Is There A Way To Use FORALL To Insert Data From An Array?
I am running oracle 19c and I want to get the best insert performance I can. Currently, I insert using INSERT /*+APPEND */ ... which is fine, but not the speeds I wanted. I read th
Solution 1:
I really get interested in what would be faster, so I've tested some possibile ways to compare them:
- simple
executemanywith no tricks. - the same with
APPEND_VALUEShint inside the statement. union allapproach you've tried in another question. This should be slower than above since it generates a really very large statement (that potentially can require more network than the data itself). It then should be parsed at DB side that will also consume a lot of time and neglect all the benefits (not talking about potential size limit). Then I'veexecutemany'ed it to test with chunks not to build a single statement for 100k records. I didn't use concatenation of values inside the statement, because wanted to keep it safe.insert all. The same downsides, but no unions. Compare it with theunionversion.- serialize the data in JSON and do deserialization at DB side with
json_table. Potentially good performance with single short statement and single data transfer with little overhead of JSON. - Your suggested
FORALLin PL/SQL wrapper procedure. Should be the same asexecutemanysince does the same, but at the database side. Overhead of transformation of the data into the collection. - The same
FORALL, but with columnar approach to pass the data: pass simple lists of column values instead of complex type. Should be much faster thanFORALLwith collection since there's no need to serialize the data into collection's type.
I've used Oracle Autonomous Database in Oracle Cloud with free account. Each method was executed for 10 times in loop with the same input dataset of 100k records, table was recreated before each test. This is the result I've got. Preparation and execution times here are data transformation at client side end DB call itself respectively.
>>> t = PerfTest(100000)
>>> t.run("exec_many", 10)
Method: exec_many.
Duration, avg: 2.3083874 s
Preparation time, avg: 0.0 s
Execution time, avg: 2.3083874 s
>>> t.run("exec_many_append", 10)
Method: exec_many_append.
Duration, avg: 2.6031369 s
Preparation time, avg: 0.0 s
Execution time, avg: 2.6031369 s
>>> t.run("union_all", 10, 10000)
Method: union_all.
Duration, avg: 27.9444233 s
Preparation time, avg: 0.0408773 s
Execution time, avg: 27.8457551 s
>>> t.run("insert_all", 10, 10000)
Method: insert_all.
Duration, avg: 70.6442494 s
Preparation time, avg: 0.0289269 s
Execution time, avg: 70.5541995 s
>>> t.run("json_table", 10)
Method: json_table.
Duration, avg: 10.4648237 s
Preparation time, avg: 9.7907693 s
Execution time, avg: 0.621006 s
>>> t.run("forall", 10)
Method: forall.
Duration, avg: 5.5622837 s
Preparation time, avg: 1.8972456000000002 s
Execution time, avg: 3.6650380999999994 s
>>> t.run("forall_columnar", 10)
Method: forall_columnar.
Duration, avg: 2.6702698000000002 s
Preparation time, avg: 0.055710800000000005 s
Execution time, avg: 2.6105702 s
>>>
The fastest way is just executemany, not so much surprise. Interesting here is that APPEND_VALUES does not improve the query and gets more time on average, so this needs more investigation.
About FORALL: as expected, individual array for each column takes less time as there's no data preparation for it. It is more or less comparable with executemany, but I think PL/SQL overhead plays some role here.
Another interesting part for me is JSON: most of the time was spent on writing LOB into database and serialization, but the query itself was very fast. Maybe write operation can be improved in some way with chuncsize or some another way to pass LOB data into select statement, but as of my code it is far from very simple and straightforward approach with executemany.
There`re also possible approaches without Python that should be faster as native tools for external data, but I didn't tested them:
Below is the code I've used for testing.
import cx_Oracle as db
import os, random, json
import datetime as dt
class PerfTest:
def __init__(self, size):
self._con = db.connect(
os.environ["ora_cloud_usr"],
os.environ["ora_cloud_pwd"],
"test_low",
encoding="UTF-8"
)
self._cur = self._con.cursor()
self.inp = [(i, "Test {i}".format(i=i), random.random()) for i in range(size)]
def __del__(self):
if self._con:
self._con.rollback()
self._con.close()
#Create objets
def setup(self):
try:
self._cur.execute("drop table rand")
#print("table dropped")
except:
pass
self._cur.execute("""create table rand(
id int,
str varchar2(100),
val number
)""")
self._cur.execute("""create or replace package pkg_test as
type ts_test is record (
id rand.id%type,
str rand.str%type,
val rand.val%type
);
type tt_test is table of ts_test index by pls_integer;
type tt_ids is table of rand.id%type index by pls_integer;
type tt_strs is table of rand.str%type index by pls_integer;
type tt_vals is table of rand.val%type index by pls_integer;
procedure write_data(p_data in tt_test);
procedure write_data_columnar(
p_ids in tt_ids,
p_strs in tt_strs,
p_vals in tt_vals
);
end;""")
self._cur.execute("""create or replace package body pkg_test as
procedure write_data(p_data in tt_test)
as
begin
forall i in indices of p_data
insert into rand(id, str, val)
values (p_data(i).id, p_data(i).str, p_data(i).val)
;
commit;
end;
procedure write_data_columnar(
p_ids in tt_ids,
p_strs in tt_strs,
p_vals in tt_vals
) as
begin
forall i in indices of p_ids
insert into rand(id, str, val)
values (p_ids(i), p_strs(i), p_vals(i))
;
commit;
end;
end;
""")
def build_union(self, size):
return """insert into rand(id, str, val)
select id, str, val from rand where 1 = 0 union all
""" + """ union all """.join(
["select :{}, :{}, :{} from dual".format(i*3+1, i*3+2, i*3+3)
for i in range(size)]
)
def build_insert_all(self, size):
return """
""".join(
["into rand(id, str, val) values (:{}, :{}, :{})".format(i*3+1, i*3+2, i*3+3)
for i in range(size)]
)
#Test case with executemany
def exec_many(self):
start = dt.datetime.now()
self._cur.executemany("insert into rand(id, str, val) values (:1, :2, :3)", self.inp)
self._con.commit()
return (dt.timedelta(0), dt.datetime.now() - start)
#The same as above but with prepared statement (no parsing)
def exec_many_append(self):
start = dt.datetime.now()
self._cur.executemany("insert /*+APPEND_VALUES*/ into rand(id, str, val) values (:1, :2, :3)", self.inp)
self._con.commit()
return (dt.timedelta(0), dt.datetime.now() - start)
#Union All approach (chunked). Should have large parse time
def union_all(self, size):
##Chunked list of big tuples
start_prepare = dt.datetime.now()
new_inp = [
tuple([item for t in r for item in t])
for r in list(zip(*[iter(self.inp)]*size))
]
new_stmt = self.build_union(size)
dur_prepare = dt.datetime.now() - start_prepare
#Execute unions
start_exec = dt.datetime.now()
self._cur.executemany(new_stmt, new_inp)
dur_exec = dt.datetime.now() - start_exec
##In case the size is not a divisor
remainder = len(self.inp) % size
if remainder > 0 :
start_prepare = dt.datetime.now()
new_stmt = self.build_union(remainder)
new_inp = tuple([
item for t in self.inp[-remainder:] for item in t
])
dur_prepare += dt.datetime.now() - start_prepare
start_exec = dt.datetime.now()
self._cur.execute(new_stmt, new_inp)
dur_exec += dt.datetime.now() - start_exec
self._con.commit()
return (dur_prepare, dur_exec)
#The same as union all, but with no need to union something
def insert_all(self, size):
##Chunked list of big tuples
start_prepare = dt.datetime.now()
new_inp = [
tuple([item for t in r for item in t])
for r in list(zip(*[iter(self.inp)]*size))
]
new_stmt = """insert all
{}
select * from dual"""
dur_prepare = dt.datetime.now() - start_prepare
#Execute
start_exec = dt.datetime.now()
self._cur.executemany(
new_stmt.format(self.build_insert_all(size)),
new_inp
)
dur_exec = dt.datetime.now() - start_exec
##In case the size is not a divisor
remainder = len(self.inp) % size
if remainder > 0 :
start_prepare = dt.datetime.now()
new_inp = tuple([
item for t in self.inp[-remainder:] for item in t
])
dur_prepare += dt.datetime.now() - start_prepare
start_exec = dt.datetime.now()
self._cur.execute(
new_stmt.format(self.build_insert_all(remainder)),
new_inp
)
dur_exec += dt.datetime.now() - start_exec
self._con.commit()
return (dur_prepare, dur_exec)
#Serialize at server side and do deserialization at DB side
def json_table(self):
start_prepare = dt.datetime.now()
new_inp = json.dumps([
{ "id":t[0], "str":t[1], "val":t[2]} for t in self.inp
])
lob_var = self._con.createlob(db.DB_TYPE_CLOB)
lob_var.write(new_inp)
start_exec = dt.datetime.now()
self._cur.execute("""
insert into rand(id, str, val)
select id, str, val
from json_table(
to_clob(:json), '$[*]'
columns
id int,
str varchar2(100),
val number
)
""", json=lob_var)
dur_exec = dt.datetime.now() - start_exec
self._con.commit()
return (start_exec - start_prepare, dur_exec)
#PL/SQL with FORALL
def forall(self):
start_prepare = dt.datetime.now()
collection_type = self._con.gettype("PKG_TEST.TT_TEST")
record_type = self._con.gettype("PKG_TEST.TS_TEST")
def recBuilder(x):
rec = record_type.newobject()
rec.ID = x[0]
rec.STR = x[1]
rec.VAL = x[2]
return rec
inp_collection = collection_type.newobject([
recBuilder(i) for i in self.inp
])
start_exec = dt.datetime.now()
self._cur.callproc("pkg_test.write_data", [inp_collection])
dur_exec = dt.datetime.now() - start_exec
return (start_exec - start_prepare, dur_exec)
#PL/SQL with FORALL and plain collections
def forall_columnar(self):
start_prepare = dt.datetime.now()
ids, strs, vals = map(list, zip(*self.inp))
start_exec = dt.datetime.now()
self._cur.callproc("pkg_test.write_data_columnar", [ids, strs, vals])
dur_exec = dt.datetime.now() - start_exec
return (start_exec - start_prepare, dur_exec)
#Run test
def run(self, method, iterations, *args):
#Cleanup schema
self.setup()
start = dt.datetime.now()
runtime = []
for i in range(iterations):
single_run = getattr(self, method)(*args)
runtime.append(single_run)
dur = dt.datetime.now() - start
dur_prep_total = sum([i.total_seconds() for i, _ in runtime])
dur_exec_total = sum([i.total_seconds() for _, i in runtime])
print("""Method: {meth}.
Duration, avg: {run_dur} s
Preparation time, avg: {prep} s
Execution time, avg: {ex} s""".format(
inp_s=len(self.inp),
meth=method,
run_dur=dur.total_seconds() / iterations,
prep=dur_prep_total / iterations,
ex=dur_exec_total / iterations
))
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