diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index 0d2033bf38681d32984b8363e9c3ad09ed32f4b2..18ad3ed255e8d5f827c776a9619b6895c45fe152 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -282,9 +282,9 @@ For input streams that receive data from the network (that is, subclasses of Net
 Note that, unlike RDDs, the default persistence level of DStreams keeps the data serialized in memory. This is further discussed in the [Performance Tuning](#memory-tuning) section. More information on different persistence levels can be found in [Spark Programming Guide](scala-programming-guide.html#rdd-persistence).
 
 # RDD Checkpointing within DStreams
-A _stateful operation_ is one which operates over multiple batches of data. This includes all window-based operations and the `updateStateByKey` operation. Because stateful operations have an infinitely growing lineage, their state must be periodically checkpointed to HDFS.
+A _stateful operation_ is one which operates over multiple batches of data. This includes all window-based operations and the `updateStateByKey` operation. 
 
-Checkpointing prevents extra computation during both failure recovery and normal operations by transparently truncating this lineage. Note that checkpointing also incurs the cost of saving to HDFS which may cause the corresponding batch to take longer to process. Hence, the interval of checkpointing needs to be set carefully. At small batch sizes (say 1 second), checkpointing every batch may significantly reduce operation throughput. Conversely, checkpointing too slowly causes the lineage and task sizes to grow which may have detrimental effects. Typically, a checkpoint interval of 5 - 10 times of sliding interval of a DStream is good setting to try.
+Because stateful operations have a dependency on previous batches of data, they continuously accumulate metadata over time. To clear this metadata, streaming supports periodic _checkpointing_ by saving intermediate data to HDFS. Note that checkpointing also incurs the cost of saving to HDFS which may cause the corresponding batch to take longer to process. Hence, the interval of checkpointing needs to be set carefully. At small batch sizes (say 1 second), checkpointing every batch may significantly reduce operation throughput. Conversely, checkpointing too slowly causes the lineage and task sizes to grow which may have detrimental effects. Typically, a checkpoint interval of 5 - 10 times of sliding interval of a DStream is good setting to try.
 
 To enable checkpointing, the developer has to provide the HDFS path to which RDD will be saved. This is done by using