You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@arrow.apache.org by "Krisztian Szucs (JIRA)" <ji...@apache.org> on 2018/09/07 11:42:00 UTC
[jira] [Updated] (ARROW-2709) [Python] write_to_dataset poor
performance when splitting
[ https://issues.apache.org/jira/browse/ARROW-2709?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Krisztian Szucs updated ARROW-2709:
-----------------------------------
Description:
Hello,
Posting this from github (master [~wesmckinn] asked for it :) )
[https://github.com/apache/arrow/issues/2138]
{code:java}
import pandas as pd
import numpy as np
import pyarrow.parquet as pq
import pyarrow as pa
idx = pd.date_range('2017-01-01 12:00:00.000', '2017-03-01 12:00:00.000', freq = 'T')
dataframe = pd.DataFrame({'numeric_col' : np.random.rand(len(idx)),
'string_col' : pd.util.testing.rands_array(8,len(idx))},
index = idx){code}
{code:java}
df["dt"] = df.index
df["dt"] = df["dt"].dt.date
table = pa.Table.from_pandas(df)
pq.write_to_dataset(table, root_path='dataset_name', partition_cols=['dt'], flavor='spark'){code}
{{this works but is inefficient memory-wise. The arrow table is a copy of the large pandas daframe and quickly saturates the RAM.}}
{{Thanks!}}
was:
Hello,
Posting this from github (master [~wesmckinn] asked for it :) )
https://github.com/apache/arrow/issues/2138
{code:java}
import pandas as pd import numpy as np import pyarrow.parquet as pq import pyarrow as pa idx = pd.date_range('2017-01-01 12:00:00.000', '2017-03-01 12:00:00.000', freq = 'T') dataframe = pd.DataFrame({'numeric_col' : np.random.rand(len(idx)), 'string_col' : pd.util.testing.rands_array(8,len(idx))}, index = idx){code}
{code:java}
df["dt"] = df.index df["dt"] = df["dt"].dt.date table = pa.Table.from_pandas(df) pq.write_to_dataset(table, root_path='dataset_name', partition_cols=['dt'], flavor='spark'){code}
{{this works but is inefficient memory-wise. The arrow table is a copy of the large pandas daframe and quickly saturates the RAM.}}
{{Thanks!}}
> [Python] write_to_dataset poor performance when splitting
> ---------------------------------------------------------
>
> Key: ARROW-2709
> URL: https://issues.apache.org/jira/browse/ARROW-2709
> Project: Apache Arrow
> Issue Type: Improvement
> Components: Python
> Reporter: Olaf
> Priority: Critical
> Labels: parquet
>
> Hello,
> Posting this from github (master [~wesmckinn] asked for it :) )
> [https://github.com/apache/arrow/issues/2138]
>
> {code:java}
> import pandas as pd
> import numpy as np
> import pyarrow.parquet as pq
> import pyarrow as pa
> idx = pd.date_range('2017-01-01 12:00:00.000', '2017-03-01 12:00:00.000', freq = 'T')
> dataframe = pd.DataFrame({'numeric_col' : np.random.rand(len(idx)),
> 'string_col' : pd.util.testing.rands_array(8,len(idx))},
> index = idx){code}
>
> {code:java}
> df["dt"] = df.index
> df["dt"] = df["dt"].dt.date
> table = pa.Table.from_pandas(df)
> pq.write_to_dataset(table, root_path='dataset_name', partition_cols=['dt'], flavor='spark'){code}
>
> {{this works but is inefficient memory-wise. The arrow table is a copy of the large pandas daframe and quickly saturates the RAM.}}
>
> {{Thanks!}}
--
This message was sent by Atlassian JIRA
(v7.6.3#76005)