Functions and Operators
Basic Mathematical Operators
Mathematical Operator Precedence
- Parenthesization
- Multiplication and division
- Addition and subtraction
Comparison Operators
Mathematical Functions
Trigonometric Functions
Geometric Functions
String Functions
Pattern-Matching Functions
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
Supported Types
Supported date_part types:
Supported interval types:
Accepted Date, Time, and Timestamp Formats
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.
Usage Notes
-
COUNT(DISTINCTx), 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 usingAPPROX_COUNT_DISTINCTwhen possible to preserve memory. When data cardinalities permit, OmniSci uses the precise implementation ofCOUNT(DISTINCTx)forAPPROX_COUNT_DISTINCT. Set the default error rate using the-hll-precision-bitsconfiguration 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
SAMPLEaggregate function, you can select non-dictionary-encoded strings that are presumed to be unique in a group. For example:If the aggregated column (user_description in the example above) is not unique within a group,
SAMPLEselects a value that might be nondeterministic because of the parallel nature of OmniSci query execution.
Miscellaneous Functions
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.
-
Create your function and save it in a .cpp file; for example, /var/lib/omnisci/udf_myFunction.cpp.
-
Add the UDF configuration flag to omnisci.conf. For example:
-
Use your function in a SQL query. For example:
Sample User-Defined Function
This function, udf_diff.cpp, returns the difference of two values from a table.
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
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.
The next line is the actual user-defined function, which returns the difference between INTEGER values x and y.
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):
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.
OmniSci returns the difference as an INTEGER value.