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Github user yucai commented on the issue: https://github.com/apache/spark/pull/22197 @cloud-fan I reverted to the previous version. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user yucai commented on the issue: https://github.com/apache/spark/pull/22197 @dongjoon-hyun Sorry for the late response, description is changed to: > Although filter "ID < 100L" is generated by Spark, it fails to pushdown into parquet actually, Spark still does the full table scan when reading. > This PR provides a case-insensitive field resolution to make it work. Let me know if you have any suggestion :). --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user cloud-fan commented on the issue: https://github.com/apache/spark/pull/22197 > Is it acceptable? apparently not... OK let's just check duplicated filed names twice: one in filter pushdown, one in column pruning. And clean it up in followup PRs. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user gatorsmile commented on the issue: https://github.com/apache/spark/pull/22197 ``` Caused by: org.apache.hadoop.hive.ql.metadata.HiveException: Duplicate column name c1 in the table definition. at org.apache.hadoop.hive.ql.metadata.Table.validateColumns(Table.java:952) at org.apache.hadoop.hive.ql.metadata.Table.checkValidity(Table.java:216) at org.apache.hadoop.hive.ql.metadata.Hive.alterTable(Hive.java:495) at org.apache.hadoop.hive.ql.metadata.Hive.alterTable(Hive.java:484) ... 88 more ``` --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user yucai commented on the issue: https://github.com/apache/spark/pull/22197 I will treat the above case as acceptable and will add a duplicated field check for the parquet schema. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user yucai commented on the issue: https://github.com/apache/spark/pull/22197 Both `catalystRequestedSchema` and `parquetSchema` are recursive structure, is there the easy way to find duplicated fields? Thanks! --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user yucai commented on the issue:
https://github.com/apache/spark/pull/22197
@cloud-fan I also think my way changes too much in this PR.
> go through the parquet schema and find duplicated field names
If user query only query non-duplicated field, this way also throws
Exception. Like below:
```
spark.range(10).selectExpr("id as a", "id as b", "id as
B").write.mode("overwrite").parquet("/tmp/data")
sql("select a from t").collect
```
Is it acceptable? Or use another PR to do the refactor first?
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Github user cloud-fan commented on the issue: https://github.com/apache/spark/pull/22197 I agree moving the `clipSchema` to the beginning is cleaner, but I feel it goes too far away from our original target: making parquet filter pushdown case insensitive when spark is case insensitive. I'd expect a very small change: at the beginning, go through the parquet schema and find duplicated field names. We can do the refactoring later, to make this PR small. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user dongjoon-hyun commented on the issue: https://github.com/apache/spark/pull/22197 Thanks. I got it. Definitely, it's irrelevant to this and an intentional regression due to that reverting. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user yucai commented on the issue:
https://github.com/apache/spark/pull/22197
@dongjoon-hyun In the **schema matched case** as you listed, it is expected
behavior in current master.
```
spark.sparkContext.hadoopConfiguration.setInt("parquet.block.size", 8 *
1024 * 1024)
spark.range(1, 40 * 1024 * 1024, 1,
1).sortWithinPartitions("id").write.mode("overwrite").parquet("/tmp/t")
sql("CREATE TABLE t (id LONG) USING parquet LOCATION '/tmp/t'")
// master and 2.3 have different plan for top limit (see below), that's why
28.4 MB are read in master
sql("select * from t where id < 100L").show()
```
This difference is probably introduced by #21573, @cloud-fan, current
master read more data than 2.3 for top limit like in
https://github.com/apache/spark/pull/22197#issuecomment-416085556 , is it a
regression or not?
Master:
2.3 branch:
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Github user dongjoon-hyun commented on the issue: https://github.com/apache/spark/pull/22197 @gatorsmile . I don't think so we have this regression on ORC data source. However, there was another JIRA report, [SPARK-25175](https://issues.apache.org/jira/browse/SPARK-25175) 5 days ago. There was not much details on it. So, I'm still monitoring that issue. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user dongjoon-hyun commented on the issue: https://github.com/apache/spark/pull/22197 @yucai . If you don't mind, could you update the PR description? This PR doesn't generate new filters here. This only changes `field resolution` logic. With and without this PR, there exists filters and there occurs filter push-downs. ``` - No filter will be pushdown. + Wrong filters will be pushed down. ``` --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user yucai commented on the issue: https://github.com/apache/spark/pull/22197 @gatorsmile I can help check `spark.sql.caseSensitive` for all the built-in data sources. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user gatorsmile commented on the issue: https://github.com/apache/spark/pull/22197 This PR is basically trying to resolve case sensitivity when the logical schema and physical schema do not match. This sounds like a general issue in all the data sources. Could any of you do us a favor? Check whether all the built-in data sources respect the conf `spark.sql.caseSensitive` in this case? --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user gatorsmile commented on the issue: https://github.com/apache/spark/pull/22197 @dongjoon-hyun Do you think we face the same issue in ORC? --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user yucai commented on the issue:
https://github.com/apache/spark/pull/22197
@cloud-fan @HyukjinKwon Seem cannot simply add `originalName` into
`ParquetSchemaType`.
Because we need exact ParquetSchemaType info for type match, like:
```
private val ParquetByteType = ParquetSchemaType(INT_8, INT32, 0, null)
private val ParquetShortType = ParquetSchemaType(INT_16, INT32, 0, null)
private val ParquetIntegerType = ParquetSchemaType(null, INT32, 0, null)
...
private val makeEq: = {
case ParquetByteType | ParquetShortType | ParquetIntegerType =>
```
I use a new case class `ParquetField` to reduce the map.
```
private case class ParquetField(
name: String,
schema: ParquetSchemaType)
```
Let me know if you are OK with this way.
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