Logs are an essential part of observing any service; without them you'll have significant blind spots. But collecting and analyzing them can be a real challenge -- especially at scale. Not only do you need to solve the basic task of collecting your logs, but you must do it in a reliable, performant, and robust manner. Nothing is more frustrating than having your logs pipeline fall on it's face during an outage, or even worse, cause the outage!
Fear not! In this guide we'll build an observability pipeline that sends logs to Kafka.
What is Kafka?
Apache Kafka is an open-source project for a distributed publish-subscribe messaging system rethought as a distributed commit log. Kafka stores messages in topics that are partitioned and replicated across multiple brokers in a cluster. Producers send messages to topics from which consumers read. These features make it an excellent candidate for durably storing logs and metrics data.
How This Guide Works
We'll be using Vector to accomplish this task. Vector is a popular open-source observability data platform. It's written in Rust, making it lightweight, ultra-fast and highly reliable. And we'll be deploying Vector as a agent.
What We'll Accomplish
We'll build an observability data platform that:
All in just a few minutes!
Install Vectorcurl --proto '=https' --tlsv1.2 -sSf https://sh.vector.dev | sh
Configure Vectorcat <<-'VECTORCFG' > ./vector.toml[sinks.out]type = "kafka"inputs = [ "my-source-or-transform-id" ]bootstrap_servers = "10.14.22.123:9092,10.14.23.332:9092"key_field = "user_id"topic = "topic-1234"encoding.codec = "json"VECTORCFG
Start Vectorvector --config ./vector.toml
Observe Vectorvector topexplain this command
Vector is powerful tool and we're just scratching the surface in this guide. Here are a few pages we recommend that demonstrate the power and flexibility of Vector: