[MINOR] Page rename (Demo: Streaming Windowed Aggregation)

docs/demo/index.md

@@ -14,7 +14,7 @@ hide:
 * [groupByKey Streaming Aggregation in Update Mode](groupByKey-count-Update.md)
 * [Internals of FlatMapGroupsWithStateExec Physical Operator](spark-sql-streaming-demo-FlatMapGroupsWithStateExec.md)
 * [Kafka Data Source](kafka-data-source.md)
-* [Streaming Aggregation](streaming-aggregation.md)
+* [Streaming Windowed Aggregation](streaming-windowed-aggregation.md)
 * [RocksDB State Store for Streaming Aggregation](rocksdb-state-store-for-streaming-aggregation.md)
 * [Streaming Query for Running Counts (Socket Source and Complete Output Mode)](groupBy-running-count-complete.md)
 * [Streaming Watermark](streaming-watermark.md)

docs/demo/streaming-aggregation.md → docs/demo/streaming-windowed-aggregation.md

@@ -3,48 +3,20 @@ hide:
   - navigation
 ---
 
-# Demo: Streaming Aggregation
+# Demo: Streaming Windowed Aggregation
 
 This demo shows a streaming query with a [streaming aggregation](../streaming-aggregation/index.md) (with [Dataset.groupBy](../operators/groupBy.md) operator) that processes data from Kafka (using [Kafka Data Source](../kafka/index.md)).
 
-!!! note
-    Please start a [Kafka cluster](kafka-data-source.md#start-kafka-cluster) and [spark-shell](kafka-data-source.md#start-spark-shell) as described in [Demo: Kafka Data Source](kafka-data-source.md).
-
-## Reset numShufflePartitions
-
-This step makes debugging easier since there is a state (store) for just one partition.
-
-```scala
-val numShufflePartitions = 1
-import org.apache.spark.sql.internal.SQLConf.SHUFFLE_PARTITIONS
-spark.sessionState.conf.setConf(SHUFFLE_PARTITIONS, numShufflePartitions)
-
-assert(spark.sessionState.conf.numShufflePartitions == numShufflePartitions)
-```
+The source code of this demo is in [spark-examples](https://github.com/jaceklaskowski/spark-examples) repository.
 
-## Load Events from Kafka
-
-```scala
-val events = spark
-  .readStream
-  .format("kafka")
-  .option("subscribe", "demo.streaming-aggregation")
-  .option("kafka.bootstrap.servers", ":9092")
-  .load
-  .select($"value" cast "string")
-  .withColumn("tokens", split($"value", ","))
-  .withColumn("id", 'tokens(0))
-  .withColumn("v", 'tokens(1) cast "int")
-  .withColumn("second", 'tokens(2) cast "long")
-  .withColumn("event_time", 'second cast "timestamp") // <-- Event time has to be a timestamp
-  .select("id", "v", "second", "event_time")
-```
-
-??? note "FIXME Consider JSON format for values"
-    JSONified values would make more sense. It'd certainly make the demo more verbose (extra JSON-specific "things") but perhaps would ease building a connection between events on the command line and their DataFrame representation.
+!!! note "Demo: Kafka Data Source"
+    Please start a [Kafka cluster](kafka-data-source.md#start-kafka-cluster) and [spark-shell](kafka-data-source.md#start-spark-shell) as described in [Demo: Kafka Data Source](kafka-data-source.md).
 
 ## Define Windowed Streaming Aggregation
 
+!!! note "Windowed Aggregation"
+    This is different from the source code in spark-examples repo as it uses windowed aggregation.
+
 Define a streaming aggregation query (using [groupBy](../operators/groupBy.md) high-level operator).
 
 The streaming query uses [Append](../OutputMode.md#Append) output mode and defines a [streaming watermark](../watermark/index.md) (using [Dataset.withWatermark](../operators/withWatermark.md) operator). Otherwise, [UnsupportedOperationChecker](../UnsupportedOperationChecker.md) would fail the query (since a watermark is required for `Append` output mode in a streaming aggregation).
@@ -113,6 +85,9 @@ ObjectHashAggregate(keys=[id#26, window#66-T10000ms], functions=[collect_list(v#
 
 ## Start Streaming Query
 
+!!! note "Append Output Mode"
+    This is different from the source code in spark-examples repo as it uses [Append](../OutputMode.md#Append) output mode.
+
 ```scala
 import java.time.Clock
 val timeOffset = Clock.systemUTC.instant.getEpochSecond
@@ -133,19 +108,7 @@ val sq = windowed
   .start
 ```
 
-The streaming query gets executed and prints out Batch 0 to the console.
-
-```text
--------------------------------------------
-Batch: 0
--------------------------------------------
-+---+------+---+-------+
-|id |window|vs |seconds|
-+---+------+---+-------+
-+---+------+---+-------+
-```
-
-### Start Diagnostic Query
+### (Optional) Start Diagnostic Query
 
 ```scala
 import java.time.Clock

docs/webui/StreamingQueryStatisticsPage.md

@@ -9,7 +9,7 @@
 
 `StreamingQueryStatisticsPage` uses `id` request parameter as the `runId` of a streaming query to [render a page](#render) of.
 !!! tip "Demo: Streaming Aggregation"
-    Use [Demo: Streaming Aggregation](../demo/streaming-aggregation.md) to learn more and monitor the statistics.
+    Use [Demo: Streaming Aggregation](../demo/streaming-windowed-aggregation.md) to learn more and monitor the statistics.
 
 ## Creating Instance
 

mkdocs.yml

@@ -431,7 +431,7 @@ nav:
     - groupByKey Streaming Aggregation in Update Mode: demo/groupByKey-count-Update.md
     - Internals of FlatMapGroupsWithStateExec Physical Operator: demo/spark-sql-streaming-demo-FlatMapGroupsWithStateExec.md
     - Kafka Data Source: demo/kafka-data-source.md
-    - Streaming Aggregation: demo/streaming-aggregation.md
+    - Streaming Windowed Aggregation: demo/streaming-windowed-aggregation.md
     - RocksDB State Store for Streaming Aggregation: demo/rocksdb-state-store-for-streaming-aggregation.md
     - Streaming Query for Running Counts (Socket Source and Complete Output Mode): demo/groupBy-running-count-complete.md
     - Streaming Watermark: demo/streaming-watermark.md