Map is a transformation, thus when you perform a map you apply a function to each element in the RDD and return a new RDD where additional transformations or actions can be called. Foreach is an action, it takes each element and applies a function, but it does not return a value.

Map and reduce are methods of RDD class, which has interface similar to scala collections. What you pass to methods map and reduce are actually anonymous function (with one param in map, and with two parameters in reduce). textFile calls provided function for every element (line of text in this context) it has.

2019年4月2日 · I have a source dataframe which has some records. I want to perform some operation on each row of this dataframe. For this purpose, the rdd.map function was used. However, looking at the logs recorded using accumulators, looks like the mapped function was called multiple times for some rows. As per the documentation, it should be called once ONLY.

2014年3月12日 · map: It returns a new RDD by applying a function to each element of the RDD. Function in .map can return only one item. flatMap: Similar to map, it returns a new RDD by applying a function to each element of the RDD, but the output is flattened. Also, function in flatMap can return a list of elements (0 or more) For Example:

2015年1月23日 · It sounds like what you really want is to save your KV RDD to a separate file for each key. Rather than creating a Map[Key, RDD[Value]] consider using a MultipleTextOutputFormat similar to the example here.

2014年1月17日 · Imp. TIP : Whenever you have heavyweight initialization that should be done once for many RDD elements rather than once per RDD element, and if this initialization, such as creation of objects from a third-party library, cannot be serialized (so that Spark can transmit it across the cluster to the worker nodes), use mapPartitions() instead of map().

RDD.map maps a function to each element of an RDD, whereas RDD.mapPartitions maps a function to each partition of an RDD. map will not change the number of elements in an RDD, while mapPartitions might very well do so. See also this answer and comments on a …

2017年7月2日 · You cannot call any RDD methods from within a UDF. When you create a UDF, it runs on the workers. RDD or dataframe operations can only run on the driver and therefore are not allowed in the UDF. It seems as if your goal is to do a UDAF (User Defined Aggregate Method). This cannot be done from pyspark. You have two options for this.

2021年4月17日 · rdd = sc.parallelize([i for i in range(5)]) rdd.map(lambda i: i**2).collect() Thus, there seems to be something flawed with the way I create or operate on my objects, but I can not track down the mistake.

2016年4月18日 · map takes a function that transforms each element of a collection: map(f: T => U) RDD[T] => RDD[U] When T is a tuple we may want to only act on the values – not the keys mapValues takes a function that maps the values in the inputs to the values in the output: mapValues(f: V => W) Where RDD[ (K, V) ] => RDD[ (K, W) ]