spark.sql.shuffle.partitions is not in Configuration docs, but a very critical parameter

I'm creating a `SparkSession` like this:

```
SparkSession
.builder().appName("foo").master("local")
.config("spark.default.parallelism", 2).getOrCreate()
```

when I run

```
((1 to 10) ++ (1 to 10)).toDS().distinct().count()
```

I get 200 partitions

```
19/12/02 10:29:34 INFO TaskSchedulerImpl: Adding task set 1.0 with 200 tasks
...
19/12/02 10:29:34 INFO TaskSetManager: Finished task 0.0 in stage 1.0 (TID 2) in 46 ms on localhost (executor driver) (1/200)
```

It is the `distinct` that is broken since `ds.rdd.getNumPartitions` gives `2`, while `ds.distinct().rdd.getNumPartitions` gives `200`. `ds.rdd.groupBy(identity).map(_._2.head)` and `ds.rdd.distinct()` work correctly.

Finally I notice that the good old `RDD` interface has a `distinct` that accepts `numPartitions` partitions, while `Dataset` does not.

.......................

According to below comments, it uses spark.sql.shuffle.partitions, which needs documenting in configuration.

> Default number of partitions in RDDs returned by transformations like join, reduceByKey, and parallelize when not set by user.

in https://spark.apache.org/docs/latest/configuration.html should say

> Default number of partitions in RDDs, but not DS/DF (see spark.sql.shuffle.partitions) returned by transformations like join, reduceByKey, and parallelize when not set by user.