event-time

Pulse is a tiny real-time data streaming framework (mini Flink/Beam) in Rust. Async (Tokio), pluggable operators, state, and I/O. Fast, modular, local-first.

A stream processing engine that stays correct when processes die. SQL or typed C++ over event time and keyed state, with exactly-once delivery and twenty-plus connectors: brokers, CDC, databases, lakehouse tables, graphs. The same pipeline runs on a single machine, inside a service or scaled out across a highly available cluster. Arrow-native C++.

Deterministic reconciliation engine with typed breaks, replayable decisions, and audit trail.

Delayed conversion labels: freshness, correctness, corrections, processing lag, and retained state.

Real-time decisions on an electricity distribution network. Deterministic code owns whether a window is closed; models only forecast, score and rank. Seven claims proved offline and against a deployed AWS estate — 0 rows published early of 3,779, 41 gate mutations refused, all six erasure legs confirmed.

Event-time windows and watermarks for Ruby. AMS Akidau, NCC-4219.

Real-time ML features from a click stream with event-time windows, where late and out-of-order events are handled explicitly; a reference engine defines the right answer and the PyFlink job is checked against it.

Streaming telemetry for AI agents: Kafka → PyFlink event-time windows (late, out-of-order events) → per-minute latency and tool-call metrics, enriched from Postgres, on a live dashboard.

Kafka streaming lab demonstrating partitioned topics, duplicate handling, late events, DLQ routing, and measured end-to-end latency.

Transactional event-time windows with durable source receipts, immutable outbox outputs, process-crash recovery tests, and lateness tradeoff experiments.

MoonBit event-time correction engine: atomic retractions, session merge/split, finality guards, downstream changelogs and replay checkpoints.

Exactly-once streaming pipeline: Kafka KRaft to Flink to ClickHouse. Zero data loss verified by killing Flink mid-stream, not by assertion.

Cross-engine output validation for event-time streaming pipelines.

Kafka -> Spark Structured Streaming -> Delta, with event-time windowing and watermarking made runnable: a dependency-free reference implementation you can execute without a JVM

Interactive F# lab for watermark lag, late events, idle partitions, incomplete windows, and delayed alerts.

Kafka-compatible streaming demo for real-time birding intelligence: event-time windows, late-event handling, stateful deduplication, hotspot metrics, and target-species alerts.

Watermark Lens - diagnostic tooling for Apache Flink watermarks, windows, and late data

Apache Flink and PyFlink streaming lab demonstrating event time, managed keyed state, checkpoints, controlled TaskManager failure, and validated state recovery.

Deterministic .NET 10 stream-processing simulator exploring event time, watermarks, late events, and correctness-latency-memory trade-offs with SVG timelines.