Note: migrated from original JIRA: https://issues.apache.org/jira/browse/ARROW-5153
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}}{{}}}
Over the course of working sets of this data, you'll have the bulk data Vec, the names column in a Vec<&String>, the address column in a Vec<Option>. 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.
Note: migrated from original JIRA: https://issues.apache.org/jira/browse/ARROW-5153
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}}{{}}}
Over the course of working sets of this data, you'll have the bulk data Vec, the names column in a Vec<&String>, the address column in a Vec<Option>. 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.