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https://issues.apache.org/jira/browse/ARROW-13187?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Simon updated ARROW-13187:
--------------------------
Description:
When one reads in a table from CSV in pyarrow version 4.0.1, it appears that
the read-in table variable is not freed (or not fast enough). I'm unsure if
this is because of pyarrow or because of the way pyarrow memory allocation
interacts with Python memory allocation. I encountered it when processing many
large CSVs sequentially.
When I run the following piece of code, the RAM memory usage increases quite
rapidly until it runs out of memory.
{code:python}
import pyarrow as pa
import pyarrow.csv
# Generate some CSV file to read in
print("Generating CSV")
with open("example.csv", "w+") as f_out:
for i in range(0, 10000000):
f_out.write("123456789,abc def ghi jkl\n")
def read_in_the_csv():
table = pa.csv.read_csv("example.csv")
print(table) # Not strictly necessary to replicate bug, table can also be
an unused variable
# This will free up the memory, as a workaround:
# table = table.slice(0, 0)
# Read in the CSV many times
print("Reading in a CSV many times")
for j in range(100000):
read_in_the_csv()
{code}
was:
When one reads in a table from CSV in pyarrow version 4.0.1, it appears that
the read-in table variable is not freed (or not fast enough). I'm unsure if
this is because of pyarrow or because of the way pyarrow memory allocation
interacts with Python memory allocation. I encountered it when processing many
large CSVs sequentially.
When I run the following piece of code, the RAM memory usage increases quite
rapidly until it runs out of memory.
{code:python}
import pyarrow as pa
import pyarrow.csv
# Generate some CSV file to read in
print("Generating CSV")
with open("example.csv", "w+") as f_out:
for i in range(0, 10000000):
f_out.write("123456789,abc def ghi jkl\n")
def read_in_the_csv():
table = pa.csv.read_csv("example.csv")
print(table) # Not strictly necessary to replicate bug, table can also be
an unused variable
# This will free up the memory, as a workaround:
# table = table.slice(0, 0)
# Read in the
print("Reading in a CSV many times")
for j in range(100000):
read_in_the_csv()
{code}
> Possibly memory not deallocated when reading in CSV
> ---------------------------------------------------
>
> Key: ARROW-13187
> URL: https://issues.apache.org/jira/browse/ARROW-13187
> Project: Apache Arrow
> Issue Type: Bug
> Components: Python
> Affects Versions: 4.0.1
> Reporter: Simon
> Priority: Minor
>
> When one reads in a table from CSV in pyarrow version 4.0.1, it appears that
> the read-in table variable is not freed (or not fast enough). I'm unsure if
> this is because of pyarrow or because of the way pyarrow memory allocation
> interacts with Python memory allocation. I encountered it when processing
> many large CSVs sequentially.
> When I run the following piece of code, the RAM memory usage increases quite
> rapidly until it runs out of memory.
> {code:python}
> import pyarrow as pa
> import pyarrow.csv
> # Generate some CSV file to read in
> print("Generating CSV")
> with open("example.csv", "w+") as f_out:
> for i in range(0, 10000000):
> f_out.write("123456789,abc def ghi jkl\n")
> def read_in_the_csv():
> table = pa.csv.read_csv("example.csv")
> print(table) # Not strictly necessary to replicate bug, table can also
> be an unused variable
> # This will free up the memory, as a workaround:
> # table = table.slice(0, 0)
> # Read in the CSV many times
> print("Reading in a CSV many times")
> for j in range(100000):
> read_in_the_csv()
> {code}
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