Kafka how to consume one topic parallel

Question

I read kafka document, still don't know how consume one topic parallel?

Suppose: I have one topic like "something happened" (don't split this topic), and I have many customers that want to consume it. So what should I do, so that multiple customers can consume it parallel? Should I use partitioning and customer groups?

I have one idea about this, but I'm not sure whether is it right.

Make many partitions about the same topic, and make one partition to one customer, so one producer must produce the same to these partitions, and every customer in the different customer group, is it right?

Answer 1

Just to add the list of answers, Confluent has a library to do this for you, like Rapids. The project is here:

https://github.com/confluentinc/parallel-consumer

It's open source.

Note: I'm the author.

Answer 2

I assume what you want is parallel consumption between customers in a publish/subscribe fashion.

Beside that, you can also have parallel consumption within a single customer in order to scale the consumer application.

Parallel consumption between customers

If by "customers" you mean different organizations which are interested in consuming topic's messages independently, all you need is consumer groups.

This is a simple publish/subscribe pattern where each customer runs its own application and read all topic's messages without interfering with others. Each customer application can be seen as a consumer group, made up by one or more Kafka consumers (whether running on a single node or spread across a cluster), all of them sharing the consumer group's identifier.

You achieve this goal regardless of partitions. In case topic is partitioned, you don't need to worry about writing the same message to all partitions. Remember that in Kafka messages are durable, a message read by a Kafka consumer is not deleted and is available to be read by other Kafka consumers from a different consumer group (until it expires). Furthermore, partitions are not meant to work like this, they help scale storage of data (at a certain point all topic's data wouldn't fit into just one node) and scale consumer applications as you can see below.

Parallel consumption within single customer

You can further parallelize, or better to say, scale the consumption of messages within a consumer group with, in fact, Kafka consumers.

Imagine topic is huge, producers write into it with an high rate, and consumer group has only one consumer: this poor consumer may struggle to keep up with the message arrival rate, especially if message processing is time-consuming too. That's the case where you need partitions and more consumers in your consumer group, so that Kafka will assign partitions to consumers to distribute reading load among them.

How partition assignment works has been already explained in other answers here, but basically for a given consumer group:

• each topic's partition is assigned exclusively to one consumer,
• a consumer might get assigned more partitions
• if consumers are more than topic's partitions, some of them will stay idle as they won't get assigned any partition to consume from.

Remember that message ordering in Kafka is guaranteed only at partition level, so if you have many partitions and ordering matters, you need to choose the right message key to partition data according to your requirements. For example if you want messages be ordered by device, a device_id would be your key that guarantees messages of the same device will be written to the same partition.

Answer 3

@Lundahl did all the didactic, I'll give you a pratical sample.

1. Create a topic for some meaning, e.g. news_events with the parallelism your consumers will need (partitions), you can calc that using the time to process one message, the number of messages you will have and the time you want to have all the messages processed.
2. Let's create consumers for that topic, you wan't to read the news and your sister or brother also, each one on your time, then every one needs a consumer group id, this way kafka will know that threads a,b,c are for one consumer group and the d,e,c are for the second consumer group, every consumer group will receive the same messages, process it at their time and won't affect each other.
3. A message will come at one or other partition, never at two, by default Kafka makes round robin to choose the partition, remember, all consumers groups can connect and read data from all the same partitions

I would suggest you to use rapids-kafka-client, a library which do that parallelism stuff for you, choose the number of threads equal the number of partitions you have, choose a consumer group, and see the magic happen.

public static void main(String[] args){
  ConsumerConfig.<String, String>builder()
      .prop(KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class.getName())
      .prop(VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class.getName())
      .prop(GROUP_ID_CONFIG, "news-app")
      .topics("news_events")
      .consumers(7)
      .callback((ctx, record) -> {
        System.out.printf("status=consumed, value=%s%n", record.value());
      })
      .build()
      .consume()
      .waitFor();
}

You can read more about consumer groups, topics and partitions here

Answer 4

Each topic in Kafka can be organized into many partitions. Partition allows for parallel consumption increasing throughput.

Producer publishes the message to a topic using the Kafka producer client library which balances the messages across the available partitions using a Partitioner. The broker to which the producer connects to takes care of sending the message to the broker which is the leader of that partition using the partition owner information in zookeeper. Consumers use Kafka’s High-level consumer library (which handles broker leader changes, managing offset info in zookeeper and figuring out partition owner info etc implicitly) to consume messages from partitions in streams; each stream may be mapped to a few partitions depending on how the consumer chooses to create the message streams.

For example, if there are 10 partitions for a topic and 3 consumer instances (C1,C2,C3 started in that order) all belonging to the same Consumer Group, we can have different consumption models that allow read parallelism as below

Each consumer uses a single stream.

In this model, when C1 starts all 10 partitions of the topic are mapped to the same stream and C1 starts consuming from that stream. When C2 starts, Kafka rebalances the partitions between the two streams. So, each stream will be assigned to 5 partitions(depending on the rebalance algorithm it might also be 4 vs 6) and each consumer consumes from its stream. Similarly, when C3 starts, the partitions are again rebalanced between the 3 streams. Note that in this model, when consuming from a stream assigned to more than one partition, the order of messages will be jumbled between partitions.

Each consumer uses more than one stream (say C1 uses 3, C2 uses 3 and C3 uses 4). In this model, when C1 starts, all the 10 partitions are assigned to the 3 streams and C1 can consume from the 3 streams concurrently using multiple threads. When C2 starts, the partitions are rebalanced between the 6 streams and similarly when C3 starts, the partitions are rebalanced between the 10 streams. Each consumer can consume concurrently from multiple streams. Note that the number of streams and partitions here are equal. In case the number of streams exceed the partitions, some streams will not get any messages as they will not be assigned any partitions.

Answer 5

Using partitions is the way of being able to parallelize the consumption of a topic. Let´s say you have 10 partitions for your topic, then you can have 10 consumers in the same consumer group reading one partition each. If you have less consumers than partitions, then they would be responsible for more than one partition each. If you have more consumers than partitions, then there would be consumers who would not get any partition assigned to them and have nothing to do except being available to replace another consumer who has died.