(datafusion-site) branch main updated: Comet 0.15.0 blog post (#170)

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     new 0f984df  Comet 0.15.0 blog post (#170)
0f984df is described below

commit 0f984df7e550b90c8e5bcc8ffcf818c51f64adbd
Author: Andy Grove <ag...@apache.org>
AuthorDate: Wed Apr 22 07:52:12 2026 -0600

    Comet 0.15.0 blog post (#170)
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+---
+layout: post
+title: Apache DataFusion Comet 0.15.0 Release
+date: 2026-04-18
+author: pmc
+categories: [subprojects]
+---
+
+<!--
+{% comment %}
+Licensed to the Apache Software Foundation (ASF) under one or more
+contributor license agreements.  See the NOTICE file distributed with
+this work for additional information regarding copyright ownership.
+The ASF licenses this file to you under the Apache License, Version 2.0
+(the "License"); you may not use this file except in compliance with
+the License.  You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+{% endcomment %}
+-->
+
+[TOC]
+
+The Apache DataFusion PMC is pleased to announce version 0.15.0 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 four weeks of development work and is the result of merging 142 PRs from 19
+contributors. See the [change log] for more information.
+
+[change log]: https://github.com/apache/datafusion-comet/blob/main/dev/changelog/0.15.0.md
+
+## Performance
+
+**Comet 0.15.0 provides a 2x speedup for TPC-H @ SF1000 (1TB), resulting in 50% cost savings.**
+
+That 2x speedup gives you a choice: finish the same Spark workload in half the time on the cluster you already
+have, or match your current Spark performance on roughly half the resources. Either way, the gain translates
+directly into lower cloud bills, reduced on-prem capacity, and lower energy usage, with no changes to your
+existing Spark SQL, DataFrame, or PySpark code. Comet runs on commodity hardware: no GPUs, FPGAs, or other
+specialized accelerators are required, so the savings come from better utilization of the infrastructure you
+already run on.
+
+<img
+src="/blog/images/comet-0.15.0/tpch_allqueries.png"
+width="100%"
+class="img-fluid"
+alt="TPC-H Overall Performance"
+/>
+
+<img
+src="/blog/images/comet-0.15.0/tpch_queries_compare.png"
+width="100%"
+class="img-fluid"
+alt="TPC-H Query-by-Query Comparison"
+/>
+
+See the [Comet Benchmarking Guide](https://datafusion.apache.org/comet/contributor-guide/benchmarking.html) for
+more details.
+
+Performance was a major theme of this release, with a series of targeted optimizations across the shuffle, scan,
+and execution layers.
+
+### Reducing JVM/Native Boundary Overhead
+
+Several changes in this release target the cost of crossing between the JVM and native sides, which can dominate
+execution time in shuffle- and broadcast-heavy workloads:
+
+- **Shuffle read path**: The native shuffle reader no longer uses FFI on the read side, removing a per-batch cost
+  that was particularly visible in shuffle-heavy queries.
+- **Broadcast exchanges**: Batches are now coalesced before broadcasting, reducing the number of small batches
+  crossing the JVM/native boundary.
+- **FFI-safe operators**: More operators are marked as FFI-safe, avoiding unnecessary deep copies when crossing
+  the JVM/native boundary.
+
+### Expanded Native Execution Coverage
+
+- **Columnar-to-row (C2R)**: Native C2R conversion is now exercised for a broader set of query shapes.
+- **`auto` scan mode**: The `auto` scan mode now enables the `native_datafusion` scan where supported, giving
+  users the benefits of the native Parquet reader without having to explicitly opt in. This is part of the
+  ongoing effort to make `native_datafusion` the default Parquet path once the deprecation of
+  `native_iceberg_compat` completes.
+
+### Memory Management
+
+- **Shared memory pools**: Unified memory pools are now shared across native execution contexts within a Spark
+  task, improving memory accounting and reducing OOMs.
+
+### Object Storage I/O
+
+- **Object store caching**: Object stores and bucket region lookups are cached, dramatically reducing DNS query
+  volume on workloads that open many files.
+- **`get_ranges` performance**: Picked up an upstream `opendal` fix that restores fast range reads from object
+  storage.
+
+Together, these changes reduce CPU and memory overhead for shuffle-heavy, broadcast-heavy, and
+object-storage-bound workloads.
+
+## Native Iceberg Reader Enabled by Default
+
+This release marks a major milestone for Iceberg users: **Comet's fully-native Iceberg reader is now enabled by
+default**. Workloads that read Iceberg tables will automatically benefit from native Rust-based scans built on
+iceberg-rust, with no additional configuration required.
+
+To support this change, the release bundles a broad set of Iceberg-focused improvements:
+
+- **Dynamic Partition Pruning (DPP)**: The native Iceberg reader supports DPP, allowing partition filters
+  derived at runtime to prune Iceberg file scans and substantially reduce I/O for star-schema-style queries.
+- **Correct classloader handling**: Iceberg classes are now loaded via the thread context classloader, resolving
+  class-loading issues in environments where the executor classloader differs from the application classloader.
+- **Continuous Iceberg CI**: Iceberg Spark integration tests now run on every PR and push to `main`, providing
+  continuous validation of the native Iceberg code path. Test diffs for Spark 3.4 were updated to keep the matrix
+  green across supported Spark versions.
+- **iceberg-rust upgrade**: Comet picks up the latest iceberg-rust, pulling in fixes for Parquet reader edge cases
+  discovered in earlier testing.
+- **Refreshed documentation**: The Iceberg user guide has been rewritten to reflect current capabilities, and the
+  contributor guide now documents how to run the Iceberg Spark test suites locally.
+
+Users who need to fall back to the previous behavior can still opt out, but we encourage the community to exercise
+the native reader and report any issues.
+
+### Sort-Merge Join Performance
+
+Comet relies heavily on sort-merge join (SMJ) because DataFusion's hash joins do not yet support spilling to
+disk. For larger-than-memory joins, SMJ is the only viable path, making its performance critical for real-world
+workloads at scale.
+
+DataFusion 53 includes several SMJ improvements that Comet 0.15.0 benefits from directly:
+
+- **Zero-copy slicing** instead of the take kernel ([datafusion#20463](https://github.com/apache/datafusion/pull/20463))
+- **Streaming output** instead of waiting for all input before emitting ([datafusion#20482](https://github.com/apache/datafusion/pull/20482))
+- **Cached row counts** to avoid O(n) recounting ([datafusion#20478](https://github.com/apache/datafusion/pull/20478))
+
+Additional SMJ work is landing in upstream DataFusion and will arrive in a future Comet release:
+
+- Specialized semi/anti join stream ([datafusion#20806](https://github.com/apache/datafusion/pull/20806))
+- Batch deferred filtering with 20–50x improvements for near-unique LEFT and FULL joins ([datafusion#21184](https://github.com/apache/datafusion/pull/21184))
+- DynComparator for ~5% TPC-H improvement ([datafusion#21484](https://github.com/apache/datafusion/pull/21484))
+- Vec-based filter state replacing HashMap ([datafusion#21517](https://github.com/apache/datafusion/pull/21517))
+- Full outer join correctness fix for NULL filter results ([datafusion#21660](https://github.com/apache/datafusion/pull/21660))
+
+With these performance improvements, the next release of Comet will enable SMJ with filters by default.
+
+## Other Key Features
+
+### New Expressions and Function Support
+
+This release adds support for the following:
+
+- **Date/time functions**: `days`, `hours`, `date_from_unix_date`
+- **String/JSON functions**: native `get_json_object` with improved performance over the fallback path
+- **Hash/math functions**: `bin`
+- **Array functions**: `sort_array`
+- **Window functions**: `LEAD` and `LAG` with `IGNORE NULLS`
+- **Aggregates**: SQL `FILTER (WHERE ...)` clauses now execute natively; `Corr` aggregate enabled
+
+### Expanded Metrics and Observability
+
+Comet metrics can now be exposed through Spark's external monitoring system, making it easier to integrate Comet
+execution statistics with existing observability dashboards. Native DataFusion scans also now report accurate
+`filesScanned` and `bytesScanned` input metrics, matching Spark's native Parquet scan reporting.
+
+## Stability and Correctness
+
+A significant portion of this release is dedicated to stability and Spark compatibility. Highlights include:
+
+- **Cast string to timestamp**: Multiple fixes for UTC timestamps, timezone handling, special formats
+  (`epoch`, `now`, etc.), and compatibility with Spark's semantics.
+- **Cast decimal to string**: Added legacy mode handling to match Spark's output formatting.
+- **String to decimal**: Support for full-width characters, null characters, and negative scale.
+- **Decimal arithmetic**: Fixes for decimal division and additional test coverage for ANSI overflow handling,
+  including scalar decimal overflow.
+- **Array expressions**: Corrected `GetArrayItem` null handling for dynamic indices; `array_append` return type
+  fixed and marked `Compatible`; audited `array_insert` for correctness; `array_compact` marked `Compatible`;
+  array-to-array cast enabled.
+- **DateTrunc/TimestampTrunc**: Fixed native crashes when the input is a literal.
+- **Ambiguous local times**: Correct handling of ambiguous and non-existent local times across DST transitions.
+- **Case-insensitive Parquet fields**: `native_datafusion` now correctly detects duplicate/ambiguous fields in
+  case-insensitive mode and falls back where appropriate.
+- **Shuffle planning**: Shuffle fallback decisions are now "sticky" across planning passes, and Comet columnar
+  shuffle is skipped for stages containing DPP scans to avoid mismatched partitioning.
+- **Error propagation**: Native error messages are now propagated through `SparkException` even when the
+  `errorClass` is empty, and file-not-found errors flow through the standard Spark error JSON path.
+- **Trigonometric compatibility**: `tan` and `atan2` are now Spark-compatible.
+
+## Dependency Upgrades
+
+This release upgrades to **DataFusion 53.1** and **Arrow 58.1**, and picks up the latest `iceberg-rust` release
+with additional reader fixes. The `jni` crate was upgraded to 0.22.4.
+
+## Deprecations and Removals
+
+The `SupportsComet` interface has been removed, along with the Java-based Iceberg integration path (which is
+fully superseded by the native Iceberg reader). See [comet#2921](https://github.com/apache/datafusion-comet/issues/2921)
+for background on the decision to standardize on the native iceberg-rust integration. The `native_iceberg_compat`
+scan remains deprecated and is expected to be removed in a future release in favor of `native_datafusion`.
+
+## Compatibility
+
+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 and Iceberg workloads and welcomes contributions
+to ongoing development.
+
+## Get Started with Comet 0.15.0
+
+Ready to try it out? Follow the [Comet 0.15.0 Installation Guide](https://datafusion.apache.org/comet/user-guide/0.15/installation.html)
+to get up and running, then point Comet at your existing Spark workloads and see the speedup for yourself.
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