<em>Kafdrop is a web UI for viewing Kafka topics and browsing consumer groups.</em> The tool displays information such as brokers, topics, partitions, consumers, and lets you view messages.
This project is a reboot of Kafdrop 2.x, dragged kicking and screaming into the world of Java 17+, Kafka 2.x, Helm and Kubernetes. It's a lightweight application that runs on Spring Boot and is dead-easy to configure, supporting SASL and TLS-secured brokers.
Optional, additional integration:
You can run the Kafdrop JAR directly, via Docker, or in Kubernetes.
java --add-opens=java.base/sun.nio.ch=ALL-UNNAMED \ -jar target/kafdrop-<version>.jar \ --kafka.brokerConnect=<host:port,host:port>,...
If unspecified, kafka.brokerConnect
defaults to localhost:9092
.
Note: As of Kafdrop 3.10.0, a ZooKeeper connection is no longer required. All necessary cluster information is retrieved via the Kafka admin API.
Open a browser and navigate to http://localhost:9000. The port can be overridden by adding the following config:
--server.port=<port> --management.server.port=<port>
Optionally, configure a schema registry connection with:
--schemaregistry.connect=http://localhost:8081
and if you also require basic auth for your schema registry connection you should add:
--schemaregistry.auth=username:password
Finally, a default message and key format (e.g. to deserialize Avro messages or keys) can optionally be configured as follows:
--message.format=AVRO
--message.keyFormat=DEFAULT
Valid format values are DEFAULT
, AVRO
, PROTOBUF
. This can also be configured at the topic level via dropdown when viewing messages.
If key format is unspecified, message format will be used for key too.
In case of protobuf message type, the definition of a message could be compiled and transmitted using a descriptor file. Thus, in order for kafdrop to recognize the message, the application will need to access to the descriptor file(s). Kafdrop will allow user to select descriptor and well as specifying name of one of the message type provided by the descriptor at runtime.
To configure a folder with protobuf descriptor file(s) (.desc), follow:
--protobufdesc.directory=/var/protobuf_desc
In case of no protobuf descriptor file being supplied the implementation will attempt to create the protobuf deserializer using the schema registry instead.
If preferred the message type could be set to default as follows:
--message.format=PROTOBUF
Images are hosted at hub.docker.com/r/obsidiandynamics/kafdrop.
Launch container in background:
docker run -d --rm -p 9000:9000 \ -e KAFKA_BROKERCONNECT=<host:port,host:port> \ -e SERVER_SERVLET_CONTEXTPATH="/" \ obsidiandynamics/kafdrop
Launch container with some specific JVM options:
docker run -d --rm -p 9000:9000 \ -e KAFKA_BROKERCONNECT=<host:port,host:port> \ -e JVM_OPTS="-Xms32M -Xmx64M" \ -e SERVER_SERVLET_CONTEXTPATH="/" \ obsidiandynamics/kafdrop
Launch container in background with protobuff definitions:
docker run -d --rm -v <path_to_protobuff_descriptor_files>:/var/protobuf_desc -p 9000:9000 \ -e KAFKA_BROKERCONNECT=<host:port,host:port> \ -e SERVER_SERVLET_CONTEXTPATH="/" \ -e CMD_ARGS="--message.format=PROTOBUF --protobufdesc.directory=/var/protobuf_desc" \ obsidiandynamics/kafdrop
Then access the web UI at http://localhost:9000.
Hey there! We hope you really like Kafdrop! Please take a moment to ⭐the repo or Tweet about it.
Clone the repository (if necessary):
git clone https://github.com/obsidiandynamics/kafdrop && cd kafdrop
Apply the chart:
helm upgrade -i kafdrop chart --set image.tag=3.x.x \ --set kafka.brokerConnect=<host:port,host:port> \ --set server.servlet.contextPath="/" \ --set cmdArgs="--message.format=AVRO --schemaregistry.connect=http://localhost:8080" \ #optional --set jvm.opts="-Xms32M -Xmx64M"
For all Helm configuration options, have a peek into chart/values.yaml.
Replace 3.x.x
with the image tag of obsidiandynamics/kafdrop. Services will be bound on port 9000 by default (node port 30900).
Note: The context path must begin with a slash.
Proxy to the Kubernetes cluster:
kubectl proxy
Navigate to http://localhost:8001/api/v1/namespaces/default/services/http:kafdrop:9000/proxy.
To install with protobuf support, a "facility" option is provided for the deployment, to mount the descriptor files folder, as well as passing the required CMD arguments, via option mountProtoDesc. Example:
helm upgrade -i kafdrop chart --set image.tag=3.x.x \ --set kafka.brokerConnect=<host:port,host:port> \ --set server.servlet.contextPath="/" \ --set mountProtoDesc.enabled=true \ --set mountProtoDesc.hostPath="<path/to/desc/folder>" \ --set jvm.opts="-Xms32M -Xmx64M"
After cloning the repository, building is just a matter of running a standard Maven build:
$ mvn clean package
The following command will generate a Docker image:
mvn assembly:single docker:build
There is a docker-compose.yaml
file that bundles a Kafka/ZooKeeper instance with Kafdrop:
cd docker-compose/kafka-kafdrop docker-compose up
Starting with version 2.0.0, Kafdrop offers a set of Kafka APIs that mirror the existing HTML views. Any existing endpoint can be returned as JSON by simply setting the Accept: application/json
header. Some endpoints are JSON only:
/topic
: Returns a list of all topics.To help document the Kafka APIs, OpenAPI Specification (OAS) has been included. The OpenAPI Specification output is available by default at the following Kafdrop URL:
/v3/api-docs
It is also possible to access the Swagger UI (the HTML views) from the following URL:
/swagger-ui.html
This can be overridden with the following configuration:
springdoc.api-docs.path=/new/oas/path
You can disable OpenAPI Specification output with the following configuration:
springdoc.api-docs.enabled=false
Starting in version 2.0.0, Kafdrop sets CORS headers for all endpoints. You can control the CORS header values with the following configurations:
cors.allowOrigins (default is *)
cors.allowMethods (default is GET,POST,PUT,DELETE)
cors.maxAge (default is 3600)
cors.allowCredentials (default is true)
cors.allowHeaders (default is Origin,Accept,X-Requested-With,Content-Type,Access-Control-Request-Method,Access-Control-Request-Headers,Authorization)
You can also disable CORS entirely with the following configuration:
cors.enabled=false
By default, you could delete a topic. If you don't want this feature, you could disable it with:
--topic.deleteEnabled=false
By default, you could create a topic. If you don't want this feature, you could disable it with:
--topic.createEnabled=false
Health and info endpoints are available at the following path: /actuator
This can be overridden with the following configuration:
management.endpoints.web.base-path=<path>
Kafdrop supports TLS (SSL) and SASL connections for encryption and authentication. This can be configured by providing a combination of the following files (placed into the Kafka root directory):
kafka.truststore.jks
: specifying the certificate for authenticating brokers, if TLS is enabled.kafka.keystore.jks
: specifying the private key to authenticate the client to the broker, if mutual TLS authentication is required.kafka.properties
: specifying the necessary configuration, including key/truststore passwords, cipher suites, enabled TLS protocol versions, username/password pairs, etc. When supplying the truststore and/or keystore files, the ssl.truststore.location
and ssl.keystore.location
properties will be assigned automatically.The three files above can be supplied to a Docker instance in base-64-encoded form via environment variables:
docker run -d --rm -p 9000:9000 \ -e KAFKA_BROKERCONNECT=<host:port,host:port> \ -e KAFKA_PROPERTIES="$(cat kafka.properties | base64)" \ -e KAFKA_TRUSTSTORE="$(cat kafka.truststore.jks | base64)" \ # optional -e KAFKA_KEYSTORE="$(cat kafka.keystore.jks | base64)" \ # optional obsidiandynamics/kafdrop
Rather than passing KAFKA_PROPERTIES
as a base64-encoded string, you can also place a pre-populated KAFKA_PROPERTIES_FILE
into the container:
cat << EOF > kafka.properties security.protocol=SASL_SSL sasl.mechanism=SCRAM-SHA-512 sasl.jaas.config=org.apache.kafka.common.security.scram.ScramLoginModule required username="foo" password="bar" EOF docker run -d --rm -p 9000:9000 \ -v $(pwd)/kafka.properties:/tmp/kafka.properties:ro \ -v $(pwd)/kafka.truststore.jks:/tmp/kafka.truststore.jks:ro \ -v $(pwd)/kafka.keystore.jks:/tmp/kafka.keystore.jks:ro \ -e KAFKA_BROKERCONNECT=<host:port,host:port> \ -e KAFKA_PROPERTIES_FILE=/tmp/kafka.properties \ -e KAFKA_TRUSTSTORE_FILE=/tmp/kafka.truststore.jks \ # optional -e KAFKA_KEYSTORE_FILE=/tmp/kafka.keystore.jks \ # optional obsidiandynamics/kafdrop
It's sometimes needed to load extra classes, e.g. for a SASL client callback handler. To facilitate that, it is possible to mount a folder with extra JARs, like this:
cat << EOF > kafka.properties security.protocol=SASL_SSL sasl.jaas.config=software.amazon.msk.auth.iam.IAMLoginModule; sasl.client.callback.handler.class=software.amazon.msk.auth.iam.IAMClientCallbackHandler EOF mkdir extra-kafdrop-classes wget --directory-prefix=extra-kafdrop-classes https://repo1.maven.org/maven2/software/amazon/msk/aws-msk-iam-auth/1.0.0/aws-msk-iam-auth-1.0.0.jar docker run -d --rm -p 9000:9000 \ -v $(pwd)/kafka.properties:/tmp/kafka.properties:ro \ -v $(pwd)/kafka.truststore.jks:/tmp/kafka.truststore.jks:ro \ -v $(pwd)/kafka.keystore.jks:/tmp/kafka.keystore.jks:ro \ -v $(pwd)/extra-kafdrop-classes:/extra-classes:ro \ -e KAFKA_BROKERCONNECT=<host:port,host:port> \ -e KAFKA_PROPERTIES_FILE=/tmp/kafka.properties \ -e KAFKA_TRUSTSTORE_FILE=/tmp/kafka.truststore.jks \ # optional -e KAFKA_KEYSTORE_FILE=/tmp/kafka.keystore.jks \ # optional obsidiandynamics/kafdrop
Name | Description |
---|---|
KAFKA_BROKERCONNECT | Bootstrap list of Kafka host/port pairs. Defaults to localhost:9092 . |
KAFKA_PROPERTIES | Additional properties to configure the broker connection (base-64 encoded). |
KAFKA_TRUSTSTORE | Certificate for broker authentication (base-64 encoded). Required for TLS/SSL. |
KAFKA_KEYSTORE | Private key for mutual TLS authentication (base-64 encoded). |
SERVER_SERVLET_CONTEXTPATH | The context path to serve requests on (must end with a / ). Defaults to / . |
SERVER_PORT | The web server port to listen on. Defaults to 9000 . |
MANAGEMENT_SERVER_PORT | The Spring Actuator server port to listen on. Defaults to 9000 . |
SCHEMAREGISTRY_CONNECT | The endpoint of Schema Registry for Avro or Protobuf message |
SCHEMAREGISTRY_AUTH | Optional basic auth credentials in the form username:password . |
CMD_ARGS | Command line arguments to Kafdrop, e.g. --message.format or --protobufdesc.directory or --server.port . |
Name | Description |
---|---|
JVM_OPTS | JVM options. E.g.JVM_OPTS: "-Xms16M -Xmx64M -Xss360K -XX:-TieredCompilation -XX:+UseStringDeduplication -noverify" |
JMX_PORT | Port to use for JMX. No default; if unspecified, JMX will not be exposed. |
HOST | The hostname to report for the RMI registry (used for JMX). Defaults to localhost . |
KAFKA_PROPERTIES_FILE | Internal location where the Kafka properties file will be written to (if KAFKA_PROPERTIES is set). Defaults to kafka.properties . |
KAFKA_TRUSTSTORE_FILE | Internal location where the truststore file will be written to (if KAFKA_TRUSTSTORE is set). Defaults to kafka.truststore.jks . |
KAFKA_KEYSTORE_FILE | Internal location where the keystore file will be written to (if KAFKA_KEYSTORE is |
字节跳动发布的AI编程神器IDE
Trae是一种自适应的集成开发环境(IDE),通过自动化和多元协作改变开发流程。利用Trae,团队能够更快速、精确地编写和部署代码,从而提高编程效率和项目交付速度。Trae具备上下文感知和代码自动完成功能,是提升开发效率的理想工具。
全能AI智能助手,随时解答生活与工作的多样问题
问小白,由元石科技研发的AI智能助手,快速准确地解答各种生活和工作问题,包括但不限于搜索、规划和社交互动,帮助用户在日常生活中提高效率,轻松管理个人事务。
实时语音翻译/同声传译工具
Transly是一个多场景的AI大语言模型驱动的同声传译、专业翻译助手,它拥有超精准的音频识别翻译能力,几乎零延迟的使用体验和支持多国语言可以让你带它走遍全球,无论你是留学生、商务人士、韩剧美剧爱好者,还是出国游玩、多国会议、跨国追星等等,都可以满足你所有需要同传的场景需求,线上线下通用,扫除语言障碍,让全世界的语言交流不再有国界。
一键生成PPT和Word,让学习生活更轻松
讯飞智文是一个利用 AI 技术的项目,能够帮助用户生成 PPT 以及各类文档。无论是商业领域的市场分析报告、年度目标制定,还是学生群体的职业生涯规划、实习避坑指南,亦或是活动策划、旅游攻略等内容,它都能提供支持,帮助用户精准表达,轻松呈现各种信息。
深度推理能力全新升级,全面对标OpenAI o1
科大讯飞的星火大模型,支持语言理解、知识问答和文本创作等多功能,适用于多种文件和业务场景,提升办公和日常生活的效率。讯飞星火是一个提供丰富智能服务的平台,涵盖科技资讯、图像创作、写作辅助、编程解答、科研文献解读等功能,能为不同需求的用户提供便捷高效的帮助,助力用户轻松获取信息、解决问题,满足多样化使用场景。
一种基于大语言模型的高效单流解耦语音令牌文本到语音合成模型
Spark-TTS 是一个基于 PyTorch 的开源文本到语音合成项目,由多个知名机构联合参与。该项目提供了高效的 LLM(大语言模型)驱动的语音合成方案,支持语音克隆和语音创建功能,可通过命令行界面(CLI)和 Web UI 两种方式使用。用户可以根据需求调整语音的性别、音高、速度等参数,生成高质量的语音。该项目适用于多种场景,如有声读物制作、智能语音助手开发等。
AI助力,做PPT更简单!
咔片是一款轻量化在线演示设计工具,借助 AI 技术,实现从内容生成到智能设计的一站式 PPT 制作服务。支持多种文档格式导入生成 PPT,提供海量模板、智能美化、素材替换等功能,适用于销售、教师、学生等各类人群,能高效制作出高品质 PPT,满足不同场景演示需求。
选题、配图、成文,一站式创作,让内容运营更高效
讯飞绘文,一个AI集成平台,支持写作、选题、配图、排版和发布。高效生成适用于各类媒体的定制内容,加速品牌传播,提升内容营销效果。
专业的AI公文写作平台,公文写作神器
AI 材料星,专业的 AI 公文写作辅助平台,为体制内工作人员提供高效的公文写作解决方案。拥有海量公文文库、9 大核心 AI 功能,支持 30 + 文稿类型生成,助力快速完成领导讲话、工作总结、述职报告等材料,提升办公效率,是体制打工人的得力写作神器。
OpenAI Agents SDK,助力开发者便捷使用 OpenAI 相关功能。
openai-agents-python 是 OpenAI 推出的一款强大 Python SDK,它为开发者提供了与 OpenAI 模型交互的高效工具,支持工具调用、结果处理、追踪等功能,涵盖多种应用场景,如研究助手、财务研究等, 能显著提升开发效率,让开发者更轻松地利用 OpenAI 的技术优势。
最新AI工具、AI资讯
独家AI资源、AI项目落地
微信扫一扫关注公众号