Spark DataFrame join后移除重复的列-Toy模板网

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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")).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
如果是scala，解决方案如下：

 val moviesWithRating = moviesDf.join(averageRatingMoviesDF, Seq("movieId"))

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")
                );