Vector

How Vector is structured

Vector is architected as a modular Rust Cargo workspace divided between the core daemon binary and specialized internal libraries:

• src/: Contains the Vector CLI, runtime lifecycle orchestration, configuration loader, pipeline topology graph builder, and component registry.
• lib/vector-core: Defines the internal unified data model representing log events, metric series (counters, gauges, sets, distributions, sketches), and trace spans.
• lib/vector-vrl: Houses the parser, compiler, standard library, and runtime evaluator for the Vector Remap Language (VRL).
• lib/vector-buffers: Implements backpressure-aware buffer queues supporting both volatile in-memory circular rings and durable disk-backed storage.
• lib/codecs: Encoders and decoders for wire protocols including JSON, NDJSON, Syslog, Apache Avro, Apache Arrow, Parquet, and OpenTelemetry Protobuf.
• lib/file-source: High-performance filesystem tailing engine with inode tracking, file rotation handling, and checkpoint persistence.

Frontend

As an infrastructure data pipeline daemon, Vector does not serve an interactive end-user web GUI directly from its binary:

• CLI Utilities & TUI: Vector provides an integrated terminal interface (vector top and vector tap) allowing operators to observe live pipeline topology throughput, buffer utilization, and inspect live data streams matching filter patterns.
• Documentation & Playground Website: The documentation portal (website/) is built with Hugo, Alpine.js, and Tailwind CSS, featuring an interactive WebAssembly-powered VRL playground for real-time expression testing.
• GraphQL & REST Admin API: An embedded Axum/Hyper HTTP server exposes runtime status, schema validation, and health checks for integration with monitoring dashboards.

Backend & APIs

Vector's internal engine executes pipelines organized into directed acyclic graphs (DAGs) of streaming components:

• Sources: Ingestion handlers that consume events from over 40 sources, including syslog listeners, HTTP endpoints, Kubernetes container logs, Kafka topics, and AWS CloudWatch.
• Transforms: Real-time processing nodes that mutate events using VRL, filter events by predicate, sample high-volume streams, or aggregate log events into metrics.
• Sinks: Outbound egress handlers that batch, compress (gzip, zstd, snappy), and transmit data to destinations such as ClickHouse, Elasticsearch, AWS S3, Datadog, Prometheus, Kafka, and Loki.
• End-to-End Acknowledgements: Vector propagates delivery confirmations backwards through the DAG, ensuring data is only acknowledged at the source once safely flushed to sinks or durable buffers.

Data & persistence

Vector processes ephemeral telemetry streams in memory but provides robust disk-based persistence for operational resilience:

• Durable Disk Buffers: When configured with type = "disk", incoming events are persisted into write-ahead log structures on disk before processing, surviving process crashes and downstream outages without message loss.
• Checkpoints & Offsets: Source position checkpoints (such as file cursor byte offsets or Kafka partition offsets) are saved periodically to state directories (/var/lib/vector) to prevent duplicate ingestion upon restarts.
• Zero Database Dependency: Vector does not require an external relational database or key-value store, keeping its operational footprint self-contained and zero-configuration.

Realtime / collaboration

Vector operates as a high-throughput streaming router engineered for sub-millisecond latency:

• Zero-Copy Streaming: Pipelines use Tokio asynchronous channels and bytes::Bytes reference-counted memory buffers to minimize copying during network packet deserialization and routing.
• Dynamic Topology Reload: Operators can trigger pipeline updates with SIGHUP or via the HTTP API, allowing Vector to re-read configuration, warm up new sinks, and cleanly drain deprecated pipelines without dropping active connections.
• Live Event Tapping: The embedded API allows authenticated administrators to attach live taps onto intermediate pipeline stages for real-time debugging without interrupting data delivery.

Build, test & deploy

The build and test pipeline is optimized for rigorous cross-platform validation:

• Cargo & Mold Linker: Uses modern Rust toolchains with mold linker in Docker for fast release compilation and link-time optimization (lto = "fat").
• Extensive Test Framework: Validates correctness using cargo-nextest, property-based testing with proptest, and dedicated end-to-end integration tests (tests/integration, tests/antithesis) verifying crash recovery under simulated hardware faults.
• Multi-Platform Packages: Generates native Debian/Ubuntu (.deb), RPM (.rpm), Alpine APK, macOS Homebrew, and static musl binaries alongside multi-architecture container images.

Self-hosting notes

Operating Vector in production requires selecting an appropriate deployment topology:

• Agent Mode: Deployed on individual virtual machines or as Kubernetes DaemonSets to collect local host metrics and tail container log files directly from /var/log/pods.
• Aggregator Mode: Deployed as a horizontally scaled cluster behind a network load balancer to terminate incoming TLS connections, perform heavyweight VRL transformations, and manage batch uploads to long-term storage sinks.
• Resource Management: Configure data_dir on persistent, high-IOPS storage when enabling disk buffers, and set memory limits according to anticipated burst sizes and batch buffers.

What to copy (and what not to)

Architectural patterns from Vector worth adopting:

• Vector Remap Language (VRL) Model: Embedding a custom, domain-specific language designed specifically for event mutation provides immense flexibility while maintaining memory safety and predictability.
• End-to-End Acknowledgment Tracking: Propagating delivery status backwards across asynchronous channel pipelines is an exemplary design for mission-critical ingestion systems.
• Feature-Gated Component Compilation: Using Cargo feature flags to compile only desired sources and sinks allows building minimal, hyper-focused binaries when binary size matters.

Patterns to avoid or treat with caution:

• Monolithic Component Matrix: Implementing dozens of proprietary cloud vendor sinks directly in the main codebase introduces immense dependency maintenance overhead; external plug-in architectures (such as WASM-based transforms) are often preferable for newer projects.