[GitHub] [spark] AmplabJenkins removed a comment on issue #26265: [SPARK-29565][ML][PYTHON] OneHotEncoder should support single-column input/output

2019-10-28 Thread GitBox
AmplabJenkins removed a comment on issue #26265: [SPARK-29565][ML][PYTHON] 
OneHotEncoder should support single-column input/output
URL: https://github.com/apache/spark/pull/26265#issuecomment-547269282
 
 
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[GitHub] [spark] AmplabJenkins removed a comment on issue #26265: [SPARK-29565][ML][PYTHON] OneHotEncoder should support single-column input/output

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AmplabJenkins removed a comment on issue #26265: [SPARK-29565][ML][PYTHON] 
OneHotEncoder should support single-column input/output
URL: https://github.com/apache/spark/pull/26265#issuecomment-547269273
 
 
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[GitHub] [spark] AmplabJenkins commented on issue #26265: [SPARK-29565][ML][PYTHON] OneHotEncoder should support single-column input/output

2019-10-28 Thread GitBox
AmplabJenkins commented on issue #26265: [SPARK-29565][ML][PYTHON] 
OneHotEncoder should support single-column input/output
URL: https://github.com/apache/spark/pull/26265#issuecomment-547269282
 
 
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[GitHub] [spark] AmplabJenkins commented on issue #26265: [SPARK-29565][ML][PYTHON] OneHotEncoder should support single-column input/output

2019-10-28 Thread GitBox
AmplabJenkins commented on issue #26265: [SPARK-29565][ML][PYTHON] 
OneHotEncoder should support single-column input/output
URL: https://github.com/apache/spark/pull/26265#issuecomment-547269273
 
 
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[GitHub] [spark] SparkQA removed a comment on issue #26265: [SPARK-29565][ML][PYTHON] OneHotEncoder should support single-column input/output

2019-10-28 Thread GitBox
SparkQA removed a comment on issue #26265: [SPARK-29565][ML][PYTHON] 
OneHotEncoder should support single-column input/output
URL: https://github.com/apache/spark/pull/26265#issuecomment-547254754
 
 
   **[Test build #112821 has 
started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/112821/testReport)**
 for PR 26265 at commit 
[`9d5d6ff`](https://github.com/apache/spark/commit/9d5d6ff3c5e8794ba14f2af2b274ed166d8350d2).


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[GitHub] [spark] SparkQA commented on issue #26265: [SPARK-29565][ML][PYTHON] OneHotEncoder should support single-column input/output

2019-10-28 Thread GitBox
SparkQA commented on issue #26265: [SPARK-29565][ML][PYTHON] OneHotEncoder 
should support single-column input/output
URL: https://github.com/apache/spark/pull/26265#issuecomment-547269089
 
 
   **[Test build #112821 has 
finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/112821/testReport)**
 for PR 26265 at commit 
[`9d5d6ff`](https://github.com/apache/spark/commit/9d5d6ff3c5e8794ba14f2af2b274ed166d8350d2).
* This patch passes all tests.
* This patch merges cleanly.
* This patch adds no public classes.


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[GitHub] [spark] maropu commented on a change in pull request #26290: [WIP][SPARK-29120][SQL][TESTS] Port create_view.sql

2019-10-28 Thread GitBox
maropu commented on a change in pull request #26290: 
[WIP][SPARK-29120][SQL][TESTS] Port create_view.sql
URL: https://github.com/apache/spark/pull/26290#discussion_r339903249
 
 

 ##
 File path: 
sql/core/src/test/resources/sql-tests/inputs/postgreSQL/create_view.sql
 ##
 @@ -0,0 +1,793 @@
+-- Portions Copyright (c) 1996-2019, PostgreSQL Global Development Group
+--
+-- CREATE VIEW
+-- 
https://github.com/postgres/postgres/blob/REL_12_STABLE/src/test/regress/sql/create_view.sql
+
+-- Spark doesn't support geometric types
+-- CREATE VIEW street AS
+--SELECT r.name, r.thepath, c.cname AS cname
+--FROM ONLY road r, real_city c
+--WHERE c.outline ## r.thepath;
+
+-- Spark doesn't support geometric types
+-- CREATE VIEW iexit AS
+--SELECT ih.name, ih.thepath,
+-- interpt_pp(ih.thepath, r.thepath) AS exit
+--FROM ihighway ih, ramp r
+--WHERE ih.thepath ## r.thepath;
+
+CREATE TABLE emp (
+  name string,
+  age int,
+  -- Spark doesn't support a geometric type `point`
+  -- location point
+  salary int,
+  manager string
+) USING parquet;
+
+CREATE VIEW toyemp AS
+   SELECT name, age, /* location ,*/ 12*salary AS annualsal
+   FROM emp;
+
+-- Spark doesn't support the COMMENT clause that is not defined in the SQL 
standard
+-- Test comments
+-- COMMENT ON VIEW noview IS 'no view';
+-- COMMENT ON VIEW toyemp IS 'is a view';
+-- COMMENT ON VIEW toyemp IS NULL;
+
+DROP VIEW toyemp;
+
+-- These views are left around mainly to exercise special cases in pg_dump.
+
+-- [SPARK-19842] Informational Referential Integrity Constraints Support in 
Spark
+-- CREATE TABLE view_base_table (key int PRIMARY KEY, data varchar(20));
+--
+-- CREATE VIEW key_dependent_view AS
+--SELECT * FROM view_base_table GROUP BY key;
+--
+-- ALTER TABLE view_base_table DROP CONSTRAINT view_base_table_pkey;  -- fails
+
+-- CREATE VIEW key_dependent_view_no_cols AS
+--SELECT FROM view_base_table GROUP BY key HAVING length(data) > 0;
+
+--
+-- CREATE OR REPLACE VIEW
+--
+
+CREATE TABLE viewtest_tbl (a int, b int) using parquet;
+-- [SPARK-29386] Copy data between a file and a table
+-- COPY viewtest_tbl FROM stdin;
+-- 5   10
+-- 10  15
+-- 15  20
+-- 20  25
+-- \.
+INSERT INTO viewtest_tbl VALUES (5, 10), (10, 15), (15, 20), (20, 25);
+
+CREATE OR REPLACE VIEW viewtest AS
+   SELECT * FROM viewtest_tbl;
+
+CREATE OR REPLACE VIEW viewtest AS
+   SELECT * FROM viewtest_tbl WHERE a > 10;
+
+SELECT * FROM viewtest;
+
+CREATE OR REPLACE VIEW viewtest AS
+   SELECT a, b FROM viewtest_tbl WHERE a > 5 ORDER BY b DESC;
+
+SELECT * FROM viewtest;
+
+-- should fail
+-- Spark can accept the DDL query below
+CREATE OR REPLACE VIEW viewtest AS
+   SELECT a FROM viewtest_tbl WHERE a <> 20;
+
+-- should fail
+-- Spark can accept the DDL query below
+CREATE OR REPLACE VIEW viewtest AS
+   SELECT 1, * FROM viewtest_tbl;
+
+-- should fail
+-- Spark can accept the DDL query below
+CREATE OR REPLACE VIEW viewtest AS
+   SELECT a, decimal(b) FROM viewtest_tbl;
+
+-- should work
+CREATE OR REPLACE VIEW viewtest AS
+   SELECT a, b, 0 AS c FROM viewtest_tbl;
+
+DROP VIEW viewtest;
+DROP TABLE viewtest_tbl;
+
+-- tests for temporary views
+
+-- Spark doesn't support the cascaded syntax below in `CREATE SCHEMA`
+-- CREATE SCHEMA temp_view_test
+-- CREATE TABLE base_table (a int, id int) using parquet
+-- CREATE TABLE base_table2 (a int, id int) using parquet;
+CREATE SCHEMA temp_view_test;
+CREATE TABLE temp_view_test.base_table (a int, id int) using parquet;
+CREATE TABLE temp_view_test.base_table2 (a int, id int) using parquet;
+
+-- Replace SET with USE
+-- SET search_path TO temp_view_test, public;
+USE temp_view_test;
+
+-- Since Spark doesn't support CREATE TEMPORARY TABLE, we used CREATE 
TEMPORARY VIEW instead
+-- CREATE TEMPORARY TABLE temp_table (a int, id int);
+CREATE TEMPORARY VIEW temp_table AS SELECT * FROM VALUES
+  (1, 1) as temp_table(a, id);
+
+-- should be created in temp_view_test schema
+CREATE VIEW v1 AS SELECT * FROM base_table;
+DESC TABLE EXTENDED v1;
+-- should be created in temp object schema
+-- [SPARK-X] Forcibly create a temporary view in CREATE VIEW if 
referencing a temporary view
+CREATE VIEW v1_temp AS SELECT * FROM temp_table;
+-- should be created in temp object schema
+CREATE TEMPORARY VIEW v2_temp AS SELECT * FROM base_table;
+DESC TABLE EXTENDED v2_temp;
+-- should be created in temp_views schema
+CREATE VIEW temp_view_test.v2 AS SELECT * FROM base_table;
+DESC TABLE EXTENDED temp_view_test.v2;
+-- should fail
+-- [SPARK-X] Forcibly create a temporary view in CREATE VIEW if 
referencing a temporary view
+CREATE VIEW temp_view_test.v3_temp AS SELECT * FROM temp_table;
+-- should fail
+-- Spark doesn't support the cascaded syntax below in `CREATE SCHEMA`
+-- CREATE SCHEMA test_view_schema
+-- CREATE TEMP VIEW testview AS SELECT 1;
+
+-- joins: if any of the join relations are temporary, the view
+-- should also be temporary
+
+-- should 

[GitHub] [spark] cloud-fan closed pull request #26269: [SPARK-29612][SQL] ALTER TABLE (RECOVER PARTITIONS) should look up catalog/table like v2 commands

2019-10-28 Thread GitBox
cloud-fan closed pull request #26269: [SPARK-29612][SQL] ALTER TABLE (RECOVER 
PARTITIONS) should look up catalog/table like v2 commands
URL: https://github.com/apache/spark/pull/26269
 
 
   


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[GitHub] [spark] maropu commented on a change in pull request #26290: [WIP][SPARK-29120][SQL][TESTS] Port create_view.sql

2019-10-28 Thread GitBox
maropu commented on a change in pull request #26290: 
[WIP][SPARK-29120][SQL][TESTS] Port create_view.sql
URL: https://github.com/apache/spark/pull/26290#discussion_r339903229
 
 

 ##
 File path: 
sql/core/src/test/resources/sql-tests/inputs/postgreSQL/create_view.sql
 ##
 @@ -0,0 +1,793 @@
+-- Portions Copyright (c) 1996-2019, PostgreSQL Global Development Group
+--
+-- CREATE VIEW
+-- 
https://github.com/postgres/postgres/blob/REL_12_STABLE/src/test/regress/sql/create_view.sql
+
+-- Spark doesn't support geometric types
+-- CREATE VIEW street AS
+--SELECT r.name, r.thepath, c.cname AS cname
+--FROM ONLY road r, real_city c
+--WHERE c.outline ## r.thepath;
+
+-- Spark doesn't support geometric types
+-- CREATE VIEW iexit AS
+--SELECT ih.name, ih.thepath,
+-- interpt_pp(ih.thepath, r.thepath) AS exit
+--FROM ihighway ih, ramp r
+--WHERE ih.thepath ## r.thepath;
+
+CREATE TABLE emp (
+  name string,
+  age int,
+  -- Spark doesn't support a geometric type `point`
+  -- location point
+  salary int,
+  manager string
+) USING parquet;
+
+CREATE VIEW toyemp AS
+   SELECT name, age, /* location ,*/ 12*salary AS annualsal
+   FROM emp;
+
+-- Spark doesn't support the COMMENT clause that is not defined in the SQL 
standard
+-- Test comments
+-- COMMENT ON VIEW noview IS 'no view';
+-- COMMENT ON VIEW toyemp IS 'is a view';
+-- COMMENT ON VIEW toyemp IS NULL;
+
+DROP VIEW toyemp;
+
+-- These views are left around mainly to exercise special cases in pg_dump.
+
+-- [SPARK-19842] Informational Referential Integrity Constraints Support in 
Spark
+-- CREATE TABLE view_base_table (key int PRIMARY KEY, data varchar(20));
+--
+-- CREATE VIEW key_dependent_view AS
+--SELECT * FROM view_base_table GROUP BY key;
+--
+-- ALTER TABLE view_base_table DROP CONSTRAINT view_base_table_pkey;  -- fails
+
+-- CREATE VIEW key_dependent_view_no_cols AS
+--SELECT FROM view_base_table GROUP BY key HAVING length(data) > 0;
+
+--
+-- CREATE OR REPLACE VIEW
+--
+
+CREATE TABLE viewtest_tbl (a int, b int) using parquet;
+-- [SPARK-29386] Copy data between a file and a table
+-- COPY viewtest_tbl FROM stdin;
+-- 5   10
+-- 10  15
+-- 15  20
+-- 20  25
+-- \.
+INSERT INTO viewtest_tbl VALUES (5, 10), (10, 15), (15, 20), (20, 25);
+
+CREATE OR REPLACE VIEW viewtest AS
+   SELECT * FROM viewtest_tbl;
+
+CREATE OR REPLACE VIEW viewtest AS
+   SELECT * FROM viewtest_tbl WHERE a > 10;
+
+SELECT * FROM viewtest;
+
+CREATE OR REPLACE VIEW viewtest AS
+   SELECT a, b FROM viewtest_tbl WHERE a > 5 ORDER BY b DESC;
+
+SELECT * FROM viewtest;
+
+-- should fail
+-- Spark can accept the DDL query below
+CREATE OR REPLACE VIEW viewtest AS
+   SELECT a FROM viewtest_tbl WHERE a <> 20;
+
+-- should fail
+-- Spark can accept the DDL query below
+CREATE OR REPLACE VIEW viewtest AS
+   SELECT 1, * FROM viewtest_tbl;
+
+-- should fail
+-- Spark can accept the DDL query below
+CREATE OR REPLACE VIEW viewtest AS
+   SELECT a, decimal(b) FROM viewtest_tbl;
+
+-- should work
+CREATE OR REPLACE VIEW viewtest AS
+   SELECT a, b, 0 AS c FROM viewtest_tbl;
+
+DROP VIEW viewtest;
+DROP TABLE viewtest_tbl;
+
+-- tests for temporary views
+
+-- Spark doesn't support the cascaded syntax below in `CREATE SCHEMA`
+-- CREATE SCHEMA temp_view_test
+-- CREATE TABLE base_table (a int, id int) using parquet
+-- CREATE TABLE base_table2 (a int, id int) using parquet;
+CREATE SCHEMA temp_view_test;
+CREATE TABLE temp_view_test.base_table (a int, id int) using parquet;
+CREATE TABLE temp_view_test.base_table2 (a int, id int) using parquet;
+
+-- Replace SET with USE
+-- SET search_path TO temp_view_test, public;
+USE temp_view_test;
+
+-- Since Spark doesn't support CREATE TEMPORARY TABLE, we used CREATE 
TEMPORARY VIEW instead
+-- CREATE TEMPORARY TABLE temp_table (a int, id int);
+CREATE TEMPORARY VIEW temp_table AS SELECT * FROM VALUES
+  (1, 1) as temp_table(a, id);
+
+-- should be created in temp_view_test schema
+CREATE VIEW v1 AS SELECT * FROM base_table;
+DESC TABLE EXTENDED v1;
+-- should be created in temp object schema
+-- [SPARK-X] Forcibly create a temporary view in CREATE VIEW if 
referencing a temporary view
+CREATE VIEW v1_temp AS SELECT * FROM temp_table;
+-- should be created in temp object schema
+CREATE TEMPORARY VIEW v2_temp AS SELECT * FROM base_table;
+DESC TABLE EXTENDED v2_temp;
+-- should be created in temp_views schema
+CREATE VIEW temp_view_test.v2 AS SELECT * FROM base_table;
+DESC TABLE EXTENDED temp_view_test.v2;
+-- should fail
+-- [SPARK-X] Forcibly create a temporary view in CREATE VIEW if 
referencing a temporary view
+CREATE VIEW temp_view_test.v3_temp AS SELECT * FROM temp_table;
+-- should fail
+-- Spark doesn't support the cascaded syntax below in `CREATE SCHEMA`
+-- CREATE SCHEMA test_view_schema
+-- CREATE TEMP VIEW testview AS SELECT 1;
+
+-- joins: if any of the join relations are temporary, the view
+-- should also be temporary
+
+-- should 

[GitHub] [spark] viirya commented on issue #26086: [SPARK-29302] Make the file name of a task for dynamic partition overwrite be unique

2019-10-28 Thread GitBox
viirya commented on issue #26086: [SPARK-29302] Make the file name of a task 
for dynamic partition overwrite be unique
URL: https://github.com/apache/spark/pull/26086#issuecomment-547268384
 
 
   Is this different to #24142? 


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[GitHub] [spark] AmplabJenkins removed a comment on issue #26134: [SPARK-29486][SQL] CalendarInterval should have 3 fields: months, days and microseconds

2019-10-28 Thread GitBox
AmplabJenkins removed a comment on issue #26134: [SPARK-29486][SQL] 
CalendarInterval should have 3 fields: months, days and microseconds
URL: https://github.com/apache/spark/pull/26134#issuecomment-547267869
 
 
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[GitHub] [spark] AmplabJenkins removed a comment on issue #26134: [SPARK-29486][SQL] CalendarInterval should have 3 fields: months, days and microseconds

2019-10-28 Thread GitBox
AmplabJenkins removed a comment on issue #26134: [SPARK-29486][SQL] 
CalendarInterval should have 3 fields: months, days and microseconds
URL: https://github.com/apache/spark/pull/26134#issuecomment-547267866
 
 
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[GitHub] [spark] AmplabJenkins commented on issue #26134: [SPARK-29486][SQL] CalendarInterval should have 3 fields: months, days and microseconds

2019-10-28 Thread GitBox
AmplabJenkins commented on issue #26134: [SPARK-29486][SQL] CalendarInterval 
should have 3 fields: months, days and microseconds
URL: https://github.com/apache/spark/pull/26134#issuecomment-547267869
 
 
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[GitHub] [spark] AmplabJenkins commented on issue #26134: [SPARK-29486][SQL] CalendarInterval should have 3 fields: months, days and microseconds

2019-10-28 Thread GitBox
AmplabJenkins commented on issue #26134: [SPARK-29486][SQL] CalendarInterval 
should have 3 fields: months, days and microseconds
URL: https://github.com/apache/spark/pull/26134#issuecomment-547267866
 
 
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[GitHub] [spark] viirya closed pull request #26087: [SPARK-29427][SQL] Create KeyValueGroupedDataset from existing columns in DataFrame

2019-10-28 Thread GitBox
viirya closed pull request #26087: [SPARK-29427][SQL] Create 
KeyValueGroupedDataset from existing columns in DataFrame
URL: https://github.com/apache/spark/pull/26087
 
 
   


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[GitHub] [spark] viirya commented on issue #26087: [SPARK-29427][SQL] Create KeyValueGroupedDataset from existing columns in DataFrame

2019-10-28 Thread GitBox
viirya commented on issue #26087: [SPARK-29427][SQL] Create 
KeyValueGroupedDataset from existing columns in DataFrame
URL: https://github.com/apache/spark/pull/26087#issuecomment-547267636
 
 
   if so, let me close this for now.


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[GitHub] [spark] maropu commented on a change in pull request #26290: [WIP][SPARK-29120][SQL][TESTS] Port create_view.sql

2019-10-28 Thread GitBox
maropu commented on a change in pull request #26290: 
[WIP][SPARK-29120][SQL][TESTS] Port create_view.sql
URL: https://github.com/apache/spark/pull/26290#discussion_r339902655
 
 

 ##
 File path: 
sql/core/src/test/resources/sql-tests/inputs/postgreSQL/create_view.sql
 ##
 @@ -0,0 +1,793 @@
+-- Portions Copyright (c) 1996-2019, PostgreSQL Global Development Group
+--
+-- CREATE VIEW
+-- 
https://github.com/postgres/postgres/blob/REL_12_STABLE/src/test/regress/sql/create_view.sql
+
+-- Spark doesn't support geometric types
+-- CREATE VIEW street AS
+--SELECT r.name, r.thepath, c.cname AS cname
+--FROM ONLY road r, real_city c
+--WHERE c.outline ## r.thepath;
+
+-- Spark doesn't support geometric types
+-- CREATE VIEW iexit AS
+--SELECT ih.name, ih.thepath,
+-- interpt_pp(ih.thepath, r.thepath) AS exit
+--FROM ihighway ih, ramp r
+--WHERE ih.thepath ## r.thepath;
+
+CREATE TABLE emp (
+  name string,
+  age int,
+  -- Spark doesn't support a geometric type `point`
+  -- location point
+  salary int,
+  manager string
+) USING parquet;
+
+CREATE VIEW toyemp AS
+   SELECT name, age, /* location ,*/ 12*salary AS annualsal
+   FROM emp;
+
+-- Spark doesn't support the COMMENT clause that is not defined in the SQL 
standard
+-- Test comments
+-- COMMENT ON VIEW noview IS 'no view';
+-- COMMENT ON VIEW toyemp IS 'is a view';
+-- COMMENT ON VIEW toyemp IS NULL;
+
+DROP VIEW toyemp;
+
+-- These views are left around mainly to exercise special cases in pg_dump.
+
+-- [SPARK-19842] Informational Referential Integrity Constraints Support in 
Spark
+-- CREATE TABLE view_base_table (key int PRIMARY KEY, data varchar(20));
+--
+-- CREATE VIEW key_dependent_view AS
+--SELECT * FROM view_base_table GROUP BY key;
+--
+-- ALTER TABLE view_base_table DROP CONSTRAINT view_base_table_pkey;  -- fails
+
+-- CREATE VIEW key_dependent_view_no_cols AS
+--SELECT FROM view_base_table GROUP BY key HAVING length(data) > 0;
+
+--
+-- CREATE OR REPLACE VIEW
+--
+
+CREATE TABLE viewtest_tbl (a int, b int) using parquet;
+-- [SPARK-29386] Copy data between a file and a table
+-- COPY viewtest_tbl FROM stdin;
+-- 5   10
+-- 10  15
+-- 15  20
+-- 20  25
+-- \.
+INSERT INTO viewtest_tbl VALUES (5, 10), (10, 15), (15, 20), (20, 25);
+
+CREATE OR REPLACE VIEW viewtest AS
+   SELECT * FROM viewtest_tbl;
+
+CREATE OR REPLACE VIEW viewtest AS
+   SELECT * FROM viewtest_tbl WHERE a > 10;
+
+SELECT * FROM viewtest;
+
+CREATE OR REPLACE VIEW viewtest AS
+   SELECT a, b FROM viewtest_tbl WHERE a > 5 ORDER BY b DESC;
+
+SELECT * FROM viewtest;
+
+-- should fail
+-- Spark can accept the DDL query below
+CREATE OR REPLACE VIEW viewtest AS
+   SELECT a FROM viewtest_tbl WHERE a <> 20;
+
+-- should fail
+-- Spark can accept the DDL query below
+CREATE OR REPLACE VIEW viewtest AS
+   SELECT 1, * FROM viewtest_tbl;
+
+-- should fail
+-- Spark can accept the DDL query below
+CREATE OR REPLACE VIEW viewtest AS
+   SELECT a, decimal(b) FROM viewtest_tbl;
+
+-- should work
+CREATE OR REPLACE VIEW viewtest AS
+   SELECT a, b, 0 AS c FROM viewtest_tbl;
+
+DROP VIEW viewtest;
+DROP TABLE viewtest_tbl;
+
+-- tests for temporary views
+
+-- Spark doesn't support the cascaded syntax below in `CREATE SCHEMA`
+-- CREATE SCHEMA temp_view_test
+-- CREATE TABLE base_table (a int, id int) using parquet
+-- CREATE TABLE base_table2 (a int, id int) using parquet;
+CREATE SCHEMA temp_view_test;
+CREATE TABLE temp_view_test.base_table (a int, id int) using parquet;
+CREATE TABLE temp_view_test.base_table2 (a int, id int) using parquet;
+
+-- Replace SET with USE
+-- SET search_path TO temp_view_test, public;
+USE temp_view_test;
+
+-- Since Spark doesn't support CREATE TEMPORARY TABLE, we used CREATE 
TEMPORARY VIEW instead
+-- CREATE TEMPORARY TABLE temp_table (a int, id int);
+CREATE TEMPORARY VIEW temp_table AS SELECT * FROM VALUES
+  (1, 1) as temp_table(a, id);
+
+-- should be created in temp_view_test schema
+CREATE VIEW v1 AS SELECT * FROM base_table;
+DESC TABLE EXTENDED v1;
+-- should be created in temp object schema
+-- [SPARK-X] Forcibly create a temporary view in CREATE VIEW if 
referencing a temporary view
+CREATE VIEW v1_temp AS SELECT * FROM temp_table;
 
 Review comment:
   ```
   // In Spark
   org.apache.spark.sql.AnalysisException
   Not allowed to create a permanent view `v1_temp` by referencing a temporary 
view `temp_table`;
   
   // In PostgreSQL
   NOTICE:  view "v1_temp" will be a temporary view
   ```


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[GitHub] [spark] SparkQA removed a comment on issue #26134: [SPARK-29486][SQL] CalendarInterval should have 3 fields: months, days and microseconds

2019-10-28 Thread GitBox
SparkQA removed a comment on issue #26134: [SPARK-29486][SQL] CalendarInterval 
should have 3 fields: months, days and microseconds
URL: https://github.com/apache/spark/pull/26134#issuecomment-547237650
 
 
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[GitHub] [spark] AmplabJenkins removed a comment on issue #26261: [SPARK-29607][SQL] Move static methods from CalendarInterval to IntervalUtils

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[GitHub] [spark] SparkQA commented on issue #26134: [SPARK-29486][SQL] CalendarInterval should have 3 fields: months, days and microseconds

2019-10-28 Thread GitBox
SparkQA commented on issue #26134: [SPARK-29486][SQL] CalendarInterval should 
have 3 fields: months, days and microseconds
URL: https://github.com/apache/spark/pull/26134#issuecomment-547267376
 
 
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* This patch passes all tests.
* This patch merges cleanly.
* This patch adds no public classes.


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[GitHub] [spark] SparkQA removed a comment on issue #26261: [SPARK-29607][SQL] Move static methods from CalendarInterval to IntervalUtils

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[GitHub] [spark] shahidki31 commented on a change in pull request #26241: [SPARK-29585][WEBUI] Fix stagePage duration

2019-10-28 Thread GitBox
shahidki31 commented on a change in pull request #26241: [SPARK-29585][WEBUI] 
Fix stagePage duration
URL: https://github.com/apache/spark/pull/26241#discussion_r339902038
 
 

 ##
 File path: core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala
 ##
 @@ -417,19 +417,8 @@ private[ui] class StageDataSource(
   case Some(t) => UIUtils.formatDate(t)
   case None => "Unknown"
 }
-val finishTime = 
stageData.completionTime.map(_.getTime()).getOrElse(currentTime)
-
-// The submission time for a stage is misleading because it counts the time
-// the stage waits to be launched. (SPARK-10930)
-val duration = stageData.firstTaskLaunchedTime.map { date =>
-  val time = date.getTime()
-  if (finishTime > time) {
-finishTime - time
-  } else {
-None
-currentTime - time
-  }
-}
+
+val duration = Some(stageData.executorRunTime)
 
 Review comment:
   Thanks @teeyog. I think this was the behavior in the previous versions of 
spark. I am not sure we need to change it. What ever you are proposing is 
`duration = executorRunTime + ser/deser time + gettingResultTime`. But, Already 
these 3 columns are getting disaplyed in the task table right? If we unify all 
would we need to display individual columns?


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[GitHub] [spark] SparkQA commented on issue #26261: [SPARK-29607][SQL] Move static methods from CalendarInterval to IntervalUtils

2019-10-28 Thread GitBox
SparkQA commented on issue #26261: [SPARK-29607][SQL] Move static methods from 
CalendarInterval to IntervalUtils
URL: https://github.com/apache/spark/pull/26261#issuecomment-547266799
 
 
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[`38f7c78`](https://github.com/apache/spark/commit/38f7c78e51ed4a19a24aee6aca08fc58dd101f20).
* This patch **fails Spark unit tests**.
* This patch merges cleanly.
* This patch adds no public classes.


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[GitHub] [spark] AmplabJenkins removed a comment on issue #26290: [WIP][SPARK-29120][SQL][TESTS] Port create_view.sql

2019-10-28 Thread GitBox
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[GitHub] [spark] maropu commented on a change in pull request #26238: [SPARK-29110][SQL][TESTS] Port window.sql (Part 4)

2019-10-28 Thread GitBox
maropu commented on a change in pull request #26238: [SPARK-29110][SQL][TESTS] 
Port window.sql (Part 4)
URL: https://github.com/apache/spark/pull/26238#discussion_r339901831
 
 

 ##
 File path: 
sql/core/src/test/resources/sql-tests/results/postgreSQL/window_part4.sql.out
 ##
 @@ -0,0 +1,507 @@
+-- Automatically generated by SQLQueryTestSuite
+-- Number of queries: 39
+
+
+-- !query 0
+SELECT i,AVG(v) OVER (ORDER BY i ROWS BETWEEN CURRENT ROW AND UNBOUNDED 
FOLLOWING)
+  FROM (VALUES(1,1),(2,2),(3,NULL),(4,NULL)) t(i,v)
+-- !query 0 schema
+struct
+-- !query 0 output
+1  1.5
+2  2.0
+3  NULL
+4  NULL
+
+
+-- !query 1
+SELECT i,AVG(v) OVER (ORDER BY i ROWS BETWEEN CURRENT ROW AND UNBOUNDED 
FOLLOWING)
+  FROM (VALUES(1,1),(2,2),(3,NULL),(4,NULL)) t(i,v)
+-- !query 1 schema
+struct
+-- !query 1 output
+1  1.5
+2  2.0
+3  NULL
+4  NULL
+
+
+-- !query 2
+SELECT i,AVG(v) OVER (ORDER BY i ROWS BETWEEN CURRENT ROW AND UNBOUNDED 
FOLLOWING)
+  FROM (VALUES(1,1),(2,2),(3,NULL),(4,NULL)) t(i,v)
+-- !query 2 schema
+struct
+-- !query 2 output
+1  1.5
+2  2.0
+3  NULL
+4  NULL
+
+
+-- !query 3
+SELECT i,AVG(v) OVER (ORDER BY i ROWS BETWEEN CURRENT ROW AND UNBOUNDED 
FOLLOWING)
+  FROM (VALUES(1,1.5),(2,2.5),(3,NULL),(4,NULL)) t(i,v)
+-- !query 3 schema
+struct
+-- !query 3 output
+1  2
+2  2.5
+3  NULL
+4  NULL
+
+
+-- !query 4
+SELECT i,SUM(v) OVER (ORDER BY i ROWS BETWEEN CURRENT ROW AND UNBOUNDED 
FOLLOWING)
+  FROM (VALUES(1,1),(2,2),(3,NULL),(4,NULL)) t(i,v)
+-- !query 4 schema
+struct
+-- !query 4 output
+1  3
+2  2
+3  NULL
+4  NULL
+
+
+-- !query 5
+SELECT i,SUM(v) OVER (ORDER BY i ROWS BETWEEN CURRENT ROW AND UNBOUNDED 
FOLLOWING)
+  FROM (VALUES(1,1),(2,2),(3,NULL),(4,NULL)) t(i,v)
+-- !query 5 schema
+struct
+-- !query 5 output
+1  3
+2  2
+3  NULL
+4  NULL
+
+
+-- !query 6
+SELECT i,SUM(v) OVER (ORDER BY i ROWS BETWEEN CURRENT ROW AND UNBOUNDED 
FOLLOWING)
+  FROM (VALUES(1,1),(2,2),(3,NULL),(4,NULL)) t(i,v)
+-- !query 6 schema
+struct
+-- !query 6 output
+1  3
+2  2
+3  NULL
+4  NULL
+
+
+-- !query 7
+SELECT i,SUM(v) OVER (ORDER BY i ROWS BETWEEN CURRENT ROW AND UNBOUNDED 
FOLLOWING)
+  FROM (VALUES(1,1.1),(2,2.2),(3,NULL),(4,NULL)) t(i,v)
+-- !query 7 schema
+struct
+-- !query 7 output
+1  3.3
+2  2.2
+3  NULL
+4  NULL
+
+
+-- !query 8
+SELECT SUM(n) OVER (ORDER BY i ROWS BETWEEN CURRENT ROW AND UNBOUNDED 
FOLLOWING)
+  FROM (VALUES(1,1.01),(2,2),(3,3)) v(i,n)
+-- !query 8 schema
+struct
+-- !query 8 output
+3
+5
+6.01
+
+
+-- !query 9
+SELECT i,COUNT(v) OVER (ORDER BY i ROWS BETWEEN CURRENT ROW AND UNBOUNDED 
FOLLOWING)
+  FROM (VALUES(1,1),(2,2),(3,NULL),(4,NULL)) t(i,v)
+-- !query 9 schema
+struct
+-- !query 9 output
+1  2
+2  1
+3  0
+4  0
+
+
+-- !query 10
+SELECT i,COUNT(*) OVER (ORDER BY i ROWS BETWEEN CURRENT ROW AND UNBOUNDED 
FOLLOWING)
+  FROM (VALUES(1,1),(2,2),(3,NULL),(4,NULL)) t(i,v)
+-- !query 10 schema
+struct
+-- !query 10 output
+1  4
+2  3
+3  2
+4  1
+
+
+-- !query 11
+SELECT VAR_POP(n) OVER (ORDER BY i ROWS BETWEEN CURRENT ROW AND UNBOUNDED 
FOLLOWING)
+  FROM (VALUES(1,600),(2,470),(3,170),(4,430),(5,300)) r(i,n)
+-- !query 11 schema
+struct
+-- !query 11 output
+0.0
+11266.
+13868.7502
+21703.9996
+4225.0
+
+
+-- !query 12
+SELECT VAR_POP(n) OVER (ORDER BY i ROWS BETWEEN CURRENT ROW AND UNBOUNDED 
FOLLOWING)
+  FROM (VALUES(1,600),(2,470),(3,170),(4,430),(5,300)) r(i,n)
+-- !query 12 schema
+struct
+-- !query 12 output
+0.0
+11266.
+13868.7502
+21703.9996
+4225.0
+
+
+-- !query 13
+SELECT VAR_POP(n) OVER (ORDER BY i ROWS BETWEEN CURRENT ROW AND UNBOUNDED 
FOLLOWING)
+  FROM (VALUES(1,600),(2,470),(3,170),(4,430),(5,300)) r(i,n)
+-- !query 13 schema
+struct
+-- !query 13 output
+0.0
+11266.
+13868.7502
+21703.9996
+4225.0
+
+
+-- !query 14
+SELECT VAR_POP(n) OVER (ORDER BY i ROWS BETWEEN CURRENT ROW AND UNBOUNDED 
FOLLOWING)
+  FROM (VALUES(1,600),(2,470),(3,170),(4,430),(5,300)) r(i,n)
+-- !query 14 schema
+struct
+-- !query 14 output
+0.0
+11266.
+13868.7502
+21703.9996
+4225.0
+
+
+-- !query 15
+SELECT VAR_SAMP(n) OVER (ORDER BY i ROWS BETWEEN CURRENT ROW AND UNBOUNDED 
FOLLOWING)
+  FROM (VALUES(1,600),(2,470),(3,170),(4,430),(5,300)) r(i,n)
+-- !query 15 schema
+struct
+-- !query 15 output
+16900.0
+18491.6668
+27129.9996
+8450.0
+NaN
+
+
+-- !query 16
+SELECT VAR_SAMP(n) OVER (ORDER BY i ROWS BETWEEN CURRENT ROW AND UNBOUNDED 
FOLLOWING)
+  FROM (VALUES(1,600),(2,470),(3,170),(4,430),(5,300)) r(i,n)
+-- !query 16 schema
+struct
+-- !query 16 output
+16900.0
+18491.6668
+27129.9996
+8450.0
+NaN
+
+
+-- !query 17
+SELECT VAR_SAMP(n) OVER (ORDER BY i ROWS BETWEEN CURRENT ROW AND UNBOUNDED 
FOLLOWING)
+  FROM (VALUES(1,600),(2,470),(3,170),(4,430),(5,300)) r(i,n)

[GitHub] [spark] AmplabJenkins removed a comment on issue #26290: [WIP][SPARK-29120][SQL][TESTS] Port create_view.sql

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[GitHub] [spark] weixiuli commented on issue #26280: [SPARK-14922][SPARK-17732][SPARK-23866][SQL] Support partition filter in ALTER TABLE DROP PARTITION and batch dropping PARTITIONS

2019-10-28 Thread GitBox
weixiuli commented on issue #26280: 
[SPARK-14922][SPARK-17732][SPARK-23866][SQL] Support partition filter in ALTER 
TABLE DROP PARTITION and batch dropping PARTITIONS
URL: https://github.com/apache/spark/pull/26280#issuecomment-547265979
 
 
   Ping @cloud-fan @mgaido91 @maropu @DazhuangSu   Kindly review, thanks.


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[GitHub] [spark] SparkQA commented on issue #26290: [WIP][SPARK-29120][SQL][TESTS] Port create_view.sql

2019-10-28 Thread GitBox
SparkQA commented on issue #26290: [WIP][SPARK-29120][SQL][TESTS] Port 
create_view.sql
URL: https://github.com/apache/spark/pull/26290#issuecomment-547265974
 
 
   **[Test build #112825 has 
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[GitHub] [spark] turboFei commented on issue #26159: [SPARK-29506][SQL] Use dynamicPartitionOverwrite in FileCommitProtocol when insert into hive table

2019-10-28 Thread GitBox
turboFei commented on issue #26159: [SPARK-29506][SQL] Use 
dynamicPartitionOverwrite in FileCommitProtocol when insert into hive table
URL: https://github.com/apache/spark/pull/26159#issuecomment-547265868
 
 
   @viirya  Hi,  for the issue mentioned by @rezasafi , I have created a ticket 
to fix it.
   Could you help take a look? Thanks in advance!


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[GitHub] [spark] maropu commented on issue #26290: [WIP][SPARK-29120][SQL][TESTS] Port create_view.sql

2019-10-28 Thread GitBox
maropu commented on issue #26290: [WIP][SPARK-29120][SQL][TESTS] Port 
create_view.sql
URL: https://github.com/apache/spark/pull/26290#issuecomment-547265805
 
 
   Since I've not filed some issues in jira, I set `WIP` now.


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[GitHub] [spark] maropu opened a new pull request #26290: [WIP][SPARK-29120][SQL][TESTS] Port create_view.sql

2019-10-28 Thread GitBox
maropu opened a new pull request #26290: [WIP][SPARK-29120][SQL][TESTS] Port 
create_view.sql
URL: https://github.com/apache/spark/pull/26290
 
 
   
   
   ### What changes were proposed in this pull request?
   
   This PR ports create_view.sql from PostgreSQL regression tests 
https://github.com/postgres/postgres/blob/REL_12_STABLE/src/test/regress/sql/create_view.sql
   
   The expected results can be found in the link: 
https://github.com/postgres/postgres/blob/REL_12_STABLE/src/test/regress/expected/create_view.out
   
   ### Why are the changes needed?
   
   To check behaviour differences between Spark and PostgreSQL
   
   ### Does this PR introduce any user-facing change?
   
   No
   
   ### How was this patch tested?
   
   Pass the Jenkins. And, Comparison with PgSQL results
   


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[GitHub] [spark] cloud-fan commented on a change in pull request #26182: [SPARK-29523][SQL] SHOW COLUMNS should do multi-catalog resolution.

2019-10-28 Thread GitBox
cloud-fan commented on a change in pull request #26182: [SPARK-29523][SQL] SHOW 
COLUMNS should do multi-catalog resolution.
URL: https://github.com/apache/spark/pull/26182#discussion_r339900738
 
 

 ##
 File path: 
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/parser/AstBuilder.scala
 ##
 @@ -2911,4 +2911,28 @@ class AstBuilder(conf: SQLConf) extends 
SqlBaseBaseVisitor[AnyRef] with Logging
   override def visitRefreshTable(ctx: RefreshTableContext): LogicalPlan = 
withOrigin(ctx) {
 RefreshTableStatement(visitMultipartIdentifier(ctx.multipartIdentifier()))
   }
+
+  /**
+   * A command for users to list the column names for a table.
+   * This function creates a [[ShowColumnsStatement]] logical plan.
+   *
+   * The syntax of using this command in SQL is:
+   * {{{
+   *   SHOW COLUMNS (FROM | IN) tableName=multipartIdentifier
+   *((FROM | IN) namespace=multipartIdentifier)?
+   * }}}
+   */
+  override def visitShowColumns(ctx: ShowColumnsContext): LogicalPlan = 
withOrigin(ctx) {
+import org.apache.spark.sql.connector.catalog.CatalogV2Implicits._
+
+val table = visitMultipartIdentifier(ctx.table)
+val namespace = Option(ctx.namespace).map(visitMultipartIdentifier)
+if (namespace.isDefined && namespace.get.length > 1 && table.length > 1) {
 
 Review comment:
   Since we don't have v2 SHOW COLUMNS yet, so in `ResolveSessionCatalog`, we 
should simply check that
   1. the table name has <= 2 parts
   2. the namespace name should have only one part if specified


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[GitHub] [spark] JkSelf commented on issue #26289: [SPARK-28560][SQL][followup] support the build side to local shuffle reader as far as possible in BroadcastHashJoin

2019-10-28 Thread GitBox
JkSelf commented on issue #26289: [SPARK-28560][SQL][followup] support the 
build side to local shuffle reader as far as possible in BroadcastHashJoin
URL: https://github.com/apache/spark/pull/26289#issuecomment-547264699
 
 
   The failed test may be not related.


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[GitHub] [spark] SparkQA commented on issue #22952: [SPARK-20568][SS] Provide option to clean up completed files in streaming query

2019-10-28 Thread GitBox
SparkQA commented on issue #22952: [SPARK-20568][SS] Provide option to clean up 
completed files in streaming query
URL: https://github.com/apache/spark/pull/22952#issuecomment-547264254
 
 
   **[Test build #112824 has 
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[GitHub] [spark] AmplabJenkins removed a comment on issue #26289: [SPARK-28560][SQL][followup] support the build side to local shuffle reader as far as possible in BroadcastHashJoin

2019-10-28 Thread GitBox
AmplabJenkins removed a comment on issue #26289: [SPARK-28560][SQL][followup] 
support the build side to local shuffle reader as far as possible in 
BroadcastHashJoin
URL: https://github.com/apache/spark/pull/26289#issuecomment-547263994
 
 
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[GitHub] [spark] HyukjinKwon commented on issue #26087: [SPARK-29427][SQL] Create KeyValueGroupedDataset from existing columns in DataFrame

2019-10-28 Thread GitBox
HyukjinKwon commented on issue #26087: [SPARK-29427][SQL] Create 
KeyValueGroupedDataset from existing columns in DataFrame
URL: https://github.com/apache/spark/pull/26087#issuecomment-547264380
 
 
   To me, adding a new API seems a bit overkill to handle rather corner case 
TBH.


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[GitHub] [spark] SparkQA commented on issue #26258: [SPARK-29604][SQL] Force initialize SessionState before initializing HiveClient in SparkSQLEnv

2019-10-28 Thread GitBox
SparkQA commented on issue #26258: [SPARK-29604][SQL] Force initialize 
SessionState before initializing HiveClient in SparkSQLEnv
URL: https://github.com/apache/spark/pull/26258#issuecomment-547264226
 
 
   **[Test build #112823 has 
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[GitHub] [spark] AmplabJenkins removed a comment on issue #26289: [SPARK-28560][SQL][followup] support the build side to local shuffle reader as far as possible in BroadcastHashJoin

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[GitHub] [spark] AmplabJenkins commented on issue #26289: [SPARK-28560][SQL][followup] support the build side to local shuffle reader as far as possible in BroadcastHashJoin

2019-10-28 Thread GitBox
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[GitHub] [spark] AmplabJenkins commented on issue #26289: [SPARK-28560][SQL][followup] support the build side to local shuffle reader as far as possible in BroadcastHashJoin

2019-10-28 Thread GitBox
AmplabJenkins commented on issue #26289: [SPARK-28560][SQL][followup] support 
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URL: https://github.com/apache/spark/pull/26289#issuecomment-547263994
 
 
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[GitHub] [spark] SparkQA removed a comment on issue #26289: [SPARK-28560][SQL][followup] support the build side to local shuffle reader as far as possible in BroadcastHashJoin

2019-10-28 Thread GitBox
SparkQA removed a comment on issue #26289: [SPARK-28560][SQL][followup] support 
the build side to local shuffle reader as far as possible in BroadcastHashJoin
URL: https://github.com/apache/spark/pull/26289#issuecomment-547244971
 
 
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[GitHub] [spark] HeartSaVioR commented on issue #26258: [SPARK-29604][SQL] Force initialize SessionState before initializing HiveClient in SparkSQLEnv

2019-10-28 Thread GitBox
HeartSaVioR commented on issue #26258: [SPARK-29604][SQL] Force initialize 
SessionState before initializing HiveClient in SparkSQLEnv
URL: https://github.com/apache/spark/pull/26258#issuecomment-547263860
 
 
   retest this, please


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[GitHub] [spark] cloud-fan commented on issue #26269: [SPARK-29612][SQL] ALTER TABLE (RECOVER PARTITIONS) should look up catalog/table like v2 commands

2019-10-28 Thread GitBox
cloud-fan commented on issue #26269: [SPARK-29612][SQL] ALTER TABLE (RECOVER 
PARTITIONS) should look up catalog/table like v2 commands
URL: https://github.com/apache/spark/pull/26269#issuecomment-547263870
 
 
   thanks, merging to master!


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[GitHub] [spark] SparkQA commented on issue #26289: [SPARK-28560][SQL][followup] support the build side to local shuffle reader as far as possible in BroadcastHashJoin

2019-10-28 Thread GitBox
SparkQA commented on issue #26289: [SPARK-28560][SQL][followup] support the 
build side to local shuffle reader as far as possible in BroadcastHashJoin
URL: https://github.com/apache/spark/pull/26289#issuecomment-547263888
 
 
   **[Test build #112816 has 
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 for PR 26289 at commit 
[`5b7ff2d`](https://github.com/apache/spark/commit/5b7ff2dc680e37032cee048f987e3809d6ebfd94).
* This patch **fails Spark unit tests**.
* This patch merges cleanly.
* This patch adds the following public classes _(experimental)_:
 * `case class OptimizeLocalShuffleReader(`


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[GitHub] [spark] HeartSaVioR commented on issue #22952: [SPARK-20568][SS] Provide option to clean up completed files in streaming query

2019-10-28 Thread GitBox
HeartSaVioR commented on issue #22952: [SPARK-20568][SS] Provide option to 
clean up completed files in streaming query
URL: https://github.com/apache/spark/pull/22952#issuecomment-547263889
 
 
   retest this, please


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[GitHub] [spark] HeartSaVioR commented on issue #26287: [SPARK-28158][SQL][FOLLOWUP] HiveUserDefinedTypeSuite: don't use RandomDataGenerator to create row for UDT backed by ArrayType

2019-10-28 Thread GitBox
HeartSaVioR commented on issue #26287: [SPARK-28158][SQL][FOLLOWUP] 
HiveUserDefinedTypeSuite: don't use RandomDataGenerator to create row for UDT 
backed by ArrayType
URL: https://github.com/apache/spark/pull/26287#issuecomment-547263642
 
 
   Thanks all for reviewing and merging!


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[GitHub] [spark] jiangxb1987 commented on issue #26243: Prepare Spark release v3.0.0-preview-rc1

2019-10-28 Thread GitBox
jiangxb1987 commented on issue #26243: Prepare Spark release v3.0.0-preview-rc1
URL: https://github.com/apache/spark/pull/26243#issuecomment-547263736
 
 
   FYI, this PR has been reverted by 
https://github.com/apache/spark/commit/b33a58c0c6a40aa312cbd72f763d5e519182c3d1


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[GitHub] [spark] jiangxb1987 closed pull request #26243: Prepare Spark release v3.0.0-preview-rc1

2019-10-28 Thread GitBox
jiangxb1987 closed pull request #26243: Prepare Spark release v3.0.0-preview-rc1
URL: https://github.com/apache/spark/pull/26243
 
 
   


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[GitHub] [spark] jiangxb1987 commented on issue #26243: Prepare Spark release v3.0.0-preview-rc1

2019-10-28 Thread GitBox
jiangxb1987 commented on issue #26243: Prepare Spark release v3.0.0-preview-rc1
URL: https://github.com/apache/spark/pull/26243#issuecomment-547262976
 
 
   Thanks, merging to master!


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[GitHub] [spark] AmplabJenkins removed a comment on issue #26134: [SPARK-29486][SQL] CalendarInterval should have 3 fields: months, days and microseconds

2019-10-28 Thread GitBox
AmplabJenkins removed a comment on issue #26134: [SPARK-29486][SQL] 
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[GitHub] [spark] AmplabJenkins removed a comment on issue #26134: [SPARK-29486][SQL] CalendarInterval should have 3 fields: months, days and microseconds

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[GitHub] [spark] AmplabJenkins commented on issue #26134: [SPARK-29486][SQL] CalendarInterval should have 3 fields: months, days and microseconds

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[GitHub] [spark] AmplabJenkins commented on issue #26134: [SPARK-29486][SQL] CalendarInterval should have 3 fields: months, days and microseconds

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[GitHub] [spark] SparkQA commented on issue #26134: [SPARK-29486][SQL] CalendarInterval should have 3 fields: months, days and microseconds

2019-10-28 Thread GitBox
SparkQA commented on issue #26134: [SPARK-29486][SQL] CalendarInterval should 
have 3 fields: months, days and microseconds
URL: https://github.com/apache/spark/pull/26134#issuecomment-547260584
 
 
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[GitHub] [spark] cloud-fan commented on a change in pull request #26182: [SPARK-29523][SQL] SHOW COLUMNS should do multi-catalog resolution.

2019-10-28 Thread GitBox
cloud-fan commented on a change in pull request #26182: [SPARK-29523][SQL] SHOW 
COLUMNS should do multi-catalog resolution.
URL: https://github.com/apache/spark/pull/26182#discussion_r339897559
 
 

 ##
 File path: 
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/parser/AstBuilder.scala
 ##
 @@ -2911,4 +2911,28 @@ class AstBuilder(conf: SQLConf) extends 
SqlBaseBaseVisitor[AnyRef] with Logging
   override def visitRefreshTable(ctx: RefreshTableContext): LogicalPlan = 
withOrigin(ctx) {
 RefreshTableStatement(visitMultipartIdentifier(ctx.multipartIdentifier()))
   }
+
+  /**
+   * A command for users to list the column names for a table.
+   * This function creates a [[ShowColumnsStatement]] logical plan.
+   *
+   * The syntax of using this command in SQL is:
+   * {{{
+   *   SHOW COLUMNS (FROM | IN) tableName=multipartIdentifier
+   *((FROM | IN) namespace=multipartIdentifier)?
+   * }}}
+   */
+  override def visitShowColumns(ctx: ShowColumnsContext): LogicalPlan = 
withOrigin(ctx) {
+import org.apache.spark.sql.connector.catalog.CatalogV2Implicits._
+
+val table = visitMultipartIdentifier(ctx.table)
+val namespace = Option(ctx.namespace).map(visitMultipartIdentifier)
+if (namespace.isDefined && namespace.get.length > 1 && table.length > 1) {
 
 Review comment:
   OK I get it. Previously we support `SHOW COLUMNS FROM db1.t1 IN db1`, and at 
runtime we check if the specified database name is the same as the db name in 
table identifier.
   
   Seems like we should move this check into the analyzer, i.e. in 
`ResolveSessionCatalog`


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[GitHub] [spark] HyukjinKwon closed pull request #25888: [SPARK-21492] [SQL] Fix memory leak issue in SMJ

2019-10-28 Thread GitBox
HyukjinKwon closed pull request #25888: [SPARK-21492] [SQL] Fix memory leak 
issue in SMJ
URL: https://github.com/apache/spark/pull/25888
 
 
   


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[GitHub] [spark] HyukjinKwon commented on issue #25888: [SPARK-21492] [SQL] Fix memory leak issue in SMJ

2019-10-28 Thread GitBox
HyukjinKwon commented on issue #25888: [SPARK-21492] [SQL] Fix memory leak 
issue in SMJ
URL: https://github.com/apache/spark/pull/25888#issuecomment-547258273
 
 
   Closing in favour of https://github.com/apache/spark/pull/26164


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[GitHub] [spark] SparkQA commented on issue #26239: [SPARK-29582][PYSPARK] Unify the behavior of pyspark.TaskContext with spark core

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[GitHub] [spark] viirya commented on a change in pull request #26182: [SPARK-29523][SQL] SHOW COLUMNS should do multi-catalog resolution.

2019-10-28 Thread GitBox
viirya commented on a change in pull request #26182: [SPARK-29523][SQL] SHOW 
COLUMNS should do multi-catalog resolution.
URL: https://github.com/apache/spark/pull/26182#discussion_r339893133
 
 

 ##
 File path: 
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/parser/AstBuilder.scala
 ##
 @@ -2911,4 +2911,28 @@ class AstBuilder(conf: SQLConf) extends 
SqlBaseBaseVisitor[AnyRef] with Logging
   override def visitRefreshTable(ctx: RefreshTableContext): LogicalPlan = 
withOrigin(ctx) {
 RefreshTableStatement(visitMultipartIdentifier(ctx.multipartIdentifier()))
   }
+
+  /**
+   * A command for users to list the column names for a table.
+   * This function creates a [[ShowColumnsStatement]] logical plan.
+   *
+   * The syntax of using this command in SQL is:
+   * {{{
+   *   SHOW COLUMNS (FROM | IN) tableName=multipartIdentifier
+   *((FROM | IN) namespace=multipartIdentifier)?
+   * }}}
+   */
+  override def visitShowColumns(ctx: ShowColumnsContext): LogicalPlan = 
withOrigin(ctx) {
+import org.apache.spark.sql.connector.catalog.CatalogV2Implicits._
+
+val table = visitMultipartIdentifier(ctx.table)
+val namespace = Option(ctx.namespace).map(visitMultipartIdentifier)
+if (namespace.isDefined && namespace.get.length > 1 && table.length > 1) {
+  val tableName = table.init
+  throw new ParseException(
+s"If namespace is multipart, table can not be multipart: 
${tableName.quoted}", ctx)
 
 Review comment:
   If the behavior is as 
https://github.com/apache/spark/pull/26182#discussion_r339892210, then the 
suggested one is more correct.


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[GitHub] [spark] SparkQA commented on issue #24898: [SPARK-22340][PYTHON] Add a mode to pin Python thread into JVM's

2019-10-28 Thread GitBox
SparkQA commented on issue #24898: [SPARK-22340][PYTHON] Add a mode to pin 
Python thread into JVM's
URL: https://github.com/apache/spark/pull/24898#issuecomment-547254785
 
 
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[GitHub] [spark] SparkQA commented on issue #26265: [SPARK-29565][ML][PYTHON] OneHotEncoder should support single-column input/output

2019-10-28 Thread GitBox
SparkQA commented on issue #26265: [SPARK-29565][ML][PYTHON] OneHotEncoder 
should support single-column input/output
URL: https://github.com/apache/spark/pull/26265#issuecomment-547254754
 
 
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[GitHub] [spark] huaxingao commented on issue #26247: [SPARK-29566][ML] Imputer should support single-column input/output

2019-10-28 Thread GitBox
huaxingao commented on issue #26247: [SPARK-29566][ML] Imputer should support 
single-column input/output
URL: https://github.com/apache/spark/pull/26247#issuecomment-547254395
 
 
   Thanks! @srowen @viirya @zhengruifeng 


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[GitHub] [spark] cloud-fan commented on a change in pull request #26182: [SPARK-29523][SQL] SHOW COLUMNS should do multi-catalog resolution.

2019-10-28 Thread GitBox
cloud-fan commented on a change in pull request #26182: [SPARK-29523][SQL] SHOW 
COLUMNS should do multi-catalog resolution.
URL: https://github.com/apache/spark/pull/26182#discussion_r339892347
 
 

 ##
 File path: 
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/parser/AstBuilder.scala
 ##
 @@ -2911,4 +2911,28 @@ class AstBuilder(conf: SQLConf) extends 
SqlBaseBaseVisitor[AnyRef] with Logging
   override def visitRefreshTable(ctx: RefreshTableContext): LogicalPlan = 
withOrigin(ctx) {
 RefreshTableStatement(visitMultipartIdentifier(ctx.multipartIdentifier()))
   }
+
+  /**
+   * A command for users to list the column names for a table.
+   * This function creates a [[ShowColumnsStatement]] logical plan.
+   *
+   * The syntax of using this command in SQL is:
+   * {{{
+   *   SHOW COLUMNS (FROM | IN) tableName=multipartIdentifier
+   *((FROM | IN) namespace=multipartIdentifier)?
+   * }}}
+   */
+  override def visitShowColumns(ctx: ShowColumnsContext): LogicalPlan = 
withOrigin(ctx) {
+import org.apache.spark.sql.connector.catalog.CatalogV2Implicits._
+
+val table = visitMultipartIdentifier(ctx.table)
+val namespace = Option(ctx.namespace).map(visitMultipartIdentifier)
+if (namespace.isDefined && namespace.get.length > 1 && table.length > 1) {
+  val tableName = table.init
+  throw new ParseException(
+s"If namespace is multipart, table can not be multipart: 
${tableName.quoted}", ctx)
 
 Review comment:
   how about "Can only specify a single-part table name if `FROM namespace` is 
specified."


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OneHotEncoder should support single-column input/output
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[GitHub] [spark] huaxingao commented on a change in pull request #26265: [SPARK-29565][ML][PYTHON] OneHotEncoder should support single-column input/output

2019-10-28 Thread GitBox
huaxingao commented on a change in pull request #26265: 
[SPARK-29565][ML][PYTHON] OneHotEncoder should support single-column 
input/output
URL: https://github.com/apache/spark/pull/26265#discussion_r339892325
 
 

 ##
 File path: mllib/src/main/scala/org/apache/spark/ml/feature/OneHotEncoder.scala
 ##
 @@ -123,6 +132,14 @@ class OneHotEncoder @Since("3.0.0") (@Since("3.0.0") 
override val uid: String)
   @Since("3.0.0")
   def this() = this(Identifiable.randomUID("oneHotEncoder"))
 
+  /** @group setParam */
+  @Since("3.0.0")
+  def setInputCol(values: String): this.type = set(inputCol, values)
+
+  /** @group setParam */
+  @Since("3.0.0")
+  def setOutputCol(values: String): this.type = set(outputCol, values)
 
 Review comment:
   updated. Thanks!


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[GitHub] [spark] cloud-fan commented on a change in pull request #26182: [SPARK-29523][SQL] SHOW COLUMNS should do multi-catalog resolution.

2019-10-28 Thread GitBox
cloud-fan commented on a change in pull request #26182: [SPARK-29523][SQL] SHOW 
COLUMNS should do multi-catalog resolution.
URL: https://github.com/apache/spark/pull/26182#discussion_r339892347
 
 

 ##
 File path: 
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/parser/AstBuilder.scala
 ##
 @@ -2911,4 +2911,28 @@ class AstBuilder(conf: SQLConf) extends 
SqlBaseBaseVisitor[AnyRef] with Logging
   override def visitRefreshTable(ctx: RefreshTableContext): LogicalPlan = 
withOrigin(ctx) {
 RefreshTableStatement(visitMultipartIdentifier(ctx.multipartIdentifier()))
   }
+
+  /**
+   * A command for users to list the column names for a table.
+   * This function creates a [[ShowColumnsStatement]] logical plan.
+   *
+   * The syntax of using this command in SQL is:
+   * {{{
+   *   SHOW COLUMNS (FROM | IN) tableName=multipartIdentifier
+   *((FROM | IN) namespace=multipartIdentifier)?
+   * }}}
+   */
+  override def visitShowColumns(ctx: ShowColumnsContext): LogicalPlan = 
withOrigin(ctx) {
+import org.apache.spark.sql.connector.catalog.CatalogV2Implicits._
+
+val table = visitMultipartIdentifier(ctx.table)
+val namespace = Option(ctx.namespace).map(visitMultipartIdentifier)
+if (namespace.isDefined && namespace.get.length > 1 && table.length > 1) {
+  val tableName = table.init
+  throw new ParseException(
+s"If namespace is multipart, table can not be multipart: 
${tableName.quoted}", ctx)
 
 Review comment:
   how about "Cannot specify multi-part table name if `FROM namespace` is 
specified."


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[GitHub] [spark] cloud-fan commented on a change in pull request #26182: [SPARK-29523][SQL] SHOW COLUMNS should do multi-catalog resolution.

2019-10-28 Thread GitBox
cloud-fan commented on a change in pull request #26182: [SPARK-29523][SQL] SHOW 
COLUMNS should do multi-catalog resolution.
URL: https://github.com/apache/spark/pull/26182#discussion_r339892210
 
 

 ##
 File path: 
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/parser/AstBuilder.scala
 ##
 @@ -2911,4 +2911,28 @@ class AstBuilder(conf: SQLConf) extends 
SqlBaseBaseVisitor[AnyRef] with Logging
   override def visitRefreshTable(ctx: RefreshTableContext): LogicalPlan = 
withOrigin(ctx) {
 RefreshTableStatement(visitMultipartIdentifier(ctx.multipartIdentifier()))
   }
+
+  /**
+   * A command for users to list the column names for a table.
+   * This function creates a [[ShowColumnsStatement]] logical plan.
+   *
+   * The syntax of using this command in SQL is:
+   * {{{
+   *   SHOW COLUMNS (FROM | IN) tableName=multipartIdentifier
+   *((FROM | IN) namespace=multipartIdentifier)?
+   * }}}
+   */
+  override def visitShowColumns(ctx: ShowColumnsContext): LogicalPlan = 
withOrigin(ctx) {
+import org.apache.spark.sql.connector.catalog.CatalogV2Implicits._
+
+val table = visitMultipartIdentifier(ctx.table)
+val namespace = Option(ctx.namespace).map(visitMultipartIdentifier)
+if (namespace.isDefined && namespace.get.length > 1 && table.length > 1) {
 
 Review comment:
   I think the logic should be: if the namespace is specified, then table name 
length must be 1.


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[GitHub] [spark] AmplabJenkins commented on issue #26265: [SPARK-29565][ML][PYTHON] OneHotEncoder should support single-column input/output

2019-10-28 Thread GitBox
AmplabJenkins commented on issue #26265: [SPARK-29565][ML][PYTHON] 
OneHotEncoder should support single-column input/output
URL: https://github.com/apache/spark/pull/26265#issuecomment-547253728
 
 
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[GitHub] [spark] AmplabJenkins commented on issue #26265: [SPARK-29565][ML][PYTHON] OneHotEncoder should support single-column input/output

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OneHotEncoder should support single-column input/output
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[GitHub] [spark] AmplabJenkins commented on issue #24898: [SPARK-22340][PYTHON] Add a mode to pin Python thread into JVM's

2019-10-28 Thread GitBox
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[GitHub] [spark] AmplabJenkins commented on issue #26265: [SPARK-29565][ML][PYTHON] OneHotEncoder should support single-column input/output

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AmplabJenkins commented on issue #26265: [SPARK-29565][ML][PYTHON] 
OneHotEncoder should support single-column input/output
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[GitHub] [spark] SparkQA commented on issue #26265: [SPARK-29565][ML][PYTHON] OneHotEncoder should support single-column input/output

2019-10-28 Thread GitBox
SparkQA commented on issue #26265: [SPARK-29565][ML][PYTHON] OneHotEncoder 
should support single-column input/output
URL: https://github.com/apache/spark/pull/26265#issuecomment-547253716
 
 
   **[Test build #112820 has 
finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/112820/testReport)**
 for PR 26265 at commit 
[`f3fa47d`](https://github.com/apache/spark/commit/f3fa47db1b875d80125226954212c9fcb57a5ae6).
* This patch **fails Python style tests**.
* This patch merges cleanly.
* This patch adds the following public classes _(experimental)_:
 * `class _OneHotEncoderParams(HasInputCol, HasInputCols, HasOutputCol, 
HasOutputCols,`


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[GitHub] [spark] AmplabJenkins removed a comment on issue #26265: [SPARK-29565][ML][PYTHON] OneHotEncoder should support single-column input/output

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[GitHub] [spark] AmplabJenkins commented on issue #26265: [SPARK-29565][ML][PYTHON] OneHotEncoder should support single-column input/output

2019-10-28 Thread GitBox
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OneHotEncoder should support single-column input/output
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[GitHub] [spark] HyukjinKwon commented on issue #24898: [SPARK-22340][PYTHON] Add a mode to pin Python thread into JVM's

2019-10-28 Thread GitBox
HyukjinKwon commented on issue #24898: [SPARK-22340][PYTHON] Add a mode to pin 
Python thread into JVM's
URL: https://github.com/apache/spark/pull/24898#issuecomment-547253383
 
 
   retest this please


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[GitHub] [spark] cloud-fan commented on issue #26243: Prepare Spark release v3.0.0-preview-rc1

2019-10-28 Thread GitBox
cloud-fan commented on issue #26243: Prepare Spark release v3.0.0-preview-rc1
URL: https://github.com/apache/spark/pull/26243#issuecomment-547253300
 
 
   LGTM


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[GitHub] [spark] HyukjinKwon commented on issue #24898: [SPARK-22340][PYTHON] Add a mode to pin Python thread into JVM's

2019-10-28 Thread GitBox
HyukjinKwon commented on issue #24898: [SPARK-22340][PYTHON] Add a mode to pin 
Python thread into JVM's
URL: https://github.com/apache/spark/pull/24898#issuecomment-547253235
 
 
   > Needs some doc updates, and I'm thinking we keep this mode as experimental 
to start
   
   Yeah, I would like to explicitly mention that this is experimental. I can 
document at `docstring` in `setJobGroup` and its friends and/or at 
`SparkContext`. @squito do you have somewhere else in your mind?
   


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[GitHub] [spark] SparkQA commented on issue #26265: [SPARK-29565][ML][PYTHON] OneHotEncoder should support single-column input/output

2019-10-28 Thread GitBox
SparkQA commented on issue #26265: [SPARK-29565][ML][PYTHON] OneHotEncoder 
should support single-column input/output
URL: https://github.com/apache/spark/pull/26265#issuecomment-547253307
 
 
   **[Test build #112820 has 
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