python spark替代方法可针对非常大的数据爆炸 [英] python spark alternative to explode for very large data
问题描述
我有一个像这样的数据框:
I have a dataframe like this:
df = spark.createDataFrame([(0, ["B","C","D","E"]),(1,["E","A","C"]),(2, ["F","A","E","B"]),(3,["E","G","A"]),(4,["A","C","E","B","D"])], ["id","items"])
会创建一个数据帧df，如下所示:
which creates a data frame df like this:
+---+-----------------+
| 0| [B, C, D, E]|
| 1| [E, A, C]|
| 2| [F, A, E, B]|
| 3| [E, G, A]|
| 4| [A, C, E, B, D]|
+---+-----------------+
我想得到这样的结果:
+---+-----+
|all|count|
+---+-----+
| F| 1|
| E| 5|
| B| 3|
| D| 2|
| C| 3|
| A| 4|
| G| 1|
+---+-----+
从本质上讲，它只是找到df["items"]中所有不同的元素，并对它们的出现频率进行计数.如果我的数据的大小更易于管理，那么我就可以这样做:
Which essentially just finds all distinct elements in df["items"] and counts their frequency. If my data was of a more manageable size, I would just do this:
all_items = df.select(explode("items").alias("all"))
result = all_items.groupby(all_items.all).count().distinct()
result.show()
但是因为我的数据在每个列表中都有数百万行和数千个元素，所以这不是一种选择.我当时想逐行执行此操作，因此我一次只能处理2个列表.因为大多数元素经常在许多行中重复(但是每行中的列表是一个集合)，所以这种方法应该可以解决我的问题.但是问题是，我真的不知道如何在Spark中执行此操作，因为我才刚刚开始学习它.有人可以帮忙吗?
But because my data has millions of rows and thousands of elements in each list, this is not an option. I was thinking of doing this row by row, so that I only work with 2 lists at a time. Because most elements are frequently repeated in many rows (but the list in each row is a set), this approach should solve my problem. But the problem is, I don't really know how to do this in Spark, as I've only just started learning it. Could anyone help, please?
推荐答案
您需要做的是减小进入爆炸状态的分区的大小.有2个选项可以执行此操作.首先，如果输入数据是可拆分的，则可以减小spark.sql.files.maxPartitionBytes的大小，以便Spark读取较小的拆分.另一种选择是在爆炸之前重新分区.
What you need to do is reduce the size of your partitions going into the explode. There are 2 options to do this. First, if your input data is splittable you can decrease the size of spark.sql.files.maxPartitionBytes so Spark reads smaller splits. The other option would be to repartition before the explode.
默认值 maxPartitionBytes的大小为128MB，因此Spark将尝试以128MB的块读取数据.如果数据不可拆分，则它将整个文件读入单个分区，在这种情况下，您需要执行repartition
The default value of maxPartitionBytes is 128MB, so Spark will attempt to read your data in 128MB chunks. If the data is not splittable then it'll read the full file into a single partition in which case you'll need to do a repartition instead.
在您的情况下，由于您正在爆炸，所以说它增加了100倍，每个分区有128MB的空间进入，最终每个分区有12GB +的空间！
In your case since you're doing an explode, say it's 100x increase with 128MB per partition going in, you're ending up with 12GB+ per partition coming out!
您可能需要考虑的另一件事是您的shuffle分区，因为您正在进行聚合.同样，在爆炸之后，您可能需要通过将spark.sql.shuffle.partitions设置为比默认值200高的值来增加聚合的分区.您可以使用Spark UI查看洗牌阶段，并查看每个任务有多少数据阅读并进行相应调整.
The other thing you may need to consider is your shuffle partitions since you're doing an aggregation. So again, you may need to increase the partitioning for the aggregation after the explode by setting spark.sql.shuffle.partitions to a higher value than the default 200. You can use the Spark UI to look at your shuffle stage and see how much data each task is reading in and adjust accordingly.
I discuss this and other tuning suggestions in the talk I just gave at Spark Summit Europe.
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