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General virtual columns support + row numbers as a first use-case - #8715
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Co-authored-by: scovich <scovich@users.noreply.github.com>
…eature tests pass
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| fn skip_records(&mut self, num_records: usize) -> Result<usize> { | ||
| // TODO: Use advance_by when it stabilizes to improve performance |
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TODO from original PR
| } | ||
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| fn row_groups(&self) -> Box<dyn Iterator<Item = &RowGroupMetaData> + '_> { | ||
| Box::new(std::iter::once(self.metadata.row_group(self.row_group_idx))) |
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this duplicates a lot, not sure if anything can be done here
| /// - If nullable: def_level = parent_def_level + 1 | ||
| /// - If required: def_level = parent_def_level | ||
| /// - rep_level = parent_rep_level (virtual fields are not repeated) | ||
| fn convert_virtual_field( |
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the name used here is not aligned with what other convert_ functions do
…hen metadata parsing may skip row groups
scovich
left a comment
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Post-merge drive by review
| // Sort ranges by ordinal to maintain original row group order | ||
| ranges.sort_by_key(|(ordinal, _)| *ordinal); |
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I don't understand this part? The row groups were supplied in some particular order (by the row_groups iterator), and we're reordering by row group ordinal instead? Wouldn't that cause row number mismatches with other columns that continue reading in the original order? It seems like we actually need:
let selected_ordinals = HashMap<i16, usize> = row_groups
.enumerate()
.map(...)
.collect::<Result<_>>()?;and then ranges needs to use that enumeration ordinal (not the row group ordinal):
if let Some(i) = selected_ordinals.get(&ordinal) {
ranges.push((i, ...);
}... so that the sorted ranges match the original row_group iterator's order?
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I messed this up when I started computing first row indexes...thank you for catching this. WIll follow up shortly with tests and the fix.
| .extension_type_name() | ||
| .map(|name| name.starts_with(VIRTUAL_PREFIX!())) | ||
| .unwrap_or(false) | ||
| } |
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| } | |
| .map_or(false, |name| name.starts_with(VIRTUAL_PREFIX!())) |
| if !is_virtual_column(field) { | ||
| panic!( |
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not a fan of panics, but if we're going to panic why not just
assert!(
is_virtual_column(field),
"...",
field.name()
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Me neither, but seemed like a unwritten rule that all these with_ methods in ArrowReaderOptions return Self rather than a Result. Please comment in the new PR if I should change that behaviour.
@alamb also checking for your opinion on this.
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Yes, the builder-like with_ functions don't give much opportunity for validity checking. We could probably use a proper ArrowReaderOptionsBuilder and do that kind of checking in build(). Or just change this one to a setter (so it's obvious it can't be chained) and return a Result<()>.
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I think it is ok to return errors when trying to build options, like
let options = options.with_virtual_columns(cols)?;If there is an error it is unlikely the code wants to continue configuring the options anyways
# Which issue does this PR close? Closes #8864. # Rationale for this change #8715 introduced row numbers feature last week. However, it had a bug, which luckily for us @scovich pointed out soon after the merge. The issue is that the row numbers are produced in ordinal-based order of the row groups, instead of user-requested order of row groups. The former is wrong, and is being fixed here by switching to user-requested order. # What changes are included in this PR? Just fixing the bug as explained above, and adding test. Also addressing two small comments from post-merge review: #8715 (review) # Are these changes tested? Yes. # Are there any user-facing changes? No, this wasn't released yet. --------- Co-authored-by: Andrew Lamb <andrew@nerdnetworks.org>
…ic on invalid input (#8867) # Which issue does this PR close? - Follow on to #8715 - related to #8863 # Rationale for this change per #8715 (comment), @scovich rightly says > not a fan of panics It is much better for a user facing API to return an error on invalid input than painc # What changes are included in this PR? 1. Make `ArrowReaderOptions::with_virtual_columns` error rather than panic on invalid input 2. Update tests to match # Are these changes tested? Yes by CI # Are there any user-facing changes? While this is an API change it was introduced in #8715 which has not yet been released. Therefore, we can make this change without breaking semver.
…rquet It would be useful to expose the virtual columns of the arrow Parquet reader in the datasource-parquet `ParquetSource` added in apache/arrow-rs#8715. Then engines can use both DataFusion's partition value machinery and the virtual columns. I made a go at it in this PR, but hit some rough edges. This is closer to an issue than a PR, but it is easier to explain with code. The virtual columns we added are a bit difficult to integrate cleanly today. They are part of the physical schema of the Parquet reader, but cannot currently be projected. We need some additional handling to avoid predicate pushdown for virtual columns, to build the correct projection mask, and to build the correct stream schema. See the changes to `opener.rs` in this PR. One alternative would be to modify the arrow-rs implementation to remove these workarounds. Then the only change to `opener.rs` would be `.with_virtual_columns(virtual_columns.to_vec())?` (and maybe even that could be avoided? See the discussion below). What would be the best way forward here? It is redundant that the user needs to specify both `Field::new("row_index", DataType::Int64, false).with_extension_type(RowNumber)`, and add the column in a special way to the reader options with `.with_virtual_columns(virtual_columns.to_vec())?`. When the extension type `RowNumber` is added, we know that it is a virtual column. All users of the `TableSchema/ParquetSource` must know that a schema is built out of three parts: the physical Parquet columns, the virtual columns and the partition columns. From a user perspective, the user would just like to supply a schema. One alternative is to only indicate the column kind using extension types, and the user only supplies a schema. That is, there would be an extension type indicating that a column is a partition column or virtual column, instead of the user supplying this information piecemeal. This may have a performance impact, as we would likely need to extract different extension type columns during planning, which could be problematic for large schemas. Signed-off-by: Jonas Irgens Kylling <jkylling@gmail.com>
…rquet It would be useful to expose the virtual columns of the arrow Parquet reader in the datasource-parquet `ParquetSource` added in apache/arrow-rs#8715. Then engines can use both DataFusion's partition value machinery and the virtual columns. I made a go at it in this PR, but hit some rough edges. This is closer to an issue than a PR, but it is easier to explain with code. The virtual columns we added are a bit difficult to integrate cleanly today. They are part of the physical schema of the Parquet reader, but cannot currently be projected. We need some additional handling to avoid predicate pushdown for virtual columns, to build the correct projection mask, and to build the correct stream schema. See the changes to `opener.rs` in this PR. One alternative would be to modify the arrow-rs implementation to remove these workarounds. Then the only change to `opener.rs` would be `.with_virtual_columns(virtual_columns.to_vec())?` (and maybe even that could be avoided? See the discussion below). What would be the best way forward here? It is redundant that the user needs to specify both `Field::new("row_index", DataType::Int64, false).with_extension_type(RowNumber)`, and add the column in a special way to the reader options with `.with_virtual_columns(virtual_columns.to_vec())?`. When the extension type `RowNumber` is added, we know that it is a virtual column. All users of the `TableSchema/ParquetSource` must know that a schema is built out of three parts: the physical Parquet columns, the virtual columns and the partition columns. From a user perspective, the user would just like to supply a schema. One alternative is to only indicate the column kind using extension types, and the user only supplies a schema. That is, there would be an extension type indicating that a column is a partition column or virtual column, instead of the user supplying this information piecemeal. This may have a performance impact, as we would likely need to extract different extension type columns during planning, which could be problematic for large schemas. Signed-off-by: Jonas Irgens Kylling <jkylling@gmail.com>
Upstream has a solution to this in arrow v57 as [apache#8715](apache#8715)
Upstream has a solution to this in arrow v57 as [apache#8715](apache#8715)
Upstream has a solution to this in arrow v57 as [apache#8715](apache#8715)
Upstream has a solution to this in arrow v57 as [apache#8715](apache#8715)
…he#8715 v57) Adds RowId support to ParquetRecordBatchReader and the async stream builder. When enabled via with_row_id(), a UInt64 column is prepended to each RecordBatch containing the file-level row offset for each row. Co-Authored-By: Thomas Peiselt <pi@kulturguerilla.org>
…he#8715 v57) Adds RowId support to ParquetRecordBatchReader and the async stream builder. When enabled via with_row_id(), a UInt64 column is prepended to each RecordBatch containing the file-level row offset for each row. Co-Authored-By: Thomas Peiselt <pi@kulturguerilla.org>
…he#8715 v57) Adds RowId support to ParquetRecordBatchReader and the async stream builder. When enabled via with_row_id(), a UInt64 column is prepended to each RecordBatch containing the file-level row offset for each row. Co-Authored-By: Thomas Peiselt <pi@kulturguerilla.org>
…he#8715 v57) Adds RowId support to ParquetRecordBatchReader and the async stream builder. When enabled via with_row_id(), a UInt64 column is prepended to each RecordBatch containing the file-level row offset for each row. Co-Authored-By: Thomas Peiselt <pi@kulturguerilla.org>
…he#8715 v57) Adds RowId support to ParquetRecordBatchReader and the async stream builder. When enabled via with_row_id(), a UInt64 column is prepended to each RecordBatch containing the file-level row offset for each row. Co-Authored-By: Thomas Peiselt <pi@kulturguerilla.org>
# Which issue does this PR close? - Closes #10381. # Rationale for this change `RowGroup.ordinal` is **optional** in the parquet-format Thrift spec with no uniformity requirement, but since #8715 the reader hard-errors on files whose row groups disagree on whether it is populated (`Inconsistent ordinal assignment: ...`). Such files are produced in the wild (e.g. Go parquet writers flushing row groups incrementally) and were readable before 57.1. This implements the plan agreed on the issue with @alamb, @etseidl and @vustef: - **All ordinals present** → honor them as written. - **None present** → sequential-fill at decode time, *unconditionally* — not just when row numbers are enabled — so downstream consumers behave identically whether the metadata was decoded fresh or reused from a prior read (the metadata-reuse hazard @vustef pointed out). - **Mixed** → leave the metadata untouched. Positional backfill could disagree with the ordinals that are present, and a partial backfill would make row-number results depend on which row groups a query happens to select. Instead, consumers that require complete ordinals fail deterministically; plain reads succeed. # What changes are included in this PR? - `thrift/mod.rs`: replace the per-row-group `OrdinalAssigner` (which errored on the first inconsistency) with a post-decode `ensure_row_group_ordinals` implementing the three cases above. - `array_reader/row_number.rs`: `RowNumberReader::try_new` rejects mixed-ordinal files up front — **even when every selected row group carries an ordinal** — so row numbering for a given file either always works or always fails, regardless of row-group pruning (@vustef's determinism concern). - `thrift/encryption.rs`: the encrypted column-metadata path used `rg.ordinal.unwrap()` for the AAD; with mixed metadata now reaching this code, return a proper error instead of panicking (@etseidl's encryption concern — files with fully-populated or fully-missing ordinals are unaffected). # Are these changes tested? - Decode round-trip tests for all three shapes (honored / sequential-filled / left untouched), including the exact Go-writer shape from the issue (first row group missing the ordinal). The mixed cases fail on main with the `Inconsistent ordinal assignment` error. - `RowNumberReader` unit tests: mixed metadata errors even for an ordinal-only selection; all-missing metadata errors for any selection. - End-to-end test: a real 4-row-group file with one ordinal stripped reads fine without virtual columns and fails deterministically with the row-number column, even when selecting only row groups that carry ordinals. - Full `parquet` test suite passes with `--all-features`. # Are there any user-facing changes? Files with mixed or absent row-group ordinal metadata are readable again (as before 57.1). Row-number virtual columns keep their strict guarantee: they now fail deterministically per file instead of depending on row-group selection. No API changes.
) # Which issue does this PR close? - Closes apache#10381. # Rationale for this change `RowGroup.ordinal` is **optional** in the parquet-format Thrift spec with no uniformity requirement, but since apache#8715 the reader hard-errors on files whose row groups disagree on whether it is populated (`Inconsistent ordinal assignment: ...`). Such files are produced in the wild (e.g. Go parquet writers flushing row groups incrementally) and were readable before 57.1. This implements the plan agreed on the issue with @alamb, @etseidl and @vustef: - **All ordinals present** → honor them as written. - **None present** → sequential-fill at decode time, *unconditionally* — not just when row numbers are enabled — so downstream consumers behave identically whether the metadata was decoded fresh or reused from a prior read (the metadata-reuse hazard @vustef pointed out). - **Mixed** → leave the metadata untouched. Positional backfill could disagree with the ordinals that are present, and a partial backfill would make row-number results depend on which row groups a query happens to select. Instead, consumers that require complete ordinals fail deterministically; plain reads succeed. # What changes are included in this PR? - `thrift/mod.rs`: replace the per-row-group `OrdinalAssigner` (which errored on the first inconsistency) with a post-decode `ensure_row_group_ordinals` implementing the three cases above. - `array_reader/row_number.rs`: `RowNumberReader::try_new` rejects mixed-ordinal files up front — **even when every selected row group carries an ordinal** — so row numbering for a given file either always works or always fails, regardless of row-group pruning (@vustef's determinism concern). - `thrift/encryption.rs`: the encrypted column-metadata path used `rg.ordinal.unwrap()` for the AAD; with mixed metadata now reaching this code, return a proper error instead of panicking (@etseidl's encryption concern — files with fully-populated or fully-missing ordinals are unaffected). # Are these changes tested? - Decode round-trip tests for all three shapes (honored / sequential-filled / left untouched), including the exact Go-writer shape from the issue (first row group missing the ordinal). The mixed cases fail on main with the `Inconsistent ordinal assignment` error. - `RowNumberReader` unit tests: mixed metadata errors even for an ordinal-only selection; all-missing metadata errors for any selection. - End-to-end test: a real 4-row-group file with one ordinal stripped reads fine without virtual columns and fails deterministically with the row-number column, even when selecting only row groups that carry ordinals. - Full `parquet` test suite passes with `--all-features`. # Are there any user-facing changes? Files with mixed or absent row-group ordinal metadata are readable again (as before 57.1). Row-number virtual columns keep their strict guarantee: they now fail deterministically per file instead of depending on row-group selection. No API changes.
Based on #7307.
Which issue does this PR close?
Rationale for this change
We need row numbers for many of the downstream features, e.g. computing unique row identifier in iceberg.
What changes are included in this PR?
New API to get row numbers as a virtual column:
This column is defined as an extension type.
Parquet metadata is propagated to the array builder to compute first row indexes.
New Virtual column is included in addition to Primitive and Group.
Are these changes tested?
Yes
Are there any user-facing changes?
This is user facing feature, and has added docstrings.
No breaking changes, at least I tried not to, by creating a duplicate of public method to add more parameters.