MINOR: Fix Streams examples in documentation

Performed minor cleanup and escaped `<` and `>` so code examples are shown correctly in the browser.

Author: Vahid Hashemian <vahidhashemian@us.ibm.com>

Reviewers: Matthias J. Sax, Guozhang Wang

Closes #2247 from vahidhashemian/doc/fix_streams_doc

docs/streams.html

@@ -279,8 +279,8 @@ from a single topic).
 <pre>
     KStreamBuilder builder = new KStreamBuilder();
 
-    KStream<String, GenericRecord> source1 = builder.stream("topic1", "topic2");
-    KTable<String, GenericRecord> source2 = builder.table("topic3", "stateStoreName");
+    KStream&lt;String, GenericRecord&gt; source1 = builder.stream("topic1", "topic2");
+    KTable&lt;String, GenericRecord&gt; source2 = builder.table("topic3", "stateStoreName");
 </pre>
 
 <h5><a id="streams_dsl_windowing" href="#streams_dsl_windowing">Windowing a stream</a></h5>
@@ -301,7 +301,7 @@ A <b>join</b> operation merges two streams based on the keys of their data recor
   </ul>
 
 Depending on the operands the following join operations are supported: <b>inner joins</b>, <b>outer joins</b> and <b>left joins</b>. Their semantics are similar to the corresponding operators in relational databases.
-a
+
 <h5><a id="streams_dsl_transform" href="#streams_dsl_transform">Transform a stream</a></h5>
 
 <p>
@@ -323,12 +323,12 @@ where users can usually pass a customized function to these functions as a param
 
 <pre>
     // written in Java 8+, using lambda expressions
-    KStream<String, GenericRecord> mapped = source1.mapValue(record -> record.get("category"));
+    KStream&lt;String, GenericRecord&gt; mapped = source1.mapValue(record -> record.get("category"));
 </pre>
 
 <p>
 Stateless transformations, by definition, do not depend on any state for processing, and hence implementation-wise
-they do not require a state store associated with the stream processor; Stateful transformations, on the other hand,
+they do not require a state store associated with the stream processor; stateful transformations, on the other hand,
 require accessing an associated state for processing and producing outputs.
 For example, in <code>join</code> and <code>aggregate</code> operations, a windowing state is usually used to store all the received records
 within the defined window boundary so far. The operators can then access these accumulated records in the store and compute
@@ -337,14 +337,14 @@ based on them.
 
 <pre>
     // written in Java 8+, using lambda expressions
-    KTable<Windowed<String>, Long> counts = source1.groupByKey().aggregate(
+    KTable&lt;Windowed&lt;String&gt;, Long&gt; counts = source1.groupByKey().aggregate(
         () -> 0L,  // initial value
         (aggKey, value, aggregate) -> aggregate + 1L,   // aggregating value
         TimeWindows.of("counts", 5000L).advanceBy(1000L), // intervals in milliseconds
         Serdes.Long() // serde for aggregated value
     );
 
-    KStream<String, String> joined = source1.leftJoin(source2,
+    KStream&lt;String, String&gt; joined = source1.leftJoin(source2,
         (record1, record2) -> record1.get("user") + "-" + record2.get("region");
     );
 </pre>
@@ -369,7 +369,7 @@ Kafka Streams provides a convenience method called <code>through</code>:
     //
     // joined.to("topic4");
     // materialized = builder.stream("topic4");
-    KStream<String, String> materialized = joined.through("topic4");
+    KStream&lt;String, String&gt; materialized = joined.through("topic4");
 </pre>