Kafka JSON Mutating

In this example we start off with a stream of JSON data being written to a Kafka topic. Our task is to consume the stream, mutate the data into a new format, filter certain items, and write the stream to a new topic.

For this example the data will be social media interactions with NLP enrichments such as sentiment and language detection, it looks something like this:

  "content": {
    "text": "This ist a public service announcement: I hate Jerry, Kate is okay.",
    "created_at": 1524599100
      "confidence": 0.07,
      "code": "de"
      "confidence": 0.93,
      "code": "en"
  "sentiment": [
       "entity": "jerry",
       "level": "negative",
       "confidence": 0.45
       "entity": "kate",
       "level": "neutral",
       "confidence": 0.08

And we wish to mutate the data such that irrelevant data is removed. Specifically, we want to reduce the language detection candidates down to a single language code, and we want to remove sentiment entities with a confidence below a certain level (0.3). The desired output from the above sample would be:

  "content": {
    "created_at": 1524599100,
    "text": "This ist a public service announcement: I hate Jerry, Kate is okay."
  "entities": [
      "name": "jerry",
      "sentiment": "negative"
  "language": "en"

Which we can accomplish using the JMESPath processor.

The main factor of concern in this example is throughput, it needs to be as high as possible, but we also need to preserve at-least-once delivery guarantees.

The full config for this example can be found here.

Since the pipeline is mostly going to be throttled by CPU the mutations execute on processing threads matching the number of logical CPUs. In order to keep the processing threads busy with traffic there are parameters in the config for tuning the number of concurrent readers to reduce input IO stalling. There is also a parameter for the message batch size, which can tuned in order to increase the throughput of the output in case it becomes the bottleneck.


    count: 8 # Try cranking this value up if your CPUs aren't maxed out
    - kafka_balanced:
        - localhost:9092 # TODO
        client_id: benthos_mutator_1
        consumer_group: benthos_mutator_group
        - data_stream
        max_batch_count: 8 # Batch size: Tune this to increase output throughput

The kafka_balanced input is used here, which automatically distributes the partitions of a topic across all consumers of the same consumer group. This allows us to have as many consumers both inside the process and as separate services as we need. It should be noted that the number of Kafka partitions for this topic should be significantly higher than the total number of consumers in order to get a good distribution of messages.

Using a broker allows us to tune the number of parallel consumers inside the process in order to reach our maximum CPU utilisation.

We also specify a max_batch_count, which is the maximum number of messages to batch together when we have a backlog of prefetched messages. The advantage of this is that a batch will be sent as a single request on the Kafka producer output. By tuning the batch size we should be able to increase our output throughput to prevent it from bottlenecking the pipeline.


  threads: 4 # This should match the number of logical CPUs
  - jmespath:
      query: |
          content: content,
          entities: sentiment[?confidence > `0.3`].{
            name: entity,
            sentiment: level
          language: max_by(language, &confidence).code

The pipeline is where we construct our processing pipelines. We set the number of threads to match our available logical CPUs in order to avoid contention. You might find slightly increasing or decreasing this number can improve throughput due to competing services on the machine.

The number of input consumers needs to be significantly higher than the number of threads here in order to prevent IO stalling.

The only processor defined here is our JMESPath query, which is where our JSON mutation comes from. You can read more about JMESPath here.


Our output is a standard Kafka producer output.