Scala Spark数据帧联合提供副本
我有一个基本数据集，其中一列有null和notnull值。 因此，我： 当我把它打印出来时，我得到了清晰的各行。但当我这么做的时候Scala Spark数据帧联合提供副本,scala,apache-spark,Scala,Apache Spark,我有一个基本数据集，其中一列有null和notnull值。 因此，我： 当我把它打印出来时，我得到了清晰的各行。但当我这么做的时候 val combined_ds = nonTrained_ds.union(trained_ds) 我从nonTrained\u-ds中获得了重复的行记录，奇怪的是，来自trained\u-ds的行不再在组合的ds中 为什么会发生这种情况 trained_ds的值为： +----------+----------------+ |unique_no |
val combined_ds = nonTrained_ds.union(trained_ds)
nonTrained\u-dstrained\u-dstrained_ds+----------+----------------+
|unique_no | running_id|
+----------+----------------+
|0456700001|16 |
|0456700004|16 |
|0456700007|16 |
|0456700010|16 |
|0456700013|16 |
|0456700016|16 |
|0456700019|16 |
|0456700022|16 |
|0456700025|16 |
|0456700028|16 |
|0456700031|16 |
|0456700034|16 |
|0456700037|16 |
|0456700040|16 |
|0456700043|16 |
|0456700046|16 |
|0456700049|16 |
|0456700052|16 |
|0456700055|16 |
|0456700058|16 |
|0456700061|16 |
|0456700064|16 |
|0456700067|16 |
|0456700070|16 |
+----------+----------------+
+----------+----------------+
|unique_no | running_id|
+----------+----------------+
|0456700002|null |
|0456700003|null |
|0456700005|null |
|0456700006|null |
|0456700008|null |
|0456700009|null |
|0456700011|null |
|0456700012|null |
|0456700014|null |
|0456700015|null |
|0456700017|null |
|0456700018|null |
|0456700020|null |
|0456700021|null |
|0456700023|null |
|0456700024|null |
|0456700026|null |
|0456700027|null |
|0456700029|null |
|0456700030|null |
|0456700032|null |
|0456700033|null |
|0456700035|null |
|0456700036|null |
|0456700038|null |
|0456700039|null |
|0456700041|null |
|0456700042|null |
|0456700044|null |
|0456700045|null |
|0456700047|null |
|0456700048|null |
|0456700050|null |
|0456700051|null |
|0456700053|null |
|0456700054|null |
|0456700056|null |
|0456700057|null |
|0456700059|null |
|0456700060|null |
|0456700062|null |
|0456700063|null |
|0456700065|null |
|0456700066|null |
|0456700068|null |
|0456700069|null |
|0456700071|null |
|0456700072|null |
+----------+----------------+
nonTrained_ds+----------+----------------+
|unique_no | running_id|
+----------+----------------+
|0456700001|16 |
|0456700004|16 |
|0456700007|16 |
|0456700010|16 |
|0456700013|16 |
|0456700016|16 |
|0456700019|16 |
|0456700022|16 |
|0456700025|16 |
|0456700028|16 |
|0456700031|16 |
|0456700034|16 |
|0456700037|16 |
|0456700040|16 |
|0456700043|16 |
|0456700046|16 |
|0456700049|16 |
|0456700052|16 |
|0456700055|16 |
|0456700058|16 |
|0456700061|16 |
|0456700064|16 |
|0456700067|16 |
|0456700070|16 |
+----------+----------------+
+----------+----------------+
|unique_no | running_id|
+----------+----------------+
|0456700002|null |
|0456700003|null |
|0456700005|null |
|0456700006|null |
|0456700008|null |
|0456700009|null |
|0456700011|null |
|0456700012|null |
|0456700014|null |
|0456700015|null |
|0456700017|null |
|0456700018|null |
|0456700020|null |
|0456700021|null |
|0456700023|null |
|0456700024|null |
|0456700026|null |
|0456700027|null |
|0456700029|null |
|0456700030|null |
|0456700032|null |
|0456700033|null |
|0456700035|null |
|0456700036|null |
|0456700038|null |
|0456700039|null |
|0456700041|null |
|0456700042|null |
|0456700044|null |
|0456700045|null |
|0456700047|null |
|0456700048|null |
|0456700050|null |
|0456700051|null |
|0456700053|null |
|0456700054|null |
|0456700056|null |
|0456700057|null |
|0456700059|null |
|0456700060|null |
|0456700062|null |
|0456700063|null |
|0456700065|null |
|0456700066|null |
|0456700068|null |
|0456700069|null |
|0456700071|null |
|0456700072|null |
+----------+----------------+
+----------+----------------+
|unique_no | running_id|
+----------+----------------+
|0456700002|null |
|0456700003|null |
|0456700005|null |
|0456700006|null |
|0456700008|null |
|0456700009|null |
|0456700011|null |
|0456700012|null |
|0456700014|null |
|0456700015|null |
|0456700017|null |
|0456700018|null |
|0456700020|null |
|0456700021|null |
|0456700023|null |
|0456700024|null |
|0456700026|null |
|0456700027|null |
|0456700029|null |
|0456700030|null |
|0456700032|null |
|0456700033|null |
|0456700035|null |
|0456700036|null |
|0456700038|null |
|0456700039|null |
|0456700041|null |
|0456700042|null |
|0456700044|null |
|0456700045|null |
|0456700047|null |
|0456700048|null |
|0456700050|null |
|0456700051|null |
|0456700053|null |
|0456700054|null |
|0456700056|null |
|0456700057|null |
|0456700059|null |
|0456700060|null |
|0456700062|null |
|0456700063|null |
|0456700065|null |
|0456700066|null |
|0456700068|null |
|0456700069|null |
|0456700071|null |
|0456700072|null |
|0456700002|16 |
|0456700005|16 |
|0456700008|16 |
|0456700011|16 |
|0456700014|16 |
|0456700017|16 |
|0456700020|16 |
|0456700023|16 |
|0456700026|16 |
|0456700029|16 |
|0456700032|16 |
|0456700035|16 |
|0456700038|16 |
|0456700041|16 |
|0456700044|16 |
|0456700047|16 |
|0456700050|16 |
|0456700053|16 |
|0456700056|16 |
|0456700059|16 |
|0456700062|16 |
|0456700065|16 |
|0456700068|16 |
|0456700071|16 |
+----------+----------------+
val nonTrained_ds = base_ds.filter(col("primary_offer_id").isNull).distinct()
val trained_ds = base_ds.filter(col("primary_offer_id").isNotNull).distinct()
可以显示重复的行和未合并的行吗？我猜测的是，您没有可视化整个数据集。如果查看组合数据集输出，值“0456700002”出现两次。不确定是否有人在问题上标记了“-1”-如果其不明确，请要求澄清。如果查看组合数据集，所有值为“16”的行都是“非训练数据”中第一列的重复项。这些是输入数据帧中唯一的数据吗？在受过训练和未受过训练的情况下？还是更多？有很多其他的栏目，但中间没有任何修改。我只是将数据集“A”提取到两个数据集中，然后将它们连接起来，得到数据集“A”。我正在使用Spark 2.0