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Posted to user@arrow.apache.org by Jacob Quinn <qu...@gmail.com> on 2022/10/05 06:48:47 UTC
Re: [Python] - Dataset API - What's happening under the hood?
Sorry for the late reply, but thought I'd chime in with a thought or two,
as I've had the chance to work on both the Arrow.jl Julia implementation as
well as recently working on a consistent cloud storage interface (for S3,
Azure, and planned GCP, also for Julia;
https://github.com/JuliaServices/CloudStore.jl).
First, to clarify, all cloud storage providers support "partial" reads in
the form of providing support for "ranged" HTTP requests (i.e. with a
"Range" header like: "Range: bytes 0-9"). So that means for a single
"object" in a cloud store, you could request specific byte ranges of that
single object to be returned.
How would that interact with stored data files? Well, for Arrow IPC/Feather
format, you could potentially do a series of these "range" requests to read
a single Feather file flatbuffer metadata, which contains the specific byte
offsets of columns within the data file. So in theory, it should be fairly
straightforward to apply
a kind of "column selection" operation where only specific columns are
actually downloaded from the cloud store, and it could be avoided to
download the entire file.
For other data formats? It's generally not as applicable since we don't
have this kind specific byte information of where certain rows/columns live
within a single object.
On the other hand, the parquet format supports a partitioning scheme that
*IS* more amenable to "partial reads", but in a slightly different way.
Instead of using HTTP Range requests, specific columns or row batches of
columns are stored as separate *objects* in the cloud store. And so by
doing a "list" type of operation
on all "objects" in the store, and reading overall metadata of the parquet
data, we could similarly do a "column selection" kind of operation by only
downloading specific *objects* from the cloud store that correspond to the
desired columns.
Hopefully that provides a little bit of clarity?
This is somewhat the overall vision that we're working towards with the
Julia implementation to hopefully provide really efficient interop with
cloud-stored data.
-Jacob Quinn
On Sun, Sep 18, 2022 at 7:47 PM Nikhil Makan <ni...@gmail.com>
wrote:
> 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 <ak...@ucsc.edu> 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 <ak...@ucsc.edu> 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 <ni...@gmail.com>
>>> 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
>>>>
>>>
Re: [Python] - Dataset API - What's happening under the hood?
Posted by Jacob Quinn <qu...@gmail.com>.
Yes, it already supports concurrent io when downloading or uploading data.
You can configure it with an initial threshold over which it will switch to
concurrent download/upload, and there are also options to configure how big
each "part" is and how many concurrent tasks to allow at a single time.
For downloads, we rely on doing an initial HEAD request on the object to
get the total size (via Content-Length header), then we do byte Range
requests according to user-provided concurrency options.
For uploads, we use the cloud-specific multipart upload APIs; so you
upload/commit each part, then at the end you commit all the parts in a
final step.
-Jacob
On Sun, Oct 9, 2022 at 6:28 PM Nikhil Makan <ni...@gmail.com> wrote:
> Thanks Jacob for the comments. Appreciate it.
>
> Out of interest does the cloud storage interface you are working on for
> Julia support concurrent io in order to improve performance with respect to
> download/uploading data. I know for pyarrow for a blob storage we have to
> use a fsspec compliant library which is adlfs. However concurrent io is
> still in the works with that library.
>
> Kind regards
> Nikhil Makan
>
> On Wed, Oct 5, 2022 at 7:49 PM Jacob Quinn <qu...@gmail.com> wrote:
>
>> Sorry for the late reply, but thought I'd chime in with a thought or two,
>> as I've had the chance to work on both the Arrow.jl Julia implementation as
>> well as recently working on a consistent cloud storage interface (for S3,
>> Azure, and planned GCP, also for Julia;
>> https://github.com/JuliaServices/CloudStore.jl).
>>
>> First, to clarify, all cloud storage providers support "partial" reads in
>> the form of providing support for "ranged" HTTP requests (i.e. with a
>> "Range" header like: "Range: bytes 0-9"). So that means for a single
>> "object" in a cloud store, you could request specific byte ranges of that
>> single object to be returned.
>>
>> How would that interact with stored data files? Well, for Arrow
>> IPC/Feather format, you could potentially do a series of these "range"
>> requests to read a single Feather file flatbuffer metadata, which contains
>> the specific byte offsets of columns within the data file. So in theory, it
>> should be fairly straightforward to apply
>> a kind of "column selection" operation where only specific columns are
>> actually downloaded from the cloud store, and it could be avoided to
>> download the entire file.
>>
>> For other data formats? It's generally not as applicable since we don't
>> have this kind specific byte information of where certain rows/columns live
>> within a single object.
>>
>> On the other hand, the parquet format supports a partitioning scheme that
>> *IS* more amenable to "partial reads", but in a slightly different way.
>> Instead of using HTTP Range requests, specific columns or row batches of
>> columns are stored as separate *objects* in the cloud store. And so by
>> doing a "list" type of operation
>> on all "objects" in the store, and reading overall metadata of the
>> parquet data, we could similarly do a "column selection" kind of operation
>> by only downloading specific *objects* from the cloud store that correspond
>> to the desired columns.
>>
>> Hopefully that provides a little bit of clarity?
>>
>> This is somewhat the overall vision that we're working towards with the
>> Julia implementation to hopefully provide really efficient interop with
>> cloud-stored data.
>>
>> -Jacob Quinn
>>
>>
>> On Sun, Sep 18, 2022 at 7:47 PM Nikhil Makan <ni...@gmail.com>
>> wrote:
>>
>>> 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 <ak...@ucsc.edu> 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 <ak...@ucsc.edu> 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 <ni...@gmail.com>
>>>>> 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
>>>>>>
>>>>>
Re: [Python] - Dataset API - What's happening under the hood?
Posted by Nikhil Makan <ni...@gmail.com>.
Thanks Jacob for the comments. Appreciate it.
Out of interest does the cloud storage interface you are working on for
Julia support concurrent io in order to improve performance with respect to
download/uploading data. I know for pyarrow for a blob storage we have to
use a fsspec compliant library which is adlfs. However concurrent io is
still in the works with that library.
Kind regards
Nikhil Makan
On Wed, Oct 5, 2022 at 7:49 PM Jacob Quinn <qu...@gmail.com> wrote:
> Sorry for the late reply, but thought I'd chime in with a thought or two,
> as I've had the chance to work on both the Arrow.jl Julia implementation as
> well as recently working on a consistent cloud storage interface (for S3,
> Azure, and planned GCP, also for Julia;
> https://github.com/JuliaServices/CloudStore.jl).
>
> First, to clarify, all cloud storage providers support "partial" reads in
> the form of providing support for "ranged" HTTP requests (i.e. with a
> "Range" header like: "Range: bytes 0-9"). So that means for a single
> "object" in a cloud store, you could request specific byte ranges of that
> single object to be returned.
>
> How would that interact with stored data files? Well, for Arrow
> IPC/Feather format, you could potentially do a series of these "range"
> requests to read a single Feather file flatbuffer metadata, which contains
> the specific byte offsets of columns within the data file. So in theory, it
> should be fairly straightforward to apply
> a kind of "column selection" operation where only specific columns are
> actually downloaded from the cloud store, and it could be avoided to
> download the entire file.
>
> For other data formats? It's generally not as applicable since we don't
> have this kind specific byte information of where certain rows/columns live
> within a single object.
>
> On the other hand, the parquet format supports a partitioning scheme that
> *IS* more amenable to "partial reads", but in a slightly different way.
> Instead of using HTTP Range requests, specific columns or row batches of
> columns are stored as separate *objects* in the cloud store. And so by
> doing a "list" type of operation
> on all "objects" in the store, and reading overall metadata of the parquet
> data, we could similarly do a "column selection" kind of operation by only
> downloading specific *objects* from the cloud store that correspond to the
> desired columns.
>
> Hopefully that provides a little bit of clarity?
>
> This is somewhat the overall vision that we're working towards with the
> Julia implementation to hopefully provide really efficient interop with
> cloud-stored data.
>
> -Jacob Quinn
>
>
> On Sun, Sep 18, 2022 at 7:47 PM Nikhil Makan <ni...@gmail.com>
> wrote:
>
>> 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 <ak...@ucsc.edu> 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 <ak...@ucsc.edu> 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 <ni...@gmail.com>
>>>> 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
>>>>>
>>>>