Tyk Streams is able to join sources and sinks with sometimes conflicting batching behaviours without sacrificing its strong delivery guarantees. It’s also able to perform powerful processing functions across batches of messages such as grouping and reduction. Therefore, batching within Tyk Streams is a mechanism that serves multiple purposes:
  1. Performance (throughput)
  2. Grouped message processing
  3. Compatibility (mixing multi and single part message protocols)

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Performance

For most users the only benefit of batching messages is improving throughput over your output protocol. For some protocols this can happen in the background and requires no configuration from you. However, if an output has a batching configuration block this means it benefits from batching and requires you to specify how you’d like your batches to be formed by configuring a batching policy: 
output:
  kafka:
    addresses: [ todo:9092 ]
    topic: tyk_stream

    # Either send batches when they reach 10 messages or when 100ms has passed
    # since the last batch.
    batching:
      count: 10
      period: 100ms
However, a small number of inputs such as kafka must be consumed sequentially (in this case by partition) and therefore benefit from specifying your batch policy at the input level instead: 
input:
  kafka:
    addresses: [ todo:9092 ]
    topics: [ tyk_input_stream ]
    batching:
      count: 10
      period: 100ms

output:
  kafka:
    addresses: [ todo:9092 ]
    topic: tyk_stream
Inputs that behave this way are documented as such and have a batching configuration block. Sometimes you may prefer to create your batches before processing in order to benefit from batch wide processing, in which case if your input doesn’t already support a batch policy you can instead use a broker, which also allows you to combine inputs with a single batch policy: 
input:
  broker:
    inputs:
      - resource: foo
      - resource: bar
    batching:
      count: 50
      period: 500ms
This also works the same with output brokers.

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Grouped Message Processing

Some processors, such as while are executed once across a whole batch. You can avoid this behaviour with the for_each processor: 
pipeline:
  processors:
    - for_each:
      - while:
          at_least_once: true
          max_loops: 0
          check: errored()
          processors:
            - catch: [] # Wipe any previous error
            - resource: foo # Attempt this processor until success
There’s a vast number of processors that specialise in operations across batches such as grouping. For example, the following processors group a batch of messages according to a metadata field and compresses them into separate .tar.gz archives: 
pipeline:
  processors:
    - group_by_value:
        value: ${! meta("kafka_partition") }
    - archive:
        format: tar
    - compress:
        algorithm: gzip

output:
  aws_s3:
    bucket: todo
    path: docs/${! meta("kafka_partition") }/${! count("files") }-${! timestamp_unix_nano() }.tar.gz
Consult our windowing guide for further examples of batched (or windowed) processing.

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Compatibility

Tyk Streams is able to read and write over protocols that support multiple part messages, and all payloads travelling through Tyk Streams are represented as a multiple part message. Therefore, all components within Tyk Streams are able to work with multiple parts in a message as standard. When messages reach an output that doesn’t support multiple parts the message is broken down into an individual message per part, and then one of two behaviours happen depending on the output. If the output supports batch sending messages then the collection of messages are sent as a single batch. Otherwise, Tyk Streams falls back to sending the messages sequentially in multiple, individual requests. This behaviour means that not only can multiple part message protocols be easily matched with single part protocols, but also the concept of multiple part messages and message batches are interchangeable within Tyk Streams.

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Shrinking Batches

A message batch (or multiple part message) can be broken down into smaller batches using the split processor: 
input:
  # Consume messages that arrive in three parts.
  resource: foo
  processors:
    # Drop the third part
    - select_parts:
        parts: [ 0, 1 ]
    # Then break our message parts into individual messages
    - split:
        size: 1
This is also useful when your input source creates batches that are too large for your output protocol: 
input:
  aws_s3:
    bucket: todo

pipeline:
  processors:
    - decompress:
        algorithm: gzip
    - unarchive:
        format: tar
    # Limit batch sizes to 5MB
    - split:
        byte_size: 5_000_000

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Batch Policy

When an input or output component has a config field batching that means it supports a batch policy. This is a mechanism that allows you to configure exactly how your batching should work on messages before they are routed to the input or output it’s associated with. Batches are considered complete and will be flushed downstream when either of the following conditions are met:
  • The byte_size field is non-zero and the total size of the batch in bytes matches or exceeds it (disregarding metadata.)
  • The count field is non-zero and the total number of messages in the batch matches or exceeds it.
  • A message added to the batch causes the check to return to true.
  • The period field is non-empty and the time since the last batch exceeds its value.
This allows you to combine conditions: 
output:
  kafka:
    addresses: [ todo:9092 ]
    topic: tyk_stream

    # Either send batches when they reach 10 messages or when 100ms has passed
    # since the last batch.
    batching:
      count: 10
      period: 100ms
A batch policy has the capability to create batches, but not to break them down.
If your configured pipeline is processing messages that are batched before they reach the batch policy then they may circumvent the conditions you’ve specified here, resulting in sizes you aren’t expecting. If you are affected by this limitation then consider breaking the batches down with a split processor before they reach the batch policy.

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Post-Batch Processing

A batch policy also has a field processors which allows you to define an optional list of processors to apply to each batch before it is flushed. This is a good place to aggregate or archive the batch into a compatible format for an output: 
output:
  http_client:
    url: http://localhost:4195/post
    batching:
      count: 10
      processors:
        - archive:
            format: lines
The above config will batch up messages and then merge them into a line delimited format before sending it over HTTP. This is an easier format to parse than the default which would have been rfc1342. During shutdown any remaining messages waiting for a batch to complete will be flushed down the pipeline.