Complex Event Processing (CEP)

The fundamental capability to identify complex patterns across multiple event streams, not just single events. CEP engines use Event Processing Languages (EPLs) to define rules that match sequences, absences, and temporal correlations.

• Sequence: Detecting an A event followed by a B event within 5 minutes.
• Absence: Alerting when an expected C event does not occur after a D event.
• Aggregation: Triggering when the sum of transaction amounts exceeds a threshold within a sliding window.

CEP is inherently time-aware, processing events based on their event time, not just system processing time. It uses various windowing strategies to scope pattern matching.

• Sliding Window: Continuously updating aggregations (e.g., "count of transactions in the last 10 minutes").
• Tumbling Window: Fixed, non-overlapping time chunks (e.g., "hourly fraud summary").
• Session Window: Dynamic windows based on periods of activity followed by inactivity, crucial for tracking user sessions.
• Watermarks: Mechanisms to handle out-of-order events and define when a window's results are considered complete.

CEP transforms raw, low-level events into higher-level, meaningful complex or derived events. This creates an event hierarchy that reduces noise and provides business-level insights.

• Raw Event: A single credit card swipe (event_type: "txn", amount: 150.00).
• Composite Event: A "Suspicious Velocity Alert" derived from 5 raw swipe events from the same card at different locations within 30 minutes.
• Derived Event: A "High-Risk Session" event that aggregates a composite velocity alert with a failed login attempt and a device fingerprint mismatch.

Beyond pattern matching, CEP engines can establish and query causal relationships between events. This allows the system to answer not just what happened, but why it happened by linking cause-and-effect across disparate sources.

• Example: Correlating a password_change event with a subsequent large_wire_transfer event on the same account, establishing a potential account takeover chain.
• Implementation: Uses event payload references and correlation IDs to build a real-time causal graph, enabling root cause analysis for fraud investigations.

CEP is a stateful paradigm. The engine must maintain the state of in-flight pattern matches, windowed aggregations, and event histories to detect patterns that span multiple events over time. This is a key differentiator from stateless stream processing.

• State Store: A high-performance, fault-tolerant storage layer (often in-memory with disk persistence) that holds partial matches.
• Example: A rule looking for "3 failed logins followed by a success" must store the count of failed logins per user as state until the pattern is completed or times out.

The ultimate goal of CEP is to trigger situationally aware actions in milliseconds. It's not just for analytics; it's an operational system that drives real-time decisions.

• Actions: Blocking a transaction, alerting a fraud analyst with full context, triggering step-up authentication, or dynamically adjusting a risk score.
• Integration: CEP engines integrate directly with the authorization flow via APIs or message queues to enforce decisions before a transaction is approved.
• Latency: Optimized for P99 latency under 10ms for high-throughput financial pipelines.