[jira] [Updated] (ARROW-8545) Allow fast writing of Decimal column to parquet

["Fons de Leeuw (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/ARROW-8545?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Fons de Leeuw updated ARROW-8545:
---------------------------------
    Description: 
Currently, when one wants to use a decimal datatype in Pandas, the only possibility is to use the `decimal.Decimal` standard-libary type. This is then an "object" column in the DataFrame.

Arrow can write a column of decimal type to Parquet, which is quite impressive given that [fastparquet does not write decimals|#data-types]] at all. However, the writing is *very* slow, in the code snippet below a factor of 4.

*Improvements*

Of course the best outcome would be if the conversion of a decimal column can be made faster, but I am not familiar enough with pandas internals to know if that's possible. (This same behavior also applies to `.to_pickle` etc.)

It would be nice, if a warning is shown that object-typed columns are being converted which is very slow. That would at least make this behavior more explicit.

Now, if fast parsing of a decimal.Decimal object column is not possible, it would be nice if a workaround is possible. For example, pass an int and then shift the dot "x" places to the left. (It is already possible to pass an int column and specify "decimal" dtype in the Arrow schema during `pa.Table.from_pandas()` but then it simply becomes a decimal without decimals.) Also, it might be nice if it can be encoded as a 128-bit byte string in the pandas column and then directly interpreted by Arrow.

*Usecase*

I need to save large dataframes (~10GB) of geospatial data with latitude/longitude. I can't use float as comparisons need to be exact, and the BigQuery "clustering" feature needs either an integer or a decimal but not a float. In the meantime, I have to do a workaround where I use only ints (the original number multiplied by 1000.)

*Snippet*
{code:java}
import decimal
from time import time

import numpy as np
import pandas as pd

d = dict()

for col in "abcdefghijklmnopqrstuvwxyz":
    d[col] = np.random.rand(int(1E7)) * 100

df = pd.DataFrame(d)

t0 = time()

df.to_parquet("/tmp/testabc.pq", engine="pyarrow")

t1 = time()

df["a"] = df["a"].round(decimals=3).astype(str).map(decimal.Decimal)

t2 = time()

df.to_parquet("/tmp/testabc_dec.pq", engine="pyarrow")

t3 = time()

print(f"Saving the normal dataframe took {t1-t0:.3f}s, with one decimal column {t3-t2:.3f}s")
# Saving the normal dataframe took 4.430s, with one decimal column 17.673s{code}
 

 

  was:
Currently, when one wants to use a decimal datatype in Pandas, the only possibility is to use the `decimal.Decimal` standard-libary type. This is then an "object" column in the DataFrame.

Arrow can write a column of decimal type to Parquet, which is quite impressive given that [fastparquet does not write decimals|[https://fastparquet.readthedocs.io/en/latest/details.html#data-types]] at all. However, the writing is *very* slow, in the code snippet below a factor of 4.

*Improvements***

Of course the best outcome would be if the conversion of a decimal column can be made faster, but I am not familiar enough with pandas internals to know if that's possible. (This same behavior also applies to `.to_pickle` etc.)

It would be nice, if a warning is shown that object-typed columns are being converted which is very slow. That would at least make this behavior more explicit.

Now, if fast parsing of a decimal.Decimal object column is not possible, it would be nice if a workaround is possible. For example, pass an int and then shift the dot "x" places to the left. (It is already possible to pass an int column and specify "decimal" dtype in the Arrow schema during `pa.Table.from_pandas()` but then it simply becomes a decimal without decimals.) Also, it might be nice if it can be encoded as a 128-bit byte string in the pandas column and then directly interpreted by Arrow.

*Usecase*

I need to save large dataframes (~10GB) of geospatial data with latitude/longitude. I can't use float as comparisons need to be exact, and the BigQuery "clustering" feature needs either an integer or a decimal but not a float. In the meantime, I have to do a workaround where I use only ints (the original number multiplied by 1000.)

*Snippet*

 
{code:java}

{code}
*import decimal
from time import time

import numpy as np
import pandas as pd

d = dict()

for col in "abcdefghijklmnopqrstuvwxyz":
    d[col] = np.random.rand(int(1E7)) * 100

df = pd.DataFrame(d)

t0 = time()

df.to_parquet("/tmp/testabc.pq", engine="pyarrow")

t1 = time()

df["a"] = df["a"].round(decimals=3).astype(str).map(decimal.Decimal)

t2 = time()

df.to_parquet("/tmp/testabc_dec.pq", engine="pyarrow")

t3 = time()

print(f"Saving the normal dataframe took \{t1-t0:.3f}s, with one decimal column \{t3-t2:.3f}s")*

*# Saving the normal dataframe took 4.430s, with one decimal column 17.673s***

 

 


> Allow fast writing of Decimal column to parquet
> -----------------------------------------------
>
>                 Key: ARROW-8545
>                 URL: https://issues.apache.org/jira/browse/ARROW-8545
>             Project: Apache Arrow
>          Issue Type: Improvement
>          Components: Python
>    Affects Versions: 0.17.0
>            Reporter: Fons de Leeuw
>            Priority: Minor
>
> Currently, when one wants to use a decimal datatype in Pandas, the only possibility is to use the `decimal.Decimal` standard-libary type. This is then an "object" column in the DataFrame.
> Arrow can write a column of decimal type to Parquet, which is quite impressive given that [fastparquet does not write decimals|#data-types]] at all. However, the writing is *very* slow, in the code snippet below a factor of 4.
> *Improvements*
> Of course the best outcome would be if the conversion of a decimal column can be made faster, but I am not familiar enough with pandas internals to know if that's possible. (This same behavior also applies to `.to_pickle` etc.)
> It would be nice, if a warning is shown that object-typed columns are being converted which is very slow. That would at least make this behavior more explicit.
> Now, if fast parsing of a decimal.Decimal object column is not possible, it would be nice if a workaround is possible. For example, pass an int and then shift the dot "x" places to the left. (It is already possible to pass an int column and specify "decimal" dtype in the Arrow schema during `pa.Table.from_pandas()` but then it simply becomes a decimal without decimals.) Also, it might be nice if it can be encoded as a 128-bit byte string in the pandas column and then directly interpreted by Arrow.
> *Usecase*
> I need to save large dataframes (~10GB) of geospatial data with latitude/longitude. I can't use float as comparisons need to be exact, and the BigQuery "clustering" feature needs either an integer or a decimal but not a float. In the meantime, I have to do a workaround where I use only ints (the original number multiplied by 1000.)
> *Snippet*
> {code:java}
> import decimal
> from time import time
> import numpy as np
> import pandas as pd
> d = dict()
> for col in "abcdefghijklmnopqrstuvwxyz":
>     d[col] = np.random.rand(int(1E7)) * 100
> df = pd.DataFrame(d)
> t0 = time()
> df.to_parquet("/tmp/testabc.pq", engine="pyarrow")
> t1 = time()
> df["a"] = df["a"].round(decimals=3).astype(str).map(decimal.Decimal)
> t2 = time()
> df.to_parquet("/tmp/testabc_dec.pq", engine="pyarrow")
> t3 = time()
> print(f"Saving the normal dataframe took {t1-t0:.3f}s, with one decimal column {t3-t2:.3f}s")
> # Saving the normal dataframe took 4.430s, with one decimal column 17.673s{code}
>  
>  



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