python pyspark如何向rdd的mappartition的回调函数中传参
时间: 2024-03-09 11:48:39 浏览: 232
在Pyspark中,可以通过使用lambda表达式将参数传递给`mapPartitions`函数的回调函数。
示例代码如下:
```
rdd = sc.parallelize([1, 2, 3, 4, 5])
def my_func(iterator, param):
for x in iterator:
yield x + param
param = 10
result = rdd.mapPartitions(lambda iterator: my_func(iterator, param)).collect()
```
在上面的例子中,`my_func`函数是我们传递给`mapPartitions`函数的回调函数。我们将参数`param`传递给`my_func`函数,并将其定义为闭包在lambda表达式中。在`my_func`函数中,我们使用`yield`语句返回转换后的元素,而不是使用`return`语句。
最后,我们使用`collect`函数将转换后的RDD收集到本地驱动程序中。
相关问题
pyspark创建sparkcontent的主程序中创建的变量在RDD的mappartition执行的函数中能否直接调用
可以直接调用,因为在Spark的分布式计算模型中,SparkContext和RDD是共享的。在Spark程序中,SparkContext是驱动程序与整个集群的连接器,而RDD则是Spark中分布式计算的基本数据模型。因此,在驱动程序中创建的变量可以在RDD的mapPartitions函数中直接使用,并且所有的RDD操作都是在SparkContext的控制下进行的。
pyspark.rdd.repartition
pyspark.rdd.repartition() is a method in PySpark that allows you to change the number of partitions in an RDD (Resilient Distributed Dataset). It shuffles the data across the partitions and creates new partitions based on the specified number.
The method takes a single argument, which is the desired number of partitions. For example, if you have an RDD with 100 partitions and you want to reduce it to 50 partitions, you can use the repartition() method as follows:
```python
rdd = rdd.repartition(50)
```
Note that repartition() is a costly operation, as it involves shuffling the data across the cluster. Therefore, it is recommended to use it only when necessary and to choose the number of partitions carefully based on the size of the data and the available resources.
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