Xander
d59cdfe999
## Which issue does this PR close? - Closes #17508 ## Rationale for this change The previous implementation used UUID-based aliasing as a workaround to prevent duplicate names for literals in Substrait plans. This approach had several drawbacks: - Non-deterministic plan names that made testing difficult (requiring UUID regex filters) - Only addressed literal naming conflicts, not the broader issue of name deduplication - Added unnecessary dependency on the `uuid` crate - Didn't properly handle cases where the same qualified name could appear with different schema representations ## What changes are included in this PR? 1. Enhanced NameTracker: Refactored to detect two types of conflicts: - Duplicate schema names: Tracked via schema_name() to prevent validate_unique_names failures (e.g., two Utf8(NULL) literals) - Ambiguous references: Tracked via qualified_name() to prevent DFSchema::check_names failures when a qualified field (e.g., left.Utf8(NULL)) and unqualified field (e.g., Utf8(NULL)) share the same column name 2. **Removed UUID dependency**: Eliminated the `uuid` crate from `datafusion/substrait` 3. **Removed literal-specific aliasing**: The UUID-based workaround in `project_rel.rs` is no longer needed as the improved NameTracker handles all naming conflicts consistently 4. **Deterministic naming**: Name conflicts now use predictable `__temp__N` suffixes instead of random UUIDs Note: This doesn't fully fix all the issues in #17508 which allow some special casing of `CAST` which are not included here. ## Are these changes tested? Yes: - Updated snapshot tests to reflect the new deterministic naming (e.g., `Utf8("people")__temp__0` instead of UUID-based names) - Modified some roundtrip tests to verify semantic equivalence (schema matching and execution) rather than exact string matching, which is more robust - All existing integration tests pass with the new naming scheme ## Are there any user-facing changes? Minimal. The generated plan names are now deterministic and more readable (using `__temp__N` suffixes instead of UUIDs), but this is primarily an internal representation change. The functional behavior and query results remain unchanged.
Apache DataFusion
DataFusion is an extensible query engine written in Rust that uses Apache Arrow as its in-memory format.
This crate provides libraries and binaries for developers building fast and feature-rich database and analytic systems, customized for particular workloads. See use cases for examples. The following related subprojects target end users:
- DataFusion Python offers a Python interface for SQL and DataFrame queries.
- DataFusion Comet is an accelerator for Apache Spark based on DataFusion.
"Out of the box," DataFusion offers SQL and DataFrame APIs, excellent performance, built-in support for CSV, Parquet, JSON, and Avro, extensive customization, and a great community.
DataFusion features a full query planner, a columnar, streaming, multi-threaded, vectorized execution engine, and partitioned data sources. You can customize DataFusion at almost all points including additional data sources, query languages, functions, custom operators and more. See the Architecture section for more details.
Here are links to important resources:
- Project Site
- Installation
- Rust Getting Started
- Rust DataFrame API
- Rust API docs
- Rust Examples
- Python DataFrame API
- Architecture
What can you do with this crate?
DataFusion is great for building projects such as domain-specific query engines, new database platforms and data pipelines, query languages and more. It lets you start quickly from a fully working engine, and then customize those features specific to your needs. See the list of known users.
Contributing to DataFusion
Please see the contributor guide and communication pages for more information.
Crate features
This crate has several features which can be specified in your Cargo.toml.
Default features:
nested_expressions: functions for working with nested types such asarray_to_stringcompression: reading files compressed withxz2,bzip2,flate2, andzstdcrypto_expressions: cryptographic functions such asmd5andsha256datetime_expressions: date and time functions such asto_timestampencoding_expressions:encodeanddecodefunctionsparquet: support for reading the Apache Parquet formatsql: support for SQL parsing and planningregex_expressions: regular expression functions, such asregexp_matchunicode_expressions: include Unicode-aware functions such ascharacter_lengthunparser: enables support to reverse LogicalPlans back into SQLrecursive_protection: uses recursive for stack overflow protection.
Optional features:
avro: support for reading the Apache Avro formatbacktrace: include backtrace information in error messagesparquet_encryption: support for using Parquet Modular Encryptionserde: enable arrow-schema'sserdefeature
DataFusion API Evolution and Deprecation Guidelines
Public methods in Apache DataFusion evolve over time: while we try to maintain a stable API, we also improve the API over time. As a result, we typically deprecate methods before removing them, according to the deprecation guidelines.
Dependencies and Cargo.lock
Following the guidance on committing Cargo.lock files, this project commits
its Cargo.lock file.
CI uses the committed Cargo.lock file, and dependencies are updated regularly
using Dependabot PRs.