Introduction​

Apache Fluss is a streaming data storage system built for real-time analytics, serving as a low-latency data layer in modern data Lakehouses. It supports sub-second streaming reads and writes, storing data in a columnar format for efficiency, and offers two flexible table types: append-only Log Tables and updatable Primary Key Tables. In practice, this means Fluss can ingest high-throughput event streams (using log tables) while also maintaining up-to-date reference data or state (using primary key tables), a combination ideal for scenarios like IoT, where you might stream sensor readings and look up information for those sensors in real-time, without the need for external K/V stores.

Background​

At the core of Fluss’s Lakehouse architecture sits the Tiering Service: a smart, policy-driven data pipeline that seamlessly bridges your real-time Fluss cluster and your cost-efficient lakehouse storage. It continuously ingests fresh events from the fluss cluster, automatically migrating older or less-frequently accessed data into colder storage tiers without interrupting ongoing queries. By balancing hot, warm, and cold storage according to configurable rules, the Tiering Service ensures that recent data remains instantly queryable while historical records are archived economically.

In this blog post we will take a deep dive and explore how Fluss’s Tiering Service orchestrates data movement, preserves consistency, and empowers scalable, high-performance analytics at optimized costs.

🌊 We are excited to announce the official release of Fluss 0.7!

This version has undergone extensive improvements in stability, architecture, performance optimization, and security, further enhancing its readiness for production environments. Over the past three months, we have completed more than 250 commits, making this release a significant milestone toward becoming a mature, production-grade streaming storage platform.

Traditional streaming data pipelines often need to join many tables or streams on a primary key to create a wide view. For example, imagine you’re building a real-time recommendation engine for an e-commerce platform. To serve highly personalized recommendations, your system needs a complete 360° view of each user, including: user preferences, past purchases, clickstream behavior, cart activity, product reviews, support tickets, ad impressions, and loyalty status.

That’s at least 8 different data sources, each producing updates independently.

Introducing the Little Otter​

Today is World Otter Day, and we are thrilled to introduce the little otter to the Fluss community! 🎉

Since open-sourced half a year ago, many community members and friends have asked us: "When will Fluss get a logo?" After more than a month of careful design work and over 30 iterations, we’re excited to finally unveil the official Fluss logo — a surfing otter! 🦦🌊

The Fluss community is pleased to announce the official release of Fluss 0.6.0. This version has undergone over three months of intensive development, bringing together the expertise and efforts of 45 contributors worldwide, with more than 200 code commits completed. Our heartfelt thanks go out to every contributor for their invaluable support!

The unification of Lakehouse and streaming storage represents a major trend in the future development of modern data lakes and streaming storage systems. Designed specifically for real-time analytics, Fluss has embraced a unified Streaming and Lakehouse architecture from its inception, enabling seamless integration into existing Lakehouse architectures.

Fluss is designed to address the demands of real-time analytics with the following key capabilities:

• Real-Time Stream Reading and Writing: Supports millisecond-level end-to-end latency.
• Columnar Stream: Optimizes storage and query efficiency.
• Streaming Updates: Enables low-latency updates to data streams.
• Changelog Generation: Supports changelog generation and consumption.
• Real-Time Lookup Queries: Facilitates instant lookup queries on primary keys.
• Streaming & Lakehouse Unification: Seamlessly integrates streaming and lakehouse storage for unified data processing.

We have discussed the challenges of using Kafka for real-time analytics in our previous blog post. Today, we are excited to introduce Fluss, a cutting-edge streaming storage system designed to power real-time analytics. We are going to explore Fluss's architecture, design principles, key features, and how it addresses the challenges of using Kafka for real-time analytics.

The industry is undergoing a clear and significant shift as big data computing transitions from offline to real-time processing. This transition is revolutionizing various sectors, including the E-commerce, automotive networking, finance, and beyond, where real-time data applications are becoming integral to operations. This evolution enables organizations to unlock greater value by leveraging real-time insights to drive business impact and enhance decision-making.

Earlier this year at Flink Forward 2024 Berlin we announced Fluss and today we are thrilled to announce open-sourcing the project. Fluss is a streaming storage system designed to power real-time analytics. It aspires to change how organizations approach real-time data by acting as the real-time data layer for the Lakehouse. Its cutting-edge design enables businesses to achieve sub-second latency, high throughput, and cost efficiency for data analytics, making it the ideal solution for modern data-driven applications.

We have historically invested a lot of effort into advancing the data streaming ecosystem, being major contributors to Apache Flink®, Apache Flink CDC, and Apache Paimon. As part of our commitment, Fluss is now open source under the Apache 2.0 license and is available on GitHub, inviting users to create the next generation of real-time architectures.