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From Wes McKinney <wesmck...@gmail.com>
Subject Re: Stored state of incremental writes to fixed size Arrow buffer?
Date Tue, 07 May 2019 16:01:31 GMT
hi John,

On Tue, May 7, 2019 at 10:53 AM John Muehlhausen <jgm@jgm.org> wrote:
>
> Wes et al, I completed a preliminary study of populating a Feather file
> incrementally.  Some notes and questions:
>
> I wrote the following dataframe to a feather file:
>             a    b
> 0  0123456789  0.0
> 1  0123456789  NaN
> 2  0123456789  NaN
> 3  0123456789  NaN
> 4        None  NaN
>
> In re-writing the flatbuffers metadata (flatc -p doesn't
> support --gen-mutable! yuck! C++ to the rescue...), it seems that
> read_feather is not affected by NumRows?  It seems to be driven entirely by
> the per-column Length values?
>
> Also, it seems as if one of the primary usages of NullCount is to determine
> whether or not a bitfield is present?  In the initialization data above I
> was careful to have a null value in each column in order to generate a
> bitfield.

Per my prior e-mails, the current Feather format is deprecated, so I'm
only willing to engage on a discussion of the official Arrow binary
protocol that we use for IPC (memory mapping) and RPC (Flight).

>
> I then wiped the bitfields in the file and set all of the string indices to
> one past the end of the blob buffer (all strings empty):
>       a   b
> 0  None NaN
> 1  None NaN
> 2  None NaN
> 3  None NaN
> 4  None NaN
>
> I then set the first record to some data by consuming some of the string
> blob and row 0 and 1 indices, also setting the double:
>
>                a    b
> 0  Hello, world!  5.0
> 1           None  NaN
> 2           None  NaN
> 3           None  NaN
> 4           None  NaN
>
> As mentioned above, NumRows seems to be ignored.  I tried adjusting each
> column Length to mask off higher rows and it worked for 4 (hide last row)
> but not for less than 4.  I take this to be due to math used to figure out
> where the buffers are relative to one another since there is only one
> metadata offset for all of: the (optional) bitset, index column and (if
> string) blobs.
>
> Populating subsequent rows would proceed in a similar way until all of the
> blob storage has been consumed, which may come before the pre-allocated
> rows have been consumed.
>
> So what does this mean for my desire to incrementally write these
> (potentially memory-mapped) pre-allocated files and/or Arrow buffers in
> memory?  Some thoughts:
>
> - If a single value (such as NumRows) were consulted to determine the table
> row dimension then updating this single value would serve to tell a reader
> which rows are relevant.  Assuming this value is updated after all other
> mutations are complete, and assuming that mutations are only appends
> (addition of rows), then concurrency control involves only ensuring that
> this value is not examined while it is being written.
>
> - NullCount presents a concurrency problem if someone reads the file after
> this field has been updated, but before NumRows has exposed the new record
> (or vice versa).  The idea previously mentioned that there will "likely
> [be] more statistics in the future" feels like it might be scope creep to
> me?  This is a data representation, not a calculation framework?  If
> NullCount had its genesis in the optional nature of the bitfield, I would
> suggest that perhaps NullCount can be dropped in favor of always supplying
> the bitfield, which in any event is already contemplated by the spec:
> "Implementations may choose to always allocate one anyway as a matter of
> convenience."  If the concern is space savings, Arrow is already an
> extremely uncompressed format.  (Compression is something I would also
> consider to be scope creep as regards Feather... compressed filesystems can
> be employed and there are other compressed dataframe formats.)  However, if
> protecting the 4 bytes required to update NowRows turns out to be no easier
> than protecting all of the statistical bytes as well as part of the same
> "critical section" (locks: yuck!!) then statistics pose no issue.  I have a
> feeling that the availability of an atomic write of 4 bytes will depend on
> the storage mechanism... memory vs memory map vs write() etc.
>
> - The elephant in the room appears to be the presumptive binary yes/no on
> mutability of Arrow buffers.  Perhaps the thought is that certain batch
> processes will be wrecked if anyone anywhere is mutating buffers in any
> way.  But keep in mind I am not proposing general mutability, only
> appending of new data.  *A huge amount of batch processing that will take
> place with Arrow is on time-series data (whether financial or otherwise).
> It is only natural that architects will want the minimal impedance mismatch
> when it comes time to grow those time series as the events occur going
> forward.*  So rather than say that I want "mutable" Arrow buffers, I would
> pitch this as a call for "immutable populated areas" of Arrow buffers
> combined with the concept that the populated area can grow up to whatever
> was preallocated.  This will not affect anyone who has "memoized" a
> dimension and wants to continue to consider the then-current data as
> immutable... it will be immutable and will always be immutable according to
> that then-current dimension.
>
> Thanks in advance for considering this feedback!  I absolutely require
> efficient row-wise growth of an Arrow-like buffer to deal with time series
> data in near real time.  I believe that preallocation is (by far) the most
> efficient way to accomplish this.  I hope to be able to use Arrow!  If I
> cannot use Arrow than I will be using a home-grown Arrow that is identical
> except for this feature, which would be very sad!  Even if Arrow itself
> could be used in this manner today, I would be hesitant to use it if the
> use-case was not protected on a go-forward basis.
>

I recommend batching your writes and using the Arrow binary streaming
protocol so you are only appending to a file rather than mutating
previously-written bytes. This use case is well defined and supported
in the software already.

https://github.com/apache/arrow/blob/master/docs/source/format/IPC.rst#streaming-format

- Wes

> Of course, I am completely open to alternative ideas and approaches!
>
> -John
>
> On Mon, May 6, 2019 at 11:39 AM Wes McKinney <wesmckinn@gmail.com> wrote:
>
> > hi John -- again, I would caution you against using Feather files for
> > issues of longevity -- the internal memory layout of those files is a
> > "dead man walking" so to speak.
> >
> > I would advise against forking the project, IMHO that is a dark path
> > that leads nowhere good. We have a large community here and we accept
> > pull requests -- I think the challenge is going to be defining the use
> > case to suitable clarity that a general purpose solution can be
> > developed.
> >
> > - Wes
> >
> >
> > On Mon, May 6, 2019 at 11:16 AM John Muehlhausen <jgm@jgm.org> wrote:
> > >
> > > François, Wes,
> > >
> > > Thanks for the feedback.  I think the most practical thing for me to do
> > is
> > > 1- write a Feather file that is structured to pre-allocate the space I
> > need
> > > (e.g. initial variable-length strings are of average size)
> > > 2- come up with code to monkey around with the values contained in the
> > > vectors so that before and after each manipulation the file is valid as I
> > > walk the rows ... this is a writer that uses memory mapping
> > > 3- check back in here once that works, assuming that it does, to see if
> > we
> > > can bless certain mutation paths
> > > 4- if we can't bless certain mutation paths, fork the project for those
> > who
> > > care more about stream processing?  That would not seem to be ideal as I
> > > think mutation in row-order across the data set is relatively low impact
> > on
> > > the overall design?
> > >
> > > Thanks again for engaging the topic!
> > > -John
> > >
> > > On Mon, May 6, 2019 at 10:26 AM Francois Saint-Jacques <
> > > fsaintjacques@gmail.com> wrote:
> > >
> > > > Hello John,
> > > >
> > > > Arrow is not yet suited for partial writes. The specification only
> > > > talks about fully frozen/immutable objects, you're in implementation
> > > > defined territory here. For example, the C++ library assumes the Array
> > > > object is immutable; it memoize the null count, and likely more
> > > > statistics in the future.
> > > >
> > > > If you want to use pre-allocated buffers and array, you can use the
> > > > column validity bitmap for this purpose, e.g. set all null by default
> > > > and flip once the row is written. It suffers from concurrency issues
> > > > (+ invalidation issues as pointed) when dealing with multiple columns.
> > > > You'll have to use a barrier of some kind, e.g. a per-batch global
> > > > atomic (if append-only), or dedicated column(s) à-la MVCC. But then,
> > > > the reader needs to be aware of this and compute a mask each time it
> > > > needs to query the partial batch.
> > > >
> > > > This is a common columnar database problem, see [1] for a recent paper
> > > > on the subject. The usual technique is to store the recent data
> > > > row-wise, and transform it in column-wise when a threshold is met akin
> > > > to a compaction phase. There was a somewhat related thread [2] lately
> > > > about streaming vs batching. In the end, I think your solution will be
> > > > very application specific.
> > > >
> > > > François
> > > >
> > > > [1] https://db.in.tum.de/downloads/publications/datablocks.pdf
> > > > [2]
> > > >
> > https://lists.apache.org/thread.html/27945533db782361143586fd77ca08e15e96e2f2a5250ff084b462d6@%3Cdev.arrow.apache.org%3E
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > >
> > > > On Mon, May 6, 2019 at 10:39 AM John Muehlhausen <jgm@jgm.org> wrote:
> > > > >
> > > > > Wes,
> > > > >
> > > > > I’m not afraid of writing my own C++ code to deal with all of this
> > on the
> > > > > writer side.  I just need a way to “append” (incrementally populate)
> > e.g.
> > > > > feather files so that a person using e.g. pyarrow doesn’t suffer
some
> > > > > catastrophic failure... and “on the side” I tell them which rows
are
> > junk
> > > > > and deal with any concurrency issues that can’t be solved in the
> > arena of
> > > > > atomicity and ordering of ops.  For now I care about basic types
but
> > > > > including variable-width strings.
> > > > >
> > > > > For event-processing, I think Arrow has to have the concept of a
> > > > partially
> > > > > full record set.  Some alternatives are:
> > > > > - have a batch size of one, thus littering the landscape with
> > trivially
> > > > > small Arrow buffers
> > > > > - artificially increase latency with a batch size larger than one,
> > but
> > > > not
> > > > > processing any data until a batch is complete
> > > > > - continuously re-write the (entire!) “main” buffer as batches
of
> > length
> > > > 1
> > > > > roll in
> > > > > - instead of one main buffer, several, and at some threshold combine
> > the
> > > > > last N length-1 batches into a length N buffer ... still an
> > inefficiency
> > > > >
> > > > > Consider the case of QAbstractTableModel as the underlying data for
a
> > > > table
> > > > > or a chart.  This visualization shows all of the data for the recent
> > past
> > > > > as well as events rolling in.  If this model interface is
> > implemented as
> > > > a
> > > > > view onto “many thousands” of individual event buffers then we
gain
> > > > nothing
> > > > > from columnar layout.  (Suppose there are tons of columns and most
of
> > > > them
> > > > > are scrolled out of the view.). Likewise we cannot re-write the
> > entire
> > > > > model on each event... time complexity blows up.  What we want is
to
> > > > have a
> > > > > large pre-allocated chunk and just change rowCount() as data is
> > > > “appended.”
> > > > >  Sure, we may run out of space and have another and another chunk
for
> > > > > future row ranges, but a handful of chunks chained together is better
> > > > than
> > > > > as many chunks as there were events!
> > > > >
> > > > > And again, having a batch size >1 and delaying the data until
a
> > batch is
> > > > > full is a non-starter.
> > > > >
> > > > > I am really hoping to see partially-filled buffers as something we
> > keep
> > > > our
> > > > > finger on moving forward!  Or else, what am I missing?
> > > > >
> > > > > -John
> > > > >
> > > > > On Mon, May 6, 2019 at 8:24 AM Wes McKinney <wesmckinn@gmail.com>
> > wrote:
> > > > >
> > > > > > hi John,
> > > > > >
> > > > > > In C++ the builder classes don't yet support writing into
> > preallocated
> > > > > > memory. It would be tricky for applications to determine a priori
> > > > > > which segments of memory to pass to the builder. It seems only
> > > > > > feasible for primitive / fixed-size types so my guess would
be
> > that a
> > > > > > separate set of interfaces would need to be developed for this
> > task.
> > > > > >
> > > > > > - Wes
> > > > > >
> > > > > > On Mon, May 6, 2019 at 8:18 AM Jacques Nadeau <jacques@apache.org>
> > > > wrote:
> > > > > > >
> > > > > > > This is more of a question of implementation versus
> > specification. An
> > > > > > arrow
> > > > > > > buffer is generally built and then sealed. In different
> > languages,
> > > > this
> > > > > > > building process works differently (a concern of the language
> > rather
> > > > than
> > > > > > > the memory specification). We don't currently allow a half
built
> > > > vector
> > > > > > to
> > > > > > > be moved to another language and then be further built.
So the
> > > > question
> > > > > > is
> > > > > > > really more concrete: what language are you looking at
and what
> > is
> > > > the
> > > > > > > specific pattern you're trying to undertake for building.
> > > > > > >
> > > > > > > If you're trying to go across independent processes (whether
the
> > same
> > > > > > > process restarted or two separate processes active
> > simultaneously)
> > > > you'll
> > > > > > > need to build up your own data structures to help with
this.
> > > > > > >
> > > > > > > On Mon, May 6, 2019 at 6:28 PM John Muehlhausen <jgm@jgm.org>
> > wrote:
> > > > > > >
> > > > > > > > Hello,
> > > > > > > >
> > > > > > > > Glad to learn of this project— good work!
> > > > > > > >
> > > > > > > > If I allocate a single chunk of memory and start building
Arrow
> > > > format
> > > > > > > > within it, does this chunk save any state regarding
my
> > progress?
> > > > > > > >
> > > > > > > > For example, suppose I allocate a column for floating
point
> > (fixed
> > > > > > width)
> > > > > > > > and a column for string (variable width).  Suppose
I start
> > > > building the
> > > > > > > > floating point column at offset X into my single buffer,
and
> > the
> > > > string
> > > > > > > > “pointer” column at offset Y into the same single
buffer, and
> > the
> > > > > > string
> > > > > > > > data elements at offset Z.
> > > > > > > >
> > > > > > > > I write one floating point number and one string,
then go away.
> > > > When I
> > > > > > > > come back to this buffer to append another value,
does the
> > buffer
> > > > > > itself
> > > > > > > > know where I would begin?  I.e. is there a differentiation
in
> > the
> > > > > > column
> > > > > > > > (or blob) data itself between the available space
and the used
> > > > space?
> > > > > > > >
> > > > > > > > Suppose I write a lot of large variable width strings
and “run
> > > > out” of
> > > > > > > > space for them before running out of space for floating
point
> > > > numbers
> > > > > > or
> > > > > > > > string pointers.  (I guessed badly when doing the
original
> > > > > > allocation.). I
> > > > > > > > consider this to be Ok since I can always “copy”
the data to
> > > > “compress
> > > > > > out”
> > > > > > > > the unused fp/pointer buckets... the choice is up
to me.
> > > > > > > >
> > > > > > > > The above applied to a (feather?) file is how I anticipate
> > > > appending
> > > > > > data
> > > > > > > > to disk... pre-allocate a mem-mapped file and gradually
fill
> > it up.
> > > > > > The
> > > > > > > > efficiency of file utilization will depend on my projections
> > > > regarding
> > > > > > > > variable-width data types, but as I said above, I
can always
> > > > re-write
> > > > > > the
> > > > > > > > file if/when this bothers me.
> > > > > > > >
> > > > > > > > Is this the recommended and supported approach for
incremental
> > > > appends?
> > > > > > > > I’m really hoping to use Arrow instead of rolling
my own, but
> > > > > > functionality
> > > > > > > > like this is absolutely key!  Hoping not to use a
side-car
> > file (or
> > > > > > memory
> > > > > > > > chunk) to store “append progress” information.
> > > > > > > >
> > > > > > > > I am brand new to this project so please forgive me
if I have
> > > > > > overlooked
> > > > > > > > something obvious.  And again, looks like great work
so far!
> > > > > > > >
> > > > > > > > Thanks!
> > > > > > > > -John
> > > > > > > >
> > > > > >
> > > >
> >

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