Architecture

This section covers the various models that combine to form Vector’s architecture. These are ordered progressively, beginning with the data model and broadening all the way to Vector’s runtime model.

Key pages

Data model
Pipeline model
Concurrency model
Buffering model
End-to-end Acknowledgements
Runtime model

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Reference
Configuration
Sinks
AMQP AppSignal AWS Cloudwatch logs AWS Cloudwatch metrics AWS Kinesis Data Firehose logs AWS Kinesis Streams logs AWS S3 AWS SNS AWS SQS Axiom Azure Blob Storage Azure Logs Ingestion Azure Monitor Logs Blackhole ClickHouse Console Databend Databricks Zerobus Datadog events Datadog logs Datadog metrics Datadog traces Doris Elasticsearch File GCP Chronicle Unstructured GCP Cloud Monitoring (formerly Stackdriver) GCP Cloud Storage GCP Stackdriver GCP PubSub GreptimeDB Logs GreptimeDB Metrics Honeycomb HTTP Humio logs Humio metrics InfluxDB logs InfluxDB metrics Kafka Keep Loki Mezmo (formerly LogDNA) MQTT NATS New Relic OpenTelemetry Papertrail Postgres Prometheus Exporter Prometheus remote write Pulsar Redis Sematext logs Sematext metrics Socket Splunk HEC logs Splunk HEC metrics StatsD Vector WebHDFS WebSocket WebSocket Server
Global options Pipeline components TLS configuration API Unit tests Schema Template syntax Secrets
CLI Environment Variables API Glossary
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