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Performance difference between write_async and write_dataframe_async #953
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I can't reproduce this behaviour in a dataset of 4 million or 8 million records. |
Hi @echangcl, |
@echangcl
|
Tried to use both pandas=1.5.3 & 2.0.3, still the same result. Write_dataframe_async is slower than write_async |
Describe what did you try to do with TM1py
![image004 copy](https://private-user-images.githubusercontent.com/62413229/258346676-d50f44c5-3f0f-42f6-af4f-ecdc47476e5b.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.9HdIY1Iz490iQ890WccRbu_d0AHS94vaCcGCzIjwJgM)
I tried to compare the performance of TI execution / write_dataframe / write_async / write_dataframe_async in TM1py.
Use a huge source file, which has more than 25 million records, to do the testing.
Found that write_async performance is much better than write_dataframe_async. is it normal?
Describe what's not working the way you expect
The only difference between testing of write_async and write_dataframe_async:
So I expect write_dataframe_async should be faster than write_async. But it's not.
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