vimmerru / vector-lib

A high-performance observability data pipeline.

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What is Vector?

Vector is a high-performance, end-to-end (agent & aggregator) observability data pipeline that puts you in control of your observability data. Collect, transform, and route all your logs, metrics, and traces to any vendors you want today and any other vendors you may want tomorrow. Vector enables dramatic cost reduction, novel data enrichment, and data security where you need it, not where it is most convenient for your vendors. Additionally, it is open source and up to 10x faster than every alternative in the space.

To get started, follow our quickstart guide or install Vector.

Principles

• Reliable - Built in Rust, Vector's primary design goal is reliability.
• End-to-end - Deploys as an agent or aggregator. Vector is a complete platform.
• Unified - Logs, metrics, and traces (coming soon). One tool for all of your data.

Use cases

• Reduce total observability costs.
• Transition vendors without disrupting workflows.
• Enhance data quality and improve insights.
• Consolidate agents and eliminate agent fatigue.
• Improve overall observability performance and reliability.

Community

• Vector is relied on by startups and enterprises like Atlassian, T-Mobile, Comcast, Zendesk, Discord, Fastly, CVS, Trivago, Tuple, Douban, Visa, Mambu, Blockfi, Claranet, Instacart, Forcepoint, and many more.
• Vector is downloaded over 100,000 times per day.
• Vector's largest user processes over 30TB daily.
• Vector has over 100 contributors and growing.

Documentation

About

• Concepts
• Under the hood
  • Architecture - data model (log, metric), pipeline model, concurrency model, runtime model
  • Networking - ARC
  • Guarantees

Setup

• Quickstart
• Installation - operating systems, package managers, platforms (Kubernetes), manual
• Deployment - roles, topologies

Reference

• Configuration
  • Sources - docker_logs, file, http, journald, kafka, socket, and many more...
  • Transforms - dedupe, filter, [geoip][docs.transforms.geoip], log_to_metric, lua, remap, and many more...
  • Sinks - aws_cloudwatch_logs, aws_s3, clickhouse, elasticsearch, gcp_cloud_storage, and many more...
  • Unit tests
• Remap Language
• API
• CLI

Administration

• Management
• Monitoring & observing
• Upgrading
• Validating

Resources

• Community - chat, calendar, @vectordotdev
• Releases
• Roadmap - vote on new features
• Policies - Code of Conduct, Privacy, Releases, Security, Versioning

Comparisons

Performance

The following performance tests demonstrate baseline performance between common protocols with the exception of the Regex Parsing test.

Test	Vector	Filebeat	FluentBit	FluentD	Logstash	SplunkUF	SplunkHF
TCP to Blackhole	86mib/s	n/a	64.4mib/s	27.7mib/s	40.6mib/s	n/a	n/a
File to TCP	76.7mib/s	7.8mib/s	35mib/s	26.1mib/s	3.1mib/s	40.1mib/s	39mib/s
Regex Parsing	13.2mib/s	n/a	20.5mib/s	2.6mib/s	4.6mib/s	n/a	7.8mib/s
TCP to HTTP	26.7mib/s	n/a	19.6mib/s	<1mib/s	2.7mib/s	n/a	n/a
TCP to TCP	69.9mib/s	5mib/s	67.1mib/s	3.9mib/s	10mib/s	70.4mib/s	7.6mib/s

To learn more about our performance tests, please see the Vector test harness.

Correctness

The following correctness tests are not exhaustive, but they demonstrate fundamental differences in quality and attention to detail:

Test	Vector	Filebeat	FluentBit	FluentD	Logstash	Splunk UF	Splunk HF
Disk Buffer Persistence	✓	✓			⚠	✓	✓
File Rotate (create)	✓	✓	✓	✓	✓	✓	✓
File Rotate (copytruncate)	✓					✓	✓
File Truncation	✓	✓	✓	✓	✓	✓	✓
Process (SIGHUP)	✓				⚠	✓	✓
JSON (wrapped)	✓	✓	✓	✓	✓	✓	✓

To learn more about our correctness tests, please see the Vector test harness.

Features

Vector is an end-to-end, unified, open data platform.

	Vector	Beats	Fluentbit	Fluentd	Logstash	Splunk UF	Splunk HF	Telegraf
End-to-end	✓							✓
Agent	✓	✓	✓			✓		✓
Aggregator	✓			✓	✓		✓	✓
Unified	✓							✓
Logs	✓	✓	✓	✓	✓	✓	✓	✓
Metrics	✓	⚠	⚠	⚠	⚠	⚠	⚠	✓
Traces	🚧
Open	✓		✓	✓				✓
Open-source	✓	✓	✓	✓	✓			✓
Vendor-neutral	✓		✓	✓				✓
Reliability	✓
Memory-safe	✓							✓
Delivery guarantees	✓					✓	✓
Multi-core	✓	✓	✓	✓	✓	✓	✓	✓

⚠ = Not interoperable, metrics are represented as structured logs

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