[
https://issues.apache.org/jira/browse/ARROW-17441?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Will Jones updated ARROW-17441:
-------------------------------
Description:
I was trying reproduce another issue involving memory pools not releasing
memory, but encountered this confusing behavior: if I create a table, then call
{{{}del table{}}}, and then {{{}pool.release_unused(){}}}, I still see
significant memory usage. On mimalloc in particular, I see no meaningful drop
in memory usage on either call.
Am I missing something? My understanding prior has been that memory will be
held onto by a memory pool, but will be forced free by release_unused; and that
system memory pool should release memory immediately. But neither of those seem
true.
{code:python}
import os
import psutil
import time
import gc
process = psutil.Process(os.getpid())
import numpy as np
from uuid import uuid4
import pyarrow as pa
def gen_batches(n_groups=200, rows_per_group=200_000):
for _ in range(n_groups):
id_val = uuid4().bytes
yield pa.table({
"x": np.random.random(rows_per_group), # This will compress poorly
"y": np.random.random(rows_per_group),
"a": pa.array(list(range(rows_per_group)), type=pa.int32()), # This
compresses with delta encoding
"id": pa.array([id_val] * rows_per_group), # This compresses with
RLE
})
def print_rss():
print(f"RSS: {process.memory_info().rss:,} bytes")
print(f"memory_pool={pa.default_memory_pool().backend_name}")
print_rss()
print("reading table")
tab = pa.concat_tables(list(gen_batches()))
print_rss()
print("deleting table")
del tab
gc.collect()
print_rss()
print("releasing unused memory")
pa.default_memory_pool().release_unused()
print_rss()
print("waiting 10 seconds")
time.sleep(10)
print_rss()
{code}
{code:none}
> ARROW_DEFAULT_MEMORY_POOL=mimalloc python test_pool.py && \
ARROW_DEFAULT_MEMORY_POOL=jemalloc python test_pool.py && \
ARROW_DEFAULT_MEMORY_POOL=system python test_pool.py
memory_pool=mimalloc
RSS: 44,449,792 bytes
reading table
RSS: 1,819,557,888 bytes
deleting table
RSS: 1,819,590,656 bytes
releasing unused memory
RSS: 1,819,852,800 bytes
waiting 10 seconds
RSS: 1,819,852,800 bytes
memory_pool=jemalloc
RSS: 45,629,440 bytes
reading table
RSS: 1,668,677,632 bytes
deleting table
RSS: 698,400,768 bytes
releasing unused memory
RSS: 699,023,360 bytes
waiting 10 seconds
RSS: 699,023,360 bytes
memory_pool=system
RSS: 44,875,776 bytes
reading table
RSS: 1,713,569,792 bytes
deleting table
RSS: 540,311,552 bytes
releasing unused memory
RSS: 540,311,552 bytes
waiting 10 seconds
RSS: 540,311,552 bytes
{code}
was:
I was trying reproduce another issue involving memory pools not releasing
memory, but encountered this confusing behavior: if I create a table, then call
{{{}del table{}}}, and then {{{}pool.release_unused(){}}}, I still see
significant memory usage. On mimalloc in particular, I see no meaningful drop
in memory usage on either call.
Am I missing something?
{code:python}
import os
import psutil
import time
import gc
process = psutil.Process(os.getpid())
import numpy as np
from uuid import uuid4
import pyarrow as pa
def gen_batches(n_groups=200, rows_per_group=200_000):
for _ in range(n_groups):
id_val = uuid4().bytes
yield pa.table({
"x": np.random.random(rows_per_group), # This will compress poorly
"y": np.random.random(rows_per_group),
"a": pa.array(list(range(rows_per_group)), type=pa.int32()), # This
compresses with delta encoding
"id": pa.array([id_val] * rows_per_group), # This compresses with
RLE
})
def print_rss():
print(f"RSS: {process.memory_info().rss:,} bytes")
print(f"memory_pool={pa.default_memory_pool().backend_name}")
print_rss()
print("reading table")
tab = pa.concat_tables(list(gen_batches()))
print_rss()
print("deleting table")
del tab
gc.collect()
print_rss()
print("releasing unused memory")
pa.default_memory_pool().release_unused()
print_rss()
print("waiting 10 seconds")
time.sleep(10)
print_rss()
{code}
{code:none}
> ARROW_DEFAULT_MEMORY_POOL=mimalloc python test_pool.py && \
ARROW_DEFAULT_MEMORY_POOL=jemalloc python test_pool.py && \
ARROW_DEFAULT_MEMORY_POOL=system python test_pool.py
memory_pool=mimalloc
RSS: 44,449,792 bytes
reading table
RSS: 1,819,557,888 bytes
deleting table
RSS: 1,819,590,656 bytes
releasing unused memory
RSS: 1,819,852,800 bytes
waiting 10 seconds
RSS: 1,819,852,800 bytes
memory_pool=jemalloc
RSS: 45,629,440 bytes
reading table
RSS: 1,668,677,632 bytes
deleting table
RSS: 698,400,768 bytes
releasing unused memory
RSS: 699,023,360 bytes
waiting 10 seconds
RSS: 699,023,360 bytes
memory_pool=system
RSS: 44,875,776 bytes
reading table
RSS: 1,713,569,792 bytes
deleting table
RSS: 540,311,552 bytes
releasing unused memory
RSS: 540,311,552 bytes
waiting 10 seconds
RSS: 540,311,552 bytes
{code}
> [Python] Memory kept after del and pool.released_unused()
> ---------------------------------------------------------
>
> Key: ARROW-17441
> URL: https://issues.apache.org/jira/browse/ARROW-17441
> Project: Apache Arrow
> Issue Type: Improvement
> Components: Python
> Affects Versions: 9.0.0
> Reporter: Will Jones
> Priority: Major
>
> I was trying reproduce another issue involving memory pools not releasing
> memory, but encountered this confusing behavior: if I create a table, then
> call {{{}del table{}}}, and then {{{}pool.release_unused(){}}}, I still see
> significant memory usage. On mimalloc in particular, I see no meaningful drop
> in memory usage on either call.
> Am I missing something? My understanding prior has been that memory will be
> held onto by a memory pool, but will be forced free by release_unused; and
> that system memory pool should release memory immediately. But neither of
> those seem true.
> {code:python}
> import os
> import psutil
> import time
> import gc
> process = psutil.Process(os.getpid())
> import numpy as np
> from uuid import uuid4
> import pyarrow as pa
> def gen_batches(n_groups=200, rows_per_group=200_000):
> for _ in range(n_groups):
> id_val = uuid4().bytes
> yield pa.table({
> "x": np.random.random(rows_per_group), # This will compress poorly
> "y": np.random.random(rows_per_group),
> "a": pa.array(list(range(rows_per_group)), type=pa.int32()), #
> This compresses with delta encoding
> "id": pa.array([id_val] * rows_per_group), # This compresses with
> RLE
> })
> def print_rss():
> print(f"RSS: {process.memory_info().rss:,} bytes")
> print(f"memory_pool={pa.default_memory_pool().backend_name}")
> print_rss()
> print("reading table")
> tab = pa.concat_tables(list(gen_batches()))
> print_rss()
> print("deleting table")
> del tab
> gc.collect()
> print_rss()
> print("releasing unused memory")
> pa.default_memory_pool().release_unused()
> print_rss()
> print("waiting 10 seconds")
> time.sleep(10)
> print_rss()
> {code}
> {code:none}
> > ARROW_DEFAULT_MEMORY_POOL=mimalloc python test_pool.py && \
> ARROW_DEFAULT_MEMORY_POOL=jemalloc python test_pool.py && \
> ARROW_DEFAULT_MEMORY_POOL=system python test_pool.py
> memory_pool=mimalloc
> RSS: 44,449,792 bytes
> reading table
> RSS: 1,819,557,888 bytes
> deleting table
> RSS: 1,819,590,656 bytes
> releasing unused memory
> RSS: 1,819,852,800 bytes
> waiting 10 seconds
> RSS: 1,819,852,800 bytes
> memory_pool=jemalloc
> RSS: 45,629,440 bytes
> reading table
> RSS: 1,668,677,632 bytes
> deleting table
> RSS: 698,400,768 bytes
> releasing unused memory
> RSS: 699,023,360 bytes
> waiting 10 seconds
> RSS: 699,023,360 bytes
> memory_pool=system
> RSS: 44,875,776 bytes
> reading table
> RSS: 1,713,569,792 bytes
> deleting table
> RSS: 540,311,552 bytes
> releasing unused memory
> RSS: 540,311,552 bytes
> waiting 10 seconds
> RSS: 540,311,552 bytes
> {code}
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