[jira] [Commented] (ARROW-5153) [Rust] [Parquet] Use IntoIter trait for write_batch/write_mini_batch

["Andrew Lamb (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/ARROW-5153?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17332027#comment-17332027 ] 

Andrew Lamb commented on ARROW-5153:
------------------------------------

Migrated to github: https://github.com/apache/arrow-rs/issues/43

> [Rust] [Parquet] Use IntoIter trait for write_batch/write_mini_batch
> --------------------------------------------------------------------
>
>                 Key: ARROW-5153
>                 URL: https://issues.apache.org/jira/browse/ARROW-5153
>             Project: Apache Arrow
>          Issue Type: Improvement
>          Components: Rust
>            Reporter: Xavier Lange
>            Priority: Major
>
> Writing data to a parquet file requires a lot of copying and intermediate Vec creation. Take a record struct like:
> {{struct MyData {}}{{  name: String,}}{{  address: Option<String>}}{{}}}
> Over the course of working sets of this data, you'll have the bulk data Vec<MyData>,  the names column in a Vec<&String>, the address column in a Vec<Option<String>>. This puts extra memory pressure on the system, at the minimum we have to allocate a Vec the same size as the bulk data even if we are using references.
> What I'm proposing is to use an IntoIter style. This will maintain backward compat as a slice automatically implements IntoIter. Where ColumnWriterImpl#write_batch goes from "values: &[T::T]"to values "values: IntoIter<Item=T::T>". Then you can do things like
> {{  write_batch(bulk.iter().map(|x| x.name), None, None)}}{{  write_batch(bulk.iter().map(|x| x.address), Some(bulk.iter().map(|x| x.is_some())), None)}}
> and you can see there's no need for an intermediate Vec, so no short-term allocations to write out the data.
> I am writing data with many columns and I think this would really help to speed things up.



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