Fix columntable materialization on stored schema (#360)

Fixes #357.

The issue here is for stored schema, the type of the schema is
`Schema{nothing, nothing}` which usually indicates tables with
many columns. Some tables implementations, however, like ARFFFiles.jl,
may choose to explicitly store _all_ schemas, even for very narrow tables.
We already have a generated branch which checks for a specialization threshold
for the known-schema case, so the fix here is fairly straightforward in just
actually checking if the stored schema # of columns is actually too many or
not.

In the end, users should be aware that `Tables.columntable` isn't a perfect,
100% kind of table implementation that is always expected to work. It was originally
meant as just a test implementation that then turned out to be fairly convenient
for REPL use. Users should note that generating a named tuple of columns from stored
schema doesn't have a way to be particularly efficient, since it necessarily has to
generate the NamedTuple type at runtime.

Author: quinnj

src/namedtuples.jl

@@ -176,9 +176,14 @@ function columntable(sch::Schema{names, types}, cols) where {names, types}
     end
 end
 
-# extremely large tables
-columntable(sch::Schema{nothing, nothing}, cols) =
-    throw(ArgumentError("input table too wide ($(length(sch.names)) columns) to convert to `NamedTuple` of `AbstractVector`s"))
+# extremely large tables or schema explicitly stored
+function columntable(sch::Schema{nothing, nothing}, cols)
+    nms = sch.names
+    if nms !== nothing && length(nms) > SPECIALIZATION_THRESHOLD
+        throw(ArgumentError("input table too wide ($(length(nms)) columns) to convert to `NamedTuple` of `AbstractVector`s"))
+    end
+    return NamedTuple{Tuple(map(Symbol, nms))}(Tuple(getarray(getcolumn(cols, nms[i])) for i = 1:length(nms)))
+end
 
 # unknown schema case
 columntable(::Nothing, cols) =

test/runtests.jl

@@ -1040,3 +1040,14 @@ end
     @test DataAPI.nrow(Tables.dictrowtable([(a=1, b=2), (a=3, b=4), (a=5, b=6)])) == 3
     @test DataAPI.ncol(Tables.dictrowtable([(a=1, b=2), (a=3, b=4), (a=5, b=6)])) == 2
 end
+
+@testset "#357" begin
+    dct = Tables.dictcolumntable((a=1:3, b=4.0:6.0, c=["7", "8", "9"]))
+    sch = Tables.schema(dct)
+    sch = Tables.Schema(sch.names, sch.types, stored=true)
+    dct = Tables.DictColumnTable(sch, getfield(dct, :values))
+    nt = Tables.columntable(dct)
+    @test nt.a == [1, 2, 3]
+    @test nt.b == [4.0, 5.0, 6.0]
+    @test nt.c == ["7", "8", "9"]
+end