I must say, I’m super excited about using Arrow and Plasma. The code you just posted worked for me at home and I’m sure I’ll figure out what I was doing wrong tomorrow at work.
Anyways, thanks so much for your help and fast replies! Sent from my iPhone > On May 16, 2018, at 7:42 PM, Robert Nishihara <robertnishih...@gmail.com> > wrote: > > 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 <cjno...@gmail.com> wrote: > >> Robert, >> >> 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 < >> robertnishih...@gmail.com >>> 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 >> to >>> 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 >> for >>> serialization. >>> >>>> On Wed, May 16, 2018 at 10:02 AM Corey Nolet <cjno...@gmail.com> 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 >> Dataframes >>>> 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 >> to >>> 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! >>>> >>> >>