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Exploring Apache GeaFlow (Incubating)'s Temporal Capabilities — Breathing New Life into Time-Series Data!

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.

    Despite data's undeniable importance, traditional graph analytics tools often struggle with dynamic data challenges:

  • Limitations of Static Analysis
        Static analysis captures only a snapshot of data at a single moment, failing to reflect trends. For instance, in device monitoring, it may overlook gradual transitions from normal to faulty states.

  • Inefficient Processing
        Traditional tools are inefficient when handling large-scale temporal data and may not meet real-time requirements. In financial risk control, delays can mean missing critical signals.

  • Lack of Flexibility
        Many tools support only one type of analysis and cannot concurrently process real-time streams and historical data.

    To address these issues, Apache GeaFlow (Incubating) innovatively introduces temporal graph computing. As a distributed stream-graph engine designed for dynamic data, GeaFlow efficiently tackles challenges posed by evolving datasets. For dynamically changing graph structures, users can seamlessly perform operations like graph traversal, pattern matching, and computations—meeting complex analytical needs. By integrating temporal dimensions with dynamic graph processing, GeaFlow offers a groundbreaking solution for real-time analytics, empowering users to extract deeper value from dynamic data.

What Is Apache GeaFlow (Incubating)?​

GeaFlow is a powerful distributed computing platform that combines graph computing and stream processing to handle dynamic graphs and temporal data efficiently. It supports complex graph algorithms and real-time analytics, making it ideal for dynamic scenarios. Key features include:

  • Distributed Architecture
    GeaFlow's framework processes ultra-large dynamic graphs (e.g., billions of nodes/edges) with high availability and scalability via partitioning and replication.

  • Seamless Integration of Stream Graphs and Temporal Graphs
    Stream graphs enable real-time updates for dynamic data, while temporal graphs add precise timestamping. Their synergy supports simultaneous real-time analysis and historical tracing.

  • Flexible Time-Window Mechanism
    Users can configure sliding or tumbling windows to analyze data trends over specific time ranges.

How Do Stream Graphs and Temporal Graphs Relate?​

1. Stream Graph​

A specialized graph structure representing evolving data dynamics. Core features:

  • Dynamic Update Mechanism
    Supports real-time CRUD operations on nodes/edges. E.g., new edges form for fund flows in financial networks, while obsolete edges disappear.
  • Event-Driven Model
    Treats each data unit (node/edge) as an event to efficiently capture changes.
  • Incremental Computation
    Computes only new/modified parts instead of reprocessing entire graphs. E.g., updating friend relationships in social networks without recalculating the entire graph.

2. Temporal Graph​

A timestamp-augmented graph where each edge/node records event timing. Core features:

  • Timestamp Management
    Assigns timestamps to all data. E.g., friendship formation times in social networks.
  • Time-Window Analysis
    Supports sliding windows (e.g., last 5 minutes) to track trends.
  • Historical Traceability
    Retains historical timestamps for retrospective analysis. E.g., auditing past anomalous transactions in risk control.

3. Synergy Between Stream and Temporal Graphs​

They complement each other:

  • Stream Graphs as the Foundation
    Stream graphs handle real-time updates; temporal graphs add time-based recording.
  • Temporal Graphs Enhance Stream Analysis
    Timestamps enable complex operations like trend prediction and window-based analytics.

4. Apache GeaFlow (Incubating)’s Implementation​

Apache GeaFlow (Incubating) unifies stream and temporal graphs through:

  • Timestamp Assignment
    Assigns processing time or event time to all data.
  • Dynamic Updates & Historical Retention
    Updates graphs in real-time while preserving historical timestamps in distributed storage.
  • Time-Window Optimization
    Uses indexing and caching (e.g., sliding-window indexes) to accelerate time-range queries.

Use Case: Tracking Indirect Social Relationships​

As social platforms grow, analyzing dynamic user interactions in real-time becomes critical for recommendations and risk detection (e.g., fake accounts).

Scenario: A platform tracks "indirect friendships"—e.g., whether user A met user C via user B, with strict time validation (A→B before B→C). This optimizes recommendations and identifies risks.

Requirements:

  1. Real-Time Processing: Capture friend-add events instantly.
  2. Time Sensitivity: Validate sequence (e.g., A adds B at 10:00; B adds C at 10:05).
  3. Efficient Queries: Rapidly identify valid triads (A→B→C) and export results.
  4. Scalability: Handle massive user data and future expansions (e.g., adding relationship weights).

DSL Implementation:

CREATE TABLE vertex_source (
    id long,
    name varchar,
    age int
) WITH (
    type='kafka',
    geaflow.dsl.kafka.servers='localhost:9092',
    geaflow.dsl.kafka.topic='vertex_source',
    geaflow.dsl.time.window.size=10
);

CREATE TABLE edge_source (
    src_id long,
    tar_id long,
    weight double,
    ts long -- Timestamp of relationship
) WITH (
    type='kafka',
    geaflow.dsl.kafka.servers='localhost:9092',
    geaflow.dsl.kafka.topic='edge_source',
    geaflow.dsl.time.window.size=10
);

CREATE GRAPH community (
    Vertex person (id bigint ID, name varchar, age int),
    Edge knows (
        src_id bigint SOURCE ID,
        tar_id bigint DESTINATION ID,
        weight double,
        ts long TIMESTAMP -- Timestamp field
    )
) WITH (storeType='rocksdb');

INSERT INTO community.person
SELECT id, name, age FROM vertex_source;

INSERT INTO community.knows
SELECT src_id, tar_id, weight, ts FROM edge_source;

CREATE TABLE tbl_result (
    a_id long,
    e1_ts long,
    b_id long,
    e2_ts long,
    c_id long
) WITH (type='file', geaflow.dsl.file.path='${target}');

USE GRAPH community;

INSERT INTO tbl_result
SELECT a_id, e1_ts, b_id, e2_ts, c_id
FROM (
    MATCH (a:person)-[e1:knows]->(b:person)-[e2:knows]->(c:person)
    WHERE e2.ts > e1.ts
    RETURN a.id AS a_id, e1.ts AS e1_ts, 
           b.id AS b_id, e2.ts AS e2_ts, c.id AS c_id
);

Workflow Explanation:

  1. Vertex Source: Kafka-consumed user data (ID, name, age) with 10s sliding windows.
  2. Edge Source: Kafka-consumed relationships (source/target IDs, weight, timestamp) in 10s windows.
  3. Graph Schema: Defines person vertices and knows edges with timestamps.
  4. Data Insertion: Loads vertices/edges into the community graph.
  5. Query: Finds triads A→B→C where B→C occurs after A→B.
  6. Result Export: Writes valid triads (A_ID, B_ID, C_ID, timestamps) to files.

Output Example:

a_id | e1_ts      | b_id | e2_ts      | c_id
1    | 1672531200 | 2    | 1672531210 | 3  -- Alice (1) met Charlie (3) via Bob (2)

Business Value:

  1. Enhanced Recommendations: Suggest potential friends (e.g., recommend Charlie to Alice).
  2. Community Detection: Identify tight-knit groups for targeted ads/events.
  3. Risk Control: Flag suspicious triads (e.g., rapid fake-account connections).
  4. User Experience: Personalize services via real-time relationship analysis.

Technical Edge:

  • Real-Time: Millisecond processing for up-to-date graphs.
  • Time-Aware: Timestamps enforce chronological validity.
  • Flexible: SQL-like syntax lowers development barriers.
  • Scalable: Handles massive dynamic graphs via incremental computation.

Core Highlights of Apache GeaFlow (Incubating)’s Temporal Capabilities​

1. Time-Aware Data Processing​

Timestamps enable precision. Apache GeaFlow (Incubating) supports:

  • 5-Minute Trend Analysis: Track real-time interaction frequency shifts.
  • 24-Hour Dynamic Patterns: Identify long-term trends (e.g., user purchase behavior).

2. Dynamic Graph + Temporal Fusion​

Captures relationship evolution:

  • Social Dynamics: Map changing friend networks over time.
  • Financial Flows: Trace real-time capital movements and risks.

3. Real-Time + Historical Data Fusion​

Unifies streaming and stored data:

  • IoT Monitoring: Predict device failures by correlating live feeds with history.
  • Risk Control: Detect anomalies via real-time/historical transaction cross-analysis.

4. Rich Built-in Algorithms​

Optimized temporal algorithms:

  • Shortest Path
  • Weakly Connected Components
  • k-Hop Neighborhood

Conclusion: Start Your Temporal Data Journey​

Dynamic data holds immense value, and GeaFlow’s temporal capabilities unlock it. Whether you’re a novice or an expert, GeaFlow empowers you to harness time-series data.

Download Apache GeaFlow (Incubating) today and explore the power of temporal analytics!

Terminology​

DSL: Domain-Specific Language. GeaFlow’s unified DSL integrates SQL and ISO/GQL for relational and graph analysis (pattern matching, algorithms), supporting hybrid table/graph operations.