diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R
index e7a166c3014c19532a009b79a8edecefe27c6266..5d6f9c042248bdb468a62a240aab13721ef894ee 100644
--- a/R/pkg/R/DataFrame.R
+++ b/R/pkg/R/DataFrame.R
@@ -593,7 +593,7 @@ setMethod("cache",
 #'
 #' Persist this SparkDataFrame with the specified storage level. For details of the
 #' supported storage levels, refer to
-#' \url{http://spark.apache.org/docs/latest/programming-guide.html#rdd-persistence}.
+#' \url{http://spark.apache.org/docs/latest/rdd-programming-guide.html#rdd-persistence}.
 #'
 #' @param x the SparkDataFrame to persist.
 #' @param newLevel storage level chosen for the persistance. See available options in
diff --git a/R/pkg/R/RDD.R b/R/pkg/R/RDD.R
index 7ad3993e9ecbc23cf4cfbbbe6c9839c2cbfa05b0..15ca212acf87f1facd8460a8dc1d1eaa504acc61 100644
--- a/R/pkg/R/RDD.R
+++ b/R/pkg/R/RDD.R
@@ -227,7 +227,7 @@ setMethod("cacheRDD",
 #'
 #' Persist this RDD with the specified storage level. For details of the
 #' supported storage levels, refer to
-#'\url{http://spark.apache.org/docs/latest/programming-guide.html#rdd-persistence}.
+#'\url{http://spark.apache.org/docs/latest/rdd-programming-guide.html#rdd-persistence}.
 #'
 #' @param x The RDD to persist
 #' @param newLevel The new storage level to be assigned
diff --git a/docs/graphx-programming-guide.md b/docs/graphx-programming-guide.md
index 76aa7b405e18c20a99260fda0c4ee5a4c90b5d48..46225dc598da877099951d7365d7bb611e66d439 100644
--- a/docs/graphx-programming-guide.md
+++ b/docs/graphx-programming-guide.md
@@ -27,7 +27,7 @@ description: GraphX graph processing library guide for Spark SPARK_VERSION_SHORT
 [EdgeContext]: api/scala/index.html#org.apache.spark.graphx.EdgeContext
 [GraphOps.collectNeighborIds]: api/scala/index.html#org.apache.spark.graphx.GraphOps@collectNeighborIds(EdgeDirection):VertexRDD[Array[VertexId]]
 [GraphOps.collectNeighbors]: api/scala/index.html#org.apache.spark.graphx.GraphOps@collectNeighbors(EdgeDirection):VertexRDD[Array[(VertexId,VD)]]
-[RDD Persistence]: programming-guide.html#rdd-persistence
+[RDD Persistence]: rdd-programming-guide.html#rdd-persistence
 [Graph.cache]: api/scala/index.html#org.apache.spark.graphx.Graph@cache():Graph[VD,ED]
 [GraphOps.pregel]: api/scala/index.html#org.apache.spark.graphx.GraphOps@pregel[A](A,Int,EdgeDirection)((VertexId,VD,A)⇒VD,(EdgeTriplet[VD,ED])⇒Iterator[(VertexId,A)],(A,A)⇒A)(ClassTag[A]):Graph[VD,ED]
 [PartitionStrategy]: api/scala/index.html#org.apache.spark.graphx.PartitionStrategy$
diff --git a/docs/index.md b/docs/index.md
index 07b6b171014ed190ec1cb684976b8ba6d1351a91..2d4607b3119bd3e2d7b263baeb3aa46eb36dd328 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -87,7 +87,7 @@ options for deployment:
 **Programming Guides:**
 
 * [Quick Start](quick-start.html): a quick introduction to the Spark API; start here!
-* [RDD Programming Guide](programming-guide.html): overview of Spark basics - RDDs (core but old API), accumulators, and broadcast variables  
+* [RDD Programming Guide](rdd-programming-guide.html): overview of Spark basics - RDDs (core but old API), accumulators, and broadcast variables  
 * [Spark SQL, Datasets, and DataFrames](sql-programming-guide.html): processing structured data with relational queries (newer API than RDDs)
 * [Structured Streaming](structured-streaming-programming-guide.html): processing structured data streams with relation queries (using Datasets and DataFrames, newer API than DStreams)
 * [Spark Streaming](streaming-programming-guide.html): processing data streams using DStreams (old API)
diff --git a/docs/ml-guide.md b/docs/ml-guide.md
index adb1c9aaefcdc089e176c4b9dffb95bca4dcb586..7aec6a40d4c640b349e33b4183c5b53b3a396fab 100644
--- a/docs/ml-guide.md
+++ b/docs/ml-guide.md
@@ -18,7 +18,7 @@ At a high level, it provides tools such as:
 
 **The MLlib RDD-based API is now in maintenance mode.**
 
-As of Spark 2.0, the [RDD](programming-guide.html#resilient-distributed-datasets-rdds)-based APIs in the `spark.mllib` package have entered maintenance mode.
+As of Spark 2.0, the [RDD](rdd-programming-guide.html#resilient-distributed-datasets-rdds)-based APIs in the `spark.mllib` package have entered maintenance mode.
 The primary Machine Learning API for Spark is now the [DataFrame](sql-programming-guide.html)-based API in the `spark.ml` package.
 
 *What are the implications?*
diff --git a/docs/mllib-optimization.md b/docs/mllib-optimization.md
index eefd7dcf1108ba7a7d07bb53ada3a586b221d800..14d76a6e41e2302f5b0897018147177a76db044f 100644
--- a/docs/mllib-optimization.md
+++ b/docs/mllib-optimization.md
@@ -116,7 +116,7 @@ is a stochastic gradient. Here `$S$` is the sampled subset of size `$|S|=$ miniB
 $\cdot n$`.
 
 In each iteration, the sampling over the distributed dataset
-([RDD](programming-guide.html#resilient-distributed-datasets-rdds)), as well as the
+([RDD](rdd-programming-guide.html#resilient-distributed-datasets-rdds)), as well as the
 computation of the sum of the partial results from each worker machine is performed by the
 standard spark routines.
 
diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md
index edefbef93feb6c089988b1348ff79237f57ea01e..642575b46dd4282da5212710e2c57770cb27c0d4 100644
--- a/docs/spark-standalone.md
+++ b/docs/spark-standalone.md
@@ -264,7 +264,7 @@ SPARK_WORKER_OPTS supports the following system properties:
 # Connecting an Application to the Cluster
 
 To run an application on the Spark cluster, simply pass the `spark://IP:PORT` URL of the master as to the [`SparkContext`
-constructor](programming-guide.html#initializing-spark).
+constructor](rdd-programming-guide.html#initializing-spark).
 
 To run an interactive Spark shell against the cluster, run the following command:
 
diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index abd4ac96536061ff0896b84f43588ce8cc8d5374..fca0cf8ff05f26a7cfe6708bd39cfcdc3fe11cbc 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -535,7 +535,7 @@ After a context is defined, you have to do the following.
 It represents a continuous stream of data, either the input data stream received from source,
 or the processed data stream generated by transforming the input stream. Internally,
 a DStream is represented by a continuous series of RDDs, which is Spark's abstraction of an immutable,
-distributed dataset (see [Spark Programming Guide](programming-guide.html#resilient-distributed-datasets-rdds) for more details). Each RDD in a DStream contains data from a certain interval,
+distributed dataset (see [Spark Programming Guide](rdd-programming-guide.html#resilient-distributed-datasets-rdds) for more details). Each RDD in a DStream contains data from a certain interval,
 as shown in the following figure.
 
 <p style="text-align: center;">
@@ -1531,7 +1531,7 @@ default persistence level is set to replicate the data to two nodes for fault-to
 
 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 the [Spark Programming Guide](programming-guide.html#rdd-persistence).
+information on different persistence levels can be found in the [Spark Programming Guide](rdd-programming-guide.html#rdd-persistence).
 
 ***
 
@@ -1720,7 +1720,13 @@ batch interval that is at least 10 seconds. It can be set by using
 
 ## Accumulators, Broadcast Variables, and Checkpoints
 
-[Accumulators](programming-guide.html#accumulators) and [Broadcast variables](programming-guide.html#broadcast-variables) cannot be recovered from checkpoint in Spark Streaming. If you enable checkpointing and use [Accumulators](programming-guide.html#accumulators) or [Broadcast variables](programming-guide.html#broadcast-variables) as well, you'll have to create lazily instantiated singleton instances for [Accumulators](programming-guide.html#accumulators) and [Broadcast variables](programming-guide.html#broadcast-variables) so that they can be re-instantiated after the driver restarts on failure. This is shown in the following example.
+[Accumulators](rdd-programming-guide.html#accumulators) and [Broadcast variables](rdd-programming-guide.html#broadcast-variables) 
+cannot be recovered from checkpoint in Spark Streaming. If you enable checkpointing and use 
+[Accumulators](rdd-programming-guide.html#accumulators) or [Broadcast variables](rdd-programming-guide.html#broadcast-variables) 
+as well, you'll have to create lazily instantiated singleton instances for 
+[Accumulators](rdd-programming-guide.html#accumulators) and [Broadcast variables](rdd-programming-guide.html#broadcast-variables) 
+so that they can be re-instantiated after the driver restarts on failure. 
+This is shown in the following example.
 
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
@@ -2182,7 +2188,7 @@ overall processing throughput of the system, its use is still recommended to ach
 consistent batch processing times. Make sure you set the CMS GC on both the driver (using `--driver-java-options` in `spark-submit`) and the executors (using [Spark configuration](configuration.html#runtime-environment) `spark.executor.extraJavaOptions`).
 
 * **Other tips**: To further reduce GC overheads, here are some more tips to try.
-    - Persist RDDs using the `OFF_HEAP` storage level. See more detail in the [Spark Programming Guide](programming-guide.html#rdd-persistence).
+    - Persist RDDs using the `OFF_HEAP` storage level. See more detail in the [Spark Programming Guide](rdd-programming-guide.html#rdd-persistence).
     - Use more executors with smaller heap sizes. This will reduce the GC pressure within each JVM heap.
 
 ***
diff --git a/docs/tuning.md b/docs/tuning.md
index 0de303a3bd9bffe23a97329cce10d767d8cc8c33..7d5f97a02fe6e24a6332a89266e05f5c657e8d5f 100644
--- a/docs/tuning.md
+++ b/docs/tuning.md
@@ -12,7 +12,7 @@ Because of the in-memory nature of most Spark computations, Spark programs can b
 by any resource in the cluster: CPU, network bandwidth, or memory.
 Most often, if the data fits in memory, the bottleneck is network bandwidth, but sometimes, you
 also need to do some tuning, such as
-[storing RDDs in serialized form](programming-guide.html#rdd-persistence), to
+[storing RDDs in serialized form](rdd-programming-guide.html#rdd-persistence), to
 decrease memory usage.
 This guide will cover two main topics: data serialization, which is crucial for good network
 performance and can also reduce memory use, and memory tuning. We also sketch several smaller topics.
@@ -155,7 +155,7 @@ pointer-based data structures and wrapper objects. There are several ways to do
 
 When your objects are still too large to efficiently store despite this tuning, a much simpler way
 to reduce memory usage is to store them in *serialized* form, using the serialized StorageLevels in
-the [RDD persistence API](programming-guide.html#rdd-persistence), such as `MEMORY_ONLY_SER`.
+the [RDD persistence API](rdd-programming-guide.html#rdd-persistence), such as `MEMORY_ONLY_SER`.
 Spark will then store each RDD partition as one large byte array.
 The only downside of storing data in serialized form is slower access times, due to having to
 deserialize each object on the fly.
@@ -262,7 +262,7 @@ number of cores in your clusters.
 
 ## Broadcasting Large Variables
 
-Using the [broadcast functionality](programming-guide.html#broadcast-variables)
+Using the [broadcast functionality](rdd-programming-guide.html#broadcast-variables)
 available in `SparkContext` can greatly reduce the size of each serialized task, and the cost
 of launching a job over a cluster. If your tasks use any large object from the driver program
 inside of them (e.g. a static lookup table), consider turning it into a broadcast variable.