vs

Apache Fluss vs Apache Paimon

Apache Fluss: Columnar streaming storage for Flink with primary-key tables and tiering into the lakehouse. Apache Paimon: Streaming lakehouse table format with LSM-based upserts and changelogs, born in Apache Flink.

key differences

  • Apache Paimon is built for larger datasets (Multi-PB) than Apache Fluss (≤ 1 PB).
  • Transactions: Apache Fluss offers limited ACID; Apache Paimon offers full.
  • Only Apache Fluss has secondary indexes, ttl / expiry.
  • Only Apache Paimon has transactions, time travel, branching.
  • Apache Paimon can run embedded in your application process.

choose apache-fluss for

  • +Real-time analytics pipelines on Flink
  • +Streaming lakehouse ingestion with fresh reads
  • +Lookup joins against changing dimension tables

best at · Streaming, Real-time analytics, Lakehouse

choose apache-paimon for

  • +Streaming CDC from databases into the lake
  • +Real-time lakehouse with Flink
  • +Incremental ETL with changelogs
  • +Partial-update wide tables

best at · Lakehouse, Streaming, Real-time analytics

overview
Primary model
Columnar / OLAP
Columnar / OLAP
Also supports
Key-Value
Relational
Type
Database engine
Embedded library
Status
Active
Active
Developer
Apache Software Foundation
Apache Software Foundation
First release
2024
2023
Latest version
1.0.0 (Sep 22, 2026)
2.1.0 (Oct 12, 2026)
License
Open source (permissive)Apache License 2.0
Open source (permissive)Apache License 2.0
Free tier
✓
✓
popularity
WhichDB Score
8.4
14.2
Rank
#323
#237
GitHub stars
2.2K
3.4K
Stack Overflow questions
–
–
scale & guarantees
Comfortable data size
Large (≤ 1 PB)
Massive (Multi-PB)
Horizontal scaling
Native
Native
Multi-region
None
None
ACID
Limited
Full
Consistency
Strong
Strong
Schema
Strict
Strict
Architecture
Distributed, Client server
Distributed, Shared storage
deployment
Self-hosted
✓
✓
Vendor-managed cloud
—
—
Serverless
—
—
Embedded
—
✓
Edge
—
—
Clouds
–
–
capabilities
Transactions
—
✓
Joins
—
—
Secondary indexes
✓
—
Full-text search
—
—
Vector search
—
—
Geospatial
—
—
JSON
—
—
Change data capture
✓
✓
Triggers
—
—
Stored procedures
—
—
Materialized views
—
—
TTL / expiry
✓
—
Pub/Sub
—
—
Columnar storage
✓
✓
In-memory
—
—
Durable persistence
✓
✓
Compression
✓
✓
Encryption at rest
—
—
Role-based access
—
—
Time travel
—
✓
Branching
—
✓
Online schema changes
—
—
Streaming ingest
✓
✓
Offline sync
—
—
Runs in WebAssembly
—
—
interfaces
Query languages
Flink SQL, Spark SQL
SQL (via Flink, Spark, Trino, StarRocks, Doris)
Client languages
Java
Java, Python
Best for
Streaming, Real-time analytics, Lakehouse
Lakehouse, Streaming, Real-time analytics

Apache Fluss vs Apache Paimon: FAQ

What is the difference between Apache Fluss and Apache Paimon?

Apache Fluss: Columnar streaming storage for Flink with primary-key tables and tiering into the lakehouse. Apache Paimon: Streaming lakehouse table format with LSM-based upserts and changelogs, born in Apache Flink. Apache Paimon is built for larger datasets (Multi-PB) than Apache Fluss (≤ 1 PB). Transactions: Apache Fluss offers limited ACID; Apache Paimon offers full. Only Apache Fluss has secondary indexes, ttl / expiry.

When should I choose Apache Fluss over Apache Paimon?

Choose Apache Fluss for real-time analytics pipelines on Flink, streaming lakehouse ingestion with fresh reads and lookup joins against changing dimension tables. Choose Apache Paimon for streaming CDC from databases into the lake, real-time lakehouse with Flink and incremental ETL with changelogs.

Is Apache Fluss or Apache Paimon more popular?

Apache Paimon ranks #237 on the WhichDB Score (14.2/100) and Apache Fluss ranks #323 (8.4/100). Popularity is not fit: check the requirements that matter for your project.

Are Apache Fluss and Apache Paimon open source?

Yes. Apache Fluss is open source. License: Apache License 2.0. Yes. Apache Paimon is open source. License: Apache License 2.0.