Use temp table with SQLAlchemy

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醉话见心
醉话见心 2021-02-06 01:31

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_DATABAS         


        
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  • 2021-02-06 02:12

    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')
    
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  • 2021-02-06 02:14

    You can try to use another solution - Process-Keyed Table

    A process-keyed table is simply a permanent table that serves as a temp table. To permit processes to use the table simultaneously, the table has an extra column to identify the process. The simplest way to do this is the global variable @@spid (@@spid is the process id in SQL Server).

    ...

    One alternative for the process-key is to use a GUID (data type uniqueidentifier).

    http://www.sommarskog.se/share_data.html#prockeyed

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