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Posted to reviews@spark.apache.org by "yaooqinn (via GitHub)" <gi...@apache.org> on 2024/03/21 07:44:38 UTC
[PR] [SPARK-47462][SQL] Add migration guide for integral type mapping changes [spark]
yaooqinn opened a new pull request, #45633:
URL: https://github.com/apache/spark/pull/45633
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Add migration guide for integral type mapping changes
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behavior change doc
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Re: [PR] [SPARK-47462][SQL][FOLLOWUP] Add migration guide for integral type mapping changes [spark]
Posted by "yaooqinn (via GitHub)" <gi...@apache.org>.
yaooqinn closed pull request #45633: [SPARK-47462][SQL][FOLLOWUP] Add migration guide for integral type mapping changes
URL: https://github.com/apache/spark/pull/45633
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Re: [PR] [SPARK-47462][SQL][FOLLOWUP] Add migration guide for integral type mapping changes [spark]
Posted by "yaooqinn (via GitHub)" <gi...@apache.org>.
yaooqinn commented on code in PR #45633:
URL: https://github.com/apache/spark/pull/45633#discussion_r1535036263
##########
docs/sql-migration-guide.md:
##########
@@ -42,6 +42,15 @@ license: |
- Since Spark 4.0, the function `to_csv` no longer supports input with the data type `STRUCT`, `ARRAY`, `MAP`, `VARIANT` and `BINARY` (because the `CSV specification` does not have standards for these data types and cannot be read back using `from_csv`), Spark will throw `DATATYPE_MISMATCH.UNSUPPORTED_INPUT_TYPE` exception.
- Since Spark 4.0, JDBC read option `preferTimestampNTZ=true` will not convert Postgres TIMESTAMP WITH TIME ZONE and TIME WITH TIME ZONE data types to TimestampNTZType, which is available in Spark 3.5.
- Since Spark 4.0, JDBC read option `preferTimestampNTZ=true` will not convert MySQL TIMESTAMP to TimestampNTZType, which is available in Spark 3.5. MySQL DATETIME is not affected.
+- Since Spark 4.0, MySQL JDBC datasource will read SMALLINT as ShortType, while in Spark 3.5 and previous, it was read as IntegerType. MEDIUMINT UNSIGNED is read as IntegerType, while in Spark 3.5 and previous, it was read as LongType. To restore the previous behavior, you can cast the column to the old type.
+
+## Upgrading from Spark SQL 3.5.1 to 3.5.2
+
+- Since 3.5.2, MySQL JDBC datasource will read TINYINT UNSIGNED as ShortType, while in 3.5.0 and 3.5.1, it was wrongly read as ByteType.
Review Comment:
SPARK-47435
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Re: [PR] [SPARK-47462][SQL][FOLLOWUP] Add migration guide for integral type mapping changes [spark]
Posted by "dongjoon-hyun (via GitHub)" <gi...@apache.org>.
dongjoon-hyun commented on code in PR #45633:
URL: https://github.com/apache/spark/pull/45633#discussion_r1534952470
##########
docs/sql-migration-guide.md:
##########
@@ -42,6 +42,7 @@ license: |
- Since Spark 4.0, the function `to_csv` no longer supports input with the data type `STRUCT`, `ARRAY`, `MAP`, `VARIANT` and `BINARY` (because the `CSV specification` does not have standards for these data types and cannot be read back using `from_csv`), Spark will throw `DATATYPE_MISMATCH.UNSUPPORTED_INPUT_TYPE` exception.
- Since Spark 4.0, JDBC read option `preferTimestampNTZ=true` will not convert Postgres TIMESTAMP WITH TIME ZONE and TIME WITH TIME ZONE data types to TimestampNTZType, which is available in Spark 3.5.
- Since Spark 4.0, JDBC read option `preferTimestampNTZ=true` will not convert MySQL TIMESTAMP to TimestampNTZType, which is available in Spark 3.5. MySQL DATETIME is not affected.
+- Since Spark 4.0, MySQL JDBC datasource will read SMALLINT as ShortType, while in Spark 3.5, it was read as IntegerType. MEDIUMINT UNSIGNED is read as IntegerType, while in Spark 3.5, it was read as LongType. To restore the previous behavior, you can cast the column to the old type.
Review Comment:
BTW, this sounds like infeasible in the production environment with thousands of jobs. WDYT?
> To restore the previous behavior, you can cast the column to the old type.
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Re: [PR] [SPARK-47462][SQL][FOLLOWUP] Add migration guide for integral type mapping changes [spark]
Posted by "dongjoon-hyun (via GitHub)" <gi...@apache.org>.
dongjoon-hyun commented on code in PR #45633:
URL: https://github.com/apache/spark/pull/45633#discussion_r1535018749
##########
docs/sql-migration-guide.md:
##########
@@ -52,6 +53,7 @@ license: |
- Since Spark 3.5, `spark.sql.optimizer.canChangeCachedPlanOutputPartitioning` is enabled by default. To restore the previous behavior, set `spark.sql.optimizer.canChangeCachedPlanOutputPartitioning` to `false`.
- Since Spark 3.5, the `array_insert` function is 1-based for negative indexes. It inserts new element at the end of input arrays for the index -1. To restore the previous behavior, set `spark.sql.legacy.negativeIndexInArrayInsert` to `true`.
- Since Spark 3.5, the Avro will throw `AnalysisException` when reading Interval types as Date or Timestamp types, or reading Decimal types with lower precision. To restore the legacy behavior, set `spark.sql.legacy.avro.allowIncompatibleSchema` to `true`
+- Since Spark 3.5, MySQL JDBC datasource will read TINYINT(n > 1) as ByteType, TINYINT UNSIGNED is read as ShortType, while in Spark 3.4 and below, they were read as IntegerType. To restore the previous behavior, you can cast the column to the old type. Note that for 3.5.0 and 3.5.1, TINYINT UNSIGNED is wrongly read as ByteType, and it is fixed in 3.5.2.
Review Comment:
Sorry, I'm re-reading this. I'm wondering if this could be misleading to the general users because we have 3 state.
- ~ 3.4
- 3.5.0 and 3.5.1
- 3.5.2
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Re: [PR] [SPARK-47462][SQL][FOLLOWUP] Add migration guide for integral type mapping changes [spark]
Posted by "yaooqinn (via GitHub)" <gi...@apache.org>.
yaooqinn commented on PR #45633:
URL: https://github.com/apache/spark/pull/45633#issuecomment-2014176937
cc @dongjoon-hyun @cloud-fan thanks
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Re: [PR] [SPARK-47462][SQL][FOLLOWUP] Add migration guide for integral type mapping changes [spark]
Posted by "yaooqinn (via GitHub)" <gi...@apache.org>.
yaooqinn commented on code in PR #45633:
URL: https://github.com/apache/spark/pull/45633#discussion_r1534955669
##########
docs/sql-migration-guide.md:
##########
@@ -42,6 +42,7 @@ license: |
- Since Spark 4.0, the function `to_csv` no longer supports input with the data type `STRUCT`, `ARRAY`, `MAP`, `VARIANT` and `BINARY` (because the `CSV specification` does not have standards for these data types and cannot be read back using `from_csv`), Spark will throw `DATATYPE_MISMATCH.UNSUPPORTED_INPUT_TYPE` exception.
- Since Spark 4.0, JDBC read option `preferTimestampNTZ=true` will not convert Postgres TIMESTAMP WITH TIME ZONE and TIME WITH TIME ZONE data types to TimestampNTZType, which is available in Spark 3.5.
- Since Spark 4.0, JDBC read option `preferTimestampNTZ=true` will not convert MySQL TIMESTAMP to TimestampNTZType, which is available in Spark 3.5. MySQL DATETIME is not affected.
+- Since Spark 4.0, MySQL JDBC datasource will read SMALLINT as ShortType, while in Spark 3.5, it was read as IntegerType. MEDIUMINT UNSIGNED is read as IntegerType, while in Spark 3.5, it was read as LongType. To restore the previous behavior, you can cast the column to the old type.
Review Comment:
> while in Spark 3.5? What is the behavior of Spark 3.4 and older?
let me revise the expression, 3.5 and previous
> BTW, this sounds like infeasible in the production environment with thousands of jobs. WDYT?
This is the best effort users can do currently.
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Re: [PR] [SPARK-47462][SQL][FOLLOWUP] Add migration guide for integral type mapping changes [spark]
Posted by "yaooqinn (via GitHub)" <gi...@apache.org>.
yaooqinn commented on code in PR #45633:
URL: https://github.com/apache/spark/pull/45633#discussion_r1535035661
##########
docs/sql-migration-guide.md:
##########
@@ -42,6 +42,15 @@ license: |
- Since Spark 4.0, the function `to_csv` no longer supports input with the data type `STRUCT`, `ARRAY`, `MAP`, `VARIANT` and `BINARY` (because the `CSV specification` does not have standards for these data types and cannot be read back using `from_csv`), Spark will throw `DATATYPE_MISMATCH.UNSUPPORTED_INPUT_TYPE` exception.
- Since Spark 4.0, JDBC read option `preferTimestampNTZ=true` will not convert Postgres TIMESTAMP WITH TIME ZONE and TIME WITH TIME ZONE data types to TimestampNTZType, which is available in Spark 3.5.
- Since Spark 4.0, JDBC read option `preferTimestampNTZ=true` will not convert MySQL TIMESTAMP to TimestampNTZType, which is available in Spark 3.5. MySQL DATETIME is not affected.
+- Since Spark 4.0, MySQL JDBC datasource will read SMALLINT as ShortType, while in Spark 3.5 and previous, it was read as IntegerType. MEDIUMINT UNSIGNED is read as IntegerType, while in Spark 3.5 and previous, it was read as LongType. To restore the previous behavior, you can cast the column to the old type.
+
+## Upgrading from Spark SQL 3.5.1 to 3.5.2
+
+- Since 3.5.2, MySQL JDBC datasource will read TINYINT UNSIGNED as ShortType, while in 3.5.0 and 3.5.1, it was wrongly read as ByteType.
+
+## Upgrading from Spark SQL 3.5.0 to 3.5.1
+
+- Since Spark 3.5.1, MySQL JDBC datasource will read TINYINT(n > 1) and TINYINT UNSIGNED as ByteType, while in Spark 3.5.0 and below, they were read as IntegerType. To restore the previous behavior, you can cast the column to the old type.
Review Comment:
https://issues.apache.org/jira/browse/SPARK-45561
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Re: [PR] [SPARK-47462][SQL][FOLLOWUP] Add migration guide for integral type mapping changes [spark]
Posted by "yaooqinn (via GitHub)" <gi...@apache.org>.
yaooqinn commented on code in PR #45633:
URL: https://github.com/apache/spark/pull/45633#discussion_r1535036805
##########
docs/sql-migration-guide.md:
##########
@@ -52,6 +53,7 @@ license: |
- Since Spark 3.5, `spark.sql.optimizer.canChangeCachedPlanOutputPartitioning` is enabled by default. To restore the previous behavior, set `spark.sql.optimizer.canChangeCachedPlanOutputPartitioning` to `false`.
- Since Spark 3.5, the `array_insert` function is 1-based for negative indexes. It inserts new element at the end of input arrays for the index -1. To restore the previous behavior, set `spark.sql.legacy.negativeIndexInArrayInsert` to `true`.
- Since Spark 3.5, the Avro will throw `AnalysisException` when reading Interval types as Date or Timestamp types, or reading Decimal types with lower precision. To restore the legacy behavior, set `spark.sql.legacy.avro.allowIncompatibleSchema` to `true`
+- Since Spark 3.5, MySQL JDBC datasource will read TINYINT(n > 1) as ByteType, TINYINT UNSIGNED is read as ShortType, while in Spark 3.4 and below, they were read as IntegerType. To restore the previous behavior, you can cast the column to the old type. Note that for 3.5.0 and 3.5.1, TINYINT UNSIGNED is wrongly read as ByteType, and it is fixed in 3.5.2.
Review Comment:
Thank you @dongjoon-hyun
Rebased the change logs according to the timeline
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Re: [PR] [SPARK-47462][SQL][FOLLOWUP] Add migration guide for integral type mapping changes [spark]
Posted by "dongjoon-hyun (via GitHub)" <gi...@apache.org>.
dongjoon-hyun commented on code in PR #45633:
URL: https://github.com/apache/spark/pull/45633#discussion_r1534951052
##########
docs/sql-migration-guide.md:
##########
@@ -52,6 +53,7 @@ license: |
- Since Spark 3.5, `spark.sql.optimizer.canChangeCachedPlanOutputPartitioning` is enabled by default. To restore the previous behavior, set `spark.sql.optimizer.canChangeCachedPlanOutputPartitioning` to `false`.
- Since Spark 3.5, the `array_insert` function is 1-based for negative indexes. It inserts new element at the end of input arrays for the index -1. To restore the previous behavior, set `spark.sql.legacy.negativeIndexInArrayInsert` to `true`.
- Since Spark 3.5, the Avro will throw `AnalysisException` when reading Interval types as Date or Timestamp types, or reading Decimal types with lower precision. To restore the legacy behavior, set `spark.sql.legacy.avro.allowIncompatibleSchema` to `true`
+- Since Spark 3.5, MySQL JDBC datasource will read TINYINT(n > 1) as ByteType, TINYINT UNSIGNED is read as ShortType, while in Spark 3.4, they were read as IntegerType. To restore the previous behavior, you can cast the column to the old type. Note that for 3.5.0 and 3.5.1, TINYINT UNSIGNED is wrongly read as ByteType, and it is fixed in 3.5.2.
Review Comment:
Thanks!
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Re: [PR] [SPARK-47462][SQL][FOLLOWUP] Add migration guide for integral type mapping changes [spark]
Posted by "dongjoon-hyun (via GitHub)" <gi...@apache.org>.
dongjoon-hyun commented on code in PR #45633:
URL: https://github.com/apache/spark/pull/45633#discussion_r1534950687
##########
docs/sql-migration-guide.md:
##########
@@ -42,6 +42,7 @@ license: |
- Since Spark 4.0, the function `to_csv` no longer supports input with the data type `STRUCT`, `ARRAY`, `MAP`, `VARIANT` and `BINARY` (because the `CSV specification` does not have standards for these data types and cannot be read back using `from_csv`), Spark will throw `DATATYPE_MISMATCH.UNSUPPORTED_INPUT_TYPE` exception.
- Since Spark 4.0, JDBC read option `preferTimestampNTZ=true` will not convert Postgres TIMESTAMP WITH TIME ZONE and TIME WITH TIME ZONE data types to TimestampNTZType, which is available in Spark 3.5.
- Since Spark 4.0, JDBC read option `preferTimestampNTZ=true` will not convert MySQL TIMESTAMP to TimestampNTZType, which is available in Spark 3.5. MySQL DATETIME is not affected.
+- Since Spark 4.0, MySQL JDBC datasource will read SMALLINT as ShortType, while in Spark 3.5, it was read as IntegerType. MEDIUMINT UNSIGNED is read as IntegerType, while in Spark 3.5, it was read as LongType. To restore the previous behavior, you can cast the column to the old type.
Review Comment:
`while in Spark 3.5`? What is the behavior of Spark 3.4 and older?
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Re: [PR] [SPARK-47462][SQL][FOLLOWUP] Add migration guide for integral type mapping changes [spark]
Posted by "yaooqinn (via GitHub)" <gi...@apache.org>.
yaooqinn commented on PR #45633:
URL: https://github.com/apache/spark/pull/45633#issuecomment-2014646702
Merged to master. Thank you @dongjoon-hyun
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Re: [PR] [SPARK-47462][SQL][FOLLOWUP] Add migration guide for integral type mapping changes [spark]
Posted by "yaooqinn (via GitHub)" <gi...@apache.org>.
yaooqinn commented on code in PR #45633:
URL: https://github.com/apache/spark/pull/45633#discussion_r1535000656
##########
docs/sql-migration-guide.md:
##########
@@ -52,6 +53,7 @@ license: |
- Since Spark 3.5, `spark.sql.optimizer.canChangeCachedPlanOutputPartitioning` is enabled by default. To restore the previous behavior, set `spark.sql.optimizer.canChangeCachedPlanOutputPartitioning` to `false`.
- Since Spark 3.5, the `array_insert` function is 1-based for negative indexes. It inserts new element at the end of input arrays for the index -1. To restore the previous behavior, set `spark.sql.legacy.negativeIndexInArrayInsert` to `true`.
- Since Spark 3.5, the Avro will throw `AnalysisException` when reading Interval types as Date or Timestamp types, or reading Decimal types with lower precision. To restore the legacy behavior, set `spark.sql.legacy.avro.allowIncompatibleSchema` to `true`
+- Since Spark 3.5, MySQL JDBC datasource will read TINYINT(n > 1) as ByteType, TINYINT UNSIGNED is read as ShortType, while in Spark 3.4, they were read as IntegerType. To restore the previous behavior, you can cast the column to the old type. Note that for 3.5.0 and 3.5.1, TINYINT UNSIGNED is wrongly read as ByteType, and it is fixed in 3.5.2.
Review Comment:
```suggestion
- Since Spark 3.5, MySQL JDBC datasource will read TINYINT(n > 1) as ByteType, TINYINT UNSIGNED is read as ShortType, while in Spark 3.4 and below, they were read as IntegerType. To restore the previous behavior, you can cast the column to the old type. Note that for 3.5.0 and 3.5.1, TINYINT UNSIGNED is wrongly read as ByteType, and it is fixed in 3.5.2.
```
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Re: [PR] [SPARK-47462][SQL][FOLLOWUP] Add migration guide for integral type mapping changes [spark]
Posted by "yaooqinn (via GitHub)" <gi...@apache.org>.
yaooqinn commented on code in PR #45633:
URL: https://github.com/apache/spark/pull/45633#discussion_r1535092704
##########
docs/sql-migration-guide.md:
##########
@@ -42,6 +42,15 @@ license: |
- Since Spark 4.0, the function `to_csv` no longer supports input with the data type `STRUCT`, `ARRAY`, `MAP`, `VARIANT` and `BINARY` (because the `CSV specification` does not have standards for these data types and cannot be read back using `from_csv`), Spark will throw `DATATYPE_MISMATCH.UNSUPPORTED_INPUT_TYPE` exception.
- Since Spark 4.0, JDBC read option `preferTimestampNTZ=true` will not convert Postgres TIMESTAMP WITH TIME ZONE and TIME WITH TIME ZONE data types to TimestampNTZType, which is available in Spark 3.5.
- Since Spark 4.0, JDBC read option `preferTimestampNTZ=true` will not convert MySQL TIMESTAMP to TimestampNTZType, which is available in Spark 3.5. MySQL DATETIME is not affected.
+- Since Spark 4.0, MySQL JDBC datasource will read SMALLINT as ShortType, while in Spark 3.5 and previous, it was read as IntegerType. MEDIUMINT UNSIGNED is read as IntegerType, while in Spark 3.5 and previous, it was read as LongType. To restore the previous behavior, you can cast the column to the old type.
+
+## Upgrading from Spark SQL 3.5.1 to 3.5.2
+
+- Since 3.5.2, MySQL JDBC datasource will read TINYINT UNSIGNED as ShortType, while in 3.5.0 and 3.5.1, it was wrongly read as ByteType.
Review Comment:
```suggestion
- Since 3.5.2, MySQL JDBC datasource will read TINYINT UNSIGNED as ShortType, while in 3.5.1, it was wrongly read as ByteType.
```
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Re: [PR] [SPARK-47462][SQL][FOLLOWUP] Add migration guide for integral type mapping changes [spark]
Posted by "dongjoon-hyun (via GitHub)" <gi...@apache.org>.
dongjoon-hyun commented on code in PR #45633:
URL: https://github.com/apache/spark/pull/45633#discussion_r1535017337
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docs/sql-migration-guide.md:
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@@ -52,6 +53,7 @@ license: |
- Since Spark 3.5, `spark.sql.optimizer.canChangeCachedPlanOutputPartitioning` is enabled by default. To restore the previous behavior, set `spark.sql.optimizer.canChangeCachedPlanOutputPartitioning` to `false`.
- Since Spark 3.5, the `array_insert` function is 1-based for negative indexes. It inserts new element at the end of input arrays for the index -1. To restore the previous behavior, set `spark.sql.legacy.negativeIndexInArrayInsert` to `true`.
- Since Spark 3.5, the Avro will throw `AnalysisException` when reading Interval types as Date or Timestamp types, or reading Decimal types with lower precision. To restore the legacy behavior, set `spark.sql.legacy.avro.allowIncompatibleSchema` to `true`
+- Since Spark 3.5, MySQL JDBC datasource will read TINYINT(n > 1) as ByteType, TINYINT UNSIGNED is read as ShortType, while in Spark 3.4 and below, they were read as IntegerType. To restore the previous behavior, you can cast the column to the old type. Note that for 3.5.0 and 3.5.1, TINYINT UNSIGNED is wrongly read as ByteType, and it is fixed in 3.5.2.
Review Comment:
`Since Spark 3.5` -> `Since Spark 3.5.2`?
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