### What changes were proposed in this pull request?
This is a follow-up of #31311 and fixes a typo in Scala 2.13 profile section in `publish-snapshot` command.
### Why are the changes needed?
To fix snapshot publishing for Scala 2.13.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Manual.
Closes#31338 from dongjoon-hyun/SPARK-34218-2.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR aims to fix Avro data source to use the decimal precision and scale of file schema.
### Why are the changes needed?
The decimal value should be interpreted with its original precision and scale. Otherwise, it returns incorrect result like the following. The schema mismatch happens when we use `userSpecifiedSchema` or there are multiple files with inconsistent schema or HiveMetastore schema is updated by the user.
```scala
scala> sql("SELECT 3.14 a").write.format("avro").save("/tmp/avro")
scala> spark.read.schema("a DECIMAL(4, 3)").format("avro").load("/tmp/avro").show
+-----+
| a|
+-----+
|0.314|
+-----+
```
### Does this PR introduce _any_ user-facing change?
Yes, this will return correct result.
### How was this patch tested?
Pass the CI with the newly added test case.
Closes#31329 from dongjoon-hyun/SPARK-34229.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Add notice about keep hive version consistence when config hive jars location
With PR #29881, if we don't keep hive version consistence. we will got below error.
```
Builtin jars can only be used when hive execution version == hive metastore version. Execution: 2.3.8 != Metastore: 1.2.1. Specify a valid path to the correct hive jars using spark.sql.hive.metastore.jars or change spark.sql.hive.metastore.version to 2.3.8.
```
### Why are the changes needed?
Make config doc detail
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Not need
Closes#31317 from AngersZhuuuu/SPARK-32852-followup.
Authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
with a simple case, the null will be passed to InsertIntoHadoopFsRelationCommand blindly, we should avoid the npe
```scala
test("NPE") {
withTable("t") {
sql(s"CREATE TABLE t(i STRING, c string) USING $format PARTITIONED BY (c)")
sql("INSERT OVERWRITE t PARTITION (c=null) VALUES ('1')")
checkAnswer(spark.table("t"), Row("1", null))
}
}
```
```logtalk
java.lang.NullPointerException
at scala.collection.immutable.StringOps$.length(StringOps.scala:51)
at scala.collection.immutable.StringOps.length(StringOps.scala:51)
at scala.collection.IndexedSeqOptimized.foreach(IndexedSeqOptimized.scala:35)
at scala.collection.IndexedSeqOptimized.foreach
at scala.collection.immutable.StringOps.foreach(StringOps.scala:33)
at org.apache.spark.sql.catalyst.catalog.ExternalCatalogUtils$.escapePathName(ExternalCatalogUtils.scala:69)
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.orig-s0.0000030000-r30676-expand-or-complete(InsertIntoHadoopFsRelationCommand.scala:231)
```
### Why are the changes needed?
a bug fix
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
new tests
Closes#31320 from yaooqinn/SPARK-34223.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1, make `silhouette` a method;
2, change return type of `setDistanceMeasure` to `this.type`;
### Why are the changes needed?
see comments in https://github.com/apache/spark/pull/28590
### Does this PR introduce _any_ user-facing change?
No, 3.1 has not been released
### How was this patch tested?
existing testsuites
Closes#31334 from zhengruifeng/31768-followup.
Authored-by: Ruifeng Zheng <ruifengz@foxmail.com>
Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
### What changes were proposed in this pull request?
On the read-side, the char length check and padding bring issues to CBO and predicate pushdown and other issues to the catalyst.
This PR reverts 6da5cdf1db that added read side length check) so that we only do length check for the write side, and data sources/vendors are responsible to enforce the char/varchar constraints for data import operations like ADD PARTITION. It doesn't make sense for Spark to report errors on the read-side if the data is already dirty.
This PR also moves the char padding to the write-side, so that it 1) avoids read side issues like CBO and filter pushdown. 2) the data source can preserve char type semantic better even if it's read by systems other than Spark.
### Why are the changes needed?
fix perf regression when tables have char/varchar type columns
closes#31278
### Does this PR introduce _any_ user-facing change?
yes, spark will not raise error for oversized char/varchar values in read side
### How was this patch tested?
modified ut
the dropped read side benchmark
```
================================================================================================
Char Varchar Read Side Perf w/o Tailing Spaces
================================================================================================
Java HotSpot(TM) 64-Bit Server VM 1.8.0_251-b08 on Mac OS X 10.16
Intel(R) Core(TM) i9-9980HK CPU 2.40GHz
Read with length 20: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
read string with length 20 1564 1573 9 63.9 15.6 1.0X
read char with length 20 1532 1551 18 65.3 15.3 1.0X
read varchar with length 20 1520 1531 13 65.8 15.2 1.0X
Java HotSpot(TM) 64-Bit Server VM 1.8.0_251-b08 on Mac OS X 10.16
Intel(R) Core(TM) i9-9980HK CPU 2.40GHz
Read with length 40: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
read string with length 40 1573 1613 41 63.6 15.7 1.0X
read char with length 40 1575 1577 2 63.5 15.7 1.0X
read varchar with length 40 1568 1576 11 63.8 15.7 1.0X
Java HotSpot(TM) 64-Bit Server VM 1.8.0_251-b08 on Mac OS X 10.16
Intel(R) Core(TM) i9-9980HK CPU 2.40GHz
Read with length 60: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
read string with length 60 1526 1540 23 65.5 15.3 1.0X
read char with length 60 1514 1539 23 66.0 15.1 1.0X
read varchar with length 60 1486 1497 10 67.3 14.9 1.0X
Java HotSpot(TM) 64-Bit Server VM 1.8.0_251-b08 on Mac OS X 10.16
Intel(R) Core(TM) i9-9980HK CPU 2.40GHz
Read with length 80: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
read string with length 80 1531 1542 19 65.3 15.3 1.0X
read char with length 80 1514 1529 15 66.0 15.1 1.0X
read varchar with length 80 1524 1565 42 65.6 15.2 1.0X
Java HotSpot(TM) 64-Bit Server VM 1.8.0_251-b08 on Mac OS X 10.16
Intel(R) Core(TM) i9-9980HK CPU 2.40GHz
Read with length 100: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
read string with length 100 1597 1623 25 62.6 16.0 1.0X
read char with length 100 1499 1512 16 66.7 15.0 1.1X
read varchar with length 100 1517 1524 8 65.9 15.2 1.1X
================================================================================================
Char Varchar Read Side Perf w/ Tailing Spaces
================================================================================================
Java HotSpot(TM) 64-Bit Server VM 1.8.0_251-b08 on Mac OS X 10.16
Intel(R) Core(TM) i9-9980HK CPU 2.40GHz
Read with length 20: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
read string with length 20 1524 1526 1 65.6 15.2 1.0X
read char with length 20 1532 1537 9 65.3 15.3 1.0X
read varchar with length 20 1520 1532 15 65.8 15.2 1.0X
Java HotSpot(TM) 64-Bit Server VM 1.8.0_251-b08 on Mac OS X 10.16
Intel(R) Core(TM) i9-9980HK CPU 2.40GHz
Read with length 40: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
read string with length 40 1556 1580 32 64.3 15.6 1.0X
read char with length 40 1600 1611 17 62.5 16.0 1.0X
read varchar with length 40 1648 1716 88 60.7 16.5 0.9X
Java HotSpot(TM) 64-Bit Server VM 1.8.0_251-b08 on Mac OS X 10.16
Intel(R) Core(TM) i9-9980HK CPU 2.40GHz
Read with length 60: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
read string with length 60 1504 1524 20 66.5 15.0 1.0X
read char with length 60 1509 1512 3 66.2 15.1 1.0X
read varchar with length 60 1519 1535 21 65.8 15.2 1.0X
Java HotSpot(TM) 64-Bit Server VM 1.8.0_251-b08 on Mac OS X 10.16
Intel(R) Core(TM) i9-9980HK CPU 2.40GHz
Read with length 80: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
read string with length 80 1640 1652 17 61.0 16.4 1.0X
read char with length 80 1625 1666 35 61.5 16.3 1.0X
read varchar with length 80 1590 1605 13 62.9 15.9 1.0X
Java HotSpot(TM) 64-Bit Server VM 1.8.0_251-b08 on Mac OS X 10.16
Intel(R) Core(TM) i9-9980HK CPU 2.40GHz
Read with length 100: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------------------------------
read string with length 100 1622 1628 5 61.6 16.2 1.0X
read char with length 100 1614 1646 30 62.0 16.1 1.0X
read varchar with length 100 1594 1606 11 62.7 15.9 1.0X
```
Closes#31281 from yaooqinn/SPARK-34192.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR aims to add `Scala 2.13` packaging and publishing.
### Why are the changes needed?
To support Scala 2.13 officially in Apache Spark 3.2.0, we need to publish the artifacts.
### Does this PR introduce _any_ user-facing change?
Yes, this will provide additional artifacts.
### How was this patch tested?
Manual.
Closes#31311 from dongjoon-hyun/SPARK-34218.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
In the PR, I propose to convert `null` partition values to `"__HIVE_DEFAULT_PARTITION__"` before storing in the `In-Memory` catalog internally. Currently, the `In-Memory` catalog maintains null partitions as `"__HIVE_DEFAULT_PARTITION__"` in file system but as `null` values in memory that could cause some issues like in SPARK-34203.
### Why are the changes needed?
`InMemoryCatalog` stores partitions in the file system in the Hive compatible form, for instance, it converts the `null` partition value to `"__HIVE_DEFAULT_PARTITION__"` but at the same time it keeps null as is internally. That causes an issue demonstrated by the example below:
```
$ ./bin/spark-shell -c spark.sql.catalogImplementation=in-memory
```
```scala
scala> spark.conf.get("spark.sql.catalogImplementation")
res0: String = in-memory
scala> sql("CREATE TABLE tbl (col1 INT, p1 STRING) USING parquet PARTITIONED BY (p1)")
res1: org.apache.spark.sql.DataFrame = []
scala> sql("INSERT OVERWRITE TABLE tbl VALUES (0, null)")
res2: org.apache.spark.sql.DataFrame = []
scala> sql("ALTER TABLE tbl DROP PARTITION (p1 = null)")
org.apache.spark.sql.catalyst.analysis.NoSuchPartitionsException: The following partitions not found in table 'tbl' database 'default':
Map(p1 -> null)
at org.apache.spark.sql.catalyst.catalog.InMemoryCatalog.dropPartitions(InMemoryCatalog.scala:440)
```
### Does this PR introduce _any_ user-facing change?
Yes. After the changes, `ALTER TABLE .. DROP PARTITION` can drop the `null` partition in `In-Memory` catalog:
```scala
scala> spark.table("tbl").show(false)
+----+----+
|col1|p1 |
+----+----+
|0 |null|
+----+----+
scala> sql("ALTER TABLE tbl DROP PARTITION (p1 = null)")
res4: org.apache.spark.sql.DataFrame = []
scala> spark.table("tbl").show(false)
+----+---+
|col1|p1 |
+----+---+
+----+---+
```
### How was this patch tested?
Added new test to `AlterTableDropPartitionSuiteBase`:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *AlterTableDropPartitionSuite"
```
Closes#31322 from MaxGekk/insert-overwrite-null-part.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
optimize: change Option[LogicalRelation] to LogicalRelation
### Why are the changes needed?
simplify code
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
Existed unit test.
Closes#31315 from monkeyboy123/spark-34067-follow-up.
Authored-by: Dereck Li <monkeyboy.ljh@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Check the name passed to `SessionCatalog.refreshTable`, and if it belongs to a temporary view, do not invalidate the relation cache.
### Why are the changes needed?
When `SessionCatalog.refreshTable` refreshes a temporary or global temporary view, it should not invalidate an entry in the relation cache associated to a table with the same name.
### Does this PR introduce _any_ user-facing change?
Should not. The change might improve performance slightly.
### How was this patch tested?
By running new UT:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *SessionCatalogSuite"
```
Closes#31265 from MaxGekk/fix-session-catalog-refresh-table.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The `RowBasedKeyValueBatch` has two different implementations depending on whether the aggregation key and value uses only fixed length data types (`FixedLengthRowBasedKeyValueBatch`) or not (`VariableLengthRowBasedKeyValueBatch`).
Before this PR the decision about the used implementation was based on by accessing the schema fields by their name.
But if two fields has the same name and one with variable length and the other with fixed length type (and all the other fields are with fixed length types) a bad decision could be made.
When `FixedLengthRowBasedKeyValueBatch` is chosen but there is a variable length field then an aggregation function could calculate with invalid values. This case is illustrated by the example used in the unit test:
`with T as (select id as a, -id as x from range(3)),
U as (select id as b, cast(id as string) as x from range(3))
select T.x, U.x, min(a) as ma, min(b) as mb from T join U on a=b group by U.x, T.x`
where the 'x' column in the left side of the join is a Long but on the right side is a String.
### Why are the changes needed?
Fixes the issue where duplicate field name aggregation has null values in the dataframe.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Added UT, tested manually on spark shell.
Closes#30788 from yliou/SPARK-33726.
Authored-by: yliou <yliou@berkeley.edu>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Make the field name matching between Avro and Catalyst schemas, on both the reader and writer paths, respect the global SQL settings for case sensitivity (i.e. case-insensitive by default). `AvroSerializer` and `AvroDeserializer` share a common utility in `AvroUtils` to search for an Avro field to match a given Catalyst field.
### Why are the changes needed?
Spark SQL is normally case-insensitive (by default), but currently when `AvroSerializer` and `AvroDeserializer` perform matching between Catalyst schemas and Avro schemas, the matching is done in a case-sensitive manner. So for example the following will fail:
```scala
val avroSchema =
"""
|{
| "type" : "record",
| "name" : "test_schema",
| "fields" : [
| {"name": "foo", "type": "int"},
| {"name": "BAR", "type": "int"}
| ]
|}
""".stripMargin
val df = Seq((1, 3), (2, 4)).toDF("FOO", "bar")
df.write.option("avroSchema", avroSchema).format("avro").save(savePath)
```
The same is true on the read path, if we assume `testAvro` has been written using the schema above, the below will fail to match the fields:
```scala
df.read.schema(new StructType().add("FOO", IntegerType).add("bar", IntegerType))
.format("avro").load(testAvro)
```
### Does this PR introduce _any_ user-facing change?
When reading Avro data, or writing Avro data using the `avroSchema` option, field matching will be performed with case sensitivity respecting the global SQL settings.
### How was this patch tested?
New tests added to `AvroSuite` to validate the case sensitivity logic in an end-to-end manner through the SQL engine.
Closes#31201 from xkrogen/xkrogen-SPARK-34133-avro-serde-casesensitivity-errormessages.
Authored-by: Erik Krogen <xkrogen@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR fixes the following integration suites to reflect the change of SPARK-33888.
* PostgresIntegrationSuite
* MySQLIntegrationSuite
* MsSqlServerIntegrationSuite
* DB2IntegrationSuite
### Why are the changes needed?
Those suites doesn't pass currently in `master` branch.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Those suites pass.
Closes#31274 from sarutak/fix-integration-suites.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR keeps partitioning of input tables in TPCDSQueryBenchmark when `--cbo` option is enabled.
https://github.com/apache/spark/pull/31011 introduced the `--cbo` option but unfortunately in that mode the table partitioning of the input data is lost. This means that the results of CBO mode is very different to non CBO mode, one example is that Dynamic Partition Pruning doesn't kick in in CBO mode.
### Why are the changes needed?
To monitor performance changed with CBO enabled.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Manually checked.
Closes#31218 from peter-toth/SPARK-34147-keep-partitioning-in-tpcdsquerybenchmark.
Authored-by: Peter Toth <peter.toth@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Compare the 3.1.1 API doc with the latest release version 3.0.1. Fix the following issues:
- Add missing `Since` annotation for new APIs
- Remove the leaking class/object in API doc
### Why are the changes needed?
Fix the issues in the Spark 3.1.1 release API docs.
### Does this PR introduce _any_ user-facing change?
Yes, API doc changes.
### How was this patch tested?
Manually test.
Closes#31271 from xuanyuanking/SPARK-34185.
Lead-authored-by: Yuanjian Li <yuanjian.li@databricks.com>
Co-authored-by: Hyukjin Kwon <gurwls223@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Adds `NullType` support for Arrow executions.
### Why are the changes needed?
As Arrow supports null type, we can convert `NullType` between PySpark and pandas with Arrow enabled.
### Does this PR introduce _any_ user-facing change?
Yes, if a user has a DataFrame including `NullType`, it will be able to convert with Arrow enabled.
### How was this patch tested?
Added tests.
Closes#31285 from ueshin/issues/SPARK-33489/arrow_nulltype.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR aims to fix the Scala 2.12 release profile in `release-build.sh`.
### Why are the changes needed?
Since 3.0.0 (SPARK-26132), the release script is using `SCALA_2_11_PROFILES` to publish Scala 2.12 artifacts.
After looking at the code, this is not a blocker because `-Pscala-2.11` is no-op in `branch-3.x`.
In addition `scala-2.12` profile is enabled by default and it's an empty profile without any configuration technically.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
This is used by release manager only.
Manually. This should land at `master/3.1/3.0`.
Closes#31310 from dongjoon-hyun/SPARK-34217.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This is a follow up of the PRs https://github.com/apache/spark/pull/31066 and https://github.com/apache/spark/pull/31304 that changed behavior of some commands regarding to table cache refreshing. The PR updates the SQL migration guide, in particular, the item which describes new behavior.
### Why are the changes needed?
To inform users about command behavior changes.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
N/A
Closes#31309 from MaxGekk/refreshTable-sql-migration-guide.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This patch uses the available offset range obtained during polling Kafka records to do offset validation check.
### Why are the changes needed?
We support non-consecutive offsets for Kafka since 2.4.0. In `fetchRecord`, we do offset validation by checking if the offset is in available offset range. But currently we obtain latest available offset range to do the check. It looks not correct as the available offset range could be changed during the batch, so the available offset range is different than the one when we polling the records from Kafka.
It is possible that an offset is valid when polling, but at the time we do the above check, it is out of latest available offset range. We will wrongly consider it as data loss case and fail the query or drop the record.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
This should pass existing unit tests.
This is hard to have unit test as the Kafka producer and the consumer is asynchronous. Further, we also need to make the offset out of new available offset range.
Closes#31275 from viirya/SPARK-34187.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This pr add partition columns for TPCDS tables. The partition column is consistent with the [TPCDSTables](https://github.com/databricks/spark-sql-perf/blob/master/src/main/scala/com/databricks/spark/sql/perf/tpcds/TPCDSTables.scala).
### Why are the changes needed?
Better track plan changes. For example, [this is the change](3fe1a93a40) after SPARK-34119.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
N/A
Closes#31243 from wangyum/SPARK-34155.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Invoke `CatalogImpl.refreshTable()` instead of `SessionCatalog.refreshTable` in v1 implementation of the `LOAD DATA` command. `SessionCatalog.refreshTable` just refreshes metadata comparing to `CatalogImpl.refreshTable()` which refreshes cached table data as well.
### Why are the changes needed?
The example below portraits the issue:
- Create a source table:
```sql
spark-sql> CREATE TABLE src_tbl (c0 int, part int) USING hive PARTITIONED BY (part);
spark-sql> INSERT INTO src_tbl PARTITION (part=0) SELECT 0;
spark-sql> SHOW TABLE EXTENDED LIKE 'src_tbl' PARTITION (part=0);
default src_tbl false Partition Values: [part=0]
Location: file:/Users/maximgekk/proj/load-data-refresh-cache/spark-warehouse/src_tbl/part=0
...
```
- Load data from the source table to a cached destination table:
```sql
spark-sql> CREATE TABLE dst_tbl (c0 int, part int) USING hive PARTITIONED BY (part);
spark-sql> INSERT INTO dst_tbl PARTITION (part=1) SELECT 1;
spark-sql> CACHE TABLE dst_tbl;
spark-sql> SELECT * FROM dst_tbl;
1 1
spark-sql> LOAD DATA LOCAL INPATH '/Users/maximgekk/proj/load-data-refresh-cache/spark-warehouse/src_tbl/part=0' INTO TABLE dst_tbl PARTITION (part=0);
spark-sql> SELECT * FROM dst_tbl;
1 1
```
The last query does not return new loaded data.
### Does this PR introduce _any_ user-facing change?
Yes. After the changes, the example above works correctly:
```sql
spark-sql> LOAD DATA LOCAL INPATH '/Users/maximgekk/proj/load-data-refresh-cache/spark-warehouse/src_tbl/part=0' INTO TABLE dst_tbl PARTITION (part=0);
spark-sql> SELECT * FROM dst_tbl;
0 0
1 1
```
### How was this patch tested?
Added new test to `org.apache.spark.sql.hive.CachedTableSuite`:
```
$ build/sbt -Phive -Phive-thriftserver "test:testOnly *CachedTableSuite"
```
Closes#31304 from MaxGekk/load-data-refresh-cache.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Rename `SessionCatalog.isTemporaryTable()` to `SessionCatalog.isTempView()`.
### Why are the changes needed?
To improve code maintenance. Currently, there are two methods that do the same but have different names:
```scala
def isTempView(nameParts: Seq[String]): Boolean
```
and
```scala
def isTemporaryTable(name: TableIdentifier): Boolean
```
### Does this PR introduce _any_ user-facing change?
Should not since `SessionCatalog` is not public API.
### How was this patch tested?
By running the existing tests:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *SessionCatalogSuite"
```
Closes#31295 from MaxGekk/replace-isTemporaryTable-by-isTempView.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
`HiveExternalCatalogVersionsSuite` can't run in orgs internal environment where access to outside internet is not allowed because `HiveExternalCatalogVersionsSuite` will download spark release package from internet.
Similar to SPARK-32998, this pr add 1 environment variables `SPARK_RELEASE_MIRROR` to let user can specify an accessible download address of spark release package and run `HiveExternalCatalogVersionsSuite` in orgs internal environment.
### Why are the changes needed?
Let `HiveExternalCatalogVersionsSuite` can run in orgs internal environment without relying on external spark release download address.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- Pass the Jenkins or GitHub Action
- Manual test with and without env variables set in internal environment can't access internet.
execute
```
mvn clean install -Dhadoop-3.2 -Phive-2.3 -Phadoop-cloud -Pmesos -Pyarn -Pkinesis-asl -Phive-thriftserver -Pspark-ganglia-lgpl -Pkubernetes -PhPhive -pl sql/hive -am -DskipTests
mvn clean install -Dhadoop-3.2 -Phive-2.3 -Phadoop-cloud -Pmesos -Pyarn -Pkinesis-asl -Phive-thriftserver -Pspark-ganglia-lgpl -Pkubernetes -PhPhive -pl sql/hive -DwildcardSuites=org.apache.spark.sql.hive.HiveExternalCatalogVersionsSuite -Dtest=none
```
**Without env**
```
HiveExternalCatalogVersionsSuite:
19:50:35.123 WARN org.apache.spark.sql.hive.HiveExternalCatalogVersionsSuite: Failed to download Spark 3.0.1 from https://archive.apache.org/dist/spark/spark-3.0.1/spark-3.0.1-bin-hadoop3.2.tgz: Network is unreachable (connect failed)
19:50:35.126 WARN org.apache.spark.sql.hive.HiveExternalCatalogVersionsSuite: Failed to download Spark 3.0.1 from https://dist.apache.org/repos/dist/release/spark/spark-3.0.1/spark-3.0.1-bin-hadoop3.2.tgz: Network is unreachable (connect failed)
org.apache.spark.sql.hive.HiveExternalCatalogVersionsSuite *** ABORTED ***
Exception encountered when invoking run on a nested suite - Unable to download Spark 3.0.1 (HiveExternalCatalogVersionsSuite.scala:125)
Run completed in 2 seconds, 669 milliseconds.
Total number of tests run: 0
Suites: completed 1, aborted 1
Tests: succeeded 0, failed 0, canceled 0, ignored 0, pending 0
```
**With env**
```
export SPARK_RELEASE_MIRROR=${spark-release.internal.com}/dist/release/
```
```
HiveExternalCatalogVersionsSuite
- backward compatibility
Run completed in 1 minute, 32 seconds.
Total number of tests run: 1
Suites: completed 2, aborted 0
Tests: succeeded 1, failed 0, canceled 0, ignored 0, pending 0
All tests passed.
```
Closes#31294 from LuciferYang/SPARK-34202.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
`KubernetesClusterManager` and `ExecutorPodsLifecycleManager` are private Spark classes.
This PR aims to move `Guava Cache` from a constructor parameter to private field of `ExecutorPodsLifecycleManager`.
### Why are the changes needed?
1. Although `KubernetesClusterManager` creates `Guava Cache`, only `ExecutorPodsLifecycleManager` uses it.
2. Although `ExecutorPodsLifecycleManager` is a Spark private class, when some users implement a new cluster manager with `ExternalClusterManager` for K8s, they can reuse `ExecutorPodsLifecycleManager`. In this case, `Guava Cache` is not good as an interface because it's a shaded class.
### Does this PR introduce _any_ user-facing change?
No. This is an Spark private.
### How was this patch tested?
Pass the existing UTs.
Closes#31297 from dongjoon-hyun/SPARK-34206.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR aims to upgrade Apache ORC from 1.6.6 to 1.6.7.
### Why are the changes needed?
Apache ORC 1.6.7 has the following fixes including [ORC-711 Support CryptoExtension in create/decryptLocalKey](https://issues.apache.org/jira/browse/ORC-711).
- https://issues.apache.org/jira/secure/ReleaseNote.jspa?projectId=12318320&version=12349470
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass the CIs with the existing tests.
Closes#31301 from dongjoon-hyun/SPARK-34208.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Added typing for keyword-only single argument udf overload.
### Why are the changes needed?
The intended use case is:
```
udf(returnType="string")
def f(x): ...
```
### Does this PR introduce _any_ user-facing change?
Yes - a new typing for udf is considered valid.
### How was this patch tested?
Existing tests.
Closes#31282 from pgrz/patch-1.
Authored-by: pgrz <grzegorski.piotr@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This is a long-standing bug that exists since we have the ambiguous self-join check. A column reference is not ambiguous if it can only come from one join side (e.g. the other side has a project to only pick a few columns). An example is
```
Join(b#1 = 3)
TableScan(t, [a#0, b#1])
Project(a#2)
TableScan(t, [a#2, b#3])
```
It's a self-join, but `b#1` is not ambiguous because it can't come from the right side, which only has column `a`.
### Why are the changes needed?
to not fail valid self-join queries.
### Does this PR introduce _any_ user-facing change?
yea as a bug fix
### How was this patch tested?
a new test
Closes#31287 from cloud-fan/self-join.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR is the same as https://github.com/apache/spark/pull/30998, but with a better UT.
In AdaptiveSparkPlanExec.getFinalPhysicalPlan, when newStages are generated, sort the new stages by class type to make sure BroadcastQueryState precede others.
This partial fix only grantee the start of materialization for BroadcastQueryStage is prior to others, but because the submission of collect job for broadcasting is run in another thread, the issue is not completely solved.
### Why are the changes needed?
When enable AQE, in getFinalPhysicalPlan, spark traversal the physical plan bottom up and create query stage for materialized part by createQueryStages and materialize those new created query stages to submit map stages or broadcasting. When ShuffleQueryStage are materializing before BroadcastQueryStage, the map stage(job) and broadcast job are submitted almost at the same time, but map stage will hold all the computing resources. If the map stage runs slow (when lots of data needs to process and the resource is limited), the broadcast job cannot be started(and finished) before spark.sql.broadcastTimeout, thus cause whole job failed (introduced in SPARK-31475).
The workaround to increase spark.sql.broadcastTimeout doesn't make sense and graceful, because the data to broadcast is very small.
The order of calling materialize can guarantee that the order of task to be scheduled in normal circumstances, but, the guarantee is not strict since the submit of broadcast job and shuffle map job are in different thread.
1. for broadcast job, call doPrepare() in main thread, and then start the real materialization in "broadcast-exchange-0" thread pool: calling getByteArrayRdd().collect() to submit collect job
2. for shuffle map job, call ShuffleExchangeExec.mapOutputStatisticsFuture() which call sparkContext.submitMapStage() directly in main thread to submit map stage
1 is trigger before 2, so in normal cases, the broadcast job will be submit first.
However, we can not control how fast the two thread runs, so the "broadcast-exchange-0" thread could run a little bit slower than main thread, result in map stage submit first. So there's still risk for the shuffle map job schedule earlier before broadcast job.
Since completely fix the issue is complex and might introduce major changes, we need more time to follow up. This partial fix is better than do nothing, it resolved most cases in SPARK-33933.
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
Add UT
Closes#31269 from zhongyu09/aqe-broadcast-partial-fix.
Authored-by: Yu Zhong <zhongyu8@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR group exception messages in `/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions`.
### Why are the changes needed?
It will largely help with standardization of error messages and its maintenance.
### Does this PR introduce _any_ user-facing change?
No. Error messages remain unchanged.
### How was this patch tested?
No new tests - pass all original tests to make sure it doesn't break any existing behavior.
Closes#31228 from beliefer/SPARK-33541.
Authored-by: beliefer <beliefer@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR fixes the issue that reading tables which contain spatial datatypes from MS SQL Server fails.
MS SQL server supports two non-standard spatial JDBC types, `geometry` and `geography` but Spark SQL can't treat them
```
java.sql.SQLException: Unrecognized SQL type -157
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.getCatalystType(JdbcUtils.scala:251)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.$anonfun$getSchema$1(JdbcUtils.scala:321)
at scala.Option.getOrElse(Option.scala:189)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.getSchema(JdbcUtils.scala:321)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD$.resolveTable(JDBCRDD.scala:63)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCRelation$.getSchema(JDBCRelation.scala:226)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcRelationProvider.createRelation(JdbcRelationProvider.scala:35)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:364)
at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:366)
at org.apache.spark.sql.DataFrameReader.$anonfun$load$2(DataFrameReader.scala:355)
at scala.Option.getOrElse(Option.scala:189)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:355)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:240)
at org.apache.spark.sql.DataFrameReader.jdbc(DataFrameReader.scala:381)
```
Considering the [data type mapping](https://docs.microsoft.com/ja-jp/sql/connect/jdbc/using-basic-data-types?view=sql-server-ver15) says, I think those spatial types can be mapped to Catalyst's `BinaryType`.
### Why are the changes needed?
To provide better support.
### Does this PR introduce _any_ user-facing change?
Yes. MS SQL Server users can use `geometry` and `geography` types in datasource tables.
### How was this patch tested?
New test case added to `MsSqlServerIntegrationSuite`.
Closes#31283 from sarutak/mssql-spatial-types.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR fixes the regression bug brought by SPARK-33888 (#30902).
After that PR merged, `PostgresDIalect#getCatalystType` throws Exception for array types.
```
[info] - Type mapping for various types *** FAILED *** (551 milliseconds)
[info] java.util.NoSuchElementException: key not found: scale
[info] at scala.collection.immutable.Map$EmptyMap$.apply(Map.scala:106)
[info] at scala.collection.immutable.Map$EmptyMap$.apply(Map.scala:104)
[info] at org.apache.spark.sql.types.Metadata.get(Metadata.scala:111)
[info] at org.apache.spark.sql.types.Metadata.getLong(Metadata.scala:51)
[info] at org.apache.spark.sql.jdbc.PostgresDialect$.getCatalystType(PostgresDialect.scala:43)
[info] at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.getSchema(JdbcUtils.scala:321)
```
### Why are the changes needed?
To fix the regression bug.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
I confirmed the test case `SPARK-22291: Conversion error when transforming array types of uuid, inet and cidr to StingType in PostgreSQL` in `PostgresIntegrationSuite` passed.
I also confirmed whether all the `v2.*IntegrationSuite` pass because this PR changed them and they passed.
Closes#31262 from sarutak/fix-postgres-dialect-regression.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR extends `StringTranslate` to support unicode characters whose code point >= `U+10000`.
### Why are the changes needed?
To make it work with wide variety of characters.
### Does this PR introduce _any_ user-facing change?
Yes. Users can use `StringTranslate` with unicode characters whose code point >= `U+10000`.
### How was this patch tested?
New assertion added to the existing test.
Closes#31164 from sarutak/extends-translate.
Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
This PR supplements the contents in the "Python Package Management".
If there is no Python installed in the local for all nodes when using `venv-pack`, job would fail as below.
```python
>>> from pyspark.sql.functions import pandas_udf
>>> pandas_udf('double')
... def pandas_plus_one(v: pd.Series) -> pd.Series:
... return v + 1
...
>>> spark.range(10).select(pandas_plus_one("id")).show()
...
Cannot run program "./environment/bin/python": error=2, No such file or directory
...
```
This is because the Python in the [packed environment via `venv-pack` has a symbolic link](https://github.com/jcrist/venv-pack/issues/5) that connects Python to the local one.
To avoid this confusion, it seems better to have an additional explanation for this.
### Why are the changes needed?
To provide more detailed information to users so that they don’t get confused
### Does this PR introduce _any_ user-facing change?
Yes, this PR fixes the part of "Python Package Management" in the "User Guide" documents.
### How was this patch tested?
Manually built the doc.
Closes#31280 from itholic/SPARK-34190.
Authored-by: itholic <haejoon309@naver.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR changes cache refreshing of v1 tables in v1 commands. In particular, v1 table dependents are not removed from the cache after this PR. Comparing to current implementation, we just clear cached data of all dependents and keep them in the cache. So, the next actions will fill in the cached data of the original v1 table and its dependents. In more details:
1. Modified the `CatalogImpl.refreshTable()` method to use `recacheByPlan()` instead of `lookupCachedData()`, `uncacheQuery()` and `cacheQuery()`. Users can call this method via public API like `spark.catalog.refreshTable()`.
2. Rewritten the part in `CatalogImpl.refreshTable()` which was responsible for table meta-data refreshing because this code stopped to work properly after removing of the second `sparkSession.table(tableIdent)`.
3. Added new private method `invalidateCachedTable()` to `SessionCatalog`. Comparing to the existing `SessionCatalog.refreshTable`, it invalidates the relation cache only. If we called `SessionCatalog.refreshTable` from `CatalogImpl.refreshTable()`, we would refresh temporary and global temporary views twice (that could lead to refreshing file index twice).
### Why are the changes needed?
1. This should improve user experience with table/view caching. For example, let's imagine that an user has cached v1 table and cached view based on the table. And the user passed the table to external library which drops/renames/adds partitions in the v1 table. Unfortunately, the user gets the view uncached after that even he/she hasn't uncached the view explicitly.
2. To improve code maintenance.
3. To reduce the amount of calls to Hive external catalog.
4. Also this should speed up table recaching.
5. To have the same behavior as for v2 tables supported by https://github.com/apache/spark/pull/31172
### Does this PR introduce _any_ user-facing change?
From the view of the correctness of query results, there are no behavior changes but the changes might influence on consuming memory and query execution time. For example:
Before:
```scala
scala> sql("CREATE TABLE tbl (c int)")
scala> sql("CACHE TABLE tbl")
scala> sql("CREATE VIEW v AS SELECT * FROM tbl")
scala> sql("CACHE TABLE v")
scala> spark.catalog.isCached("v")
res6: Boolean = true
scala> spark.catalog.refreshTable("tbl")
scala> spark.catalog.isCached("v")
res8: Boolean = false
```
After:
```scala
scala> spark.catalog.refreshTable("tbl")
scala> spark.catalog.isCached("v")
res8: Boolean = true
```
### How was this patch tested?
1. Added new unit tests that create a view, a temporary view and a global temporary view on top of v1/v2 tables, and refresh the base table via `ALTER TABLE .. ADD/DROP/RENAME PARTITION`.
2. By running the unified test suites:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *AlterTableAddPartitionSuite"
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *AlterTableDropPartitionSuite"
# build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *AlterTableRenamePartitionSuite"
```
Closes#31206 from MaxGekk/refreshTable-recache-by-plan.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add `drop table` in charvarchar sql test.
### Why are the changes needed?
1. `drop table` is also a test case, for better coverage.
2. It's more clear to drop table which created in current test.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Add test.
Closes#31277 from ulysses-you/SPARK-33901-FOLLOWUP.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add tests to check that v2 table dependents are re-cached after table altering via the commands:
- `ALTER TABLE .. ADD PARTITION`
- `ALTER TABLE .. DROP PARTITION`
- `ALTER TABLE .. RENAME PARTITION`
### Why are the changes needed?
To improve test coverage and prevent regressions.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running the modified test suites:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *.AlterTableAddPartitionSuite"
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *.AlterTableDropPartitionSuite"
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *.AlterTableRenamePartitionSuite"
```
Closes#31250 from MaxGekk/check-v2-dependents-recached.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The current implement of `UnboundedOffsetWindowFunctionFrame` and `UnboundedPrecedingOffsetWindowFunctionFrame` only support `nth_value` that respect nulls. So nth_value will execute with `UnboundedWindowFunctionFrame` and `UnboundedPrecedingWindowFunctionFrame`.
`UnboundedWindowFunctionFrame` and `UnboundedPrecedingWindowFunctionFrame` will call `updateExpressions` of `nth_value` multiple times.
### Why are the changes needed?
Improve performance for nth_value ignore nulls over offset window
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Jenkins test
Closes#31178 from beliefer/SPARK-34096.
Authored-by: beliefer <beliefer@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
For varchar(N), we currently trim all spaces first to check whether the remained length exceeds, it not necessary to visit them all but at most to those after N.
### Why are the changes needed?
improve varchar performance for write side
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
benchmark and existing ut
Closes#31253 from yaooqinn/SPARK-34164.
Authored-by: Kent Yao <yao@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
When ruby version is 3.0, jekyll server will failed with
```
yi.zhu$ SKIP_API=1 jekyll serve --watch
Configuration file: /Users/yi.zhu/Documents/project/Angerszhuuuu/spark/docs/_config.yml
Source: /Users/yi.zhu/Documents/project/Angerszhuuuu/spark/docs
Destination: /Users/yi.zhu/Documents/project/Angerszhuuuu/spark/docs/_site
Incremental build: disabled. Enable with --incremental
Generating...
done in 5.085 seconds.
Auto-regeneration: enabled for '/Users/yi.zhu/Documents/project/Angerszhuuuu/spark/docs'
------------------------------------------------
Jekyll 4.2.0 Please append `--trace` to the `serve` command
for any additional information or backtrace.
------------------------------------------------
<internal:/usr/local/Cellar/ruby/3.0.0_1/lib/ruby/3.0.0/rubygems/core_ext/kernel_require.rb>:85:in `require': cannot load such file -- webrick (LoadError)
from <internal:/usr/local/Cellar/ruby/3.0.0_1/lib/ruby/3.0.0/rubygems/core_ext/kernel_require.rb>:85:in `require'
from /Users/yi.zhu/.gem/ruby/3.0.0/gems/jekyll-4.2.0/lib/jekyll/commands/serve/servlet.rb:3:in `<top (required)>'
from /Users/yi.zhu/.gem/ruby/3.0.0/gems/jekyll-4.2.0/lib/jekyll/commands/serve.rb:179:in `require_relative'
from /Users/yi.zhu/.gem/ruby/3.0.0/gems/jekyll-4.2.0/lib/jekyll/commands/serve.rb:179:in `setup'
from /Users/yi.zhu/.gem/ruby/3.0.0/gems/jekyll-4.2.0/lib/jekyll/commands/serve.rb💯in `process'
from /Users/yi.zhu/.gem/ruby/3.0.0/gems/jekyll-4.2.0/lib/jekyll/command.rb:91:in `block in process_with_graceful_fail'
from /Users/yi.zhu/.gem/ruby/3.0.0/gems/jekyll-4.2.0/lib/jekyll/command.rb:91:in `each'
from /Users/yi.zhu/.gem/ruby/3.0.0/gems/jekyll-4.2.0/lib/jekyll/command.rb:91:in `process_with_graceful_fail'
from /Users/yi.zhu/.gem/ruby/3.0.0/gems/jekyll-4.2.0/lib/jekyll/commands/serve.rb:86:in `block (2 levels) in init_with_program'
from /Users/yi.zhu/.gem/ruby/3.0.0/gems/mercenary-0.4.0/lib/mercenary/command.rb:221:in `block in execute'
from /Users/yi.zhu/.gem/ruby/3.0.0/gems/mercenary-0.4.0/lib/mercenary/command.rb:221:in `each'
from /Users/yi.zhu/.gem/ruby/3.0.0/gems/mercenary-0.4.0/lib/mercenary/command.rb:221:in `execute'
from /Users/yi.zhu/.gem/ruby/3.0.0/gems/mercenary-0.4.0/lib/mercenary/program.rb:44:in `go'
from /Users/yi.zhu/.gem/ruby/3.0.0/gems/mercenary-0.4.0/lib/mercenary.rb:21:in `program'
from /Users/yi.zhu/.gem/ruby/3.0.0/gems/jekyll-4.2.0/exe/jekyll:15:in `<top (required)>'
from /usr/local/bin/jekyll:23:in `load'
from /usr/local/bin/jekyll:23:in `<main>'
```
This issue is solved in https://github.com/jekyll/jekyll/issues/8523
### Why are the changes needed?
Fix build issue
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Not need
Closes#31263 from AngersZhuuuu/SPARK-34181.
Lead-authored-by: Angerszhuuuu <angers.zhu@gmail.com>
Co-authored-by: AngersZhuuuu <angers.zhu@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
determine the numParts by numNodes
### Why are the changes needed?
current model saving may generate too many small files,
a tree model can be too large to single partition (a RandomForestClassificationModel with numTrees=100 and depth=20, its size is 226M)
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
existing testsuites
Closes#31090 from zhengruifeng/treemodel_single_part.
Authored-by: Ruifeng Zheng <ruifengz@foxmail.com>
Signed-off-by: Ruifeng Zheng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
This test fails flakily. I found it failing in 1 out of 80 runs.
```
Expected -0.35667494393873245 and -0.41914521201224453 to be within 0.15 using relative tolerance.
```
Increasing relative tolerance to 0.2 should improve flakiness.
```
0.2 * 0.35667494393873245 = 0.071 > 0.062 = |-0.35667494393873245 - (-0.41914521201224453)|
```
### Why are the changes needed?
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
Closes#31266 from Loquats/NaiveBayesSuite-reltol.
Authored-by: Andy Zhang <yue.zhang@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Update Avro dependency to version 1.10.1
### Why are the changes needed?
To catch up multiple improvements of Avro as well as fix security issues on transitive dependencies.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Since there were no API changes required we just run the tests
Closes#31232 from iemejia/SPARK-27733-avro-upgrade.
Authored-by: Ismaël Mejía <iemejia@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
There is one Java UT error when testing sql/hive module independently in Scala 2.13 after SPARK-33212, the error message as follow:
```
[ERROR] Tests run: 2, Failures: 0, Errors: 1, Skipped: 0, Time elapsed: 20.353 s <<< FAILURE! - in org.apache.spark.sql.hive.JavaDataFrameSuite
[ERROR] org.apache.spark.sql.hive.JavaDataFrameSuite.testUDAF Time elapsed: 18.548 s <<< ERROR!
java.lang.NoClassDefFoundError: scala/collection/parallel/TaskSupport
at org.apache.spark.sql.hive.JavaDataFrameSuite.checkAnswer(JavaDataFrameSuite.java:41)
at org.apache.spark.sql.hive.JavaDataFrameSuite.testUDAF(JavaDataFrameSuite.java:92)
Caused by: java.lang.ClassNotFoundException: scala.collection.parallel.TaskSupport
at org.apache.spark.sql.hive.JavaDataFrameSuite.checkAnswer(JavaDataFrameSuite.java:41)
at org.apache.spark.sql.hive.JavaDataFrameSuite.testUDAF(JavaDataFrameSuite.java:92)
```
This pr add a Scala-2.13 profile with dependency of `scala-parallel-collections_` to `sql/hive` module to fix the Java UT in Scala 2.13.
### Why are the changes needed?
Recover the independent mvn test ability of sql/hive module in Scala 2.13.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- Pass the Jenkins or GitHub Action
- Manual test
```
dev/change-scala-version.sh 2.13
mvn clean install -Dhadoop-3.2 -Phive-2.3 -Phadoop-cloud -Pmesos -Pyarn -Pkinesis-asl -Phive-thriftserver -Pspark-ganglia-lgpl -Pkubernetes -Phive -Pscala-2.13 -pl sql/hive -am -DskipTests
mvn test -Dhadoop-3.2 -Phive-2.3 -Phadoop-cloud -Pmesos -Pyarn -Pkinesis-asl -Phive-thriftserver -Pspark-ganglia-lgpl -Pkubernetes -Phive -Pscala-2.13 -pl sql/hive
```
**Before**
```
[ERROR] Tests run: 2, Failures: 0, Errors: 1, Skipped: 0, Time elapsed: 18.725 s <<< FAILURE! - in org.apache.spark.sql.hive.JavaDataFrameSuite
[ERROR] org.apache.spark.sql.hive.JavaDataFrameSuite.testUDAF Time elapsed: 16.853 s <<< ERROR!
java.lang.NoClassDefFoundError: scala/collection/parallel/TaskSupport
at org.apache.spark.sql.hive.JavaDataFrameSuite.checkAnswer(JavaDataFrameSuite.java:41)
at org.apache.spark.sql.hive.JavaDataFrameSuite.testUDAF(JavaDataFrameSuite.java:92)
Caused by: java.lang.ClassNotFoundException: scala.collection.parallel.TaskSupport
at org.apache.spark.sql.hive.JavaDataFrameSuite.checkAnswer(JavaDataFrameSuite.java:41)
at org.apache.spark.sql.hive.JavaDataFrameSuite.testUDAF(JavaDataFrameSuite.java:92)
[INFO] Running org.apache.spark.sql.hive.JavaMetastoreDataSourcesSuite
16:15:36.186 WARN org.apache.spark.sql.hive.test.TestHiveExternalCatalog: Couldn't find corresponding Hive SerDe for data source provider org.apache.spark.sql.json. Persisting data source table `default`.`javasavedtable` into Hive metastore in Spark SQL specific format, which is NOT compatible with Hive.
16:15:36.288 WARN org.apache.hadoop.hive.ql.session.SessionState: METASTORE_FILTER_HOOK will be ignored, since hive.security.authorization.manager is set to instance of HiveAuthorizerFactory.
16:15:36.396 WARN org.apache.hadoop.hive.conf.HiveConf: HiveConf of name hive.internal.ss.authz.settings.applied.marker does not exist
16:15:36.397 WARN org.apache.hadoop.hive.conf.HiveConf: HiveConf of name hive.stats.jdbc.timeout does not exist
16:15:36.397 WARN org.apache.hadoop.hive.conf.HiveConf: HiveConf of name hive.stats.retries.wait does not exist
[INFO] Tests run: 1, Failures: 0, Errors: 0, Skipped: 0, Time elapsed: 3.481 s - in org.apache.spark.sql.hive.JavaMetastoreDataSourcesSuite
[INFO]
[INFO] Results:
[INFO]
[ERROR] Errors:
[ERROR] JavaDataFrameSuite.testUDAF:92->checkAnswer:41 » NoClassDefFound scala/collect...
[INFO]
[ERROR] Tests run: 3, Failures: 0, Errors: 1, Skipped: 0
```
**After**
```
[INFO] Tests run: 2, Failures: 0, Errors: 0, Skipped: 0, Time elapsed: 19.287 s - in org.apache.spark.sql.hive.JavaDataFrameSuite
[INFO] Running org.apache.spark.sql.hive.JavaMetastoreDataSourcesSuite
16:12:16.697 WARN org.apache.spark.sql.hive.test.TestHiveExternalCatalog: Couldn't find corresponding Hive SerDe for data source provider org.apache.spark.sql.json. Persisting data source table `default`.`javasavedtable` into Hive metastore in Spark SQL specific format, which is NOT compatible with Hive.
16:12:17.540 WARN org.apache.hadoop.hive.ql.session.SessionState: METASTORE_FILTER_HOOK will be ignored, since hive.security.authorization.manager is set to instance of HiveAuthorizerFactory.
16:12:17.653 WARN org.apache.hadoop.hive.conf.HiveConf: HiveConf of name hive.internal.ss.authz.settings.applied.marker does not exist
16:12:17.653 WARN org.apache.hadoop.hive.conf.HiveConf: HiveConf of name hive.stats.jdbc.timeout does not exist
16:12:17.654 WARN org.apache.hadoop.hive.conf.HiveConf: HiveConf of name hive.stats.retries.wait does not exist
[INFO] Tests run: 1, Failures: 0, Errors: 0, Skipped: 0, Time elapsed: 3.58 s - in org.apache.spark.sql.hive.JavaMetastoreDataSourcesSuite
[INFO]
[INFO] Results:
[INFO]
[INFO] Tests run: 3, Failures: 0, Errors: 0, Skipped: 0
```
Closes#31259 from LuciferYang/SPARK-34176.
Authored-by: yangjie01 <yangjie01@baidu.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Call `copyTagsFrom` for the new node created by `MultiInstanceRelation.newInstance()`.
### Why are the changes needed?
```scala
val df = spark.range(2)
df.join(df, df("id") <=> df("id")).show()
```
For this query, it's supposed to be non-ambiguous join by the rule `DetectAmbiguousSelfJoin` because of the same attribute reference in the condition:
537a49fc09/sql/core/src/main/scala/org/apache/spark/sql/execution/analysis/DetectAmbiguousSelfJoin.scala (L125)
However, `DetectAmbiguousSelfJoin` can not apply this prediction due to the right side plan doesn't contain the dataset_id TreeNodeTag, which is missing after `MultiInstanceRelation.newInstance`. That's why we should preserve the tags info for the copied node.
Fortunately, the query is still considered as non-ambiguous join because `DetectAmbiguousSelfJoin` only checks the left side plan and the reference is the same as the left side plan. However, this's not the expected behavior but only a coincidence.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Updated a unit test
Closes#31260 from Ngone51/fix-missing-tags.
Authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add executor number check.
### Why are the changes needed?
The test `decommission workers ensure that shuffle output is regenerated even with shuffle service` assumes it has two executor and both of two tasks can execute concurrently.
The two tasks will execute serially if there only one executor. The result is test is unexpceted. E.g.
```
[info] 5 did not equal 4 Expected 4 tasks but got List(0:0:0:0-SUCCESS, 0:0:1:0-FAILED, 0:0:1:1-SUCCESS, 0:1:0:0-SUCCESS, 1:0:0:0-SUCCESS) (DecommissionWorkerSuite.scala:190)
```
The failed task due to the first task finished and decommission the worker.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Pass exists test.
Closes#31255 from ulysses-you/SPARK-34166.
Authored-by: ulysses-you <ulyssesyou18@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR aims to add a note for Apache Arrow project's `PyArrow` compatibility for Python 3.9.
### Why are the changes needed?
Although Apache Spark documentation claims `Spark runs on Java 8/11, Scala 2.12, Python 3.6+ and R 3.5+.`,
Apache Arrow's `PyArrow` is not compatible with Python 3.9.x yet. Without installing `PyArrow` library, PySpark UTs passed without any problem. So, it would be enough to add a note for this limitation and the compatibility link of Apache Arrow website.
- https://arrow.apache.org/docs/python/install.html#python-compatibility
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
**BEFORE**
<img width="804" alt="Screen Shot 2021-01-19 at 1 45 07 PM" src="https://user-images.githubusercontent.com/9700541/105096867-8fbdbe00-5a5c-11eb-88f7-8caae2427583.png">
**AFTER**
<img width="908" alt="Screen Shot 2021-01-19 at 7 06 41 PM" src="https://user-images.githubusercontent.com/9700541/105121661-85fe7f80-5a89-11eb-8af7-1b37e12c55c1.png">
Closes#31251 from dongjoon-hyun/SPARK-34162.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This passes original SQL text to `CacheTableAsSelect` command in DSv1 and v2 so that it will be stored instead of the analyzed logical plan, similar to `CREATE VIEW` command.
In addition, this changes the behavior of dropping temporary view to also invalidate dependent caches in a cascade, when the config `SQLConf.STORE_ANALYZED_PLAN_FOR_VIEW` is false (which is the default value).
### Why are the changes needed?
Currently, after creating a temporary view with `CACHE TABLE ... AS SELECT` command, the view can still be queried even after the source table is dropped or replaced (in v2). This can cause correctness issue.
For instance, in the following:
```sql
> CREATE TABLE t ...;
> CACHE TABLE v AS SELECT * FROM t;
> DROP TABLE t;
> SELECT * FROM v;
```
The last select query still returns the old (and stale) result instead of fail. Note that the cache is already invalidated as part of dropping table `t`, but the temporary view `v` still exist.
On the other hand, the following:
```sql
> CREATE TABLE t ...;
> CREATE TEMPORARY VIEW v AS SELECT * FROM t;
> CACHE TABLE v;
> DROP TABLE t;
> SELECT * FROM v;
```
will throw "Table or view not found" error in the last select query.
This is related to #30567 which aligns the behavior of temporary view and global view by storing the original SQL text for temporary view, as opposed to the analyzed logical plan. However, the PR only handles `CreateView` case but not the `CacheTableAsSelect` case.
This also changes uncache logic and use cascade invalidation for temporary views created above. This is to align its behavior to how a permanent view is handled as of today, and also to avoid potential issues where a dependent view becomes invalid while its data is still kept in cache.
### Does this PR introduce _any_ user-facing change?
Yes, now when `SQLConf.STORE_ANALYZED_PLAN_FOR_VIEW` is set to false (the default value), whenever a table/permanent view/temp view that a cached view depends on is dropped, the cached view itself will become invalid during analysis, i.e., user will get "Table or view not found" error. In addition, when the dependent is a temp view in the previous case, the cache itself will also be invalidated.
### How was this patch tested?
Modified/Enhanced some existing tests.
Closes#31107 from sunchao/SPARK-34052.
Lead-authored-by: Chao Sun <sunchao@apple.com>
Co-authored-by: Chao Sun <sunchao@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. Port DS V2 tests from `AlterTablePartitionV2SQLSuite ` to the test suite `v2.AlterTableRecoverPartitionsSuite`.
2. Port DS v1 tests from `DDLSuite` to `v1.AlterTableRecoverPartitionsSuiteBase`.
### Why are the changes needed?
To improve test coverage.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By running new test suites:
```
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *AlterTableRecoverPartitionsParserSuite"
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *AlterTableRecoverPartitionsSuite"
$ build/sbt -Phive-2.3 -Phive-thriftserver "test:testOnly *CatalogedDDLSuite"
```
Closes#31105 from MaxGekk/unify-recover-partitions-tests.
Authored-by: Max Gekk <max.gekk@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
