Many thanks Marco. Points noted and other points/criticism are equally welcome. In a forum like this we do not disagree, we just agree to differ so to speak and share ideas.
I will review my code and take onboard your suggestions. regards, Mich LinkedIn * https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* *Disclaimer:* Use it at your own risk. Any and all responsibility for any loss, damage or destruction of data or any other property which may arise from relying on this email's technical content is explicitly disclaimed. The author will in no case be liable for any monetary damages arising from such loss, damage or destruction. On Tue, 9 Feb 2021 at 18:09, Sofia’s World <mmistr...@gmail.com> wrote: > Hey Mich > my 2 cents on top of Jerry's. > for reusable @fixtures across your tests, i'd leverage conftest.py and put > all of them there -if number is not too big. OW. as you say, you can > create tests\fixtures where you place all of them there > > in term of extractHiveDAta.... for a @fixture it is doing too much > A fixture in pytest - anyone correct if wrong - its just an object you can > reuse across tests, something like this below. it should contain very > minimal code.. I'd say not more than 3 lines.. > > @fixture > def spark(): > return SparkSession().... > > def test_mydataframe(spark): > mydf = spark.table("mypreferredtable") > > It seems to me your extractHiveDAta is doing too much. > IMHO it should be: > > @pytest.fixture > def hive_extractor(): > return <s> > > @pytext.fixture > def default_config(): > return <a default instance of your config> > > def test_extraction_from_hive(spark, hive_extractor, default_config): > tableName = config['GCPVariables']['sourceTable'] > fullyQualifiedTableName = config['hiveVariables']['DSDB'] + '.' + > tableName > house_df = s.loadTableFromHiveJDBC(spark_session, > fullyQualifiedTableName) > # To test your dataframe, do something like this > test_df_pandas = <pandas>.from_csv("""regionName,col2,col3 > Kensington and > chelsea,Value2,Value3""") > test_df = spark.createDataFrame(test_df_pandas) > result_df = house_df.subtract(test_df) > self.assertEquals(0, result_df.count()) > > as always, pls feel free to disagree.... havent done much on pytest/ > fixtures but this is how i'd restructure...... > > hth > Marco > > > > On Tue, Feb 9, 2021 at 5:37 PM Mich Talebzadeh <mich.talebza...@gmail.com> > wrote: > >> Interesting points Jerry. I do not know how much atomising the unit test >> brings benefit. >> >> For example we have >> >> @pytest.fixture(scope = "session") >> def extractHiveData(): >> # read data through jdbc from Hive >> spark_session = s.spark_session(ctest['common']['appName']) >> tableName = config['GCPVariables']['sourceTable'] >> fullyQualifiedTableName = config['hiveVariables']['DSDB'] + '.' + >> tableName >> house_df = s.loadTableFromHiveJDBC(spark_session, >> fullyQualifiedTableName) >> # sample data selected equally n rows from Kensington and Chelsea and >> n rows from City of Westminster >> num_rows = int(ctest['statics']['read_df_rows']/2) >> house_df = house_df.filter(col("regionname") == "Kensington and >> Chelsea").limit(num_rows).unionAll(house_df.filter(col("regionname") == >> "City of Westminster").limit(num_rows)) >> return house_df >> >> Notes: >> >> That spark_session is imported from a packaged and has been tested many >> times >> >> The config static values are read through a python file config.py in turn >> reading a yml file config.yml >> >> The important ones to test is house_df, the data frame to read from the >> Hive table. That can fail for a variety of reasons. >> >> >> 1. The Hive driver used is old or out of date >> 2. The Hive driver does not support kerberized access that may be the >> case in production >> >> So any unit testing is going to be limited by scope. Also another point >> being is that if the extract data module fails then you are going to know >> that by calling it and probably can be rectified pretty quick. It is always >> the issue of coverage. How much testing needs to be covered. >> >> >> HTH >> >> >> >> LinkedIn * >> https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw >> <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* >> >> >> >> >> >> *Disclaimer:* Use it at your own risk. Any and all responsibility for >> any loss, damage or destruction of data or any other property which may >> arise from relying on this email's technical content is explicitly >> disclaimed. The author will in no case be liable for any monetary damages >> arising from such loss, damage or destruction. >> >> >> >> >> On Tue, 9 Feb 2021 at 16:34, Jerry Vinokurov <grapesmo...@gmail.com> >> wrote: >> >>> Sure, I think it makes sense in many cases to break things up like this. >>> Looking at your other example I'd say that you might want to break up >>> extractHiveData into several fixtures (one for session, one for config, one >>> for the df) because in my experience fixtures like those are reused >>> constantly across a test suite. In general I try to keep my fixtures to one >>> concrete task only, so that if I find myself repeating a pattern I just >>> factor it out into another fixture. >>> >>> On Tue, Feb 9, 2021 at 11:14 AM Mich Talebzadeh < >>> mich.talebza...@gmail.com> wrote: >>> >>>> Thanks Jerry for your comments. >>>> >>>> The easiest option and I concur is to have all these fixture files >>>> currently under fixtures package lumped together in conftest.py under >>>> * tests* package. >>>> >>>> Then you can get away all together from fixtures and it works. However, >>>> I gather plug and play becomes less manageable when you have a large number >>>> of fixtures (large being relative here). My main modules (not tests) are >>>> designed to do ETL from any database that supports JDBC connections (bar >>>> Google BigQuery that only works correctly with Spark API). You specify your >>>> source DB and target DB in yml file for any pluggable JDBC database >>>> >>>> Going back to Pytest, please check this reference below for the reason >>>> for fixtures packaging >>>> >>>> How to modularize your py.test fixtures (github.com) >>>> <https://gist.github.com/peterhurford/09f7dcda0ab04b95c026c60fa49c2a68> >>>> >>>> With regard to your other point on fixtures (a fixture in each file), I >>>> have this fixture *loadIntoMysqlTable() *where it uses the data frame >>>> created in* extractHiveData*, reads sample records from Hive and >>>> populates MySql test table. The input needed is the Dataframe that is >>>> constructed in the fixture module extractHiveData which has been passed as >>>> parameter to this. This is the only way it seems to work through my tests >>>> >>>> >>>> @pytest.fixture(scope = "session") >>>> def extractHiveData(): >>>> # read data through jdbc from Hive >>>> spark_session = s.spark_session(ctest['common']['appName']) >>>> tableName = config['GCPVariables']['sourceTable'] >>>> fullyQualifiedTableName = config['hiveVariables']['DSDB'] + '.' + >>>> tableName >>>> house_df = s.loadTableFromHiveJDBC(spark_session, >>>> fullyQualifiedTableName) >>>> # sample data selected equally n rows from Kensington and Chelsea >>>> and n rows from City of Westminster >>>> num_rows = int(ctest['statics']['read_df_rows']/2) >>>> house_df = house_df.filter(col("regionname") == "Kensington and >>>> Chelsea").limit(num_rows).unionAll(house_df.filter(col("regionname") == >>>> "City of Westminster").limit(num_rows)) >>>> return house_df >>>> >>>> @pytest.fixture(scope = "session") >>>> def loadIntoMysqlTable(*extractHiveData*): >>>> try: >>>> *extractHiveData*. \ >>>> write. \ >>>> format("jdbc"). \ >>>> option("url", test_url). \ >>>> option("dbtable", ctest['statics']['sourceTable']). \ >>>> option("user", ctest['statics']['user']). \ >>>> option("password", ctest['statics']['password']). \ >>>> option("driver", ctest['statics']['driver']). \ >>>> mode(ctest['statics']['mode']). \ >>>> save() >>>> return True >>>> except Exception as e: >>>> print(f"""{e}, quitting""") >>>> sys.exit(1) >>>> >>>> Thanks again. >>>> >>>> >>>> Mich >>>> >>>> >>>> LinkedIn * >>>> https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw >>>> <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* >>>> >>>> >>>> >>>> >>>> >>>> *Disclaimer:* Use it at your own risk. Any and all responsibility for >>>> any loss, damage or destruction of data or any other property which may >>>> arise from relying on this email's technical content is explicitly >>>> disclaimed. The author will in no case be liable for any monetary damages >>>> arising from such loss, damage or destruction. >>>> >>>> >>>> >>>> >>>> On Tue, 9 Feb 2021 at 15:47, Jerry Vinokurov <grapesmo...@gmail.com> >>>> wrote: >>>> >>>>> Hi Mich, >>>>> >>>>> I'm a bit confused by what you mean when you say that you cannot call >>>>> a fixture in another fixture. The fixtures resolve dependencies among >>>>> themselves by means of their named parameters. So that means that if I >>>>> have >>>>> a fixture >>>>> >>>>> @pytest.fixture >>>>> def fixture1(): >>>>> return SomeObj() >>>>> >>>>> and another fixture >>>>> >>>>> @pytest.fixture >>>>> def fixture2(fixture1) >>>>> return do_something_with_obj(fixture1) >>>>> >>>>> my second fixture will simply receive the object created by the first. >>>>> As such, you do not need to "call" the second fixture at all. Of course, >>>>> if >>>>> you had some use case where you were constructing an object in the second >>>>> fixture, you could have the first return a class, or you could have it >>>>> return a function. In fact, I have fixtures in a project that do both. >>>>> Here >>>>> they are: >>>>> >>>>> @pytest.fixture >>>>> def func(): >>>>> >>>>> def foo(x, y, z): >>>>> >>>>> return (x + y) * z >>>>> >>>>> return foo >>>>> >>>>> That's a fixture that returns a function, and any test using the func >>>>> fixture would receive that actual function as a value, which could then be >>>>> invoked by calling e.g. func(1, 2, 3). Here's another fixture that's >>>>> more like what you're doing: >>>>> >>>>> >>>>> @pytest.fixture >>>>> def data_frame(): >>>>> >>>>> return pd.DataFrame.from_records([(1, 2, 3), (4, 5, 6)], >>>>> columns=['x', 'y', 'z']) >>>>> >>>>> This one just returns a data frame that can be operated on. >>>>> >>>>> Looking at your setup, I don't want to say that it's wrong per se (it >>>>> could be very appropriate to your specific project to split things up >>>>> among >>>>> these many files) but I would say that it's not idiomatic usage of pytest >>>>> fixtures, in my experience. It feels to me like you're jumping through a >>>>> lot of hoops to set up something that could be done quite easily and >>>>> compactly in conftest.py. I do want to emphasize that there is no >>>>> limitation on how fixtures can be used within functions or within other >>>>> fixtures (which are also just functions), since the result of the fixture >>>>> call is just some Python object. >>>>> >>>>> Hope this helps, >>>>> Jerry >>>>> >>>>> On Tue, Feb 9, 2021 at 10:18 AM Mich Talebzadeh < >>>>> mich.talebza...@gmail.com> wrote: >>>>> >>>>>> I was a bit confused with the use of fixtures in Pytest with the >>>>>> dataframes passed as an input pipeline from one fixture to another. I >>>>>> wrote >>>>>> this after spending some time on it. As usual it is heuristic rather than >>>>>> anything overtly by the book so to speak. >>>>>> >>>>>> In PySpark and PyCharm you can ETTL from Hive to BigQuery or from >>>>>> Oracle to Hive etc. However, for PyTest, I decided to use MySql as a >>>>>> database of choice for testing with a small sample of data (200 rows). I >>>>>> mentioned Fixtures. Simply put "Fixtures are* functions, which will >>>>>> run before each test function to which it is applied, to prepare data*. >>>>>> Fixtures are used to feed some data to the tests such as database >>>>>> connections". If you have ordering like Read data (Extract), do >>>>>> something with it( Transform) and save it somewhere (Load), using Spark >>>>>> then these are all happening in memory with data frames feeding each >>>>>> other. >>>>>> >>>>>> The crucial thing to remember is that fixtures pass functions to each >>>>>> other as parameters not by invoking them directly! >>>>>> >>>>>> Example ## This is correct @pytest.fixture(scope = "session") def >>>>>> transformData(readSourceData): ## fixture passed as parameter # this is >>>>>> incorrect (cannot call a fixture in another fixture) read_df = >>>>>> readSourceData() So This operation becomes >>>>>> >>>>>> transformation_df = readSourceData. \ select( \ .... >>>>>> >>>>>> Say in PyCharm under tests package, you create a package "fixtures" >>>>>> (just a name nothing to do with "fixture") and in there you put your ETL >>>>>> python modules that prepare data for you. Example >>>>>> >>>>>> ### file --> saveData.py @pytest.fixture(scope = "session") def >>>>>> saveData(transformData): # Write to test target table try: >>>>>> transformData. \ >>>>>> write. \ format("jdbc"). \ .... >>>>>> >>>>>> >>>>>> You then drive this test by creating a file called *conftest.py * >>>>>> under* tests* package. You can then instantiate your fixture files >>>>>> by referencing them in this file as below >>>>>> >>>>>> import pytest from tests.fixtures.extractHiveData import >>>>>> extractHiveData from tests.fixtures.loadIntoMysqlTable import >>>>>> loadIntoMysqlTable from tests.fixtures.readSavedData import readSavedData >>>>>> from tests.fixtures.readSourceData import readSourceData from >>>>>> tests.fixtures.transformData import transformData from >>>>>> tests.fixtures.saveData import saveData from tests.fixtures.readSavedData >>>>>> import readSavedData >>>>>> >>>>>> Then you have your test Python file say *test_oracle.py* under >>>>>> package tests and then put assertions there >>>>>> >>>>>> import pytest from src.config import ctest >>>>>> @pytest.mark.usefixtures("extractHiveData") def >>>>>> test_extract(extractHiveData): assert extractHiveData.count() > 0 >>>>>> @pytest.mark.usefixtures("loadIntoMysqlTable") def >>>>>> test_loadIntoMysqlTable(loadIntoMysqlTable): assert loadIntoMysqlTable >>>>>> @pytest.mark.usefixtures("readSavedData") def >>>>>> test_readSourceData(readSourceData): assert readSourceData.count() == >>>>>> ctest['statics']['read_df_rows'] >>>>>> @pytest.mark.usefixtures("transformData") def >>>>>> test_transformData(transformData): assert transformData.count() == >>>>>> ctest['statics']['transformation_df_rows'] >>>>>> @pytest.mark.usefixtures("saveData") def test_saveData(saveData): assert >>>>>> saveData >>>>>> @pytest.mark.usefixtures("readSavedData") >>>>>> def test_readSavedData(transformData, readSavedData): assert >>>>>> readSavedData.subtract(transformData).count() == 0 >>>>>> >>>>>> This is an illustration from PyCharm about directory structure unders >>>>>> tests >>>>>> >>>>>> >>>>>> [image: image.png] >>>>>> >>>>>> >>>>>> Let me know your thoughts. >>>>>> >>>>>> >>>>>> Cheers, >>>>>> >>>>>> >>>>>> Mich >>>>>> >>>>>> >>>>>> LinkedIn * >>>>>> https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw >>>>>> <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> *Disclaimer:* Use it at your own risk. Any and all responsibility >>>>>> for any loss, damage or destruction of data or any other property which >>>>>> may >>>>>> arise from relying on this email's technical content is explicitly >>>>>> disclaimed. The author will in no case be liable for any monetary damages >>>>>> arising from such loss, damage or destruction. >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>> >>>>> -- >>>>> http://www.google.com/profiles/grapesmoker >>>>> >>>> >>> >>> -- >>> http://www.google.com/profiles/grapesmoker >>> >>
