Hacktoberfest 2026: le issue che i maintainer hanno segnato per ottobre, aperte e adatte ai principianti. Sfoglia le issue Hacktoberfest

Reusing intermediate results causes memory issues

Aperta
  • Qualcosa non va?
#854 6 commenti 0 reazioni 1 assegnatario Vedi su GitHub

@phofl ci sta già lavorando.

Dal 14/2/2024.

Valutazione

Questa issue non è ancora stata valutata.

Descrizione

Problem

Whenever we reuse intermediate results and there is a pipeline breaker (such as shuffles, joins, reductions, or groupby operations), it forces us to materialize the entire intermediate result (thus, it is breaking the pipelining that we could utilize for reuse for example between multiple element-wise operations).

This result materialization puts a hard limit on our ability to scale as I have observed in multiple TPC-H benchmark queries.

To illustrate this, run these two snippets on a cluster of your choice

With full intermediate result materialization

from dask_expr.datasets import timeseries

from distributed import Client

if __name__ == "__main__":
    with Client() as client:
        print(client.dashboard_link)
        df = timeseries(start="2000-01-01", end="2020-12-31", freq="100ms", dtypes={"x": float})
        # To compute mean, we have to fully materialize df, and we won't free its data
        # until we have reused the chunks to compute a partial of the sum. 
        mean = df["x"].mean()
        df[df["x"] > mean].sum().compute()

Without full intermediate result materialization

from dask_expr.datasets import timeseries

from distributed import Client

if __name__ == "__main__":
    with Client() as client:
        print(client.dashboard_link)
        df = timeseries(start="2000-01-01", end="2020-12-31", freq="100ms", dtypes={"x": float})
        # Compute the mean beforehand so that we don't have to keep all of `df` in memory
        mean = df["x"].mean().compute()
        df[df["x"] > mean].sum().compute()

Possible solution

The easiest approach would be to never reuse any intermediate results. This has a few downsides:

  • Non-deterministic functions will lead to unexpected results
  • We waste a lot of computational resources on recomputations

...but it will allow us to scale.

We can certainly get smarter about intermediate result materialization, but this will require some effort depending on how smart we want to be. (There's a body of (ongoing) research and implementations in the database world we could draw from.)

Lingua principale
Python
Stelle
89
Fork
26
Nessuna PR unita negli ultimi 30g

Preparare l'ambiente

Come iniziare

  1. Leggi tutta la issue e poi la guida ai contributi del progetto.
  2. Commenta sulla issue per dire che te ne occupi tu — evita che due persone facciano lo stesso lavoro.
  3. Fai un fork del repository e lavora su un branch.
  4. Apri una pull request che faccia riferimento al numero della issue.

Altre issue di dask/dask-expr

Tutte le issue di dask/dask-expr

Issue simili

Altre issue su Python

Ricevi le nuove issue nella tua casella

Un breve riepilogo di issue GitHub adatte ai principianti.