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实时数据库复制工具

排名更新于 2026年6月30日

For 数据库间的实时复制, the strongest matches are debezium/debezium (Debezium is the leading open-source change data capture platform), apache/flink-cdc (Apache Flink CDC is a distributed streaming data integration) and airbytehq/airbyte (Airbyte is a data integration platform that directly supports). alibaba/otter and benthosdev/benthos round out the shortlist. Each is ranked by relevance to your query, popularity and recent activity.

用于在分布式数据库系统之间实时同步和复制数据的开源软件解决方案。

实时数据库复制工具

用 AI 发现最棒的仓库。我们将通过 AI 为您搜索最匹配的仓库。
  • debezium/debeziumdebezium 的头像

    debezium/debezium

    12,421在 GitHub 上查看↗

    Debezium is a distributed change data capture platform that streams row-level database modifications as real-time events. By parsing database transaction logs, the system broadcasts structural and data changes to message brokers, enabling reactive processing and data integration across distributed architectures. The platform utilizes log-based capture to extract modifications directly from transaction logs, ensuring minimal impact on source system performance while maintaining the original commit order of operations. It employs database-specific connector adapters to translate proprietary bin

    Debezium is the leading open-source change data capture platform that streams row-level database changes in real time via Kafka, with built-in connectors for multiple databases, schema mapping, fault-tolerant delivery, and both snapshot and continuous streaming modes — exactly the kind of tool this search is after.

    JavaChange Data CaptureChange Data Capture Streams
    在 GitHub 上查看↗12,421
  • apache/flink-cdcapache 的头像

    apache/flink-cdc

    6,430在 GitHub 上查看↗

    This project is a streaming data integration framework that captures real-time database changes and synchronizes them with downstream systems. It operates as a distributed streaming ETL and database synchronizer, reading database logs and snapshots to propagate row-level modifications to target sinks. The system supports declarative data integration, allowing users to define source-to-sink data flows using SQL or YAML configurations. It distinguishes itself by automating schema evolution to maintain synchronization when source structures change and ensuring exactly-once delivery and processin

    Apache Flink CDC is a distributed streaming data integration framework built precisely for real-time database replication via change data capture, supporting multiple connectors (MySQL, PostgreSQL, etc.), exactly-once delivery, schema evolution, and both snapshot and continuous streaming modes—directly matching your requirement for low-latency heterogeneous database replication.

    JavaChange Data CaptureExactly-Once Processing GuaranteesExactly-Once Processing Semantics
    在 GitHub 上查看↗6,430
  • airbytehq/airbyteairbytehq 的头像

    airbytehq/airbyte

    21,472在 GitHub 上查看↗

    Airbyte is a data integration platform designed to synchronize information between diverse applications, databases, and data warehouses. It functions as an extract, transform, and load orchestrator that manages automated data movement workflows across cloud, on-premise, and hybrid environments. The platform provides a standardized interface for connectors, enabling the movement of structured and unstructured data while maintaining stateful checkpoints for reliable incremental syncing. The platform distinguishes itself through a containerized architecture that isolates connectors to prevent de

    Airbyte is a data integration platform that directly supports change data capture (CDC) and real-time streaming replication between heterogeneous databases, with multiple connectors, schema mapping, fault tolerance via checkpoints, and both batch and continuous sync modes — covering every feature this search requires.

    PythonChange Data CaptureChange Data Capture ServicesChange Data Capture Tools
    在 GitHub 上查看↗21,472
  • alibaba/otteralibaba 的头像

    alibaba/otter

    8,127在 GitHub 上查看↗

    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 co

    Otter is a distributed change data capture tool that replicates data between databases in real time by parsing database transaction logs, and it supports multi-region synchronization, data transformation, and consistency assurance—squarely matching your need for a low-latency CDC replication system.

    JavaChange Data Capture ToolsReal-Time Data Replication
    在 GitHub 上查看↗8,127
  • benthosdev/benthosbenthosdev 的头像

    benthosdev/benthos

    8,681在 GitHub 上查看↗

    Benthos is a stream processing engine and data integration pipeline used for routing, transforming, and connecting data streams between diverse sources and sinks. It functions as event routing middleware and a change data capture tool, streaming real-time database modifications as discrete events for downstream processing. The system utilizes a declarative pipeline configuration, where data flow and processing logic are defined in a single static file. It features a specialized domain-specific language for mapping, filtering, and enriching data payloads, allowing for complex transformations w

    Benthos is a stream processing engine and data integration pipeline that explicitly functions as a change data capture tool, streaming real‑time database modifications across diverse sources and sinks with built‑in transformation, schema mapping, and at‑least‑once delivery guarantees, making it a strong fit for low‑latency database replication.

    GoChange Data CaptureChange Data Capture Tools
    在 GitHub 上查看↗8,681
  • alibaba/canalalibaba 的头像

    alibaba/canal

    29,697在 GitHub 上查看↗

    Canal is a database replication middleware that performs change data capture by simulating a database replica. It monitors transaction logs to stream incremental data modifications to downstream systems in real time, acting as an event streaming infrastructure that transforms low-level binary logs into structured, consumable message streams. The project distinguishes itself through a high-throughput architecture that utilizes concurrent multi-threaded parsing and stateful log position tracking to ensure reliable data delivery. It employs a pluggable sink architecture that decouples data extra

    Alibaba Canal is a change data capture middleware that monitors database transaction logs to stream incremental changes in real time, fitting the search for a CDC replication tool, though its focus on MySQL as the source makes it narrower than a fully heterogeneous solution.

    JavaChange Data Capture ServicesChange Data Capture Tools
    在 GitHub 上查看↗29,697
  • redpanda-data/connectredpanda-data 的头像

    redpanda-data/connect

    8,681在 GitHub 上查看↗

    Connect is a Kafka data integration platform and stream processing engine used to build declarative pipelines that move and transform messages between Kafka topics and external sources. It functions as a Kafka Connect framework and a change data capture tool, streaming real-time database modifications to synchronize data across distributed environments. The project differentiates itself through a dedicated mapping language for mutating and reshaping message payloads and the ability to execute custom processing logic within a sandboxed WebAssembly runtime. It also provides an observability pip

    Redpanda Connect explicitly describes itself as a change data capture tool that streams real-time database modifications to synchronize data across distributed environments, and its declarative pipelines, dedicated mapping language, and delivery-guarantee tags directly address the required CDC, real-time streaming, schema transformation, and fault-tolerance features.

    GoChange Data CaptureChange Data Capture Tools
    在 GitHub 上查看↗8,681
  • apache/seatunnelapache 的头像

    apache/seatunnel

    9,427在 GitHub 上查看↗

    SeaTunnel is a distributed data integration engine designed to synchronize structured and unstructured data across diverse sources and sinks. It functions as a multi-engine execution framework that can run data integration tasks across different distributed computing backends to optimize workload performance. The project is distinguished by a visual data pipeline designer for configuring workflows without manual code and a specialized change data capture tool for streaming incremental database updates. It also includes an enrichment pipeline that integrates large language models and embedding

    SeaTunnel is a distributed data integration engine with a dedicated change data capture (CDC) tool for streaming incremental database updates, supporting real-time streaming, multiple database connectors, schema mapping/transformations, fault tolerance, and both batch and continuous modes—directly matching your request for a low-latency CDC-based replication tool.

    JavaCDC SynchronizationChange Data Capture Tools
    在 GitHub 上查看↗9,427
  • electric-sql/electricelectric-sql 的头像

    electric-sql/electric

    9,909在 GitHub 上查看↗

    Electric is a Postgres data synchronization engine and replication proxy designed to enable local-first software. It replicates data from Postgres databases to client-side stores in real time using logical replication, allowing applications to maintain a local embedded database for offline access and low-latency updates. The system distinguishes itself by using shapes to filter and authorize specific subsets of database rows and columns before streaming them to clients or edge workers. It further supports multi-user collaboration by integrating a conflict-free replicated data type framework t

    Electric is a Postgres CDC replication engine that streams data in real time to client-side stores using logical replication, which fits the core need for low-latency data replication, but its focus on Postgres-to-local-first clients means it may not support the wide range of heterogeneous source and target databases the visitor might be looking for.

    ElixirChange Data Capture StreamsReal-time Data Synchronization
    在 GitHub 上查看↗9,909
  • apache/shardingsphereapache 的头像

    apache/shardingsphere

    20,737在 GitHub 上查看↗

    ShardingSphere is a distributed SQL database middleware that provides sharding, read-write splitting, and distributed transaction management for relational databases. It functions as a layer that intercepts SQL queries to distribute data across multiple physical database instances for horizontal scaling. The project is distinguished by its ability to operate as either a standalone transparent database proxy or via direct integration as a JDBC driver. It features a SQL dialect translator that parses queries into abstract syntax trees to convert syntax between different database engines, enabli

    ShardingSphere provides change data capture (CDC) and data migration capabilities as part of its distributed SQL middleware, allowing near-real-time replication between heterogeneous databases using its SQL dialect translator and CDC sync features.

    JavaCDC SynchronizationChange Data Capture
    在 GitHub 上查看↗20,737
  • 2ndquadrant/pglogical2ndQuadrant 的头像

    2ndQuadrant/pglogical

    1,225在 GitHub 上查看↗

    Logical Replication extension for PostgreSQL 17, 16, 15, 14, 13, 12, 11, 10, 9.6, 9.5, 9.4 (Postgres), providing much faster replication than Slony, Bucardo or Londiste, as well as cross-version upgrades.

    pglogical is a PostgreSQL extension that delivers change data capture (CDC) via logical replication, enabling real‑time streaming of changes between PostgreSQL databases — it fits the replication‑tool category exactly but is limited to PostgreSQL‑only environments, not heterogeneous replication.

    CExtensions and Plugins
    在 GitHub 上查看↗1,225
  • shayonj/pg_easy_replicateshayonj 的头像

    shayonj/pg_easy_replicate

    1,034在 GitHub 上查看↗

    pgeasyreplicate is a command-line utility designed to orchestrate database migrations and logical replication between PostgreSQL instances. It functions as a migration orchestrator that manages the entire lifecycle of data synchronization, from initial schema propagation to the final transition of application traffic. The tool distinguishes itself through state-aware switchover logic, which coordinates the movement of operations by verifying replication lag and enforcing write locks to prevent data loss. It provides granular control over data movement by allowing users to organize tables in

    pgeasyreplicate is a Ruby tool that simplifies setting up PostgreSQL logical replication for minimal-downtime switchovers, making it a CDC-based replication tool for Postgres databases—though it is limited to homogeneous PostgreSQL replication and does not support heterogeneous databases or multiple connectors.

    RubyAtomic Database MigrationsDatabase Migration SwitchoversDatabase Replication Orchestrators
    在 GitHub 上查看↗1,034
一览前 10 名对比
仓库Star 数语言许可证最后推送
debezium/debezium12.4KJavaapache-2.02026年2月20日
apache/flink-cdc6.4KJavaApache-2.02026年6月16日
airbytehq/airbyte21.5KPythonNOASSERTION2026年6月16日
alibaba/otter8.1KJavaApache-2.02024年5月25日
benthosdev/benthos8.7KGo—2026年6月16日
alibaba/canal29.7KJavaApache-2.02026年6月4日
redpanda-data/connect8.7KGo—2026年6月16日
apache/seatunnel9.4KJavaApache-2.02026年6月23日
electric-sql/electric9.9KElixirapache-2.02026年2月20日
apache/shardingsphere20.7KJavaApache-2.02026年6月23日

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