Programmable Push Filters

You can use Redpanda Console’s programmable push filters to search for a specific message within a Kafka topic. Programmable push filters enable you to write a TypeScript/JavaScript function body that runs in the backend and is called for every individual Kafka record. The code has to return a boolean. If your code returns true, the backend sends the record to the frontend. Otherwise the record will be skipped and Redpanda Console will continue to consume records until the selected number of max search results has been reached or "the end" of the topic hit.

Redpanda Console injects the following variables into your function, which you can use in your filter code:

  • partitionId - the record’s partition ID

  • offset - the record’s offset within its' partition

  • key - the record’s key in its decoded form

  • value - the record’s value in its decoded form

Keys and values are passed into your JavaScript code in their decoded form. That means the deserialization logic (for example, decode an Avro serialized byte array to a JSON object)is applied first, before injecting it into the JavaScript function. If your message is presented as a JSON object in the UI, you can also access it like a JavaScript object in your filter code.

If you have a series of Avro, JSON or Protobuf encoded record values which deserialize to JSON objects like this:

    "event_type": "BASKET_ITEM_ADDED",
    "event_id": "777036dd-1bac-499c-993a-8cc86cee3ccc"
    "item": {
        "id": "895e443a-f1b7-4fe5-ad66-b9adfe5420b9",
        "name": "milk"
return == "895e443a-f1b7-4fe5-ad66-b9adfe5420b9"

Redpanda Console allows you to use more than one filter function at the same time. If you start a search while having multiple filters active, they are combined with a logical OR. Accordingly, as soon as one function returns true, the record will be sent to the frontend.

Resource usage and performance

You can use Redpanda Console’s filter engine against topics with millions of messages, as the filter code is evaluated in the backend where more resources are available. However, while the filter engine is fairly efficient it will potentially consume all available CPU resources and may cause significant network traffic due to the number of consumed Kafka messages.

Usually the performance is constrained by the available CPU resources. Depending on the used JavaScript code and the messages, the expected performance is around ~15k-20k filtered messages per second for each available core. The request is only processed on a single instance and cannot be shared across multiple instances.