Stream Monitoring

Real-Time Alerting on Streaming Data

Evaluate alert conditions continuously over Kafka streams using SQL. RisingWave detects threshold breaches within milliseconds — no polling, no cron jobs, no Flink cluster.

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Sub-Second

Detection Latency

Alert conditions evaluate continuously as events arrive — breaches detected within milliseconds of the triggering event

SQL

Alert Logic

Express complex alert conditions — windowed counts, rate-of-change, cross-stream correlations — in standard SQL without code

PostgreSQL

Integration Interface

Query alert state or stream alert triggers to Slack, PagerDuty, or any webhook-based notification system via PostgreSQL

Multi-Source

Event Sources

Alert on Kafka streams, database CDC events, and HTTP sources simultaneously within a single SQL alert definition

Why Streaming Alerts

Why do alerting systems need continuous stream evaluation?

Polling-based alerting systems check conditions every 30 seconds or every minute. In that window, a fraud transaction can complete, an SLA breach can affect thousands of users, or an attack can escalate. Continuous stream evaluation checks conditions against every event as it arrives, reducing detection latency from minutes to milliseconds.

Factor	Polling / Cron	RisingWave
Detection Latency	Minutes (polling/cron)	Sub-second (streaming)
Alert Logic	Code-based CEP or scripts	Declarative SQL conditions
Infrastructure	Flink cluster or Lambda	Single SQL system
False Positive Control	Manual deduplication code	SQL window deduplication

→Detect breaches within milliseconds of the triggering event rather than at the next polling interval
→Express complex multi-condition alert logic — correlated events, rate-of-change, cross-stream thresholds — in SQL
→Eliminate alert storms using SQL window deduplication without custom code
→Alert on Kafka streams, database CDC changes, and other sources with the same SQL framework

Use Cases

What types of alerts benefit from continuous stream evaluation?

Any alerting use case where minutes of detection latency causes measurable harm. Infrastructure incidents, fraud, business threshold violations, and security events all have lower blast radius when detected in sub-second rather than at the next polling interval.

Infrastructure and SLA Monitoring

Detect p99 latency spikes, error rate increases, and throughput drops from application event streams within seconds — before SLA breaches affect customers

Fraud and Anomaly Alerts

Evaluate multi-condition fraud signals — transaction velocity, unusual amounts, geographic anomalies — continuously over card and account event streams for real-time fraud alerts

Business Threshold Alerts

Alert operations teams when revenue per hour drops below target, cart abandonment rate spikes, or inventory levels breach reorder points based on real-time event streams

Security and Compliance Events

Monitor authentication failures, privilege escalation attempts, and unusual access patterns from log event streams, triggering security alerts within seconds of suspicious activity

How It Works

How does RisingWave evaluate streaming alert conditions?

RisingWave ingests event streams from Kafka and continuously evaluates SQL-defined alert conditions using materialized views. Alert state — which conditions are active, when they last triggered — is maintained as incrementally updated SQL results. Your notification service polls or subscribes to the alert materialized view, emitting notifications when conditions are met.

→Create a Kafka source in RisingWave pointing to your event topic using a SQL CREATE SOURCE statement
→Define alert conditions as SQL materialized views with window aggregations — COUNT(*) OVER 5 MINUTES, AVG() > threshold
→Use stream-table joins to enrich alert conditions with configuration data, SLA thresholds, or user context
→Query the alert materialized view from a notification service — trigger Slack, PagerDuty, or email when conditions are active
→Add SQL WHERE clauses to deduplicate alerts within cooldown windows, preventing notification storms

Frequently Asked Questions

How do I build a real-time alerting system on streaming data?

How does RisingWave compare to Apache Flink for stream-based alerting?

How do I avoid alert storms with streaming alerts?

Can I alert on database changes as well as Kafka events?

Detect issues in milliseconds, not minutes

Define alert conditions in SQL over your Kafka streams and start getting sub-second notifications without Flink or custom code.

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