py-dataframe-show-reader is a library that reads the output of an Apache
Spark DataFrame.show() statement into a PySpark DataFrame.
The primary intended use of the functions in this library is to be used to enable writing more concise and easy-to-read tests of data transformations than would otherwise be possible.
Imagine we are working on a Python method that uses PySpark to perform a data transformation that is not terribly complex, but complex enough that we would like to verify that it performs as intended. For example, consider this function that accepts a PySpark Dataframe containing daily sales figures and returns a Dataframe containing weekly sales summaries:
def summarize_weekly_sales(df_to_average: DataFrame): return (df_to_average .groupby(f.weekofyear('date').alias('week_of_year')) .agg(f.avg('units_sold').alias('avg_units_sold'), f.sum('gross_sales').alias('gross_sales')))
This is not a complex method, but perhaps we would like to verify that:
- Dates are in fact grouped into different weeks.
- Units sold are actually averaged and not summed.
- Gross sales are truly summed and not averaged.
A unit testing purist might argue that each of these assertions should be covered by a separate test method, but there are at least two reasons why one might choose not to do that.
Practical experience tells us that detectable overhead is incurred for each separate PySpark transformation test, so we may want to limit the number of separate tests in order to keep our full test suite running in a reasonable duration.
When working with data sets as we do in Spark or SQL, particularly when using aggregate, grouping and window functions, interactions between different rows can be easy to overlook. Tweaking a query to fix an aggregate function like a summation might inadvertently break the intended behavior of a windowing function in the query. A change to the query might allow a summation-only unit test to pass while leaving broken window function behavior undetected because we have neglected to update the window-function-only unit test.
If we accept that we'd like to use a single test to verify the three requirements of our query, we need three rows in our input DataFrame.
Using unadorned pytest, our test might look like this:
def test_without_dataframe_show_reader(spark_session: SparkSession): input_rows = [ Row( date=datetime(2019, 1, 1), units_sold=10, gross_sales=100, ), Row( date=datetime(2019, 1, 2), units_sold=20, gross_sales=200, ), Row( date=datetime(2019, 1, 8), units_sold=80, gross_sales=800, ), ] input_df = spark_session.createDataFrame(input_rows) result = summarize_weekly_sales(input_df).collect() assert 2 == len(result) # Number of rows assert 3 == len(result) # Number of columns assert 1 == result['week_of_year'] assert 15 == result['avg_units_sold'] assert 300 == result['gross_sales'] assert 2 == result['week_of_year'] assert 80 == result['avg_units_sold'] assert 800 == result['gross_sales']
Using the DataFrame Show Reader, our test could instead look like this:
def test_using_dataframe_show_reader(spark_session: SparkSession): input_df = show_output_to_df(""" +-------------------+----------+-----------+ |date |units_sold|gross_sales| +-------------------+----------+-----------+ |2019-01-01 00:00:00|10 |100 | |2019-01-02 00:00:00|20 |200 | |2019-01-08 00:00:00|80 |800 | This date is in week 2. +-------------------+----------+-----------+ """, spark_session) expected_df = show_output_to_df(""" +------------+--------------+-----------+ |week_of_year|avg_units_sold|gross_sales| [int |double |double ] +------------+--------------+-----------+ |1 |15 |300 | |2 |80 |800 | +------------+--------------+-----------+ """, spark_session) assert_equal(expected_df, summarize_weekly_sales(input_df))
In the second test example, the
show_output_to_df function accepts as input
a string containing the output of a Spark DataFrame.show() call and
"rehydrates" it into a new Spark DataFrame instance that can be used for
In the first version, the setup portion of the test contains eighteen lines, and it may take a few moments to digest the contents of the input rows. In the second version, the setup portion contains nine lines and displays the input data in a more concise tabular form that may be easier for other programmers (and our future selves) to digest when we need to maintain this code down the road.
If the method under test was more complicated and required more rows and/or columns in order to adequately test, the length of the first test format would grow much more quickly than that of the test using the DataFrame Show Reader.
Notice also that the
show_output_to_df function gives us a convenient way
to create an
expected_df to pass to the
assert_equal function (to check
DataFrame equality )that is included in the package. In addition to allowing
this compact display of the expected numbers of rows and columns and data,
assert_equal checks that the DataFrame schemas match, which the first
version of the test does not do.
Running the Tests
- Clone the git repo.
cdinto the root level of the repo.
- At a terminal command line, run
To install the package for use in your own package, run:
pip install py-dataframe-show-reader
Who Maintains DataFrame Show Reader?
DataFrame Show Reader is the work of the community. The core committers and maintainers are responsible for reviewing and merging PRs, as well as steering conversation around new feature requests. If you would like to become a maintainer, please contact us.