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fluss

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UpdatedJune 2, 2026 at 19:57

Apache Fluss Incubating streaming storage expertise for real-time analytics, streamhouse and lakehouse architectures. Use when the user mentions Fluss, streaming storage, columnar streams, sub-second ingestion, tiered storage, Flink Delta Join, Paimon/Iceberg/Lance tiering, Spark access to streams, or Fluss table design.

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SKILL.md

readonly

name	fluss
description	Apache Fluss Incubating streaming storage expertise for real-time analytics, streamhouse and lakehouse architectures. Use when the user mentions Fluss, streaming storage, columnar streams, sub-second ingestion, tiered storage, Flink Delta Join, Paimon/Iceberg/Lance tiering, Spark access to streams, or Fluss table design.

Apache Fluss Expert

Use this skill for Fluss table design, low-latency stream storage, Flink integration, tiering to lakehouse formats, and operational planning.

Current Facts

Current stable: Apache Fluss 0.9.1.
Status: Apache Incubator project. Incubating releases are not yet fully endorsed ASF products.
Important 0.9 line features: Spark integration, complex nested types, zero-copy schema evolution, aggregation merge engine, auto-increment dictionary tables, $changelog and $binlog virtual tables, compacted log format, dynamic sink shuffle, KV snapshot leases, cluster rebalance, Azure Blob/ADLS Gen2 support, and Java Client POJO support.
Clients: Fluss Rust, Python, and C++ client 0.1.0 has been announced; do not describe Python SDK as only future roadmap.
Flink CDC: use current Flink CDC 3.6.0 guidance unless working in a pinned 3.5 environment.
Docker examples: prefer fluss/fluss:0.9.1 for current stable examples.

How To Use

Determine whether the table should be a log table or primary-key table.
Determine hot/cold architecture: Fluss only, Fluss tiered to Paimon, Fluss tiered to Iceberg, or Lance-oriented AI/vector ingestion.
For Flink jobs, align Fluss connector, Flink version, and CDC version before writing examples.

Design Rules

Use Fluss for hot, sub-second stream/table access; use Paimon/Iceberg for cold historical lakehouse storage.
Use log tables for append-only events and primary-key tables for mutable keyed state or CDC.
Size buckets for parallelism and avoid unnecessary small-file/tablet overhead.
Use $changelog/$binlog virtual tables for audit, replay, CDC, and ML reproducibility scenarios.
Use aggregation merge engine when pushing high-cardinality aggregate state into storage simplifies Flink state.

Update Checklist

Recheck Fluss downloads before changing stable versions.
Recheck client SDK maturity before recommending Python/C++/Rust client use in production.

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name	fluss
description	Apache Fluss Incubating streaming storage expertise for real-time analytics, streamhouse and lakehouse architectures. Use when the user mentions Fluss, streaming storage, columnar streams, sub-second ingestion, tiered storage, Flink Delta Join, Paimon/Iceberg/Lance tiering, Spark access to streams, or Fluss table design.

Apache Fluss Expert

Use this skill for Fluss table design, low-latency stream storage, Flink integration, tiering to lakehouse formats, and operational planning.

Current Facts

Current stable: Apache Fluss 0.9.1.
Status: Apache Incubator project. Incubating releases are not yet fully endorsed ASF products.
Important 0.9 line features: Spark integration, complex nested types, zero-copy schema evolution, aggregation merge engine, auto-increment dictionary tables, $changelog and $binlog virtual tables, compacted log format, dynamic sink shuffle, KV snapshot leases, cluster rebalance, Azure Blob/ADLS Gen2 support, and Java Client POJO support.
Clients: Fluss Rust, Python, and C++ client 0.1.0 has been announced; do not describe Python SDK as only future roadmap.
Flink CDC: use current Flink CDC 3.6.0 guidance unless working in a pinned 3.5 environment.
Docker examples: prefer fluss/fluss:0.9.1 for current stable examples.

How To Use

Determine whether the table should be a log table or primary-key table.
Determine hot/cold architecture: Fluss only, Fluss tiered to Paimon, Fluss tiered to Iceberg, or Lance-oriented AI/vector ingestion.
For Flink jobs, align Fluss connector, Flink version, and CDC version before writing examples.

Design Rules

Use Fluss for hot, sub-second stream/table access; use Paimon/Iceberg for cold historical lakehouse storage.
Use log tables for append-only events and primary-key tables for mutable keyed state or CDC.
Size buckets for parallelism and avoid unnecessary small-file/tablet overhead.
Use $changelog/$binlog virtual tables for audit, replay, CDC, and ML reproducibility scenarios.
Use aggregation merge engine when pushing high-cardinality aggregate state into storage simplifies Flink state.

Update Checklist

Recheck Fluss downloads before changing stable versions.
Recheck client SDK maturity before recommending Python/C++/Rust client use in production.