I am trying to use use a temp table with SQLAlchemy and join it against an existing table. This is what I have so far
engine = db.get_engine(db.app, 'MY_DATABASE')
df = pd.DataFrame({"id": [1, 2, 3], "value": [100, 200, 300], "date": [date.today(), date.today(), date.today()]})
temp_table = db.Table('#temp_table',
db.Column('id', db.Integer),
db.Column('value', db.Integer),
db.Column('date', db.DateTime))
temp_table.create(engine)
df.to_sql(name='tempdb.dbo.#temp_table',
con=engine,
if_exists='append',
index=False)
query = db.session.query(ExistingTable.id).join(temp_table, temp_table.c.id == ExistingTable.id)
out_df = pd.read_sql(query.statement, engine)
temp_table.drop(engine)
return out_df.to_dict('records')
This doesn't return any results because the insert statements that to_sql
does don't get run (I think this is because they are run using sp_prepexec
, but I'm not entirely sure about that).
I then tried just writing out the SQL statement (CREATE TABLE #temp_table...
, INSERT INTO #temp_table...
, SELECT [id] FROM...
) and then running pd.read_sql(query, engine)
. I get the error message
This result object does not return rows. It has been closed automatically.
I guess this is because the statement does more than just SELECT
?
How can I fix this issue (either solution would work, although the first would be preferable as it avoids hard-coded SQL). To be clear, I can't modify the schema in the existing database—it's a vendor database.
In case the number of records to be inserted in the temporary table is small/moderate, one possibility would be to use a literal subquery
or a values CTE
instead of creating temporary table.
# MODEL
class ExistingTable(Base):
__tablename__ = 'existing_table'
id = sa.Column(sa.Integer, primary_key=True)
name = sa.Column(sa.String)
# ...
Assume also following data is to be inserted into temp
table:
# This data retrieved from another database and used for filtering
rows = [
(1, 100, datetime.date(2017, 1, 1)),
(3, 300, datetime.date(2017, 3, 1)),
(5, 500, datetime.date(2017, 5, 1)),
]
Create a CTE or a sub-query containing that data:
stmts = [
# @NOTE: optimization to reduce the size of the statement:
# make type cast only for first row, for other rows DB engine will infer
sa.select([
sa.cast(sa.literal(i), sa.Integer).label("id"),
sa.cast(sa.literal(v), sa.Integer).label("value"),
sa.cast(sa.literal(d), sa.DateTime).label("date"),
]) if idx == 0 else
sa.select([sa.literal(i), sa.literal(v), sa.literal(d)]) # no type cast
for idx, (i, v, d) in enumerate(rows)
]
subquery = sa.union_all(*stmts)
# Choose one option below.
# I personally prefer B because one could reuse the CTE multiple times in the same query
# subquery = subquery.alias("temp_table") # option A
subquery = subquery.cte(name="temp_table") # option B
Create final query with the required joins and filters:
query = (
session
.query(ExistingTable.id)
.join(subquery, subquery.c.id == ExistingTable.id)
# .filter(subquery.c.date >= XXX_DATE)
)
# TEMP: Test result output
for res in query:
print(res)
Finally, get pandas data frame:
out_df = pd.read_sql(query.statement, engine)
result = out_df.to_dict('records')