spark RDD (Resilient Distributed DataSet)

1. 스파크 클러스터
   클러스터란 여러 대의 서버가 마치 한대의 서버처럼 동작하는 것을 뜻합니다. 스파크는 클러스터 환경에서 동작하며 대량의 데이터를 여러 서버에서 병렬 처리합니다.

2. 분산 데이터로서의 RDD
   RDD는 Resilient Distrubuted Datasets으로, ‘회복력을 가진 분산 데이터 집합’이란 뜻입니다. (Resilient : 회복력이 있는) 데이터를 처리하는 과정에서 문제가 발생하더라도 스스로 복구할 수 있는 것을 의미합니다.

• RDD 는 여러 분산 노드에 걸쳐 저장되는, 변경이 불가능한 데이터의 집합입니다.
• RDD 를 변경하기 위해선 새로운 RDD 를 생성해야합니다.
• RDD 는 2가지 Operation 을 사용해 조작할 수 있습니다.

3. 트랜스포메이션과 액션
   RDD가 제공하는 연산은 크게 트랜스포메이션과 액션이 있습니다. 트랜스포메이션은 RDD의 변형을 일으키는 연산이고, 실제로 동작이 수행되지는 않습니다. 반면에, action은 동작을 수행해서 원하는 타입의 결과를 만들어내는 것으로 saveAsTextFile로 수행됩니다. 따라서, saveAsTextFile은 action 연산에 해당됩니다.

Transformation

• map(func)
  Return a new distributed dataset formed by passing each element of the source through a function func.
• filter(func)
  Return a new dataset formed by selecting those elements of the source on which func returns true.
• flatMap(func)
  Similar to map, but each input item can be mapped to 0 or more output items (so func should return a Seq rather than a single item).
• mapPartitions(func)
  Similar to map, but runs separately on each partition (block) of the RDD, so func must be of type Iterator T => Iterator U when running on an RDD of type T.
• mapPartitionsWithIndex(func)
  Similar to mapPartitions, but also provides func with an integer value representing the index of the partition, so func must be of type (Int, Iterator T) => Iterator U when running on an RDD of type T.
• sample(withReplacement, fraction, seed)
  Sample a fraction fraction of the data, with or without replacement, using a given random number generator seed.
• union(otherDataset)
  Return a new dataset that contains the union of the elements in the source dataset and the argument.
• intersection(otherDataset)
  Return a new RDD that contains the intersection of elements in the source dataset and the argument.
• distinct([numPartitions]))
  Return a new dataset that contains the distinct elements of the source dataset.
• groupByKey([numPartitions])
  When called on a dataset of (K, V) pairs, returns a dataset of (K, Iterable) pairs.
  Note: If you are grouping in order to perform an aggregation (such as a sum or average) over each key, using reduceByKey or aggregateByKey will yield much better performance.
  Note: By default, the level of parallelism in the output depends on the number of partitions of the parent RDD. You can pass an optional numPartitions argument to set a different number of tasks.
• reduceByKey(func, [numPartitions])
  When called on a dataset of (K, V) pairs, returns a dataset of (K, V) pairs where the values for each key are aggregated using the given reduce function func, which must be of type (V,V) => V. Like in groupByKey, the number of reduce tasks is configurable through an optional second argument.
• aggregateByKey(zeroValue)(seqOp, combOp, [numPartitions])
  When called on a dataset of (K, V) pairs, returns a dataset of (K, U) pairs where the values for each key are aggregated using the given combine functions and a neutral "zero" value. Allows an aggregated value type that is different than the input value type, while avoiding unnecessary allocations. Like in groupByKey, the number of reduce tasks is configurable through an optional second argument.
• sortByKey([ascending], [numPartitions])
  When called on a dataset of (K, V) pairs where K implements Ordered, returns a dataset of (K, V) pairs sorted by keys in ascending or descending order, as specified in the boolean ascending argument.
• join(otherDataset, [numPartitions])
  When called on datasets of type (K, V) and (K, W), returns a dataset of (K, (V, W)) pairs with all pairs of elements for each key. Outer joins are supported through leftOuterJoin, rightOuterJoin, and fullOuterJoin.
• cogroup(otherDataset, [numPartitions])
  When called on datasets of type (K, V) and (K, W), returns a dataset of (K, (Iterable, Iterable)) tuples. This operation is also called groupWith.
• cartesian(otherDataset)
  When called on datasets of types T and U, returns a dataset of (T, U) pairs (all pairs of elements).
• pipe(command, [envVars])
  Pipe each partition of the RDD through a shell command, e.g. a Perl or bash script. RDD elements are written to the process's stdin and lines output to its stdout are returned as an RDD of strings.
• coalesce(numPartitions)
  Decrease the number of partitions in the RDD to numPartitions. Useful for running operations more efficiently after filtering down a large dataset.
• repartition(numPartitions)
  Reshuffle the data in the RDD randomly to create either more or fewer partitions and balance it across them. This always shuffles all data over the network.
• repartitionAndSortWithinPartitions(partitioner)
  Repartition the RDD according to the given partitioner and, within each resulting partition, sort records by their keys. This is more efficient than calling repartition and then sorting within each partition because it can push the sorting down into the shuffle machinery.

Action

• 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)
  same
• saveAsObjectFile(path)
  same
• 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. See Understanding closures for more details.