Github user BenFradet commented on a diff in the pull request:
https://github.com/apache/spark/pull/10060#discussion_r47008015
--- Diff: docs/sql-programming-guide.md ---
@@ -428,6 +461,45 @@ df <- sql(sqlContext, "SELECT * FROM table")
</div>
+## Creating Datasets
+
+Datasets are similar to RDDs, however, instead of using Java Serialization
or Kryo they use
+a specialized [Encoder](api/scala/index.html#org.apache.spark.sql.Encoder)
to serialize the objects
+for processing or transmitting over the network. While both encoders and
standard serialization are
+responsible for during an object into bytes, encoders are code generated
dynamically and use a format
+that allows Spark to perform many operations like filtering, sorting and
hashing without deserialzing
+the back into an object.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+{% highlight scala %}
+// Encoders for most common types are automatically provided by importing
sqlContext.implicits._
+val ds = Seq(1, 2, 3).toDS()
+ds.map(_ + 1).collect() // Returns: Array(2, 3, 4)
+
+// Encoders are also created for case classes.
+case class Person(name: String, age: Long)
+val ds = Seq(Person("Andy", 32)).toDS()
+
+// DataFrames can be converted to a Dataset by providing a class. Mapping
will be done by name.
--- End diff --
2 whitespaces here too
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