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og:description: A solo developer shares a weekly progress update on StreamFusion, a Rust-based stream processing accelerator for Apache Flink. Highlights include shipping...
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# StreamFusion Supports Parquet Writing! | Personal Projects With Ex-Google SWE, Ep 38

**[Jordan has no life](https://daily.dev/sources/jordan-has-no-life)** · 16 min read · 2 upvotes · 0 comments

## Summary

A solo developer shares a weekly progress update on StreamFusion, a Rust-based stream processing accelerator for Apache Flink. Highlights include shipping experimental Parquet writer support that flushes column chunks to reduce memory footprint, simplifying the Kafka connector after realizing a fully native Rust implementation wasn't worth the added complexity, exploring Apache Fluss for columnar message brokering, rewriting the protobuf serialization layer to use code-generated deserializers, and starting work on a Delta Lake sink. The developer also recounts an AI coding assistant hallucinating and forking a connector's internals during unsupervised work, prompting plans to add guardrails via unit tests.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.youtube.com/watch?v=t8c1CocHqEM>

## Questions this post answers

### Why did StreamFusion drop its native Rust Kafka client in favor of using the JVM Kafka client with Java-to-Rust data copying?

Profiling showed that copying bytes between the Java heap and Rust off-heap memory was not the actual bottleneck in the stream processing pipeline, and a native Rust Kafka client only offered a few extra percentage points of performance while adding huge complexity around consumer/producer configuration parity, Kerberos, SSL, and other security handshake protocols. For an early-stage, single-maintainer project, that complexity wasn't worth the marginal gain.

_Track pragmatic tradeoffs like this Rust versus JVM Kafka client decision on daily.dev while building your own streaming pipelines._

### How does a Rust-based Parquet writer reduce memory footprint compared to Flink's stock Java Parquet writer?

Flink's standard Java Parquet writer flushes columns one column chunk at a time, while a Rust implementation using lower-level Parquet writing APIs can flush each column chunk to disk as soon as it's fully encoded within a row group, rather than waiting for the entire row group to be buffered in memory. This lowers peak memory usage, which matters for a system designed to also reduce memory footprint alongside CPU cost.

_Compare memory-conscious Parquet writer designs on daily.dev when optimizing your own streaming data pipelines._

### Why is Apache Fluss's Flink connector inefficient for column-oriented streaming systems?

Fluss stores data internally as Arrow rows, which allows efficient column projection pruning, but its Flink connector converts that data back to row-by-row format before handing it off, even though consuming systems designed around column batches would prefer to receive Arrow batches directly. Work is underway to expose Arrow-batch reads for both append-only and primary-key tables to avoid this unnecessary row conversion.

_Follow emerging streaming broker tradeoffs like this on daily.dev when evaluating Fluss or similar systems for your architecture._

---

Tags: [#rust](https://daily.dev/tags/rust), [#big-data](https://daily.dev/tags/big-data), [#apache-kafka](https://daily.dev/tags/apache-kafka), [#apache-flink](https://daily.dev/tags/apache-flink)

[View this post on daily.dev](https://daily.dev/posts/streamfusion-supports-parquet-writing-personal-projects-with-ex-google-swe-ep-38-lmlyh5wx0)

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