使用 Homebrew, pacman, scoop, zypper 安装 kafka

项目链接

• 主页

命令和别名

• connect-distributed
• connect-mirror-maker
• connect-plugin-path
• connect-standalone
• kafka-acls
• kafka-broker-api-versions
• kafka-client-metrics
• kafka-cluster
• kafka-configs
• kafka-console-consumer
• kafka-console-producer
• kafka-console-share-consumer
• kafka-consumer-groups
• kafka-consumer-perf-test
• kafka-delegation-tokens
• kafka-delete-records
• kafka-dump-log
• kafka-e2e-latency
• kafka-features
• kafka-get-offsets
• kafka-groups
• kafka-jmx
• kafka-leader-election
• kafka-log-dirs
• kafka-metadata-quorum
• kafka-metadata-shell
• kafka-producer-perf-test
• kafka-reassign-partitions
• kafka-replica-verification
• kafka-run-class
• kafka-server-start
• kafka-server-stop

项目历史

LinkedIn announced Kafka publicly on June 16, 2011 as a distributed publish-subscribe messaging system, open sourced under the Apache 2.0 license and submitted to the Apache Incubator. The announcement described Kafka as a system for high-throughput event data and noted that a paper had appeared at NetDB 2011.

The Apache GitHub repository was created in August 2011. Kafka graduated from the Apache Incubator on October 23, 2012, and Apache project records identify the Kafka committee as established in November 2012.

Over time Kafka expanded beyond a broker and client protocol into a broader event-streaming platform. Kafka Connect, Kafka Streams, MirrorMaker, security features, storage tooling, metadata tooling, and the Kafka Improvement Proposal process all became part of the operational and governance surface around the package.

采用历史

Kafka's adoption followed from its original LinkedIn use case: durable, high-throughput movement of event data between many producers and consumers. Its Apache governance and Java/Scala ecosystem helped it become a common foundation for streaming analytics, log aggregation, change-data capture, event-driven services, and data pipelines.

Package-manager adoption reflects two overlapping audiences. Developers install Kafka locally to run brokers and command scripts for development, while operators use the same scripts to inspect and administer clusters. Homebrew, Scoop, Pacman, and other package entries expose Kafka as a developer workstation dependency as much as a server component.

使用方式

The Homebrew package installs many command-line entry points, including kafka-topics, kafka-console-producer, kafka-console-consumer, kafka-consumer-groups, kafka-configs, kafka-server-start, kafka-storage, and Kafka Connect scripts. That makes it both a runnable local broker distribution and a CLI toolbox.

Kafka is used when systems need durable ordered logs, consumer groups, replayable event streams, and integration points between services and data systems. The official documentation and quickstart center the core loop: start a broker, create topics, write events, read events, and build producers, consumers, Streams, or Connect workloads.

为什么软件包爱好者会关心

Apache Kafka is significant in package databases because one formula exposes an entire infrastructure platform plus dozens of command shims. It is a good stress test for package metadata: executable lists are long, config files are distribution-relative, and the package name refers to a server, protocol ecosystem, and CLI family at once.

It is also historically important because many later packages in this batch exist to make Kafka easier to operate: kaf and kafkactl provide friendlier CLIs, while cloud-specific plugins exist because managed Kafka-compatible services introduced authentication and provider-specific edge cases.

时间线

• 2011-06-16: LinkedIn announced Kafka as an open-source distributed publish-subscribe messaging system.
• 2011-08: Apache Kafka GitHub repository created.
• 2012-10-23: Kafka graduated from the Apache Incubator.
• 2012-11: Apache Kafka committee established.
• 2015 onward: Kafka's ecosystem expanded around Streams, Connect, managed services, and Kafka Improvement Proposals.

Related projects

• Kafka's close project family includes Kafka Connect, Kafka Streams, MirrorMaker, Schema Registry implementations, kcat, kaf, kafkactl, Apache Flink, Apache Pulsar, and managed Kafka-compatible services such as Amazon MSK, Confluent Cloud, and Azure Event Hubs for Kafka clients.

来源

• https://api.github.com/repos/apache/kafka
• https://engineering.linkedin.com/27/project-kafka-distributed-publish-subscribe-messaging-system-reaches-v06
• https://formulae.brew.sh/formula/kafka
• https://github.com/apache/kafka
• https://incubator.apache.org/projects/kafka.html
• https://kafka.apache.org/documentation/
• https://kafka.apache.org/quickstart
• https://projects.apache.org/committee.html?kafka=