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linkedin/databus

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3,678 星标·735 分支·Java·Apache-2.0·4 次浏览

Databus

Databus is a distributed change data capture system and database transaction log streamer. It extracts data modifications from source logs and streams them to downstream consumers to keep secondary data stores and caches consistent.

The system functions as a source-agnostic data pipeline and distributed event relay. It uses a network of processes to decouple database sources from consumers, routing change streams across a network to multiple targets regardless of the original source system.

The project covers real-time data synchronization and distributed pipeline monitoring. This includes the ability to capture database changes, distribute those streams via relays, and track connection requests to debug network connectivity.

Features

  • Change Data Capture - Extracts data modifications from transaction logs to maintain a consistent source of truth across secondary data stores.
  • Source-Agnostic Data Pipelines - Provides a distributed architecture that routes captured database events to multiple targets regardless of the original source system.
  • Distributed Data Pipelines - Implements a distributed data pipeline that moves captured database changes through a network of relays to maintain consistency.
  • Change Data Capture Systems - Streams database modifications from source logs to downstream consumers through a decoupled delivery pipeline.
  • Distributed Event Distribution - Routes captured data changes through a relay system to decouple primary sources from multiple independent consumers.
  • Change - Decouples database sources from consumers by routing change streams through intermediate relay processes.
  • Change Data Relays - Routes captured database changes through relay processes to decouple sources from multiple downstream consumers.
  • Real-time Data Synchronization - Moves transaction log updates to search indexes or caches to maintain consistency across a distributed architecture.
  • Database Change Streaming - Moves captured database changes from a source to downstream applications through a distributed delivery pipeline.
  • Event Relay Networks - Implements a network of relay processes that decouple database sources from consumers by routing change streams.
  • Transaction Log Parsers - Extracts database modifications by parsing transaction log trail files and converting them into a portable stream format.
  • Change Data Capture Streams - Routes captured data changes from primary sources to search indexes or caches to keep them consistent.
  • Database Transaction Log Parsers - Extracts data changes from transaction logs to keep secondary data stores and caches consistent.
  • Agnostic Event Formats - Standardizes database changes into a generic event format to enable a source-agnostic data pipeline.
  • Data Pipelines - Source-agnostic distributed change data capture system.

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Databus 的开源替代方案

相似的开源项目,按与 Databus 的功能重合度排序。
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    Maxwell is a MySQL change data capture tool and binlog streaming application that converts database modifications into structured JSON events. It functions as a data pipeline that reads MySQL binary logs to synchronize changes across external indices, search engines, and distributed messaging systems such as Kafka. The project provides capabilities to maintain persistent audit trails by recording a chronological history of all database modifications. It enables real-time data synchronization and event-driven architecture integration by streaming database changes to external platforms to trigg

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  • apache/flink-cdcapache 的头像

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    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

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  • supabase/realtimesupabase 的头像

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    Realtime is a real-time data distribution and synchronization engine that enables applications to stream database changes and coordinate state between clients. It functions as a synchronization layer that monitors database write-ahead logs to provide change data capture and pushes updates to authorized clients via WebSockets. The project features a real-time presence server for tracking the online status of active users and a broadcast service for sending ephemeral messages without database persistence. It organizes communication through channel-based message routing and uses a structured JSO

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  • rethinkdb/rethinkdbrethinkdb 的头像

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    RethinkDB is a distributed, document-oriented database designed to store and manage JSON-formatted data across scalable clusters. It utilizes a custom log-structured storage engine with B-Tree indexing to ensure high-performance disk I/O and data persistence. The system maintains high availability through automatic sharding and replication, employing a primary-replica voting consensus mechanism to handle node failures and ensure consistent cluster operations. A defining characteristic of the platform is its reactive changefeed engine, which allows applications to subscribe to live data update

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查看 Databus 的所有 30 个替代方案→

常见问题解答

linkedin/databus 是做什么的?

Databus is a distributed change data capture system and database transaction log streamer. It extracts data modifications from source logs and streams them to downstream consumers to keep secondary data stores and caches consistent.

linkedin/databus 的主要功能有哪些?

linkedin/databus 的主要功能包括:Change Data Capture, Source-Agnostic Data Pipelines, Distributed Data Pipelines, Change Data Capture Systems, Distributed Event Distribution, Change, Change Data Relays, Real-time Data Synchronization。

linkedin/databus 有哪些开源替代品?

linkedin/databus 的开源替代品包括: zendesk/maxwell — Maxwell is a MySQL change data capture tool and binlog streaming application that converts database modifications into… apache/flink-cdc — This project is a streaming data integration framework that captures real-time database changes and synchronizes them… supabase/realtime — Realtime is a real-time data distribution and synchronization engine that enables applications to stream database… rethinkdb/rethinkdb — RethinkDB is a distributed, document-oriented database designed to store and manage JSON-formatted data across… datalinkdc/dinky — Dinky is a real-time data platform for developing, deploying, and operating streaming applications based on Apache… risingwavelabs/risingwave — RisingWave is a cloud-native streaming database and real-time analytics engine that uses standard SQL to process…