Repository: kafka Updated Branches: refs/heads/trunk 21d7e6f19 -> 6626b058c
MINOR: Fix Streams examples in documentation Performed minor cleanup and escaped `<` and `>` so code examples are shown correctly in the browser. Author: Vahid Hashemian <[email protected]> Reviewers: Matthias J. Sax, Guozhang Wang Closes #2247 from vahidhashemian/doc/fix_streams_doc Project: http://git-wip-us.apache.org/repos/asf/kafka/repo Commit: http://git-wip-us.apache.org/repos/asf/kafka/commit/6626b058 Tree: http://git-wip-us.apache.org/repos/asf/kafka/tree/6626b058 Diff: http://git-wip-us.apache.org/repos/asf/kafka/diff/6626b058 Branch: refs/heads/trunk Commit: 6626b058c7893ce1192456b3fc7617f98da6f3cc Parents: 21d7e6f Author: Vahid Hashemian <[email protected]> Authored: Tue Dec 13 09:44:11 2016 -0800 Committer: Guozhang Wang <[email protected]> Committed: Tue Dec 13 09:44:11 2016 -0800 ---------------------------------------------------------------------- docs/streams.html | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/kafka/blob/6626b058/docs/streams.html ---------------------------------------------------------------------- diff --git a/docs/streams.html b/docs/streams.html index 74620ec..306b2a5 100644 --- a/docs/streams.html +++ b/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<String, GenericRecord> source1 = builder.stream("topic1", "topic2"); + KTable<String, GenericRecord> 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<String, GenericRecord> 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<Windowed<String>, Long> 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<String, String> 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<String, String> materialized = joined.through("topic4"); </pre>
