I would like to upsert my pandas DataFrame into a SQL Server table. This question has a workable solution for PostgreSQL, but T-SQL does not have an ON CONFLICT
There are two options:
MERGE
statement instead of INSERT ... ON CONFLICT
.UPDATE
statement with a JOIN
, followed by a conditional INSERT
.The T-SQL documentation for MERGE says:
Performance Tip: The conditional behavior described for the MERGE statement works best when the two tables have a complex mixture of matching characteristics. For example, inserting a row if it doesn't exist, or updating a row if it matches. When simply updating one table based on the rows of another table, improve the performance and scalability with basic INSERT, UPDATE, and DELETE statements.
In many cases it is faster and less complicated to simply use the separate UPDATE
and INSERT
statements.
engine = sa.create_engine(
connection_uri, fast_executemany=True, isolation_level="SERIALIZABLE"
)
with engine.begin() as conn:
# step 0.0 - create test environment
conn.execute(sa.text("DROP TABLE IF EXISTS main_table"))
conn.execute(
sa.text(
"CREATE TABLE main_table (id int primary key, txt varchar(50))"
)
)
conn.execute(
sa.text(
"INSERT INTO main_table (id, txt) VALUES (1, 'row 1 old text')"
)
)
# step 0.1 - create DataFrame to UPSERT
df = pd.DataFrame(
[(2, "new row 2 text"), (1, "row 1 new text")], columns=["id", "txt"]
)
# step 1 - upload DataFrame to temporary table
df.to_sql("#temp_table", conn, index=False, if_exists="replace")
# step 2 - merge temp_table into main_table
conn.execute(
sa.text("""\
UPDATE main SET main.txt = temp.txt
FROM main_table main INNER JOIN #temp_table temp
ON main.id = temp.id
"""
)
)
conn.execute(
sa.text("""\
INSERT INTO main_table (id, txt)
SELECT id, txt FROM #temp_table
WHERE id NOT IN (SELECT id FROM main_table)
"""
)
)
# step 3 - confirm results
result = conn.execute(sa.text("SELECT * FROM main_table ORDER BY id")).fetchall()
print(result) # [(1, 'row 1 new text'), (2, 'new row 2 text')]