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new a39d447677 MINOR: fix streams tutorial (#12251)
a39d447677 is described below
commit a39d447677d2886c463bd11377896a0f7f5bf6dd
Author: Okada Haruki <[email protected]>
AuthorDate: Sat Jun 4 14:23:11 2022 +0900
MINOR: fix streams tutorial (#12251)
Reviewers: Luke Chen <[email protected]>
---
docs/streams/tutorial.html | 14 +++++++-------
1 file changed, 7 insertions(+), 7 deletions(-)
diff --git a/docs/streams/tutorial.html b/docs/streams/tutorial.html
index a526de568a..017d779682 100644
--- a/docs/streams/tutorial.html
+++ b/docs/streams/tutorial.html
@@ -452,7 +452,7 @@ source.flatMapValues(new ValueMapper<String,
Iterable<String>>() {
<p>
Note that the <code>count</code> operator has a
<code>Materialized</code> parameter that specifies that the
running count should be stored in a state store named
<code>counts-store</code>.
- This <code>Counts</code> store can be queried in real-time, with
details described in the <a
href="/{{version}}/documentation/streams/developer-guide#streams_interactive_queries">Developer
Manual</a>.
+ This <code>counts-store</code> store can be queried in real-time, with
details described in the <a
href="/{{version}}/documentation/streams/developer-guide#streams_interactive_queries">Developer
Manual</a>.
</p>
<p>
@@ -490,10 +490,10 @@ Sub-topologies:
Processor: KSTREAM-FLATMAPVALUES-0000000001(stores: []) -->
KSTREAM-KEY-SELECT-0000000002 <-- KSTREAM-SOURCE-0000000000
Processor: KSTREAM-KEY-SELECT-0000000002(stores: []) -->
KSTREAM-FILTER-0000000005 <-- KSTREAM-FLATMAPVALUES-0000000001
Processor: KSTREAM-FILTER-0000000005(stores: []) -->
KSTREAM-SINK-0000000004 <-- KSTREAM-KEY-SELECT-0000000002
- Sink: KSTREAM-SINK-0000000004(topic: Counts-repartition) <--
KSTREAM-FILTER-0000000005
+ Sink: KSTREAM-SINK-0000000004(topic: counts-store-repartition) <--
KSTREAM-FILTER-0000000005
Sub-topology: 1
- Source: KSTREAM-SOURCE-0000000006(topics: Counts-repartition) -->
KSTREAM-AGGREGATE-0000000003
- Processor: KSTREAM-AGGREGATE-0000000003(stores: [Counts]) -->
KTABLE-TOSTREAM-0000000007 <-- KSTREAM-SOURCE-0000000006
+ Source: KSTREAM-SOURCE-0000000006(topics: counts-store-repartition) -->
KSTREAM-AGGREGATE-0000000003
+ Processor: KSTREAM-AGGREGATE-0000000003(stores: [counts-store]) -->
KTABLE-TOSTREAM-0000000007 <-- KSTREAM-SOURCE-0000000006
Processor: KTABLE-TOSTREAM-0000000007(stores: []) -->
KSTREAM-SINK-0000000008 <-- KSTREAM-AGGREGATE-0000000003
Sink: KSTREAM-SINK-0000000008(topic: streams-wordcount-output) <--
KTABLE-TOSTREAM-0000000007
Global Stores:
@@ -501,14 +501,14 @@ Global Stores:
<p>
As we can see above, the topology now contains two disconnected
sub-topologies.
- The first sub-topology's sink node
<code>KSTREAM-SINK-0000000004</code> will write to a repartition topic
<code>Counts-repartition</code>,
+ The first sub-topology's sink node
<code>KSTREAM-SINK-0000000004</code> will write to a repartition topic
<code>counts-store-repartition</code>,
which will be read by the second sub-topology's source node
<code>KSTREAM-SOURCE-0000000006</code>.
The repartition topic is used to "shuffle" the source stream by its
aggregation key, which is in this case the value string.
In addition, inside the first sub-topology a stateless
<code>KSTREAM-FILTER-0000000005</code> node is injected between the grouping
<code>KSTREAM-KEY-SELECT-0000000002</code> node and the sink node to filter out
any intermediate record whose aggregate key is empty.
</p>
<p>
- In the second sub-topology, the aggregation node
<code>KSTREAM-AGGREGATE-0000000003</code> is associated with a state store
named <code>Counts</code> (the name is specified by the user in the
<code>count</code> operator).
- Upon receiving each record from its upcoming stream source node, the
aggregation processor will first query its associated <code>Counts</code> store
to get the current count for that key, augment by one, and then write the new
count back to the store.
+ In the second sub-topology, the aggregation node
<code>KSTREAM-AGGREGATE-0000000003</code> is associated with a state store
named <code>counts-store</code> (the name is specified by the user in the
<code>count</code> operator).
+ Upon receiving each record from its upcoming stream source node, the
aggregation processor will first query its associated <code>counts-store</code>
store to get the current count for that key, augment by one, and then write the
new count back to the store.
Each updated count for the key will also be piped downstream to the
<code>KTABLE-TOSTREAM-0000000007</code> node, which interpret this update
stream as a record stream before further piping to the sink node
<code>KSTREAM-SINK-0000000008</code> for writing back to Kafka.
</p>
