vegabook
I’m loving Adbc which is super fast! I’m creating Explorer DataFrames like this:
Adbc.Connection.query!(Suprabonds.AdbcConn, "Select * from trace")
|> Adbc.Result.to_map
|> Explorer.DataFrame.new
Just wondering if this is the best way? For large numbers of rows the to_map intermediate stage is taking quite a long time.
Does Explorer use Apache Arrow under the hood as well? If so can we go directly from an Adbc.Result to an Explorer.DataFrame without the to_map stage?
What’s the fastest way of getting from Adbc.Result to Explorer.DataFrame?
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josevalim
Use
Explorer.DataFrame.from_query!(Suprabonds.AdbcConn, "Select * from trace")instead. It will allocate the underling arrow data only once and pass it directly to Explorer.vegabook
Needs an empty list for options but otherwise works great. Thanks.
aspett
When I use Explorer.DataFrame.from_query with a query that does a join across two tables that have same named columns (e.g. post.id, author.id), Explorer is unhappy because it sees two columns with the same name even though they’re from different tables.
Is there anything smart I can do to solve that? Ideally want to avoid aliasing every column.
Perhaps if the resulting DF had columns in format
.
table.colthat would helpe.g.
SELECT p1.id, p1.title, a1.id, a1.name FROM posts p1 JOIN authors ON p1.author_id = a.idmight return