For Question 2: At a glance, I don't see anything in adlfs or azure that is able to do partial reads of a blob. If you're using block blobs, then likely you would want to store blocks of your file as separate blocks of a blob, and then you can do partial data transfers that way. I could be misunderstanding the SDKs or how Azure stores data, but my guess is that a whole blob is retrieved and then the local file is able to support partial, block-based reads as you expect from local filesystems. You may be able to double check how much data is being retrieved by looking at where adlfs is mounting your blob storage.
For Question 3: you can memory map remote files, it's just that every page fault will be even more expensive than for local files. I am not sure how to tell the dataset API to do memory mapping, and I'm not sure how well that would work over adlfs. For Question 4: Can you try using `pc.scalar(1000)` as shown in the first code excerpt in [1]: >> x, y = pa.scalar(7.8), pa.scalar(9.3) >> pc.multiply(x, y) <pyarrow.DoubleScalar: 72.54> [1]: https://arrow.apache.org/docs/python/compute.html#standard-compute-functions Aldrin Montana Computer Science PhD Student UC Santa Cruz On Thu, Sep 8, 2022 at 8:26 PM Nikhil Makan <[email protected]> wrote: > Hi There, > > I have been experimenting with Tabular Datasets > <https://arrow.apache.org/docs/python/dataset.html> for data that can be > larger than memory and had a few questions related to what's going on > under the hood and how to work with it (I understand it is still > experimental). > > *Question 1: Reading Data from Azure Blob Storage* > Now I know the filesystems don't fully support this yet, but there is an > fsspec compatible library (adlfs) which is shown in the file system > example > <https://arrow.apache.org/docs/python/filesystems.html#using-fsspec-compatible-filesystems-with-arrow> > which > I have used. Example below with the nyc taxi dataset, where I am pulling > the whole dataset through and writing to disk to the feather format. > > import adlfs > import pyarrow.dataset as ds > > fs = adlfs.AzureBlobFileSystem(account_name='azureopendatastorage') > > dataset = ds.dataset('nyctlc/green/', filesystem=fs, format='parquet') > > scanner = dataset.scanner() > ds.write_dataset(scanner, f'taxinyc/green/feather/', format='feather') > > This could be something on the Azure side but I find I am being > bottlenecked on the download speed and have noticed if I spin up multiple > Python sessions (or in my case interactive windows) I can increase my > throughput. Hence I can download each year of the taxinyc dataset in > separate interactive windows and increase my bandwidth consumed. The tabular > dataset <https://arrow.apache.org/docs/python/dataset.html> documentation > notes 'optionally parallel reading.' Do you know how I can control this? Or > perhaps control the number of concurrent connections. Or has this got > nothing to do with the arrow and sits purley on the Azure side? I have > increased the io thread count from the default 8 to 16 and saw no > difference, but could still spin up more interactive windows to maximise > bandwidth. > > *Question 2: Reading Filtered Data from Azure Blob Storage* > Unfortunately I don't quite have a repeatable example here. However using > the same data above, only this time I have each year as a feather file > instead of a parquet file. I have uploaded this to my own Azure blob > storage account. > I am trying to read a subset of this data from the blob storage by > selecting columns and filtering the data. The final result should be a > dataframe that takes up around 240 mb of memory (I have tested this by > working with the data locally). However when I run this by connecting to > the Azure blob storage it takes over an hour to run and it's clear it's > downloading a lot more data than I would have thought. Given the file > formats are feather that supports random access I would have thought I > would only have to download the 240 mb? > > Is there more going on in the background? Perhaps I am using this > incorrectly? > > import adlfs > import pyarrow.dataset as ds > > connection_string = '' > fs = adlfs.AzureBlobFileSystem(connection_string=connection_string,) > > ds_f = ds.dataset("taxinyc/green/feather/", format='feather') > > df = ( > ds_f > .scanner( > columns={ # Selections and Projections > 'passengerCount': ds.field(('passengerCount'))*1000, > 'tripDistance': ds.field(('tripDistance')) > }, > filter=(ds.field('vendorID') == 1) > ) > .to_table() > .to_pandas() > ) > > df.info() > > *Question 3: How is memory mapping being applied?* > Does the Dataset API make use of memory mapping? Do I have the correct > understanding that memory mapping is only intended for dealing with large > data stored on a local file system. Where as data stored on a cloud file > system in the feather format effectively cannot be memory mapped? > > *Question 4: Projections* > I noticed in the scanner function when projecting a column I am unable to > use any compute functions (I get a Type Error: only other expressions > allowed as arguments) yet I am able to multiply this using standard python > arithmetic. > > 'passengerCount': ds.field(('passengerCount'))*1000, > > 'passengerCount': pc.multiply(ds.field(('passengerCount')),1000), > > Is this correct or am I to process this using an iterator via record batch > <https://arrow.apache.org/docs/python/dataset.html#iterative-out-of-core-or-streaming-reads> > to > do this out of core? Is it actually even doing it out of core with " *1000 > ". > > Thanks for your help in advance. I have been following the Arrow project > for the last two years but have only recently decided to dive into it in > depth to explore it for various use cases. I am particularly interested in > the out-of-core data processing and the interaction with cloud storages to > retrieve only a selection of data from feather files. Hopefully at some > point when I have enough knowledge I can contribute to this amazing project. > > Kind regards > Nikhil Makan >
