Fluent Bit: Official Manual
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1.8
1.8
  • Fluent Bit v1.8 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
    • Yocto / Embedded Linux
    • Windows
  • Administration
    • Configuring Fluent Bit
      • Format and Schema
      • Configuration File
      • Variables
      • Commands
      • Upstream Servers
      • Unit Sizes
      • Multiline Parsing
      • Record Accessor
    • 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
      • Node Exporter Metrics
      • Collectd
      • CPU Metrics
      • Disk I/O Metrics
      • Docker Metrics
      • Docker Events
      • Dummy
      • Exec
      • Fluent Bit Metrics
      • Forward
      • Head
      • HTTP
      • Health
      • Kernel Logs
      • Memory Metrics
      • MQTT
      • Network I/O Metrics
      • Process Metrics
      • Random
      • Serial Interface
      • Standard Input
      • StatsD
      • Syslog
      • Systemd
      • Tail
      • TCP
      • Thermal
      • Windows Event Log
    • 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
      • Rewrite Tag
      • Standard Output
      • Throttle
      • Tensorflow
    • Outputs
      • Prometheus Exporter
      • Prometheus Remote Write
      • Amazon CloudWatch
      • Amazon Kinesis Data Firehose
      • Amazon Kinesis Data Streams
      • Amazon S3
      • Azure Log Analytics
      • Azure Blob
      • Google Cloud BigQuery
      • Counter
      • Datadog
      • Elasticsearch
      • File
      • FlowCounter
      • Forward
      • GELF
      • HTTP
      • InfluxDB
      • Kafka
      • Kafka REST Proxy
      • LogDNA
      • Loki
      • NATS
      • New Relic
      • NULL
      • PostgreSQL
      • Slack
      • Stackdriver
      • Standard Output
      • Splunk
      • 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. Concepts

Buffering

Performance and Data Safety

Last updated 3 years ago

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When processes data, it uses the system memory (heap) as a primary and temporal place to store the record logs before they get delivered, on this private memory area the records are processed.

Buffering refers to the ability to store the records somewhere, and while they are processed and delivered, still be able to store more. Buffering in memory is the fastest mechanism, but there are certain scenarios where the mechanism requires special strategies to deal with , data safety or reduce memory consumption by the service in constraint environments.

Network failures or latency on third party service is pretty common, and on scenarios where we cannot deliver data fast enough as we receive new data to process, we likely will face backpressure.

Our buffering strategies are designed to solve problems associated with backpressure and general delivery failures.

Fluent Bit as buffering strategies, offers a primary buffering mechanism in memory and an optional secondary one using the file system. With this hybrid solution you can adjust to any use case safety and keep a high performance while processing your data.

Both mechanisms are not exclusive and when the data is ready to be processed or delivered it will be always in memory, while other data in the queue might be in the file system until is ready to be processed and moved up to memory.

To learn more about the buffering configuration in Fluent Bit, please jump to the section.

Fluent Bit
backpressure
Buffering & Storage