Traditional log shippers like Filebeat and Fluentd handle simple “collect-and-forward” scenarios but fall short of meeting the complex data pipeline needs of modern cloud-native architectures. Vector, as a next-generation observability data pipeline, provides a unified Source-Transform-Sink architecture that handles the collection, processing, and routing of logs, metrics, and traces within a single pipeline.
Vector Core Architecture
Vector’s core design philosophy divides the data pipeline into three composable stages:
[sources.syslog]
type = "syslog"
address = "0.0.0.0:514"
[sources.kubernetes_logs]
type = "kubernetes_logs"
[transforms.parser]
type = "remap"
inputs = ["kubernetes_logs"]
source = '''
.level = downcase(string!(.level))
.timestamp = parse_timestamp!(.timestamp, "%+")
'''
[transforms.filter]
type = "filter"
inputs = ["syslog"]
condition = '.severity != "debug"'
[sinks.elasticsearch]
type = "elasticsearch"
inputs = ["parser", "filter"]
endpoint = "http://elasticsearch:9200"
[sinks.s3_archive]
type = "aws_s3"
inputs = ["parser"]
bucket = "logs-archive"
compression = "gzip"Performance Advantages
Built with Rust, Vector delivers memory safety and exceptional performance. Under identical hardware conditions, it achieves 3-5x the throughput of Filebeat while consuming only one-third the memory of Fluentd. Its zero-copy data transport and backpressure-aware mechanisms ensure stability under high load.
Data Enrichment and Routing
The Vector Remap Language (VRL) enables rich data cleaning, transformation, and enrichment operations. More powerfully, Vector supports multi-sink routing, allowing the same data stream to be sent simultaneously to Elasticsearch, Kafka, S3, and Prometheus — eliminating the need to deploy multiple agents.