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zendesk/maxwell

0
View on GitHub↗
4,254 stars·1,042 forks·Java·24 viewsmaxwells-daemon.io↗

Maxwell

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 trigger workflows and notify microservices.

The system covers broad functional areas including data bootstrapping through initial snapshots, schema version management, and event filtering. It incorporates traffic management through partition-key routing and provides monitoring via health checks and performance metrics exposed through an HTTP endpoint.

Connections to databases and streaming producers are secured using SSL and encrypted communication.

Features

  • MySQL Change Data Capture - Reading MySQL binlogs to turn database modifications into JSON events for streaming to other systems.
  • Change Data Capture - Provides a system for streaming row-level database inserts, updates, and deletes as a sequence of JSON events.
  • Change Data Capture - Writes captured database changes as formatted JSON data to external destinations for downstream processing.
  • Change Data Capture Tools - Reads MySQL binary logs and converts database modifications into JSON events for streaming platforms.
  • Change Filters - Provides rules for including or excluding specific tables, columns, or rows from the change stream using regex or name rules.
  • Snapshot Bootstrapping - Captures a point-in-time copy of existing table data to seed the stream before real-time capture begins.
  • Database-to-Kafka Connectors - Streams real-time database changes from MySQL into Kafka and other distributed messaging systems.
  • Partition-Based Routing - Distributes events across streaming partitions using keys to ensure strict chronological ordering of database updates.
  • Binlog Streaming Proxies - Monitors MySQL binary logs to synchronize data changes across external indices and search engines.
  • Snapshot-Based Bootstrapping - Capture a snapshot of existing table data and stream it as initial events before starting real-time capture.
  • Real-time Data Synchronization - Keeps external search indices and secondary databases consistent by streaming live updates from a primary MySQL store.
  • Stream Processing Pipelines - Implements a high-throughput data pipeline that feeds database changes into streaming platforms with batching and partitioning.
  • Database Audit Trails - Captures a chronological history of all database modifications to maintain persistent audit trails.
  • Log Position Recovery - Tracks and restores the last processed binary log position to ensure continuous data capture after a crash or failover.
  • Messaging Platform Integrations - Integrates with messaging platforms to route captured database events for inter-service communication and transformation.
  • Database Change Streaming - Reads database binary logs and streams real-time row and column updates to external platforms.
  • Service-to-Service Streaming - Sends database change events to message brokers to trigger workflows and notify other microservices.
  • Entity Audit Logs - Creates a persistent and chronological history of every database modification for compliance and tracking.
  • Event-Driven Architectures - Routes database changes to Kafka or Kinesis to trigger workflows and notify microservices.
  • Batch Write Buffering - Implements batch write buffering to increase throughput and reduce network overhead when streaming database changes.
  • Database Record Auditing - Records every change made to a database, tracking mutations of individual records with old and new values.
  • Search Index Synchronization - Streams database change logs to external search engines in real-time to maintain consistent search indices.
  • Schema Change Logs - Monitors table and column modifications as events and records them in schema change logs.
  • Schema Version Tracking - Tracks database schema changes and manages version history to maintain an accurate structural record.
  • Schema History Compaction - Tracks database structure changes over time and rolls up old versions to limit storage growth.
  • Dynamic Configuration Hot-Loading - Modify operational settings via an HTTP endpoint to change behavior without restarting the application.
  • Binlog Position Recovery - Restores the log reading position when a slave is promoted to master to ensure continuous data capture.
  • Batch-Buffered Stream Writing - Pack multiple small records into a single larger batch to increase throughput and prevent throttling during stream writes.
  • Structured JSON Loggers - Transforms raw database change logs into structured JSON records for downstream processing and storage.
  • Hot-Reload Configuration Managers - Provides a management interface for applying operational configuration changes at runtime without restarting the application.
  • Change Data Capture - Tool that streams MySQL binlog changes as JSON to Kafka.

Star history

Star history chart for zendesk/maxwellStar history chart for zendesk/maxwell

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Frequently asked questions

What does zendesk/maxwell do?

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.

What are the main features of zendesk/maxwell?

The main features of zendesk/maxwell are: MySQL Change Data Capture, Change Data Capture, Change Data Capture Tools, Change Filters, Snapshot Bootstrapping, Database-to-Kafka Connectors, Partition-Based Routing, Binlog Streaming Proxies.

Which projects share features with zendesk/maxwell?

Projects with overlapping indexed features include: paper-trail-gem/paper_trail — PaperTrail is a Ruby on Rails versioning library and database audit trail system. It tracks changes to database models… linkedin/databus — Databus is a distributed change data capture system and database transaction log streamer. It extracts data… hazelcast/hazelcast — Hazelcast is a distributed data platform that combines an in-memory data grid with a stream processing engine to… datalinkdc/dinky — Dinky is a real-time data platform for developing, deploying, and operating streaming applications based on Apache… debezium/debezium — Debezium is a distributed change data capture platform that streams row-level database modifications as real-time… electric-sql/electric — Electric is a Postgres data synchronization engine and replication proxy designed to enable local-first software. It…

Projects sharing features with Maxwell

These projects share indexed features with Maxwell. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • paper-trail-gem/paper_trailpaper-trail-gem avatar

    paper-trail-gem/paper_trail

    7,022View on GitHub↗

    PaperTrail is a Ruby on Rails versioning library and database audit trail system. It tracks changes to database models by capturing historical snapshots of object states and associating every modification with the specific user or process responsible for the update. The project provides a model state reification tool to reconstruct model instances as they existed at a specific point in time and a data recovery framework to restore deleted database records by reifying their last known version. The system covers a broad range of capabilities including version history navigation, changeset-base

    Rubyactiverecordauditlog
    View on GitHub↗7,022
  • linkedin/databuslinkedin avatar

    linkedin/databus

    3,678View on GitHub↗

    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 t

    Java
    View on GitHub↗3,678
  • datalinkdc/dinkyDataLinkDC avatar

    DataLinkDC/dinky

    3,740View on GitHub↗

    Dinky is a real-time data platform for developing, deploying, and operating streaming applications based on Apache Flink. It functions as a SQL streaming IDE and a real-time data pipeline orchestrator, providing a web-based environment for writing and verifying queries with integrated logic plan visualization and lineage tracking. The platform acts as a distributed cluster manager, allowing the registration, monitoring, and administration of multiple processing clusters from a centralized interface. It also serves as a change data capture integration tool, synchronizing real-time database cha

    Javadatalakedatawarehouseflink
    View on GitHub↗3,740
  • hazelcast/hazelcasthazelcast avatar

    hazelcast/hazelcast

    6,570View on GitHub↗

    Hazelcast is a distributed data platform that combines an in-memory data grid with a stream processing engine to support real-time analytics and event-driven applications. It functions as a partitioned, distributed key-value store that replicates data across cluster nodes to provide low-latency access and high availability. The platform also serves as a distributed SQL query engine, allowing users to execute standard SQL statements against both in-memory datasets and external data sources. What distinguishes Hazelcast is its use of a distributed consensus subsystem to maintain strongly consis

    Javabig-datacachingdata-in-motion
    View on GitHub↗6,570
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