diff --git a/docs/configuration.md b/docs/configuration.md
index f292bfbb7dcd65c4ea9799d34e29499e0928e92c..673cdb371a5127b6ada54626bc30a300a011c2d9 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -1228,7 +1228,7 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td><code>spark.streaming.receiver.writeAheadLogs.enable</code></td>
+  <td><code>spark.streaming.receiver.writeAheadLog.enable</code></td>
   <td>false</td>
   <td>
     Enable write ahead logs for receivers. All the input data received through receivers
diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index 01450efe35e553538450594ae8816df302910435..e37a2bb37b9a48e70afbe2ff1eed7bc4b06d3a45 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -1574,7 +1574,7 @@ To run a Spark Streaming applications, you need to have the following.
   recovery, thus ensuring zero data loss (discussed in detail in the
   [Fault-tolerance Semantics](#fault-tolerance-semantics) section). This can be enabled by setting
   the [configuration parameter](configuration.html#spark-streaming)
-  `spark.streaming.receiver.writeAheadLogs.enable` to `true`. However, these stronger semantics may
+  `spark.streaming.receiver.writeAheadLog.enable` to `true`. However, these stronger semantics may
   come at the cost of the receiving throughput of individual receivers. This can be corrected by
   running [more receivers in parallel](#level-of-parallelism-in-data-receiving)
   to increase aggregate throughput. Additionally, it is recommended that the replication of the