org.apache.spark.sql.AnalysisException: Reference 'var' is ambiguous, could be: var#166, var#167.;

GitHub | hiltonmbr | 6 months ago
  1. 0

    GitHub comment 111#233146888

    GitHub | 6 months ago | hiltonmbr
    org.apache.spark.sql.AnalysisException: Reference 'var' is ambiguous, could be: var#166, var#167.;
  2. 0

    GitHub comment 111#233147755

    GitHub | 6 months ago | kevinushey
    org.apache.spark.sql.AnalysisException: Reference 'var' is ambiguous, could be: var#6, var#7.;
  3. 0

    Duplicate columns in Spark Dataframe

    Stack Overflow | 1 year ago | Bamqf
    org.apache.spark.sql.AnalysisException: Reference 'Email' is ambiguous, could be: Email#350, Email#361.;
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  5. 0

    GitHub comment 141#156318742

    GitHub | 1 year ago | jerryivanhoe
    org.apache.spark.sql.AnalysisException: Non-local session path expected to be non-null;
  6. 0

    [jira] [Updated] (SPARK-14231) JSON data source fails to infer floats as decimal when precision is bigger than 38 or scale is bigger than precision.

    spark-issues | 10 months ago | Davies Liu (JIRA)
    org.apache.spark.sql.AnalysisException: Decimal scale (2) cannot be greater than precision (1).;

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    Root Cause Analysis

    1. org.apache.spark.sql.AnalysisException

      Reference 'var' is ambiguous, could be: var#166, var#167.;

      at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve()
    2. Spark Project Catalyst
      LogicalPlan$$anonfun$resolve$1.apply
      1. org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:264)
      2. org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveQuoted(LogicalPlan.scala:168)
      3. org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolve$1.apply(LogicalPlan.scala:130)
      4. org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolve$1.apply(LogicalPlan.scala:129)
      4 frames
    3. Scala
      IterableLike$class.foreach
      1. scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
      2. scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
      3. scala.collection.Iterator$class.foreach(Iterator.scala:893)
      4. scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
      5. scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
      5 frames
    4. org.apache.spark
      StructType.foreach
      1. org.apache.spark.sql.types.StructType.foreach(StructType.scala:94)
      1 frame