Python - Performance comparison for set() vs {}
A discussion following this question left me wondering, so I decided to run a few tests and compare the creation time of set((x,y,z))
vs. {x,y,z}
for creating sets in Python (I'm using Python 3.7).
I compared the two methods using time
and timeit
.
Both were consistent* with the following results:
test1 = """
my_set1 = set((1, 2, 3))
"""
print(timeit(test1))
Result: 0.30240735499999993
test2 = """
my_set2 = {1,2,3}
"""
print(timeit(test2))
Result: 0.10771795900000003
So the second method was almost 3 times faster than the first.
This was quite a surprising difference to me.
What is happening under the hood to optimize the performance of the set literal over the set()
method in such a way?
* Note: I only show the results of the timeit
tests since they are averaged over many samples, and thus perhaps more reliable, but the results when testing with time
showed similar differences in both cases.
python python-3.x performance set
add a comment |
A discussion following this question left me wondering, so I decided to run a few tests and compare the creation time of set((x,y,z))
vs. {x,y,z}
for creating sets in Python (I'm using Python 3.7).
I compared the two methods using time
and timeit
.
Both were consistent* with the following results:
test1 = """
my_set1 = set((1, 2, 3))
"""
print(timeit(test1))
Result: 0.30240735499999993
test2 = """
my_set2 = {1,2,3}
"""
print(timeit(test2))
Result: 0.10771795900000003
So the second method was almost 3 times faster than the first.
This was quite a surprising difference to me.
What is happening under the hood to optimize the performance of the set literal over the set()
method in such a way?
* Note: I only show the results of the timeit
tests since they are averaged over many samples, and thus perhaps more reliable, but the results when testing with time
showed similar differences in both cases.
python python-3.x performance set
add a comment |
A discussion following this question left me wondering, so I decided to run a few tests and compare the creation time of set((x,y,z))
vs. {x,y,z}
for creating sets in Python (I'm using Python 3.7).
I compared the two methods using time
and timeit
.
Both were consistent* with the following results:
test1 = """
my_set1 = set((1, 2, 3))
"""
print(timeit(test1))
Result: 0.30240735499999993
test2 = """
my_set2 = {1,2,3}
"""
print(timeit(test2))
Result: 0.10771795900000003
So the second method was almost 3 times faster than the first.
This was quite a surprising difference to me.
What is happening under the hood to optimize the performance of the set literal over the set()
method in such a way?
* Note: I only show the results of the timeit
tests since they are averaged over many samples, and thus perhaps more reliable, but the results when testing with time
showed similar differences in both cases.
python python-3.x performance set
A discussion following this question left me wondering, so I decided to run a few tests and compare the creation time of set((x,y,z))
vs. {x,y,z}
for creating sets in Python (I'm using Python 3.7).
I compared the two methods using time
and timeit
.
Both were consistent* with the following results:
test1 = """
my_set1 = set((1, 2, 3))
"""
print(timeit(test1))
Result: 0.30240735499999993
test2 = """
my_set2 = {1,2,3}
"""
print(timeit(test2))
Result: 0.10771795900000003
So the second method was almost 3 times faster than the first.
This was quite a surprising difference to me.
What is happening under the hood to optimize the performance of the set literal over the set()
method in such a way?
* Note: I only show the results of the timeit
tests since they are averaged over many samples, and thus perhaps more reliable, but the results when testing with time
showed similar differences in both cases.
python python-3.x performance set
python python-3.x performance set
edited 51 mins ago
asked 59 mins ago
YuvalG
268114
268114
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
(This is in response to code that has now been edited out of the initial question) You forgot to call the functions in the second case. Making the appropriate modifications, the results are as expected:
test1 = """
def foo1():
my_set1 = set((1, 2, 3))
foo1()
"""
timeit(test1)
# 0.48808742000255734
test2 = """
def foo2():
my_set2 = {1,2,3}
foo2()
"""
timeit(test2)
# 0.3064506609807722
Now, the reason for the difference in timings is because set()
is a function call requiring a lookup into the symbol table, whereas the {...}
set construction is an artefact of the syntax, and is much faster.
The difference is obvious when observing the disassembled byte code.
import dis
dis.dis("set((1, 2, 3))")
1 0 LOAD_NAME 0 (set)
2 LOAD_CONST 3 ((1, 2, 3))
4 CALL_FUNCTION 1
6 RETURN_VALUE
dis.dis("{1, 2, 3}")
1 0 LOAD_CONST 0 (1)
2 LOAD_CONST 1 (2)
4 LOAD_CONST 2 (3)
6 BUILD_SET 3
8 RETURN_VALUE
In the first case, a function call is made by the instruction CALL_FUNCTION
on the tuple (1, 2, 3)
(which also comes with its own overhead, although minor—it is loaded as a constant via LOAD_CONST
), whereas in the second instruction is just a BUILD_SET
call, which is more efficient.
Re: your question regarding the time taken for tuple construction, we see this is actually negligible:
timeit("""(1, 2, 3)""")
# 0.01858693000394851
timeit("""{1, 2, 3}""")
# 0.11971827200613916
This is because python interns small tuples (you can see this clearly from the LOAD_CONST
instruction above), so the time taken is negligible.
For larger sequences, we see similar behaviour. {..}
syntax is faster at constructing sets using set comprehensions as opposed to set()
which has to build the set from a generator.
timeit("""set(i for i in range(10000))""", number=1000)
# 0.9775058150407858
timeit("""{i for i in range(10000)}""", number=1000)
# 0.5508635920123197
Interestingly, however, set()
is optimised for range
:
timeit("""set(range(10000))""", number=1000)
# 0.3746800610097125
This happens to be faster than the set construction. You will see similar behaviour for other sequences (such as list
s).
My recommendation would be to use the {...}
set comprehension when constructing set literals, and as an alternative to passing a generator comprehension to set()
; and instead use set()
to convert an existing sequence/iterable to a set.
1
He is also creating a tuple and then pass it to the set function, does the tuple creation times count?
– Daniel Mesejo
55 mins ago
2
@DanielMesejo Perhaps, but I cannot be sure. In this case, maybe not as I believe python interns (caches) tuples, so after the first couple of timeits that might not cause much of a difference in timings. But in theory, yes, it will contribute.
– coldspeed
53 mins ago
Whoops! Silly me, that definitely explains it. Editing the question, the first part is still rather intruiging, I'd love a more in depth view of what's happening. Was also wondering about what @DanielMesejo asked.
– YuvalG
52 mins ago
1
@DanielMesejo might be wrong but from the bytecode, it seems it does not create the tuple, it is built as a constant when python code is initially parsed. It justLOAD_CONST
s the tuple. The overhead comes after, building the set from that tuple.
– spectras
51 mins ago
@spectras You're right, that actually proves the point that the tuple is cached after the initial call.
– coldspeed
48 mins ago
|
show 1 more comment
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(This is in response to code that has now been edited out of the initial question) You forgot to call the functions in the second case. Making the appropriate modifications, the results are as expected:
test1 = """
def foo1():
my_set1 = set((1, 2, 3))
foo1()
"""
timeit(test1)
# 0.48808742000255734
test2 = """
def foo2():
my_set2 = {1,2,3}
foo2()
"""
timeit(test2)
# 0.3064506609807722
Now, the reason for the difference in timings is because set()
is a function call requiring a lookup into the symbol table, whereas the {...}
set construction is an artefact of the syntax, and is much faster.
The difference is obvious when observing the disassembled byte code.
import dis
dis.dis("set((1, 2, 3))")
1 0 LOAD_NAME 0 (set)
2 LOAD_CONST 3 ((1, 2, 3))
4 CALL_FUNCTION 1
6 RETURN_VALUE
dis.dis("{1, 2, 3}")
1 0 LOAD_CONST 0 (1)
2 LOAD_CONST 1 (2)
4 LOAD_CONST 2 (3)
6 BUILD_SET 3
8 RETURN_VALUE
In the first case, a function call is made by the instruction CALL_FUNCTION
on the tuple (1, 2, 3)
(which also comes with its own overhead, although minor—it is loaded as a constant via LOAD_CONST
), whereas in the second instruction is just a BUILD_SET
call, which is more efficient.
Re: your question regarding the time taken for tuple construction, we see this is actually negligible:
timeit("""(1, 2, 3)""")
# 0.01858693000394851
timeit("""{1, 2, 3}""")
# 0.11971827200613916
This is because python interns small tuples (you can see this clearly from the LOAD_CONST
instruction above), so the time taken is negligible.
For larger sequences, we see similar behaviour. {..}
syntax is faster at constructing sets using set comprehensions as opposed to set()
which has to build the set from a generator.
timeit("""set(i for i in range(10000))""", number=1000)
# 0.9775058150407858
timeit("""{i for i in range(10000)}""", number=1000)
# 0.5508635920123197
Interestingly, however, set()
is optimised for range
:
timeit("""set(range(10000))""", number=1000)
# 0.3746800610097125
This happens to be faster than the set construction. You will see similar behaviour for other sequences (such as list
s).
My recommendation would be to use the {...}
set comprehension when constructing set literals, and as an alternative to passing a generator comprehension to set()
; and instead use set()
to convert an existing sequence/iterable to a set.
1
He is also creating a tuple and then pass it to the set function, does the tuple creation times count?
– Daniel Mesejo
55 mins ago
2
@DanielMesejo Perhaps, but I cannot be sure. In this case, maybe not as I believe python interns (caches) tuples, so after the first couple of timeits that might not cause much of a difference in timings. But in theory, yes, it will contribute.
– coldspeed
53 mins ago
Whoops! Silly me, that definitely explains it. Editing the question, the first part is still rather intruiging, I'd love a more in depth view of what's happening. Was also wondering about what @DanielMesejo asked.
– YuvalG
52 mins ago
1
@DanielMesejo might be wrong but from the bytecode, it seems it does not create the tuple, it is built as a constant when python code is initially parsed. It justLOAD_CONST
s the tuple. The overhead comes after, building the set from that tuple.
– spectras
51 mins ago
@spectras You're right, that actually proves the point that the tuple is cached after the initial call.
– coldspeed
48 mins ago
|
show 1 more comment
(This is in response to code that has now been edited out of the initial question) You forgot to call the functions in the second case. Making the appropriate modifications, the results are as expected:
test1 = """
def foo1():
my_set1 = set((1, 2, 3))
foo1()
"""
timeit(test1)
# 0.48808742000255734
test2 = """
def foo2():
my_set2 = {1,2,3}
foo2()
"""
timeit(test2)
# 0.3064506609807722
Now, the reason for the difference in timings is because set()
is a function call requiring a lookup into the symbol table, whereas the {...}
set construction is an artefact of the syntax, and is much faster.
The difference is obvious when observing the disassembled byte code.
import dis
dis.dis("set((1, 2, 3))")
1 0 LOAD_NAME 0 (set)
2 LOAD_CONST 3 ((1, 2, 3))
4 CALL_FUNCTION 1
6 RETURN_VALUE
dis.dis("{1, 2, 3}")
1 0 LOAD_CONST 0 (1)
2 LOAD_CONST 1 (2)
4 LOAD_CONST 2 (3)
6 BUILD_SET 3
8 RETURN_VALUE
In the first case, a function call is made by the instruction CALL_FUNCTION
on the tuple (1, 2, 3)
(which also comes with its own overhead, although minor—it is loaded as a constant via LOAD_CONST
), whereas in the second instruction is just a BUILD_SET
call, which is more efficient.
Re: your question regarding the time taken for tuple construction, we see this is actually negligible:
timeit("""(1, 2, 3)""")
# 0.01858693000394851
timeit("""{1, 2, 3}""")
# 0.11971827200613916
This is because python interns small tuples (you can see this clearly from the LOAD_CONST
instruction above), so the time taken is negligible.
For larger sequences, we see similar behaviour. {..}
syntax is faster at constructing sets using set comprehensions as opposed to set()
which has to build the set from a generator.
timeit("""set(i for i in range(10000))""", number=1000)
# 0.9775058150407858
timeit("""{i for i in range(10000)}""", number=1000)
# 0.5508635920123197
Interestingly, however, set()
is optimised for range
:
timeit("""set(range(10000))""", number=1000)
# 0.3746800610097125
This happens to be faster than the set construction. You will see similar behaviour for other sequences (such as list
s).
My recommendation would be to use the {...}
set comprehension when constructing set literals, and as an alternative to passing a generator comprehension to set()
; and instead use set()
to convert an existing sequence/iterable to a set.
1
He is also creating a tuple and then pass it to the set function, does the tuple creation times count?
– Daniel Mesejo
55 mins ago
2
@DanielMesejo Perhaps, but I cannot be sure. In this case, maybe not as I believe python interns (caches) tuples, so after the first couple of timeits that might not cause much of a difference in timings. But in theory, yes, it will contribute.
– coldspeed
53 mins ago
Whoops! Silly me, that definitely explains it. Editing the question, the first part is still rather intruiging, I'd love a more in depth view of what's happening. Was also wondering about what @DanielMesejo asked.
– YuvalG
52 mins ago
1
@DanielMesejo might be wrong but from the bytecode, it seems it does not create the tuple, it is built as a constant when python code is initially parsed. It justLOAD_CONST
s the tuple. The overhead comes after, building the set from that tuple.
– spectras
51 mins ago
@spectras You're right, that actually proves the point that the tuple is cached after the initial call.
– coldspeed
48 mins ago
|
show 1 more comment
(This is in response to code that has now been edited out of the initial question) You forgot to call the functions in the second case. Making the appropriate modifications, the results are as expected:
test1 = """
def foo1():
my_set1 = set((1, 2, 3))
foo1()
"""
timeit(test1)
# 0.48808742000255734
test2 = """
def foo2():
my_set2 = {1,2,3}
foo2()
"""
timeit(test2)
# 0.3064506609807722
Now, the reason for the difference in timings is because set()
is a function call requiring a lookup into the symbol table, whereas the {...}
set construction is an artefact of the syntax, and is much faster.
The difference is obvious when observing the disassembled byte code.
import dis
dis.dis("set((1, 2, 3))")
1 0 LOAD_NAME 0 (set)
2 LOAD_CONST 3 ((1, 2, 3))
4 CALL_FUNCTION 1
6 RETURN_VALUE
dis.dis("{1, 2, 3}")
1 0 LOAD_CONST 0 (1)
2 LOAD_CONST 1 (2)
4 LOAD_CONST 2 (3)
6 BUILD_SET 3
8 RETURN_VALUE
In the first case, a function call is made by the instruction CALL_FUNCTION
on the tuple (1, 2, 3)
(which also comes with its own overhead, although minor—it is loaded as a constant via LOAD_CONST
), whereas in the second instruction is just a BUILD_SET
call, which is more efficient.
Re: your question regarding the time taken for tuple construction, we see this is actually negligible:
timeit("""(1, 2, 3)""")
# 0.01858693000394851
timeit("""{1, 2, 3}""")
# 0.11971827200613916
This is because python interns small tuples (you can see this clearly from the LOAD_CONST
instruction above), so the time taken is negligible.
For larger sequences, we see similar behaviour. {..}
syntax is faster at constructing sets using set comprehensions as opposed to set()
which has to build the set from a generator.
timeit("""set(i for i in range(10000))""", number=1000)
# 0.9775058150407858
timeit("""{i for i in range(10000)}""", number=1000)
# 0.5508635920123197
Interestingly, however, set()
is optimised for range
:
timeit("""set(range(10000))""", number=1000)
# 0.3746800610097125
This happens to be faster than the set construction. You will see similar behaviour for other sequences (such as list
s).
My recommendation would be to use the {...}
set comprehension when constructing set literals, and as an alternative to passing a generator comprehension to set()
; and instead use set()
to convert an existing sequence/iterable to a set.
(This is in response to code that has now been edited out of the initial question) You forgot to call the functions in the second case. Making the appropriate modifications, the results are as expected:
test1 = """
def foo1():
my_set1 = set((1, 2, 3))
foo1()
"""
timeit(test1)
# 0.48808742000255734
test2 = """
def foo2():
my_set2 = {1,2,3}
foo2()
"""
timeit(test2)
# 0.3064506609807722
Now, the reason for the difference in timings is because set()
is a function call requiring a lookup into the symbol table, whereas the {...}
set construction is an artefact of the syntax, and is much faster.
The difference is obvious when observing the disassembled byte code.
import dis
dis.dis("set((1, 2, 3))")
1 0 LOAD_NAME 0 (set)
2 LOAD_CONST 3 ((1, 2, 3))
4 CALL_FUNCTION 1
6 RETURN_VALUE
dis.dis("{1, 2, 3}")
1 0 LOAD_CONST 0 (1)
2 LOAD_CONST 1 (2)
4 LOAD_CONST 2 (3)
6 BUILD_SET 3
8 RETURN_VALUE
In the first case, a function call is made by the instruction CALL_FUNCTION
on the tuple (1, 2, 3)
(which also comes with its own overhead, although minor—it is loaded as a constant via LOAD_CONST
), whereas in the second instruction is just a BUILD_SET
call, which is more efficient.
Re: your question regarding the time taken for tuple construction, we see this is actually negligible:
timeit("""(1, 2, 3)""")
# 0.01858693000394851
timeit("""{1, 2, 3}""")
# 0.11971827200613916
This is because python interns small tuples (you can see this clearly from the LOAD_CONST
instruction above), so the time taken is negligible.
For larger sequences, we see similar behaviour. {..}
syntax is faster at constructing sets using set comprehensions as opposed to set()
which has to build the set from a generator.
timeit("""set(i for i in range(10000))""", number=1000)
# 0.9775058150407858
timeit("""{i for i in range(10000)}""", number=1000)
# 0.5508635920123197
Interestingly, however, set()
is optimised for range
:
timeit("""set(range(10000))""", number=1000)
# 0.3746800610097125
This happens to be faster than the set construction. You will see similar behaviour for other sequences (such as list
s).
My recommendation would be to use the {...}
set comprehension when constructing set literals, and as an alternative to passing a generator comprehension to set()
; and instead use set()
to convert an existing sequence/iterable to a set.
edited 28 mins ago
answered 56 mins ago
coldspeed
119k19113192
119k19113192
1
He is also creating a tuple and then pass it to the set function, does the tuple creation times count?
– Daniel Mesejo
55 mins ago
2
@DanielMesejo Perhaps, but I cannot be sure. In this case, maybe not as I believe python interns (caches) tuples, so after the first couple of timeits that might not cause much of a difference in timings. But in theory, yes, it will contribute.
– coldspeed
53 mins ago
Whoops! Silly me, that definitely explains it. Editing the question, the first part is still rather intruiging, I'd love a more in depth view of what's happening. Was also wondering about what @DanielMesejo asked.
– YuvalG
52 mins ago
1
@DanielMesejo might be wrong but from the bytecode, it seems it does not create the tuple, it is built as a constant when python code is initially parsed. It justLOAD_CONST
s the tuple. The overhead comes after, building the set from that tuple.
– spectras
51 mins ago
@spectras You're right, that actually proves the point that the tuple is cached after the initial call.
– coldspeed
48 mins ago
|
show 1 more comment
1
He is also creating a tuple and then pass it to the set function, does the tuple creation times count?
– Daniel Mesejo
55 mins ago
2
@DanielMesejo Perhaps, but I cannot be sure. In this case, maybe not as I believe python interns (caches) tuples, so after the first couple of timeits that might not cause much of a difference in timings. But in theory, yes, it will contribute.
– coldspeed
53 mins ago
Whoops! Silly me, that definitely explains it. Editing the question, the first part is still rather intruiging, I'd love a more in depth view of what's happening. Was also wondering about what @DanielMesejo asked.
– YuvalG
52 mins ago
1
@DanielMesejo might be wrong but from the bytecode, it seems it does not create the tuple, it is built as a constant when python code is initially parsed. It justLOAD_CONST
s the tuple. The overhead comes after, building the set from that tuple.
– spectras
51 mins ago
@spectras You're right, that actually proves the point that the tuple is cached after the initial call.
– coldspeed
48 mins ago
1
1
He is also creating a tuple and then pass it to the set function, does the tuple creation times count?
– Daniel Mesejo
55 mins ago
He is also creating a tuple and then pass it to the set function, does the tuple creation times count?
– Daniel Mesejo
55 mins ago
2
2
@DanielMesejo Perhaps, but I cannot be sure. In this case, maybe not as I believe python interns (caches) tuples, so after the first couple of timeits that might not cause much of a difference in timings. But in theory, yes, it will contribute.
– coldspeed
53 mins ago
@DanielMesejo Perhaps, but I cannot be sure. In this case, maybe not as I believe python interns (caches) tuples, so after the first couple of timeits that might not cause much of a difference in timings. But in theory, yes, it will contribute.
– coldspeed
53 mins ago
Whoops! Silly me, that definitely explains it. Editing the question, the first part is still rather intruiging, I'd love a more in depth view of what's happening. Was also wondering about what @DanielMesejo asked.
– YuvalG
52 mins ago
Whoops! Silly me, that definitely explains it. Editing the question, the first part is still rather intruiging, I'd love a more in depth view of what's happening. Was also wondering about what @DanielMesejo asked.
– YuvalG
52 mins ago
1
1
@DanielMesejo might be wrong but from the bytecode, it seems it does not create the tuple, it is built as a constant when python code is initially parsed. It just
LOAD_CONST
s the tuple. The overhead comes after, building the set from that tuple.– spectras
51 mins ago
@DanielMesejo might be wrong but from the bytecode, it seems it does not create the tuple, it is built as a constant when python code is initially parsed. It just
LOAD_CONST
s the tuple. The overhead comes after, building the set from that tuple.– spectras
51 mins ago
@spectras You're right, that actually proves the point that the tuple is cached after the initial call.
– coldspeed
48 mins ago
@spectras You're right, that actually proves the point that the tuple is cached after the initial call.
– coldspeed
48 mins ago
|
show 1 more comment
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