Comparing two array.array objects is much slower than it ought to be.The whole point of array.array is to be efficient: "array — Efficient arrays of numeric values"But comparing them is orders of magnitude less efficient than comparing tuples or lists of numbers.  It would seem that array's __eq__ is converting every single number into an int, float, etc. first instead of just comparing the arrays in their native format.If arrays can be copied efficiently, and bytearray can be copied or compared efficiently, there's no good reason for array's equality test to be so stupid.example code:-------------from timeit import timeitsetup = '''from array import arraya = array("I", %s)ac = a.__copy__b = ac()t = tuple(a)u = t[:1] + t[1:]'''for init in ("[1]*10", "[0xffffffff]*10", "[1]*1000", "[0xffffffff]*1000"): print("\n", init) for action in ("ac()", "a == b", "a == ac()", "t == u"):  print(("%6.2f" % timeit(action, setup % init)), action)results:-------- [1]*10  0.31 ac()  0.50 a == b  0.73 a == ac()  0.17 t == u [0xffffffff]*10  0.29 ac()  1.59 a == b  1.87 a == ac()  0.15 t == u [1]*1000  0.84 ac() 37.06 a == b 37.72 a == ac()  2.91 t == u [0xffffffff]*1000  0.84 ac()146.03 a == b145.97 a == ac()  2.90 t == u

You're correct about what is going on; aside from bypassing a bounds check (when not compiled with asserts enabled), the function it uses to get each index is the same as that used to implement indexing at the Python layer. It looks up the getitem function appropriate to the type code over and over, then calls it to create the PyLongObject and performs a rich compare.The existing behavior is probably necessary to work with array subclasses, but it's also incredibly slow as you noticed. Main question is whether to keep the slow path for subclasses, or (effectively) require that array subclasses overriding __getitem__ also override he rich comparison operators to make them work as expected.For cases where the signedness and element size are identical, it's trivial to acquire readonly buffers for both arrays and directly compare the memory (with memcmp for EQ/NE or size 1 elements, wmemcmp for appropriately sized wider elements, and simple loops for anything else).

I noticed the issue is still there in Python 3.6.But I can't see why array subclasses should be the reason for this implementation.By looking at getarrayitem, it looks like __getitem__ does not play any role in how the elements are accessed.Consider the attached example, where SubclassedArray.__getitem__ is overridden to always return 0: nonetheless, equality checks with an array.array containing the same elements always succeed.> For cases where the signedness and element size are identical, it's trivial to acquire readonly buffers for both arrays and directly compare the memoryI would argue that _integerness_ sholud also be identical: otherwisearray("l", range(10)) == array("f", range(10))would evaluate to False, while it is True in the current implementation.

Added a PR with a fast path that triggers when compared arrays store values of the same type. In this fast path, no Python objects are created. For big arrays the runtime reduction can reach 50-100x.It's possible to optimize the comparison loop a bit more - for example array('B') comparison could use memcmp and become extra 10x faster, and other types could receive similar treatment - but I wanted to keep the code relatively simple.Code duplication with macros is a bit ugly, but that's the best I could come up with so far.Benchmark results:Test                                Before      After       % of old timeEqual, same stored type (new fast path)array('i', range(0))                20.4ns      22.07ns     108.15%array('i', range(1))                33.39ns     22.32ns     66.86%array('i', range(10))               152.0ns     31.21ns     20.54%array('i', range(10000))            447.7us     6.571us     1.47%array('i', range(100000))           4.626ms     67.24us     1.45%array('i', [1<<30]*100000))         5.234ms     65.8us      1.26%array('B', range(10))               151.8ns     28.53ns     18.79%array('B', range(250))              3.14us      194.5ns     6.19%array('B', [1,2,3]*1000)            37.76us     2.088us     5.53%array('d', range(10))               311.9ns     31.22ns     10.01%array('d', range(100000))           2.889ms     99.3us      3.44%Equal, different types (slow path)array('X', range(0))                20.37ns     19.45ns     95.48%array('X', range(1))                34.87ns     34.42ns     98.72%array('X', range(10))               169.1ns     169.0ns     99.97%array('X', range(10000))            462.2us     444.8us     96.23%array('X', range(100000))           4.752ms     4.571ms     96.20%Not equal: first element (X) differentarray('i', [X] + [1,2,3]*10000)     42.77ns     21.84ns     51.06%Not equal: last element (X) differentarray('i', [1,2,3]*10000 + [X])     375.4us     19.8us      5.27%

Not saying that this issue shouldn't be fixed, but Numpy arrays are a much more powerful and well-optimized replacement for array.array.

The PR looks very reasonable.  This is a nice optimization.

New changeset7c17e2304b9387f321c813516bf134e4f0bd332a by Antoine Pitrou (Adrian Wielgosik) in branch 'master':bpo-24700: Add a fast path for comparing array.array of equal type (#3009)https://github.com/python/cpython/commit/7c17e2304b9387f321c813516bf134e4f0bd332a

Adrian's PR was merged. Thank you Adrian!