I am using Spring Boot #kafkaListener in my application. Lets assume I use below configuration -
Topic Partitions : 2
spring.kafka.listener.concurrency : 2
group-id : TEST_GRP_ID
Acknowledgement : Manual
My question is ,
As per my knowledge Concurrency will create parallel thread to consume message.
So, thread 1 consumed the batch of records and thread 2 consumed the batch of records in this case processing of the messages will sequential and then commit the offset?
If I have two instances of the micro service in my cloud environment (in production more partition and more instances), then how concurrency will work? In each instance will create two parallel thread for my Kafka consumer?
How can I improve performance of my consumer or how can I make fast consumption and processing of the messages?
Your understanding is not too far from the truth. In fact only one consumer per partition can exist for the given group. The concurrency number gives us an approximate number of target consumers. And independently of microservice instances only two maximum consumers can exist if you have only two partitions in your topic.
So, to increase a performance you need to have more than 2 partition or more topics to consume, then they all can be distributed between your instances and their consumers evenly.
See more info in Apache Kafka docs: https://docs.confluent.io/platform/current/clients/consumer.html
✓you are having concurrency as 2 , which means 2 containers will be created to your listener.
✓As you are having 2 partitions in topic , messages from both the partitions will be consumed and processed parallelly.
✓When you spin up one more instance with same group name , the first thing that will happen is Group Rebalance .
✓Despite this event , as at any point of time only one consumer from a specific consumer group can be there for a partition , In the end , only 2 containers will be listening to messages and other 2 containers just remain idle.
✓In order to achieve more scalability , we need to add more number of partitions to the topic there by we can have more number of active listener containers
Related
I have 1 application as a producer(scheduler) of jobs and N applications as an consumers (executors).
Is there any way how to prioritize execution of particular job using Quartz?
f.e.: at one moment i have 1000 jobs in queue and i need to execute 1 particular job with the highest priority
One possible solution could be set up different group name for the primary jobs. But is it possible to set up for 1 of my consumer apps to execute only jobs with particular group name?
Thanks in advance for reply.
I am trying to create a Kafka Streams service where
I am trying to initialize a cache in a processor, that will then be updated by consuming messages with a topic say "nodeStateChanged" for a partition key lets say locationId.
I need to check the node state when I consume another topic lets say "Report" again keyed by the same locationId. Effectively I am joining with the table created by nodeStateChanged.
How do I ensure that all the updates for nodeStateChanged fall on the same instance as the Report topic so that the lookup for a location is possible when a new report is recieved. Do 1 and 2 need to be created by the same topology or it okay to create two seperate topologies that share the same APPLICATION_ID_CONFIG.
You don't need to do anything. Kafka Streams will always co-partition topics. Ie, if you have a sub-topology that reads from multiple topics with N partitions each, you get N tasks and each task is processing corresponding partitions, ie, task 0 processes partitions zero of both input topics, task 1 processes partitions one of both input topics, etc.
I have producer which produces some messages(10 for example).
There are n partitions and a consumer group with n consumers.
Kafka system will distribute the messages among the consumers.
How do I combine the messages of all the consumers in one place so that I have 10 messages.
I am using Kafka with Spring.
Create a consumer group with only one consumer, then you will get the records from all the partitions in one place.
My understanding as per Kafka stream documentation,
Maximum possible parallel tasks is equal to maximum number of partitions of a topic among all topics in a cluster.
I have around 60 topics at Kafka cluster. Each topic has single partition only.
Is it possible to achieve scalability/parallelism with Kafka stream for my Kafka cluster?
Do you want to do the same computation over all topics? For this, I would recommend to introduce an extra topic with many partitions that you use to scale out:
// using new 1.0 API
StreamsBuilder builder = new StreamsBuilder():
KStream parallelizedStream = builder
.stream(/* subscribe to all topics at once*/)
.through("topic-with-many-partitions");
// apply computation
parallelizedStream...
Note: You need to create the topic "topic-with-many-partitions" manually before starting your Streams application
Pro Tip:
The topic "topic-with-many-partitions" can have a very short retention time as it's only used for scaling and must not hold data long term.
Update
If you have 10 topic T1 to T10 with a single partitions each, the program from above will execute as follows (with TN being the dummy topic with 10 partitions):
T1-0 --+ +--> TN-0 --> T1_1
... --+--> T0_0 --+--> ... --> ...
T10-0 --+ +--> TN-10 --> T1_10
The first part of your program will only read all 10 input topics and write it back into 10 partitions of TN. Afterwards, you can get up to 10 parallel tasks, each processing one input partition. If you start 10 KafakStreams instances, only one will execute T0_0, and each will alsa one T1_x running.
I use logstash to transfer data from Kafka to Elasticsearch and I'm getting the following error:
WARN org.apache.kafka.clients.consumer.internals.ConsumerCoordinator - Auto offset commit failed for group kafka-es-sink: Commit cannot be completed since the group has already rebalanced and assigned the partitions to another member. This means that the time between subsequent calls to poll() was longer than the configured session.timeout.ms, which typically implies that the poll loop is spending too much time message processing. You can address this either by increasing the session timeout or by reducing the maximum size of batches returned in poll() with max.poll.records.
I tried to adjust the session timeout (to 30000) and max poll records (to 250).
The topic produces 1000 events per seconds in avro format. There are 10 partitions (2 servers) and two logstash instances with 5 consumer threads each.
I have no problems with other topics with ~100-300 events per second.
I think it should be a config issue because I also have a second connector between Kafka and Elasticsearch on the same topic which works fine (confluent's kafka-connect-elasticsearch)
The main aim is to compare kafka connect and logstash as connector. Maybe anyone has also some experience in general?