Package: exasol-udf-mock-python
UDF Mock for PythonThis projects provides a mock runner for Python3 UDFs which allows you
to test your UDFs locally without a database.Note:This project is in a very early development phase.
Please, be aware that the behavior of the mock runner doesn't perfectly
reflect the behaviors of the UDFs inside the database and that the interface still can change.
In any case, you need to verify your UDFs with integrations test inside the database.Getting startedAttention:We changed the default branch to main and the master branch is deprecated.Installing via pippip install udf-mock-pythonInstalling via poetryAdd it to yourtool.poetry.dependenciesortool.poetry.dev-dependencies[tool.poetry.dev-dependencies]
udf-mock-python = "^0.1.0"
...How to use the MockThe mock runner runs your python UDF in a python environment in which
no external variables, functions or classes are visble.
This means in practice, you can only use things you defined inside your
UDF and what gets provided by the UDF frameworks,
such as exa.meta and the context for the run function.
This includes imports, variables, functions, classes and so on.You define a UDF in this framework within in a wrapper function.
This wrapper function then contains all necessary imports, functions,
variables and classes.
You then handover the wrapper function to theUDFMockExecutorwhich runs the UDF inside if the isolated python environment.
The following example shows, how you use this framework:
The following example shows the general setup for a test with the Mock:def udf_wrapper():

    def run(ctx):
        return ctx.t1+1, ctx.t2+1.1, ctx.t3+"1"

executor = UDFMockExecutor()
meta = MockMetaData(
    script_code_wrapper_function=udf_wrapper,
    input_type="SCALAR",
    input_columns=[Column("t1", int, "INTEGER"),
                   Column("t2", float, "FLOAT"),
                   Column("t3", str, "VARCHAR(20000)")],
    output_type="RETURNS",
    output_columns=[Column("t1", int, "INTEGER"),
                    Column("t2", float, "FLOAT"),
                    Column("t3", str, "VARCHAR(20000)")]
)
exa = MockExaEnvironment(meta)
result = executor.run([Group([(1,1.0,"1"), (5,5.0,"5"), (6,6.0,"6")])], exa)Checkout thetestsfor more information about, how to use the Mock.Limitations or missing featuresSome of the following limitations are fundamental, other are missing
feature and might get removed by later releases:Data type checks for outputs are more strict as in real UDFsNo support for Import or Export Specification or Virtual Schema adapterNo support for dynamic input and output parametersNo support for exa.import_scriptNo BucketFSExecution is not isolated in a containerCan access and manipulate the file system of the system running the MockUDF inside of the database only can write /tmp to tmp and
only see the file system of the script-language container and the mounted bucketfsCan use all python package available in the system running the MockIf you use package which are currently not available in the script-language containers,
you need create your own container for testing inside of the databaseDoes not emulate the ressource limitations which get a applied in the databaseOnly one instance of the UDF gets executedNo support for Python2, because Python2 is officially End of Life