Skip to main content

Interpolation

Benthos allows you to dynamically set config fields with environment variables anywhere within a config file using the syntax ${<variable-name>} (or ${<variable-name>:<default-value>} in order to specify a default value). This is useful for setting environment specific fields such as addresses:

input:
  kafka:
    addresses: [ "${BROKERS}" ]
    consumer_group: benthos_bridge_consumer
    topics: [ "haha_business" ]
BROKERS="foo:9092,bar:9092" benthos -c ./config.yaml

If a literal string is required that matches this pattern (${foo}) you can escape it with double brackets. For example, the string ${{foo}} is read as the literal ${foo}.

Bloblang Queries​

Some Benthos fields also support Bloblang function interpolations, which are much more powerful expressions that allow you to query the contents of messages and perform arithmetic. The syntax of a function interpolation is ${!<bloblang expression>}, where the contents are a bloblang query (the right-hand-side of a bloblang map) including a range of functions. For example, with the following config:

output:
  kafka:
    addresses: [ "TODO:6379" ]
    topic: 'dope-${! json("topic") }'

A message with the contents {"topic":"foo","message":"hello world"} would be routed to the Kafka topic dope-foo.

If a literal string is required that matches this pattern (${!foo}) then, similar to environment variables, you can escape it with double brackets. For example, the string ${{!foo}} would be read as the literal ${!foo}.

Bloblang supports arithmetic, boolean operators, coalesce and mapping expressions. For more in-depth details about the language check out the docs.

Examples​

Reference Metadata​

A common usecase for interpolated functions is dynamic routing at the output level using metadata:

output:
  kafka:
    addresses: [ TODO ]
    topic: ${! meta("output_topic") }
    key: ${! meta("key") }

Coalesce and Mapping​

Bloblang supports coalesce and mapping, which makes it easy to extract values from slightly varying data structures:

pipeline:
  processors:
    - cache:
        resource: foocache
        operator: set
        key: '${! json().message.(foo | bar).id }'
        value: '${! content() }'

Here's a map of inputs to resulting values:

{"foo":{"a":{"baz":"from_a"},"c":{"baz":"from_c"}}} -> from_a
{"foo":{"b":{"baz":"from_b"},"c":{"baz":"from_c"}}} -> from_b
{"foo":{"b":null,"c":{"baz":"from_c"}}}             -> from_c

Delayed Processing​

We have a stream of JSON documents each with a unix timestamp field doc.received_at which is set when our platform receives it. We wish to only process messages an hour after they were received. We can achieve this by running the sleep processor using an interpolation function to calculate the seconds needed to wait for:

pipeline:
  processors:
  - sleep:
      duration: '${! 3600 - ( timestamp_unix() - json("doc.created_at").number() ) }s'

If the calculated result is less than or equal to zero the processor does not sleep at all. If the value of doc.created_at is a string then our method .number() will attempt to parse it into a number.