Why Are Temporal Capabilities So Crucial?​

In today's digital era, data has become a core resource driving decisions and innovation. However, data is not just static numbers or relationships—it constantly evolves over time. Whether tracking real-time fluctuations in stock markets, dynamic interactions in social networks, or status updates from IoT devices, the temporal dimension is key to understanding this data. For example:

• In finance, the sequence of transactions determines the direction of capital flow.
• In social networks, user interactions (likes, comments) evolve over time.
• In IoT, timestamped sensor data reflects changes in device status.

Problem Background​

In streaming computing, data rarely arrives all at once but is continuously input and processed. Similarly, in graph computing/graph querying scenarios, vertices and edges are constantly read from data sources to construct graphs incrementally. In incremental graph queries, the graph evolves continuously, leading to different query results across graph versions. When new vertices/edges form an updated graph version, recomputing through the entire graph incurs high overhead and duplicates historical computations. Since historical data has already been processed, ideally only the delta-affected portions should be computed/queried without full-graph re-execution.

Author: Lin Litao

1. Introduction: The Dilemma and Breakthrough in Traditional Data Warehouses​

1. Contextual Problem: When Data Association Becomes a Business Pain Point​

• Financial Anti-Fraud Scenario: In anti-fraud analysis, complex multi-layered fund chain mining often relies on multi-table JOIN operations for intricate multi-hop tracking. Analyst teams spend days writing SQL scripts, and the final query can take hours — by which time the funds have already been laundered. This reveals a deep contradiction in traditional data warehouses: the misalignment between the relational paradigm and real-world networked business logic, often leading to high query latency and complex logic.
• Marketing Analysis Scenario: In analyzing marketing business relationships, identifying potential VIP customers through social connection chains requires advanced data analysis skills. Although tools like DeepInsight AI Copilot now allow users to quickly generate dimensions and metrics with 80% accuracy via large models and integrate them into self-service dashboards, these analyses often involve deep user associations, which perform poorly when expressed intuitively in SQL.

March 2025 saw the release of streaming graph computing engine Apache GeaFlow (Incubating) v0.6.4. This version implements multiple significant feature updates, including:

• 🍀 Experimental support for storing GeaFlow graph data in Paimon data lake
• 🍀 Enhanced graph data warehouse capabilities: Supports relational access to graph entities
• 🍀 Unified memory manager support
• 🍀 RBO rule extensions: New MatchEdgeLabelFilterRemoveRule and MatchIdFilterSimplifyRule
• 🍀 Support for incremental matching operators

Author: Kunyu; Reviewer: Dongshuo.

In a previous article "Stream4Graph: Incremental Computation on Dynamic Graphs", we introduced how introducing incremental computation into graph computing—essentially combining "graphs + streams"—allowed Apache GeaFlow (Incubating) to significantly outperform Spark GraphX in terms of performance. Now, the question arises: when we introduce graph computing capabilities into stream computing—combining "streams + graphs"—how does GeaFlow compare to Flink's associative computation performance?

In today’s era, data is being generated at an unprecedented speed and scale, and real-time processing of massive datasets has wide applications in various fields such as anomaly detection, search recommendations, and financial transactions. As one of the core technologies for real-time data processing, stream computing has become increasingly important.

Author: Zhang Qi

It's well known that when we need to perform correlation analysis on data, we typically use SQL join operations. However, Cartesian product calculations during SQL joins require maintaining a large number of intermediate results, which significantly impacts overall data analysis performance. In contrast, graph-based approaches maintain data correlations, transforming correlation analysis into graph traversal operations and greatly reducing the cost of data analysis.

However, with the continuous growth in data scale and increasing demand for real-time processing, efficiently solving real-time computation problems on large-scale graph data has become increasingly urgent. Traditional computing engines such as Spark and Flink are gradually falling short of meeting the growing business demands for graph data processing. Therefore, designing a real-time processing engine tailored for large-scale graph data will bring significant advancements to big data processing technologies.

Stream graph computing engine Apache GeaFlow (Incubating), which combines the technical advantages of graph processing and stream processing. It implements incremental computation capabilities on dynamic graphs, enhancing real-time performance in high-performance correlation analysis. In the following sections, we will introduce the characteristics of graph computing technology, how the industry addresses large-scale real-time graph computing challenges, and GeaFlow's performance in dynamic graph computation.