Skip to content

test: add it for produce & scan log for all supported datatypes #205

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

Open
linguoxuan wants to merge 2 commits into
base: main
Choose a base branch
from
+525 −39
Open

test: add it for produce & scan log for all supported datatypes #205

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
8bea16b
test: add it for produce & scan log for all supported datatypes
Jan 23, 2026
a6f5e1f
support timestamps before the Unix epoch.
Jan 24, 2026
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
173 changes: 134 additions & 39 deletions crates/fluss/src/row/column.rs
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -17,8 +17,11 @@

use crate::row::InternalRow;
use arrow::array::{
Array, AsArray, BinaryArray, Decimal128Array, FixedSizeBinaryArray, Float32Array, Float64Array,
Int8Array, Int16Array, Int32Array, Int64Array, RecordBatch, StringArray,
Array, AsArray, BinaryArray, Date32Array, Decimal128Array, FixedSizeBinaryArray, Float32Array,
Float64Array, Int8Array, Int16Array, Int32Array, Int64Array, RecordBatch, StringArray,
Time32MillisecondArray, Time32SecondArray, Time64MicrosecondArray, Time64NanosecondArray,
TimestampMicrosecondArray, TimestampMillisecondArray, TimestampNanosecondArray,
TimestampSecondArray,
};
use arrow::datatypes::{DataType as ArrowDataType, TimeUnit};
use std::sync::Arc;
Expand Down Expand Up @@ -67,35 +70,110 @@ impl ColumnarRow {
) -> T {
let schema = self.record_batch.schema();
let arrow_field = schema.field(pos);
let value = self.get_long(pos);
let column = self.record_batch.column(pos);

match arrow_field.data_type() {
ArrowDataType::Timestamp(time_unit, _) => {
// Convert based on Arrow TimeUnit
let (millis, nanos) = match time_unit {
TimeUnit::Second => (value * 1000, 0),
TimeUnit::Millisecond => (value, 0),
TimeUnit::Microsecond => {
let millis = value / 1000;
let nanos = ((value % 1000) * 1000) as i32;
(millis, nanos)
}
TimeUnit::Nanosecond => {
let millis = value / 1_000_000;
let nanos = (value % 1_000_000) as i32;
(millis, nanos)
}
};

if nanos == 0 {
construct_compact(millis)
} else {
// nanos is guaranteed to be in valid range [0, 999_999] by arithmetic
construct_with_nanos(millis, nanos)
.expect("nanos in valid range by construction")
// Read value based on the actual Arrow timestamp type
let value = match arrow_field.data_type() {
ArrowDataType::Timestamp(TimeUnit::Second, _) => column
.as_any()
.downcast_ref::<TimestampSecondArray>()
.expect("Expected TimestampSecondArray")
.value(self.row_id),
ArrowDataType::Timestamp(TimeUnit::Millisecond, _) => column
.as_any()
.downcast_ref::<TimestampMillisecondArray>()
.expect("Expected TimestampMillisecondArray")
.value(self.row_id),
ArrowDataType::Timestamp(TimeUnit::Microsecond, _) => column
.as_any()
.downcast_ref::<TimestampMicrosecondArray>()
.expect("Expected TimestampMicrosecondArray")
.value(self.row_id),
ArrowDataType::Timestamp(TimeUnit::Nanosecond, _) => column
.as_any()
.downcast_ref::<TimestampNanosecondArray>()
.expect("Expected TimestampNanosecondArray")
.value(self.row_id),
other => panic!("Expected Timestamp column at position {pos}, got {other:?}"),
};

// Convert based on Arrow TimeUnit
let (millis, nanos) = match arrow_field.data_type() {
ArrowDataType::Timestamp(time_unit, _) => match time_unit {
TimeUnit::Second => (value * 1000, 0),
TimeUnit::Millisecond => (value, 0),
TimeUnit::Microsecond => {
// Use Euclidean division so that nanos is always non-negative,
// even for timestamps before the Unix epoch.
let millis = value.div_euclid(1000);
let nanos = (value.rem_euclid(1000) * 1000) as i32;
(millis, nanos)
}
TimeUnit::Nanosecond => {
// Use Euclidean division so that nanos is always in [0, 999_999].
let millis = value.div_euclid(1_000_000);
let nanos = value.rem_euclid(1_000_000) as i32;
(millis, nanos)
}
},
_ => unreachable!(),
};

if nanos == 0 {
construct_compact(millis)
} else {
// nanos is guaranteed to be in valid range [0, 999_999] by arithmetic
construct_with_nanos(millis, nanos).expect("nanos in valid range by construction")
}
}

/// Read date value from Arrow Date32Array
fn read_date_from_arrow(&self, pos: usize) -> i32 {
self.record_batch
.column(pos)
.as_any()
.downcast_ref::<Date32Array>()
.expect("Expected Date32Array")
.value(self.row_id)
}

/// Read time value from Arrow Time32/Time64 arrays, converting to milliseconds
fn read_time_from_arrow(&self, pos: usize) -> i32 {
let schema = self.record_batch.schema();
let arrow_field = schema.field(pos);
let column = self.record_batch.column(pos);

match arrow_field.data_type() {
ArrowDataType::Time32(TimeUnit::Second) => {
let value = column
.as_any()
.downcast_ref::<Time32SecondArray>()
.expect("Expected Time32SecondArray")
.value(self.row_id);
value * 1000 // Convert seconds to milliseconds
}
other => panic!("Expected Timestamp column at position {pos}, got {other:?}"),
ArrowDataType::Time32(TimeUnit::Millisecond) => column
.as_any()
.downcast_ref::<Time32MillisecondArray>()
.expect("Expected Time32MillisecondArray")
.value(self.row_id),
ArrowDataType::Time64(TimeUnit::Microsecond) => {
let value = column
.as_any()
.downcast_ref::<Time64MicrosecondArray>()
.expect("Expected Time64MicrosecondArray")
.value(self.row_id);
(value / 1000) as i32 // Convert microseconds to milliseconds
}
ArrowDataType::Time64(TimeUnit::Nanosecond) => {
let value = column
.as_any()
.downcast_ref::<Time64NanosecondArray>()
.expect("Expected Time64NanosecondArray")
.value(self.row_id);
(value / 1_000_000) as i32 // Convert nanoseconds to milliseconds
}
other => panic!("Expected Time column at position {pos}, got {other:?}"),
}
}
}
Expand Down Expand Up @@ -223,11 +301,11 @@ impl InternalRow for ColumnarRow {
}

fn get_date(&self, pos: usize) -> crate::row::datum::Date {
crate::row::datum::Date::new(self.get_int(pos))
crate::row::datum::Date::new(self.read_date_from_arrow(pos))
}

fn get_time(&self, pos: usize) -> crate::row::datum::Time {
crate::row::datum::Time::new(self.get_int(pos))
crate::row::datum::Time::new(self.read_time_from_arrow(pos))
}

fn get_timestamp_ntz(&self, pos: usize, precision: u32) -> crate::row::datum::TimestampNtz {
Expand All @@ -253,16 +331,33 @@ impl InternalRow for ColumnarRow {
}

fn get_char(&self, pos: usize, _length: usize) -> &str {
let array = self
.record_batch
.column(pos)
.as_any()
.downcast_ref::<FixedSizeBinaryArray>()
.expect("Expected fixed-size binary array for char type");
let column = self.record_batch.column(pos);
let schema = self.record_batch.schema();
let arrow_field = schema.field(pos);

let bytes = array.value(self.row_id);
// don't check length, following java client
std::str::from_utf8(bytes).expect("Invalid UTF-8 in char field")
match arrow_field.data_type() {
ArrowDataType::FixedSizeBinary(_) => {
// KV table format: char stored as FixedSizeBinary
let array = column
.as_any()
.downcast_ref::<FixedSizeBinaryArray>()
.expect("Expected fixed-size binary array for char type");
let bytes = array.value(self.row_id);
// don't check length, following java client
std::str::from_utf8(bytes).expect("Invalid UTF-8 in char field")
}
ArrowDataType::Utf8 => {
// Log table format: char stored as Utf8
column
.as_any()
.downcast_ref::<StringArray>()
.expect("Expected String array for char type")
.value(self.row_id)
}
other => panic!(
"Expected FixedSizeBinary or Utf8 for char type at position {pos}, got {other:?}"
),
}
}

fn get_string(&self, pos: usize) -> &str {
Expand Down
Loading
Loading