Note

This documentation is for a prior release of Kinetica. For the latest documentation, click here.

Example UDF (Non-CUDA) - Sum of Squares

The following is a complete example, using the Python API, of a non-CUDA UDF that takes a list of input tables and corresponding output tables (must be the same number) and, for each record of each input table, sums the squares of input table columns and saves the result to the corresponding output table column; i.e.:

in.a² + in.b² + ... + in.n² -> out.a

Download & Run

This example will contain the following Python scripts (click to download):

A UDF management program, udf_sos_manager.py , written using the Python API, which creates the input & output tables, and creates the UDF and executes it.
A UDF, udf_sos_proc.py , written using the Python UDF API, which contains the sum-of-squares example.

After copying these scripts to a local directory, the example can be run as follows, specifying the database URL, username, & password:

Run Example
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$ python3 udf_sos_manager.py init <url> <username> <password>
$ python3 udf_sos_manager.py exec <url> <username> <password>

Verify the results, using a SQL client (KiSQL), Kinetica Workbench, or other:

The udf_sos_py_in_table table is created in the user's default schema (ki_home, unless a different one was assigned during account creation)
A matching udf_sos_py_out_table table is created in the same schema
The udf_sos_py_in_table contains 10,000 records of random data
The udf_sos_py_out_table contains the sum of square of the two columns from udf_sos_py_in_table.

To show the source columns and the sum-of-squares together, run the following query:

Show Sum of Squares Calculations
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SELECT
   sos_in.id,
   x1 || '^2 + ' || x2 || '^2 = ' || y AS "Equation"
FROM
   udf_sos_py_in_table sos_in,
   udf_sos_py_out_table sos_out
WHERE
   sos_in.id = sos_out.id
ORDER BY
   id

UDF Detail

The example UDF uses a single table, udf_sos_py_in_table, as input and a corresponding table, udf_sos_py_out_table, for output.

The input table will contain two float columns and be populated with 10,000 pairs of randomly-generated numbers. The output table will contain one float column that will hold the sums calculated by the UDF. Both tables will also contain an int column that is the calculation identifier, allowing the input data to be matched up with the output data after the UDF has run.

Note

The UDF will assume the first column of the input table, as defined in the original table creation process, is the identifier field. All of the remaining columns after the first will be used in the sum-of-squares calculation.

The UDF will calculate the sum of the squares of each of the 10,000 pairs of numbers and insert into the output table the corresponding 10,000 sums.

Initialization (udf_sos_manager.py init)

The init option invokes the init() function in the udf_sos_manager.py script. This function will create the input table for the UDF to use as the source of the calculations and the output table into which the results will be inserted. It also populates the input data using the standard Kinetica Python API, all outside of the UDF execution framework.

Several aspects of the initialization process are noteworthy:

The external database connection, indicative of the use of the standard Kinetica Python API--the UDF itself will not have this, as it runs within the database:
Connect to the Database
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kinetica = gpudb.GPUdb(host=[args.url], username=args.username, password=args.password)

Input and output table creation:

Create Input Table
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input_table_obj = gpudb.GPUdbTable(
    _type = [
        ["id", "int", gpudb.GPUdbColumnProperty.INT16, gpudb.GPUdbColumnProperty.PRIMARY_KEY],
        ["x1", "float"],
        ["x2", "float"]
    ],
    name = input_table,
    db = kinetica
)

Populate Input Table Data
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records = []
for val in range(1, MAX_RECORDS+1):
    records.append([val, random.gauss(1, 1), random.gauss(1, 2)])
input_table_obj.insert_records(records)

Create Results Table
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gpudb.GPUdbTable(
    _type = [
        ["id", "int", gpudb.GPUdbColumnProperty.INT16, gpudb.GPUdbColumnProperty.PRIMARY_KEY],
        ["y", "float"]
    ],
    name = output_table,
    db = kinetica
)

UDF (udf_sos_proc.py)

The udf_sos_proc.py script is the UDF itself. It uses the Kinetica Python UDF API to compute the sums of squares of input table columns and output those sums to the output table. It runs within the UDF execution framework, and as such, is not called directly--instead, it is registered and launched by udf_sos_manager.py.

Noteworthy in the UDF are the following:

The initial call to ProcData() to access the database:
Begin UDF
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proc_data = ProcData()
The size of the output table must be specified before writing to it:
Size Results Table to Match the Input Table
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out_table.size = in_table.size

The sum-of-squares computation and writing to the output table:

Compute Sum-of-Squares and Write to Output Table
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# Loop through the remaining input table columns
for in_column in islice(in_table, 1, None):
    # For every record value in the column...
    for calc_num in range(0, in_table.size):
        # Add the square of that value to the corresponding output column
        y[calc_num] += in_column[calc_num] ** 2

The final call to complete() to mark the process as finished and ready for clean-up:
End UDF
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proc_data.complete()

Execution (udf_sos_manager.py exec)

The exec option invokes the exec() function in the udf_sos_manager.py script. This function will read the UDF script in as bytes, and create a UDF, uploading the script to the database. The function will then execute the UDF.

The registration step associates a name with the UDF execution code contained in udf_sos_proc.py, the command ( python3 ) and arguments (the name of the proc script) to use to run it, and that it will run in distributed mode.
Create UDF
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response = kinetica.create_proc(PROC_NAME, 'distributed', files, 'python3', [PROC_FILE_NAME], {})
The execution step invokes the UDF by name, passing in the input & output table names against which the UDF will execute.
Execute UDF
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response = kinetica.execute_proc(PROC_NAME, {}, {}, [input_table], {}, [output_table], {})