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Brief Demo

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plumber is a CLI devtool for inspecting, piping, messaging and redirecting data in message systems like Kafka, RabbitMQ , GCP PubSub and many more. [1]

The tool enables you to:

  • Safely view the contents of your data streams
  • Write plain or encoded data to any system
  • Route data from one place to another
  • Decode protobuf/avro/thrift/JSON data in real-time
    • Support for both Deep and Shallow protobuf envelope types
    • Support for google.protobuf.Any fields
  • Relay data to the Batch platform
  • Ship change data capture events to Batch platform
  • Replay events into a message system on your local network
  • And many other features (for a full list: plumber -h)

[1] It's like curl for messaging systems.

Why do you need it?

Messaging systems are black boxes - gaining visibility into what is passing through them is an involved process that requires you to write brittle consumer code that you will eventually throw away.

plumber enables you to stop wasting time writing throw-away code - use it to look into your queues and data streams, use it to connect disparate systems together or use it for debugging your event driven systems.


Brief Demo


Via brew

$ brew tap batchcorp/public
$ brew install plumber


Plumber is a single binary, to install you simply need to download it, give it executable permissions and call it from your shell. Here's an example set of commands to do this:

$ curl -L -o plumber
$ chmod +x plumber
$ mv plumber /usr/local/bin/plumber


Write messages

❯ plumber write kafka --topics test --input foo
INFO[0000] Successfully wrote message to topic 'test'    backend=kafka
INFO[0000] Successfully wrote '1' message(s)             pkg=plumber

Read message(s)

❯ plumber read kafka --topics test
INFO[0000] Initializing (could take a minute or two) ...  backend=kafka

------------- [Count: 1 Received at: 2021-11-30T12:51:32-08:00] -------------------

| Key                  |                                     NONE |
| topic                |                                     test |
| Offset               |                                        8 |
| Partition            |                                        0 |
| Header(s)            |                                     NONE |


NOTE: Add -f to perform a continuous read (like tail -f)

Write messages via pipe

Write multiple messages

NOTE: Multiple messages are separated by a newline.

$ cat mydata.txt

$ cat mydata.txt | plumber write kafka --topics foo

INFO[0000] Successfully wrote message to topic 'foo'  pkg=kafka/write.go
INFO[0000] Successfully wrote message to topic 'foo'  pkg=kafka/write.go
INFO[0000] Successfully wrote message to topic 'foo'  pkg=kafka/write.go

Write each element of a JSON array as a message

$ cat mydata.json
[{"key": "value1"},{"key": "value2"}]

$ cat mydata.json | plumber write kafka --topics foo --json-array

INFO[0000] Successfully wrote message to topic 'foo'  pkg=kafka/write.go
INFO[0000] Successfully wrote message to topic 'foo'  pkg=kafka/write.go


Getting Help

A full list of available flags can be displayed by using the --help flag after different parts of the command:

$ plumber --help
$ plumber read --help
$ plumber read kafka --help


  • Encode & decode for multiple formats
    • Protobuf (Deep and Shallow envelope)
    • Avro
    • Thrift
    • Flatbuffer
    • GZip
    • JSON
    • JSONPB (protobuf serialized as JSON)
    • Base64
  • --continuous support (ie. tail -f)
  • Support for most messaging systems
  • Supports writing via string, file or pipe
  • Observe, relay and archive messaging data
  • Single-binary, zero-config, easy-install

Hmm, what is this Batch thing?

We are distributed system enthusiasts that started a company called Batch.

Our company focuses on solving data stream observability for complex systems and workflows. Our goal is to allow everyone to build asynchronous systems, without the fear of introducing too much complexity.

While working on our company, we built a tool for reading and writing messages from our messaging systems and realized that there is a serious lack of tooling in this space.

We wanted a swiss army knife type of tool for working with messaging systems (we use Kafka and RabbitMQ internally), so we created plumber.

Why the name plumber?

We consider ourselves "internet plumbers" of sort - so the name seemed to fit :)

Supported Messaging Systems

  • Kafka
  • RabbitMQ
  • RabbitMQ Streams
  • Google Cloud Platform PubSub
  • MQTT
  • Amazon Kinesis Streams (NEW)
  • Amazon SQS
  • Amazon SNS (Publishing)
  • ActiveMQ (STOMP protocol)
  • Azure Service Bus
  • Azure Event Hub
  • NATS
  • NATS Streaming (Jetstream)
  • Redis-PubSub
  • Redis-Streams
  • Postgres CDC (Change Data Capture)
  • MongoDB CDC (Change Data Capture)
  • Apache Pulsar
  • NSQ
  • KubeMQ

NOTE: If your messaging tech is not supported - submit an issue and we'll do our best to make it happen!


You need to ensure that you are using the same consumer group on all plumber instances.


Make sure that all instances of plumber are pointed to the same queue.

Note on boolean flags

In order to flip a boolean flag to false, prepend --no to the flag.

ie. --queue-declare is true by default. To make it false, use --no-queue-declare.


plumber can now act as a replay destination (tunnel). Tunnel mode allows you to run an instance of plumber, on your local network, which will then be available in the Batch platform as a replay destination.

This mitigates the need make firewall changes to replay messages from a Batch collection back to your message bus.

See for full documentation.

High Performance & High Availability

plumber comes with a "server" mode which will cause plumber to operate as a highly available cluster.

You can read more about "server mode" here.

Server mode examples can be found in docs/


Huge shoutout to jhump and for his excellent protoreflect library, without which plumber would not be anywhere near as easy to implement. Thank you!


To push a new plumber release:

  1. git tag v0.18.0 master
  2. git push origin v0.18.0
  3. Watch the github action
  4. New release should be automatically created under
  5. Update release to include any relevant info
  6. Update homebrew SHA and version references


We love contributions! Prior to sending us a PR, open an issue to discuss what you intend to work on. When ready to open PR - add good tests and let's get this thing merged!