Need a help in getting the best design solution for creating Kafka consumers.
Will be having multiple topics and those can be like groups say for example
10 topics that are used to send out emails (10 count is chosen because will be getting more client traffic and want to dedicate a topic per client like each topic for one client so that others will not be delayed or waited)
10 topics to process a business logic and the 10 count explanation is same as above.
Now with this usage what's the best way to design Kafka consumers? Consumer dedicated to each topic ? or is there a way where we can scale up consumer dynamically by passing in which topic it needs to subscribe? For sure will be deploying this in containers but want suggestions on how to get started with consumer part with dynamic scalability and common code. And what's the best technology to implement this type of kafka consumers? (dotnet/java/python) ?
Also please do suggest if partitions make sense in this kind of design so that we can leverage consumer groups.
Consumers belonging to a same consumer group are assigned partitions in a topic.
In kafka, a topic can have multiple partitions. The consumers consume the messages of a particular topic from their assigned partition(s). The messages in the partition are ordered by sequential offsets.
Now, topic-wide record order is not important, you generally want to start with a higher number of partitions in a topic. Let's say start with 100 partitions. Your data will be distributed across the 100 partitions in a topic, assuming null keys or at least 100 unique key values with non colliding hashes, as record keys determine partitioning. If topic order is important, you're limited to one partition, and therefore one consumer thread; however, this thread can separate consumption from processing by loading records into alternative data structures (a queue) for processing.
You can now have 10 consumers consuming from 100 partitions. Each consumer will be assigned to about 10 partitions, and they will consume the messages in a round-robin fashion.
If you want to scale out, you simply increase the number of consumers. If you double the number of consumer to 20 then each consumer will process 5 partitions, thus, you get double throughput.
Let's assume the scenario where I have four partitions and a consumer group having twenty consumers.
So only four consumers will be working and sixteen will be in starvation mode. How to optimize that sixteen other consumers so we will have every consumer in working, or how we can optimize this situation?
This is desired behaviour. Kafka consumers guarantee that each record in a given partition will be consumed in order; if a partition were to have more than one consumer for a given group, this would no longer hold up.
Additionally, consumer groups can keep check of their progress through any given partition on a topic. If multiple consumers for the same group were competing, recording the offset would become nonsensical.
I guess you'd want to size your consumer groups when you spin up your application's nodes, but remember that the extra consumers in the group will add resiliency as well (maybe a little more than you bargained for in this case!)
From my understanding partitions and consumers are tied up into a 1:1 relationship in which a single consumer processes a partition. However is there such a way to repartition in the middle of processing?
We are currently trying to optimize a process in which the topic gets consumed across a group but there are cases in which the data processing needs to take longer on a certain consumer while others are already idle. Its like data cleansing where a certain partition might no longer need cleansing while others require fuzzy matching thereby adding complexity to the task a consumer performs.
Your understanding with regards to partitions and consumers is not quite right.
If you have N partitions, then you can have up to N consumers within the same consumer group each of which reading from a single partition. When you have less consumers than partitions, then some of the consumers will read from more than one partition. Also, if you have more consumers than partitions then some of the consumers will be inactive and will receive no messages at all.
If you have one consumer per partition, then some of the partitions might receive more messages and this is why some of your consumers might be idle while some others might still processing some messages. Note that messages are not always inserted into topic partitions in a round-robin fashion as messages with the same key are placed into the same partition.
in kafka topics are partitioned, and even if you can add partitions to a topic there is no repartitioning: all the data already written to a partition stays there, new data will be partitioned among the existing partitions (in a round robin fashion if you do not define keys, otherwise one key will always land in the same partition as long as you do not add partitions.)
But if you have a consumer group, and you add or remove consumers to this group, there is a group rebalancing where each consumer receives its share of partitions to exclusively consume from.
So if you have 3 partitions (with evenly distributed messages among them) and 2 consumers (in the same group) one consumer will have twice as much messages to handle than the other; with 3 consumers each one will consume one partition; with 4 consumers one will stay idle...
So as you already have evenly distributed messages (which is good), you should have as many consumers as you have partitions, and if it is still not fast enough you may add n partitions and n consumers. (For sure you could also try to optimize the consumer but that is another story...)
Added to answer comment:
Once a consumer -- from a given group -- is consuming a partition, it will continue to do so and will be the only one from the group consuming this partition, even if a lot of other consumers from the same group are idle. In one group a partition is never shared between consumers. (If the consumer crashes, another one will continue the work, and if a new consumer enters the group a rebalance will occur, but anyway only one consumer will work on one partition at a given time).
So one approach, as said in your comment would be to distribute the load evenly over the partitions. Another approach, would be to have a topic dedicated to expensive jobs, let it have a lot of partitions and a lot of consumers; and let the topic for non-expensive jobs have fever consumers.
Last approach that I would not recommend would be to not use the consumer group features and to manage yourself how you consume from Kafka, by using assign and seek methods from the consumer. (See KafkaConsumer JavaDoc for more information). Spark Structured Streaming for example is using that approach, but it is much more complex...
Currently I am having only one partition of topic and only one consumer.
But my consumer runs slow so I want to add new consumers to the same topic.
But at the same time I also want all the consumers to process exclusive set of messages.
I am not able to determine how many partitions should I create so that I can add consumers whenever I want to increase throughput and to process exclusive messages.
If you have N partitions for a topic, you can start a consumer group with N consumers, and they will each get to work on one of the partitions.
Each consumer will get a disparate set of messages (unless there is fail-over involved, then some messages may be delivered more than once) that way, and if the workload is evenly spread amongst all partitions, this will load-balance properly.
If you have fewer than N consumers, some of them will get more than one partition, but they will still not share partitions and each will get roughly the same number of partitions.
If you have more than N consumers, some will stay idle (but can act as hot standbys in case one of the others drops out).
The key to all this is that you put these consumers into the same consumer group. That is the way to indicate to Kafka that they should divide the partitions between them. If you started N consumer groups instead, each group would get all messages in parallel (each message would be processed N times).
The main downside to partitioning is that you lose the total ordering of messages. Messages are delivered in-order only with respect to other messages in the same partition. This may or may not be a problem for your application (and if it is, you can re-arrange things in your application by looking at timestamps).
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?
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.
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.
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.
#Lundahl did all the didactic, I'll give you a pratical sample.
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.
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.
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
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.