Spark数据集转换为数组 [英] Spark Data set transformation to array

问题描述

我有一个像下面这样的数据集；其中col1的值重复多次，而col2的值唯一.这个原始数据集几乎可以容纳十亿行，所以我不想使用collect或collect_list，因为它不会针对我的用例进行横向扩展.

I have a dataset like below; with values of col1 repeating multiple times and unique values of col2. This original dataset can almost a billion rows, so I do not want to use collect or collect_list as it will not scale-out for my use case.

原始数据集:

+---------------------|
|    col1  |    col2  |
+---------------------|
|    AA|    11        |
|    BB|    21        |
|    AA|    12        |
|    AA|    13        |
|    BB|    22        |
|    CC|    33        |
+---------------------|

我想将数据集转换为以下数组格式.newColumn作为col2的数组.

I want to transform the dataset into the following array format. newColumn as an array of col2.

转换后的数据集:

+---------------------|
|col1  |     newColumn|
+---------------------|
|    AA|    [11,12,13]|
|    BB|    [21,22]   |
|    CC|    [33]      |
+---------------------|

我已经看到了此解决方案，但是它使用collect_list并且不会扩展到大数据集.

I have seen this solution, but it uses collect_list and will not scale-out on big datasets.

推荐答案

使用spark的内置函数始终是最好的方法.我看不到使用collect_list函数的问题.只要您有足够的内存，这将是最好的方法.优化工作的一种方法是将数据保存为实木复合地板，按A列进行存储并将其保存为表格.最好将其划分为均匀分布数据的某些列.

Using the inbuilt functions of spark are always the best way. I see no problem in using the collect_list function. As long as you have sufficient memory, this would be the best way. One way of optimizing your job would be to save your data as parquet , bucket it by column A and saving it as a table. Better would be to also partition it by some column that evenly distributes data.

例如，

df_stored = #load your data from csv or parquet or any format'
spark.catalog.setCurrentDatabase(database_name)
df_stored.write.mode("overwrite").format("parquet").partitionBy(part_col).bucketBy(10,"col1").option("path",savepath).saveAsTable(tablename)
df_analysis = spark.table(tablename)
df_aggreg = df_analysis.groupby('col1').agg(F.collect_list(col('col2')))

这将加快聚合速度，并避免大量改组.试试吧

This would speeden up the aggregation and avoid a lot of shuffle. try it out

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