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Posted to github@arrow.apache.org by GitBox <gi...@apache.org> on 2022/11/28 17:31:35 UTC
[GitHub] [arrow-rs] tustvold opened a new pull request, #3212: Support StructArray in Row Format (#3159)
tustvold opened a new pull request, #3212:
URL: https://github.com/apache/arrow-rs/pull/3212
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Part of #3159
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[GitHub] [arrow-rs] ursabot commented on pull request #3212: Support StructArray in Row Format (#3159)
Posted by GitBox <gi...@apache.org>.
ursabot commented on PR #3212:
URL: https://github.com/apache/arrow-rs/pull/3212#issuecomment-1330440364
Benchmark runs are scheduled for baseline = b2bfe9c5fb4f09ef9f7b1fd67ec767f6d42cf8d8 and contender = 733d32e90b67bbc62bcff6fc4aa1873d43d4e686. 733d32e90b67bbc62bcff6fc4aa1873d43d4e686 is a master commit associated with this PR. Results will be available as each benchmark for each run completes.
Conbench compare runs links:
[Skipped :warning: Benchmarking of arrow-rs-commits is not supported on ec2-t3-xlarge-us-east-2] [ec2-t3-xlarge-us-east-2](https://conbench.ursa.dev/compare/runs/93ef16f285f74b6e956dcc3fda9033d8...90d19da96c394af5a6231f973a78d021/)
[Skipped :warning: Benchmarking of arrow-rs-commits is not supported on test-mac-arm] [test-mac-arm](https://conbench.ursa.dev/compare/runs/ffd7e2533c6c400388d9825e88b3999a...1e6cb17d62ce4b99ba8d6f6e07efa72a/)
[Skipped :warning: Benchmarking of arrow-rs-commits is not supported on ursa-i9-9960x] [ursa-i9-9960x](https://conbench.ursa.dev/compare/runs/4917e89e25294c2db56486f84b228fcf...32dbbdaf952246c3bb8a56cd5da446f7/)
[Skipped :warning: Benchmarking of arrow-rs-commits is not supported on ursa-thinkcentre-m75q] [ursa-thinkcentre-m75q](https://conbench.ursa.dev/compare/runs/009dcbf0e1da40b7975e45b4c88ffce8...685225b2370948719ad516153439a557/)
Buildkite builds:
Supported benchmarks:
ec2-t3-xlarge-us-east-2: Supported benchmark langs: Python, R. Runs only benchmarks with cloud = True
test-mac-arm: Supported benchmark langs: C++, Python, R
ursa-i9-9960x: Supported benchmark langs: Python, R, JavaScript
ursa-thinkcentre-m75q: Supported benchmark langs: C++, Java
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[GitHub] [arrow-rs] tustvold merged pull request #3212: Support StructArray in Row Format (#3159)
Posted by GitBox <gi...@apache.org>.
tustvold merged PR #3212:
URL: https://github.com/apache/arrow-rs/pull/3212
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[GitHub] [arrow-rs] tustvold commented on a diff in pull request #3212: Support StructArray in Row Format (#3159)
Posted by GitBox <gi...@apache.org>.
tustvold commented on code in PR #3212:
URL: https://github.com/apache/arrow-rs/pull/3212#discussion_r1033978965
##########
arrow/src/row/mod.rs:
##########
@@ -872,35 +980,59 @@ fn encode_column(
out: &mut Rows,
column: &ArrayRef,
opts: SortOptions,
- dictionary: Option<&[Option<&[u8]>]>,
+ encoder: &Encoder<'_>,
) {
- downcast_primitive_array! {
- column => fixed::encode(out, column, opts),
- DataType::Null => {}
- DataType::Boolean => fixed::encode(out, as_boolean_array(column), opts),
- DataType::Binary => {
- variable::encode(out, as_generic_binary_array::<i32>(column).iter(), opts)
+ match encoder {
+ Encoder::Stateless => {
+ downcast_primitive_array! {
+ column => fixed::encode(out, column, opts),
+ DataType::Null => {}
+ DataType::Boolean => fixed::encode(out, as_boolean_array(column), opts),
+ DataType::Binary => {
+ variable::encode(out, as_generic_binary_array::<i32>(column).iter(), opts)
+ }
+ DataType::LargeBinary => {
+ variable::encode(out, as_generic_binary_array::<i64>(column).iter(), opts)
+ }
+ DataType::Utf8 => variable::encode(
+ out,
+ as_string_array(column).iter().map(|x| x.map(|x| x.as_bytes())),
+ opts,
+ ),
+ DataType::LargeUtf8 => variable::encode(
+ out,
+ as_largestring_array(column)
+ .iter()
+ .map(|x| x.map(|x| x.as_bytes())),
+ opts,
+ ),
+ _ => unreachable!(),
+ }
}
- DataType::LargeBinary => {
- variable::encode(out, as_generic_binary_array::<i64>(column).iter(), opts)
+ Encoder::Dictionary(dict) => {
+ downcast_dictionary_array! {
+ column => encode_dictionary(out, column, dict, opts),
+ _ => unreachable!()
+ }
}
- DataType::Utf8 => variable::encode(
- out,
- as_string_array(column).iter().map(|x| x.map(|x| x.as_bytes())),
- opts,
- ),
- DataType::LargeUtf8 => variable::encode(
- out,
- as_largestring_array(column)
- .iter()
- .map(|x| x.map(|x| x.as_bytes())),
- opts,
- ),
- DataType::Dictionary(_, _) => downcast_dictionary_array! {
- column => encode_dictionary(out, column, dictionary.unwrap(), opts),
- _ => unreachable!()
+ Encoder::Struct(rows, null) => {
+ let array = as_struct_array(column.as_ref());
+ let null_sentinel = null_sentinel(opts);
+ out.offsets
+ .iter_mut()
+ .skip(1)
+ .enumerate()
+ .for_each(|(idx, offset)| {
+ let (row, sentinel) = match array.is_valid(idx) {
+ true => (rows.row(idx), 0x01),
+ false => (*null, null_sentinel),
+ };
+ let end_offset = *offset + 1 + row.as_ref().len();
+ out.buffer[*offset] = sentinel;
+ out.buffer[*offset + 1..end_offset].copy_from_slice(row.as_ref());
Review Comment:
I'm working on adding a diagram to show this more clearly
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[GitHub] [arrow-rs] tustvold commented on a diff in pull request #3212: Support StructArray in Row Format (#3159)
Posted by GitBox <gi...@apache.org>.
tustvold commented on code in PR #3212:
URL: https://github.com/apache/arrow-rs/pull/3212#discussion_r1033972417
##########
arrow/src/row/mod.rs:
##########
@@ -872,35 +980,59 @@ fn encode_column(
out: &mut Rows,
column: &ArrayRef,
opts: SortOptions,
- dictionary: Option<&[Option<&[u8]>]>,
+ encoder: &Encoder<'_>,
) {
- downcast_primitive_array! {
- column => fixed::encode(out, column, opts),
- DataType::Null => {}
- DataType::Boolean => fixed::encode(out, as_boolean_array(column), opts),
- DataType::Binary => {
- variable::encode(out, as_generic_binary_array::<i32>(column).iter(), opts)
+ match encoder {
+ Encoder::Stateless => {
+ downcast_primitive_array! {
+ column => fixed::encode(out, column, opts),
+ DataType::Null => {}
+ DataType::Boolean => fixed::encode(out, as_boolean_array(column), opts),
+ DataType::Binary => {
+ variable::encode(out, as_generic_binary_array::<i32>(column).iter(), opts)
+ }
+ DataType::LargeBinary => {
+ variable::encode(out, as_generic_binary_array::<i64>(column).iter(), opts)
+ }
+ DataType::Utf8 => variable::encode(
+ out,
+ as_string_array(column).iter().map(|x| x.map(|x| x.as_bytes())),
+ opts,
+ ),
+ DataType::LargeUtf8 => variable::encode(
+ out,
+ as_largestring_array(column)
+ .iter()
+ .map(|x| x.map(|x| x.as_bytes())),
+ opts,
+ ),
+ _ => unreachable!(),
+ }
}
- DataType::LargeBinary => {
- variable::encode(out, as_generic_binary_array::<i64>(column).iter(), opts)
+ Encoder::Dictionary(dict) => {
+ downcast_dictionary_array! {
+ column => encode_dictionary(out, column, dict, opts),
+ _ => unreachable!()
+ }
}
- DataType::Utf8 => variable::encode(
- out,
- as_string_array(column).iter().map(|x| x.map(|x| x.as_bytes())),
- opts,
- ),
- DataType::LargeUtf8 => variable::encode(
- out,
- as_largestring_array(column)
- .iter()
- .map(|x| x.map(|x| x.as_bytes())),
- opts,
- ),
- DataType::Dictionary(_, _) => downcast_dictionary_array! {
- column => encode_dictionary(out, column, dictionary.unwrap(), opts),
- _ => unreachable!()
+ Encoder::Struct(rows, null) => {
+ let array = as_struct_array(column.as_ref());
+ let null_sentinel = null_sentinel(opts);
+ out.offsets
+ .iter_mut()
+ .skip(1)
+ .enumerate()
+ .for_each(|(idx, offset)| {
+ let (row, sentinel) = match array.is_valid(idx) {
+ true => (rows.row(idx), 0x01),
+ false => (*null, null_sentinel),
+ };
+ let end_offset = *offset + 1 + row.as_ref().len();
+ out.buffer[*offset] = sentinel;
+ out.buffer[*offset + 1..end_offset].copy_from_slice(row.as_ref());
Review Comment:
> aren't accidentally compared with the contents of another field
The same way as normal, effectively this approach flattens the schema in a depth first manner. Lists will need encoding in this way, that's still to come :smile:
> Also I wonder why not encode the struct array directly into out rather than copy it from another Rows?
Nulls, we need to not encode the values if the `StructArray` contains a null. Otherwise you would potentially establish an ordering between nulls, based on the "masked" values.
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[GitHub] [arrow-rs] tustvold commented on a diff in pull request #3212: Support StructArray in Row Format (#3159)
Posted by GitBox <gi...@apache.org>.
tustvold commented on code in PR #3212:
URL: https://github.com/apache/arrow-rs/pull/3212#discussion_r1033974877
##########
arrow/src/row/mod.rs:
##########
@@ -307,6 +308,13 @@ mod variable;
///
/// Input Row Format
/// ```
+///
+/// ## Struct Encoding
+///
+/// A null is encoded as a `0_u8`.
Review Comment:
This is consistent with the nomenclature for the other encodings, with the inverting for null ordering discussed in a later heading
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[GitHub] [arrow-rs] alamb commented on a diff in pull request #3212: Support StructArray in Row Format (#3159)
Posted by GitBox <gi...@apache.org>.
alamb commented on code in PR #3212:
URL: https://github.com/apache/arrow-rs/pull/3212#discussion_r1033987056
##########
arrow/src/row/mod.rs:
##########
@@ -872,35 +980,59 @@ fn encode_column(
out: &mut Rows,
column: &ArrayRef,
opts: SortOptions,
- dictionary: Option<&[Option<&[u8]>]>,
+ encoder: &Encoder<'_>,
) {
- downcast_primitive_array! {
- column => fixed::encode(out, column, opts),
- DataType::Null => {}
- DataType::Boolean => fixed::encode(out, as_boolean_array(column), opts),
- DataType::Binary => {
- variable::encode(out, as_generic_binary_array::<i32>(column).iter(), opts)
+ match encoder {
+ Encoder::Stateless => {
+ downcast_primitive_array! {
+ column => fixed::encode(out, column, opts),
+ DataType::Null => {}
+ DataType::Boolean => fixed::encode(out, as_boolean_array(column), opts),
+ DataType::Binary => {
+ variable::encode(out, as_generic_binary_array::<i32>(column).iter(), opts)
+ }
+ DataType::LargeBinary => {
+ variable::encode(out, as_generic_binary_array::<i64>(column).iter(), opts)
+ }
+ DataType::Utf8 => variable::encode(
+ out,
+ as_string_array(column).iter().map(|x| x.map(|x| x.as_bytes())),
+ opts,
+ ),
+ DataType::LargeUtf8 => variable::encode(
+ out,
+ as_largestring_array(column)
+ .iter()
+ .map(|x| x.map(|x| x.as_bytes())),
+ opts,
+ ),
+ _ => unreachable!(),
+ }
}
- DataType::LargeBinary => {
- variable::encode(out, as_generic_binary_array::<i64>(column).iter(), opts)
+ Encoder::Dictionary(dict) => {
+ downcast_dictionary_array! {
+ column => encode_dictionary(out, column, dict, opts),
+ _ => unreachable!()
+ }
}
- DataType::Utf8 => variable::encode(
- out,
- as_string_array(column).iter().map(|x| x.map(|x| x.as_bytes())),
- opts,
- ),
- DataType::LargeUtf8 => variable::encode(
- out,
- as_largestring_array(column)
- .iter()
- .map(|x| x.map(|x| x.as_bytes())),
- opts,
- ),
- DataType::Dictionary(_, _) => downcast_dictionary_array! {
- column => encode_dictionary(out, column, dictionary.unwrap(), opts),
- _ => unreachable!()
+ Encoder::Struct(rows, null) => {
+ let array = as_struct_array(column.as_ref());
+ let null_sentinel = null_sentinel(opts);
+ out.offsets
+ .iter_mut()
+ .skip(1)
+ .enumerate()
+ .for_each(|(idx, offset)| {
+ let (row, sentinel) = match array.is_valid(idx) {
+ true => (rows.row(idx), 0x01),
+ false => (*null, null_sentinel),
+ };
+ let end_offset = *offset + 1 + row.as_ref().len();
+ out.buffer[*offset] = sentinel;
+ out.buffer[*offset + 1..end_offset].copy_from_slice(row.as_ref());
Review Comment:
> Nulls, we need to not encode the values if the StructArray contains a null. Otherwise you would potentially establish an ordering between nulls, based on the "masked" values.
This explanation makes sense -- I suggest you put it as a comment somewhere as it was not obvious to me while reading this PR.
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[GitHub] [arrow-rs] tustvold commented on a diff in pull request #3212: Support StructArray in Row Format (#3159)
Posted by GitBox <gi...@apache.org>.
tustvold commented on code in PR #3212:
URL: https://github.com/apache/arrow-rs/pull/3212#discussion_r1033973275
##########
arrow/src/row/mod.rs:
##########
@@ -332,8 +340,99 @@ mod variable;
#[derive(Debug)]
pub struct RowConverter {
fields: Arc<[SortField]>,
- /// interning state for column `i`, if column`i` is a dictionary
- interners: Vec<Option<Box<OrderPreservingInterner>>>,
+ /// State for codecs
+ codecs: Vec<Codec>,
+}
+
+#[derive(Debug)]
+enum Codec {
+ /// No additional codec state is necessary
+ Stateless,
+ // The interner used to encode dictionary values
+ Dictionary(OrderPreservingInterner),
+ // A row converter for the child fields
+ // and the encoding of a row containing only nulls
+ Struct(RowConverter, OwnedRow),
+}
+
+impl Codec {
+ fn new(sort_field: &SortField) -> Result<Self> {
+ match &sort_field.data_type {
+ DataType::Dictionary(_, _) => Ok(Self::Dictionary(Default::default())),
+ d if !d.is_nested() => Ok(Self::Stateless),
+ DataType::Struct(f) => {
+ let sort_fields = f
+ .iter()
+ .map(|x| {
+ SortField::new_with_options(
+ x.data_type().clone(),
+ sort_field.options,
+ )
+ })
+ .collect();
+
+ let mut converter = RowConverter::new(sort_fields)?;
+ let nulls: Vec<_> =
+ f.iter().map(|x| new_null_array(x.data_type(), 1)).collect();
+
+ let nulls = converter.convert_columns(&nulls)?;
+ let owned = OwnedRow {
+ data: nulls.buffer,
+ config: nulls.config,
+ };
+
+ Ok(Self::Struct(converter, owned))
+ }
+ _ => Err(ArrowError::NotYetImplemented(format!(
+ "not yet implemented: {:?}",
+ sort_field.data_type
+ ))),
+ }
+ }
+
+ fn encoder(&mut self, array: &dyn Array) -> Result<Encoder<'_>> {
+ match self {
+ Codec::Stateless => Ok(Encoder::Stateless),
+ Codec::Dictionary(interner) => {
+ let values = downcast_dictionary_array! {
+ array => array.values(),
+ _ => unreachable!()
+ };
+
+ let mapping = compute_dictionary_mapping(interner, values)
+ .into_iter()
+ .map(|maybe_interned| {
+ maybe_interned.map(|interned| interner.normalized_key(interned))
+ })
+ .collect();
+
+ Ok(Encoder::Dictionary(mapping))
+ }
+ Codec::Struct(converter, null) => {
+ let v = as_struct_array(array);
+ let rows = converter.convert_columns(v.columns())?;
Review Comment:
Yup - we just flatten the schema, so a `Struct{Int32, Int32}, Struct{Float32}` is encoded similarly to `Int32,Int32,Float32` albeit with additional nullability for the structs
##########
arrow/src/row/mod.rs:
##########
@@ -783,54 +881,64 @@ fn null_sentinel(options: SortOptions) -> u8 {
}
/// Computes the length of each encoded [`Rows`] and returns an empty [`Rows`]
-fn new_empty_rows(
- cols: &[ArrayRef],
- dictionaries: &[Option<Vec<Option<&[u8]>>>],
- config: RowConfig,
-) -> Rows {
+fn new_empty_rows(cols: &[ArrayRef], encoders: &[Encoder], config: RowConfig) -> Rows {
use fixed::FixedLengthEncoding;
let num_rows = cols.first().map(|x| x.len()).unwrap_or(0);
let mut lengths = vec![0; num_rows];
- for (array, dict) in cols.iter().zip(dictionaries) {
- downcast_primitive_array! {
- array => lengths.iter_mut().for_each(|x| *x += fixed::encoded_len(array)),
- DataType::Null => {},
- DataType::Boolean => lengths.iter_mut().for_each(|x| *x += bool::ENCODED_LEN),
- DataType::Binary => as_generic_binary_array::<i32>(array)
- .iter()
- .zip(lengths.iter_mut())
- .for_each(|(slice, length)| *length += variable::encoded_len(slice)),
- DataType::LargeBinary => as_generic_binary_array::<i64>(array)
- .iter()
- .zip(lengths.iter_mut())
- .for_each(|(slice, length)| *length += variable::encoded_len(slice)),
- DataType::Utf8 => as_string_array(array)
- .iter()
- .zip(lengths.iter_mut())
- .for_each(|(slice, length)| {
- *length += variable::encoded_len(slice.map(|x| x.as_bytes()))
- }),
- DataType::LargeUtf8 => as_largestring_array(array)
- .iter()
- .zip(lengths.iter_mut())
- .for_each(|(slice, length)| {
- *length += variable::encoded_len(slice.map(|x| x.as_bytes()))
- }),
- DataType::Dictionary(_, _) => downcast_dictionary_array! {
- array => {
- let dict = dict.as_ref().unwrap();
- for (v, length) in array.keys().iter().zip(lengths.iter_mut()) {
- match v.and_then(|v| dict[v as usize]) {
- Some(k) => *length += k.len() + 1,
- None => *length += 1,
+ for (array, encoder) in cols.iter().zip(encoders) {
+ match encoder {
+ Encoder::Stateless => {
+ downcast_primitive_array! {
+ array => lengths.iter_mut().for_each(|x| *x += fixed::encoded_len(array)),
+ DataType::Null => {},
+ DataType::Boolean => lengths.iter_mut().for_each(|x| *x += bool::ENCODED_LEN),
+ DataType::Binary => as_generic_binary_array::<i32>(array)
+ .iter()
+ .zip(lengths.iter_mut())
+ .for_each(|(slice, length)| *length += variable::encoded_len(slice)),
+ DataType::LargeBinary => as_generic_binary_array::<i64>(array)
+ .iter()
+ .zip(lengths.iter_mut())
+ .for_each(|(slice, length)| *length += variable::encoded_len(slice)),
+ DataType::Utf8 => as_string_array(array)
Review Comment:
Oh yes, most definitely :grin:
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[GitHub] [arrow-rs] alamb commented on a diff in pull request #3212: Support StructArray in Row Format (#3159)
Posted by GitBox <gi...@apache.org>.
alamb commented on code in PR #3212:
URL: https://github.com/apache/arrow-rs/pull/3212#discussion_r1033954065
##########
arrow/src/row/mod.rs:
##########
@@ -332,8 +340,99 @@ mod variable;
#[derive(Debug)]
pub struct RowConverter {
fields: Arc<[SortField]>,
- /// interning state for column `i`, if column`i` is a dictionary
- interners: Vec<Option<Box<OrderPreservingInterner>>>,
+ /// State for codecs
+ codecs: Vec<Codec>,
+}
+
+#[derive(Debug)]
+enum Codec {
Review Comment:
extracting this into Codec` is a nice generalization
##########
arrow/src/row/mod.rs:
##########
@@ -307,6 +308,13 @@ mod variable;
///
/// Input Row Format
/// ```
+///
+/// ## Struct Encoding
+///
+/// A null is encoded as a `0_u8`.
Review Comment:
```suggestion
/// A null is encoded as a `0_u8` or `0xFF_u8`
```
##########
arrow/src/row/mod.rs:
##########
@@ -332,8 +340,99 @@ mod variable;
#[derive(Debug)]
pub struct RowConverter {
fields: Arc<[SortField]>,
- /// interning state for column `i`, if column`i` is a dictionary
- interners: Vec<Option<Box<OrderPreservingInterner>>>,
+ /// State for codecs
+ codecs: Vec<Codec>,
+}
+
+#[derive(Debug)]
+enum Codec {
+ /// No additional codec state is necessary
+ Stateless,
+ // The interner used to encode dictionary values
+ Dictionary(OrderPreservingInterner),
+ // A row converter for the child fields
+ // and the encoding of a row containing only nulls
+ Struct(RowConverter, OwnedRow),
+}
+
+impl Codec {
+ fn new(sort_field: &SortField) -> Result<Self> {
+ match &sort_field.data_type {
+ DataType::Dictionary(_, _) => Ok(Self::Dictionary(Default::default())),
+ d if !d.is_nested() => Ok(Self::Stateless),
+ DataType::Struct(f) => {
+ let sort_fields = f
+ .iter()
+ .map(|x| {
+ SortField::new_with_options(
+ x.data_type().clone(),
+ sort_field.options,
+ )
+ })
+ .collect();
+
+ let mut converter = RowConverter::new(sort_fields)?;
+ let nulls: Vec<_> =
+ f.iter().map(|x| new_null_array(x.data_type(), 1)).collect();
+
+ let nulls = converter.convert_columns(&nulls)?;
+ let owned = OwnedRow {
+ data: nulls.buffer,
+ config: nulls.config,
+ };
+
+ Ok(Self::Struct(converter, owned))
+ }
+ _ => Err(ArrowError::NotYetImplemented(format!(
+ "not yet implemented: {:?}",
+ sort_field.data_type
+ ))),
+ }
+ }
+
+ fn encoder(&mut self, array: &dyn Array) -> Result<Encoder<'_>> {
+ match self {
+ Codec::Stateless => Ok(Encoder::Stateless),
+ Codec::Dictionary(interner) => {
+ let values = downcast_dictionary_array! {
+ array => array.values(),
+ _ => unreachable!()
+ };
+
+ let mapping = compute_dictionary_mapping(interner, values)
+ .into_iter()
+ .map(|maybe_interned| {
+ maybe_interned.map(|interned| interner.normalized_key(interned))
+ })
+ .collect();
+
+ Ok(Encoder::Dictionary(mapping))
+ }
+ Codec::Struct(converter, null) => {
+ let v = as_struct_array(array);
+ let rows = converter.convert_columns(v.columns())?;
Review Comment:
Is it true that this code converts the struct array to row format now and then the individual rows are copied over one by one into the resulting format?
In other words, the row format form of the struct array is copied?
##########
arrow/src/row/mod.rs:
##########
@@ -783,54 +881,64 @@ fn null_sentinel(options: SortOptions) -> u8 {
}
/// Computes the length of each encoded [`Rows`] and returns an empty [`Rows`]
-fn new_empty_rows(
- cols: &[ArrayRef],
- dictionaries: &[Option<Vec<Option<&[u8]>>>],
- config: RowConfig,
-) -> Rows {
+fn new_empty_rows(cols: &[ArrayRef], encoders: &[Encoder], config: RowConfig) -> Rows {
use fixed::FixedLengthEncoding;
let num_rows = cols.first().map(|x| x.len()).unwrap_or(0);
let mut lengths = vec![0; num_rows];
- for (array, dict) in cols.iter().zip(dictionaries) {
- downcast_primitive_array! {
- array => lengths.iter_mut().for_each(|x| *x += fixed::encoded_len(array)),
- DataType::Null => {},
- DataType::Boolean => lengths.iter_mut().for_each(|x| *x += bool::ENCODED_LEN),
- DataType::Binary => as_generic_binary_array::<i32>(array)
- .iter()
- .zip(lengths.iter_mut())
- .for_each(|(slice, length)| *length += variable::encoded_len(slice)),
- DataType::LargeBinary => as_generic_binary_array::<i64>(array)
- .iter()
- .zip(lengths.iter_mut())
- .for_each(|(slice, length)| *length += variable::encoded_len(slice)),
- DataType::Utf8 => as_string_array(array)
- .iter()
- .zip(lengths.iter_mut())
- .for_each(|(slice, length)| {
- *length += variable::encoded_len(slice.map(|x| x.as_bytes()))
- }),
- DataType::LargeUtf8 => as_largestring_array(array)
- .iter()
- .zip(lengths.iter_mut())
- .for_each(|(slice, length)| {
- *length += variable::encoded_len(slice.map(|x| x.as_bytes()))
- }),
- DataType::Dictionary(_, _) => downcast_dictionary_array! {
- array => {
- let dict = dict.as_ref().unwrap();
- for (v, length) in array.keys().iter().zip(lengths.iter_mut()) {
- match v.and_then(|v| dict[v as usize]) {
- Some(k) => *length += k.len() + 1,
- None => *length += 1,
+ for (array, encoder) in cols.iter().zip(encoders) {
+ match encoder {
+ Encoder::Stateless => {
+ downcast_primitive_array! {
+ array => lengths.iter_mut().for_each(|x| *x += fixed::encoded_len(array)),
+ DataType::Null => {},
+ DataType::Boolean => lengths.iter_mut().for_each(|x| *x += bool::ENCODED_LEN),
+ DataType::Binary => as_generic_binary_array::<i32>(array)
+ .iter()
+ .zip(lengths.iter_mut())
+ .for_each(|(slice, length)| *length += variable::encoded_len(slice)),
+ DataType::LargeBinary => as_generic_binary_array::<i64>(array)
+ .iter()
+ .zip(lengths.iter_mut())
+ .for_each(|(slice, length)| *length += variable::encoded_len(slice)),
+ DataType::Utf8 => as_string_array(array)
Review Comment:
Seeing this list of types I think it is going to conflict with https://github.com/apache/arrow-rs/pull/3182
##########
arrow/src/row/mod.rs:
##########
@@ -332,8 +340,99 @@ mod variable;
#[derive(Debug)]
pub struct RowConverter {
fields: Arc<[SortField]>,
- /// interning state for column `i`, if column`i` is a dictionary
- interners: Vec<Option<Box<OrderPreservingInterner>>>,
+ /// State for codecs
+ codecs: Vec<Codec>,
+}
+
+#[derive(Debug)]
+enum Codec {
+ /// No additional codec state is necessary
+ Stateless,
+ // The interner used to encode dictionary values
+ Dictionary(OrderPreservingInterner),
+ // A row converter for the child fields
+ // and the encoding of a row containing only nulls
+ Struct(RowConverter, OwnedRow),
+}
+
+impl Codec {
+ fn new(sort_field: &SortField) -> Result<Self> {
+ match &sort_field.data_type {
+ DataType::Dictionary(_, _) => Ok(Self::Dictionary(Default::default())),
+ d if !d.is_nested() => Ok(Self::Stateless),
+ DataType::Struct(f) => {
+ let sort_fields = f
+ .iter()
+ .map(|x| {
+ SortField::new_with_options(
+ x.data_type().clone(),
+ sort_field.options,
+ )
+ })
+ .collect();
+
+ let mut converter = RowConverter::new(sort_fields)?;
+ let nulls: Vec<_> =
+ f.iter().map(|x| new_null_array(x.data_type(), 1)).collect();
+
+ let nulls = converter.convert_columns(&nulls)?;
+ let owned = OwnedRow {
+ data: nulls.buffer,
+ config: nulls.config,
+ };
+
+ Ok(Self::Struct(converter, owned))
+ }
+ _ => Err(ArrowError::NotYetImplemented(format!(
+ "not yet implemented: {:?}",
Review Comment:
```suggestion
"Row format support not yet implemented for: {:?}",
```
##########
arrow/src/row/mod.rs:
##########
@@ -307,6 +308,13 @@ mod variable;
///
/// Input Row Format
/// ```
+///
+/// ## Struct Encoding
Review Comment:
👍
##########
arrow/src/row/mod.rs:
##########
@@ -1329,6 +1502,54 @@ mod tests {
assert_eq!(&cols[0], &a);
}
+ #[test]
+ fn test_struct() {
Review Comment:
Would it be valuable to more than one level (aka a struct array with a struct array child)? Also, how about a struct array with another type?
##########
arrow/src/row/mod.rs:
##########
@@ -872,35 +980,59 @@ fn encode_column(
out: &mut Rows,
column: &ArrayRef,
opts: SortOptions,
- dictionary: Option<&[Option<&[u8]>]>,
+ encoder: &Encoder<'_>,
) {
- downcast_primitive_array! {
- column => fixed::encode(out, column, opts),
- DataType::Null => {}
- DataType::Boolean => fixed::encode(out, as_boolean_array(column), opts),
- DataType::Binary => {
- variable::encode(out, as_generic_binary_array::<i32>(column).iter(), opts)
+ match encoder {
+ Encoder::Stateless => {
+ downcast_primitive_array! {
+ column => fixed::encode(out, column, opts),
+ DataType::Null => {}
+ DataType::Boolean => fixed::encode(out, as_boolean_array(column), opts),
+ DataType::Binary => {
+ variable::encode(out, as_generic_binary_array::<i32>(column).iter(), opts)
+ }
+ DataType::LargeBinary => {
+ variable::encode(out, as_generic_binary_array::<i64>(column).iter(), opts)
+ }
+ DataType::Utf8 => variable::encode(
+ out,
+ as_string_array(column).iter().map(|x| x.map(|x| x.as_bytes())),
+ opts,
+ ),
+ DataType::LargeUtf8 => variable::encode(
+ out,
+ as_largestring_array(column)
+ .iter()
+ .map(|x| x.map(|x| x.as_bytes())),
+ opts,
+ ),
+ _ => unreachable!(),
+ }
}
- DataType::LargeBinary => {
- variable::encode(out, as_generic_binary_array::<i64>(column).iter(), opts)
+ Encoder::Dictionary(dict) => {
+ downcast_dictionary_array! {
+ column => encode_dictionary(out, column, dict, opts),
+ _ => unreachable!()
+ }
}
- DataType::Utf8 => variable::encode(
- out,
- as_string_array(column).iter().map(|x| x.map(|x| x.as_bytes())),
- opts,
- ),
- DataType::LargeUtf8 => variable::encode(
- out,
- as_largestring_array(column)
- .iter()
- .map(|x| x.map(|x| x.as_bytes())),
- opts,
- ),
- DataType::Dictionary(_, _) => downcast_dictionary_array! {
- column => encode_dictionary(out, column, dictionary.unwrap(), opts),
- _ => unreachable!()
+ Encoder::Struct(rows, null) => {
+ let array = as_struct_array(column.as_ref());
+ let null_sentinel = null_sentinel(opts);
+ out.offsets
+ .iter_mut()
+ .skip(1)
+ .enumerate()
+ .for_each(|(idx, offset)| {
+ let (row, sentinel) = match array.is_valid(idx) {
+ true => (rows.row(idx), 0x01),
+ false => (*null, null_sentinel),
+ };
+ let end_offset = *offset + 1 + row.as_ref().len();
+ out.buffer[*offset] = sentinel;
+ out.buffer[*offset + 1..end_offset].copy_from_slice(row.as_ref());
Review Comment:
Since the row format of Struct arrays can be variable length, how are we ensuring that the contents of one struct array aren't accidentally compared with the contents of another field (e.g. the COBs encoding approach for variable length strings).
Also I wonder why not encode the struct array directly into `out` rather than copy it from another `Rows`?
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