> For the complete documentation index, see [llms.txt](https://george-jen.gitbook.io/data-science-and-apache-spark/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://george-jen.gitbook.io/data-science-and-apache-spark/working-with-key-value-pair.md).

# Working with key value pair

While most Spark operations work on RDDs containing any type of objects, a few special operations are only available on RDDs of key-value pairs. The most common ones are such as grouping or aggregating the elements by a key.

In Scala, these operations are automatically available on RDDs containing Tuple2 objects (the built-in tuples in the language, created by simply writing (a, b)). The key-value pair operations are available in the PairRDDFunctions class, which automatically wraps around an RDD of tuples.

For example, the following code uses the reduceByKey operation on key-value pairs to count how many times each line of text occurs in a file

```
val rdd=sc.parallelize(Seq("a","b","x","a"))
val pairs = rdd.map(s => (s, 1))
val counts = pairs.reduceByKey((a, b) => a + b)
counts.collect.foreach(println)
/*
(x,1)
(b,1)
(a,2)
*/
```


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