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#------------------------------------------------------------------------------
# Copyright (c) 2018, 2022, Oracle and/or its affiliates.
#
# This software is dual-licensed to you under the Universal Permissive License
# (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl and Apache License
# 2.0 as shown at http://www.apache.org/licenses/LICENSE-2.0. You may choose
# either license.
#
# If you elect to accept the software under the Apache License, Version 2.0,
# the following applies:
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#------------------------------------------------------------------------------
#------------------------------------------------------------------------------
# ref_cursor.py
#
# Demonstrates the use of REF CURSORS.
#------------------------------------------------------------------------------
import time
import oracledb
import sample_env
# determine whether to use python-oracledb thin mode or thick mode
if not sample_env.get_is_thin():
oracledb.init_oracle_client(lib_dir=sample_env.get_oracle_client())
connection = oracledb.connect(user=sample_env.get_main_user(),
password=sample_env.get_main_password(),
dsn=sample_env.get_connect_string())
with connection.cursor() as cursor:
ref_cursor = connection.cursor()
cursor.callproc("myrefcursorproc", (2, 6, ref_cursor))
print("Rows between 2 and 6:")
for row in ref_cursor:
print(row)
print()
ref_cursor = connection.cursor()
cursor.callproc("myrefcursorproc", (8, 9, ref_cursor))
print("Rows between 8 and 9:")
for row in ref_cursor:
print(row)
print()
#--------------------------------------------------------------------------
# Setting prefetchrows and arraysize of a REF CURSOR can improve
# performance when fetching a large number of rows by reducing network
# round-trips.
#--------------------------------------------------------------------------
# Truncate the table used for this demo
cursor.execute("truncate table TestTempTable")
# Populate the table with a large number of rows
num_rows = 50000
sql = "insert into TestTempTable (IntCol) values (:1)"
data = [(n + 1,) for n in range(num_rows)]
cursor.executemany(sql, data)
# Perform an untuned fetch
ref_cursor = connection.cursor()
print("ref_cursor.prefetchrows =", ref_cursor.prefetchrows,
"ref_cursor.arraysize =", ref_cursor.arraysize)
start = time.time()
sum_rows = 0
cursor.callproc("myrefcursorproc2", [ref_cursor])
for row in ref_cursor:
sum_rows += row[0]
elapsed = (time.time() - start)
print("Sum of IntCol for", num_rows, "rows is ", sum_rows,
"in", elapsed, "seconds")
print()
# Repeat the call but increase the internal arraysize and prefetch row
# buffers for the REF CURSOR to tune the number of round-trips to the
# database
ref_cursor = connection.cursor()
ref_cursor.prefetchrows = 1000
ref_cursor.arraysize = 1000
print("ref_cursor.prefetchrows =", ref_cursor.prefetchrows,
"ref_cursor.arraysize =", ref_cursor.arraysize)
start = time.time()
sum_rows = 0
cursor.callproc("myrefcursorproc2", [ref_cursor])
for row in ref_cursor:
sum_rows += row[0]
elapsed = (time.time() - start)
print("Sum of IntCol for", num_rows, "rows is ", sum_rows,
"in", elapsed, "seconds")
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