Skip to content

feat: Add support for Timestamp data types in data page statistics. #11123

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Jun 26, 2024
Merged

feat: Add support for Timestamp data types in data page statistics. #11123

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
c63875b
feat: Add support for Timestamp data types in data page statistics.
Jun 25, 2024
d29737c
Simplify array creation using with_timezone_opt.
Jun 26, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
38 changes: 13 additions & 25 deletions datafusion/core/src/datasource/physical_plan/parquet/statistics.rs
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -354,32 +354,11 @@ macro_rules! get_statistics {
))),
DataType::Timestamp(unit, timezone) =>{
let iter = [<$stat_type_prefix Int64StatsIterator>]::new($iterator).map(|x| x.copied());

Ok(match unit {
TimeUnit::Second => {
Arc::new(match timezone {
Some(tz) => TimestampSecondArray::from_iter(iter).with_timezone(tz.clone()),
None => TimestampSecondArray::from_iter(iter),
})
}
TimeUnit::Millisecond => {
Arc::new(match timezone {
Some(tz) => TimestampMillisecondArray::from_iter(iter).with_timezone(tz.clone()),
None => TimestampMillisecondArray::from_iter(iter),
})
}
TimeUnit::Microsecond => {
Arc::new(match timezone {
Some(tz) => TimestampMicrosecondArray::from_iter(iter).with_timezone(tz.clone()),
None => TimestampMicrosecondArray::from_iter(iter),
})
}
TimeUnit::Nanosecond => {
Arc::new(match timezone {
Some(tz) => TimestampNanosecondArray::from_iter(iter).with_timezone(tz.clone()),
None => TimestampNanosecondArray::from_iter(iter),
})
}
Copy link
Contributor Author

@efredine efredine Jun 26, 2024

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Applied simplification using with_timezone_opt to the get_statistics macro as well.

TimeUnit::Second => Arc::new(TimestampSecondArray::from_iter(iter).with_timezone_opt(timezone.clone())),
TimeUnit::Millisecond => Arc::new(TimestampMillisecondArray::from_iter(iter).with_timezone_opt(timezone.clone())),
TimeUnit::Microsecond => Arc::new(TimestampMicrosecondArray::from_iter(iter).with_timezone_opt(timezone.clone())),
TimeUnit::Nanosecond => Arc::new(TimestampNanosecondArray::from_iter(iter).with_timezone_opt(timezone.clone())),
Comment on lines +358 to +361
Copy link
Member

@Weijun-H Weijun-H Jun 26, 2024

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

👍

})
},
DataType::Time32(unit) => {
Expand Down Expand Up @@ -713,6 +692,15 @@ macro_rules! get_data_page_statistics {
)),
Some(DataType::Float32) => Ok(Arc::new(Float32Array::from_iter([<$stat_type_prefix Float32DataPageStatsIterator>]::new($iterator).flatten()))),
Some(DataType::Float64) => Ok(Arc::new(Float64Array::from_iter([<$stat_type_prefix Float64DataPageStatsIterator>]::new($iterator).flatten()))),
Copy link
Contributor Author

@efredine efredine Jun 26, 2024

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Should this repeated code be extracted? I assume it would need to be put into a macro?

Copy link
Contributor

@alamb alamb Jun 26, 2024

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Which repeated code do you mean?

If you wnated to more fully macroize this table and avoid repetition that sounds like a great idea to me (though perhaps as a follow on PR)

Copy link
Contributor Author

@efredine efredine Jun 26, 2024

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

The four lines in the match unit expression are exactly the same in both macros.

Some(DataType::Timestamp(unit, timezone)) => {
let iter = [<$stat_type_prefix Int64DataPageStatsIterator>]::new($iterator).flatten();
Ok(match unit {
TimeUnit::Second => Arc::new(TimestampSecondArray::from_iter(iter).with_timezone_opt(timezone.clone())),
TimeUnit::Millisecond => Arc::new(TimestampMillisecondArray::from_iter(iter).with_timezone_opt(timezone.clone())),
TimeUnit::Microsecond => Arc::new(TimestampMicrosecondArray::from_iter(iter).with_timezone_opt(timezone.clone())),
TimeUnit::Nanosecond => Arc::new(TimestampNanosecondArray::from_iter(iter).with_timezone_opt(timezone.clone())),
})
},
_ => unimplemented!()
}
}
Expand Down
32 changes: 16 additions & 16 deletions datafusion/core/tests/parquet/arrow_statistics.rs
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -747,7 +747,7 @@ async fn test_timestamp() {
// row counts are [5, 5, 5, 5]
expected_row_counts: Some(UInt64Array::from(vec![5, 5, 5, 5])),
column_name: "nanos",
check: Check::RowGroup,
check: Check::Both,
}
.run();

Expand Down Expand Up @@ -776,7 +776,7 @@ async fn test_timestamp() {
// row counts are [5, 5, 5, 5]
expected_row_counts: Some(UInt64Array::from(vec![5, 5, 5, 5])),
column_name: "nanos_timezoned",
check: Check::RowGroup,
check: Check::Both,
}
.run();

Expand All @@ -798,7 +798,7 @@ async fn test_timestamp() {
expected_null_counts: UInt64Array::from(vec![1, 1, 1, 1]),
expected_row_counts: Some(UInt64Array::from(vec![5, 5, 5, 5])),
column_name: "micros",
check: Check::RowGroup,
check: Check::Both,
}
.run();

Expand Down Expand Up @@ -827,7 +827,7 @@ async fn test_timestamp() {
// row counts are [5, 5, 5, 5]
expected_row_counts: Some(UInt64Array::from(vec![5, 5, 5, 5])),
column_name: "micros_timezoned",
check: Check::RowGroup,
check: Check::Both,
}
.run();

Expand All @@ -849,7 +849,7 @@ async fn test_timestamp() {
expected_null_counts: UInt64Array::from(vec![1, 1, 1, 1]),
expected_row_counts: Some(UInt64Array::from(vec![5, 5, 5, 5])),
column_name: "millis",
check: Check::RowGroup,
check: Check::Both,
}
.run();

Expand Down Expand Up @@ -878,7 +878,7 @@ async fn test_timestamp() {
// row counts are [5, 5, 5, 5]
expected_row_counts: Some(UInt64Array::from(vec![5, 5, 5, 5])),
column_name: "millis_timezoned",
check: Check::RowGroup,
check: Check::Both,
}
.run();

Expand All @@ -900,7 +900,7 @@ async fn test_timestamp() {
expected_null_counts: UInt64Array::from(vec![1, 1, 1, 1]),
expected_row_counts: Some(UInt64Array::from(vec![5, 5, 5, 5])),
column_name: "seconds",
check: Check::RowGroup,
check: Check::Both,
}
.run();

Expand Down Expand Up @@ -929,7 +929,7 @@ async fn test_timestamp() {
// row counts are [5, 5, 5, 5]
expected_row_counts: Some(UInt64Array::from(vec![5, 5, 5, 5])),
column_name: "seconds_timezoned",
check: Check::RowGroup,
check: Check::Both,
}
.run();
}
Expand Down Expand Up @@ -975,7 +975,7 @@ async fn test_timestamp_diff_rg_sizes() {
// row counts are [8, 8, 4]
expected_row_counts: Some(UInt64Array::from(vec![8, 8, 4])),
column_name: "nanos",
check: Check::RowGroup,
check: Check::Both,
}
.run();

Expand All @@ -1002,7 +1002,7 @@ async fn test_timestamp_diff_rg_sizes() {
// row counts are [8, 8, 4]
expected_row_counts: Some(UInt64Array::from(vec![8, 8, 4])),
column_name: "nanos_timezoned",
check: Check::RowGroup,
check: Check::Both,
}
.run();

Expand All @@ -1022,7 +1022,7 @@ async fn test_timestamp_diff_rg_sizes() {
expected_null_counts: UInt64Array::from(vec![1, 2, 1]),
expected_row_counts: Some(UInt64Array::from(vec![8, 8, 4])),
column_name: "micros",
check: Check::RowGroup,
check: Check::Both,
}
.run();

Expand All @@ -1049,7 +1049,7 @@ async fn test_timestamp_diff_rg_sizes() {
// row counts are [8, 8, 4]
expected_row_counts: Some(UInt64Array::from(vec![8, 8, 4])),
column_name: "micros_timezoned",
check: Check::RowGroup,
check: Check::Both,
}
.run();

Expand All @@ -1069,7 +1069,7 @@ async fn test_timestamp_diff_rg_sizes() {
expected_null_counts: UInt64Array::from(vec![1, 2, 1]),
expected_row_counts: Some(UInt64Array::from(vec![8, 8, 4])),
column_name: "millis",
check: Check::RowGroup,
check: Check::Both,
}
.run();

Expand All @@ -1096,7 +1096,7 @@ async fn test_timestamp_diff_rg_sizes() {
// row counts are [8, 8, 4]
expected_row_counts: Some(UInt64Array::from(vec![8, 8, 4])),
column_name: "millis_timezoned",
check: Check::RowGroup,
check: Check::Both,
}
.run();

Expand All @@ -1116,7 +1116,7 @@ async fn test_timestamp_diff_rg_sizes() {
expected_null_counts: UInt64Array::from(vec![1, 2, 1]),
expected_row_counts: Some(UInt64Array::from(vec![8, 8, 4])),
column_name: "seconds",
check: Check::RowGroup,
check: Check::Both,
}
.run();

Expand All @@ -1143,7 +1143,7 @@ async fn test_timestamp_diff_rg_sizes() {
// row counts are [8, 8, 4]
expected_row_counts: Some(UInt64Array::from(vec![8, 8, 4])),
column_name: "seconds_timezoned",
check: Check::RowGroup,
check: Check::Both,
}
.run();
}
Expand Down