using pd.read_sql() to extract large data (>5 million records) from oracle database, making the sql execution very slow - python-2.7

Question

Initially tried using pd.read_sql().
Then I tried using sqlalchemy, query objects but none of these methods are
useful as the sql getting executed for long time and it never ends.
I tried using Hints.
I guess the problem is the following: Pandas creates a cursor object in the
background. With cx_Oracle we cannot influence the "arraysize" parameter which
will be used thereby, i.e. always the default value of 100 will be used which
is far too small.
CODE:
import pandas as pd
import Configuration.Settings as CS
import DataAccess.Databases as SDB
import sqlalchemy
import cx_Oracle
dfs = []
DBM = SDB.Database(CS.DB_PRM,PrintDebugMessages=False,ClientInfo="Loader")
sql = '''
WITH
l AS
(
SELECT DISTINCT /*+ materialize */
hcz.hcz_lwzv_id AS lwzv_id
FROM
pm_mbt_materialbasictypes mbt
INNER JOIN pm_mpt_materialproducttypes mpt ON mpt.mpt_mbt_id = mbt.mbt_id
INNER JOIN pm_msl_materialsublots msl ON msl.msl_mpt_id = mpt.mpt_id
INNER JOIN pm_historycompattributes hca ON hca.hca_msl_id = msl.msl_id AND hca.hca_ignoreflag = 0
INNER JOIN pm_tpm_testdefprogrammodes tpm ON tpm.tpm_id = hca.hca_tpm_id
inner join pm_tin_testdefinsertions tin on tin.tin_id = tpm.tpm_tin_id
INNER JOIN pm_hcz_history_comp_zones hcz ON hcz.hcz_hcp_id = hca.hca_hcp_id
WHERE
mbt.mbt_name = :input1 and tin.tin_name = 'x1' and
hca.hca_testendday < '2018-5-31' and hca.hca_testendday > '2018-05-30'
),
TPL as
(
select /*+ materialize */
*
from
(
select
ut.ut_id,
ut.ut_basic_type,
ut.ut_insertion,
ut.ut_testprogram_name,
ut.ut_revision
from
pm_updated_testprogram ut
where
ut.ut_basic_type = :input1 and ut.ut_insertion = :input2
order by
ut.ut_revision desc
) where rownum = 1
)
SELECT /*+ FIRST_ROWS */
rcl.rcl_lotidentifier AS LOT,
lwzv.lwzv_wafer_id AS WAFER,
pzd.pzd_zone_name AS ZONE,
tte.tte_tpm_id||'~'||tte.tte_testnumber||'~'||tte.tte_testname AS Test_Identifier,
case when ppd.ppd_measurement_result > 1e15 then NULL else SFROUND(ppd.ppd_measurement_result,6) END AS Test_Results
FROM
TPL
left JOIN pm_pcm_details pcm on pcm.pcm_ut_id = TPL.ut_id
left JOIN pm_tin_testdefinsertions tin ON tin.tin_name = TPL.ut_insertion
left JOIN pm_tpr_testdefprograms tpr ON tpr.tpr_name = TPL.ut_testprogram_name and tpr.tpr_revision = TPL.ut_revision
left JOIN pm_tpm_testdefprogrammodes tpm ON tpm.tpm_tpr_id = tpr.tpr_id and tpm.tpm_tin_id = tin.tin_id
left JOIN pm_tte_testdeftests tte on tte.tte_tpm_id = tpm.tpm_id and tte.tte_testnumber = pcm.pcm_testnumber
cross join l
left JOIN pm_lwzv_info lwzv ON lwzv.lwzv_id = l.lwzv_id
left JOIN pm_rcl_resultschipidlots rcl ON rcl.rcl_id = lwzv.lwzv_rcl_id
left JOIN pm_pcm_zone_def pzd ON pzd.pzd_basic_type = TPL.ut_basic_type and pzd.pzd_pcm_x = lwzv.lwzv_pcm_x and pzd.pzd_pcm_y = lwzv.lwzv_pcm_y
left JOIN pm_pcm_par_data ppd ON ppd.ppd_lwzv_id = l.lwzv_id and ppd.ppd_tte_id = tte.tte_id
'''
#method1: using query objects.
Q = DBM.getQueryObject(sql)
Q.execute({"input1":'xxxx',"input2":'yyyy'})
while not Q.AtEndOfResultset:
print Q
#method2: using sqlalchemy
connectstring = "oracle+cx_oracle://username:Password#(description=
(address_list=(address=(protocol=tcp)(host=tnsconnect string)
(port=pertnumber)))(connect_data=(sid=xxxx)))"
engine = sqlalchemy.create_engine(connectstring, arraysize=10000)
df_p = pd.read_sql(sql, params=
{"input1":'xxxx',"input2":'yyyy'}, con=engine)
#method3: using pd.read_sql()
df_p = pd.read_sql_query(SQL_PCM, params=
{"input1":'xxxx',"input2":'yyyy'},
coerce_float=True, con= DBM.Connection)
It would be great if some one could help me out in this. Thanks in advance.

Answer 1

And yet another possibility to adjust the array size without needing to create oraaccess.xml as suggested by Chris. This may not work with the rest of your code as is, but it should give you an idea of how to proceed if you wish to try this approach!
class Connection(cx_Oracle.Connection):
def __init__(self):
super(Connection, self).__init__("user/pw#dsn")
def cursor(self):
c = super(Connection, self).cursor()
c.arraysize = 5000
return c
engine = sqlalchemy.create_engine(creator=Connection)
pandas.read_sql(sql, engine)

Answer 2

Here's another alternative to experiment with.
Set a prefetch size by using the external configuration available to Oracle Call Interface programs like cx_Oracle. This overrides internal settings used by OCI programs. Create an oraaccess.xml file:
<?xml version="1.0"?>
<oraaccess xmlns="http://xmlns.oracle.com/oci/oraaccess"
xmlns:oci="http://xmlns.oracle.com/oci/oraaccess"
schemaLocation="http://xmlns.oracle.com/oci/oraaccess
http://xmlns.oracle.com/oci/oraaccess.xsd">
<default_parameters>
<prefetch>
<rows>1000</rows>
</prefetch>
</default_parameters>
</oraaccess>
If you use tnsnames.ora or sqlnet.ora for cx_Oracle, then put the oraaccess.xml file in the same directory. Otherwise, create a new directory and set the environment variable TNS_ADMIN to that directory name.
cx_Oracle needs to be using Oracle Client 12c, or later, libraries.
Experiment with different sizes.
See OCI Client-Side Deployment Parameters Using oraaccess.xml.