alibaba/otter
Otter
Otter is a distributed database synchronization system and change data capture tool designed to replicate data between databases across multiple geographic regions. It functions as a synchronization orchestrator and ETL data pipeline that mirrors records and associated files in real time.
The system employs incremental log parsing to capture database changes and utilizes a consistency-based convergence algorithm and loop-avoidance logic to manage bi-directional replication. It processes data through a pipeline of selection, extraction, transformation, and loading to handle joins and format conversions before delivering records to target tables.
The platform includes a distributed coordination layer to manage worker node state and schedule large-scale synchronization tasks across remote data centers. Supporting capabilities cover synchronization health monitoring for tracking replication lag and throughput, as well as administrative access control for managing system configurations.
Features
- Database Replication Tools - A distributed system designed for replicating database records across remote data centers with built-in loop avoidance.
- Change Data Capture Tools - Captures and parses incremental database logs to mirror records and associated files in real time.
- ETL Workflows - Implements ETL workflows to extract data from sources and apply transformations before target delivery.
- Data Replication - Synchronizes data between databases using consistency algorithms and loop-avoidance to ensure convergence.
- Real-Time Data Replication - Parses incremental database logs to replicate data from source to target databases in near real-time.
- Distributed Data Synchronization Systems - Replicates data between databases across multiple geographic regions using incremental log parsing for near real-time consistency.
- Regional Replication - Replicates database records across geographically distant data centers to improve local read performance.
- Distributed Task Orchestrators - Provides a coordination layer to manage worker nodes and schedule large-scale data replication tasks across distributed environments.
- Worker Node Management - Manages a fleet of distributed worker nodes through a central controller that pushes configurations and monitors lag.
- Distributed Coordination Services - Provides a distributed coordination service to manage shared state and synchronize node configurations across geographic regions.
- Database Transaction Log Parsers - Parses database redo logs to extract data changes for replication without impacting source database performance.
- Data Transformation Pipelines - Processes database records through a multi-stage pipeline of selection, extraction, transformation, and loading.
- Distributed Cluster Coordination - Coordinates nodes across multiple geographic regions using a shared source of truth to optimize read efficiency.
- Replicated Data Convergence - Implements a consistency-based convergence algorithm to ensure final data agreement across disparate data centers.
- Data Source Synchronizers - Coordinates data flow by configuring data sources, parsing rules, and target tables.
- Replication Loop Avoidance - Implements loop-avoidance logic to prevent infinite data loops during bi-directional synchronization.
- Real-Time Data Streaming - Captures incremental database log changes and transforms them for near real-time updates in a data pipeline.
- Replication Health Monitors - Tracks replication lag, throughput, and progress to ensure data consistency across geographically distributed environments.
Star history
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