Github user ahmed-mahran commented on a diff in the pull request:
https://github.com/apache/spark/pull/14234#discussion_r71073776
--- Diff: docs/structured-streaming-programming-guide.md ---
@@ -1093,12 +1067,10 @@ 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.
+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.
## Recovering from Failures with Checkpointing
-In case of a failure or intentional shutdown, you can recover the previous
progress and state of a previous query, and continue where it left off. This is
done using checkpointing and write ahead logs. You can configure a query with a
checkpoint location, and the query will save all the progress information (i.e.
range of offsets processed in each trigger), and the running aggregates (e.g.
word counts in the quick example) will be saved the checkpoint location. As of
Spark 2.0, this checkpoint location has to be a path in a HDFS compatible file
system, and can be set as an option in the DataStreamWriter when [starting a
query](#starting-streaming-queries).
--- End diff --
Added anchor `[quick example](#quick-example)`
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