Github user zsxwing commented on a diff in the pull request:
https://github.com/apache/spark/pull/15897#discussion_r88307731
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -1087,9 +1087,185 @@ spark.streams().awaitAnyTermination() # block
until any one of them terminates
</div>
</div>
-Finally, for asynchronous monitoring of streaming queries, you can create
and attach a `StreamingQueryListener`
-([Scala](api/scala/index.html#org.apache.spark.sql.streaming.StreamingQueryListener)/[Java](api/java/org/apache/spark/sql/streaming/StreamingQueryListener.html)
docs),
-which will give you regular callback-based updates when queries are
started and terminated.
+
+## Monitoring Streaming Queries
+There are two ways you can monitor queries. You can directly get the
current status
+of an active query using `streamingQuery.status`, which will return a
`StreamingQueryStatus` object
+([Scala](api/scala/index.html#org.apache.spark.sql.streaming.StreamingQueryStatus)/[Java](api/java/org/apache/spark/sql/streaming/StreamingQueryStatus.html)/[Python](api/python/pyspark.sql.html#pyspark.sql.streaming.StreamingQueryStatus)
docs)
+that has all the details like current ingestion rates, processing rates,
average latency,
+details of the currently active trigger, etc.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+{% highlight scala %}
+val query: StreamingQuery = ...
+
+println(query.status)
+
+/* Will print the current status of the query
+
+Status of query 'queryName'
+ Query id: 1
+ Status timestamp: 123
+ Input rate: 15.5 rows/sec
+ Processing rate 23.5 rows/sec
+ Latency: 345.0 ms
+ Trigger details:
+ batchId: 5
+ isDataPresentInTrigger: true
+ isTriggerActive: true
+ latency.getBatch.total: 20
+ latency.getOffset.total: 10
+ numRows.input.total: 100
+ Source statuses [1 source]:
+ Source 1 - MySource1
+ Available offset: 0
+ Input rate: 15.5 rows/sec
+ Processing rate: 23.5 rows/sec
+ Trigger details:
+ numRows.input.source: 100
+ latency.getOffset.source: 10
+ latency.getBatch.source: 20
+ Sink status - MySink
+ Committed offsets: [1, -]
+*/
+{% endhighlight %}
+
+</div>
+<div data-lang="java" markdown="1">
+
+{% highlight java %}
+StreamingQuery query = ...
+
+System.out.println(query.status);
+
+/* Will print the current status of the query
+
+Status of query 'queryName'
+ Query id: 1
+ Status timestamp: 123
+ Input rate: 15.5 rows/sec
+ Processing rate 23.5 rows/sec
+ Latency: 345.0 ms
+ Trigger details:
+ batchId: 5
+ isDataPresentInTrigger: true
+ isTriggerActive: true
+ latency.getBatch.total: 20
+ latency.getOffset.total: 10
+ numRows.input.total: 100
+ Source statuses [1 source]:
+ Source 1 - MySource1
+ Available offset: 0
+ Input rate: 15.5 rows/sec
+ Processing rate: 23.5 rows/sec
+ Trigger details:
+ numRows.input.source: 100
+ latency.getOffset.source: 10
+ latency.getBatch.source: 20
+ Sink status - MySink
+ Committed offsets: [1, -]
+*/
+{% endhighlight %}
+
+</div>
+<div data-lang="python" markdown="1">
+
+{% highlight python %}
+query = ... // a StreamingQuery
+
+print(query.status)
+
+'''
+Will print the current status of the query
+
+Status of query 'queryName'
+ Query id: 1
+ Status timestamp: 123
+ Input rate: 15.5 rows/sec
+ Processing rate 23.5 rows/sec
+ Latency: 345.0 ms
+ Trigger details:
+ batchId: 5
+ isDataPresentInTrigger: true
+ isTriggerActive: true
+ latency.getBatch.total: 20
+ latency.getOffset.total: 10
+ numRows.input.total: 100
+ Source statuses [1 source]:
+ Source 1 - MySource1
+ Available offset: 0
+ Input rate: 15.5 rows/sec
+ Processing rate: 23.5 rows/sec
+ Trigger details:
+ numRows.input.source: 100
+ latency.getOffset.source: 10
+ latency.getBatch.source: 20
+ Sink status - MySink
+ Committed offsets: [1, -]
+'''
+{% endhighlight %}
+
+</div>
+</div>
+
+
+You can also asynchronously monitor all queries associated with a
+`SparkSession` by attaching a `StreamingQueryListener`
+([Scala](api/scala/index.html#org.apache.spark.sql.streaming.StreamingQueryListener)/[Java](api/java/org/apache/spark/sql/streaming/StreamingQueryListener.html)
docs).
+Once you attach your custom `StreamingQueryListener` object with
+`sparkSession.streams.attachListener()`, you will get callbacks when a
query is started and
+stopped and when there is progress made in an active query. Here is an
example,
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+{% highlight scala %}
+val spark: SparkSession = ...
+
+spark.streams.addListener(new StreamingQueryListener() {
+
+ override def onQueryStarted(queryStarted: QueryStartedEvent): Unit = {
+ println("Query started: " + queryTerminated.queryStatus.name)
+ }
+ override def onQueryTerminated(queryTerminated: QueryTerminatedEvent):
Unit = {
+ println("Query terminated: " + queryTerminated.queryStatus.name)
+ }
+ override def onQueryProgress(queryProgress: QueryProgressEvent): Unit
= {
+ println("Query made progress: " + queryProgress.queryStatus)
+ }
+})
+{% endhighlight %}
+
+</div>
+<div data-lang="java" markdown="1">
+
+{% highlight java %}
+SparkSession spark = ...
+
+spark.streams.addListener(new StreamingQueryListener() {
+
+ @Overrides void onQueryStarted(QueryStartedEvent queryStarted) {
+ System.out.println("Query started: " +
queryTerminated.queryStatus.name);
+ }
+ @Overrides void onQueryTerminated(QueryTerminatedEvent
queryTerminated) {
+ System.out.println("Query terminated: " +
queryTerminated.queryStatus.name);
--- End diff --
nit: queryTerminated.queryStatus.name ->
queryTerminated.queryStatus().name()
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