Spark DataFrame join后移除重复的列
在Spark，两个DataFrame做join操作后，会出现重复的列。例如：
Dataset<Row> moviesWithRating = moviesDF.join(averageRatingMoviesDF,moviesDF.col("movieId").equalTo(averageRatingMoviesDF.col("movieId")));
其schema如下：
//moviesWithRating.printSchema();/*** root* |-- _id: struct (nullable = true)* | |-- oid: string (nullable = true)* |-- actors: string (nullable = true)* |-- description: string (nullable = true)* |-- directors: string (nullable = true)* |-- genres: string (nullable = true)* |-- issue: string (nullable = true)* |-- language: string (nullable = true)* |-- movieId: integer (nullable = true)* |-- shoot: string (nullable = true)* |-- timeLong: string (nullable = true)* |-- title: string (nullable = true)* |-- movieId: integer (nullable = true)* |-- avgRating: double (nullable = true)*/
我们在继续操作这个DataFrame时，可能就会报错，如下：org.apache.spark.sql.AnalysisException: Reference ‘movieId’ is ambiguous
解决方案有两种方法可以用来移除重复的列
- 方法一：join表达式使用字符串数组（用于join的列）
Seq<String> joinColumns = JavaConversions.asScalaBuffer(Arrays.asList("movieId", "movieId")).toList();Dataset<Row> moviesWithRating = moviesDF.join(averageRatingMoviesDF,joinColumns,"inner");
这里DataFrame moviesDF和averageRatingMoviesDF使用了movieId和movieId两列来做join，返回的结果会对这两列去重
scala解决方案：
df1.join(df2, Seq("id","name"),"left") // df1和df2使用了id和name两列来做join,返回的结果会对这两列去
- 方法二：使用select返回指定的列
Dataset<Row> moviesWithRating = moviesDF.join(averageRatingMoviesDF,moviesDF.col("movieId").equalTo(averageRatingMoviesDF.col("movieId"))).select(moviesDF.col("movieId"),col("actors"),col("description"),col("directors"),col("genres"),col("issue"),col("language"),col("shoot"),col("timeLong"),col("title"),col("avgRating"));
说明：
如果列较少, 推荐使用第二种.
如果列较多, 推荐使用第一种.