What is a JavaRDD?

(If you’re new to Spark, JavaRDD is a distributed collection of objects, in this case lines of text in a file. We can apply operations to these objects that will automatically be parallelized across a cluster.)

What is a Java RDD?

Resilient Distributed Datasets (RDD) is a fundamental data structure of Spark. It is an immutable distributed collection of objects. RDDs can contain any type of Python, Java, or Scala objects, including user-defined classes. Formally, an RDD is a read-only, partitioned collection of records.

What is Java pair RDD?

1. There is a distinction because some operations ( aggregateByKey , groupByKey , etc) need to have a Key to group by, then a value to put into the grouped result. JavaPairRDD is there to declare the contract to the developer that a Key and Value is required.

How does spark sort RDD?

Spark RDD sortByKey() Syntax This function takes two optional arguments; ascending as Boolean and numPartitions as an integer. ascending is used to specify the order of the sort, by default, it is true meaning ascending order, use false for descending order.

What is JavaSparkContext?

public class JavaSparkContext extends java.lang.Object implements java.io.Closeable. A Java-friendly version of SparkContext that returns JavaRDD s and works with Java collections instead of Scala ones. Only one SparkContext may be active per JVM. You must stop() the active SparkContext before creating a new one.

What is RDD DataFrame and dataset?

Conceptually, consider DataFrame as an alias for a collection of generic objects Dataset[Row], where a Row is a generic untyped JVM object. Dataset, by contrast, is a collection of strongly-typed JVM objects, dictated by a case class you define in Scala or a class in Java.

What is RDD and DataFrame in Spark?

RDD – RDD is a distributed collection of data elements spread across many machines in the cluster. RDDs are a set of Java or Scala objects representing data. DataFrame – A DataFrame is a distributed collection of data organized into named columns. It is conceptually equal to a table in a relational database.

What is RDD explain properties of RDD?

Immutable and Read-only Since, RDDs are immutable, which means unchangeable over time. That property helps to maintain consistency when we perform further computations. As we can not make any change in RDD once created, it can only get transformed into new RDDs. This is possible through its transformations processes.

What is the difference between RDD and pair RDD?

pairRDD operations are applied on each key/element in parallel. Operations on RDD (like flatMap) are applied to the whole collection. Spark provides special operations on RDDs containing key/value pairs. These RDDs are called pair RDDs.

What is reduce by key in Spark?

In Spark, the reduceByKey function is a frequently used transformation operation that performs aggregation of data. It receives key-value pairs (K, V) as an input, aggregates the values based on the key and generates a dataset of (K, V) pairs as an output.

How to redistribute data in a javardd?

Internally, this uses a shuffle to redistribute data. If you are decreasing the number of partitions in this RDD, consider using coalesce , which can avoid performing a shuffle. Return a sampled subset of this RDD with a random seed.

How to randomly split a javardd in spark?

Set this RDD’s storage level to persist its values across operations after the first time it is computed. Randomly splits this RDD with the provided weights. Randomly splits this RDD with the provided weights.

Why do we use partition size in javardd?

Uses this partitioner/partition size, because even if other is huge, the resulting RDD will be less than or equal to us. Return an RDD with the elements from this that are not in other. Return an RDD with the elements from this that are not in other.

How to increase the parallelism of a javardd?

Return a new RDD that has exactly numPartitions partitions. Can increase or decrease the level of parallelism in this RDD. Internally, this uses a shuffle to redistribute data. If you are decreasing the number of partitions in this RDD, consider using coalesce , which can avoid performing a shuffle.