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https://issues.apache.org/jira/browse/SPARK-14948?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15432253#comment-15432253
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Alexander Bij commented on SPARK-14948:
---------------------------------------
The same problem occurs with *Spark 2.0.0*. This block can be executed in the
Spark-shell:
{code}
val rdd = sc.parallelize(List("a" -> 1, "b" -> 1, "a" -> 2))
val df = rdd.toDF(Seq("letter", "number"): _*)
val agg_df =
df.groupBy("letter").agg(max("number")).withColumnRenamed("max(number)", "max")
// Error occurs (start with agg_df):
agg_df.join(df, agg_df("letter") === df("letter") and agg_df("max") ===
df("number"), "inner")
// Join the other way around works (start with df):
df.join(agg_df, agg_df("letter") === df("letter") and agg_df("max") ===
df("number"), "inner").show()
{code}
> Exception when joining DataFrames derived form the same DataFrame
> -----------------------------------------------------------------
>
> Key: SPARK-14948
> URL: https://issues.apache.org/jira/browse/SPARK-14948
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.6.0
> Reporter: Saurabh Santhosh
>
> h2. Spark Analyser is throwing the following exception in a specific scenario
> :
> h2. Exception :
> org.apache.spark.sql.AnalysisException: resolved attribute(s) F1#3 missing
> from asd#5,F2#4,F1#6,F2#7 in operator !Project [asd#5,F1#3];
> at
> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.failAnalysis(CheckAnalysis.scala:38)
> h2. Code :
> {code:title=SparkClient.java|borderStyle=solid}
> StructField[] fields = new StructField[2];
> fields[0] = new StructField("F1", DataTypes.StringType, true,
> Metadata.empty());
> fields[1] = new StructField("F2", DataTypes.StringType, true,
> Metadata.empty());
> JavaRDD<Row> rdd =
>
> sparkClient.getJavaSparkContext().parallelize(Arrays.asList(RowFactory.create("a",
> "b")));
> DataFrame df = sparkClient.getSparkHiveContext().createDataFrame(rdd, new
> StructType(fields));
> sparkClient.getSparkHiveContext().registerDataFrameAsTable(df, "t1");
> DataFrame aliasedDf = sparkClient.getSparkHiveContext().sql("select F1 as
> asd, F2 from t1");
> sparkClient.getSparkHiveContext().registerDataFrameAsTable(aliasedDf,
> "t2");
> sparkClient.getSparkHiveContext().registerDataFrameAsTable(df, "t3");
>
> DataFrame join = aliasedDf.join(df,
> aliasedDf.col("F2").equalTo(df.col("F2")), "inner");
> DataFrame select = join.select(aliasedDf.col("asd"), df.col("F1"));
> select.collect();
> {code}
> h2. Observations :
> * This issue is related to the Data Type of Fields of the initial Data
> Frame.(If the Data Type is not String, it will work.)
> * It works fine if the data frame is registered as a temporary table and an
> sql (select a.asd,b.F1 from t2 a inner join t3 b on a.F2=b.F2) is written.
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