Repository: spark
Updated Branches:
  refs/heads/master 1a48c0047 -> 08913ce00


fixed a typo

idempotant -> idempotent

Author: Seigneurin, Alexis (CONT) <[email protected]>

Closes #14833 from aseigneurin/fix-typo.


Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/08913ce0
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/08913ce0
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/08913ce0

Branch: refs/heads/master
Commit: 08913ce0002a80a989489a31b7353f5ec4a5849f
Parents: 1a48c00
Author: Seigneurin, Alexis (CONT) <[email protected]>
Authored: Mon Aug 29 13:12:10 2016 +0100
Committer: Sean Owen <[email protected]>
Committed: Mon Aug 29 13:12:10 2016 +0100

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 docs/structured-streaming-programming-guide.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)
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http://git-wip-us.apache.org/repos/asf/spark/blob/08913ce0/docs/structured-streaming-programming-guide.md
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diff --git a/docs/structured-streaming-programming-guide.md 
b/docs/structured-streaming-programming-guide.md
index 090b14f..8a88e06 100644
--- a/docs/structured-streaming-programming-guide.md
+++ b/docs/structured-streaming-programming-guide.md
@@ -406,7 +406,7 @@ Furthermore, this model naturally handles data that has 
arrived later than expec
 
 ## Fault Tolerance Semantics
 Delivering end-to-end exactly-once semantics was one of key goals behind the 
design of Structured Streaming. To achieve that, we have designed the 
Structured Streaming sources, the sinks and the execution engine to reliably 
track the exact progress of the processing so that it can handle any kind of 
failure by restarting and/or reprocessing. Every streaming source is assumed to 
have offsets (similar to Kafka offsets, or Kinesis sequence numbers)
-to track the read position in the stream. The engine uses checkpointing and 
write ahead logs to record the offset range of the data being processed in each 
trigger. The streaming sinks are designed to be idempotent for handling 
reprocessing. Together, using replayable sources and idempotant sinks, 
Structured Streaming can ensure **end-to-end exactly-once semantics** under any 
failure.
+to track the read position in the stream. The engine uses checkpointing and 
write ahead logs to record the offset range of the data being processed in each 
trigger. The streaming sinks are designed to be idempotent for handling 
reprocessing. Together, using replayable sources and idempotent sinks, 
Structured Streaming can ensure **end-to-end exactly-once semantics** under any 
failure.
 
 # API using Datasets and DataFrames
 Since Spark 2.0, DataFrames and Datasets can represent static, bounded data, 
as well as streaming, unbounded data. Similar to static Datasets/DataFrames, 
you can use the common entry point `SparkSession` 
([Scala](api/scala/index.html#org.apache.spark.sql.SparkSession)/


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