[
https://issues.apache.org/jira/browse/SPARK-38615?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Gengliang Wang updated SPARK-38615:
-----------------------------------
Description:
Currently, there is not enough error context for runtime ANSI failures.
In the following example, the error message only tells that there is a "divide
by zero" error, without pointing out where the exact SQL statement is.
{code:java}
> SELECT
ss1.ca_county,
ss1.d_year,
ws2.web_sales / ws1.web_sales web_q1_q2_increase,
ss2.store_sales / ss1.store_sales store_q1_q2_increase,
ws3.web_sales / ws2.web_sales web_q2_q3_increase,
ss3.store_sales / ss2.store_sales store_q2_q3_increase
FROM
ss ss1, ss ss2, ss ss3, ws ws1, ws ws2, ws ws3
WHERE
ss1.d_qoy = 1
AND ss1.d_year = 2000
AND ss1.ca_county = ss2.ca_county
AND ss2.d_qoy = 2
AND ss2.d_year = 2000
AND ss2.ca_county = ss3.ca_county
AND ss3.d_qoy = 3
AND ss3.d_year = 2000
AND ss1.ca_county = ws1.ca_county
AND ws1.d_qoy = 1
AND ws1.d_year = 2000
AND ws1.ca_county = ws2.ca_county
AND ws2.d_qoy = 2
AND ws2.d_year = 2000
AND ws1.ca_county = ws3.ca_county
AND ws3.d_qoy = 3
AND ws3.d_year = 2000
AND CASE WHEN ws1.web_sales > 0
THEN ws2.web_sales / ws1.web_sales
ELSE NULL END
> CASE WHEN ss1.store_sales > 0
THEN ss2.store_sales / ss1.store_sales
ELSE NULL END
AND CASE WHEN ws2.web_sales > 0
THEN ws3.web_sales / ws2.web_sales
ELSE NULL END
> CASE WHEN ss2.store_sales > 0
THEN ss3.store_sales / ss2.store_sales
ELSE NULL END
ORDER BY ss1.ca_county
{code}
{code:java}
org.apache.spark.SparkArithmeticException: divide by zero at
org.apache.spark.sql.errors.QueryExecutionErrors$.divideByZeroError(QueryExecutionErrors.scala:140)
at
org.apache.spark.sql.catalyst.expressions.DivModLike.eval(arithmetic.scala:437)
at
org.apache.spark.sql.catalyst.expressions.DivModLike.eval$(arithmetic.scala:425)
at org.apache.spark.sql.catalyst.expressions.Divide.eval(arithmetic.scala:534)
...{code}
I suggest that we provide details in the error message, including:
* the problematic expression from the original SQL query, e.g.
"ss3.store_sales / ss2.store_sales store_q2_q3_increase"
* the line number and starting char position of the problematic expression, in
case of queries like "select a + b from t1 union select a + b from t2"
So that the error message will be precise
{code:java}
org.apache.spark.SparkArithmeticException: divide by zero
SparkArithmeticException: divide by zero. To return NULL instead, use
'try_divide'. If necessary set spark.sql.ansi.enabled to false (except for ANSI
interval type) to bypass this error.
== SQL(line 2, position 43) ==
ws2.web_sales / ws1.web_sales web_q1_q2, ss2.store_sales / ss1.store_sales
store_q1_q2
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
{code}
[^SQL Error Attribution Framework.pdf]
was:
Currently, there is not enough error context for runtime ANSI failures.
In the following example, the error message only tells that there is a "divide
by zero" error, without pointing out where the exact SQL statement is.
{code:java}
> SELECT
ss1.ca_county,
ss1.d_year,
ws2.web_sales / ws1.web_sales web_q1_q2_increase,
ss2.store_sales / ss1.store_sales store_q1_q2_increase,
ws3.web_sales / ws2.web_sales web_q2_q3_increase,
ss3.store_sales / ss2.store_sales store_q2_q3_increase
FROM
ss ss1, ss ss2, ss ss3, ws ws1, ws ws2, ws ws3
WHERE
ss1.d_qoy = 1
AND ss1.d_year = 2000
AND ss1.ca_county = ss2.ca_county
AND ss2.d_qoy = 2
AND ss2.d_year = 2000
AND ss2.ca_county = ss3.ca_county
AND ss3.d_qoy = 3
AND ss3.d_year = 2000
AND ss1.ca_county = ws1.ca_county
AND ws1.d_qoy = 1
AND ws1.d_year = 2000
AND ws1.ca_county = ws2.ca_county
AND ws2.d_qoy = 2
AND ws2.d_year = 2000
AND ws1.ca_county = ws3.ca_county
AND ws3.d_qoy = 3
AND ws3.d_year = 2000
AND CASE WHEN ws1.web_sales > 0
THEN ws2.web_sales / ws1.web_sales
ELSE NULL END
> CASE WHEN ss1.store_sales > 0
THEN ss2.store_sales / ss1.store_sales
ELSE NULL END
AND CASE WHEN ws2.web_sales > 0
THEN ws3.web_sales / ws2.web_sales
ELSE NULL END
> CASE WHEN ss2.store_sales > 0
THEN ss3.store_sales / ss2.store_sales
ELSE NULL END
ORDER BY ss1.ca_county
{code}
{code:java}
org.apache.spark.SparkArithmeticException: divide by zero at
org.apache.spark.sql.errors.QueryExecutionErrors$.divideByZeroError(QueryExecutionErrors.scala:140)
at
org.apache.spark.sql.catalyst.expressions.DivModLike.eval(arithmetic.scala:437)
at
org.apache.spark.sql.catalyst.expressions.DivModLike.eval$(arithmetic.scala:425)
at org.apache.spark.sql.catalyst.expressions.Divide.eval(arithmetic.scala:534)
{code}
I suggest that we provide details in the error message, including:
* the problematic expression from the original SQL query, e.g.
"ss3.store_sales / ss2.store_sales store_q2_q3_increase"
* the line number and starting char position of the problematic expression, in
case of queries like "select a + b from t1 union select a + b from t2"
> SQL Error Attribution Framework
> -------------------------------
>
> Key: SPARK-38615
> URL: https://issues.apache.org/jira/browse/SPARK-38615
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 3.3.0
> Reporter: Gengliang Wang
> Priority: Major
> Attachments: SQL Error Attribution Framework.pdf
>
>
> Currently, there is not enough error context for runtime ANSI failures.
> In the following example, the error message only tells that there is a
> "divide by zero" error, without pointing out where the exact SQL statement is.
> {code:java}
> > SELECT
> ss1.ca_county,
> ss1.d_year,
> ws2.web_sales / ws1.web_sales web_q1_q2_increase,
> ss2.store_sales / ss1.store_sales store_q1_q2_increase,
> ws3.web_sales / ws2.web_sales web_q2_q3_increase,
> ss3.store_sales / ss2.store_sales store_q2_q3_increase
> FROM
> ss ss1, ss ss2, ss ss3, ws ws1, ws ws2, ws ws3
> WHERE
> ss1.d_qoy = 1
> AND ss1.d_year = 2000
> AND ss1.ca_county = ss2.ca_county
> AND ss2.d_qoy = 2
> AND ss2.d_year = 2000
> AND ss2.ca_county = ss3.ca_county
> AND ss3.d_qoy = 3
> AND ss3.d_year = 2000
> AND ss1.ca_county = ws1.ca_county
> AND ws1.d_qoy = 1
> AND ws1.d_year = 2000
> AND ws1.ca_county = ws2.ca_county
> AND ws2.d_qoy = 2
> AND ws2.d_year = 2000
> AND ws1.ca_county = ws3.ca_county
> AND ws3.d_qoy = 3
> AND ws3.d_year = 2000
> AND CASE WHEN ws1.web_sales > 0
> THEN ws2.web_sales / ws1.web_sales
> ELSE NULL END
> > CASE WHEN ss1.store_sales > 0
> THEN ss2.store_sales / ss1.store_sales
> ELSE NULL END
> AND CASE WHEN ws2.web_sales > 0
> THEN ws3.web_sales / ws2.web_sales
> ELSE NULL END
> > CASE WHEN ss2.store_sales > 0
> THEN ss3.store_sales / ss2.store_sales
> ELSE NULL END
> ORDER BY ss1.ca_county
> {code}
> {code:java}
> org.apache.spark.SparkArithmeticException: divide by zero at
> org.apache.spark.sql.errors.QueryExecutionErrors$.divideByZeroError(QueryExecutionErrors.scala:140)
> at
> org.apache.spark.sql.catalyst.expressions.DivModLike.eval(arithmetic.scala:437)
> at
> org.apache.spark.sql.catalyst.expressions.DivModLike.eval$(arithmetic.scala:425)
> at
> org.apache.spark.sql.catalyst.expressions.Divide.eval(arithmetic.scala:534)
> ...{code}
>
> I suggest that we provide details in the error message, including:
> * the problematic expression from the original SQL query, e.g.
> "ss3.store_sales / ss2.store_sales store_q2_q3_increase"
> * the line number and starting char position of the problematic expression,
> in case of queries like "select a + b from t1 union select a + b from t2"
> So that the error message will be precise
> {code:java}
> org.apache.spark.SparkArithmeticException: divide by zero
> SparkArithmeticException: divide by zero. To return NULL instead, use
> 'try_divide'. If necessary set spark.sql.ansi.enabled to false (except for
> ANSI interval type) to bypass this error.
> == SQL(line 2, position 43) ==
> ws2.web_sales / ws1.web_sales web_q1_q2, ss2.store_sales / ss1.store_sales
> store_q1_q2
> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
> {code}
> [^SQL Error Attribution Framework.pdf]
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