Comments (2)
I just love those performance reports!
This does look suspicious--thanks for the report. I don't yet have a plausible explanation for why some threads would stall. The simplest "I have no idea what's going on, but let's take a stab at a solution anyway" thing to try would be to change the Lock
to RLock
in afar/_printing.py
.
I'll try to investigate this further tonight.
from afar.
I can reproduce the issue.
I have ruled out some of the weird bits of afar
, such as using locks around updating builtins.print
and using a custom channel to send messages from the worker to the client. It's really not clear to me what else in afar
would even be relevant in causing any issues.
run_afar
is a long running task that runs other tasks. This could be the source of the issue. Aha! To test this, I just reproduced the issue by running the following (w/o afar
):
def run():
ddf_cpu = dd.read_parquet(
"s3://coiled-datasets/timeseries/20-years/parquet",
storage_options={"anon": True, "use_ssl": True},
split_row_groups=True,
engine="pyarrow",
)
return ddf_cpu.groupby('name').x.mean().compute()
with performance_report(filename="read_pq_groupby_mean_CPU_pyarrow_run.html"):
client.submit(run).result()
So, I don't think afar
is to blame. Maybe this is related to dask/distributed#5332
from afar.
Related Issues (10)
- Option to not return Futures HOT 1
- Error when using `%%time` Jupyter magic HOT 2
- Large object warning HOT 1
- Send `print` statements back to the client HOT 3
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- IPython magic? HOT 5
- Repr of final expression no longer displayed in Jupyter with afar 0.4.0 HOT 4
- [Bug] Afar not working with cudf/dask-cudf/gpu-numba HOT 4
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from afar.