Resilient Distributed Datasets (RDDs) are a distributed memory abstraction that allows programmers to perform in-memory computations on large clusters while retaining the fault tolerance of data flow models like MapReduce.
Reading Spark method sortByKey : sortByKey([ascending], [numTasks]) When called on a dataset of (K, V) pairs where K implements Ordered, …
scala apache-spark rddI'd like to select a range of elements in a Spark RDD. For example, I have an RDD with a …
apache-spark rddI am looking for some better explanation of the aggregate functionality that is available via spark in python. The example …
python apache-spark lambda aggregate rddI need to generate a full list of row_numbers for a data table with many columns. In SQL, this …
sql apache-spark row-number rddI read up on the documentation of HashPartitioner. Unfortunately nothing much was explained except for the API calls. I am …
scala apache-spark rdd partitioningAssume df1 and df2 are two DataFrames in Apache Spark, computed using two different mechanisms, e.g., Spark SQL vs. …
scala apache-spark dataframe apache-spark-sql rddDefinition says: RDD is immutable distributed collection of objects I don't quite understand what does it mean. Is it like …
scala hadoop apache-spark rddI have a RDD structure RDD[(String, String)] and I want to create 2 Lists (one for each dimension of the …
scala list apache-spark rddGiven that the HashPartitioner docs say: [HashPartitioner] implements hash-based partitioning using Java's Object.hashCode. Say I want to partition DeviceData …
scala apache-spark rddI have a data frame with following type col1|col2|col3|col4 xxxx|yyyy|zzzz|[1111],[2222] I want my output to …
python apache-spark pyspark rdd