SQL Query Hangs When Using scan_parquet off S3 about polars HOT 5 CLOSED

import os

os.environ["POLARS_FORCE_ASYNC"] = "1"
os.environ["POLARS_MAX_THREADS"] = "1"

import polars as pl

df = pl.Series("x", [1]).to_frame()

p = ".env/x.parquet"
df.write_parquet(p)

print(pl.collect_all([pl.scan_parquet(p)]))

import polars as pl
import boto3
session = boto3.session.Session()
credentials = session.get_credentials().get_frozen_credentials()
storage_options = {
        "aws_access_key_id": credentials.access_key,
        "aws_secret_access_key": credentials.secret_key,
        "aws_session_token": credentials.token,
        "aws_region": session.region_name,
    }
df0 = pl.scan_parquet("s3://bucket/df0.parquet",
                    storage_options=storage_options)
df1 = pl.scan_parquet("s3://bucket/df1.parquet",
                    storage_options=storage_options)
dfboth = df0.join(df1, on="bar", how="inner")
dfboth = dfboth.filter(pl.col("bar").is_in((0, 1))).collect()
print(dfboth)

--------Version info---------
Polars:               0.20.16
Index type:           UInt32
Platform:             Linux-5.15.0-1055-aws-aarch64-with-glibc2.31
Python:               3.11.5 (main, Sep 11 2023, 13:14:08) [GCC 11.2.0]

----Optional dependencies----
adbc_driver_manager:  <not installed>
cloudpickle:          3.0.0 # upgraded
connectorx:           <not installed>
deltalake:            <not installed>
fastexcel:            <not installed>
fsspec:               2024.3.1 # upgraded
gevent:               <not installed>
hvplot:               0.9.0
matplotlib:           3.8.0
numpy:                1.24.3
openpyxl:             3.0.10
pandas:               2.1.4
pyarrow:              15.0.2 # upgraded
pydantic:             2.6.4 # upgraded
pyiceberg:            <not installed>
pyxlsb:               <not installed>
sqlalchemy:           2.0.21
xlsx2csv:             <not installed>
xlsxwriter:           3.1.9

df_pl = pl.concat(
    (pl.scan_parquet(c.url, storage_options=storage_options) for c in results),
    how="diagonal",
    # Ideally this reduces the memory usage
    rechunk=False,
).collect()