 1005
  },
  {
    "rank": 0.5,
    "custid": "C41",
    "orderno": 1001
  },
  {
    "rank": 1,
    "custid": "C41",
    "orderno": 1006
  }
]

dense_rank

Syntax:

DENSE_RANK() OVER ([window-partition-clause] [window-order-clause])

Returns the dense rank of the current tuple – that is, the number of distinct tuples preceding this tuple in the current window partition, plus one.

The tuples are ordered by the window order clause. If any tuples are tied, they will have the same rank. If the window order clause is omitted, the function returns the same result (1) for each tuple.

For this function, when any tuples have the same rank, the rank of the next tuple will be consecutive, so there will not be a gap in the sequence of returned values. For example, if there are five tuples ranked 3, the next dense rank is 4.
Arguments:
- None.
Clauses:
- (Optional) Window Partition Clause.
- (Optional) Window Order Clause.
Return Value:
- An integer, greater than or equal to 1.

Example:

Find the dense rank of all orders by number of items.

FROM orders AS o
SELECT o.orderno, LEN(o.items) AS items,
DENSE_RANK() OVER (
  ORDER BY LEN(o.items)
) AS `rank`
ORDER BY `rank`, o.orderno;

The expected result is:

[
  {
    "items": 0,
    "rank": 1,
    "orderno": 1009
  },
  {
    "items": 1,
    "rank": 2,
    "orderno": 1008
  },
  {
    "items": 2,
    "rank": 3,
    "orderno": 1001
  },
  {
    "items": 2,
    "rank": 3,
    "orderno": 1002
  },
  {
    "items": 2,
    "rank": 3,
    "orderno": 1003
  },
  {
    "items": 2,
    "rank": 3,
    "orderno": 1004
  },
  {
    "items": 2,
    "rank": 3,
    "orderno": 1007
  },
  {
    "items": 3,
    "rank": 4,
    "orderno": 1006
  },
  {
    "items": 4,
    "rank": 5,
    "orderno": 1005
  }
]

first_value

Syntax:

FIRST_VALUE(expr) [nulls-modifier] OVER (window-definition)

Returns the requested value from the first tuple in the current window frame, where the window frame is specified by the window definition.
Arguments:
- expr: The value that you want to return from the first tuple in the window frame. ^[¹^]
Modifiers:
- NULLS Modifier: (Optional) Determines how NULL or MISSING values are treated when finding the first value in the window frame.
  - IGNORE NULLS: If the values for any tuples evaluate to NULL or MISSING, those tuples are ignored when finding the first tuple. In this case, the function returns the first non-NULL, non-MISSING value.
  - RESPECT NULLS: If the values for any tuples evaluate to NULL or MISSING, those tuples are included when finding the first tuple.
  If this modifier is omitted, the default is RESPECT NULLS.
Clauses:
- (Optional) Window Partition Clause.
- (Optional) Window Order Clause.
- (Optional) Window Frame Clause.
Return Value:
- The specified value from the first tuple. The order of the tuples is determined by the window order clause.
- NULL, if the frame was empty or if all values were NULL or MISSING and the IGNORE NULLS modifier was specified.
- In the following cases, this function may return unpredictable results.
  - If the window order clause is omitted.
  - If the window frame is defined by ROWS, and there are tied tuples in the window frame.
- To make the function return deterministic results, add a window order clause, or add further ordering terms to the window order clause so that no tuples are tied.
- If the window frame is defined by RANGE or GROUPS, and there are tied tuples in the window frame, the function returns the first value of the input expression.

Example:

For each order, show the customer and the value, including the value of the smallest order from that customer.

FROM orders AS o
LET revenue = ROUND((
  FROM o.items
  SELECT VALUE SUM(qty * price)
)[0], 2)
SELECT o.custid, o.orderno, revenue,
FIRST_VALUE(revenue) OVER (
  PARTITION BY o.custid
  ORDER BY revenue
) AS smallest_order;

The expected result is:

[
  {
    "custid": "C13",
    "orderno": 1009,
    "revenue": null,
    "smallest_order": null
  },
  {
    "custid": "C13",
    "orderno": 1007,
    "revenue": 130.45,
    "smallest_order": null
  },
  {
    "custid": "C13",
    "orderno": 1008,
    "revenue": 1999.8,
    "smallest_order": null
  },
  {
    "custid": "C13",
    "orderno": 1002,
    "revenue": 10906.55,
    "smallest_order": null
  },
  {
    "custid": "C31",
    "orderno": 1003,
    "revenue": 477.95,
    "smallest_order": 477.95
  },
  {
    "custid": "C35",
    "orderno": 1004,
    "revenue": 199.94,
    "smallest_order": 199.94
  },
  {
    "custid": "C37",
    "orderno": 1005,
    "revenue": 4639.92,
    "smallest_order": 4639.92
  },
  {
    "custid": "C41",
    "orderno": 1001,
    "revenue": 157.73,
    "smallest_order": 157.73
  },
  {
    "custid": "C41",
    "orderno": 1006,
    "revenue": 18847.58,
    "smallest_order": 157.73
  }
]

lag

Syntax:

LAG(expr[, offset[, default]]) [nulls-modifier] OVER ([window-partition-clause] [window-order-clause])

Returns the value from a tuple at a given offset prior to the current tuple position.

The window order clause determines the sort order of the tuples. If the window order clause is omitted, the return values may be unpredictable.
Arguments:
- expr: The value that you want to return from the offset tuple. ^[¹^]
- offset: (Optional) A positive integer. If omitted, the default is 1.
- default: (Optional) The value to return when the offset goes out of partition scope. If omitted, the default is NULL.
Modifiers:
- NULLS Modifier: (Optional) Determines how NULL or MISSING values are treated when finding the offset tuple in the window partition.
  - IGNORE NULLS: If the values for any tuples evaluate to NULL or MISSING, those tuples are ignored when finding the offset tuple.
  - RESPECT NULLS: If the values for any tuples evaluate to NULL or MISSING, those tuples are included when finding the offset tuple.
  If this modifier is omitted, the default is RESPECT NULLS.
Clauses:
- (Optional) Window Partition Clause.
- (Optional) Window Order Clause.
Return Value:
- The specified value from the offset tuple.
- If the offset tuple is out of partition scope, it returns the default value, or NULL if no default is specified.

Example:

For each order, show the customer and the value, including the value of the next-smallest order from that customer.

FROM orders AS o
LET revenue = ROUND((
  FROM o.items
  SELECT VALUE SUM(qty * price)
)[0], 2)
SELECT o.custid, o.orderno, revenue,
LAG(revenue, 1, "No smaller order") OVER (
  PARTITION BY o.custid
  ORDER BY revenue
) AS next_smallest_order;

The expected result is:

[
  {
    "custid": "C13",
    "orderno": 1009,
    "revenue": null,
    "next_smallest_order": "No smaller order"
  },
  {
    "custid": "C13",
    "orderno": 1007,
    "revenue": 130.45,
    "next_smallest_order": null
  },
  {
    "custid": "C13",
    "orderno": 1008,
    "revenue": 1999.8,
    "next_smallest_order": 130.45
  },
  {
    "custid": "C13",
    "orderno": 1002,
    "revenue": 10906.55,
    "next_smallest_order": 1999.8
  },
  {
    "custid": "C31",
    "orderno": 1003,
    "revenue": 477.95,
    "next_smallest_order": "No smaller order"
  },
  {
    "custid": "C35",
    "orderno": 1004,
    "revenue": 199.94,
    "next_smallest_order": "No smaller order"
  },
  {
    "custid": "C37",
    "orderno": 1005,
    "revenue": 4639.92,
    "next_smallest_order": "No smaller order"
  },
  {
    "custid": "C41",
    "orderno": 1001,
    "revenue": 157.73,
    "next_smallest_order": "No smaller order"
  },
  {
    "custid": "C41",
    "orderno": 1006,
    "revenue": 18847.58,
    "next_smallest_order": 157.73
  }
]

last_value

Syntax:

LAST_VALUE(expr) [nulls-modifier] OVER (window-definition)

Returns the requested value from the last tuple in the current window frame, where the window frame is specified by the window definition.
Arguments:
- expr: The value that you want to return from the last tuple in the window frame. ^[¹^]
Modifiers:
- NULLS Modifier: (Optional) Determines how NULL or MISSING values are treated when finding the last tuple in the window frame.
  - IGNORE NULLS: If the values for any tuples evaluate to NULL or MISSING, those tuples are ignored when finding the last tuple. In this case, the function returns the last non-NULL, non-MISSING value.
  - RESPECT NULLS: If the values for any tuples evaluate to NULL or MISSING, those tuples are included when finding the last tuple.
  If this modifier is omitted, the default is RESPECT NULLS.
Clauses:
- (Optional) Window Partition Clause.
- (Optional) Window Order Clause.
- (Optional) Window Frame Clause.
Return Value:
- The specified value from the last tuple. The order of the tuples is determined by the window order clause.
- NULL, if the frame was empty or if all values were NULL or MISSING and the IGNORE NULLS modifier was specified.
- In the following cases, this function may return unpredictable results.
  - If the window order clause is omitted.
  - If the window frame clause is omitted.
  - If the window frame is defined by ROWS, and there are tied tuples in the window frame.
- To make the function return deterministic results, add a window order clause, or add further ordering terms to the window order clause so that no tuples are tied.
- If the window frame is defined by RANGE or GROUPS, and there are tied tuples in the window frame, the function returns the last value of the input expression.

Example:

For each order, show the customer and the value, including the value of the largest order from that customer.

FROM orders AS o
LET revenue = ROUND((
  FROM o.items
  SELECT VALUE SUM(qty * price)
)[0], 2)
SELECT o.custid, o.orderno, revenue,
LAST_VALUE(revenue) OVER (
  PARTITION BY o.custid
  ORDER BY revenue
  ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING -- ➊
) AS largest_order;

The expected result is:

[
  {
    "custid": "C13",
    "orderno": 1009,
    "revenue": null,
    "largest_order": 10906.55
  },
  {
    "custid": "C13",
    "orderno": 1007,
    "revenue": 130.45,
    "largest_order": 10906.55
  },
  {
    "custid": "C13",
    "orderno": 1008,
    "revenue": 1999.8,
    "largest_order": 10906.55
  },
  {
    "custid": "C13",
    "orderno": 1002,
    "revenue": 10906.55,
    "largest_order": 10906.55
  },
  {
    "custid": "C31",
    "orderno": 1003,
    "revenue": 477.95,
    "largest_order": 477.95
  },
  {
    "custid": "C35",
    "orderno": 1004,
    "revenue": 199.94,
    "largest_order": 199.94
  },
  {
    "custid": "C37",
    "orderno": 1005,
    "revenue": 4639.92,
    "largest_order": 4639.92
  },
  {
    "custid": "C41",
    "orderno": 1001,
    "revenue": 157.73,
    "largest_order": 18847.58
  },
  {
    "custid": "C41",
    "orderno": 1006,
    "revenue": 18847.58,
    "largest_order": 18847.58
  }
]

➀ This clause specifies that the window frame should extend to the end of the window partition. Without this clause, the end point of the window frame would always be the current tuple. This would mean that the largest order would always be the same as the current order.

lead

Syntax:

LEAD(expr[, offset[, default]]) [nulls-modifier] OVER ([window-partition-clause] [window-order-clause])

Returns the value from a tuple at a given offset ahead of the current tuple position.

The window order clause determines the sort order of the tuples. If the window order clause is omitted, the return values may be unpredictable.
Arguments:
- expr: The value that you want to return from the offset tuple. ^[¹^]
- offset: (Optional) A positive integer. If omitted, the default is 1.
- default: (Optional) The value to return when the offset goes out of window partition scope. If omitted, the default is NULL.
Modifiers:
- NULLS Modifier: (Optional) Determines how NULL or MISSING values are treated when finding the offset tuple in the window partition.
  - IGNORE NULLS: If the values for any tuples evaluate to NULL or MISSING, those tuples are ignored when finding the offset tuple.
  - RESPECT NULLS: If the values for any tuples evaluate to NULL or MISSING, those tuples are included when finding the offset tuple.
  If this modifier is omitted, the default is RESPECT NULLS.
Clauses:
- (Optional) Window Partition Clause.
- (Optional) Window Order Clause.
Return Value:
- The specified value from the offset tuple.
- If the offset tuple is out of partition scope, it returns the default value, or NULL if no default is specified.

Example:

For each order, show the customer and the value, including the value of the next-largest order from that customer.

FROM orders AS o
LET revenue = ROUND((
  FROM o.items
  SELECT VALUE SUM(qty * price)
)[0], 2)
SELECT o.custid, o.orderno, revenue,
LEAD(revenue, 1, "No larger order") OVER (
  PARTITION BY o.custid
  ORDER BY revenue
) AS next_largest_order;

The expected result is:

[
  {
    "custid": "C13",
    "orderno": 1009,
    "revenue": null,
    "next_largest_order": 130.45
  },
  {
    "custid": "C13",
    "orderno": 1007,
    "revenue": 130.45,
    "next_largest_order": 1999.8
  },
  {
    "custid": "C13",
    "orderno": 1008,
    "revenue": 1999.8,
    "next_largest_order": 10906.55
  },
  {
    "custid": "C13",
    "orderno": 1002,
    "revenue": 10906.55,
    "next_largest_order": "No larger order"
  },
  {
    "custid": "C31",
    "orderno": 1003,
    "revenue": 477.95,
    "next_largest_order": "No larger order"
  },
  {
    "custid": "C35",
    "orderno": 1004,
    "revenue": 199.94,
    "next_largest_order": "No larger order"
  },
  {
    "custid": "C37",
    "orderno": 1005,
    "revenue": 4639.92,
    "next_largest_order": "No larger order"
  },
  {
    "custid": "C41",
    "orderno": 1001,
    "revenue": 157.73,
    "next_largest_order": 18847.58
  },
  {
    "custid": "C41",
    "orderno": 1006,
    "revenue": 18847.58,
    "next_largest_order": "No larger order"
  }
]

nth_value

Syntax:

NTH_VALUE(expr, offset) [from-modifier] [nulls-modifier] OVER (window-definition)

Returns the requested value from a tuple in the current window frame, where the window frame is specified by the window definition.
Arguments:
- expr: The value that you want to return from the offset tuple in the window frame. ^[¹^]
- offset: The number of the offset tuple within the window frame, counting from 1.
Modifiers:
- FROM Modifier: (Optional) Determines where the function starts counting the offset.
  - FROM FIRST: Counting starts at the first tuple in the window frame. In this case, an offset of 1 is the first tuple in the window frame, 2 is the second tuple, and so on.
  - FROM LAST: Counting starts at the last tuple in the window frame. In this case, an offset of 1 is the last tuple in the window frame, 2 is the second-to-last tuple, and so on.
  The order of the tuples is determined by the window order clause. If this modifier is omitted, the default is FROM FIRST.
- NULLS Modifier: (Optional) Determines how NULL or MISSING values are treated when finding the offset tuple in the window frame.
  - IGNORE NULLS: If the values for any tuples evaluate to NULL or MISSING, those tuples are ignored when finding the offset tuple.
  - RESPECT NULLS: If the values for any tuples evaluate to NULL or MISSING, those tuples are included when finding the offset tuple.
  If this modifier is omitted, the default is RESPECT NULLS.
Clauses:
- (Optional) Window Partition Clause.
- (Optional) Window Order Clause.
- (Optional) Window Frame Clause.
Return Value:
- The specified value from the offset tuple.
- In the following cases, this function may return unpredictable results.
  - If the window order clause is omitted.
  - If the window frame is defined by ROWS, and there are tied tuples in the window frame.
- To make the function return deterministic results, add a window order clause, or add further ordering terms to the window order clause so that no tuples are tied.
- If the window frame is defined by RANGE or GROUPS, and there are tied tuples in the window frame, the function returns the first value of the input expression when counting FROM FIRST, or the last value of the input expression when counting FROM LAST.

Example 1:

For each order, show the customer and the value, including the value of the second smallest order from that customer.

FROM orders AS o
LET revenue = ROUND((
  FROM o.items
  SELECT VALUE SUM(qty * price)
)[0], 2)
SELECT o.custid, o.orderno, revenue,
NTH_VALUE(revenue, 2) FROM FIRST OVER (
  PARTITION BY o.custid
  ORDER BY revenue
  ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING -- ➊
) AS smallest_order_but_1;

The expected result is:

[
  {
    "custid": "C13",
    "orderno": 1009,
    "revenue": null,
    "smallest_order_but_1": 130.45
  },
  {
    "custid": "C13",
    "orderno": 1007,
    "revenue": 130.45, // ➋
    "smallest_order_but_1": 130.45
  },
  {
    "custid": "C13",
    "orderno": 1008,
    "revenue": 1999.8,
    "smallest_order_but_1": 130.45
  },
  {
    "custid": "C13",
    "orderno": 1002,
    "revenue": 10906.55,
    "smallest_order_but_1": 130.45
  },
  {
    "custid": "C31",
    "orderno": 1003,
    "revenue": 477.95,
    "smallest_order_but_1": null
  },
  {
    "custid": "C35",
    "orderno": 1004,
    "revenue": 199.94,
    "smallest_order_but_1": null
  },
  {
    "custid": "C37",
    "orderno": 1005,
    "revenue": 4639.92,
    "smallest_order_but_1": null
  },
  {
    "custid": "C41",
    "orderno": 1001,
    "revenue": 157.73,
    "smallest_order_but_1": 18847.58
  },
  {
    "custid": "C41",
    "orderno": 1006,
    "revenue": 18847.58, // ➋
    "smallest_order_but_1": 18847.58
  }
]

➀ This clause specifies that the window frame should extend to the end of the window partition. Without this clause, the end point of the window frame would always be the current tuple. This would mean that for the smallest order, the function would be unable to find the route with the second smallest order.

➁ The second smallest order from this customer.

Example 2:

For each order, show the customer and the value, including the value of the second largest order from that customer.

FROM orders AS o
LET revenue = ROUND((
  FROM o.items
  SELECT VALUE SUM(qty * price)
)[0], 2)
SELECT o.custid, o.orderno, revenue,
NTH_VALUE(revenue, 2) FROM LAST OVER (
  PARTITION BY o.custid
  ORDER BY revenue
  ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING -- ➊
) AS largest_order_but_1;

The expected result is:

[
  {
    "custid": "C13",
    "orderno": 1002,
    "revenue": 10906.55,
    "largest_order_but_1": 1999.8
  },
  {
    "custid": "C13",
    "orderno": 1008,
    "revenue": 1999.8, // ➋
    "largest_order_but_1": 1999.8
  },
  {
    "custid": "C13",
    "orderno": 1007,
    "revenue": 130.45,
    "largest_order_but_1": 1999.8
  },
  {
    "custid": "C13",
    "orderno": 1009,
    "revenue": null,
    "largest_order_but_1": 1999.8
  },
  {
    "custid": "C31",
    "orderno": 1003,
    "revenue": 477.95,
    "largest_order_but_1": null
  },
  {
    "custid": "C35",
    "orderno": 1004,
    "revenue": 199.94,
    "largest_order_but_1": null
  },
  {
    "custid": "C37",
    "orderno": 1005,
    "revenue": 4639.92,
    "largest_order_but_1": null
  },
  {
    "custid": "C41",
    "orderno": 1006,
    "revenue": 18847.58,
    "largest_order_but_1": 157.73
  },
  {
    "custid": "C41",
    "orderno": 1001,
    "revenue": 157.73, // ➋
    "largest_order_but_1": 157.73
  }
]

➀ This clause specifies that the window frame should extend to the end of the window partition. Without this clause, the end point of the window frame would always be the current tuple. This would mean the function would be unable to find the second largest order for smaller orders.

➁ The second largest order from this customer.

ntile

Syntax:

NTILE(num_tiles) OVER ([window-partition-clause] [window-order-clause])

Divides the window partition into the specified number of tiles, and allocates each tuple in the window partition to a tile, so that as far as possible each tile has an equal number of tuples. When the set of tuples is not equally divisible by the number of tiles, the function puts more tuples into the lower-numbered tiles. For each tuple, the function returns the number of the tile into which that tuple was placed.

The window order clause determines the sort order of the tuples. If the window order clause is omitted then the tuples are processed in an undefined order.
Arguments:
- num_tiles: The number of tiles into which you want to divide the window partition. This argument can be an expression and must evaluate to a number. If the number is not an integer, it will be truncated.
Clauses:
- (Optional) Window Partition Clause.
- (Optional) Window Order Clause.
Return Value:
- An value greater than or equal to 1 and less than or equal to the number of tiles.

Example:

Allocate each order to one of three tiles by value.

FROM orders AS o
LET revenue = ROUND((
  FROM o.items
  SELECT VALUE SUM(qty * price)
)[0], 2)
SELECT o.orderno, revenue,
NTILE(3) OVER (
  ORDER BY revenue
) AS `ntile`;

The expected result is:

[
  {
    "ntile": 1,
    "orderno": 1009,
    "revenue": null
  },
  {
    "ntile": 1,
    "orderno": 1007,
    "revenue": 130.45
  },
  {
    "ntile": 1,
    "orderno": 1001,
    "revenue": 157.73
  },
  {
    "ntile": 2,
    "orderno": 1004,
    "revenue": 199.94
  },
  {
    "ntile": 2,
    "orderno": 1003,
    "revenue": 477.95
  },
  {
    "ntile": 2,
    "orderno": 1008,
    "revenue": 1999.8
  },
  {
    "ntile": 3,
    "orderno": 1005,
    "revenue": 4639.92
  },
  {
    "ntile": 3,
    "orderno": 1002,
    "revenue": 10906.55
  },
  {
    "ntile": 3,
    "orderno": 1006,
    "revenue": 18847.58
  }
]

percent_rank

Syntax:

PERCENT_RANK() OVER ([window-partition-clause] [window-order-clause])

Returns the percentile rank of the current tuple – that is, the rank of the tuples minus one, divided by the total number of tuples in the window partition minus one.

The window order clause determines the sort order of the tuples. If the window order clause is omitted, the function returns the same result (0) for each tuple.
Arguments:
- None.
Clauses:
- (Optional) Window Partition Clause.
- (Optional) Window Order Clause.
Return Value:
- A number between 0 and 1. The higher the value, the higher the ranking.

Example:

For each customer, find the percentile rank of all orders by order number.

FROM orders AS o
SELECT o.custid, o.orderno, PERCENT_RANK() OVER (
  PARTITION BY o.custid
  ORDER BY o.orderno
) AS `rank`;

The expected result is:

[
  {
    "rank": 0,
    "custid": "C13",
    "orderno": 1002
  },
  {
    "rank": 0.3333333333333333,
    "custid": "C13",
    "orderno": 1007
  },
  {
    "rank": 0.6666666666666666,
    "custid": "C13",
    "orderno": 1008
  },
  {
    "rank": 1,
    "custid": "C13",
    "orderno": 1009
  },
  {
    "rank": 0,
    "custid": "C31",
    "orderno": 1003
  },
  {
    "rank": 0,
    "custid": "C35",
    "orderno": 1004
  },
  {
    "rank": 0,
    "custid": "C37",
    "orderno": 1005
  },
  {
    "rank": 0,
    "custid": "C41",
    "orderno": 1001
  },
  {
    "rank": 1,
    "custid": "C41",
    "orderno": 1006
  }
]

rank

Syntax:

RANK() OVER ([window-partition-clause] [window-order-clause])

Returns the rank of the current tuple – that is, the number of distinct tuples preceding this tuple in the current window partition, plus one.

The tuples are ordered by the window order clause. If any tuples are tied, they will have the same rank. If the window order clause is omitted, the function returns the same result (1) for each tuple.

When any tuples have the same rank, the rank of the next tuple will include all preceding tuples, so there may be a gap in the sequence of returned values. For example, if there are five tuples ranked 3, the next rank is 8.

To avoid gaps in the returned values, use the DENSE_RANK() function instead.
Arguments:
- None.
Clauses:
- (Optional) Window Partition Clause.
- (Optional) Window Order Clause.
Return Value:
- An integer, greater than or equal to 1.

Example:

Find the rank of all orders by number of items.

FROM orders AS o
SELECT o.orderno, LEN(o.items) AS items,
RANK() OVER (
  ORDER BY LEN(o.items)
) AS `rank`;

The expected result is:

[
  {
    "items": 0,
    "rank": 1,
    "orderno": 1009
  },
  {
    "items": 1,
    "rank": 2,
    "orderno": 1008
  },
  {
    "items": 2,
    "rank": 3,
    "orderno": 1004
  },
  {
    "items": 2,
    "rank": 3,
    "orderno": 1007
  },
  {
    "items": 2,
    "rank": 3,
    "orderno": 1002
  },
  {
    "items": 2,
    "rank": 3,
    "orderno": 1001
  },
  {
    "items": 2,
    "rank": 3,
    "orderno": 1003
  },
  {
    "items": 3,
    "rank": 8,
    "orderno": 1006
  },
  {
    "items": 4,
    "rank": 9,
    "orderno": 1005
  }
]

ratio_to_report

Syntax:

RATIO_TO_REPORT(expr) OVER (window-definition)

Returns the fractional ratio of the specified value for each tuple to the sum of values for all tuples in the window frame.
Arguments:
- expr: The value for which you want to calculate the fractional ratio. ^[¹^]
Clauses:
- (Optional) Window Partition Clause.
- (Optional) Window Order Clause.
- (Optional) Window Frame Clause.
Return Value:
- A number between 0 and 1, representing the fractional ratio of the value for the current tuple to the sum of values for all tuples in the current window frame. The sum of returned values for all tuples in the current window frame is 1.
- If the input expression does not evaluate to a number, or the sum of values for all tuples is zero, it returns NULL.

Example:

For each customer, calculate the value of each order as a fraction of the total value of all orders.

FROM orders AS o
LET revenue = ROUND((
  FROM o.items
  SELECT VALUE SUM(qty * price)
)[0], 2)
SELECT o.custid, o.orderno,
RATIO_TO_REPORT(revenue) OVER (
  PARTITION BY o.custid
) AS fractional_ratio;

The expected result is:

[
  {
    "custid": "C13",
    "orderno": 1007,
    "fractional_ratio": 0.010006289887088855
  },
  {
    "custid": "C13",
    "orderno": 1002,
    "fractional_ratio": 0.8365971710849288
  },
  {
    "custid": "C13",
    "orderno": 1009,
    "fractional_ratio": null
  },
  {
    "custid": "C13",
    "orderno": 1008,
    "fractional_ratio": 0.15339653902798234
  },
  {
    "custid": "C31",
    "orderno": 1003,
    "fractional_ratio": 1
  },
  {
    "custid": "C35",
    "orderno": 1004,
    "fractional_ratio": 1
  },
  {
    "custid": "C37",
    "orderno": 1005,
    "fractional_ratio": 1
  },
  {
    "custid": "C41",
    "orderno": 1006,
    "fractional_ratio": 0.9917007404772666
  },
  {
    "custid": "C41",
    "orderno": 1001,
    "fractional_ratio": 0.008299259522733382
  }
]

row_number

Syntax:

ROW_NUMBER() OVER ([window-partition-clause] [window-order-clause])

Returns a unique row number for every tuple in every window partition. In each window partition, the row numbering starts at 1.

The window order clause determines the sort order of the tuples. If the window order clause is omitted, the return values may be unpredictable.
Arguments:
- None.
Clauses:
- (Optional) Window Partition Clause.
- (Optional) Window Order Clause.
Return Value:
- An integer, greater than or equal to 1.

Example:

For each customer, number all orders by value.

FROM orders AS o
LET revenue = ROUND((
  FROM o.items
  SELECT VALUE SUM(qty * price)
)[0], 2)
SELECT o.custid, o.orderno,
ROW_NUMBER() OVER (
  PARTITION BY o.custid
  ORDER BY revenue
) AS `row`;

The expected result is:

[
  {
    "row": 1,
    "custid": "C13",
    "orderno": 1009
  },
  {
    "row": 2,
    "custid": "C13",
    "orderno": 1007
  },
  {
    "row": 3,
    "custid": "C13",
    "orderno": 1008
  },
  {
    "row": 4,
    "custid": "C13",
    "orderno": 1002
  },
  {
    "row": 1,
    "custid": "C31",
    "orderno": 1003
  },
  {
    "row": 1,
    "custid": "C35",
    "orderno": 1004
  },
  {
    "row": 1,
    "custid": "C37",
    "orderno": 1005
  },
  {
    "row": 1,
    "custid": "C41",
    "orderno": 1001
  },
  {
    "row": 2,
    "custid": "C41",
    "orderno": 1006
  }
]

1. If the query contains the GROUP BY clause or any aggregate functions, this expression must only depend on GROUP BY expressions or aggregate functions.

Builtin Functions

abs

acos

asin

atan

atan2

ceil

cos

cosh

degrees

e

exp

floor

ln

log

pi

power

radians

round

sign

sin

sinh

sqrt

tan

tanh

trunc

round_half_to_even

concat

contains

ends_with

initcap (or title)

length

lower

ltrim

position

regexp_contains

regexp_like

regexp_position

regexp_replace

repeat

replace

reverse

rtrim

split

starts_with

substr

trim

upper

string_concat

string_join

string_to_codepoint

codepoint_to_string

substring_before

substring_after

parse_binary

print_binary

binary_length

sub_binary

binary_concat

create_point

create_line

create_rectangle

create_circle

create_polygon

get_x/get_y

get_points

get_center/get_radius

spatial_distance

spatial_area

spatial_intersect

spatial_cell

edit_distance

edit_distance_check

edit_distance_contains

similarity_jaccard

similarity_jaccard_check

word_tokens

get_year/get_month/get_day/get_hour/get_minute/get_second/get_millisecond

adjust_datetime_for_timezone

adjust_time_for_timezone