Site: add Iceberg Rust and DataFusion Comet blog post

site/docs/blog/.authors.yml

@@ -18,3 +18,7 @@ authors:
     name: Apache Iceberg PMC
     description: Apache Iceberg PMC
     avatar: assets/images/iceberg-logo-icon.png
+  mbutrovich:
+    name: Matt Butrovich
+    description: Apache Iceberg Contributor, Apache DataFusion PMC Member
+    avatar: https://github.com/mbutrovich.png

site/docs/blog/posts/2026-07-13-accelerating-iceberg-rust-development-with-datafusion-comet.md

@@ -0,0 +1,229 @@
+---
+date: 2026-07-13
+title: Accelerating Apache Spark Queries (and Iceberg Rust Development) with Apache DataFusion Comet
+slug: accelerating-iceberg-rust-development-with-datafusion-comet  # this is the blog url
+authors:
+  - mbutrovich
+categories:
+  - blog
+---
+
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+
+Apache Iceberg's ecosystem spans multiple query engines and language implementations that work
+together to give users a consistent experience across the data lakehouse. This post explores one
+integration within that ecosystem, [Iceberg Rust](https://github.com/apache/iceberg-rust) and
+[Apache DataFusion Comet](https://datafusion.apache.org/comet/), and the two benefits their
+relationship brings.
+Comet accelerates Apache Spark's reads over Iceberg tables by running them natively through Iceberg
+Rust. That same integration turns Iceberg Java's nearly 10,000 Spark tests into a differential-testing
+harness whose benefits run both ways: Iceberg Rust gets exercised against a broad corpus of
+real-world scenarios, and the comparison has even caught bugs in Iceberg Java. The resulting fixes
+land upstream and benefit every project built on these libraries, not just Comet, as the Iceberg and
+DataFusion communities build on each other's strengths.
+
+<!-- more -->
+
+## Background
+
+Apache Iceberg provides a universal table format that serves as a foundation for modern data
+lakehouse
+platforms. With Iceberg, users store their tables with the benefit of being able to access
+and modify their data from a number of different query engines.
+The
+[Iceberg Java repository](https://github.com/apache/iceberg), the *de facto* reference
+implementation of the Iceberg specification, ships a mature [Apache Spark](https://spark.apache.org)
+integration. Beyond querying their data, teams also use Spark for table maintenance like compaction and
+snapshot expiration.
+In addition to Java, the Iceberg community maintains a number
+of other Iceberg implementations like [C++](https://github.com/apache/iceberg-cpp),
+[Go](https://github.com/apache/iceberg-go), [Python](https://github.com/apache/iceberg-python), and
+[Rust](https://github.com/apache/iceberg-rust).
+These other implementations benefit not only from the Iceberg specification, but also the lessons
+learned and design decisions of the Java project's community. The Java repository's extensive
+test suites, for instance, include nearly 10,000 correctness tests driven by Spark (as of Iceberg
+1.11 with Spark 4.1). Each implementation maintains its own test suite and can look to Iceberg Java
+as a reference for both correct behavior and test coverage. None of them, however, can run Java's
+tests directly against their own code.
+
+While Spark remains widely used for working with Iceberg, a number of projects exist to accelerate
+its JVM-backed execution. One such solution is Comet, which Apple donated in 2024 as a subproject of
+the [Apache DataFusion](https://datafusion.apache.org) query engine. Comet's native execution engine
+aims to run CPU-bound jobs faster and IO-bound jobs with fewer resources. As we will see, it does
+more than speed up queries: the same design that makes it fast also makes it a tool for accelerating
+Iceberg Rust's development.
+
+## Accelerating Spark Queries with Comet
+
+Comet builds upon several related Apache projects including DataFusion (for its efficient operator
+implementations like joins and aggregations), [Arrow-rs](https://github.com/apache/arrow-rs)
+(for its standardized in-memory format and robust Parquet reader), and both the Java and Rust
+implementations of Iceberg. To accelerate Spark queries, Comet
+intercepts execution
+at the physical plan level. After Spark has parsed, planned, and optimized a user's query,
+Comet's JVM code runs as one final optimizer rule to convert Spark plan nodes to Comet plan nodes.
+These Comet plan nodes have a superpower: they execute in DataFusion's Rust engine over columnar Arrow
+data.
+
+<figure markdown="span">![Comet converts a Spark physical plan into an equivalent DataFusion physical plan](../../assets/images/2026-07-13-comet-plan-translation.png){ width="750" }<figcaption>Comet converts a Spark physical plan into an equivalent DataFusion physical plan.</figcaption></figure>
+
+So how does Comet use *both* Iceberg libraries to accelerate Spark queries over Iceberg tables?
+As previously mentioned, Iceberg provides robust integrations with Spark, enabling users to query
+their Iceberg tables regardless of the Spark API they are using (*e.g.*, SQL, Scala, or PySpark).
+Iceberg relies on Spark's
+[`Data Source v2`](https://spark.apache.org/docs/4.2.0-preview4/sql-data-sources-v2.html) API to
+integrate with query planning, a process that
+Apache Iceberg PMC member Russell Spitzer recently described in a talk titled 
+["An Extremely Technical Overview of How Apache Iceberg Planning Actually Works"](https://www.youtube.com/watch?v=kJaD0WuQ1Bg).
+The short version of the talk is that given a query reading an Iceberg table, the Java 
+library inspects table metadata (*e.g.*, version history, schema, statistics, file layout) to
+construct `FileScanTask` objects. These objects describe the low-level operations (*e.g.*, file paths
+and byte ranges) needed to read the table and feed data to downstream query operators.
+
+Comet still relies on Iceberg Java for this planning. Acceleration is possible because Iceberg Rust
+has its own `FileScanTask`, so Comet uses it as the common abstraction between the two libraries: it
+takes the `FileScanTask` objects that Iceberg Java produced and hands them to Iceberg Rust, which
+reads the described files into the in-memory Arrow batches that feed the rest of the plan.
+
+<figure markdown="span">![Comet translates Iceberg Java's FileScanTask objects into Iceberg Rust's FileScanTask objects](../../assets/images/2026-07-13-comet-task-translation.png){ width="750" }<figcaption>Comet translates Iceberg Java's <code>FileScanTask</code> objects into Iceberg Rust's <code>FileScanTask</code> objects.</figcaption></figure>
+
+To measure Comet's impact on real workloads, the AWS Data on EKS team benchmarked Comet against
+Spark alone on the TPC-DS 3 TB workload over Iceberg tables. Comet completed the suite roughly
+40% faster (2,803.80s versus 4,665.47s) and accelerated 102 of the 103 TPC-DS queries, with only
+a single query regressing. See the
+[full benchmark writeup](https://awslabs.github.io/data-on-eks/docs/benchmarks/spark-datafusion-comet-benchmark)
+for the complete methodology and per-query results.
+
+<figure markdown="span">![TPC-DS 3 TB on Iceberg: Spark with Comet completes in 2803.8s versus 4665.47s for Spark with Iceberg](../../assets/images/2026-07-13-comet-tpcds.png){ width="500" }<figcaption>TPC-DS 3 TB (Iceberg) on AWS EKS: Spark with Comet completes the suite ~40% faster.</figcaption></figure> 
+
+Raw speed only matters if the answers are correct. Comet prioritizes correctness and compatibility
+with the libraries it accelerates. In addition to its own exhaustive test suites, Comet goes
+further by running Iceberg Java's Spark test suites with Comet enabled as regression tests,
+continuously checking the native path against the same corpus that guards the reference
+implementation.
+
+Comet uses Iceberg Rust to accelerate reads but does not yet accelerate writes, which still go
+through Iceberg Java. Even among table reads, it does not accelerate all of them. For example, Comet currently falls back to
+Iceberg Java any time it encounters a table using [table format version
+3](https://iceberg.apache.org/spec/#version-3-extended-types-and-capabilities) or newer. 
+This fallback behavior can be due to gaps in Comet or gaps in the underlying Iceberg Rust library.
+A consequence is that when Comet runs Iceberg Java's Spark suites, many tests silently take the
+Iceberg Java path rather than exercising Comet's native execution, so not every passing test
+reflects an accelerated read. That same graceful fallback, however, is also what makes these
+suites useful for improving Iceberg Rust itself.
+
+## Accelerating Iceberg Rust Development with Comet
+
+While the specification remains the reference for Iceberg developers, the lessons learned and
+edge cases encountered by the Java implementation provide an excellent corpus for other
+implementers. Historically, a non-Java implementation could only study that corpus and reimplement
+equivalent tests by hand. Comet changes that: it lets Iceberg Rust execute directly against Iceberg
+Java's Spark test suites. To our knowledge, no other Iceberg implementation (*e.g.*, C++ or Go) has
+any comparable way to test itself against the Java corpus.
+
+Comet accelerates queries by keeping Iceberg Java's planning and swapping in native execution.
+Accelerating development reuses that same split. Iceberg Java and Spark handle planning and produce
+a trusted result, so they serve as an oracle. Comet and Iceberg Rust handle native execution, so
+they become the system under test. Running them side by side is a form of differential testing: a
+query that Comet executes natively should return exactly what Spark returns on its own, and any
+difference points to a gap in Iceberg Rust or in Comet's translation between the two libraries.
+
+<figure markdown="span">![Differential testing: shared Iceberg Java planning feeds both Spark's JVM execution and Comet's native execution, and their results are compared](../../assets/images/2026-07-13-comet-differential-testing.png){ width="750" }<figcaption>Planning is held constant while execution varies: Spark's JVM path is the trusted oracle, Comet's native path (via Iceberg Rust) is the system under test, and any difference in results flags a gap.</figcaption></figure>
+
+Comet's fallback behavior is what makes this practical. By default, Comet falls back to Iceberg Java
+whenever it encounters a feature that Iceberg Rust cannot yet handle. Relaxing a fallback forces the
+native path and exposes exactly where it breaks, which turns the process into ordinary test-driven
+development against Iceberg Java's suite of nearly 10,000 Spark tests. A developer relaxes a fallback,
+runs the tests that exercise the feature, inspects what the Java planner produces, implements
+whatever Iceberg Rust is missing to match it, wires up any new plan-conversion logic Comet needs, and
+re-runs the suite to confirm the native path now passes.
+
+<figure markdown="span">![The development loop: relax a Comet fallback, run Iceberg Java's tests, characterize the failures, implement the fix in Iceberg Rust, wire up plan conversion in Comet, and re-run to confirm](../../assets/images/2026-07-13-comet-tdd-loop.png){ width="500" }<figcaption>Relaxing a fallback turns Iceberg Java's Spark tests into a test-driven development loop for both Iceberg Rust and Comet.</figcaption></figure>
+
+The first iterations are noisy. Early on, a single test run could produce hundreds of failures
+buried in enormous logs. To make that tractable, contributors have leaned on AI assistants to digest
+the volume of test output and characterize the failures by root cause, so they can tackle whichever
+gap accounts for the most. The humans still reason about the underlying code themselves; the AI just
+turns a wall of red into a prioritized backlog.
+
+This model is already producing results, with Comet contributors submitting [over 40 pull
+requests](https://github.com/search?q=repo%3Aapache%2Ficeberg-rust+is%3Apr+author%3Ambutrovich+author%3Aparthchandra+author%3Ahsiang-c&type=pullrequests)
+to Iceberg Rust spanning bug fixes, new features, and performance optimizations. For example, Comet has recently begun adding [preliminary support for table format version
+3](https://github.com/apache/datafusion-comet/pull/4887), reading deletion vectors against
+an in-progress Iceberg Rust branch. Contributors are now peeling those fixes off into standalone
+Iceberg Rust contributions. Similarly, [adding Iceberg 1.11 support to
+Comet](https://github.com/apache/datafusion-comet/pull/4840) surfaced two bugs in Iceberg Rust that
+Comet contributors [quickly](https://github.com/apache/iceberg-rust/pull/2781)
+[fixed](https://github.com/apache/iceberg-rust/pull/2783). Future contributions could follow the
+same model to close the rest of the table format version 3 gap in Iceberg Rust: new data types
+(variant, geometry, and geography), row lineage, default column values, and table encryption. The
+write path is an opportunity too, since the same approach could bootstrap native write support next.
+
+Crucially, none of these contributions are Comet-specific. They land upstream in Iceberg Rust and
+close feature gaps with Iceberg Java, so every system built on the library benefits, not just
+Comet. For the developers building Iceberg Rust, the payoff is direct: instead of mirroring Iceberg
+Java's tests by hand, they get a stream of real, production-hardened behaviors to implement and
+verify against, so the library matures faster and ships with more confidence. Comet is simply the
+workload that surfaces the gaps; the fixes belong to the whole community.
+
+The comparison cuts both ways. Iceberg Java is usually the oracle, but sometimes Iceberg Rust's
+behavior is the reference for the correct result. For example, Comet helped validate the fix for
+[a bug in Iceberg Java's manifest delete file size after a rewrite table
+action](https://github.com/apache/iceberg/pull/15470), confirming the corrected behavior against
+Iceberg Rust.
+
+This workflow is becoming part of how both projects test. When a
+Comet contributor fixes a bug or adds a feature on an Iceberg Rust branch, they
+typically open a Comet draft pull request that points at that branch and demonstrates previously
+failing Iceberg Java tests passing end to end. The same setup also serves as an informal way to
+validate Iceberg Rust release candidates. Comet is not a formal CI check for Iceberg Rust, but the
+Iceberg Rust community encourages developers to run their changes through Comet when validating a
+new feature.
+
+On its own, an open table format is little more than data at rest. Paired with an open source query
+engine like DataFusion, it becomes the foundation of an open data platform. The work described here
+is a small but growing example of what that looks like in practice: two communities building on
+each other's strengths to accelerate Iceberg on both fronts. Users who query Iceberg get faster
+results, and the developers who build it get a faster path to shipping and validating new features.
+We are thrilled by the deepening collaboration between the Iceberg and DataFusion communities, and we
+encourage anyone interested to find a way to get involved.
+
+## Getting Involved
+
+Both Comet and Iceberg Rust welcome contributions. Comet tracks work through
+GitHub [issues](https://github.com/apache/datafusion-comet/issues) and discussion happens through the
+[Apache DataFusion communication channels](https://datafusion.apache.org/contributor-guide/communication.html),
+while Iceberg Rust uses GitHub [issues](https://github.com/apache/iceberg-rust/issues) and the
+[Apache Iceberg community channels](https://iceberg.apache.org/community/).
+
+There are several ways to get involved:
+
+- Give Comet a try to accelerate your Spark queries over Iceberg tables: see the
+  [Comet user guide](https://datafusion.apache.org/comet/user-guide/index.html) to get started,
+  point it at your existing workloads, and report any issues you encounter.
+- Help close the gaps that cause Comet to fall back to Iceberg Java by contributing features to
+  [Iceberg Rust](https://github.com/apache/iceberg-rust).
+- Review the contributor guides for
+  [Comet](https://datafusion.apache.org/comet/contributor-guide/index.html) and
+  [Iceberg Rust](https://github.com/apache/iceberg-rust/blob/main/CONTRIBUTING.md).
+- Look for good first issues in
+  [Comet](https://github.com/apache/datafusion-comet/issues) and
+  [Iceberg Rust](https://github.com/apache/iceberg-rust/issues).
+
+For more information, visit the [Comet](https://github.com/apache/datafusion-comet) and
+[Iceberg Rust](https://github.com/apache/iceberg-rust) repositories.