diff options
Diffstat (limited to 'core')
5 files changed, 9 insertions, 10 deletions
diff --git a/core/src/main/scala/org/apache/spark/Accumulator.scala b/core/src/main/scala/org/apache/spark/Accumulator.scala index 7bea636c94..9d5fbefc82 100644 --- a/core/src/main/scala/org/apache/spark/Accumulator.scala +++ b/core/src/main/scala/org/apache/spark/Accumulator.scala @@ -24,8 +24,7 @@ package org.apache.spark * They can be used to implement counters (as in MapReduce) or sums. Spark natively supports * accumulators of numeric value types, and programmers can add support for new types. * - * An accumulator is created from an initial value `v` by calling - * [[SparkContext#accumulator SparkContext.accumulator]]. + * An accumulator is created from an initial value `v` by calling `SparkContext.accumulator`. * Tasks running on the cluster can then add to it using the `+=` operator. * However, they cannot read its value. Only the driver program can read the accumulator's value, * using its [[#value]] method. diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala index 766aea213a..9544475ff0 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala @@ -166,7 +166,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)]) * Return a subset of this RDD sampled by key (via stratified sampling) containing exactly * math.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key). * - * This method differs from [[sampleByKey]] in that we make additional passes over the RDD to + * This method differs from `sampleByKey` in that we make additional passes over the RDD to * create a sample size that's exactly equal to the sum of math.ceil(numItems * samplingRate) * over all key values with a 99.99% confidence. When sampling without replacement, we need one * additional pass over the RDD to guarantee sample size; when sampling with replacement, we need @@ -184,7 +184,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)]) * Return a subset of this RDD sampled by key (via stratified sampling) containing exactly * math.ceil(numItems * samplingRate) for each stratum (group of pairs with the same key). * - * This method differs from [[sampleByKey]] in that we make additional passes over the RDD to + * This method differs from `sampleByKey` in that we make additional passes over the RDD to * create a sample size that's exactly equal to the sum of math.ceil(numItems * samplingRate) * over all key values with a 99.99% confidence. When sampling without replacement, we need one * additional pass over the RDD to guarantee sample size; when sampling with replacement, we need diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala index eda16d957c..91ae1002ab 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala @@ -393,7 +393,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { def treeReduce(f: JFunction2[T, T, T], depth: Int): T = rdd.treeReduce(f, depth) /** - * [[org.apache.spark.api.java.JavaRDDLike#treeReduce]] with suggested depth 2. + * `org.apache.spark.api.java.JavaRDDLike.treeReduce` with suggested depth 2. */ def treeReduce(f: JFunction2[T, T, T]): T = treeReduce(f, 2) @@ -440,7 +440,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { } /** - * [[org.apache.spark.api.java.JavaRDDLike#treeAggregate]] with suggested depth 2. + * `org.apache.spark.api.java.JavaRDDLike.treeAggregate` with suggested depth 2. */ def treeAggregate[U]( zeroValue: U, diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala index ec12b9963e..d7bfdbad84 100644 --- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala @@ -109,7 +109,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * functions. This method is here for backward compatibility. It does not provide combiner * classtag information to the shuffle. * - * @see [[combineByKeyWithClassTag]] + * @see `combineByKeyWithClassTag` */ def combineByKey[C]( createCombiner: V => C, @@ -127,7 +127,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * This method is here for backward compatibility. It does not provide combiner * classtag information to the shuffle. * - * @see [[combineByKeyWithClassTag]] + * @see `combineByKeyWithClassTag` */ def combineByKey[C]( createCombiner: V => C, @@ -608,7 +608,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * existing partitioner/parallelism level. This method is here for backward compatibility. It * does not provide combiner classtag information to the shuffle. * - * @see [[combineByKeyWithClassTag]] + * @see `combineByKeyWithClassTag` */ def combineByKey[C]( createCombiner: V => C, diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala index 374abccf6a..a7e01f397e 100644 --- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala @@ -1841,7 +1841,7 @@ abstract class RDD[T: ClassTag]( * Defines implicit functions that provide extra functionalities on RDDs of specific types. * * For example, [[RDD.rddToPairRDDFunctions]] converts an RDD into a [[PairRDDFunctions]] for - * key-value-pair RDDs, and enabling extra functionalities such as [[PairRDDFunctions.reduceByKey]]. + * key-value-pair RDDs, and enabling extra functionalities such as `PairRDDFunctions.reduceByKey`. */ object RDD { |