将 kafka-streams 与自定义分区器一起使用

Question

I want to join a KStream with a KTable. Both have a different key but are co-partitioned using a custom partitioner. However, the join does not produce and results.

The KStream has the following structure
- key: House - Group
- value: User
The KTable has the following structure
- key: User - Group
- value: Address

To make sure every insert both topics are processed in insertion order, I'm using a custom Partitioner where I'm partitioning both topics using the Group part of each key.

I want to end up with a stream of the following structure:
- key: House - Group
- value: User - Address

For this I'm doing the following:

val streamsBuilder = streamBuilderHolder.streamsBuilder
val houseToUser = streamsBuilder.stream<HouseGroup, User>("houseToUser")
val userToAddress = streamsBuilder.table<UserGroup, Address>("userToAddress")
val result: KStream<HouseGroup, UserWithAddress> = houseToUser
        .map { k: HouseGroup, v: User ->
            val newKey = UserGroup(v, k.group)
            val newVal = UserHouse(v, k.house)
            KeyValue(newKey, newVal)
        }
        .join(userToAddress) { v1: UserHouse, v2: Address ->
            UserHouseWithAddress(v1, v2)
        }
        .map{k: UserGroup, v: UserHouseWithAddress ->
            val newKey = HouseGroup(v.house, k.group)
            val newVal = UserWithAddress(k.user, v.address)
            KeyValue(newKey, newVal)
        }

This expected a matching join but that did not work.

I guess the obvious solution is to join with a global table and let go of the custom partitioner. However, I still don't understand why the above would not work.

Answer 1

I think the lack of matching is caused because different partitioners are used.

For your input topic CustomPartitioner is used. Kafka Streams be default uses org.apache.kafka.clients.producer.internals.DefaultPartitioner .

In your code just before KStream::join you have called KStream::map . KStream::map function enforced repartitioning before KStream::join . During repartioning messages are flushed to Kafka ( $AppName-KSTREAM-MAP-000000000X-repartition topic). To spread messages Kafka Streams uses defined partitioner (property: ProducerConfig.PARTITIONER_CLASS_CONFIG ). Summarizing: messages with same keys might be in different partition for " repartition topic " and for " KTable topic "

Solution in your case will be set your custom partition in properties for your Kafka Streams application ( props.put(ProducerConfig.PARTITIONER_CLASS_CONFIG, "com.example.CustomPartitioner" )

For debugging you can check repartition topic ( $AppName-KSTREAM-MAP-000000000X-repartition ). Messages with same keys like input topic might be in different partitions (different number)

Documentation about Join co-partitioning requirements

Answer 2

Try this, works for me.

static async System.Threading.Tasks.Task Main(string[] args)
        {
 
            int count = 0;
            string line = null;

            var appConfig = getAppConfig(Enviroment.Dev);
            var schemaRegistrConfig = getSchemmaRegistryConfig(appConfig);
            var registry = new CachedSchemaRegistryClient(schemaRegistrConfig);
            var serializer = new AvroSerializer<YourAvroSchemaClass>(registry);

            var adminClient = new AdminClientBuilder(new AdminClientConfig( getClientConfig(appConfig))).Build();
            var topics = new List<TopicSpecification>(){ new TopicSpecification { Name = appConfig.OutputTopic, NumPartitions = 11}};

            await adminClient.CreateTopicsAsync(topics);

            var producerConfig = getProducerConfig(appConfig);

            var producer = new ProducerBuilder<string, byte[]>(producerConfig)
                .SetPartitioner(appConfig.OutputTopic, (string topicName, int partitionCount, ReadOnlySpan<byte> keyData, bool keyIsNull) =>
             {
                 var keyValueInInt = Convert.ToInt32(System.Text.UTF8Encoding.UTF8.GetString(keyData.ToArray()));
                 return (Partition)Math.Floor((double)(keyValueInInt % partitionCount));
             }).Build();

            using (producer)
            {
                Console.WriteLine($"Start to load data from : {appConfig.DataFileName}: { DateTime.Now} ");
                var watch = new Stopwatch();
                watch.Start();
                try
                {
                    var stream = new StreamReader(appConfig.DataFileName);
                    while ((line = stream.ReadLine()) != null)
                    {
                        var message = parseLine(line);
                        var data = await serializer.SerializeAsync(message.Value, new SerializationContext(MessageComponentType.Value, appConfig.OutputTopic));
                        producer.Produce(appConfig.OutputTopic, new Message<string, byte[]> { Key = message.Key, Value = data });
 
                        if (count++ % 1000 == 0)
                        {
                            producer.Flush();
                            Console.WriteLine($"Write ... {count} in {watch.Elapsed.TotalSeconds} seconds");
                        }
                    }
                    producer.Flush();
                }
                catch (ProduceException<Null, string> e)
                {
                    Console.WriteLine($"Line: {line}");
                    Console.WriteLine($"Delivery failed: {e.Error.Reason}");
                    System.Environment.Exit(101);
                }
        finally
        {
            producer.Flush();
           
                }
            }
        }