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Build a Real-Time Transit Monitor

Tutorial

Build a real-time transit monitoring system that ingests GTFS feeds, stores time-series data in Apache Ignite 3, and runs SQL-based monitoring queries.

ignite3gridgain9
Intermediate|2-3 hours|getting-started
Tested on
note

This tutorial uses Apache Ignite 3. The same code and commands work identically with GridGain 9. Substitute the GridGain 9 Docker image and Maven coordinates from How to Start a Cluster with Docker Compose if you are using GridGain.

Transit data combines real-time streams, time-series storage, geospatial tracking, and complex queries, making it an ideal context for learning Ignite's distributed data capabilities. In this tutorial, you build a real-time transit monitoring system that exercises all of these patterns.

What You Learn​

  • Connect to real-time GTFS transit data feeds
  • Model time-series data with Ignite 3 schema annotations
  • Build a batch ingestion pipeline with transaction support
  • Write SQL analytics queries against distributed data
  • Detect service anomalies using polling-based monitoring

What You Build​

By the end of this tutorial, the application produces a live console dashboard:

╔══════════════════════════════════════════════════════╗
║            TRANSIT MONITORING DASHBOARD              ║
╚══════════════════════════════════════════════════════╝
Current time: 2023-05-21 14:22:38

SUMMARY VIEW
────────────────────────────────────────────────────────────────
ACTIVE VEHICLES BY ROUTE (last 15 minutes)
• Route 14      : 32 vehicles ↑
• Route 5       : 30 vehicles =
• Route 38      : 28 vehicles ↓
• Route 1       : 26 vehicles =
• Route 8       : 24 vehicles ↑

VEHICLE STATUS DISTRIBUTION
• IN_TRANSIT_TO  : 342 vehicles ↑
• STOPPED_AT     : 198 vehicles ↓

DATA INGESTION STATUS
• Status: Running
• Total records fetched: 45,280
• Total records stored: 45,280
• Last fetch count: 540
• Last fetch time: 876ms
• Running time: 01:23:45
• Ingestion rate: 9.12 records/second

Views rotate automatically every 10 seconds
Press ENTER at any time to exit

The application connects to real-time transit data feeds, stores vehicle positions in a distributed Ignite database, analyzes movement patterns, and alerts operators to potential service disruptions. The system architecture includes:

  • A clustered Ignite database for distributed storage
  • A data ingestion pipeline for real-time updates
  • SQL-based analytics for operational insights
  • A monitoring system for service disruptions
  • A real-time console dashboard for visualization

Prerequisites​

  • Java 11 or later installed and properly configured
  • Maven 3.6+ for dependency management
  • Docker 20.10+ and Docker Compose for running the Ignite 3 cluster
  • IDE such as IntelliJ IDEA or VS Code with Java extensions
  • Basic Java knowledge, including familiarity with classes, interfaces, and collections
  • Some SQL experience for understanding the query examples

Tutorial Modules​

This tutorial consists of 6 modules that build incrementally:

  1. Project Setup - Configure environment and dependencies
  2. Understanding GTFS - The transit data format and Ignite schema
  3. GTFS Client - Build the data fetching component
  4. Data Ingestion - Create the data pipeline
  5. Service Monitoring - Implement analytics and alerts
  6. Putting It All Together - Complete application integration

Source Code​

The complete source code is available on GitHub:

Repository: https://github.com/maglietti/transit-monitor

git clone https://github.com/maglietti/transit-monitor.git
cd transit-monitor

Each module builds on the previous ones, with clear checkpoints to validate progress. Complete the modules in a single session or spread them across multiple days.

Start with Module 1: Project Setup to set up the project structure and configure the Ignite cluster.