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
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So my question is this: If i have a server running Kafka (And zookeeper), and another machine only consuming messages, does the consumer machine need to run zookeeper too? Or does the server take care of all?
No.
Role of Zookeeper in Kafka is:
Broker registration: (cluster membership) with heartbeats mechanism to keep the list current
Storing topic configuration: which topics exist, how many partitions each
has, where are the replicas, who is the preferred leader, list of ISR for
partitions
Electing controller: The controller is one of the brokers and is responsible for maintaining the leader/follower relationship for all the partitions.
So Zookeeper is required only for kafka broker. There is no need to have Zookeper on the producer or consumer side.
The consumer does not need zookeeper
You have not mentioned which version of Kafka or the clients you're using.
Kafka consumers using 0.8 store their offsets in Zookeeper, so it is required for them. However, no, you would not run Zookeeper and consumers on the same server
From 0.9 and later, clients are separate from needing it (unless you want to manage external connections to Zookeeper on your own for storing data)
Are there any downsides to running the same producer and consumer code for all nodes in the cluster? If there are 8 nodes in the cluster (8 consumer, 8 kafka broker, and 8 producers), would 8 producers be running at the same time in the cluster then? Is there a way to modify cluster so that only one producer runs at a time?
Kafka cluster is nothing but Kafka brokers running under a distributed consensus. Kafka cluster is agnostic about number of producers and consumers running around it. Producers and consumers are clients of the Kafka cluster. Producers will stream data to Kafka and consumers consume the data from Kafka. Within Kafka cluster data will be distributed within topics. Topics are sharded using partitions. If multiple consumers belong to the same consumer group consumers can work in a self healing fashion.
Is there a way to modify cluster so that only one producer runs at a
time?
If you intend to run a single producer at certain point of time, you don't need to make any change within cluster.
Are there any downsides to running the same producer and consumer code for all nodes in the cluster?
The primary downsides here would be scalability and memory usage.
Producers and Consumers are not required to run on Brokers. Producers should be deployed where data is being generated (or running as separate hosts, like Kafka Connect workers).
Consumers should be scaled out independently based on the throughput and ordering guarantees that you need in your downstream systems.
There is nothing that says 8 brokers requires 8 producers and 8 consumers; partitions are what matters more
If you have N partitions in a topic, you can only scale to N active consumers anyway, and infinitely many producers
8 brokers can hold lots of partitions for any given topic
Running a single producer is an implementation of your own code. The broker cannot force it.
I have Nifi cluster of and Kafka is also installed there.
Created one topic with 5 partitions, start consuming that topic with one gourp-id. So that each partition will get unique messages.
Now I created the 5 ConsumeKafka_1_0 processors having the intent of getting unique messages on each consumer side. But only 2 of the ConsumeKafka_1_0 are consuming all the messages rest is setting ideal.
Now what I did is started the 5 command line Kafka consumer, and what happened is, I was able to see the all the partitions are getting the messages and able to consume them from command line consumer in round-robin fashion only.
Also, I tried descried the Kafka group and what I saw was only 2 of the Nifi ConsumeKafka_1_0 is consuming all the 5 partitions and rest is ideal, see the snapshot.
Would you please let me what I am doing wrong here with Nifi consumer processor.
Note - i used Nifi version is 1.5 and Kafka version is 1.0.
I've written this article which explains how the integration with Kafka works:
https://bryanbende.com/development/2016/09/15/apache-nifi-and-apache-kafka
The Apache Kafka client (used by NiFi) is what assigns partitions to the consumers.
Typically if you had a 5 node NiFi cluster, with 1 ConsumeKafka processor on the canvas with 1 concurrent task, then each node would be consuming 1 partition.
I am using Kafka client library comes with Kafka 0.11.0.1. I noticed that using kafkaconsumer does not need to configure zookeeper anymore. Does that mean zookeep server will automatically be located by the kafka bootstrap server?
Since Kafka 0.9 the KafkaConsumer implementation stores offsets commit and consumer group information in Kafka brokers themselves. This eliminates the zookeeper dependency and increases the scalability of the consumers.
I deploy a kafka cluster on three hosts.And deploy consumers on the same hosts.
How i to let the consumer consume the nearest broker's partitions.for example,host a's consumer just consume the partitions which belong to host a.
Kafka doesn't work that way. The clients will connect to all three brokers and produce and consume from all three brokers in parallel based on which nodes are currently the leader for each topic partition.