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1) Spark is a fast and general cluster computing system.
2) Spark supports a rich set of higher level tools including:
- Spark SQL: For SQL query language and structure data processing system
- MLlib: for machine learning
- GraphX: for machine learning
- Spark Streaming: for streaming processing
SPEED: Its enables applications in hadoop clusters to run upto:
Declaring variables: Var x: Int = 7 var x = 7 //type inferred val y = 'hi' //read only Functions: def square(x: int): int = x*x def square(x: int): int = { x*x } def announce(text: string)= { println(text) } | Java equivalent int x = 7; final string y = 'hello'; Java equivalent: int square(int x){ return x*x; } void announce(string text){ system.out.println(text); } |
| Method on Seq[T] | Explanation |
| map(f: T => U): Seq[U] | Each element is result of f |
| flatMap(f: T => Seq[U]): Seq[U] | One to many map |
| filter(f: T => Boolean): Seq[T] | Keep elements passing f |
| exists(f: T => Boolean): Boolean | True if one element passes f |
| forall(f: T => Boolean): Boolean | True is all elements pass |
| reduce(f:(T, T) => T); T | Merge elements using f |
| groupBy(f:T => K): Map[K, List[T]] | Group elements by f |
| sortBy(f: T => K): Seq[T] | sort elements |
Wordcount: count how often each word appear in a collection of text documents.
Void map(string doc_id, string text): for each word w insegment(text): emit (w, '1'); void reduce (string word, Iterator group): int count = 0; for each pc in group; count+= int(pc); emit (word, string(count)); |
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