Stream Cube: Streaming Big Data Computing Engine

The World’s Premier Feature Calculation Engine

Experience the future of real-time analytics with StreamCube, the industry-leading engine designed for high-performance metrics and feature calculation. Built on a cutting-edge unified storage and compute architecture, StreamCube delivers unparalleled speed—boasting microsecond-level response times and millisecond-level data freshness. Efficiency meets power: with just a minimal node footprint, StreamCube achieves million-level throughput, making it cost-effective without compromising on performance. It is purpose-built for complex time-series aggregation, capable of managing rolling and sliding windows spanning over 180 days. Equipped with dozens of built-in operators for Complex Event Processing (CEP) and volatility calculations, StreamCube turns massive data streams into instant, actionable insights.

Stream Cube has made breakthroughs in global technical challenges faced in the field of real-time data processing, including:

  • Real-time incremental computation of complex operators
  • Dynamic time-window calculations for time-series data
  • Identification of complex risk event sequences in time series
  • Real-time intelligent decision-making for high-dimensional time-series data

These successes effectively meet the high concurrency and low latency demands of big data processing in both industry and scientific research.

Primary Application Fields of Stream Cube

Key Features & Advantages of Stream Cube

Extremely High Concurrency & Ultra-Low Latency

Cluster deployment with a small number of nodes can achieve over 3 million TPS, with an average latency of less than 1 millisecond.

High Availability & High Scalability

It features a built-in distributed cache that can smoothly scale to multiple nodes when memory is insufficient. It also provides a multi-replica consistency storage mechanism based on the Paxos algorithm, ensuring high reliability of data storage.

Support for complex event computation

Supports, but is not limited to, algorithms such as time windows movement, volatility judgement, concentration judgement, continuous increment, and continuous decrement.

Loosely-Coupled & Cross-Platform

Components are designed with loose coupling, allowing for seamless integration with other platforms.

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