I am using a single cluster multi-broker kafka cluster with spark streaming to fetch data. the problem i am facing is under one topic same message get sent across all brokers.
How do I limit kafka to send only one message per broker so that there is no duplication of messages under one topic?
Related
I need to push data from multiple Kafka producers to a seperate Kafka broker. Say I have 3 Kafka servers. From Kafka 1 and 2, I need to push the data to Kafka 3 like below, is it possible?
Kafka has built in replication across brokers. Your producer can only write to one broker at any time for any topic in the cluster.
If you have separate clusters, use MirrorMaker to replicate topics
There are some misunderstood in your questions.
1. There is no Kafka Server
Kafka is a cluster, which means that all "servers" work together as a unique server. This means that when you send a message to a Kafka Cluster, you don't know which broker will accept this message.
You need to use the correct names for questions. When you say "Kafka broker" you mean a Kafka instance in a cluster. There is no "Kafka Server".
2. Do you need to replicate your data? Or just send the same message to two Kafka Clusters?
You need to replicate your message, this means that you have just one message that exists in two brokers, you need to set your topic replication.
3. Do you need the same message in two Clusters?
Use Mirror Maker
I have a concern regarding the Debizum outbox pattern ,that is when the Kafka connect consumes messages from outbox table and trying to produce to kafka topic ,if the kafka brokers are down does the debizum retries the message delivery or just fail?
Do we have any configuration for kafka produce fail scenario?
I have Kafka cluster with three brokers and zookeeper instances. Kept the replication factor of 2 for each partition.
i want to understand the impact of publishing messages to single node in a cluster by giving one broker address. Will this broker sends message to other brokers if messages fit into partitions hold by other brokers?
can someone explain how internal sync works or else point to resources.
giving one broker address
Even if you give one address, the bootstrap protocol returns all brokers to the client.
The partitioner logic determines which partition in which broker to send the data to - you target partitions, not brokers in the client.
Currently we are using Apache NiFi to consume messages via Kafka consumer. Output of kafka consumer is connected to hive processor.
I'm looking into how to run kafka consumer instance on a nifi cluster.
I have 3 nodes of nifi cluster and a kafka topic which have 3 partitions, I want the kafka consumer to be able run on each node so each consumer can poll message from one of topic partitions.
After I started the kafka consumer processor ,i can only see that the kafka consumer always run on a single node but not all nodes.
Is there any configuration that I missed?
NiFi uses the Apache Kafka client which is what performs the assignment of consumers to partitions. When you start the processor, assuming you have it set to 1 concurrent task, then you should have 1 consumer on each node of your cluster, and each consumer should get assigned a different partition.
https://bryanbende.com/development/2016/09/15/apache-nifi-and-apache-kafka
The producers send messages by setting up a list of Kafka Broker as follows.
props.put("bootstrap.servers", "127.0.0.1:9092,127.0.0.1:9092,127.0.0.1:9092");
I wonder "producers" how to know that which of the three brokers knew which one had a partition leader.
For a typical distributed server, either you have a load bearing server or have a virtual IP, but for Kafka, how is it loaded?
Does the producers program try to connect to one broker at random and look for a broker with a partition leader?
A Kafka cluster contains multiple broker instances. At any given time, exactly one broker is the leader while the remaining are the in-sync-replicas (ISR) which contain the replicated data. When the leader broker is taken down unexpectedly, one of the ISR becomes the leader.
Kafka chooses one broker’s partition’s replicas as leader using ZooKeeper. When a producer publishes a message to a partition in a topic, it is forwarded to its leader.
According to Kafka documentation:
The partitions of the log are distributed over the servers in the
Kafka cluster with each server handling data and requests for a share
of the partitions. Each partition is replicated across a configurable
number of servers for fault tolerance.
Each partition has one server which acts as the "leader" and zero or
more servers which act as "followers". The leader handles all read and
write requests for the partition while the followers passively
replicate the leader. If the leader fails, one of the followers will
automatically become the new leader. Each server acts as a leader for
some of its partitions and a follower for others so load is well
balanced within the cluster.
You can find topic and partition leader using this piece of code.
EDIT:
The producer sends a meta request with a list of topics to one of the brokers you supplied when configuring the producer.
The response from the broker contains a list of partitions in those topics and the leader for each partition. The producer caches this information and therefore, it knows where to redirect the messages.
It's quite an old question but I have the same question and after researched, I want to share the answer cuz I hope it can help others.
To determine leader of a partition, producer uses a request type called a metadata request, which includes a list of topics the producer is interested in.
The broker will response specifies which partitions exist in the topics, the replicas for each partition, and which replica is the leader.
Metadata requests can be sent to any broker because all brokers have a metadata cache that contains this information.