The STATS ... BY function supports these aggregate functions:

AVGedit

Syntax

AVG(column)

Description

The average of a numeric field.

Supported types

The result is always a double no matter the input type.

Example

FROM employees
| STATS AVG(height)

COUNTedit

Syntax

COUNT([input])

Parameters

Description

Returns the total number (count) of input values.

Supported types

Can take any field type as input.

Examples

FROM employees
| STATS COUNT(height)

To count the number of rows, use COUNT() or COUNT(*):

FROM employees
| STATS count = COUNT(*) BY languages
| SORT languages DESC

COUNT_DISTINCTedit

Syntax

COUNT_DISTINCT(column[, precision])

Parameters

Description

Returns the approximate number of distinct values.

Counts are approximateedit

Computing exact counts requires loading values into a set and returning its size. This doesn’t scale when working on high-cardinality sets and/or large values as the required memory usage and the need to communicate those per-shard sets between nodes would utilize too many resources of the cluster.

This COUNT_DISTINCT function is based on the HyperLogLog++ algorithm, which counts based on the hashes of the values with some interesting properties:

For a precision threshold of c, the implementation that we are using requires about c * 8 bytes.

The following chart shows how the error varies before and after the threshold:

For all 3 thresholds, counts have been accurate up to the configured threshold. Although not guaranteed, this is likely to be the case. Accuracy in practice depends on the dataset in question. In general, most datasets show consistently good accuracy. Also note that even with a threshold as low as 100, the error remains very low (1-6% as seen in the above graph) even when counting millions of items.

The HyperLogLog++ algorithm depends on the leading zeros of hashed values, the exact distributions of hashes in a dataset can affect the accuracy of the cardinality.

The COUNT_DISTINCT function takes an optional second parameter to configure the precision.

Supported types

Can take any field type as input.

Examples

FROM hosts
| STATS COUNT_DISTINCT(ip0), COUNT_DISTINCT(ip1)

With the optional second parameter to configure the precision:

FROM hosts
| STATS COUNT_DISTINCT(ip0, 80000), COUNT_DISTINCT(ip1, 5)

MAXedit

The maximum value of a numeric field.

FROM employees
| STATS MAX(languages)

MEDIANedit

The value that is greater than half of all values and less than half of all values, also known as the 50% PERCENTILE.

FROM employees
| STATS MEDIAN(salary), PERCENTILE(salary, 50)

MEDIAN_ABSOLUTE_DEVIATIONedit

The median absolute deviation, a measure of variability. It is a robust statistic, meaning that it is useful for describing data that may have outliers, or may not be normally distributed. For such data it can be more descriptive than standard deviation.

It is calculated as the median of each data point’s deviation from the median of the entire sample. That is, for a random variable X, the median absolute deviation is median(|median(X) - Xi|).

FROM employees
| STATS MEDIAN(salary), MEDIAN_ABSOLUTE_DEVIATION(salary)

MINedit

The minimum value of a numeric field.

FROM employees
| STATS MIN(languages)

PERCENTILEedit

The value at which a certain percentage of observed values occur. For example, the 95th percentile is the value which is greater than 95% of the observed values and the 50th percentile is the MEDIAN.

FROM employees
| STATS p0 = PERCENTILE(salary,  0)
     , p50 = PERCENTILE(salary, 50)
     , p99 = PERCENTILE(salary, 99)

PERCENTILE is (usually) approximateedit

There are many different algorithms to calculate percentiles. The naive implementation simply stores all the values in a sorted array. To find the 50th percentile, you simply find the value that is at my_array[count(my_array) * 0.5].

Clearly, the naive implementation does not scale — the sorted array grows linearly with the number of values in your dataset. To calculate percentiles across potentially billions of values in an Elasticsearch cluster, approximate percentiles are calculated.

The algorithm used by the percentile metric is called TDigest (introduced by Ted Dunning in Computing Accurate Quantiles using T-Digests).

When using this metric, there are a few guidelines to keep in mind:

The following chart shows the relative error on a uniform distribution depending on the number of collected values and the requested percentile:

It shows how precision is better for extreme percentiles. The reason why error diminishes for large number of values is that the law of large numbers makes the distribution of values more and more uniform and the t-digest tree can do a better job at summarizing it. It would not be the case on more skewed distributions.

SUMedit

The sum of a numeric field.

FROM employees
| STATS SUM(languages)

ES|QL supports these mathematical functions:

ABSedit

Syntax

Parameters

Description

Returns the absolute value.

Supported types

Examples

ROW number = -1.0
| EVAL abs_number = ABS(number)

FROM employees
| KEEP first_name, last_name, height
| EVAL abs_height = ABS(0.0 - height)

ACOSedit

Syntax

Parameters

Description

Returns the arccosine of n as an angle, expressed in radians.

Supported types

Example

ROW a=.9
| EVAL acos=ACOS(a)

ASINedit

Syntax

Parameters

Description

Returns the arcsine of the input numeric expression as an angle, expressed in radians.

Supported types

Example

ROW a=.9
| EVAL asin=ASIN(a)

ATANedit

Syntax

Parameters

Description

Returns the arctangent of the input numeric expression as an angle, expressed in radians.

Supported types

Example

ROW a=12.9
| EVAL atan=ATAN(a)

ATAN2edit

Syntax

Parameters

Description

The angle between the positive x-axis and the ray from the origin to the point (x , y) in the Cartesian plane, expressed in radians.

Supported types

Example

ROW y=12.9, x=.6
| EVAL atan2=ATAN2(y, x)

CEILedit

Syntax

Parameters

Description

Round a number up to the nearest integer.

Supported types

Example

ROW a=1.8
| EVAL a=CEIL(a)

COSedit

Syntax

Parameters

Description

Returns the cosine of n. Input expected in radians.

Supported types

Example

ROW a=1.8
| EVAL cos=COS(a)

COSHedit

Syntax

Parameters

Supported types

Description

Returns the hyperbolic cosine.

Example

ROW a=1.8
| EVAL cosh=COSH(a)

Eedit

Euler’s number.

ROW E()

FLOORedit

Round a number down to the nearest integer.

ROW a=1.8
| EVAL a=FLOOR(a)

Supported types:

LOG10edit

Returns the log base 10. The input can be any numeric value, the return value is always a double.

Logs of negative numbers are NaN. Logs of infinites are infinite, as is the log of 0.

ROW d = 1000.0
| EVAL s = LOG10(d)

Supported types:

PIedit

The ratio of a circle’s circumference to its diameter.

ROW PI()

POWedit

Returns the value of a base (first argument) raised to the power of an exponent (second argument). Both arguments must be numeric. The output is always a double. Note that it is still possible to overflow a double result here; in that case, null will be returned.

ROW base = 2.0, exponent = 2
| EVAL result = POW(base, exponent)

Fractional exponentsedit

The exponent can be a fraction, which is similar to performing a root. For example, the exponent of 0.5 will give the square root of the base:

ROW base = 4, exponent = 0.5
| EVAL s = POW(base, exponent)

Table of supported input and output typesedit

For clarity, the following table describes the output result type for all combinations of numeric input types:

ROUNDedit

Rounds a number to the closest number with the specified number of digits. Defaults to 0 digits if no number of digits is provided. If the specified number of digits is negative, rounds to the number of digits left of the decimal point.

FROM employees
| KEEP first_name, last_name, height
| EVAL height_ft = ROUND(height * 3.281, 1)

SINedit

Sine trigonometric function.

ROW a=1.8
| EVAL sin=SIN(a)

Supported types:

SINHedit

Sine hyperbolic function.

ROW a=1.8
| EVAL sinh=SINH(a)

Supported types:

SQRTedit

Returns the square root of a number. The input can be any numeric value, the return value is always a double.

Square roots of negative numbers are NaN. Square roots of infinites are infinite.

ROW d = 100.0
| EVAL s = SQRT(d)

Supported types:

TANedit

Tangent trigonometric function.

ROW a=1.8
| EVAL tan=TAN(a)

Supported types:

TANHedit

Tangent hyperbolic function.

ROW a=1.8
| EVAL tanh=TANH(a)

Supported types:

TAUedit

The ratio of a circle’s circumference to its radius.

ROW TAU()

ES|QL supports these string functions:

CONCATedit

Syntax

CONCAT(string1, string2[, ..., stringN])

Parameters

Description

Concatenates two or more strings.

Example

FROM employees
| KEEP first_name, last_name
| EVAL fullname = CONCAT(first_name, " ", last_name)

LEFTedit

Return the substring that extracts length chars from the string starting from the left.

FROM employees
| KEEP last_name
| EVAL left = LEFT(last_name, 3)
| SORT last_name ASC
| LIMIT 5

Supported types:

LENGTHedit

Returns the character length of a string.

FROM employees
| KEEP first_name, last_name, height
| EVAL fn_length = LENGTH(first_name)

LTRIMedit

Removes leading whitespaces from strings.

ROW message = "   some text  ",  color = " red "
| EVAL message = LTRIM(message)
| EVAL color = LTRIM(color)
| EVAL message = CONCAT("'", message, "'")
| EVAL color = CONCAT("'", color, "'")

Supported types:

REPLACEedit

The function substitutes in the string (1st argument) any match of the regular expression (2nd argument) with the replacement string (3rd argument).

If any of the arguments are NULL, the result is NULL.

ROW str = "Hello World"
| EVAL str = REPLACE(str, "World", "Universe")
| KEEP str

RIGHTedit

Return the substring that extracts length chars from the string starting from the right.

FROM employees
| KEEP last_name
| EVAL right = RIGHT(last_name, 3)
| SORT last_name ASC
| LIMIT 5

Supported types:

RTRIMedit

Removes trailing whitespaces from strings.

ROW message = "   some text  ",  color = " red "
| EVAL message = RTRIM(message)
| EVAL color = RTRIM(color)
| EVAL message = CONCAT("'", message, "'")
| EVAL color = CONCAT("'", color, "'")

Supported types:

SPLITedit

Split a single valued string into multiple strings. For example:

ROW words="foo;bar;baz;qux;quux;corge"
| EVAL word = SPLIT(words, ";")

Which splits "foo;bar;baz;qux;quux;corge" on ; and returns an array:

SUBSTRINGedit

Returns a substring of a string, specified by a start position and an optional length. This example returns the first three characters of every last name:

FROM employees
| KEEP last_name
| EVAL ln_sub = SUBSTRING(last_name, 1, 3)

A negative start position is interpreted as being relative to the end of the string. This example returns the last three characters of of every last name:

FROM employees
| KEEP last_name
| EVAL ln_sub = SUBSTRING(last_name, -3, 3)

If length is omitted, substring returns the remainder of the string. This example returns all characters except for the first:

FROM employees
| KEEP last_name
| EVAL ln_sub = SUBSTRING(last_name, 2)

TRIMedit

Removes leading and trailing whitespaces from strings.

ROW message = "   some text  ",  color = " red "
| EVAL message = TRIM(message)
| EVAL color = TRIM(color)

Supported types:

ES|QL supports these date-time functions:

AUTO_BUCKETedit

Syntax

AUTO_BUCKET(field, buckets, from, to)

Parameters

Description

Creates human-friendly buckets and returns a value for each row that corresponds to the resulting bucket the row falls into.

Using a target number of buckets, a start of a range, and an end of a range, AUTO_BUCKET picks an appropriate bucket size to generate the target number of buckets or fewer. For example, asking for at most 20 buckets over a year results in monthly buckets:

FROM employees
| WHERE hire_date >= "1985-01-01T00:00:00Z" AND hire_date < "1986-01-01T00:00:00Z"
| EVAL month = AUTO_BUCKET(hire_date, 20, "1985-01-01T00:00:00Z", "1986-01-01T00:00:00Z")
| KEEP hire_date, month
| SORT hire_date

The goal isn’t to provide exactly the target number of buckets, it’s to pick a range that people are comfortable with that provides at most the target number of buckets.

Combine AUTO_BUCKET with STATS ... BY to create a histogram:

FROM employees
| WHERE hire_date >= "1985-01-01T00:00:00Z" AND hire_date < "1986-01-01T00:00:00Z"
| EVAL month = AUTO_BUCKET(hire_date, 20, "1985-01-01T00:00:00Z", "1986-01-01T00:00:00Z")
| STATS hires_per_month = COUNT(*) BY month
| SORT month

Asking for more buckets can result in a smaller range. For example, asking for at most 100 buckets in a year results in weekly buckets:

FROM employees
| WHERE hire_date >= "1985-01-01T00:00:00Z" AND hire_date < "1986-01-01T00:00:00Z"
| EVAL week = AUTO_BUCKET(hire_date, 100, "1985-01-01T00:00:00Z", "1986-01-01T00:00:00Z")
| STATS hires_per_week = COUNT(*) BY week
| SORT week

AUTO_BUCKET can also operate on numeric fields. For example, to create a salary histogram:

FROM employees
| EVAL bs = AUTO_BUCKET(salary, 20, 25324, 74999)
| STATS COUNT(*) by bs
| SORT bs

Unlike the earlier example that intentionally filters on a date range, you rarely want to filter on a numeric range. You have to find the min and max separately. ES|QL doesn’t yet have an easy way to do that automatically.

Examples

Create hourly buckets for the last 24 hours, and calculate the number of events per hour:

FROM sample_data
| WHERE @timestamp >= NOW() - 1 day and @timestamp < NOW()
| EVAL bucket = AUTO_BUCKET(@timestamp, 25, DATE_FORMAT(NOW() - 1 day), DATE_FORMAT(NOW()))
| STATS COUNT(*) BY bucket

Create monthly buckets for the year 1985, and calculate the average salary by hiring month:

FROM employees
| WHERE hire_date >= "1985-01-01T00:00:00Z" AND hire_date < "1986-01-01T00:00:00Z"
| EVAL bucket = AUTO_BUCKET(hire_date, 20, "1985-01-01T00:00:00Z", "1986-01-01T00:00:00Z")
| STATS AVG(salary) BY bucket
| SORT bucket

DATE_EXTRACTedit

Syntax

DATE_EXTRACT(date_part, date)

Parameters

Description

Extracts parts of a date, like year, month, day, hour.

Examples

ROW date = DATE_PARSE("yyyy-MM-dd", "2022-05-06")
| EVAL year = DATE_EXTRACT("year", date)

Find all events that occurred outside of business hours (before 9 AM or after 5 PM), on any given date:

FROM sample_data
| WHERE DATE_EXTRACT("hour_of_day", @timestamp) < 9 AND DATE_EXTRACT("hour_of_day", @timestamp) >= 17

DATE_FORMATedit

Syntax

DATE_FORMAT([format,] date)

Parameters

Description

Returns a string representation of a date, in the provided format.

Example

FROM employees
| KEEP first_name, last_name, hire_date
| EVAL hired = DATE_FORMAT("YYYY-MM-dd", hire_date)

DATE_PARSEedit

Syntax

DATE_PARSE([format,] date_string)

Parameters

Description

Returns a date by parsing the second argument using the format specified in the first argument.

Example

ROW date_string = "2022-05-06"
| EVAL date = DATE_PARSE("yyyy-MM-dd", date_string)

DATE_TRUNCedit

Syntax

DATE_TRUNC(interval, date)

Parameters

Description

Rounds down a date to the closest interval.

Examples

FROM employees
| KEEP first_name, last_name, hire_date
| EVAL year_hired = DATE_TRUNC(1 year, hire_date)

Combine DATE_TRUNC with STATS ... BY to create date histograms. For example, the number of hires per year:

FROM employees
| EVAL year = DATE_TRUNC(1 year, hire_date)
| STATS hires = COUNT(emp_no) BY year
| SORT year

Or an hourly error rate:

FROM sample_data
| EVAL error = CASE(message LIKE "*error*", 1, 0)
| EVAL hour = DATE_TRUNC(1 hour, @timestamp)
| STATS error_rate = AVG(error) by hour
| SORT hour

NOWedit

Returns current date and time.

ROW current_date = NOW()

ES|QL supports these type conversion functions:

TO_BOOLEANedit

Converts an input value to a boolean value.

The input can be a single- or multi-valued field or an expression. The input type must be of a string or numeric type.

A string value of "true" will be case-insensitive converted to the Boolean true. For anything else, including the empty string, the function will return false. For example:

ROW str = ["true", "TRuE", "false", "", "yes", "1"]
| EVAL bool = TO_BOOLEAN(str)

The numerical value of 0 will be converted to false, anything else will be converted to true.

Alias: TO_BOOL

TO_CARTESIANPOINTedit

Converts an input value to a point value.

The input can be a single- or multi-valued field or an expression. The input type must be a string or a cartesian point.

A string will only be successfully converted if it respects the WKT Point format:

row wkt = ["POINT(4297.11 -1475.53)", "POINT(7580.93 2272.77)"]
| mv_expand wkt
| eval pt = to_cartesianpoint(wkt)

TO_DATETIMEedit

Converts an input value to a date value.

The input can be a single- or multi-valued field or an expression. The input type must be of a string or numeric type.

A string will only be successfully converted if it’s respecting the format yyyy-MM-dd'T'HH:mm:ss.SSS'Z' (to convert dates in other formats, use DATE_PARSE). For example:

ROW string = ["1953-09-02T00:00:00.000Z", "1964-06-02T00:00:00.000Z", "1964-06-02 00:00:00"]
| EVAL datetime = TO_DATETIME(string)

Note that in this example, the last value in the source multi-valued field has not been converted. The reason being that if the date format is not respected, the conversion will result in a null value. When this happens a Warning header is added to the response. The header will provide information on the source of the failure:

"Line 1:112: evaluation of [TO_DATETIME(string)] failed, treating result as null. Only first 20 failures recorded."

A following header will contain the failure reason and the offending value:

"java.lang.IllegalArgumentException: failed to parse date field [1964-06-02 00:00:00] with format [yyyy-MM-dd'T'HH:mm:ss.SSS'Z']"

If the input parameter is of a numeric type, its value will be interpreted as milliseconds since the Unix epoch. For example:

ROW int = [0, 1]
| EVAL dt = TO_DATETIME(int)

Alias: TO_DT

TO_DEGREESedit

Converts a number in radians to degrees.

The input can be a single- or multi-valued field or an expression. The input type must be of a numeric type and result is always double.

Example:

ROW rad = [1.57, 3.14, 4.71]
| EVAL deg = TO_DEGREES(rad)

TO_DOUBLEedit

Converts an input value to a double value.

The input can be a single- or multi-valued field or an expression. The input type must be of a boolean, date, string or numeric type.

Example:

ROW str1 = "5.20128E11", str2 = "foo"
| EVAL dbl = TO_DOUBLE("520128000000"), dbl1 = TO_DOUBLE(str1), dbl2 = TO_DOUBLE(str2)

Note that in this example, the last conversion of the string isn’t possible. When this happens, the result is a null value. In this case a Warning header is added to the response. The header will provide information on the source of the failure:

"Line 1:115: evaluation of [TO_DOUBLE(str2)] failed, treating result as null. Only first 20 failures recorded."

A following header will contain the failure reason and the offending value:

"java.lang.NumberFormatException: For input string: \"foo\""

If the input parameter is of a date type, its value will be interpreted as milliseconds since the Unix epoch, converted to double.

Boolean true will be converted to double 1.0, false to 0.0.

Alias: TO_DBL

TO_GEOPOINTedit

Converts an input value to a geo_point value.

The input can be a single- or multi-valued field or an expression. The input type must be a string or a geo_point.

A string will only be successfully converted if it respects the WKT Point format:

row wkt = "POINT(42.97109630194 14.7552534413725)"
| eval pt = to_geopoint(wkt)

TO_INTEGERedit

Converts an input value to an integer value.

The input can be a single- or multi-valued field or an expression. The input type must be of a boolean, date, string or numeric type.

Example:

ROW long = [5013792, 2147483647, 501379200000]
| EVAL int = TO_INTEGER(long)

Note that in this example, the last value of the multi-valued field cannot be converted as an integer. When this happens, the result is a null value. In this case a Warning header is added to the response. The header will provide information on the source of the failure:

"Line 1:61: evaluation of [TO_INTEGER(long)] failed, treating result as null. Only first 20 failures recorded."

A following header will contain the failure reason and the offending value:

"org.elasticsearch.xpack.ql.InvalidArgumentException: [501379200000] out of [integer] range"

If the input parameter is of a date type, its value will be interpreted as milliseconds since the Unix epoch, converted to integer.

Boolean true will be converted to integer 1, false to 0.

Alias: TO_INT

TO_IPedit

Converts an input string to an IP value.

The input can be a single- or multi-valued field or an expression.

Example:

ROW str1 = "1.1.1.1", str2 = "foo"
| EVAL ip1 = TO_IP(str1), ip2 = TO_IP(str2)
| WHERE CIDR_MATCH(ip1, "1.0.0.0/8")

Note that in the example above the last conversion of the string isn’t possible. When this happens, the result is a null value. In this case a Warning header is added to the response. The header will provide information on the source of the failure:

"Line 1:68: evaluation of [TO_IP(str2)] failed, treating result as null. Only first 20 failures recorded."

A following header will contain the failure reason and the offending value:

"java.lang.IllegalArgumentException: 'foo' is not an IP string literal."

TO_LONGedit

Converts an input value to a long value.

The input can be a single- or multi-valued field or an expression. The input type must be of a boolean, date, string or numeric type.

Example:

ROW str1 = "2147483648", str2 = "2147483648.2", str3 = "foo"
| EVAL long1 = TO_LONG(str1), long2 = TO_LONG(str2), long3 = TO_LONG(str3)

Note that in this example, the last conversion of the string isn’t possible. When this happens, the result is a null value. In this case a Warning header is added to the response. The header will provide information on the source of the failure:

"Line 1:113: evaluation of [TO_LONG(str3)] failed, treating result as null. Only first 20 failures recorded."

A following header will contain the failure reason and the offending value:

"java.lang.NumberFormatException: For input string: \"foo\""

If the input parameter is of a date type, its value will be interpreted as milliseconds since the Unix epoch, converted to long.

Boolean true will be converted to long 1, false to 0.

TO_RADIANSedit

Converts a number in degrees to radians.

The input can be a single- or multi-valued field or an expression. The input type must be of a numeric type and result is always double.

Example:

ROW deg = [90.0, 180.0, 270.0]
| EVAL rad = TO_RADIANS(deg)

TO_STRINGedit

Converts a field into a string. For example:

ROW a=10
| EVAL j = TO_STRING(a)

It also works fine on multivalued fields:

ROW a=[10, 9, 8]
| EVAL j = TO_STRING(a)

Alias: TO_STR

Supported types:

TO_UNSIGNED_LONGedit

Converts an input value to an unsigned long value.

The input can be a single- or multi-valued field or an expression. The input type must be of a boolean, date, string or numeric type.

Example:

ROW str1 = "2147483648", str2 = "2147483648.2", str3 = "foo"
| EVAL long1 = TO_UNSIGNED_LONG(str1), long2 = TO_ULONG(str2), long3 = TO_UL(str3)

Note that in this example, the last conversion of the string isn’t possible. When this happens, the result is a null value. In this case a Warning header is added to the response. The header will provide information on the source of the failure:

"Line 1:133: evaluation of [TO_UL(str3)] failed, treating result as null. Only first 20 failures recorded."

A following header will contain the failure reason and the offending value:

"java.lang.NumberFormatException: Character f is neither a decimal digit number, decimal point, nor \"e\" notation exponential mark."

If the input parameter is of a date type, its value will be interpreted as milliseconds since the Unix epoch, converted to unsigned long.

Boolean true will be converted to unsigned long 1, false to 0.

Alias: TO_ULONG, TO_UL

TO_VERSIONedit

Converts an input string to a version value. For example:

ROW v = TO_VERSION("1.2.3")

The input can be a single- or multi-valued field or an expression.

Alias: TO_VER

Supported types:

Conditional functions return one of their arguments by evaluating in an if-else manner. ES|QL supports these conditional functions:

CASEedit

Syntax

CASE(condition1, value1[, ..., conditionN, valueN][, default_value])

Parameters

Description

Accepts pairs of conditions and values. The function returns the value that belongs to the first condition that evaluates to true.

If the number of arguments is odd, the last argument is the default value which is returned when no condition matches. If the number of arguments is even, and no condition matches, the function returns null.

Example

Determine whether employees are monolingual, bilingual, or polyglot:

FROM employees
| EVAL type = CASE(
    languages <= 1, "monolingual",
    languages <= 2, "bilingual",
     "polyglot")
| KEEP emp_no, languages, type

Calculate the total connection success rate based on log messages:

FROM sample_data
| EVAL successful = CASE(
    STARTS_WITH(message, "Connected to"), 1,
    message == "Connection error", 0
  )
| STATS success_rate = AVG(successful)

Calculate an hourly error rate as a percentage of the total number of log messages:

FROM sample_data
| EVAL error = CASE(message LIKE "*error*", 1, 0)
| EVAL hour = DATE_TRUNC(1 hour, @timestamp)
| STATS error_rate = AVG(error) by hour
| SORT hour

COALESCEedit

Syntax

COALESCE(expression1 [, ..., expressionN])

Parameters

Description

Returns the first of its arguments that is not null. If all arguments are null, it returns null.

Example

ROW a=null, b="b"
| EVAL COALESCE(a, b)

GREATESTedit

Returns the maximum value from many columns. This is similar to MV_MAX except it’s intended to run on multiple columns at once.

ROW a = 10, b = 20
| EVAL g = GREATEST(a, b)

Supported types:

LEASTedit

Returns the minimum value from many columns. This is similar to MV_MIN except it’s intended to run on multiple columns at once.

ROW a = 10, b = 20
| EVAL l = LEAST(a, b)

Supported types:

ES|QL supports these multivalue functions:

MV_AVGedit

Converts a multivalued field into a single valued field containing the average of all of the values. For example:

ROW a=[3, 5, 1, 6]
| EVAL avg_a = MV_AVG(a)

MV_CONCATedit

Converts a multivalued string field into a single valued field containing the concatenation of all values separated by a delimiter:

ROW a=["foo", "zoo", "bar"]
| EVAL j = MV_CONCAT(a, ", ")

If you want to concat non-string fields call TO_STRING on them first:

ROW a=[10, 9, 8]
| EVAL j = MV_CONCAT(TO_STRING(a), ", ")

Supported types:

MV_COUNTedit

Converts a multivalued field into a single valued field containing a count of the number of values:

ROW a=["foo", "zoo", "bar"]
| EVAL count_a = MV_COUNT(a)

Supported types:

MV_DEDUPEedit

Removes duplicates from a multivalued field. For example:

ROW a=["foo", "foo", "bar", "foo"]
| EVAL dedupe_a = MV_DEDUPE(a)

Supported types:

MV_MAXedit

Converts a multivalued field into a single valued field containing the maximum value. For example:

ROW a=[3, 5, 1]
| EVAL max_a = MV_MAX(a)

It can be used by any field type, including keyword fields. In that case picks the last string, comparing their utf-8 representation byte by byte:

ROW a=["foo", "zoo", "bar"]
| EVAL max_a = MV_MAX(a)

Supported types:

MV_MEDIANedit

Converts a multivalued field into a single valued field containing the median value. For example:

ROW a=[3, 5, 1]
| EVAL median_a = MV_MEDIAN(a)

It can be used by any numeric field type and returns a value of the same type. If the row has an even number of values for a column the result will be the average of the middle two entries. If the field is not floating point then the average rounds down:

ROW a=[3, 7, 1, 6]
| EVAL median_a = MV_MEDIAN(a)

MV_MINedit

Converts a multivalued field into a single valued field containing the minimum value. For example:

ROW a=[2, 1]
| EVAL min_a = MV_MIN(a)

It can be used by any field type, including keyword fields. In that case picks the first string, comparing their utf-8 representation byte by byte:

ROW a=["foo", "bar"]
| EVAL min_a = MV_MIN(a)

Supported types:

MV_SUMedit

Converts a multivalued field into a single valued field containing the sum of all of the values. For example:

ROW a=[3, 5, 6]
| EVAL sum_a = MV_SUM(a)

Boolean operators for comparing against one or multiple expressions.

Binary operatorsedit

These binary comparison operators are supported:

Logical operatorsedit

The following logical operators are supported:

IS NULL and IS NOT NULL predicatesedit

For NULL comparison, use the IS NULL and IS NOT NULL predicates:

FROM employees
| WHERE birth_date IS NULL
| KEEP first_name, last_name
| SORT first_name
| LIMIT 3

FROM employees
| WHERE is_rehired IS NOT NULL
| STATS COUNT(emp_no)

CIDR_MATCHedit

Syntax

CIDR_MATCH(ip, block1[, ..., blockN])

Parameters

Description

Returns true if the provided IP is contained in one of the provided CIDR blocks.

Example

FROM hosts
| WHERE CIDR_MATCH(ip1, "127.0.0.2/32", "127.0.0.3/32")
| KEEP card, host, ip0, ip1

ENDS_WITHedit

Returns a boolean that indicates whether a keyword string ends with another string:

FROM employees
| KEEP last_name
| EVAL ln_E = ENDS_WITH(last_name, "d")

Supported types:

INedit

The IN operator allows testing whether a field or expression equals an element in a list of literals, fields or expressions:

ROW a = 1, b = 4, c = 3
| WHERE c-a IN (3, b / 2, a)

IS_FINITEedit

Syntax

Parameters

Description

Returns a boolean that indicates whether its input is a finite number.

Supported types

Example

ROW d = 1.0
| EVAL s = IS_FINITE(d/0)

IS_INFINITEedit

Syntax

Parameters

Description

Returns a boolean that indicates whether its input is an infinite number.

Supported types

Example

ROW d = 1.0
| EVAL s = IS_INFINITE(d/0)

IS_NANedit

Syntax

Parameters

Description

Returns a boolean that indicates whether its input is Not-a-Number (NaN).

Supported types

Example

ROW d = 1.0
| EVAL s = IS_NAN(d)

LIKEedit

Use LIKE to filter data based on string patterns using wildcards. LIKE usually acts on a field placed on the left-hand side of the operator, but it can also act on a constant (literal) expression. The right-hand side of the operator represents the pattern.

The following wildcard characters are supported:

FROM employees
| WHERE first_name LIKE "?b*"
| KEEP first_name, last_name