varpulis-engine-wasm 0.9.0

Documentation · Live Demo · Quick Start · Examples · Benchmarks

stream FraudAlert = Events
    .where(type == "login") as e1
    -> Events.where(type == "transfer") as e2
    -> Events.where(type == "transfer") as e3
    .within(5m)
    .where(e2.amount + e3.amount > 10000)
    .forecast(confidence: 0.8, horizon: 2m)
    .alert(webhook: "https://ops.example.com/fraud", message: "User {e1.user}: ${e2.amount + e3.amount}")
    .emit(user: e1.user, total: e2.amount + e3.amount)

Login → two transfers over $10K within 5 minutes. .forecast() fires before the pattern completes. No other open-source CEP engine does this.

Quick Start

curl -sSf https://raw.githubusercontent.com/varpulis/varpulis/main/scripts/install.sh | sh
varpulis interactive --no-tui

vpl> event Tick: price: float
vpl> stream Spike = Tick .where(price > 100) .emit(alert: "spike", price: price)
vpl> Tick { price: 42.0 }
vpl> Tick { price: 150.0 }
→ Spike: {"alert":"spike","price":150}
vpl> :save spike_detector.vpl

Copy-paste. 30 seconds. No files, no connectors, no Docker.

The default varpulis interactive opens a split-pane TUI with topology, live events, input, and metrics. Add --no-tui for a plain text shell, --json for agent automation.

Why Varpulis?

¹ Approximate figures from published benchmarks and vendor documentation; workload-dependent.

.forecast() is unique. It uses Probabilistic Suffix Trees to predict that a pattern is about to complete — before the final event arrives. Combined with Hawkes process intensity estimation and conformal prediction intervals, it turns reactive CEP into proactive alerting.

Performance

Single core. Detailed benchmarks →

Connectors

Each connector is an independent crate. The default binary includes all; build with --features mqtt,kafka for a minimal binary.

Features

• Pipeline operators: .where(), .window(), .aggregate(), .emit(), .to(), .alert()
• SASE+ patterns: sequences (->), Kleene closures (+, *), negation (AND NOT)
• Forecasting: .forecast() — PST-based prediction with confidence and horizon
• Alert webhooks: .alert(webhook: "url", message: "{field}") — fire-and-forget
• Windows: tumbling, sliding, session, count-based
• Aggregations: 15+ functions (sum, avg, ema, percentile, stddev, ...) — SIMD-accelerated
• Joins: inner, LEFT, RIGHT, FULL outer with null-fill
• Imperative: var, if/else, while, for, functions, lambdas
• Compile-time meta-programming: for row in 0..4: generates streams

• Interactive TUI with split-pane topology/events/metrics (varpulis interactive)
• Schema inference from sample data (varpulis infer --input data.jsonl)
• Pipeline trace / explain mode (--trace)
• Watch mode with auto-reload (--watch)
• VS Code extension (LSP: diagnostics, completion, hover, go-to-definition)
• MCP server for AI-assisted development
• JSON-line protocol for agent automation (--json)

• Single binary, Docker, Kubernetes (Helm chart included)
• Coordinator/worker cluster with Raft consensus
• Multi-tenant SaaS mode with RBAC and SSO/OIDC
• Prometheus metrics, OpenTelemetry tracing, Grafana dashboards
• RocksDB state persistence with optional AES-256-GCM encryption
• Circuit breaker, dead letter queue, backpressure signaling

Documentation

Contributing

Contributions welcome — see CONTRIBUTING.md.

License

Dual-licensed under MIT or Apache-2.0.

Acknowledgments

SASE/SASE+ — Wu et al. SIGMOD 2006, Agrawal et al. SIGMOD 2008 · Hamlet — Poppe et al. SIGMOD 2021 · Built with Pest and Tower-LSP