README.md

# Kaffe

An opinionated, highly specific, Elixir wrapper around
[Brod](https://github.com/klarna/brod): the Erlang Kafka client.
:coffee:

**NOTE**: Although we're using this in production at Spreedly it is still under active development. The API may change and there may be serious bugs we've yet to encounter.

## Installation

  1. Add `kaffe` to your list of dependencies in `mix.exs`:

      ```elixir
      def deps do
        [{:kaffe, "~> 1.0"}]
      end
      ```

  2. Ensure `kaffe` is started with your application:

      ```elixir
      def application do
        [applications: [:logger, :kaffe]]
      end
      ```

  3. Configure a Kaffe Consumer and/or Producer

## Kaffe Consumer Usage

Kaffe has two primary modes of message consumption.

Single message consumers process one message at a time using the `:brod_group_subscriber` behavior. This supports consumers in a consumer group in a single node (e.g., Heroku dyno). For such consumers, this is the place to start as it's the simplest mode of operation.

Batch message consumers receive a list of messages and work as part of the `:brod_group_member` behavior. This has a few important benefits:

1. Group members assign a "subscriber" to each partition in the topic. Because Kafka topics scale with partitions, having a worker per partition usually increases throughput.
2. Group members correctly handle partition assignments across multiple clients in a consumer group. This means that this mode of operation will scale horizontally (e.g., multiple dynos on Heroku).
3. Downstream processing that benefits from batching (like writing to another Kafka topic) is more easily supported.

### Kaffe Consumer - Single Message Consumer

1. Add a `handle_message/1` function to a local module (e.g. `MessageProcessor`). This function will be called with each Kafka message as a map. Each message map will include the topic and partition in addition to the normal Kafka message metadata.

    The module's `handle_message/1` function _must_ return `:ok` or Kaffe will throw an error. In normal (synchronous consumer) operation the Kaffe consumer will block until your `handle_message/1` function returns `:ok`.

    ### Example

      ```elixir
      defmodule MessageProcessor do
        def handle_message(%{key: key, value: value} = message) do
          IO.inspect message
          IO.puts "#{key}: #{value}"
          :ok # The handle_message function MUST return :ok
        end
      end
      ```

    ### Message Structure

    ```elixir
    %{
      attributes: 0,
      crc: 1914336469,
      key: "kafka message key",
      magic_byte: 0,
      offset: 41,
      partition: 17,
      topic: "some-kafka-topic",
      value: "the actual kafka message value is here",
      ts: 1234567890123, # timestamp in milliseconds
      ts_type: :append  # timestamp type: :undefined | :create | :append
    }
    ```

2. Configure your Kaffe Consumer in your mix config

      ```elixir
      config :kaffe,
        consumer: [
          endpoints: [kafka: 9092], # that's [hostname: kafka_port]
          topics: ["interesting-topic"], # the topic(s) that will be consumed
          consumer_group: "your-app-consumer-group", # the consumer group for tracking offsets in Kafka
          message_handler: MessageProcessor, # the module from Step 1 that will process messages

          # optional
          async_message_ack: false, # see "async message acknowledgement" below
          start_with_earliest_message: true # default false
        ],
      ```

    The `start_with_earliest_message` field controls where your consumer group starts when it starts for the very first time. Once offsets have been committed to Kafka then they will supercede this option. If omitted then your consumer group will start processing from the most recent messages in the topic instead of consuming all available messages.

    ### Heroku Configuration

    To configure a Kaffe Consumer for a Heroku Kafka compatible environment including SSL omit the `endpoint` and instead set `heroku_kafka_env: true`

    ```elixir
    config :kaffe,
      consumer: [
        heroku_kafka_env: true,
        topics: ["interesting-topic"],
        consumer_group: "your-app-consumer-group",
        message_handler: MessageProcessor
      ]
    ```

    With that setting in place Kaffe will automatically pull required info from the following ENV variables:

    - `KAFKA_URL`
    - `KAFKA_CLIENT_CERT`
    - `KAFKA_CLIENT_CERT_KEY`
    - `KAFKA_TRUSTED_CERT` (not used yet)

3. Add `Kaffe.Consumer` as a worker in your supervision tree

      ```elixir
      worker(Kaffe.Consumer, [])
      ```

### Kaffe GroupMember - Batch Message Consumer

1. Define a `handle_messages/1` function in the provided module.

      `handle_messages/1` This function (note the pluralization) will be called with a *list of messages*, with each message as a map. Each message map will include the topic and partition in addition to the normal Kafka message metadata.

      The module's `handle_messages/1` function _must_ return `:ok` or Kaffe will throw an error. The Kaffe consumer will block until your `handle_messages/1` function returns `:ok`.

      ```elixir
      defmodule MessageProcessor
        def handle_messages(messages) do
          for %{key: key, value: value} = message <- messages do
            IO.inspect message
            IO.puts "#{key}: #{value}"
          end
          :ok # Important!
        end
      end
      ```

2. The configuration options for the `GroupMember` consumer are a
   superset of those for `Kaffe.Consumer`, except for
   `:async_message_ack`, which is not supported. The additional options
   are:

      `:rebalance_delay_ms` which is the time to allow for rebalancing
      among workers. The default is 10,000, which should give the
      consumers time to rebalance when scaling.

      `:max_bytes` limits the number of message bytes received from Kafka
      for a particular topic subscriber. The default is 1MB. This
      parameter might need tuning depending on the number of partitions
      in the topics being read (there is one subscriber per topic per
      partition). For example, if you are reading from two topics, each
      with 32 partitions, there is the potential of 64MB in buffered
      messages at any one time.

      `:offset_reset_policy` controls how the subscriber handles an
      expired offset. See the Kafka consumer option,
      [`auto.offset.reset`](https://kafka.apache.org/documentation/#newconsumerconfigs).
      Valid values for this option are:

      - `:reset_to_earliest` - reset to the earliest available offset
      - `:reset_to_latest` - reset to the latest offset
      - `:reset_by_subscriber` - The subscriber receives the `OffsetOutOfRange` error

      More information in the [Brod
      consumer](https://github.com/klarna/brod/blob/master/src/brod_consumer.erl).

      `:worker_allocation_strategy` controls how workers are allocated with respect to consumed topics and partitions.
      - `:worker_per_partition` - this is the default (for backward compatibilty) and allocates a single worker per partition across topics. This is useful for managing concurrent processing of messages that may be received from any consumed topic.
      - `:worker_per_topic_partition` - this strategy allocates a worker per topic partition. This means there will be a worker for every topic partition consumed. Unless you need to control concurrency across topics, you should use this strategy.

      ```elixir
      config :kaffe,
        consumer: [
          endpoints: [kafka: 9092],
          topics: ["interesting-topic"],
          consumer_group: "your-app-consumer-group",
          message_handler: MessageProcessor,
          offset_reset_policy: :reset_to_latest,
          max_bytes: 500_000,
          worker_allocation_strategy: :worker_per_topic_partition,
        ],
      ```

  3. Add `Kaffe.GroupMemberSupervisor` as a supervisor in your
     supervision tree

       ```elixir
       supervisor(Kaffe.GroupMemberSupervisor, [])
       ```

### async message acknowledgement

If you need asynchronous message consumption:

1. Add a `handle_message/2` function to your processing module. This function will be called with the Consumer `pid` and the Kafka message. When your processing is complete you will need to call `Kaffe.Consumer.ack(pid, message)` to acknowledge the offset.

2. Set `async` to true when you start the Kaffe.Consumer

      ```elixir
      consumer_group = "demo-commitlog-consumer"
      topic = "commitlog"
      message_handler = MessageProcessor
      async = true

      worker(Kaffe.Consumer, [consumer_group, topics, message_handler, async])

      # … in your message handler module

      def handle_message(pid, message) do
        spawn_message_processing_worker(pid, message)
        :ok # MUST return :ok
      end

      # … somewhere in your system when the worker is finished processing

      Kaffe.Consumer.ack(pid, message)
      ```

**NOTE**: Asynchronous consumption means your system will no longer provide any backpressure to the Kaffe.Consumer. You will also need to add robust measures to your system to ensure that no messages are lost in processing. IE if you spawn 5 workers processing a series of asynchronous messages from Kafka and 1 of them crashes without acknowledgement then it's possible and likely that the message will be skipped entirely.

Kafka only tracks a single numeric offset, not individual messages. If a message fails and a later offset is committed then the failed message will _not_ be sent again.

It's possible that your topic and system are entirely ok with losing some messages (i.e. frequent metrics that aren't individually important).

## Kaffe Producer Usage

`Kaffe.Producer` handles producing messages to Kafka and will automatically select the topic partitions per message or can be given a function to call to determine the partition per message.

Configure your Kaffe Producer in your mix config

      ```elixir
      config :kaffe,
        producer: [
          endpoints: [kafka: 9092], # [hostname: port]
          topics: ["kafka-topic"],

          # optional
          partition_strategy: :md5
        ]
      ```

The `partition_strategy` setting can be one of:

- `:md5`: (default) provides even and deterministic distrbution of the messages over the available partitions based on an MD5 hash of the key
- `:random`: select a random partition for each message
- function: a given function to call to determine the correct partition

You can also set any of the Brod producer configuration options in the `producer` section - see [the Brod sources](https://github.com/klarna/brod/blob/master/src/brod_producer.erl#L90) for a list of keys and their meaning.

### Heroku Configuration

To configure a Kaffe Producer for a Heroku Kafka compatible environment including SSL omit the `endpoint` and instead set `heroku_kafka_env: true`

    ```elixir
    config :kaffe,
      producer: [
        heroku_kafka_env: true,
        topics: ["kafka-topic"],

        # optional
        partition_strategy: :md5
      ]
    ```

With that setting in place Kaffe will automatically pull required info from the following ENV variables:

    - `KAFKA_URL`
    - `KAFKA_CLIENT_CERT`
    - `KAFKA_CLIENT_CERT_KEY`
    - `KAFKA_TRUSTED_CERT`

### Producing to Kafka

Currently only synchronous message production is supported.

There are several ways to produce:

- `topic`/`message_list` - Produce each message in the list to the given `topic`. The messages are produced to the correct partition based on the configured partitioning strategy.

    Each item in the list is a tuple of the key and value: `{key, value}`.

    ```elixir
    Kaffe.Producer.produce_sync("topic", [{"key1", "value1"}, {"key2", "value2"}])
    ```

- `topic`/`partition`/`message_list` - Produce each message in the list to the given `topic`/`partition`.

    Each item in the list is a tuple of the key and value: `{key, value}`.

    ```elixir
    Kaffe.Producer.produce_sync("topic", 2, [{"key1", "value1"}, {"key2", "value2"}])
    ```

- `key`/`value` - The key/value will be produced to the first topic given to the producer when it was started. The partition will be selected with the chosen strategy or given function.

    ```elixir
    Kaffe.Producer.produce_sync("key", "value")
    ```

- `topic`/`key`/`value` - The key/value will be produced to the given topic.

    ```elixir
    Kaffe.Producer.produce_sync("whitelist", "key", "value")
    ```

- `topic`/`partition`/`key`/`value` - The key/value will be produced to the given topic/partition.

    ```elixir
    Kaffe.Producer.produce_sync("whitelist", 2, "key", "value")
    ```

    **NOTE**: With this approach Kaffe will not calculate the next partition since it assumes you're taking over that job by giving it a specific partition.


## Testing

### Setup

In order to run the end to end tests, a Kafka topic is required. It must:

* be named `kaffe-test`
* have 32 partitions

If using the `kafka-topics.sh` script that comes with the Kafka distribution, you may use something like:

```bash
kafka-topics.sh --zookeeper localhost:2181 --create --partitions 32 --replication-factor 1 --topic kaffe-test
```

### Running

```bash
# unit tests
mix test
# end to end test
mix test --only e2e
```