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[GitHub] [spark] Udbhav30 commented on issue #25233: [SPARK-28391][SQL][PYTHON][TESTS] Convert and port 'pgSQL/select_implicit.sql' into UDF test base

Udbhav30 commented on issue #25233: [SPARK-28391][SQL][PYTHON][TESTS] Convert and port 'pgSQL/select_implicit.sql' into UDF test base URL: https://github.com/apache/spark/pull/25233#issuecomment-514062796 Hi @HyukjinKwon can you please review this thanks

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[GitHub] [spark] Udbhav30 opened a new pull request #25233: [SPARK-28391][SQL][PYTHON][TESTS] Convert and port 'pgSQL/select_implicit.sql' into UDF test base

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zhengruifeng edited a comment on issue #25178: [SPARK-28421][ML] SparseVector.apply performance optimization URL: https://github.com/apache/spark/pull/25178#issuecomment-514035619 I test the perf among current impl (`apply`) , direct binary-search (`apply2`), binary-seach with extra range

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zhengruifeng edited a comment on issue #25178: [SPARK-28421][ML] SparseVector.apply performance optimization URL: https://github.com/apache/spark/pull/25178#issuecomment-514041986 The expected cost without range check is `E(cost(apply2)) = log(NNZ)`; while the one with range check is

[GitHub] [spark] zhengruifeng edited a comment on issue #25178: [SPARK-28421][ML] SparseVector.apply performance optimization

zhengruifeng edited a comment on issue #25178: [SPARK-28421][ML] SparseVector.apply performance optimization URL: https://github.com/apache/spark/pull/25178#issuecomment-514041986 The expected cost without range check is `E(cost(apply2)) = log(NNZ)`; while the one with range check is

[GitHub] [spark] maropu commented on a change in pull request #25172: [SPARK-28412][SQL] ANSI SQL: OVERLAY function support byte array

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[GitHub] [spark] HeartSaVioR commented on issue #25135: [SPARK-28367][SS] Use new KafkaConsumer.poll API in Kafka connector

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[GitHub] [spark] SparkQA commented on issue #24958: [SPARK-28153][PYTHON] Use AtomicReference at InputFileBlockHolder (to support input_file_name with Python UDF)

SparkQA commented on issue #24958: [SPARK-28153][PYTHON] Use AtomicReference at InputFileBlockHolder (to support input_file_name with Python UDF) URL: https://github.com/apache/spark/pull/24958#issuecomment-514044796 **[Test build #108027 has

[GitHub] [spark] dongjoon-hyun closed pull request #25225: [SPARK-28469][SQL] Change CalendarIntervalType's readable string representation from calendarinterval to interval

dongjoon-hyun closed pull request #25225: [SPARK-28469][SQL] Change CalendarIntervalType's readable string representation from calendarinterval to interval URL: https://github.com/apache/spark/pull/25225 This is an

[GitHub] [spark] dongjoon-hyun commented on issue #25189: [SPARK-28435][SQL] Support cast StringType to IntervalType for SQL interface

dongjoon-hyun commented on issue #25189: [SPARK-28435][SQL] Support cast StringType to IntervalType for SQL interface URL: https://github.com/apache/spark/pull/25189#issuecomment-514044307 #25225 is merged now. Could you rebase this PR?

[GitHub] [spark] dongjoon-hyun commented on issue #25225: [SPARK-28469][SQL] Change CalendarIntervalType's readable string representation from calendarinterval to interval

dongjoon-hyun commented on issue #25225: [SPARK-28469][SQL] Change CalendarIntervalType's readable string representation from calendarinterval to interval URL: https://github.com/apache/spark/pull/25225#issuecomment-514043510 Yep. I think it's okay by itself. Thank you for the pointer.

[GitHub] [spark] SparkQA commented on issue #25161: [SPARK-28390][SQL][PYTHON][TESTS] Convert and port 'pgSQL/select_having.sql' into UDF test base

SparkQA commented on issue #25161: [SPARK-28390][SQL][PYTHON][TESTS] Convert and port 'pgSQL/select_having.sql' into UDF test base URL: https://github.com/apache/spark/pull/25161#issuecomment-514042578 **[Test build #108031 has

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[GitHub] [spark] AmplabJenkins commented on issue #25161: [SPARK-28390][SQL][PYTHON][TESTS] Convert and port 'pgSQL/select_having.sql' into UDF test base

AmplabJenkins commented on issue #25161: [SPARK-28390][SQL][PYTHON][TESTS] Convert and port 'pgSQL/select_having.sql' into UDF test base URL: https://github.com/apache/spark/pull/25161#issuecomment-514042253 Merged build finished. Test PASSed.

[GitHub] [spark] AmplabJenkins commented on issue #25161: [SPARK-28390][SQL][PYTHON][TESTS] Convert and port 'pgSQL/select_having.sql' into UDF test base

AmplabJenkins commented on issue #25161: [SPARK-28390][SQL][PYTHON][TESTS] Convert and port 'pgSQL/select_having.sql' into UDF test base URL: https://github.com/apache/spark/pull/25161#issuecomment-514042256 Test PASSed. Refer to this link for build results (access rights to CI server

[GitHub] [spark] zhengruifeng commented on issue #25178: [SPARK-28421][ML] SparseVector.apply performance optimization

zhengruifeng commented on issue #25178: [SPARK-28421][ML] SparseVector.apply performance optimization URL: https://github.com/apache/spark/pull/25178#issuecomment-514041986 The expected cost without range check is `E(cost(apply2)) = log(NNZ)`; while the one with range check is

[GitHub] [spark] shivusondur commented on a change in pull request #25161: [SPARK-28390][SQL][PYTHON][TESTS] Convert and port 'pgSQL/select_having.sql' into UDF test base

shivusondur commented on a change in pull request #25161: [SPARK-28390][SQL][PYTHON][TESTS] Convert and port 'pgSQL/select_having.sql' into UDF test base URL: https://github.com/apache/spark/pull/25161#discussion_r306117675 ## File path:

[GitHub] [spark] SparkQA commented on issue #25134: [SPARK-28366][CORE] Logging in driver when loading single large unsplittable file

SparkQA commented on issue #25134: [SPARK-28366][CORE] Logging in driver when loading single large unsplittable file URL: https://github.com/apache/spark/pull/25134#issuecomment-514041249 **[Test build #108030 has

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[GitHub] [spark] AmplabJenkins removed a comment on issue #25134: [SPARK-28366][CORE] Logging in driver when loading single large unsplittable file

AmplabJenkins removed a comment on issue #25134: [SPARK-28366][CORE] Logging in driver when loading single large unsplittable file URL: https://github.com/apache/spark/pull/25134#issuecomment-514040916 Merged build finished. Test PASSed.

[GitHub] [spark] AmplabJenkins commented on issue #25134: [SPARK-28366][CORE] Logging in driver when loading single large unsplittable file

AmplabJenkins commented on issue #25134: [SPARK-28366][CORE] Logging in driver when loading single large unsplittable file URL: https://github.com/apache/spark/pull/25134#issuecomment-514040916 Merged build finished. Test PASSed.

[GitHub] [spark] AmplabJenkins commented on issue #25134: [SPARK-28366][CORE] Logging in driver when loading single large unsplittable file

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[GitHub] [spark] maropu commented on a change in pull request #25172: [SPARK-28412][SQL] ANSI SQL: OVERLAY function support byte array

maropu commented on a change in pull request #25172: [SPARK-28412][SQL] ANSI SQL: OVERLAY function support byte array URL: https://github.com/apache/spark/pull/25172#discussion_r306114834 ## File path:

[GitHub] [spark] gczsjdy commented on a change in pull request #25007: [SPARK-28209][CORE][SHUFFLE] Proposed new shuffle writer API

gczsjdy commented on a change in pull request #25007: [SPARK-28209][CORE][SHUFFLE] Proposed new shuffle writer API URL: https://github.com/apache/spark/pull/25007#discussion_r306100833 ## File path: core/src/main/java/org/apache/spark/shuffle/sort/io/LocalDiskShuffleDataIO.java

[GitHub] [spark] gczsjdy commented on a change in pull request #25007: [SPARK-28209][CORE][SHUFFLE] Proposed new shuffle writer API

gczsjdy commented on a change in pull request #25007: [SPARK-28209][CORE][SHUFFLE] Proposed new shuffle writer API URL: https://github.com/apache/spark/pull/25007#discussion_r306109354 ## File path:

[GitHub] [spark] zhengruifeng commented on issue #25178: [SPARK-28421][ML] SparseVector.apply performance optimization

zhengruifeng commented on issue #25178: [SPARK-28421][ML] SparseVector.apply performance optimization URL: https://github.com/apache/spark/pull/25178#issuecomment-514035619 I test the perf among current impl (`apply`) , direct binary-search (`apply2`), binary-seach with extra range check

[GitHub] [spark] ulysses-you commented on a change in pull request #25198: [SPARK-28443][SQL] Spark sql add exception when create field type NullType

ulysses-you commented on a change in pull request #25198: [SPARK-28443][SQL] Spark sql add exception when create field type NullType URL: https://github.com/apache/spark/pull/25198#discussion_r306109902 ## File path:

[GitHub] [spark] beliefer commented on a change in pull request #25172: [SPARK-28412][SQL] ANSI SQL: OVERLAY function support byte array

beliefer commented on a change in pull request #25172: [SPARK-28412][SQL] ANSI SQL: OVERLAY function support byte array URL: https://github.com/apache/spark/pull/25172#discussion_r306106058 ## File path:

[GitHub] [spark] SparkQA commented on issue #25134: [SPARK-28366][CORE] Logging in driver when loading single large unsplittable file

SparkQA commented on issue #25134: [SPARK-28366][CORE] Logging in driver when loading single large unsplittable file URL: https://github.com/apache/spark/pull/25134#issuecomment-514028148 **[Test build #108029 has

[GitHub] [spark] AmplabJenkins removed a comment on issue #25134: [SPARK-28366][CORE] Logging in driver when loading single large unsplittable file

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[GitHub] [spark] AmplabJenkins commented on issue #25134: [SPARK-28366][CORE] Logging in driver when loading single large unsplittable file

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