> The dataset API still makes use of multiple cores though correct? Yes. It tries to use enough threads to ensure it is using the full processor.
> How does this then relate to the filesystems interface and native support for > HDFS, GCFS and S3. Do these exhibit the same issue? No, they should not. I am not aware of the specifics of the Azure issue but I know we can handle concurrent reads on the native S3 and GCFS filesystems and I assume we can on HDFS but I have never set HDFS up myself. > Further to this are per my earlier discussions on this thread we are unable > to do partial reads of a blob in Azure, I wanted to know if that is possible > with any of the other three that have native support. i.e. can we filter the > data downloaded from these instead of downloading everything and then > filtering? There are two questions here: 1. Can we read part of a file from the filesystem? Yes, all filesystem implementations that I am aware of support this. 2. Can we reduce the amount of data read by using a filter? Parquet is the only format that has some form of this. We can eliminate entire row groups from consideration if there is a pushdown filter and the row group statistics are informative enough. All formats (including Azure) support some pushdown support through partitioning. If the files are stored in a partitioned manner we can possible eliminate entire directories from consideration with a pushdown filter (e.g. if the filter is year==2004 then we can eliminate the directory `/year=2003/month=July`) There is more we could do here, both with parquet (page-level indices) and IPC (e.g. arrow/feather files which have no statistics at the moment). We just need someone that has the time and energy to implement it. > I don't think I quite follow this. Happy to be pointed to some documentation > to read more on this by the way. I have yet to find a good guide that explains this so I'll make a brief attempt at clarification. 1. If you read in a buffer of data from the disk, and then you discard that buffer (e.g. delete it), then that physical memory can be returned to the OS (barring fragmentation) regardless of how it was read in (e.g. memory mapping or regular file). 2. If you read in a buffer of data from the disk, and you do not discard that buffer, then that physical memory cannot be returned to the OS unless it is swapped out. This is true regardless of how it was read in. A. Normal reads A normal read will first copy the buffer from the disk to the kernel page cache. The caller controls exactly when this I/O occurs because it will be during the call to `read()`. The kernel will then perform a memcpy from the kernel page cache to a user-space caller-provided buffer that the caller has allocated. ``` // Caller provides the buffer void* my_buffer = malloc(100); // Read happens here, can control exactly when it happens read(fp, my_buffer, 100); ``` B. Memory mapped reads A memory mapped read will first copy the buffer from the disk to the kernel page cache. The caller doesn't have much control over when this I/O occurs because it will happen whenever the kernel decides it needs the page in the page cache to be populated (usually when the user first tries to access the data and a page fault occurs). It will then give the user a pointer directly into the kernel page cache. // Buffer provided by the kernel, no read happens here void* my_buffer = mmap(...); // ... // ... // This line probably triggers a page fault and blocks while the data is read from the disk. int x = ((int*)my_buffer)[0]; > I thought the basic idea behind memory mapping is that the data structure has > the same representation on disk as it does in memory therefore allowing it to > not consume additional memory when reading it No. We cannot, for example, do arithmetic on data that is on the disk. The CPU requires that data first be brought into RAM. > So would the dataset API process multiple files potentially quicker without > memory mapping. Probably not. If your data happened to already be fully in the kernel page cache then yes, the dataset API would probably process the file slightly faster. However, this would typically only be true if your entire dataset (or at least the working set you are dealing with) is smaller than your physical RAM and has been accessed recently. If the data is not already in the kernel page cache then memory mapping will probably have a negative effect. This is because the page faults come at unexpected times and can block the process at times we don't expect it to. > Also correct me if I am wrong, but memory mapping is related to the ipc > format only, formats such as parquet cannot take advantage of this? Any format can use memory mapped I/O. SO....why bother? Memory mapping is more typically used for IPC. In particular, it can be used to perform true zero-copy IPC. This IPC would only be true zero copy with the IPC format. Process A memory maps a file. Process A populates that region of memory with a table it generates in some way. Process A sends a control signal to process B that the table is ready. Process B memory maps the same file (we know it will be in the kernel page cache because we just used this RAM to write to it). Process B operates on the data in some way. On Tue, Sep 20, 2022 at 4:46 PM Nikhil Makan <[email protected]> wrote: > > Hi Weston, thanks for the response! > > > I would say that this is always a problem. In the datasets API the > goal is to maximize the resource usage within a single process. Now, > it may be a known or expected problem :) > > The dataset API still makes use of multiple cores though correct? > How does this then relate to the filesystems interface and native support for > HDFS, GCFS and S3. Do these exhibit the same issue? Further to this are per > my earlier discussions on this thread we are unable to do partial reads of a > blob in Azure, I wanted to know if that is possible with any of the other > three that have native support. i.e. can we filter the data downloaded from > these instead of downloading everything and then filtering? > > > I think the benefits of memory mapping are rather subtle and often > misleading. Datasets can make use of memory mapping for local > filesystems. Doing so will, at best, have a slight performance > benefit (avoiding a memcpy) but would most likely decrease performance > (by introducing I/O where it is not expected) and it will have no > effect whatsoever on the amount of RAM used. > > I don't think I quite follow this. Happy to be pointed to some documentation > to read more on this by the way. I thought the basic idea behind memory > mapping is that the data structure has the same representation on disk as it > does in memory therefore allowing it to not consume additional memory when > reading it, which is typical with normal I/O operations with reading files. > So would the dataset API process multiple files potentially quicker without > memory mapping. Also correct me if I am wrong, but memory mapping is related > to the ipc format only, formats such as parquet cannot take advantage of this? > > Kind regards > Nikhil Makan > > > On Tue, Sep 20, 2022 at 5:12 AM Weston Pace <[email protected]> wrote: >> >> Sorry for the slow reply. >> >> > 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. >> >> I would say that this is always a problem. In the datasets API the >> goal is to maximize the resource usage within a single process. Now, >> it may be a known or expected problem :) >> >> > 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? >> >> I think the benefits of memory mapping are rather subtle and often >> misleading. Datasets can make use of memory mapping for local >> filesystems. Doing so will, at best, have a slight performance >> benefit (avoiding a memcpy) but would most likely decrease performance >> (by introducing I/O where it is not expected) and it will have no >> effect whatsoever on the amount of RAM used. >> >> > 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. >> >> Yes. However the functions that get mapped can sometimes be >> surprising. Specifically, logical operations map to the _kleene >> variation and arithmetic maps to the _checked variation. You can find >> the implementation at [1]. For multiplication this boils down to: >> >> ``` >> @staticmethod >> cdef Expression _expr_or_scalar(object expr): >> if isinstance(expr, Expression): >> return (<Expression> expr) >> return (<Expression> Expression._scalar(expr)) >> >> ... >> >> def __mul__(Expression self, other): >> other = Expression._expr_or_scalar(other) >> return Expression._call("multiply_checked", [self, other]) >> ``` >> >> >> On Mon, Sep 19, 2022 at 12:52 AM Jacek Pliszka <[email protected]> >> wrote: >> > >> > Re 2. In Python Azure SDK there is logic for partial blob read: >> > >> > https://learn.microsoft.com/en-us/python/api/azure-storage-blob/azure.storage.blob.blobclient?view=azure-python#azure-storage-blob-blobclient-query-blob >> > >> > However I was unable to use it as it does not support parquet files >> > with decimal columns and these are the ones I have. >> > >> > BR >> > >> > J >> > >> > pt., 16 wrz 2022 o 02:26 Aldrin <[email protected]> napisał(a): >> > > >> > > 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 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 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 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 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
