Thanks Aldrin for the response on this. Question 1: For reference to anyone else who reads this, it appears adlfs does not support concurrent io and this is currently being developed. https://github.com/fsspec/adlfs/issues/268
Question 2: Noted your points. I am using block blobs. If I am understanding you correctly are you suggesting just splitting the data up into separate blobs? This way if I filter the data it only downloads the blobs that are required? This would seem to only work if you know beforehand what the filter could be so you can split your data accordingly. However, if you wanted to return two columns of all the data I assume this would still result in all the blobs being downloaded. I also came across this https://learn.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-query-acceleration-how-to?tabs=python. However this is only for csv/json files. Are you aware of partial reading where we take advantage of the columnar format such as arrow/parquet being implemented in any other storages suchs as Google Cloud Storage or Amazon S3? I know PyArrow has native support for GCS and S3 however I ran this test example against S3 and no real improvements. Seems to be the same issue where the whole file is downloaded. import pyarrow.dataset as ds ds_f = ds.dataset( "s3://voltrondata-labs-datasets/nyc-taxi/year=2019/month=1") ds_f.head(10) df = ( ds_f .scanner( columns={ # Selections and Projections 'passengerCount': ds.field(('passengerCount')), }, ) .to_table() .to_pandas() ) df.info() Question 3; Thanks, Noted. Question 4: Tried this: 'passengerCount': pc.multiply(ds.field(('passengerCount')),pa.scalar(1000)) Same issue -> Type Error: only other expressions allowed as arguments However it works with this: 'passengerCount': pc.multiply(ds.field(('passengerCount')),pc.scalar(1000)) This works as well as noted previosuly, so I assume the python operators are mapped across similar to what happens when you use the operators against a numpy or pandas series it just executes a np.multiply or pd. multiply in the background. 'passengerCount': ds.field(('passengerCount'))*1000, Kind regards Nikhil Makan On Fri, Sep 16, 2022 at 12:28 PM Aldrin <[email protected]> wrote: > (oh, sorry I misread `pa.scalar` as `pc.scalar`, so please try > `pyarrow.scalar` per the documentation) > > Aldrin Montana > Computer Science PhD Student > UC Santa Cruz > > > On Thu, Sep 15, 2022 at 5:26 PM Aldrin <[email protected]> wrote: > >> 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 >>> >>
