issue #2 - catch case where identical rows are used in succession for the upsert

[dgcaron]

This PR applies the proposed changes as described in #2 before the change the new test case yielded the following result:

+------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------------+
|                                           df1                                            |                                            df2                                            |
+------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------------+
| Row(pkey=1, attr='A', cur=True, effective_date=datetime.date(2019, 1, 1), end_date=None) | Row(pkey=1, attr='A', cur=True, effective_date=datetime.date(2019, 1, 1), end_date=None) |
| Row(pkey=1, attr='A', cur=True, effective_date=datetime.date(2019, 1, 1), end_date=None) | Row(pkey=2, attr='B', cur=True, effective_date=datetime.date(2019, 1, 1), end_date=None) |
| Row(pkey=2, attr='B', cur=True, effective_date=datetime.date(2019, 1, 1), end_date=None) | Row(pkey=4, attr='D', cur=True, effective_date=datetime.date(2019, 1, 1), end_date=None) |
| Row(pkey=4, attr='D', cur=True, effective_date=datetime.date(2019, 1, 1), end_date=None) |                                            None                                           |
+------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------------+

the update is equal to the existing record 1 so no action should be taken but a new record is created instead without ending the previous record with id . with the change applied the record is untouched

+------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------------+
|                                           df1                                            |                                            df2                                            |
+------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------------+
| Row(pkey=1, attr='A', cur=True, effective_date=datetime.date(2019, 1, 1), end_date=None) | Row(pkey=1, attr='A', cur=True, effective_date=datetime.date(2019, 1, 1), end_date=None) |
| Row(pkey=1, attr='A', cur=True, effective_date=datetime.date(2019, 1, 1), end_date=None) | Row(pkey=2, attr='B', cur=True, effective_date=datetime.date(2019, 1, 1), end_date=None) |
| Row(pkey=2, attr='B', cur=True, effective_date=datetime.date(2019, 1, 1), end_date=None) | Row(pkey=4, attr='D', cur=True, effective_date=datetime.date(2019, 1, 1), end_date=None) ||
+------------------------------------------------------------------------------------------+-------------------------------------------------------------------------------------------+

[dgcaron]

This change only accounts for the duplicate rows, there is another issue with this statement but that requires a more elaborate change and i wanted to split the issues in two seperate PR's

[MrPowers]

@dgcaron - thanks for this contribution.

@danielbeach - can you take a look and let us know if this change looks alright to you?

[MrPowers]

LGTM

[MrPowers]

there is another issue with this statement but that requires a more elaborate change and i wanted to split the issues in two seperate PR's

@dgcaron - can you please make another PR with the other fixes? I promise we'll get it reviewed and merged faster.

[dgcaron]

The changes proposed are explained #121.. fixing it properly requires a breaking change in the current call so you might want to give your thoughts before I start writing code :)