`chunksize` in `read_sql_table` sometimes is not accurate
I have a table containing about 28 million rows. I read the data in 1 million row chunks:
```python
params = urllib.parse.quote_plus("DRIVER={SQL Server Native Client 11.0};"
                                 "SERVER=xxxx;"
                                 "DATABASE=xxxx;"
                                 "Trusted_Connection=yes")
engine = sa.create_engine("mssql+pyodbc:///?odbc_connect={}".format(params))

pd_frame_generator = pd.read_sql_table(table_name='myTable',
                                        con=engine,
                                        columns=['x', 'y', 'z'] ,
                                        chunksize=1000000
                                       )
```
But sometime one or two more rows are read. The following is the number of lines after exporting the read data into csv (one extra row is normal because of the header. Abnormal ones are morked in red):
![pd_read_sql](https://user-images.githubusercontent.com/15974735/63714998-c1930c80-c7f7-11e9-9dae-9b3fbfd8a7f7.png)

#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.0.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
machine          : AMD64
processor        : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : None.None

pandas           : 0.25.1
numpy            : 1.15.1
pytz             : 2018.5
dateutil         : 2.7.3
pip              : 18.0
setuptools       : 39.0.1
Cython           : None
pytest           : 3.9.3
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : 4.2.5
html5lib         : 1.0.1
pymysql          : None
psycopg2         : 2.7.7 (dt dec pq3 ext lo64)
jinja2           : 2.10
IPython          : 7.0.1
pandas_datareader: None
bs4              : 4.7.1
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.2.5
matplotlib       : 2.2.3
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : 1.1.0
sqlalchemy       : 1.3.1
tables           : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None

</details>
