PySpark reduceByKey() transformation is used to merge the values of each key using an associative reduce function on PySpark RDD. It is a wider
1), ('b',3), ('c',2)]>>> >>> rdd.sortByKey(False).collect()[('c',2), ('b',3), ('a',1)]>>> # 把元祖里面的两个元素想象成字典的key: value,ByKey自然是根据Key来进行操作>>> # 可显然我们是想根据value来进行排序,根据出现...
目录1、创建键值对RDD 从文件加载 通过并行集合创建 2、常用的键值对RDD转换操作(reduceByKey和groupByKey) 3、keys,values.sortByKey... keys:把key取出形成新的RDD values:与keys同理sortByKey():默认按Key升序排序(false为降序)sortBy():.sortBy(_._2,false)按值降序 ...
for key, values in result: print(f"{key}: {values}") In the above example of the wide transformation, the groupByKey operation requires data from different partitions to be shuffled and combined based on the key. This involves data movement across the cluster, making it a wide transformation...
reduceByKey(lambda a, b: a + b) reduceByKeyRdd.sortByKey(False).collect() reduceByKeyRdd.map(lambda x:(x[1],x[0])).sortByKey(False).map(lambda x:(x[1],x[0])).collect() def my_union(): a = sc.parallelize([1,2,3]) b = sc.parallelize([3,4,5]) print(a.union(b...
reduceByKey– ThereduceByKey()combines the values associated with each key using the provided function. In our scenario, it aggregates the word strings by using the sum function on the corresponding values. The result of our RDD outcome comprises distinct words along with their respective counts...
reduceByKey :针对K-V型RDD,自动按照key进行分组,然后根据提供的聚合逻辑,完成组内数据(value)的聚合操作,返回聚合后的K-V值 rdd1 = sc.parallelize([('a',1),('a',1),('b',1),('b',1),('b',1)]) print(rdd1.reduceByKey(lambda a,b:a+b).collect()) # 输出 ''' [('b', 3), ...
reduceByKey(lambda x,y:x+y) #(2, 131) high_rating_movies = clean_data.map(lambda x: (x[2],x[1])).\ filter(lambda y: y[1] >= 4).\ mapValues(lambda x: 1).\ reduceByKey(lambda x,y: x+y) #(2, 51) mchr = movie_counts.leftOuterJoin(high_rating_movies) ...
rdd2.foldByKey().collect() rdd2.keyBy().collect() # 减少 rdd.reduceByKey(lambda x,y: x+y).collect() # 根据key合并rdd中的值 rdd.reduce(lambda x,y: x+y) # 展开 # 分组 rdd2.groupBy(lambda x: x%2).mapValues(list).collect() ...
$SPARK_HOME/bin/spark-submit reduce.py Output − The output of the above command is −Adding all the elements -> 15 join(other, numPartitions = None)It returns RDD with a pair of elements with the matching keys and all the values for that particular key. In the following example, ...