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Functions and Operators
Functions and Operators (DML)

Basic Mathematical Operators

Operator
Description
+numeric
Returns numeric
numeric
Returns negative value of numeric
numeric1 + numeric2
Sum of numeric1 and numeric2
numeric1 numeric2
Difference of numeric1 and numeric2
numeric1 * numeric2
Product of numeric1 and numeric2
numeric1 / numeric2
Quotient (numeric1 divided by numeric2)

Mathematical Operator Precedence

1. 1.
Parenthesization
2. 2.
Multiplication and division
3. 3.

Comparison Operators

Operator
Description
=
Equals
<>
Not equals
>
Greater than
>=
Greater than or equal to
<
Less than
<=
Less than or equal to
BETWEEN x AND y
Is a value within a range
NOT BETWEEN x AND y
Is a value not within a range
IS NULL
Is a value that is null
IS NOT NULL
Is a value that is not null
NULLIF(x, y)
Compare expressions x and y. If different, return x. If they are the same, return null. For example, if a dataset uses ‘NA’ for null values, you can use this statement to return null using SELECT NULLIF(field_name,'NA').
IS TRUE
True if a value resolves to TRUE.
IS NOT TRUE
True if a value resolves to FALSE.

Mathematical Functions

Function
Description
ABS(x)
Returns the absolute value of x
CEIL(x)
Returns the smallest integer not less than the argument
DEGREES(x)
EXP(x)
Returns the value of e to the power of x
FLOOR(x)
Returns the largest integer not greater than the argument
LN(x)
Returns the natural logarithm of x
LOG(x)
Returns the natural logarithm of x
LOG10(x)
Returns the base-10 logarithm of the specified float expression x
MOD(x,y)
Returns the remainder of int x divided by int y
PI()
Returns the value of pi
POWER(x,y)
Returns the value of x raised to the power of y
ROUND(x)
Rounds x to the nearest integer value, but does not change the data type. For example, the double value 4.1 rounds to the double value 4.
ROUND_TO_DIGIT (x,y)
Rounds x to y decimal places
SIGN(x)
Returns the sign of x as -1, 0, 1 if x is negative, zero, or positive
SQRT(x)
Returns the square root of x.
TRUNCATE(x,y)
Truncates x to y decimal places
WIDTH_BUCKET(target,lower-boundary,upper-boundary,bucket-count)
Define equal-width intervals (buckets) in a range between the lower boundary and the upper boundary, and returns the bucket number to which the target expression is assigned.
• target - A constant, column variable, or general expression for which a bucket number is returned.
• lower-boundary - Lower boundary for the range of values to be partitioned equally.
• upper-boundary - Upper boundary for the range of values to be partitioned equally.
• partition_count - Number of equal-width buckets in the range defined by the lower and upper boundaries.
Expressions can be constants, column variables, or general expressions.
Example Create 10 age buckets of equal size, with lower bound 0 and upper bound 100 ([0,10], [10,20]... [90,100]), and classify the
age of a customer accordingly:
SELECT WIDTH_BUCKET(age, 0, 100, 10) FROM customer;
For example, a customer of age 34 is assigned to bucket 3 ([30,40]) and the function returns the value 3.

Trigonometric Functions

Function
Description
ACOS(x)
Returns the arc cosine of x
ASIN(x)
Returns the arc sine of x
ATAN(x)
Returns the arc tangent of x
ATAN2(x,y)
Returns the arc tangent of x and y
COS(x)
Returns the cosine of x
COT(x)
Returns the cotangent of x
SIN(x)
Returns the sine of x
TAN(x)
Returns the tangent of x

Geometric Functions

Function
Description
DISTANCE_IN_METERS(fromLon, fromLat, toLon, toLat)
Calculates distance in meters between two WGS84 positions.
CONV_4326_900913_X(x)
Converts WGS84 latitude to WGS84 Web Mercator x coordinate.
CONV_4326_900913_Y(y)
Converts WGS84 longitude to WGS84 Web Mercator y coordinate.

String Functions

Function
Description
CHAR_LENGTH(str)
Returns the number of characters in a string. Only works with unencoded fields (ENCODING set to none).
KEY_FOR_STRING(str)
Returns the dictionary key of a dictionary-encoded string column.
LENGTH(str)
Returns the length of a string in bytes. Only works with unencoded fields (ENCODING set to none).

Pattern-matching Functions

Name
Example
Description
str LIKE pattern
'ab' LIKE 'ab'
Returns true if the string matches the pattern (case-sensitive)
str NOT LIKE pattern
'ab' NOT LIKE 'cd'
Returns true if the string does not match the pattern
str ILIKE pattern
'AB' ILIKE 'ab'
Returns true if the string matches the pattern (case-insensitive)
str REGEXP POSIX pattern
'^[a-z]+r#x27;
Lowercase string ending with r
REGEXP_LIKE ( str , POSIX pattern )
'^[hc]at'
cat or hat
Usage Notes
The following wildcard characters are supported by LIKE and ILIKE:
• % matches any number of characters, including zero characters.
• _ matches exactly one character.

Date/Time Functions

Function
Description
CURRENT_DATE
CURRENT_DATE()
Returns the current date in the GMT time zone.
Example:
SELECT CURRENT_DATE();
CURRENT_TIME
CURRENT_TIME()
Returns the current time of day in the GMT time zone.
Example:
SELECT CURRENT_TIME();
CURRENT_TIMESTAMP
CURRENT_TIMESTAMP()
NOW()
Return the current timestamp in the GMT time zone.
Example:
SELECT CURRENT_TIMESTAMP();
DATE_TRUNC(date_part, timestamp)
Truncates the timestamp to the specified date_part. DATE_TRUNC(week,...) starts on Monday (ISO), which is different than EXTRACT(dow,...), which starts on Sunday.
Example:
SELECT DATE_TRUNC(MINUTE, arr_timestamp) Arrival FROM flights_2008_10k LIMIT 10;
EXTRACT(date_part FROM timestamp)
Returns the specified date_part from timestamp.
Example:
SELECT EXTRACT(HOUR FROM arr_timestamp) Arrival_Hour FROM flights_2008_10k LIMIT 10;
INTERVAL 'count' date_part
Adds or Subtracts count date_part units from a timestamp. Note that 'count' is enclosed in single quotes.
Example:
SELECT arr_timestamp + INTERVAL '10' YEAR FROM flights_2008_10k LIMIT 10;
Adds an interval of count date_part to timestamp or date and returns signed date_part units in the provided timestamp or date form.
Example:
SELECT TIMESTAMPADD(DAY, 14, arr_timestamp) Fortnight FROM flights_2008_10k LIMIT 10;
TIMESTAMPDIFF(date_part, timestamp1, timestamp2)
Subtracts timestamp1 from timestamp2 and returns the result in signed date_part units.
Example:
SELECT TIMESTAMPDIFF(MINUTE, arr_timestamp, dep_timestamp) Flight_Time FROM flights_2008_10k LIMIT 10;
DATEDIFF('date_part', date, date)
Returns the difference between two dates, calculated to the lowest level of the date_part you specify. For example, if you set the date_part as DAY, only the year, month, and day are used to calculate the result. Other fields, such as hour and minute, are ignored.
Example:
SELECT DATEDIFF('YEAR', plane_issue_date, now()) Years_In_Service FROM flights_2008_10k LIMIT 10;
Returns a date after a specified time/date interval has been added.
Example:
SELECT DATEADD('MINUTE', 6000, dep_timestamp) Arrival_Estimate FROM flights_2008_10k LIMIT 10;
DATEPART('interval', date | timestamp)
Returns a specified part of a given date or timestamp as an integer value. Note that 'interval' must be enclosed in single quotes.
Example:
SELECT DATEPART('YEAR', plane_issue_date) Year_Issued FROM flights_2008_10k LIMIT 10;

Supported Types

Supported date_part types:
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DATE_TRUNC [YEAR, QUARTER, MONTH, DAY, HOUR, MINUTE, SECOND, MILLISECOND,
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MICROSECOND, NANOSECOND, MILLENNIUM, CENTURY, DECADE, WEEK,
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WEEK_SUNDAY, QUARTERDAY]
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EXTRACT [YEAR, QUARTER, MONTH, DAY, HOUR, MINUTE, SECOND, MILLISECOND,
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MICROSECOND, NANOSECOND, DOW, ISODOW, DOY, EPOCH, QUARTERDAY,
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WEEK, WEEK_SUNDAY, DATEEPOCH]
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DATEDIFF [YEAR, QUARTER, MONTH, DAY, HOUR, MINUTE, SECOND, MILLISECOND,
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MICROSECOND, NANOSECOND, WEEK]
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Supported interval types:
1
2
HOUR, MINUTE, SECOND, MILLISECOND, MICROSECOND, NANOSECOND]
3
TIMESTAMPADD [YEAR, QUARTER, MONTH, WEEKDAY, DAY, HOUR, MINUTE,
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SECOND, MILLISECOND, MICROSECOND, NANOSECOND]
5
DATEPART [YEAR, QUARTER, MONTH, DAYOFYEAR, QUARTERDAY, WEEKDAY, DAY, HOUR,
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MINUTE, SECOND, MILLISECOND, MICROSECOND, NANOSECOND]
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Accepted Date, Time, and Timestamp Formats

Datatype
Formats
Examples
DATE
YYYY-MM-DD
2013-10-31
DATE
MM/DD/YYYY
10/31/2013
DATE
DD-MON-YY
31-Oct-13
DATE
DD/Mon/YYYY
31/Oct/2013
EPOCH
1383262225
TIME
HH:MM
23:49
TIME
HHMMSS
234901
TIME
HH:MM:SS
23:49:01
TIMESTAMP
DATE TIME
31-Oct-13 23:49:01
TIMESTAMP
DATETTIME
31-Oct-13T23:49:01
TIMESTAMP
DATE:TIME
11/31/2013:234901
TIMESTAMP
DATE TIME ZONE
31-Oct-13 11:30:25 -0800
TIMESTAMP
DATE HH.MM.SS PM
31-Oct-13 11.30.25pm
TIMESTAMP
DATE HH:MM:SS PM
31-Oct-13 11:30:25pm
TIMESTAMP
1383262225

Usage Notes

• For two-digit years, years 69-99 are assumed to be previous century (for example, 1969), and 0-68 are assumed to be current century (for example, 2016).
• For four-digit years, negative years (BC) are not supported.
• Hours are expressed in 24-hour format.
• When time components are separated by colons, you can write them as one or two digits.
• Months are case insensitive. You can spell them out or abbreviate to three characters.
• For timestamps, decimal seconds are ignored. Time zone offsets are written as +/-HHMM.
• For timestamps, a numeric string is converted to +/- seconds since January 1, 1970. Supported timestamps range from -30610224000 (January 1, 1000) through 29379456000 (December 31, 2900).
• On output, dates are formatted as YYYY-MM-DD. Times are formatted as HH:MM:SS.
• Linux EPOCH values range from -30610224000 (1/1/1000) through 185542587100800 (1/1/5885487). Complete range in years: +/-5,883,517 around epoch.

Statistical and Aggregate Functions

Both double-precision (standard) and single-precision floating point statistical functions are provided. Single-precision functions run faster on GPUs but might cause overflow errors.
Double-precision FP Function
Single-precision FP Function
Description
AVG(x)
Returns the average value of x
COUNT()
Returns the count of the number of rows returned
COUNT(DISTINCT x)
Returns the count of distinct values of x
APPROX_COUNT_DISTINCT(x, e)
Returns the approximate count of distinct values of x with defined expected error rate e, where e is an integer from 1 to 100. If no value is set for e, the approximate count is calculated using the system-widehll-precision-bits configuration parameter.
APPROX_MEDIAN(x)
Returns the approximate median of x. Two server configuration parameters affect memory usage:
Accuracy of APPROX_MEDIAN depends on the distribution of data; see Usage Notes.
APPROX_PERCENTILE(x,y)
Returns the approximate quantile of x, where y is the value between 0 and 1.
For example, y=0 returns MIN(x), y=1 returns MAX(x), and y=0.5 returns APPROX_MEDIAN(x).
MAX(x)
Returns the maximum value of x
MIN(x)
Returns the minimum value of x
SINGLE_VALUE
Returns the input value if there is only one distinct value in the input; otherwise, the query fails.
SUM(x)
Returns the sum of the values of x
SAMPLE(x)
Returns one sample value from aggregated column x. For example, the following query returns population grouped by city, along with one value from the state column for each group:
Note: This was previously LAST_SAMPLE, which is now deprecated.
CORRELATION(x, y)
CORRELATION_FLOAT(x, y)
Alias of CORR. Returns the coefficient of correlation of a set of number pairs.
CORR(x, y)
CORR_FLOAT(x, y)
Returns the coefficient of correlation of a set of number pairs.
COVAR_POP(x, y)
COVAR_POP_FLOAT(x, y)
Returns the population covariance of a set of number pairs.
COVAR_SAMP(x, y)
COVAR_SAMP_FLOAT(x, y)
Returns the sample covariance of a set of number pairs.
STDDEV(x)
STDDEV_FLOAT(x)
Alias of STDDEV_SAMP. Returns sample standard deviation of the value.
STDDEV_POP(x)
STDDEV_POP_FLOAT(x)
Returns the population standard the standard deviation of the value.
STDDEV_SAMP(x)
STDDEV_SAMP_FLOAT(x)
Returns the sample standard deviation of the value.
VARIANCE(x)
VARIANCE_FLOAT(x)
Alias of VAR_SAMP. Returns the sample variance of the value.
VAR_POP(x)
VAR_POP_FLOAT(x)
Returns the population variance sample variance of the value.
VAR_SAMP(x)
VAR_SAMP_FLOAT(x)
Returns the sample variance of the value.

Usage Notes

• COUNT(DISTINCT x), especially when used in conjunction with GROUP BY, can require a very large amount of memory to keep track of all distinct values in large tables with large cardinalities. To avoid this large overhead, use APPROX_COUNT_DISTINCT.
• APPROX_COUNT_DISTINCT(x, e) gives an approximate count of the value x, based on an expected error rate defined in e. The error rate is an integer value from 1 to 100. The lower the value of e, the higher the precision, and the higher the memory cost. Select a value for e based on the level of precision required. On large tables with large cardinalities, consider using APPROX_COUNT_DISTINCT when possible to preserve memory. When data cardinalities permit, OmniSci uses the precise implementation of COUNT(DISTINCT x) for APPROX_COUNT_DISTINCT. Set the default error rate using the -hll-precision-bits configuration parameter.
• The accuracy of APPROX_MEDIAN (x) upon the distribution of data. For example:
• For 100,000,000 integers (1, 2, 3, ... 100M) in random order, APPROX_MEDIAN can provide a highly accurate answer 5+ significant digits.
• For 100,000,001 integers, where 50,000,000 have value of 0 and 50,000,001 have value of 1, APPROX_MEDIAN returns a value close to 0.5, even though the median is 1.
• Currently, OmniSci does not support grouping by non-dictionary-encoded strings. However, with the SAMPLE aggregate function, you can select non-dictionary-encoded strings that are presumed to be unique in a group. For example:
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SELECT user_name, SAMPLE(user_decription) FROM tweets GROUP BY user_name;
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If the aggregated column (user_description in the example above) is not unique within a group, SAMPLE selects a value that might be nondeterministic because of the parallel nature of OmniSci query execution.

Miscellaneous Functions

Function
Description
SAMPLE_RATIO(x)
Returns a Boolean value, with the probability of True being returned for a row equal to the input argument. The input argument is a numeric value between 0.0 and 1.0. Negative input values (return False), input values greater than 1.0 returns True, and null input values return False.
The result of the function is deterministic per row; that is, all calls of the operator for a given row return the same result. The sample ratio is probabilistic, but is generally within a thousandth of a percentile of the actual range when the underlying dataset is millions of records or larger.
The following example filters approximately 50% of the rows from t and returns a count that is approximately half the number of rows in t:
SELECT COUNT(*) FROM t WHERE SAMPLE_RATIO(0.5)

User-Defined Functions

You can create your own C++ functions and use them in your SQL queries.
• User-defined Functions (UDFs) require clang++ version 9. You can verify the version installed using the command clang++ --version.
• UDFs currently allow any authenticated user to register and execute a runtime function. By default, runtime UDFs are globally disabled but can be enabled with the runtime flag enable-runtime-udf.
1. 1.
Create your function and save it in a .cpp file; for example, /var/lib/omnisci/udf_myFunction.cpp.
2. 2.
Add the UDF configuration flag to omnisci.conf. For example:
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udf = "/var/lib/omnisci/udf_myFunction.cpp"
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3. 3.
Use your function in a SQL query. For example:
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SELECT udf_myFunction FROM myTable
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Sample User-Defined Function

This function, udf_diff.cpp, returns the difference of two values from a table.
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#include <cstdint>
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#if defined(_CUDA_ARCH) && defined(CUDACC) && defined(clang_)
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#define DEVICE _device_
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#define NEVER_INLINE
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#define ALWAYS_INLINE
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#else
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#define DEVICE
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#define NEVER_INLINE _attribute_((noinline))
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#define ALWAYS_INLINE _attribute_((always_inline))
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#endif
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#define EXTENSION_NOINLINE extern "C" NEVER_INLINE DEVICE EXTENSION_NOINLINE int32_t udf_diff(const int32_t x, const int32_t y) { return x - y; }
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Code Commentary

Include the standard integer library, which supports the following datatypes:
• bool
• int8_t (cstdint), char
• int16_t (cstdint), short
• int32_t (cstdint), int
• int64_t (cstdint), size_t
• float
• double
• void
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#include <cstdint>
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The next four lines are boilerplate code that allows OmniSci to determine whether the server is running with GPUs. OmniSci chooses whether it should compile the function inline to achieve the best possible performance.
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#if defined(_CUDA_ARCH) && defined(CUDACC) && defined(clang_)
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#define DEVICE _device_
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#define NEVER_INLINE
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#define ALWAYS_INLINE
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#else
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#define DEVICE
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#define NEVER_INLINE _attribute_((noinline))
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#define ALWAYS_INLINE _attribute_((always_inline))
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#endif
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The next line is the actual user-defined function, which returns the difference between INTEGER values x and y.
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EXTENSION_NOINLINE int32_t udf_diff(const int32_t x, const int32_t y) { return x - y; }
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To run the udf_diff function, add this line to your /var/lib/omnisci/omnisci.conf file (in this example, the .cpp file is stored at /var/lib/omnisci/udf_diff.cpp):
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udf = "/var/lib/omnisci/udf_diff.cpp"
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Restart the OmniSci server.
Use your command from an OmniSci SQL client to query, for example, a table named myTable that contains the INTEGER columns myInt1 and myInt2.
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SELECT udf_diff(myInt1, myInt2) FROM myTable LIMIT 1;
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OmniSci returns the difference as an INTEGER value.