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Vector: High-Performance Observability Data Pipeline

Explore Vector, a high-performance observability data pipeline that collects, transforms, and routes all your logs, metrics, and traces to any destination. Designed for scalability and reliability.

Rust
Added on 2025年6月15日
View on GitHub
Vector: High-Performance Observability Data Pipeline preview
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Project Introduction

Summary

This project delivers a high-performance, vendor-neutral data pipeline specifically built for collecting, transforming, and routing all types of observability data (logs, metrics, traces). It aims to provide a single source of truth for data pipelines, improving efficiency and control.

Problem Solved

Managing observability data from diverse sources, formats, and destinations is complex, resource-intensive, and often unreliable. This project provides a single, high-performance tool to simplify this entire workflow.

Core Features

Unified Data Collection

Efficiently collect data from a wide array of sources including files, sockets, APIs, and popular platforms.

In-Stream Transformation

Process and transform data in flight using powerful built-in functions for parsing, filtering, sampling, and enrichment.

Flexible Data Routing

Route transformed data to multiple destinations like object storage, databases, monitoring systems, and data warehouses.

Reliable Delivery

Handle backpressure and ensure data delivery with robust buffering, retries, and acknowledgments.

Tech Stack

Rust
Tokio
serde
prost
Kafka
Protobuf
Kubernetes

Use Cases

Vector can be deployed in various scenarios where efficient and reliable data collection, transformation, and routing are critical.

Unified Data Aggregation

Details

Aggregate logs, metrics, and traces from applications, containers, and infrastructure nodes into a unified stream.

User Value

Centralize observability data management, simplifying monitoring and troubleshooting across distributed systems.

Conditional Data Routing

Details

Route specific data types (e.g., security logs) to a dedicated SIEM system, while sending operational logs to an analytics platform, and metrics to a time-series database.

User Value

Ensure data lands in the most appropriate system for analysis and storage, optimizing cost and accessibility.

Data Transformation and Masking

Details

Filter out sensitive information (e.g., PII) from logs before sending them to external systems or long-term storage.

User Value

Enhance data privacy and compliance by processing data in-stream according to predefined rules.

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