Fluent Bit: Official Manual
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1.9
1.9
  • Fluent Bit v1.9 Documentation
  • About
    • What is Fluent Bit?
    • A Brief History of Fluent Bit
    • Fluentd & Fluent Bit
    • License
  • Concepts
    • Key Concepts
    • Buffering
    • Data Pipeline
      • Input
      • Parser
      • Filter
      • Buffer
      • Router
      • Output
  • Installation
    • Getting Started with Fluent Bit
    • Upgrade Notes
    • Supported Platforms
    • Requirements
    • Sources
      • Download Source Code
      • Build and Install
      • Build with Static Configuration
    • Linux Packages
      • Amazon Linux
      • Redhat / CentOS
      • Debian
      • Ubuntu
      • Raspbian / Raspberry Pi
    • Docker
    • Containers on AWS
    • Amazon EC2
    • Kubernetes
    • macOS
    • Windows
    • Yocto / Embedded Linux
  • Administration
    • Configuring Fluent Bit
      • Classic mode
        • Format and Schema
        • Configuration File
        • Variables
        • Commands
        • Upstream Servers
        • Record Accessor
      • YAML Configuration
        • Configuration File
      • Unit Sizes
      • Multiline Parsing
    • Security
    • Buffering & Storage
    • Backpressure
    • Scheduling and Retries
    • Networking
    • Memory Management
    • Monitoring
    • Dump Internals / Signal
    • HTTP Proxy
  • Local Testing
    • Validating your Data and Structure
    • Running a Logging Pipeline Locally
  • Data Pipeline
    • Pipeline Monitoring
    • Inputs
      • Collectd
      • CPU Log Based Metrics
      • Disk I/O Log Based Metrics
      • Docker Log Based Metrics
      • Docker Events
      • Dummy
      • Exec
      • Fluent Bit Metrics
      • Forward
      • Head
      • HTTP
      • Health
      • Kernel Logs
      • Memory Metrics
      • MQTT
      • Network I/O Log Based Metrics
      • NGINX Exporter Metrics
      • Node Exporter Metrics
      • Process Log Based Metrics
      • Prometheus Scrape Metrics
      • Random
      • Serial Interface
      • Standard Input
      • StatsD
      • Syslog
      • Systemd
      • Tail
      • TCP
      • Thermal
      • Windows Event Log
      • Windows Event Log (winevtlog)
      • Windows Exporter Metrics
    • Parsers
      • Configuring Parser
      • JSON
      • Regular Expression
      • LTSV
      • Logfmt
      • Decoders
    • Filters
      • AWS Metadata
      • CheckList
      • Expect
      • GeoIP2 Filter
      • Grep
      • Kubernetes
      • Lua
      • Parser
      • Record Modifier
      • Modify
      • Multiline
      • Nest
      • Nightfall
      • Rewrite Tag
      • Standard Output
      • Throttle
      • Tensorflow
    • Outputs
      • Amazon CloudWatch
      • Amazon Kinesis Data Firehose
      • Amazon Kinesis Data Streams
      • Amazon S3
      • Azure Blob
      • Azure Log Analytics
      • Counter
      • Datadog
      • Elasticsearch
      • File
      • FlowCounter
      • Forward
      • GELF
      • Google Cloud BigQuery
      • HTTP
      • InfluxDB
      • Kafka
      • Kafka REST Proxy
      • LogDNA
      • Loki
      • NATS
      • New Relic
      • NULL
      • Observe
      • OpenSearch
      • OpenTelemetry
      • PostgreSQL
      • Prometheus Exporter
      • Prometheus Remote Write
      • SkyWalking
      • Slack
      • Splunk
      • Stackdriver
      • Standard Output
      • Syslog
      • TCP & TLS
      • Treasure Data
      • WebSocket
  • Stream Processing
    • Introduction to Stream Processing
    • Overview
    • Changelog
    • Getting Started
      • Fluent Bit + SQL
      • Check Keys and NULL values
      • Hands On! 101
  • Fluent Bit for Developers
    • C Library API
    • Ingest Records Manually
    • Golang Output Plugins
    • Developer guide for beginners on contributing to Fluent Bit
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  1. Stream Processing

Introduction to Stream Processing

Last updated 2 years ago

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is a fast and flexible Log processor that aims to collect, parse, filter and deliver logs to remote databases, so Data Analysis can be performed.

Data Analysis usually happens after the data is stored and indexed in a database, but for real-time and complex analysis needs, process the data while it's still in motion in the Log processor brings a lot of advantages and this approach is called Stream Processing on the Edge.

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