[Pandas-dev] Rewriting some of internals of pandas in C/C++? / Roadmap

[Jeff Reback]

Wes your last is noted as well. I *think* we can actually do this now (well
there is a PR out there).

On Tue, Dec 29, 2015 at 4:12 PM, Wes McKinney <wesmckinn at gmail.com> wrote:

> The other huge thing this will enable is to do is copy-on-write for
> various kinds of views, which should cut down on some of the defensive
> copying in the library and reduce memory usage.
>
> On Tue, Dec 29, 2015 at 1:02 PM, Wes McKinney <wesmckinn at gmail.com> wrote:
> > Basically the approach is
> >
> > 1) Base dtype type
> > 2) Base array type with K >= 1 dimensions
> > 3) Base scalar type
> > 4) Base index type
> > 5) "Wrapper" subclasses for all NumPy types fitting into categories
> > #1, #2, #3, #4
> > 6) Subclasses for pandas-specific types like category, datetimeTZ, etc.
> > 7) NDFrame as cpcloud wrote is just a list of these
> >
> > Indexes and axis labels / column names can get layered on top.
> >
> > After we do all this we can look at adding nested types (arrays, maps,
> > structs) to better support JSON.
> >
> > - Wes
> >
> > On Tue, Dec 29, 2015 at 12:14 PM, Phillip Cloud <cpcloud at gmail.com>
> wrote:
> >> Maybe this is saying the same thing as Wes, but how far would something
> like
> >> this get us?
> >>
> >> // warning: things are probably not this simple
> >>
> >> struct data_array_t {
> >>     void *primitive;  // scalar data
> >>     data_array_t *nested; // nested data
> >>     boost::dynamic_bitset isnull;  // might have to create our own to
> avoid
> >> boost
> >>     schema_t schema;  // not sure exactly what this looks like
> >> };
> >>
> >> typedef std::map<string, data_array_t> data_frame_t;  // probably not
> this
> >> simple
> >>
> >> To answer Jeff’s use-case question: I think that the use cases are 1)
> >> freedom from numpy (mostly) 2) no more block manager which frees us
> from the
> >> limitations of the block memory layout. In particular, the ability to
> take
> >> advantage of memory mapped IO would be a big win IMO.
> >>
> >>
> >> On Tue, Dec 29, 2015 at 2:50 PM Wes McKinney <wesmckinn at gmail.com>
> wrote:
> >>>
> >>> I will write a more detailed response to some of these things after
> >>> the new year, but, in particular, re: missing values, can you or
> >>> someone tell me why creating an object that contains a NumPy array and
> >>> a bitmap is not sufficient? If we we can add a lightweight C/C++ class
> >>> layer between NumPy function calls (e.g. arithmetic) and pandas
> >>> function calls, then I see no reason why we cannot have
> >>>
> >>> Int32Array->add
> >>>
> >>> and
> >>>
> >>> Float32Array->add
> >>>
> >>> do the right thing (the former would be responsible for bitmasking to
> >>> propagate NA values; the latter would defer to NumPy). If we can put
> >>> all the internals of pandas objects inside a black box, we can add
> >>> layers of virtual function indirection without a performance penalty
> >>> (e.g. adding more interpreter overhead with more abstraction layers
> >>> does add up to a perf penalty).
> >>>
> >>> I don't think this is too scary -- I would be willing to create a
> >>> small POC C++ library to prototype something like what I'm talking
> >>> about.
> >>>
> >>> Since pandas has limited points of contact with NumPy I don't think
> >>> this would end up being too onerous.
> >>>
> >>> For the record, I'm pretty allergic to "advanced C++"; I think it is a
> >>> useful tool if you pick a sane 20% subset of the C++11 spec and follow
> >>> Google C++ style it's not very inaccessible to intermediate
> >>> developers. More or less "C plus OOP and easier object lifetime
> >>> management (shared/unique_ptr, etc.)". As soon as you add a lot of
> >>> template metaprogramming C++ library development quickly becomes
> >>> inaccessible except to the C++-Jedi.
> >>>
> >>> Maybe let's start a Google document on "pandas roadmap" where we can
> >>> break down the 1-2 year goals and some of these infrastructure issues
> >>> and have our discussion there? (obviously publish this someplace once
> >>> we're done)
> >>>
> >>> - Wes
> >>>
> >>> On Fri, Dec 25, 2015 at 2:14 PM, Jeff Reback <jeffreback at gmail.com>
> wrote:
> >>> > Here are some of my thoughts about pandas Roadmap / status and some
> >>> > responses to Wes's thoughts.
> >>> >
> >>> > In the last few (and upcoming) major releases we have been made the
> >>> > following changes:
> >>> >
> >>> > - dtype enhancements (Categorical, Timedelta, Datetime w/tz) & making
> >>> > these
> >>> > first class objects
> >>> > - code refactoring to remove subclassing of ndarrays for Series &
> Index
> >>> > - carving out / deprecating non-core parts of pandas
> >>> >   - datareader
> >>> >   - SparsePanel, WidePanel & other aliases (TImeSeries)
> >>> >   - rpy, rplot, irow et al.
> >>> >   - google-analytics
> >>> > - API changes to make things more consistent
> >>> >   - pd.rolling/expanding * -> .rolling/expanding (this is in master
> now)
> >>> >   - .resample becoming a full defered like groupby.
> >>> >   - multi-index slicing along any level (obviates need for .xs) and
> >>> > allows
> >>> > assignment
> >>> >   - .loc/.iloc - for the most part obviates use of .ix
> >>> >   - .pipe & .assign
> >>> >   - plotting accessors
> >>> >   - fixing of the sorting API
> >>> > - many performance enhancements both micro & macro (e.g. release GIL)
> >>> >
> >>> > Some on-deck enhancements are (meaning these are basically ready to
> go
> >>> > in):
> >>> >   - IntervalIndex (and eventually make PeriodIndex just a sub-class
> of
> >>> > this)
> >>> >   - RangeIndex
> >>> >
> >>> > so lots of changes, though nothing really earth shaking, just more
> >>> > convenience, reducing magicness somewhat
> >>> > and providing flexibility.
> >>> >
> >>> > Of course we are getting increasing issues, mostly bug reports (and
> lots
> >>> > of
> >>> > dupes), some edge case enhancements
> >>> > which can add to the existing API's and of course, requests to expand
> >>> > the
> >>> > (already) large code to other usecases.
> >>> > Balancing this are a good many pull-requests from many different
> users,
> >>> > some
> >>> > even deep into the internals.
> >>> >
> >>> > Here are some things that I have talked about and could be considered
> >>> > for
> >>> > the roadmap. Disclaimer: I do work for Continuum
> >>> > but these views are of course my own; furthermore obviously I am a
> bit
> >>> > more
> >>> > familiar with some of the 'sponsored' open-source
> >>> > libraries, but always open to new things.
> >>> >
> >>> > - integration / automatic deferral to numba for JIT (this would be
> thru
> >>> > .apply)
> >>> > - automatic deferal to dask from groubpy where appropriate / maybe a
> >>> > .to_parallel (to simply return a dask.DataFrame object)
> >>> > - incorporation of quantities / units (as part of the dtype)
> >>> > - use of DyND to allow missing values for int dtypes
> >>> > - make Period a first class dtype.
> >>> > - provide some copy-on-write semantics to alleviate the
> chained-indexing
> >>> > issues which occasionaly come up with the mis-use of the indexing API
> >>> > - allow a 'policy' to automatically provide column blocks for
> dict-like
> >>> > input (e.g. each column would be a block), this would allow a
> pass-thru
> >>> > API
> >>> > where you could
> >>> > put in numpy arrays where you have views and have them preserved
> rather
> >>> > than
> >>> > copied automatically. Note that this would also allow what I call
> >>> > 'split'
> >>> > where a passed in
> >>> > multi-dim numpy array could be split up to individual blocks (which
> >>> > actually
> >>> > gives a nice perf boost after the splitting costs).
> >>> >
> >>> > In working towards some of these goals. I have come to the opinion
> that
> >>> > it
> >>> > would make sense to have a neutral API protocol layer
> >>> > that would allow us to swap out different engines as needed, for
> >>> > particular
> >>> > dtypes, or *maybe* out-of-core type computations. E.g.
> >>> > imagine that we replaced the in-memory block structure with a bclolz
> /
> >>> > memap
> >>> > type; in theory this should be 'easy' and just work.
> >>> > I could also see us adopting *some* of the SFrame code to allow
> easier
> >>> > interop with this API layer.
> >>> >
> >>> > In practice, I think a nice API layer would need to be created to
> make
> >>> > this
> >>> > clean / nice.
> >>> >
> >>> > So this comes around to Wes's point about creating a c++ library for
> the
> >>> > internals (and possibly even some of the indexing routines).
> >>> > In an ideal world, or course this would be desirable. Getting there
> is a
> >>> > bit
> >>> > non-trivial I think, and IMHO might not be worth the effort. I don't
> >>> > really see big performance bottlenecks. We *already* defer much of
> the
> >>> > computation to libraries like numexpr & bottleneck (where
> appropriate).
> >>> > Adding numba / dask to the list would be helpful.
> >>> >
> >>> > I think that almost all performance issues are the result of:
> >>> >
> >>> > a) gross misuse of the pandas API. How much code have you seen that
> does
> >>> > df.apply(lambda x: x.sum())
> >>> > b) routines which operate column-by-column rather block-by-block and
> are
> >>> > in
> >>> > python space (e.g. we have an issue right now about .quantile)
> >>> >
> >>> > So I am glossing over a big goal of having a c++ library that
> represents
> >>> > the
> >>> > pandas internals. This would by definition have a c-API that so
> >>> > you *could* use pandas like semantics in c/c++ and just have it work
> >>> > (and
> >>> > then pandas would be a thin wrapper around this library).
> >>> >
> >>> > I am not averse to this, but I think would be quite a big effort, and
> >>> > not a
> >>> > huge perf boost IMHO. Further there are a number of API issues w.r.t.
> >>> > indexing
> >>> > which need to be clarified / worked out (e.g. should we simply
> deprecate
> >>> > [])
> >>> > that are much easier to test / figure out in python space.
> >>> >
> >>> > I also thing that we have quite a large number of contributors.
> Moving
> >>> > to
> >>> > c++ might make the internals a bit more impenetrable that the current
> >>> > internals.
> >>> > (though this would allow c++ people to contribute, so that might
> balance
> >>> > out).
> >>> >
> >>> > We have a limited core of devs whom right now are familar with
> things.
> >>> > If
> >>> > someone happened to have a starting base for a c++ library, then I
> might
> >>> > change
> >>> > opinions here.
> >>> >
> >>> >
> >>> > my 4c.
> >>> >
> >>> > Jeff
> >>> >
> >>> >
> >>> >
> >>> >
> >>> > On Thu, Dec 24, 2015 at 7:18 PM, Wes McKinney <wesmckinn at gmail.com>
> >>> > wrote:
> >>> >>
> >>> >> Deep thoughts during the holidays.
> >>> >>
> >>> >> I might be out of line here, but the interpreter-heaviness of the
> >>> >> inside of pandas objects is likely to be a long-term liability and
> >>> >> source of performance problems and technical debt.
> >>> >>
> >>> >> Has anyone put any thought into planning and beginning to execute
> on a
> >>> >> rewrite that moves as much as possible of the internals into native
> /
> >>> >> compiled code? I'm talking about:
> >>> >>
> >>> >> - pandas/core/internals
> >>> >> - indexing and assignment
> >>> >> - much of pandas/core/common
> >>> >> - categorical and custom dtypes
> >>> >> - all indexing mechanisms
> >>> >>
> >>> >> I'm concerned we've already exposed too much internals to users, so
> >>> >> this might lead to a lot of API breakage, but it might be for the
> >>> >> Greater Good. As a first step, beginning a partial migration of
> >>> >> internals into some C++ classes that encapsulate the insides of
> >>> >> DataFrame objects and implement indexing and block-level
> manipulations
> >>> >> would be a good place to start. I think you could do this wouldn't
> too
> >>> >> much disruption.
> >>> >>
> >>> >> As part of this internal retooling we might give consideration to
> >>> >> alternative data structures for representing data internal to pandas
> >>> >> objects. Now in 2015/2016, continuing to be hamstrung by NumPy's
> >>> >> limitations feels somewhat anachronistic. User code is riddled with
> >>> >> workarounds for data type fidelity issues and the like. Like,
> really,
> >>> >> why not add a bitndarray (similar to ilanschnell/bitarray) for
> storing
> >>> >> nullness for problematic types and hide this from the user? =)
> >>> >>
> >>> >> Since we are now a NumFOCUS-sponsored project, I feel like we might
> >>> >> consider establishing some formal governance over pandas and
> >>> >> publishing meetings notes and roadmap documents describing plans for
> >>> >> the project and meetings notes from committers. There's no real
> >>> >> "committer culture" for NumFOCUS projects like there is with the
> >>> >> Apache Software Foundation, but we might try leading by example!
> >>> >>
> >>> >> Also, I believe pandas as a project has reached a level of
> importance
> >>> >> where we ought to consider planning and execution on larger scale
> >>> >> undertakings such as this for safeguarding the future.
> >>> >>
> >>> >> As for myself, well, I have my hands full in Big Data-land. I wish I
> >>> >> could be helping more with pandas, but there a quite a few
> fundamental
> >>> >> issues (like data interoperability nested data handling and file
> >>> >> format support — e.g. Parquet, see
> >>> >>
> >>> >>
> >>> >>
> http://wesmckinney.com/blog/the-problem-with-the-data-science-language-wars/
> )
> >>> >> preventing Python from being more useful in industry analytics
> >>> >> applications.
> >>> >>
> >>> >> Aside: one of the bigger mistakes I made with pandas's API design
> was
> >>> >> making it acceptable to call class constructors — like
> >>> >> pandas.DataFrame — directly (versus factory functions). Sorry about
> >>> >> that! If we could convince everyone to start writing
> pandas.data_frame
> >>> >> or dataframe instead of using the class reference it would help a
> lot
> >>> >> with code cleanup. It's hard to plan for these things — NumPy
> >>> >> interoperability seemed a lot more important in 2008 than it does
> now,
> >>> >> so I forgive myself.
> >>> >>
> >>> >> cheers and best wishes for 2016,
> >>> >> Wes
> >>> >> _______________________________________________
> >>> >> Pandas-dev mailing list
> >>> >> Pandas-dev at python.org
> >>> >> https://mail.python.org/mailman/listinfo/pandas-dev
> >>> >
> >>> >
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