Re: PyArrow & Python Multiprocessing
You should be able to do something like the following.
# Start the store.
plasma_store -s /tmp/store -m 1000000000
Then in Python, do the following:
import pandas as pd
import pyarrow.plasma as plasma
import numpy as np
client = plasma.connect('/tmp/store', '', 0)
series = pd.Series(np.zeros(100))
object_id = client.put(series)
And yes, I would create a separate Plasma client for each process. I don't
think you'll be able to pickle a Plasma client object successfully (it has
a socket connection to the store).
On Wed, May 16, 2018 at 3:43 PM Corey Nolet <cjnolet@xxxxxxxxx> wrote:
> Thank you for the quick response. I've been playing around for a few hours
> to get a feel for how this works.
> If I understand correctly, it's better to have the Plasma client objects
> instantiated within each separate process? Weird things seemed to happen
> when I attempted to share a single one. I was assuming that the pickle
> serialization by python multiprocessing would have been serializing the
> connection info and re-instantiating on the other side but that didn't seem
> to be the case.
> I managed to load up a gigantic set of CSV files into Dataframes. Now I'm
> attempting to read the chunks, perform a groupby-aggregate, and write the
> results back to the Plasma store. Unless I'm mistaken, there doesn't seem
> to be a very direct way of accomplishing this. When I tried converting the
> Series object into a Plasma Array and just doing a client.put(array) I get
> a pickling error. Unless maybe I'm misunderstanding the architecture here,
> I believe that error would have been referring to attempts to serialize the
> object into a file? I would hope that the data isn't all being sent to the
> single Plasma server (or sent over sockets for that matter).
> What would be the recommended strategy for serializing Pandas Series
> objects? I really like the StreamWriter concept here but there does not
> seem to be a direct way (or documentation) to accomplish this.
> Thanks again.
> On Wed, May 16, 2018 at 1:28 PM, Robert Nishihara <
> > wrote:
> > Take a look at the Plasma object store
> > https://arrow.apache.org/docs/python/plasma.html.
> > Here's an example using it (along with multiprocessing to sort a pandas
> > dataframe)
> > https://github.com/apache/arrow/blob/master/python/
> > examples/plasma/sorting/sort_df.py.
> > It's possible the example is a bit out of date.
> > You may be interested in taking a look at Ray
> > https://github.com/ray-project/ray. We use Plasma/Arrow under the hood
> > do all of these things but hide a lot of the bookkeeping (like object ID
> > generation). For your setting, you can think of it as a replacement for
> > Python multiprocessing that automatically uses shared memory and Arrow
> > serialization.
> > On Wed, May 16, 2018 at 10:02 AM Corey Nolet <cjnolet@xxxxxxxxx> wrote:
> > > I've been reading through the PyArrow documentation and trying to
> > > understand how to use the tool effectively for IPC (using zero-copy).
> > >
> > > I'm on a system with 586 cores & 1TB of ram. I'm using Panda's
> > > to process several 10's of gigs of data in memory and the pickling that
> > is
> > > done by Python's multiprocessing API is very wasteful.
> > >
> > > I'm running a little hand-built map-reduce where I chunk the dataframe
> > into
> > > N_mappers number of chunks, run some processing on them, then run some
> > > number N_reducers to finalize the operation. What I'd like to be able
> > do
> > > is chunk up the dataframe into Arrow Buffer objects and just have each
> > > mapped task read their respective Buffer object with the guarantee of
> > > zero-copy.
> > >
> > > I see there's a couple Filesystem abstractions for doing memory-mapped
> > > files. Durability isn't something I need and I'm willing to forego the
> > > expense of putting the files on disk.
> > >
> > > Is it possible to write the data directly to memory and pass just the
> > > reference around to the different processes? What's the recommended way
> > to
> > > accomplish my goal here?
> > >
> > >
> > > Thanks in advance!
> > >