Real-Time Geospatial Analytics with Streaming SQL

Geospatial streaming processes location-based events — GPS coordinates, check-ins, delivery tracking, ride requests — in real time. Streaming SQL computes distance, proximity, geofencing, and spatial aggregations.

Overview

Why Streaming?

Traditional batch approaches compute these metrics hourly or daily. Streaming SQL computes them continuously — every event is processed within milliseconds, and materialized views always reflect the current state.

Implementation Pattern

-- Universal streaming pattern
CREATE SOURCE events (...) WITH (connector='kafka', topic='events', ...);

CREATE MATERIALIZED VIEW metrics AS
SELECT dimension, COUNT(*) as count, SUM(amount) as total,
  AVG(amount) as avg_amount, MAX(ts) as last_event
FROM events WHERE ts > NOW() - INTERVAL '24 hours'
GROUP BY dimension;

-- Query with any PostgreSQL client
SELECT * FROM metrics ORDER BY count DESC;

Architecture

Data Sources → RisingWave (SQL Processing) → Materialized Views (Serving)
                                            → Iceberg Sink (Historical)

RisingWave provides both real-time serving (via PostgreSQL protocol) and historical storage (via Iceberg sink). Applications, dashboards, and AI agents query the same materialized views.

Key Benefits

Sub-second freshness: Views update with every event
SQL-only: No Java, no custom code, no streaming frameworks
PostgreSQL compatible: Use psql, Grafana, Metabase, any PG driver
Open source: Apache 2.0, self-hostable, no vendor lock-in
S3 state: Elastic scaling, fast recovery, cost-efficient storage

Frequently Asked Questions

How fresh is the data?

RisingWave materialized views update within milliseconds of each event. Point queries return in 10-20ms p99. This is real-time — not near-real-time.

Do I need to change my application code?

No. RisingWave speaks PostgreSQL protocol. Any application that queries PostgreSQL can query RisingWave without code changes. Just change the connection string.