Woodpecker

Key Features

Woodpecker provides a powerful solution for cloud-native logging with high performance and reliability

Cloud-Native WAL

Uses cloud object storage as the durable storage layer, ensuring scalability and cost-effectiveness.

High-Throughput Writes

Optimized for cloud storage with specialized write strategies to maximize sequential write throughput.

Efficient Log Reads

Utilizes memory management and prefetching strategies to optimize sequential log access.

Ordered Durability

Guarantees strict sequential ordering for log persistence.

Flexible Deployment

Can be deployed as a standalone service or integrated as an embedded library in your application.

Resilient & Fault-Tolerant

Leverages cloud reliability features for strong durability guarantees.

System Architecture

Woodpecker is designed with a modular architecture that separates concerns into distinct layers, enabling efficient data flow and management.

The system consists of three main layers: Client Layer, Server Layer, and Storage Layer, each with specific responsibilities and components.

Core Components

The building blocks of the Woodpecker system

Client Layer	Server Layer	Storage Layer
Client Log Writer/Reader Segment Handler	Log Store Segment Processor LogFile Writer/Reader	Object Storage Local File System Cache System

Core Concepts

Understanding the fundamental data model of Woodpecker

Log: Represents a continuous data stream
Segment: Logical partition of a log
LogFile: Physical storage unit within a segment
Fragment: Shard of a log file, the basic storage unit

Key Workflows

How data flows through the Woodpecker system

Write Flow

Client calls AppendAsync to add data to the log
Data is first written to memory buffer
When specific conditions are met, Sync operation is triggered
Sync persists data to storage system as log file fragments
Client receives acknowledgment of successful write

Read Flow

Client creates a Reader for a specific range
Reader locates the appropriate segment through segment manager
LogFile is retrieved from the segment, and data is read from fragments
Data may be retrieved from cache or object storage

Configuration & Tuning

Key parameters for optimizing Woodpecker performance

MaxEntries: Maximum number of entries in the buffer
MaxBytes: Maximum buffer size in bytes
MaxInterval: Maximum sync interval (milliseconds)
MaxFlushThreads: Maximum number of flush threads
MaxFlushSize: Maximum size of a single fragment
MaxMemory: Maximum memory usage for cache

Future Development

Planned improvements and enhancements

Short-term Goals

Improve test coverage and system stability
Optimize memory usage and reduce resource consumption
Enhance monitoring and diagnostic capabilities

Mid-term Goals

Support more storage backends (beyond MinIO)
Implement more advanced caching strategies
Provide richer client interfaces and tools

Long-term Vision

Distributed deployment support
Advanced data analysis and query capabilities
Integration with more ecosystems