Comet 0.14 blog post

content/blog/2026-03-18-datafusion-comet-0.14.0.md

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+---
+layout: post
+title: Apache DataFusion Comet 0.14.0 Release
+date: 2026-03-18
+author: pmc
+categories: [subprojects]
+---
+
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+
+[TOC]
+
+The Apache DataFusion PMC is pleased to announce version 0.14.0 of the [Comet](https://datafusion.apache.org/comet/) subproject.
+
+Comet is an accelerator for Apache Spark that translates Spark physical plans to DataFusion physical plans for
+improved performance and efficiency without requiring any code changes.
+
+This release covers approximately eight weeks of development work and is the result of merging 189 PRs from 21
+contributors. See the [change log] for more information.
+
+[change log]: https://github.com/apache/datafusion-comet/blob/main/dev/changelog/0.14.0.md
+
+## Key Features
+
+### Native Columnar-to-Row Conversion
+
+Comet now uses a native columnar-to-row (C2R) conversion by default. This
+feature replaces Comet's JVM-based columnar-to-row transition with a native Rust implementation, reducing JVM memory overhead
+when data flows from Comet's native execution back to Spark operators that require row-based input.
+
+### Native Iceberg Improvements
+
+Comet's fully-native Iceberg integration received several enhancements:
+
+**Per-Partition Plan Serialization**: `CometExecRDD` now supports per-partition plan data, reducing serialization
+overhead for native Iceberg scans and enabling dynamic partition pruning (DPP).
+
+**Vended Credentials**: Native Iceberg scans now support passing vended credentials from the catalog, improving
+integration with cloud storage services.
+
+**Performance Optimizations**:
+
+- Single-pass `FileScanTask` validation for reduced planning overhead
+- Configurable data file concurrency via `spark.comet.scan.icebergNative.dataFileConcurrency`
+- Channel-based executor thread parking instead of `yield_now()` for reduced CPU overhead
+- Reuse of `CometConf` and native utility instances in batch decoding
+
+### New Expressions
+
+This release adds support for the following expressions:
+
+- Date/time functions: `make_date`, `next_day`
+- String functions: `right`, `string_split`
+- Math functions: `width_bucket`, `crc32`
+- Map functions: `map_contains_key`, `map_from_entries`
+- Conversion functions: `to_csv`, `luhn_check`
+- Cast support: date to timestamp, integer to timestamp, numeric to timestamp, integer to binary, boolean to decimal, date to numeric
+
+### ANSI Mode Error Messages
+
+ANSI SQL mode now produces proper error messages matching Spark's expected output, improving compatibility for
+workloads that rely on strict SQL error handling.
+
+### DataFusion Configuration Passthrough
+
+DataFusion session-level configurations can now be set directly from Spark using the `spark.comet.datafusion.*`
+prefix. This enables tuning DataFusion internals such as batch sizes and memory limits without modifying Comet code.
+
+## Performance Improvements
+
+This release includes extensive performance optimizations:
+
+- **Sum aggregation**: Specialized implementations for each eval mode eliminate per-row mode checks
+- **Contains expression**: SIMD-based scalar pattern search for faster string matching
+- **Batch coalescing**: Reduced IPC schema overhead in `BufBatchWriter` by coalescing small batches
+- **Tokio runtime**: Worker threads now initialize from `spark.executor.cores` for better resource utilization
+- **Decimal expressions**: Optimized decimal arithmetic operations
+- **Row-to-columnar transition**: Improved performance for JVM shuffle data conversion
+- **Aligned pointer reads**: Optimized `SparkUnsafeRow` field accessors using aligned memory reads
+
+## Deprecations and Removals
+
+The deprecated `native_comet` scan mode has been removed. Use `native_datafusion` instead. Note 
+that the `native_iceberg_compat` scan is now deprecated and will be removed from a future release. 
+
+## Compatibility
+
+This release upgrades to DataFusion 52.3, Arrow 57.3, and iceberg-rust 0.9.0. Published binaries now target
+x86-64-v3 and neoverse-n1 CPU architectures for improved performance on modern hardware.
+
+Supported platforms include Spark 3.4.3, 3.5.4-3.5.8, and Spark 4.0.x with various JDK and Scala combinations.
+
+The community encourages users to test Comet with existing Spark workloads and welcomes contributions to ongoing development.