RDDs are created by starting with a file in the Hadoop file system (or
any other Hadoop-supported file system), or an existing Scala collection
in the driver program, and transforming it. Users may also ask Spark to
persist an RDD in memory, allowing it to be reused efficiently across
parallel operations. Finally, RDD.
RDD can be created below three method
- Parallelizing already existing collection in driver program.
- Referencing a dataset in an external storage system (e.g. HDFS, Hbase, shared file system).
- Creating RDD from already existing RDDs.
RDD basic commands
collect(): Its returns all the elements of the dataset as an array at the driver program, and using for loop on this array, print elements of RDD.
collect(): Its returns all the elements of the dataset as an array at the driver program, and using for loop on this array, print elements of RDD.
rdd=sc.parallelize([1,2,3,4,5])
rdd.collect()
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)
count(): It count the elements of the RDD.
rdd.count()
![](data:image/png;base64,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)
var rdd=sc.parallelize(List(1,2,3,4,5))
rdd.getNumPartitions
a = sc.parallelize(range(10), 5)
a.glom().collect()
Best case Scenario is that we should make RDD in following way:
rdd.collect()
count(): It count the elements of the RDD.
rdd.count()
take(n):Read elements of an RDD where n is the number of elements.
rdd.take(3)
first: Restun first element of an RDD.
rdd.first
foreach: It is an action. It does not return any value. It
executes input function on each element of an RDD. It executes the
function on each item in RDD. It is good for writing database or
publishing to web services.
rdd=sc.parallelize([1,2,3,4,5])
def f(x): print(x)
rdd.foreach(f)
def f(x): print(x)
rdd.foreach(f)
To Find the Number of Partitions
var rdd=sc.parallelize(List(1,2,3,4,5))
rdd.partitions.length
rdd.partitions.length
var rdd=sc.parallelize(List(1,2,3,4,5))
rdd.getNumPartitions
a = sc.parallelize(range(10), 5)
a.glom().collect()
Best case Scenario is that we should make RDD in following way:
numbers of cores in Cluster = no. of partitions
Consider
the size of wc-data.txt is of 1280 MB and Default block size is 128 MB.
So there will be 10 blocks created and 10 default partitions(1 per
block). For a better performance, we can increase the number of
partitions on each block. Below code will create 20 partitions on 10
blocks(2 partitions/block). Performance will be improved but need to
make sure that each cluster is running on 2 cores minimum.
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