didi/knowstreaming

一站式云原生实时流数据平台,通过0侵入、插件化构建企业级Kafka服务,极大降低操作、存储和管理实时流数据门槛

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Summary Information

Updated 36 minutes ago
Added to GitGenius on September 9th, 2026
Created on March 19th, 2020
Open Issues & Pull Requests: 157 (+0)
GitHub issues: Enabled
Number of forks: 1,300
Total Stargazers: 7,180 (+0)
Total Subscribers: 113 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 14.9 hours
Mean response time: 32.1 days
90th percentile: 52.9 days
Tracked items: 25

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Issue Activity (beta)

Open issues: 25
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 695 days
Stale 30+ days: 24
Stale 90+ days: 23

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • type: bug (3)
  • type: question (3)
  • type: discussion (2)
  • dev: front-end (1)
  • dev: project-manager (1)

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Detailed Description

KnowStreaming is a cloud-native Kafka management and control platform that enables non-invasive multi-cluster Kafka administration at enterprise scale.

The tool addresses the operational complexity of managing Kafka clusters across versions and deployment modes without requiring modifications to Apache Kafka itself. It works by providing a centralized management interface that discovers and onboards Kafka clusters ranging from version 0.10.x through 3.x.x, whether running with ZooKeeper or Raft consensus. The platform consolidates cluster monitoring, alerting, resource governance, and disaster recovery capabilities into a single dashboard, translating low-level CLI operations into visual, guided workflows.

Teams should adopt this tool if they operate multiple Kafka clusters and want to reduce the operational burden on their infrastructure staff. It suits organizations seeking to standardize Kafka management practices across environments without rewriting existing deployments. The platform emphasizes accessibility, allowing operators without deep Kafka expertise to perform complex administrative tasks through its graphical interface. It provides specialized capabilities including cluster health analysis, multi-dimensional metrics dashboards, automated health inspection across cluster dimensions, load balancing, topic replica scaling, and replica migration.

The project maintains active development with regular commits and ongoing issue resolution. The maintainers actively solicit user feedback and deployment information to guide future improvements. The codebase is written in Java and designed with horizontal scalability in mind, allowing operators to add nodes to increase collection and service capacity. The architecture supports hot-pluggable enterprise features, indicating a modular approach to extending functionality for observability integration, resource governance, and multi-region disaster recovery scenarios.