I think the fallback graph approach might still be useful in the case of
parsing with unions allowed, albeit with a much broader graph.
For example,
INT64 + STRING -> UNION(INT64, STRING)
T + UNION (*) -> UNION(T, *)
# ...
Related: how should ordinarily convertible types be handled in the context
of unions? For example, if we have a column of mostly INT64 with a few
DOUBLE mixed in, should the column become DOUBLE or UNION(INT64, DOUBLE)?
WRT foregoing type inference altogether, I don't think we can make that
work; users are still going to expect to be able to just parse a CSV or
line-delimited JSON file without specifying column types explicitly. With
that said the idea of using schemas is very interesting. How would you use
a protobuf file to express that a string field should be parsed as a
timestamp? (IIRC, protobuf doesn't have built-in time types)
On Fri, Nov 30, 2018, 10:28 Francois Saint-Jacques <
fsaintjacq...@networkdump.com> wrote:
> Hello,
>
> With JSON and other "typed" formats (msgpack, protobuf, ...) you need to
> take account unions, e.g.
>
> {a: "herp", b: 10}
> {a: true, c: "derp"}
>
> The type for `a` would be union<string, bool>.
>
> I think we should also evaluate into investing at ingesting different
> schema DSL (protobuf idl, json-schema) to avoid inference entirely.
>
> On Fri, Nov 30, 2018 at 9:43 AM Ben Kietzman <ben.kietz...@rstudio.com>
> wrote:
>
> > Hi Antoine,
> >
> > The conversion of previous blocks is part of the fall back mechanism I'm
> > trying to describe. When type inference fails (even in a different
> block),
> > conversion of all blocks of the column is attempted to the next type in
> the
> > fallback graph.
> >
> > If there is no problem with the fallback graph model, the API would
> > probably look like a reusable LoosenType- something which simplifies
> > querying for the loosened type when inference fails.
> >
> > Unrelated: I forgot to include some edges in the json graph
> >
> > NULL -> BOOL
> > NULL -> INT64 -> DOUBLE
> > NULL -> TIMESTAMP -> STRING -> BINARY
> > NULL -> STRUCT
> > NULL -> LIST
> >
> > On Fri, Nov 30, 2018, 04:52 Antoine Pitrou <anto...@python.org> wrote:
> >
> > >
> > > Hi Ben,
> > >
> > > Le 30/11/2018 à 02:19, Ben Kietzman a écrit :
> > > > Currently, to figure out which types may be inferred and under which
> > > > circumstances they will be inferred involves digging through code. I
> > > think
> > > > it would be useful to have an API for expressing type inference
> rules.
> > > > Ideally this would be provided as utility functions alongside
> > > > StringConverter and used by anything which does type inference while
> > > > parsing/unboxing.
> > >
> > > It may be a bit more complicated. For example, a CSV file is parsed by
> > > blocks, and each block produces an array chunk. But when the type of a
> > > later block changes due to type inference failing on the current type,
> > > all previous blocks must be parsed again.
> > >
> > > So I'm curious what you would make the API look like.
> > >
> > > > By contrast, when reading JSON (which is explicit about numbers vs
> > > > strings), the graph would be:
> > > >
> > > > NULL -> BOOL
> > > > NULL -> INT64 -> DOUBLE
> > > > NULL -> TIMESTAMP -> STRING -> BINARY
> > > >
> > > > Seem reasonable?
> > > > Is there a case which isn't covered by a fallback graph as above?
> > >
> > > I have no idea. Someone else may be able to answer your question.
> > >
> > > Regards
> > >
> > > Antoine.
> > >
> >
>
>
> --
> Sent from my jetpack.
>
