Timeout of 0 is impossible

hw-kafka-client

Kafka bindings for Haskell backed by the librdkafka C module.

Ecosystem

HaskellWorks Kafka ecosystem is described here: https://github.com/haskell-works/hw-kafka

Consumer

High level consumers are supported by librdkafka starting from version 0.9. High-level consumers provide an abstraction for consuming messages from multiple partitions and topics. They also address scalability (up to a number of partitions) by providing automatic rebalancing functionality. When a new consumer joins a consumer group the set of consumers attempts to "rebalance" the load to assign partitions to each consumer.

Consumer example

See Running integration tests locally to learn how to configure a local environment.

cabal build --flag examples

or

cabal run kafka-client-example --flag examples

A working consumer example can be found here: ConsumerExample.hs
To run an example please compile with the examples flag.

import Control.Exception (bracket)
import Kafka.Consumer

-- Global consumer properties
consumerProps :: ConsumerProperties
consumerProps = brokersList ["localhost:9092"]
             <> groupId "consumer_example_group"
             <> noAutoCommit
             <> logLevel KafkaLogInfo

-- Subscription to topics
consumerSub :: Subscription
consumerSub = topics ["kafka-client-example-topic"]
           <> offsetReset Earliest

-- Running an example
runConsumerExample :: IO ()
runConsumerExample = do
    res <- bracket mkConsumer clConsumer runHandler
    print res
    where
      mkConsumer = newConsumer consumerProps consumerSub
      clConsumer (Left err) = return (Left err)
      clConsumer (Right kc) = maybe (Right ()) Left <$> closeConsumer kc
      runHandler (Left err) = return (Left err)
      runHandler (Right kc) = processMessages kc

-------------------------------------------------------------------
processMessages :: KafkaConsumer -> IO (Either KafkaError ())
processMessages kafka = do
    replicateM_ 10 $ do
      msg <- pollMessage kafka (Timeout 1000)
      putStrLn $ "Message: " <> show msg
      err <- commitAllOffsets OffsetCommit kafka
      putStrLn $ "Offsets: " <> maybe "Committed." show err
    return $ Right ()

Producer

kafka-client producer supports sending messages to multiple topics. Target topic name is a part of each message that is to be sent by produceMessage.

A working producer example can be found here: ProducerExample.hs

Delivery reports

Kafka Producer maintains its own internal queue for outgoing messages. Calling produceMessage does not mean that the message is actually written to Kafka, it only means that the message is put to that outgoing queue and that the producer will (eventually) push it to Kafka.

However, it is not always possible for the producer to send messages to Kafka. Network problems or Kafka cluster being offline can prevent the producer from doing it.

When a message cannot be sent to Kafka for some time (see message.timeout.ms configuration option), the message is dropped from the outgoing queue and the delivery report indicating an error is raised.

It is possible to configure hw-kafka-client to set an infinite message timeout so the message is never dropped from the queue:

producerProps :: ProducerProperties
producerProps = brokersList ["localhost:9092"]
             <> sendTimeout (Timeout 0)           -- for librdkafka "0" means "infinite" (see https://github.com/edenhill/librdkafka/issues/2015)

Delivery reports provide the way to detect when producer experiences problems sending messages to Kafka.

Currently hw-kafka-client only supports delivery error callbacks:

producerProps :: ProducerProperties
producerProps = brokersList ["localhost:9092"]
             <> setCallback (deliveryCallback print)

In the example above when the producer cannot deliver the message to Kafka, the error will be printed (and the message will be dropped).

Producer example

{-# LANGUAGE OverloadedStrings #-}
import Control.Exception (bracket)
import Control.Monad (forM_)
import Data.ByteString (ByteString)
import Kafka.Producer

-- Global producer properties
producerProps :: ProducerProperties
producerProps = brokersList ["localhost:9092"]
             <> logLevel KafkaLogDebug

-- Topic to send messages to
targetTopic :: TopicName
targetTopic = "kafka-client-example-topic"

-- Run an example
runProducerExample :: IO ()
runProducerExample =
    bracket mkProducer clProducer runHandler >>= print
    where
      mkProducer = newProducer producerProps
      clProducer (Left _)     = return ()
      clProducer (Right prod) = closeProducer prod
      runHandler (Left err)   = return $ Left err
      runHandler (Right prod) = sendMessages prod

sendMessages :: KafkaProducer -> IO (Either KafkaError ())
sendMessages prod = do
  err1 <- produceMessage prod (mkMessage Nothing (Just "test from producer") )
  forM_ err1 print

  err2 <- produceMessage prod (mkMessage (Just "key") (Just "test from producer (with key)"))
  forM_ err2 print

  return $ Right ()

mkMessage :: Maybe ByteString -> Maybe ByteString -> ProducerRecord
mkMessage k v = ProducerRecord
                  { prTopic = targetTopic
                  , prPartition = UnassignedPartition
                  , prKey = k
                  , prValue = v
                  }

Synchronous sending of messages

Because of the asynchronous nature of librdkafka, there is no API to provide synchronous production of messages. It is, however, possible to combine the delivery reports feature with that of callbacks. This can be done using the Kafka.Producer.produceMessage' function.

produceMessage' :: MonadIO m
                => KafkaProducer
                -> ProducerRecord
                -> (DeliveryReport -> IO ())
                -> m (Either ImmediateError ())

Using this function, you can provide a callback which will be invoked upon the produced message's delivery report. With a little help of MVars or similar, you can in fact, create a synchronous-like interface.

sendMessageSync :: MonadIO m
                => KafkaProducer
                -> ProducerRecord
                -> m (Either KafkaError Offset)
sendMessageSync producer record = liftIO $ do
  -- Create an empty MVar:
  var <- newEmptyMVar

  -- Produce the message and use the callback to put the delivery report in the
  -- MVar:
  res <- produceMessage' producer record (putMVar var)

  case res of
    Left (ImmediateError err) ->
      pure (Left err)
    Right () -> do
      -- Flush producer queue to make sure you don't get stuck waiting for the
      -- message to send:
      flushProducer producer

      -- Wait for the message's delivery report and map accordingly:
      takeMVar var >>= return . \case
        DeliverySuccess _ offset -> Right offset
        DeliveryFailure _ err    -> Left err
        NoMessageError err       -> Left err

Note: this is a semi-naive solution as this waits forever (or until librdkafka times out). You should make sure that your configuration reflects the behavior you want out of this functionality.

Running integration tests locally

shell.nix can be used to provide a working environment that is enough to build and test hw-kafka-client.

To be able to run tests locally, $KAFKA_TEST_BROKER environment variable is expected to be set (use shell.nix or export manually).

$KAFKA_TEST_BROKER should contain an IP address of an accessible Kafka broker that will be used to run integration tests against.

With Docker Compose this variable is used to configure Kafka broker to listen on this address:

$ docker-compose up

After that, integration tests can switched on with using 'it' flag:

$ cabal test --test-show-details=direct --flag it

Credits

This project is inspired by Haskakafka which unfortunately doesn't seem to be actively maintained.

haskell-works / hw-kafka-client Goto Github PK

hw-kafka-client's Introduction

hw-kafka-client

Ecosystem

Consumer

Consumer example

Producer

Delivery reports

Producer example

Synchronous sending of messages

Running integration tests locally

Credits

hw-kafka-client's People

Contributors

Stargazers

Watchers

Forkers

hw-kafka-client's Issues

Recommend Projects

Recommend Topics

Recommend Org