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new d187d3c13 [website] Release 0.8 blog post and update Download page
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Author: Jark Wu <[email protected]>
AuthorDate: Sun Nov 9 23:00:29 2025 +0800
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+---
+title: "Announcing Apache Fluss (Incubating) 0.8: Streaming Lakehouse for Data
+ AI"
+sidebar_label: "Announcing Apache Fluss 0.8"
+authors: [giannis, jark]
+date: 2025-11-09
+tags: [releases]
+---
+
+
+
+š We are excited to announce the official release of **Apache Fluss
(Incubating) 0.8**!
+
+This is our first release under the incubator of the Apache Software
Foundation, marking a significant milestone in our journey to provide a robust
streaming storage platform for real-time analytics.
+
+Over the past four months, the community has made tremendous progress,
delivering nearly 400 commits that push the boundaries of the Streaming
Lakehouse ecosystem. This release includes multiple stability optimizations and
introduces deeper integrations, performance breakthroughs, and next-generation
stream processing capabilities. Highlights:
+
+* š§ Enhanced Streaming Lakehouse capabilities with full support for [Apache
Iceberg](https://iceberg.apache.org/) and
[Lance](https://lancedb.github.io/lance/)
+* ā” Introduction of [Delta
Joins](https://cwiki.apache.org/confluence/display/FLINK/FLIP-486%3A+Introduce+A+New+DeltaJoin)
with Flink, a game-changing innovation that redefines efficiency in stream
processing by minimizing state and maximizing speed.
+* š§ Supports hot updates for both cluster configurations and table
configurations
+
+Apache Fluss 0.8 marks the beginning of a new era in streaming:
+**real-time**, **unified**, and **zero-state**, purpose-built to power the
next generation of data platforms with **low-latency performance**,
**scalability**, and **architectural simplicity**.
+
+<!-- truncate -->
+
+
+
+## Streaming Lakehouse for Iceberg
+
+A key highlight of Fluss 0.8 is the introduction of **Streaming Lakehouse for
Apache Iceberg**
([FIP-3](https://cwiki.apache.org/confluence/display/FLUSS/FIP-3%3A+Support+tiering+Fluss+data+to+Iceberg)),
+which transforms Iceberg from a batch-oriented table format into a
continuously updating Lakehouse. Apache Fluss acts as the **real-time ingestion
and storage layer**, writing fresh data and updates into Iceberg with
guaranteed ordering and exactly-once semantics.
+
+This enables real-time data on Fluss to be tiered as Apache Iceberg tables,
while providing table semantics like partitioning and bucketing on a single
copy of data.
+Moreover, it solves Icebergās long-standing update limitations through Flussās
**native support for upserts and deletes** and its **built-in compaction
service**,
+which automatically merges small files and maintains optimized Iceberg
snapshots.
+
+Key benefits include:
+- **Unified Architecture**: Fluss handles sub-second streaming reads and
writes, while Iceberg stores compacted historical data.
+- **Native Updates and Deletes**: Fluss efficiently applies changes and tiers
them into Iceberg without rewrite jobs.
+- **Built-in Compaction Service**: The built-in service maintains snapshot
efficiency with no external tooling.
+- **Efficient Backfilling**: Enables lightning-fast backfill of historical
data from Iceberg for streaming processing.
+- **Lower Cost**: Reduce storage cost by tiering cold data to Iceberg while
keeping hot data in Fluss, eliminating the need for duplicate storage.
+- **Lower Latency**: Sub-second data freshness for Iceberg tables by Union
Read from Fluss and Iceberg.
+
+```yaml title='server.yaml'
+# Iceberg configuration
+datalake.format: iceberg
+
+# the catalog config about Iceberg, assuming using Hadoop catalog,
+datalake.iceberg.type: hadoop
+datalake.iceberg.warehouse: /path/to/iceberg
+```
+
+You can find more detailed instructions in the [Iceberg Lakehouse
documentation](/docs/streaming-lakehouse/integrate-data-lakes/iceberg/).
+
+## Real-Time Multimodal AI Analytics with Lance
+
+Another major enhancement in Fluss 0.8 is the addition of **Streaming
Lakehouse support for [Lance](https://github.com/lancedb/lance)**
([FIP-5](https://cwiki.apache.org/confluence/display/FLUSS/FIP-5%3A+Support+tiering+Fluss+data+to+Lance)),
+a modern columnar and vector-native data format designed for AI and machine
learning workloads.
+This integration extends Apache Fluss towards being a real-time ingestion
platform for multi-modal data & AI,
+not just traditional tabular streams, but also embeddings, vectors, and
unstructured features used in AI systems.
+With this release, Fluss can continuously ingest, update, and tier data into
Lance tables with guaranteed ordering and freshness,
+enabling fast synchronization between streaming pipelines and downstream ML or
retrieval applications.
+
+Key benefits include:
+
+- **Unified multi-modal data ingestion**: Stream tabular, vector, and
embedding data into Lance in real time.
+- **AI/ML-ready storage**: Keep feature vectors and embeddings continuously
up-to-date for model training or inference.
+- **Low-latency analytics and retrieval**: Fast, continuous updates enable
Lance data to be immediately usable for real-time search and recommendation.
+- **Simplified architecture**: Eliminates complex ETL pipelines between
streaming systems and vector databases.
+
+Seamless integration: combines Flussās high-throughput streaming engine with
Lanceās efficient columnar persistence for consistent, multi-modal data
management.
+
+```yaml title='server.yaml'
+datalake.format: lance
+datalake.lance.warehouse: s3://<bucket>
+datalake.lance.endpoint: <endpoint>
+datalake.lance.allow_http: true
+datalake.lance.access_key_id: <access_key_id>
+datalake.lance.secret_access_key: <secret_access_key>
+```
+
+See the [LanceDB blog post](https://lancedb.com/blog/fluss-integration/) for
the full integration. You also can find more detailed instructions in the
[Lance Lakehouse
documentation](/docs/streaming-lakehouse/integrate-data-lakes/lance/).
+
+## Flink 2.1
+
+Apache Fluss is now fully compatible with **Apache Flink 2.1**, ensuring
seamless integration with the latest Flink runtime and APIs.
+This update strengthens Flussās role as a unified streaming storage layer,
providing reliable performance and consistency for modern data pipelines built
on Flink.
+
+### Delta Join
+
+The Delta Join is a major step towards the era of zero-state streaming joins.
This release introduces support for Delta Joins with Apache Flink.
+By externalizing state into Fluss tables, Flink performs joins incrementally
on data deltas, without maintaining large states.
+This architecture reduces CPU and memory usage by **up to 80%**, eliminates
over **100 TB of state** as witnessed in the first production use cases from
[early adopters](blog/2025-08-07-taobao-practice.md),
+and cuts checkpoint durations from **90 seconds to just 1 second**. Because
all data lives natively in Fluss tables,
+thereās **no state bootstrapping**; pipelines start instantly, stay
lightweight, and achieve efficiency for real-time analytics at scale.
+
+Below is a performance comparison (CPU, memory, state size, checkpoint
interval) between Delta Join and Stream-Stream Join, as evaluated by Taobaoās
Search & Recommendation Systems team.
+
+
+
+
+
+
+You can find more detailed instructions in the [Delta Join
documentation](/docs/engine-flink/delta-joins/).
+
+### Materialized Table
+
+Apache Fluss 0.8 introduces support for Flink Materialized Tables, enabling
seamless, low-latency materializations directly over Fluss streams.
+Flinkās Materialized Table turns a SQL query into a continuously or
periodically refreshed result table with a defined freshness target (e.g.,
seconds or minutes).
+With Fluss as the underlying streaming source, users can declaratively build
real-time tables that stay up to date without custom orchestration.
+This integration unifies batch and streaming ETL: Fluss delivers
high-throughput, low-latency data, while Flink continuously maintains derived
tables for analytics,
+APIs, and downstream workloads, providing real-time, consistent data pipelines
with minimal operational overhead.
+This integration further strengthens the batch & stream unification.
+
+```sql title="Flink SQL"
+-- 1. create a materialized table with 10 seconds freshness
+CREATE MATERIALIZED TABLE fluss.dw.sales_summary
+FRESHNESS = INTERVAL '10' SECOND
+AS SELECT
+ product,
+ SUM(quantity) AS total_sales,
+ CURRENT_TIMESTAMP() AS last_updated
+FROM fluss.dw.sales_detail
+GROUP BY product;
+
+-- 2. suspend data refresh for the materialized table
+ALTER MATERIALIZED TABLE dwd_orders SUSPEND;
+
+-- 3. resume data refresh for the materialized table
+ALTER MATERIALIZED TABLE dwd_orders RESUME
+-- Set table option via WITH clause
+WITH(
+ 'sink.parallelism' = '10'
+);
+```
+
+You can find more detailed instructions in the [Materialized Table
documentation](/docs/engine-flink/ddl/#materialized-table).
+
+## Stability
+
+In this release, we have made significant improvements in the stability and
reliability of Apache Fluss under large-scale production workloads.
+Through continuous validation across multiple business units within Alibaba
Group, and **especially through large-scale workloads during the Alibaba's
Double 11 peak traffic**, we have resolved over 35 stability-related issues.
+These improvements substantially enhance Flussās robustness in
mission-critical streaming use cases.
+
+Key improvements include:
+- **[Graceful Shutdown](/docs/maintenance/operations/graceful-shutdown/)**:
Fluss supports cluster rolling upgrade, and we introduced a graceful shutdown
mechanism for TabletServers in this version. During shutdown, leadership is
proactively migrated before termination, ensuring that read/write latency
remains unaffected during rolling upgrades.
+- **Accelerated Coordinator Event Processing**: Optimized the Coordinatorās
event handling mechanism through asynchronous processing and batched ZooKeeper
operations. As a result, all events are now processed in milliseconds.
+- **Faster Coordinator Recovery**: Parallelized initialization cuts
Coordinator startup time from 10 minutes to just 20 seconds in production-scale
benchmarks, this dramatically improves service availability and recovery speed.
+- **Optimized Server Metrics**: Refined metric granularity and reporting logic
to reduce telemetry volume by 90% while preserving full observability.
+- **Enhanced Metadata Performance**: Addressed metadata bottlenecks during
mass client restarts by strengthening the server local cache and introducing
asynchronous ZooKeeper operations. This reduces metadata request latency from
>10 seconds to milliseconds, ensuring stable client reconnection under load.
+
+With these foundational stability improvements, Fluss 0.8 is now
production-ready for the most demanding real-time workloads, including
Alibabaās annual Double 11 global shopping festival.
+
+## Dynamic Configuration
+
+Starting with Fluss version 0.8, certain **cluster-level configurations** and
**table-level configurations** can be updated dynamically, without requiring a
cluster restart or table recreation. This enables operators and developers to
adjust system behavior in real time, improving operational agility and
minimizing downtime.
+
+### Dynamic Cluster Configs
+
+Fluss now supports runtime updates for cluster configuration parameters. These
changes take effect immediately across the cluster after being applied through
the API.
+
+```java title="Java Client"
+Admin admin = connection.getAdmin();
+Collection<AlterConfig> configsToUpdate = Arrays.asList(
+ new AlterConfig("datalake.format", "paimon", AlterConfigOpType.SET));
+admin.alterClusterConfigs(configsToUpdate)
+```
+
+### Dynamic Table Configs
+
+Fluss now supports update options dynamically on a table using the `ALTER
TABLE ... SET` statement. This supports all the client-wise options (like
`scan.startup.mode`) and some storage-wise options (like
`table.datalake.enabled`).
+
+```sql title="Flink SQL"
+-- Enable lakehouse storage for the given table
+ALTER TABLE my_table SET ('table.datalake.enabled' = 'true');
+```
+
+When you issue a `ALTER TABLE ... SET` command to update storage options on a
table, the Fluss cluster validates and applies the new configuration
immediately. The updated settings are propagated to all TabletServers and
CoordinatorServer components, ensuring consistent behavior going forward.
+
+This capability is especially useful for tuning performance, adapting to
changing data patterns, or complying with evolving data governance
requirementsāall without service interruption.
+
+You can find more detailed instructions in the [Updating Configs
documentation](/docs/maintenance/operations/updating-configs/).
+
+## Helm Charts
+
+This release also introduced Helm Charts. With this addition, users can now
deploy and manage a full Fluss cluster using [Helm](https://helm.sh/).
+The Helm chart simplifies provisioning, upgrades, and scaling by packaging
configuration, manifests, and dependencies into a single, versioned release.
+This should help users running Fluss on Kubernetes faster, more reliably, and
with easier integration into existing CI/CD and observability setups,
significantly lowering the barrier for teams adopting Fluss in production.
+
+You can find more detailed instructions in the [Deploying with Helm
documentation](/docs/install-deploy/deploying-with-helm/).
+
+## Java Version Upgrade
+
+Starting with Fluss 0.8, the project has upgraded its default Java language
version from **Java 8 to Java 11**. Accordingly, the official binary
distribution is now compiled with Java 11.
+As a result: The minimum required Java version for running Fluss clusters or
using Fluss connectors/clients is now Java 11. Besides, we recommend using Java
17 for production deployments of Fluss server components, as it offers better
performance and long-term support.
+
+While the source code still maintains source compatibility with Java 8,
official support for Java 8 is deprecated and will be removed in a future
release. If you must run Fluss on Java 8, you can manually build the project
from source.
+
+## Ecosystem
+
+The Apache Fluss community is actively expanding Fluss beyond the JVM
ecosystem with new **native clients** for Rust and Python, enabling seamless
integration across modern data and AI workflows.
+Weāve established an [official
repository](https://github.com/apache/fluss-rust) to host both the Rust and
Python clients, developed with performance, safety, and developer experience in
mind:
+
+- **š¦ Rust Client**: Built on async I/O, zero-copy columnar streaming (via
Apache Arrow), and Rustās memory safety guarantees, this client unlocks
high-performance query integration with native OLAP engines like DuckDB and
StarRocks.
+- **š Python Client**: Built as a native binding on top of the Rust client, it
allows Python developers to interact with Fluss tables and streams directly
from data science, ML, and analytics workflows.
+
+The Rust and Python clients are maintained in a [separate
repository](https://github.com/apache/fluss-rust) to allow for faster iteration
and releases, and therefore are not part of the Fluss 0.8 release.
+However, the community is actively stabilizing the clients and plans to
release them soon.
+
+## Upgrade Notes
+
+The Fluss community is committed to delivering a smooth upgrade experience.
This release maintains compatibility at the levels of network protocols and
storage formats, with full bidirectional compatibility between clients and
servers:
+- Clients from version 0.7 can seamlessly connect to version 0.8 servers,
+- Clients from version 0.8 are also compatible with version 0.7 servers.
+
+However, Fluss 0.8 is the first official release since the project entered the
Apache Incubator, and it includes changes such as package path updates (e.g.,
groupId and Java package names). As a result, applications that depend on the
Fluss SDK will need to make corresponding code adjustments when upgrading to
version 0.8. Please refer to the [upgrade
notes](/docs/maintenance/operations/upgrade-notes-0.8/) for a comprehensive
list of adjustments to make and issues to check during the upg [...]
+
+For a detailed list of all changes in this release, please refer to the
[release notes](https://github.com/apache/fluss/releases/tag/v0.8.0-incubating).
+
+## List of contributors
+The Apache Fluss community would like to express gratitude to all the
contributors who made this release possible:
+
+> Alibaba-HZY, CaoZhen, CenterCode, CodeDrinks, David, Giannis Polyzos,
Hemanth Savasere, Hongshun Wang, Jark Wu, Jensen, Junbo Wang, Kerwin, Leonard
Xu, Liebing, Maggie Cao, Mahesh Sambaram, MehulBatra, Michael Koepf, Rafael
Sousa, Rion Williams, Ron, Sergey Nuyanzin, SeungMin, Wang Cheng,
XianmingZhou00, Xuyang, Yang Guo, Yang Wang, Yunchi Pang, ZijunZhao, Zmm,
andybj0228, buvb, cxxwang, dependabot[bot], jackylee, leosanqing, naivedogger,
ocean.wy, pisceslj, totalo, xiaochen, xiaozhou, [...]
+
+Apache Fluss is under active development. Be sure to stay updated on the
project, give it a try and if you like it,
+donāt forget to give it some ā¤ļø via ā on
[GitHub](https://github.com/apache/fluss).
\ No newline at end of file
diff --git a/website/src/pages/downloads.md b/website/src/pages/downloads.md
index a184485d5..962d9dba9 100644
--- a/website/src/pages/downloads.md
+++ b/website/src/pages/downloads.md
@@ -2,7 +2,31 @@
> Apache Fluss (Incubating) is a streaming storage built for real-time
> analytics which can serve as the real-time data layer for Lakehouse
> architectures.
-**Note:** We are currently preparing for the first release under the Apache
Incubator. Previous releases were non-Apache releases.
+Apache Fluss 0.8.0 is the latest stable release.
+
+## Helm Chart
+Repo: https://downloads.apache.org/incubator/fluss/helm-chart
+
+## Apache Fluss 0.8.0
+
+| Artifact | Signature | SHA |
+|----------|---------|---------|
+| [Fluss Binary
Release](https://downloads.apache.org/incubator/fluss/fluss-0.8.0-incubating/fluss-0.8.0-incubating-bin.tgz)
|
[.asc](https://downloads.apache.org/incubator/fluss/fluss-0.8.0-incubating/fluss-0.8.0-incubating-bin.tgz.asc)
|
[.sha512](https://downloads.apache.org/incubator/fluss/fluss-0.8.0-incubating/fluss-0.8.0-incubating-bin.tgz.sha512)
|
+| [Fluss Source
Release](https://downloads.apache.org/incubator/fluss/fluss-0.8.0-incubating/fluss-0.8.0-incubating-src.tgz)
|
[.asc](https://downloads.apache.org/incubator/fluss/fluss-0.8.0-incubating/fluss-0.8.0-incubating-src.tgz.asc)
|
[.sha512](https://downloads.apache.org/incubator/fluss/fluss-0.8.0-incubating/fluss-0.8.0-incubating-src.tgz.sha512)
|
+| [Fluss Helm
Chart](https://downloads.apache.org/incubator/fluss/helm-chart/0.8.0-incubating/fluss-0.8.0-incubating.tgz)
|
[.asc](https://downloads.apache.org/incubator/fluss/helm-chart/0.8.0-incubating/fluss-0.8.0-incubating.tgz.asc)
|
[.sha512](https://downloads.apache.org/incubator/fluss/helm-chart/0.8.0-incubating/fluss-0.8.0-incubating.tgz.sha512)
|
+| [Fluss Tiering Service
Jar](https://repo1.maven.org/maven2/org/apache/fluss/fluss-flink-tiering/0.8.0-incubating/fluss-flink-tiering-0.8.0-incubating.jar)
|
[.asc](https://repo1.maven.org/maven2/org/apache/fluss/fluss-flink-tiering/0.8.0-incubating/fluss-flink-tiering-0.8.0-incubating.jar.asc)
|
[.sha1](https://repo1.maven.org/maven2/org/apache/fluss/fluss-flink-tiering/0.8.0-incubating/fluss-flink-tiering-0.8.0-incubating.jar.sha1)
|
+| [Connector Jar for Flink
1.20](https://repo1.maven.org/maven2/org/apache/fluss/fluss-flink-1.20/0.8.0-incubating/fluss-flink-1.20-0.8.0-incubating.jar)
|
[.asc](https://repo1.maven.org/maven2/org/apache/fluss/fluss-flink-1.20/0.8.0-incubating/fluss-flink-1.20-0.8.0-incubating.jar.asc)
|
[.sha1](https://repo1.maven.org/maven2/org/apache/fluss/fluss-flink-1.20/0.8.0-incubating/fluss-flink-1.20-0.8.0-incubating.jar.sha1)
|
+| [Connector Jar for Flink
1.19](https://repo1.maven.org/maven2/org/apache/fluss/fluss-flink-1.19/0.8.0-incubating/fluss-flink-1.19-0.8.0-incubating.jar)
|
[.asc](https://repo1.maven.org/maven2/org/apache/fluss/fluss-flink-1.19/0.8.0-incubating/fluss-flink-1.19-0.8.0-incubating.jar.asc)
|
[.sha1](https://repo1.maven.org/maven2/org/apache/fluss/fluss-flink-1.19/0.8.0-incubating/fluss-flink-1.19-0.8.0-incubating.jar.sha1)
|
+| [Connector Jar for Flink
1.18](https://repo1.maven.org/maven2/org/apache/fluss/fluss-flink-1.18/0.8.0-incubating/fluss-flink-1.18-0.8.0-incubating.jar)
|
[.asc](https://repo1.maven.org/maven2/org/apache/fluss/fluss-flink-1.18/0.8.0-incubating/fluss-flink-1.18-0.8.0-incubating.jar.asc)
|
[.sha1](https://repo1.maven.org/maven2/org/apache/fluss/fluss-flink-1.18/0.8.0-incubating/fluss-flink-1.18-0.8.0-incubating.jar.sha1)
|
+| [Filesystem Jar for AWS
S3](https://repo1.maven.org/maven2/org/apache/fluss/fluss-fs-s3/0.8.0-incubating/fluss-fs-s3-0.8.0-incubating.jar)
|
[.asc](https://repo1.maven.org/maven2/org/apache/fluss/fluss-fs-s3/0.8.0-incubating/fluss-fs-s3-0.8.0-incubating.jar.asc)
|
[.sha1](https://repo1.maven.org/maven2/org/apache/fluss/fluss-fs-s3/0.8.0-incubating/fluss-fs-s3-0.8.0-incubating.jar.sha1)
|
+| [Filesystem Jar for Aliyun
OSS](https://repo1.maven.org/maven2/org/apache/fluss/fluss-fs-oss/0.8.0-incubating/fluss-fs-oss-0.8.0-incubating.jar)
|
[.asc](https://repo1.maven.org/maven2/org/apache/fluss/fluss-fs-oss/0.8.0-incubating/fluss-fs-oss-0.8.0-incubating.jar.asc)
|
[.sha1](https://repo1.maven.org/maven2/org/apache/fluss/fluss-fs-oss/0.8.0-incubating/fluss-fs-oss-0.8.0-incubating.jar.sha1)
|
+| [Filesystem Jar for
HDFS](https://repo1.maven.org/maven2/org/apache/fluss/fluss-fs-hdfs/0.8.0-incubating/fluss-fs-hdfs-0.8.0-incubating.jar)
|
[.asc](https://repo1.maven.org/maven2/org/apache/fluss/fluss-fs-hdfs/0.8.0-incubating/fluss-fs-hdfs-0.8.0-incubating.jar.asc)
|
[.sha1](https://repo1.maven.org/maven2/org/apache/fluss/fluss-fs-hdfs/0.8.0-incubating/fluss-fs-hdfs-0.8.0-incubating.jar.sha1)
|
+| [Filesystem Jar for Google Cloud
Storage](https://repo1.maven.org/maven2/org/apache/fluss/fluss-fs-gs/0.8.0-incubating/fluss-fs-gs-0.8.0-incubating.jar)
|
[.asc](https://repo1.maven.org/maven2/org/apache/fluss/fluss-fs-gs/0.8.0-incubating/fluss-fs-gs-0.8.0-incubating.jar.asc)
|
[.sha1](https://repo1.maven.org/maven2/org/apache/fluss/fluss-fs-gs/0.8.0-incubating/fluss-fs-gs-0.8.0-incubating.jar.sha1)
|
+| [Filesystem Jar for Huawei
OBS](https://repo1.maven.org/maven2/org/apache/fluss/fluss-fs-obs/0.8.0-incubating/fluss-fs-obs-0.8.0-incubating.jar)
|
[.asc](https://repo1.maven.org/maven2/org/apache/fluss/fluss-fs-obs/0.8.0-incubating/fluss-fs-obs-0.8.0-incubating.jar.asc)
|
[.sha1](https://repo1.maven.org/maven2/org/apache/fluss/fluss-fs-obs/0.8.0-incubating/fluss-fs-obs-0.8.0-incubating.jar.sha1)
|
+
+Read the [release blog](/blog/releases/0.8/) about the new features and
significant improvements in the Apache Fluss 0.8.0 release.
+
+------------------
## Previous Releases (Non-Apache)
