Creating a schema data health expectation in Palantir Foundry Code Repositories

Question

I have a dataset that is the output of a Python transform defined in Palantir Foundry Code Repository. It has certain columns, but given that over time the data may change I want to validate these columns(around 73) holds in the future.

How can I create a data health expectation or check to ensure that all 73 columns holds in future?

Answer 1

You can use expectations to make assertions about which columns exist in your output schema.

See the official docs for schema expectations.

There are 3 kinds of schema expectations:

# Assert some columns exist.
E.schema().contains({'col1': type1, 'col2': type2})

# Assert the schema contains only columns from the given set (but not necessarily all of them).
E.schema().is_subset_of({'col1': type1, 'col2': type2})

# Assert the schema contains exactly the given columns.
E.schema().equals({'col1': type1, 'col2': type2})

Additionally, for checking a single column, you can use E.col('col1').exists(). But for 73 columns you're better off going with E.schema().

So for a more fleshed-out example, you might have something like:

from transforms.api import transform_df, Check, Input, Output
import transforms.expectations as E
from pyspark.sql import types as T

COLUMNS_WHICH_MUST_EXIST = {
    'string_column': T.StringType(),
    'number_column': T.IntegerType(),
    # ...and 71 more.
}

@transform_df(
    Output("ri.foundry.main.dataset.abcdef", checks=[
        Check(E.schema().contains(COLUMNS_WHICH_MUST_EXIST), "contains important columns"),
    ]),
    input_data=Input("ri.foundry.main.dataset.12345678"),
)
def compute(input_data):
    # ... your logic here

Also see the official docs for expectation checks for more details of the options available.