Kafka partitions Explained.
As all we know, Kafka topics are divided into number of partitions. A topic can have zero or more number of partitions. Topic partition is a way of implementing parallelism in Kafka.Producer can write data into different partitions parallelly and consumers in a consumer group can read them parallelly. Each consumer in a consumer group is bound to a particular partition.Kafka always give single partitions data to one consumer thread. So the degree of parallelism is based on the number of partitions and number of consumers in a consumer group. you cannot have more number of consumers than the total number of partitions. If you are adding more number of consumers, extra consumers will be idle. Of-course, you can start with less number of consumers than number of partitions and you can add the consumer later. When you add a new consumer, group coordinator will coordinate and assign one particular partition for the new consumer.
In producer side, you can produce data to a topic without worrying about the partitions.By default Kafka will use default partition-er. The Producer config property partitioner.class sets the partitioner. By default partitioner.class is set to org.apache.kafka.clients.producer.internals.DefaultPartitioner. The default partitioner partitions using the hash of the record key, if the record has a key.for example, If you want to send particular customer data to a particular partition, you can use some unique id as record key for each customer. Kafka will use hash of this key to find exact topic partition for that customer, this way we can kafka will make sure same each partition holds one particular customer data. The default partitioner partitions using round-robin if the record has no key.You can define a custom partitioner as well.
As all we know, Kafka topics are divided into number of partitions. A topic can have zero or more number of partitions. Topic partition is a way of implementing parallelism in Kafka.Producer can write data into different partitions parallelly and consumers in a consumer group can read them parallelly. Each consumer in a consumer group is bound to a particular partition.Kafka always give single partitions data to one consumer thread. So the degree of parallelism is based on the number of partitions and number of consumers in a consumer group. you cannot have more number of consumers than the total number of partitions. If you are adding more number of consumers, extra consumers will be idle. Of-course, you can start with less number of consumers than number of partitions and you can add the consumer later. When you add a new consumer, group coordinator will coordinate and assign one particular partition for the new consumer.
In producer side, you can produce data to a topic without worrying about the partitions.By default Kafka will use default partition-er. The Producer config property partitioner.class sets the partitioner. By default partitioner.class is set to org.apache.kafka.clients.producer.internals.DefaultPartitioner. The default partitioner partitions using the hash of the record key, if the record has a key.for example, If you want to send particular customer data to a particular partition, you can use some unique id as record key for each customer. Kafka will use hash of this key to find exact topic partition for that customer, this way we can kafka will make sure same each partition holds one particular customer data. The default partitioner partitions using round-robin if the record has no key.You can define a custom partitioner as well.
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