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Posted to reviews@spark.apache.org by adrian-wang <gi...@git.apache.org> on 2015/03/09 07:31:42 UTC
[GitHub] spark pull request: [SPARK-6201] [SQL] promote string and do widen...
GitHub user adrian-wang opened a pull request:
https://github.com/apache/spark/pull/4945
[SPARK-6201] [SQL] promote string and do widen types for IN
@huangjs
Acutally spark sql will first go through analysis period, in which we do widen types and promote strings, and then optimization, where constant IN will be converted into INSET.
So it turn out that we only need to fix this for IN.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/adrian-wang/spark inset
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/4945.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #4945
----
commit 6c838c410bb228f8086410ffc2e84b3d5a9e42e1
Author: Daoyuan Wang <da...@intel.com>
Date: 2015-03-09T06:27:59Z
promote string and do widen types for IN
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[GitHub] spark pull request: [SPARK-6201] [SQL] promote string and do widen...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/4945#issuecomment-77993513
[Test build #28420 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/28420/consoleFull) for PR 4945 at commit [`cd72593`](https://github.com/apache/spark/commit/cd7259367b5fa56eb6fc0f9601b5f8fb118cef17).
* This patch **passes all tests**.
* This patch merges cleanly.
* This patch adds no public classes.
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[GitHub] spark pull request: [SPARK-6201] [SQL] promote string and do widen...
Posted by liancheng <gi...@git.apache.org>.
Github user liancheng commented on a diff in the pull request:
https://github.com/apache/spark/pull/4945#discussion_r26054354
--- Diff: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala ---
@@ -269,6 +285,14 @@ trait HiveTypeCoercion {
i.makeCopy(Array(Cast(a, StringType), b.map(Cast(_, StringType))))
case i @ In(a, b) if a.dataType == TimestampType && b.forall(_.dataType == DateType) =>
i.makeCopy(Array(Cast(a, StringType), b.map(Cast(_, StringType))))
+ case i @ In(a, b) if a.dataType == StringType
+ && b.exists(_.dataType.isInstanceOf[NumericType]) =>
+ i.makeCopy(Array(Cast(a, DoubleType), b))
--- End diff --
As I've commented on the JIRA ticket, this is not the behavior of Hive. Hive actually converts the numerics in the constant set into strings.
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[GitHub] spark pull request: [SPARK-6201] [SQL] promote string and do widen...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/4945#issuecomment-95043931
[Test build #30732 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/30732/consoleFull) for PR 4945 at commit [`581fa1c`](https://github.com/apache/spark/commit/581fa1cedbc3a8bba104572202a928b48f723b89).
* This patch **fails Spark unit tests**.
* This patch merges cleanly.
* This patch adds no public classes.
* This patch does not change any dependencies.
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[GitHub] spark pull request: [SPARK-6201] [SQL] promote string and do widen...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/4945#issuecomment-77993517
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[GitHub] spark pull request: [SPARK-6201] [SQL] promote string and do widen...
Posted by adrian-wang <gi...@git.apache.org>.
Github user adrian-wang commented on the pull request:
https://github.com/apache/spark/pull/4945#issuecomment-77998513
@liancheng That's reasonable, not every string could be converted into numeric types.
Hive specified about this as
/**
* GenericUDFIn
*
* Example usage:
* SELECT key FROM src WHERE key IN ("238", "1");
*
* From MySQL page on IN(): To comply with the SQL standard, IN returns NULL
* not only if the expression on the left hand side is NULL, but also if no
* match is found in the list and one of the expressions in the list is NULL.
*
* Also noteworthy: type conversion behavior is different from MySQL. With
* expr IN expr1, expr2... in MySQL, exprN will each be converted into the same
* type as expr. In the Hive implementation, all expr(N) will be converted into
* a common type for conversion consistency with other UDF's, and to prevent
* conversions from a big type to a small type (e.g. int to tinyint)
*/
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[GitHub] spark pull request: [SPARK-6201] [SQL] promote string and do widen...
Posted by liancheng <gi...@git.apache.org>.
Github user liancheng commented on the pull request:
https://github.com/apache/spark/pull/4945#issuecomment-94008753
@adrian-wang @marmbrus Sorry for the late reply. Yeah, the MySQL way also seems reasonable to me.
In both Spark SQL and MySQL, `IN` is treated more like an operator, which has its own reasonable type coercion rules. However, in Hive, `IN` is defined as a UDF, which follows general UDF argument type coercion rules, but those rules doesn't make sense here.
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[GitHub] spark pull request: [SPARK-6201] [SQL] promote string and do widen...
Posted by chenghao-intel <gi...@git.apache.org>.
Github user chenghao-intel commented on a diff in the pull request:
https://github.com/apache/spark/pull/4945#discussion_r26044586
--- Diff: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala ---
@@ -269,6 +285,14 @@ trait HiveTypeCoercion {
i.makeCopy(Array(Cast(a, StringType), b.map(Cast(_, StringType))))
case i @ In(a, b) if a.dataType == TimestampType && b.forall(_.dataType == DateType) =>
i.makeCopy(Array(Cast(a, StringType), b.map(Cast(_, StringType))))
+ case i @ In(a, b) if a.dataType == StringType
+ && b.exists(_.dataType.isInstanceOf[NumericType]) =>
+ i.makeCopy(Array(Cast(a, DoubleType), b))
+ case i @ In(a, b) if b.exists(_.dataType == StringType)
+ && a.dataType.isInstanceOf[NumericType] =>
+ i.makeCopy(Array(a, b.map(_.dataType match{
+ case StringType => Cast(a, DoubleType)
--- End diff --
Causes unmatched exception?
```scala
case StringType => Cast(a, DoubleType)
case x => x
```
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[GitHub] spark pull request: [SPARK-6201] [SQL] promote string and do widen...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/4945#issuecomment-77988265
[Test build #28420 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/28420/consoleFull) for PR 4945 at commit [`cd72593`](https://github.com/apache/spark/commit/cd7259367b5fa56eb6fc0f9601b5f8fb118cef17).
* This patch merges cleanly.
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[GitHub] spark pull request: [SPARK-6201] [SQL] promote string and do widen...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/4945#issuecomment-77812210
[Test build #28382 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/28382/consoleFull) for PR 4945 at commit [`6c838c4`](https://github.com/apache/spark/commit/6c838c410bb228f8086410ffc2e84b3d5a9e42e1).
* This patch **passes all tests**.
* This patch merges cleanly.
* This patch adds no public classes.
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[GitHub] spark pull request: [SPARK-6201] [SQL] promote string and do widen...
Posted by marmbrus <gi...@git.apache.org>.
Github user marmbrus commented on the pull request:
https://github.com/apache/spark/pull/4945#issuecomment-89094024
What is the status here? I haven't looked closely but this seems reasonable to me. However, I'd like to see more hive comparison tests that specifically test edge cases (different sized numbers, strings that can't be converted to numbers, etc) to make sure we are compatible with what they are doing.
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[GitHub] spark pull request: [SPARK-6201] [SQL] promote string and do widen...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/4945#issuecomment-95043939
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[GitHub] spark pull request: [SPARK-6201] [SQL] promote string and do widen...
Posted by adrian-wang <gi...@git.apache.org>.
Github user adrian-wang commented on the pull request:
https://github.com/apache/spark/pull/4945#issuecomment-93671496
If we don't like what hive does here, we can do it in MySQL way: Convert all expressions in the list to the type of left handle of IN. In fact the main differences here is that Spark SQL promotes strings to numeric when do type coercion, while Hive seems do the contrary.
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[GitHub] spark pull request: [SPARK-6201] [SQL] promote string and do widen...
Posted by liancheng <gi...@git.apache.org>.
Github user liancheng commented on the pull request:
https://github.com/apache/spark/pull/4945#issuecomment-89621194
The thing that makes me hesitant here is whether we should stick to Hive, because Hive's behavior is actually error prone and unintuitive. In Hive, `IN` is implemented as a UDF, and function argument type coercion rules apply here.
Take `"1.00" IN (1.0, 2.0)` as an example, `"1.00"`, `1.0`, and `2.0` are all arguments of `GenericUDFIn`. When doing type coercion, `1.0` and `2.0` is first converted to string `"1.0"` and `"2.0"`, and then compared with `"1.00"`, thus returns false.
Personally I think maybe we should just throw an exception if the left side of `IN` has different data types from the right side.
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[GitHub] spark pull request: [SPARK-6201] [SQL] promote string and do widen...
Posted by liancheng <gi...@git.apache.org>.
Github user liancheng commented on a diff in the pull request:
https://github.com/apache/spark/pull/4945#discussion_r26054740
--- Diff: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala ---
@@ -269,6 +285,14 @@ trait HiveTypeCoercion {
i.makeCopy(Array(Cast(a, StringType), b.map(Cast(_, StringType))))
case i @ In(a, b) if a.dataType == TimestampType && b.forall(_.dataType == DateType) =>
i.makeCopy(Array(Cast(a, StringType), b.map(Cast(_, StringType))))
+ case i @ In(a, b) if a.dataType == StringType
+ && b.exists(_.dataType.isInstanceOf[NumericType]) =>
+ i.makeCopy(Array(Cast(a, DoubleType), b))
+ case i @ In(a, b) if b.exists(_.dataType == StringType)
+ && a.dataType.isInstanceOf[NumericType] =>
+ i.makeCopy(Array(a, b.map(_.dataType match{
+ case StringType => Cast(a, DoubleType)
--- End diff --
Same as above.
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[GitHub] spark pull request: [SPARK-6201] [SQL] promote string and do widen...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/4945#issuecomment-95069393
[Test build #30734 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/30734/consoleFull) for PR 4945 at commit [`71e05cc`](https://github.com/apache/spark/commit/71e05ccae24c5c47fc38bbe79df0ac2d3b97fdd0).
* This patch **passes all tests**.
* This patch merges cleanly.
* This patch adds no public classes.
* This patch does not change any dependencies.
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[GitHub] spark pull request: [SPARK-6201] [SQL] promote string and do widen...
Posted by chenghao-intel <gi...@git.apache.org>.
Github user chenghao-intel commented on a diff in the pull request:
https://github.com/apache/spark/pull/4945#discussion_r26044965
--- Diff: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala ---
@@ -220,6 +220,22 @@ trait HiveTypeCoercion {
b.makeCopy(Array(newLeft, newRight))
}.getOrElse(b) // If there is no applicable conversion, leave expression unchanged.
}
+
+ // Also widen types for InExpressions.
+ case q: LogicalPlan => q transformExpressions {
+ // Skip nodes who's children have not been resolved yet.
+ case e if !e.childrenResolved => e
+
+ case i @ In(a, b) if b.exists(_.dataType != a.dataType) =>
+ b.map(_.dataType).foldLeft(None: Option[DataType])((r, c) => r match {
+ case None => Some(c)
+ case Some(dt) => findTightestCommonType(dt, c)
+ }) match {
+ // If there is no applicable conversion, leave expression unchanged.
+ case None => i.makeCopy(Array(a, b))
--- End diff --
Leave it as `i` instead of the `i.makeCopy(..)`? Or throwing exception?
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[GitHub] spark pull request: [SPARK-6201] [SQL] promote string and do widen...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/4945#issuecomment-77806425
[Test build #28382 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/28382/consoleFull) for PR 4945 at commit [`6c838c4`](https://github.com/apache/spark/commit/6c838c410bb228f8086410ffc2e84b3d5a9e42e1).
* This patch merges cleanly.
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[GitHub] spark pull request: [SPARK-6201] [SQL] promote string and do widen...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/4945#issuecomment-77812220
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[GitHub] spark pull request: [SPARK-6201] [SQL] promote string and do widen...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/4945#issuecomment-96773860
[Test build #31058 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/31058/consoleFull) for PR 4945 at commit [`71e05cc`](https://github.com/apache/spark/commit/71e05ccae24c5c47fc38bbe79df0ac2d3b97fdd0).
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[GitHub] spark pull request: [SPARK-6201] [SQL] promote string and do widen...
Posted by marmbrus <gi...@git.apache.org>.
Github user marmbrus commented on the pull request:
https://github.com/apache/spark/pull/4945#issuecomment-92554707
Any comments on @liancheng 's suggestion?
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[GitHub] spark pull request: [SPARK-6201] [SQL] promote string and do widen...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/4945#issuecomment-95042053
[Test build #30732 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/30732/consoleFull) for PR 4945 at commit [`581fa1c`](https://github.com/apache/spark/commit/581fa1cedbc3a8bba104572202a928b48f723b89).
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[GitHub] spark pull request: [SPARK-6201] [SQL] promote string and do widen...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
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[GitHub] spark pull request: [SPARK-6201] [SQL] promote string and do widen...
Posted by SparkQA <gi...@git.apache.org>.
Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/4945#issuecomment-95047309
[Test build #30734 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/30734/consoleFull) for PR 4945 at commit [`71e05cc`](https://github.com/apache/spark/commit/71e05ccae24c5c47fc38bbe79df0ac2d3b97fdd0).
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[GitHub] spark pull request: [SPARK-6201] [SQL] promote string and do widen...
Posted by marmbrus <gi...@git.apache.org>.
Github user marmbrus commented on the pull request:
https://github.com/apache/spark/pull/4945#issuecomment-93867757
The mysql way seems reasonable to me. @liancheng ?
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