You are not qualified as an Apache Spark developer until you know what is a Resilient Distributed Dataset (RDD). It is the fundamental technique to represent data in the Spark memory. There are advanced data representation techniques like DataFrame built on top of RDD. However, it is always better to start with the most basic dataset: RDD. RDD is nothing other than a data structure with some special properties or features.
We all know that Apache Spark is a distributed general-purpose cluster-computing framework. There are some common problems faced in a distributed environment including but not limited to:
- Remote access of data is expensive
- High chance of failure
- Runtime errors are expensive and hard to track
- Wasting computing power is way too expensive
RDD is designed to address the abovementioned problems. In the following section, you will see the properties of RDD and how it solves these problems.
RDD is Distributed
As the name suggests, Resilient Distributed Dataset is a distributed dataset by nature which means you can safely distribute RDDs across nodes and execute operations on them. This property of RDD solves the problem of expensive remote access of data in distributed computing because an RDD can be migrated to a node which is depending on the data stored in that RDD.
RDD is Resilient & Immutable
Another property of RDD is its ability to recover quickly (Resilient) which comes with an additional requirement: Immutability. When we write a Spark program, we are creating a directed acyclic graph which generates an RDD after every transform operations.
For example, consider the Spark application we developed in Spark 01: Movie Rating Counter:
package com.javahelps.spark
import org.apache.spark.SparkContext
object MovieRatingsCounter {
def main(args: Array[String]): Unit = {
val sc = new SparkContext("local[*]", "MovieRatingsCounter")
// Read a text file
val data = sc.textFile("/tmp/ml-latest-small/ratings.csv")
// Extract the first row which is the header
val header = data.first();
// Filter out the header from the dataset
val filteredData = data.filter(row => row != header)
// Extract rating from line as float
val ratingData = filteredData.map(line => line.split(',')(2).toFloat)
val result = ratingData.countByValue() // Count number of occurrences of each ratings
println(result)
}
}In this code, the
sc.textFile function creates the first RDD (referred by the variable data) of String. The next Transformation operation filter creates a new RDD (referred by te variable filteredData). Similarly, the map is another Transformation operation which creates another RDD. Finally, the countByValue is an Action operation which gets the number of occurrences of each rating and returns it as a Scala object. The directed acyclic graph of RDDs created in the above code is shown in the following diagram.
The important point to notice here is that none of these Spark operations modify an existing RDD. Instead, they create a new RDD. This behavior is known as immutability of RDD. Immutabe RDDs allow Spark to rebuild an RDD from the previous RDD in the pipeline if there is a failure. For example, in the above pipeline, if the
filteredData RDD is failed for some reason, it can be rebuilt by applying the same filter operation on the previous RDD: data. If RDDs are mutable there is no guarantee that the data RDD will be in the same form as it was at the first time Spark called the filter operation on it. By being immutable and resilient, RDD handles the failure of nodes in a distributed environment.RDD is Compile-time Type-safe
RDDs are type-safe similar to arrays in Java. In the example provided in the last section, the
As a Spark developer, always remember that until you call an Action operation on an RDD, your Spark cluster will not do anything for you. For example, if you print the
In a nutshell, a Spark developer must know the following facts about RDDs:
Feel free to comment below if you have any questions.
data RDD and the filteredData RDD are RDDs with the type of String. Therefore, you can perform only String related operations on those RDDs. Similarly, ratingData RDD is a Float type RDD so that you can operate Float related operations on that RDD. It helps developers to avoid runtime errors which are hard to track in a distributed runtime.RDD is Lazy
RDDs support two types of operations: Transformation operation and Action operation. Transformation operations create new RDD from another RDD without modifying them (because RDDs are immutable). The following table lists some of the common transformations supported by Spark.| Action | Meaning |
|---|---|
reduce(func) | Aggregate the elements of the dataset using a function func (which takes two arguments and returns one). The function should be commutative and associative so that it can be computed correctly in parallel. |
collect() | Return all the elements of the dataset as an array at the driver program. This is usually useful after a filter or other operation that returns a sufficiently small subset of the data. |
count() | Return the number of elements in the dataset. |
first() | Return the first element of the dataset (similar to take(1)). |
take(n) | Return an array with the first n elements of the dataset. |
takeSample(withReplacement, num, [seed]) | Return an array with a random sample of num elements of the dataset, with or without replacement, optionally pre-specifying a random number generator seed. |
takeOrdered(n, [ordering]) | Return the first n elements of the RDD using either their natural order or a custom comparator. |
saveAsTextFile(path) | Write the elements of the dataset as a text file (or set of text files) in a given directory in the local filesystem, HDFS or any other Hadoop-supported file system. Spark will call toString on each element to convert it to a line of text in the file. |
saveAsSequenceFile(path) | Write the elements of the dataset as a Hadoop SequenceFile in a given path in the local filesystem, HDFS or any other Hadoop-supported file system. This is available on RDDs of key-value pairs that implement Hadoop's Writable interface. In Scala, it is also available on types that are implicitly convertible to Writable (Spark includes conversions for basic types like Int, Double, String, etc). |
saveAsObjectFile(path) | Write the elements of the dataset in a simple format using Java serialization, which can then be loaded using SparkContext.objectFile(). |
countByKey() | Only available on RDDs of type (K, V). Returns a hashmap of (K, Int) pairs with the count of each key. |
foreach(func) | Run a function func on each element of the dataset. This is usually done for side effects such as updating an accumulator or interacting with external storage systems. Note: modifying variables other than Accumulators outside of the foreach() may result in undefined behavior. |
ratingData as shown below instead of calling the countByValue action method, you will get an output saying that you are printing an RDD not the list of ratings.package com.javahelps.spark
import org.apache.spark.SparkContext
object MovieRatingsCounter {
def main(args: Array[String]): Unit = {
val sc = new SparkContext("local[*]", "MovieRatingsCounter")
// Read a text file
val data = sc.textFile("/tmp/ml-latest-small/ratings.csv")
// Extract the first row which is the header
val header = data.first();
// Filter out the header from the dataset
val filteredData = data.filter(row => row != header)
// Extract rating from line as float
val ratingData = filteredData.map(line => line.split(',')(2).toFloat)
println(ratingData)
}
}In a nutshell, a Spark developer must know the following facts about RDDs:
- You cannot modify an RDD because RDDs are immutable
- RDDs are lazy so you need to call an action method at the end of a pipeline to get the output
- Though an RDD may looks like a local variable in your code, at the runtime it can be shared across a cluster of nodes.
Feel free to comment below if you have any questions.
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