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Posted to issues@spark.apache.org by "Yuming Wang (Jira)" <ji...@apache.org> on 2022/05/13 04:44:00 UTC

[jira] [Updated] (SPARK-39173) The error message is different if disable broadcast join

     [ https://issues.apache.org/jira/browse/SPARK-39173?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Yuming Wang updated SPARK-39173:
--------------------------------
    Description: 
How to reproduce this issue:
{code:scala}
Seq(-1, 1000000000L).foreach { broadcastThreshold =>
  withSQLConf(
    SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> broadcastThreshold.toString,
    SQLConf.ANSI_ENABLED.key -> "true") {
    val df = sql(
      """
        |SELECT
        |  item.i_brand_id brand_id,
        |  avg(ss_ext_sales_price) avg_agg
        |FROM store_sales, item
        |WHERE store_sales.ss_item_sk = item.i_item_sk
        |GROUP BY item.i_brand_id
      """.stripMargin)
    df.collect()
  }
}
{code}

{noformat}
Error message: org.apache.spark.SparkArithmeticException: [CANNOT_CHANGE_DECIMAL_PRECISION] Decimal(expanded,999999999999999999999999999999999.28175,38,5}) cannot be represented as Decimal(38, 6). If necessary set "spark.sql.ansi.enabled" to false to bypass this error.
Error message: org.apache.spark.SparkArithmeticException: [ARITHMETIC_OVERFLOW] Overflow in sum of decimals. If necessary set spark.sql.ansi.enabled to false (except for ANSI interval type) to bypass this error.
{noformat}


  was:
How to reproduce this issue:
{code:scala}
Seq(-1, 1000000000L).foreach { broadcastThreshold =>
  withSQLConf(
    SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> broadcastThreshold.toString,
    SQLConf.ANSI_ENABLED.key -> "true") {
    val df = sql(
      """
        |SELECT
        |  item.i_brand_id brand_id,
        |  avg(ss_ext_sales_price) avg_agg
        |FROM store_sales, item
        |WHERE store_sales.ss_item_sk = item.i_item_sk
        |GROUP BY item.i_brand_id
      """.stripMargin)
    df.collect()
  }
}
{code}

{noformat}
Error message: Job aborted due to stage failure: Task 0 in stage 10.0 failed 1 times, most recent failure: Lost task 0.0 in stage 10.0 (TID 9) (localhost executor driver): org.apache.spark.SparkArithmeticException: [CANNOT_CHANGE_DECIMAL_PRECISION] Decimal(expanded,999999999999999999999999999999999.28175,38,5}) cannot be represented as Decimal(38, 6). If necessary set "spark.sql.ansi.enabled" to false to bypass this error.
Error message: Job aborted due to stage failure: Task 0 in stage 14.0 failed 1 times, most recent failure: Lost task 0.0 in stage 14.0 (TID 14) (localhost executor driver): org.apache.spark.SparkArithmeticException: [ARITHMETIC_OVERFLOW] Overflow in sum of decimals. If necessary set spark.sql.ansi.enabled to false (except for ANSI interval type) to bypass this error.
{noformat}



> The error message is different if disable broadcast join
> --------------------------------------------------------
>
>                 Key: SPARK-39173
>                 URL: https://issues.apache.org/jira/browse/SPARK-39173
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 3.4.0
>            Reporter: Yuming Wang
>            Priority: Major
>
> How to reproduce this issue:
> {code:scala}
> Seq(-1, 1000000000L).foreach { broadcastThreshold =>
>   withSQLConf(
>     SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> broadcastThreshold.toString,
>     SQLConf.ANSI_ENABLED.key -> "true") {
>     val df = sql(
>       """
>         |SELECT
>         |  item.i_brand_id brand_id,
>         |  avg(ss_ext_sales_price) avg_agg
>         |FROM store_sales, item
>         |WHERE store_sales.ss_item_sk = item.i_item_sk
>         |GROUP BY item.i_brand_id
>       """.stripMargin)
>     df.collect()
>   }
> }
> {code}
> {noformat}
> Error message: org.apache.spark.SparkArithmeticException: [CANNOT_CHANGE_DECIMAL_PRECISION] Decimal(expanded,999999999999999999999999999999999.28175,38,5}) cannot be represented as Decimal(38, 6). If necessary set "spark.sql.ansi.enabled" to false to bypass this error.
> Error message: org.apache.spark.SparkArithmeticException: [ARITHMETIC_OVERFLOW] Overflow in sum of decimals. If necessary set spark.sql.ansi.enabled to false (except for ANSI interval type) to bypass this error.
> {noformat}



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