Which Kafkautils function is used to create an RDD based on offset ranges?

createRDD
createRDD. :: Experimental :: Create a RDD from Kafka using offset ranges for each topic and partition.

What is PreferConsistent in Kafka?

PreferConsistent as shown above. This will distribute partitions evenly across available executors. If your executors are on the same hosts as your Kafka brokers, use PreferBrokers , which will prefer to schedule partitions on the Kafka leader for that partition.

What is Kafka Utils?

Kafka-Utils is a library containing tools to interact with kafka clusters and manage them. The tool provides utilities like listing of all the clusters, balancing the partition distribution across brokers and replication-groups, managing consumer groups, rolling-restart of the cluster, cluster healthchecks.

Which function is used to get current Kafka offsets for an RDD?

You can use transform() instead of foreachRDD() as your first method call in order to access offsets, then call further Spark methods. However, be aware that the one-to-one mapping between RDD partition and Kafka partition does not remain after any methods that shuffle or repartition, e.g. reduceByKey() or window().

What is Autocommit in Kafka?

Using auto-commit gives you “at least once” delivery: Kafka guarantees that no messages will be missed, but duplicates are possible. Auto-commit basically works as a cron with a period set through the auto.commit.interval.ms configuration property.

What is the difference between spark streaming and structured streaming?

Both the Apache Spark streaming and the structured streaming models use micro- (or mini-) batching as their primary processing mechanisms. But it is the detail that changes. Ergo, Apache Spark uses DStreams, while structured streaming uses DataFrames to process these streams of data pouring into the analytics engine.

Why Kafka is used with Spark?

Kafka is a potential messaging and integration platform for Spark streaming. Kafka act as the central hub for real-time streams of data and are processed using complex algorithms in Spark Streaming.

What is Spark Streaming?

Spark Streaming is an extension of the core Spark API that allows data engineers and data scientists to process real-time data from various sources including (but not limited to) Kafka, Flume, and Amazon Kinesis. This processed data can be pushed out to file systems, databases, and live dashboards.

What is spark streaming?

Where Kafka offset is stored?

Offsets in Kafka are stored as messages in a separate topic named ‘__consumer_offsets’ . Each consumer commits a message into the topic at periodic intervals.

Who maintains the offset in Kafka?

As each message is received by Kafka, it allocates a message ID to the message. Kafka then maintains the message ID offset on a by consumer and by partition basis to track consumption. Kafka brokers keep track of both what is sent to the consumer and what is acknowledged by the consumer by using two offset values.

What is Enable_auto_commit_config?

If the auto acknowledgement is disabled by using this property ConsumerConfig. ENABLE_AUTO_COMMIT_CONFIG , Then you have to set the acknowledgement mode on container level to MANUAL and don’t commit the offset because by default it is set to BATCH.