### What changes were proposed in this pull request?
This pr removes the nonstandard `SET OWNER` syntax for namespaces and changes the owner reserved properties from `ownerName` and `ownerType` to `owner`.
### Why are the changes needed?
the `SET OWNER` syntax for namespaces is hive-specific and non-sql standard, we need a more future-proofing design before we implement user-facing changes for SQL security issues
### Does this PR introduce any user-facing change?
no, just revert an unpublic syntax
### How was this patch tested?
modified uts
Closes#27300 from yaooqinn/SPARK-30591.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add optimizer rule PruneHiveTablePartitions pruning hive table partitions based on filters on partition columns.
Doing so, the total size of pruned partitions may be small enough for broadcast join in JoinSelection strategy.
### Why are the changes needed?
In JoinSelection strategy, spark use the "plan.stats.sizeInBytes" to decide whether the plan is suitable for broadcast join.
Currently, "plan.stats.sizeInBytes" does not take "pruned partitions" into account, so it may miss some broadcast join and take sort-merge join instead, which will definitely impact join performance.
This PR aim at taking "pruned partitions" into account for hive table in "plan.stats.sizeInBytes" and then improve performance by using broadcast join if possible.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
Added unit tests.
This is based on #25919, credits should go to lianhuiwang and advancedxy.
Closes#26805 from fuwhu/SPARK-15616.
Authored-by: fuwhu <bestwwg@163.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add `owner` property to v2 table, it is reversed by `TableCatalog`, indicates the table's owner.
### Why are the changes needed?
enhance ownership management of catalog API
### Does this PR introduce any user-facing change?
yes, add 1 reserved property - `owner` , and it is not allowed to use in OPTIONS/TBLPROPERTIES anymore, only if legacy on
### How was this patch tested?
add uts
Closes#27249 from yaooqinn/SPARK-30019.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Use the new framework to resolve the ALTER TABLE commands.
This PR also refactors ALTER TABLE logical plans such that they extend a base class `AlterTable`. Each plan now implements `def changes: Seq[TableChange]` for any table change operations.
Additionally, `UnresolvedV2Relation` and its usage is completely removed.
### Why are the changes needed?
This is a part of effort to make the relation lookup behavior consistent: [SPARK-29900](https://issues.apache.org/jira/browse/SPARK-29900).
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Updated existing tests
Closes#27243 from imback82/v2commands_newframework.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR aims to add these test cases for resolution of ORC table location reported by [SPARK-25993](https://issues.apache.org/jira/browse/SPARK-25993)
also add corresponding test cases for Parquet table.
### Why are the changes needed?
The current behavior is complex, this test case suites are designed to prevent the accidental behavior change. This pr is rebased on master, the original pr is [23108](https://github.com/apache/spark/pull/23108)
### Does this PR introduce any user-facing change?
No. This adds test cases only.
### How was this patch tested?
This is a new test case.
Closes#27130 from kevinyu98/spark-25993-2.
Authored-by: Kevin Yu <qyu@us.ibm.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Use the new framework to resolve the SHOW TBLPROPERTIES command. This PR along with #27243 should update all the existing V2 commands with `UnresolvedV2Relation`.
### Why are the changes needed?
This is a part of effort to make the relation lookup behavior consistent: [SPARK-2990](https://issues.apache.org/jira/browse/SPARK-29900).
### Does this PR introduce any user-facing change?
Yes `SHOW TBLPROPERTIES temp_view` now fails with `AnalysisException` will be thrown with a message `temp_view is a temp view not table`. Previously, it was returning empty row.
### How was this patch tested?
Existing tests
Closes#26921 from imback82/consistnet_v2command.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Add a `V1Scan` interface, so that data source v1 implementations can migrate to DS v2 much easier.
### Why are the changes needed?
It's a lot of work to migrate v1 sources to DS v2. The new API added here can allow v1 sources to go through v2 code paths without implementing all the Batch, Stream, PartitionReaderFactory, ... stuff.
We already have a v1 write fallback API after https://github.com/apache/spark/pull/25348
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
new test suite
Closes#26231 from cloud-fan/v1-read-fallback.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Use the new framework to resolve the DESCRIBE TABLE command.
The v1 DESCRIBE TABLE command supports both table and view. Checked with Hive and Presto, they don't have DESCRIBE TABLE syntax but only DESCRIBE, which supports both table and view:
1. https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL#LanguageManualDDL-DescribeTable/View/MaterializedView/Column
2. https://prestodb.io/docs/current/sql/describe.html
We should make it clear that DESCRIBE support both table and view, by renaming the command to `DescribeRelation`.
This PR also tunes the framework a little bit to support the case that a command accepts both table and view.
### Why are the changes needed?
This is a part of effort to make the relation lookup behavior consistent: SPARK-29900.
Note that I make a separate PR here instead of #26921, as I need to update the framework to support a new use case: accept both table and view.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
existing tests
Closes#27187 from cloud-fan/describe.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
This PR intends to improve partition pruning for nondeterministic expressions in Hive tables:
Before this PR:
```
scala> sql("""create table test(id int) partitioned by (dt string)""")
scala> sql("""select * from test where dt='20190101' and rand() < 0.5""").explain()
== Physical Plan ==
*(1) Filter ((isnotnull(dt#19) AND (dt#19 = 20190101)) AND (rand(6515336563966543616) < 0.5))
+- Scan hive default.test [id#18, dt#19], HiveTableRelation `default`.`test`, org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, [id#18], [dt#19], Statistics(sizeInBytes=8.0 EiB)
```
After this PR:
```
== Physical Plan ==
*(1) Filter (rand(-9163956883277176328) < 0.5)
+- Scan hive default.test [id#0, dt#1], HiveTableRelation `default`.`test`, org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, [id#0], [dt#1], Statistics(sizeInBytes=8.0 EiB), [isnotnull(dt#1), (dt#1 = 20190101)]
```
This PR is the rework of #24118.
### Why are the changes needed?
For better performance.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Unit tests added.
Closes#27219 from maropu/SPARK-26736.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
Fix all the failed tests when enable AQE.
### Why are the changes needed?
Run more tests with AQE to catch bugs, and make it easier to enable AQE by default in the future.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing unit tests
Closes#26813 from JkSelf/enableAQEDefault.
Authored-by: jiake <ke.a.jia@intel.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In the PR, I propose to replace `.collect()`, `.count()` and `.foreach(_ => ())` in SQL benchmarks and use the `NoOp` datasource. I added an implicit class to `SqlBasedBenchmark` with the `.noop()` method. It can be used in benchmark like: `ds.noop()`. The last one is unfolded to `ds.write.format("noop").mode(Overwrite).save()`.
### Why are the changes needed?
To avoid additional overhead that `collect()` (and other actions) has. For example, `.collect()` has to convert values according to external types and pull data to the driver. This can hide actual performance regressions or improvements of benchmarked operations.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Re-run all modified benchmarks using Amazon EC2.
| Item | Description |
| ---- | ----|
| Region | us-west-2 (Oregon) |
| Instance | r3.xlarge (spot instance) |
| AMI | ami-06f2f779464715dc5 (ubuntu/images/hvm-ssd/ubuntu-bionic-18.04-amd64-server-20190722.1) |
| Java | OpenJDK8/10 |
- Run `TPCDSQueryBenchmark` using instructions from the PR #26049
```
# `spark-tpcds-datagen` needs this. (JDK8)
$ git clone https://github.com/apache/spark.git -b branch-2.4 --depth 1 spark-2.4
$ export SPARK_HOME=$PWD
$ ./build/mvn clean package -DskipTests
# Generate data. (JDK8)
$ git clone gitgithub.com:maropu/spark-tpcds-datagen.git
$ cd spark-tpcds-datagen/
$ build/mvn clean package
$ mkdir -p /data/tpcds
$ ./bin/dsdgen --output-location /data/tpcds/s1 // This need `Spark 2.4`
```
- Other benchmarks ran by the script:
```
#!/usr/bin/env python3
import os
from sparktestsupport.shellutils import run_cmd
benchmarks = [
['sql/test', 'org.apache.spark.sql.execution.benchmark.AggregateBenchmark'],
['avro/test', 'org.apache.spark.sql.execution.benchmark.AvroReadBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.BloomFilterBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.DataSourceReadBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.DateTimeBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.ExtractBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.FilterPushdownBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.InExpressionBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.IntervalBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.JoinBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.MakeDateTimeBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.MiscBenchmark'],
['hive/test', 'org.apache.spark.sql.execution.benchmark.ObjectHashAggregateExecBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.OrcNestedSchemaPruningBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.OrcV2NestedSchemaPruningBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.ParquetNestedSchemaPruningBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.RangeBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.UDFBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.WideSchemaBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.benchmark.WideTableBenchmark'],
['hive/test', 'org.apache.spark.sql.hive.orc.OrcReadBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.datasources.csv.CSVBenchmark'],
['sql/test', 'org.apache.spark.sql.execution.datasources.json.JsonBenchmark']
]
print('Set SPARK_GENERATE_BENCHMARK_FILES=1')
os.environ['SPARK_GENERATE_BENCHMARK_FILES'] = '1'
for b in benchmarks:
print("Run benchmark: %s" % b[1])
run_cmd(['build/sbt', '%s:runMain %s' % (b[0], b[1])])
```
Closes#27078 from MaxGekk/noop-in-benchmarks.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
Defines a new subclass of UDF: `UserDefinedAggregator`. Also allows `Aggregator` to be registered as a udf. Under the hood, the implementation is based on the internal `TypedImperativeAggregate` class that spark's predefined aggregators make use of. The effect is that custom user defined aggregators are now serialized only on partition boundaries instead of being serialized and deserialized at each input row.
The two new modes of using `Aggregator` are as follows:
```scala
val agg: Aggregator[IN, BUF, OUT] = // typed aggregator
val udaf1 = UserDefinedAggregator(agg)
val udaf2 = spark.udf.register("agg", agg)
```
## How was this patch tested?
Unit testing has been added that corresponds to the testing suites for `UserDefinedAggregateFunction`. Additionally, unit tests explicitly count the number of aggregator ser/de cycles to ensure that it is governed only by the number of data partitions.
To evaluate the performance impact, I did two comparisons.
The code and REPL results are recorded on [this gist](https://gist.github.com/erikerlandson/b0e106a4dbaf7f80b4f4f3a21f05f892)
To characterize its behavior I benchmarked both a relatively simple aggregator and then an aggregator with a complex structure (a t-digest).
### performance
The following compares the new `Aggregator` based aggregation against UDAF. In this scenario, the new aggregation is about 100x faster. The difference in performance impact depends on the complexity of the aggregator. For very simple aggregators (e.g. implementing 'sum', etc), the performance impact is more like 25-30%.
```scala
scala> import scala.util.Random._, org.apache.spark.sql.Row, org.apache.spark.tdigest._
import scala.util.Random._
import org.apache.spark.sql.Row
import org.apache.spark.tdigest._
scala> val data = sc.parallelize(Vector.fill(50000){(nextInt(2), nextGaussian, nextGaussian.toFloat)}, 5).toDF("cat", "x1", "x2")
data: org.apache.spark.sql.DataFrame = [cat: int, x1: double ... 1 more field]
scala> val udaf = TDigestUDAF(0.5, 0)
udaf: org.apache.spark.tdigest.TDigestUDAF = TDigestUDAF(0.5,0)
scala> val bs = Benchmark.sample(10) { data.agg(udaf($"x1"), udaf($"x2")).first }
bs: Array[(Double, org.apache.spark.sql.Row)] = Array((16.523,[TDigestSQL(TDigest(0.5,0,130,TDigestMap(-4.9171836327285225 -> (1.0, 1.0), -3.9615949140987685 -> (1.0, 2.0), -3.792874086327091 -> (0.7500781537109753, 2.7500781537109753), -3.720534874164185 -> (1.796754196108008, 4.546832349818983), -3.702105588052377 -> (0.4531676501810167, 5.0), -3.665883591332569 -> (2.3434687534153142, 7.343468753415314), -3.649982231368131 -> (0.6565312465846858, 8.0), -3.5914188829817744 -> (4.0, 12.0), -3.530472305581248 -> (4.0, 16.0), -3.4060489584449467 -> (2.9372251939818383, 18.93722519398184), -3.3000694035428486 -> (8.12412890252889, 27.061354096510726), -3.2250016655261877 -> (8.30564453211017, 35.3669986286209), -3.180537395623448 -> (6.001782561137285, 41.3687811...
scala> bs.map(_._1)
res0: Array[Double] = Array(16.523, 17.138, 17.863, 17.801, 17.769, 17.786, 17.744, 17.8, 17.939, 17.854)
scala> val agg = TDigestAggregator(0.5, 0)
agg: org.apache.spark.tdigest.TDigestAggregator = TDigestAggregator(0.5,0)
scala> val udaa = spark.udf.register("tdigest", agg)
udaa: org.apache.spark.sql.expressions.UserDefinedAggregator[Double,org.apache.spark.tdigest.TDigestSQL,org.apache.spark.tdigest.TDigestSQL] = UserDefinedAggregator(TDigestAggregator(0.5,0),None,true,true)
scala> val bs = Benchmark.sample(10) { data.agg(udaa($"x1"), udaa($"x2")).first }
bs: Array[(Double, org.apache.spark.sql.Row)] = Array((0.313,[TDigestSQL(TDigest(0.5,0,130,TDigestMap(-4.9171836327285225 -> (1.0, 1.0), -3.9615949140987685 -> (1.0, 2.0), -3.792874086327091 -> (0.7500781537109753, 2.7500781537109753), -3.720534874164185 -> (1.796754196108008, 4.546832349818983), -3.702105588052377 -> (0.4531676501810167, 5.0), -3.665883591332569 -> (2.3434687534153142, 7.343468753415314), -3.649982231368131 -> (0.6565312465846858, 8.0), -3.5914188829817744 -> (4.0, 12.0), -3.530472305581248 -> (4.0, 16.0), -3.4060489584449467 -> (2.9372251939818383, 18.93722519398184), -3.3000694035428486 -> (8.12412890252889, 27.061354096510726), -3.2250016655261877 -> (8.30564453211017, 35.3669986286209), -3.180537395623448 -> (6.001782561137285, 41.36878118...
scala> bs.map(_._1)
res1: Array[Double] = Array(0.313, 0.193, 0.175, 0.185, 0.174, 0.176, 0.16, 0.186, 0.171, 0.179)
scala>
```
Closes#25024 from erikerlandson/spark-27296.
Authored-by: Erik Erlandson <eerlands@redhat.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Currently data columns are displayed in one line for show create table command, when the table has many columns (to make things even worse, columns may have long names or comments), the displayed result is really hard to read.
To improve readability, we print each column in a separate line. Note that other systems like Hive/MySQL also display in this way.
Also, for data columns, table properties and options, we put the right parenthesis to the end of the last column/property/option, instead of occupying a separate line.
### Why are the changes needed?
for better readability
### Does this PR introduce any user-facing change?
before the change:
```
spark-sql> show create table test_table;
CREATE TABLE `test_table` (`col1` INT COMMENT 'This is comment for column 1', `col2` STRING COMMENT 'This is comment for column 2', `col3` DOUBLE COMMENT 'This is comment for column 3')
USING parquet
OPTIONS (
`bar` '2',
`foo` '1'
)
TBLPROPERTIES (
'a' = 'x',
'b' = 'y'
)
```
after the change:
```
spark-sql> show create table test_table;
CREATE TABLE `test_table` (
`col1` INT COMMENT 'This is comment for column 1',
`col2` STRING COMMENT 'This is comment for column 2',
`col3` DOUBLE COMMENT 'This is comment for column 3')
USING parquet
OPTIONS (
`bar` '2',
`foo` '1')
TBLPROPERTIES (
'a' = 'x',
'b' = 'y')
```
### How was this patch tested?
modified existing tests
Closes#27147 from wzhfy/multi_line_columns.
Authored-by: Zhenhua Wang <wzh_zju@163.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
In this pull request, we are going to support `SET OWNER` syntax for databases and namespaces,
```sql
ALTER (DATABASE|SCHEME|NAMESPACE) database_name SET OWNER [USER|ROLE|GROUP] user_or_role_group;
```
Before this commit 332e252a14, we didn't care much about ownerships for the catalog objects. In 332e252a14, we determined to use properties to store ownership staff, and temporarily used `alter database ... set dbproperties ...` to support switch ownership of a database. This PR aims to use the formal syntax to replace it.
In hive, `ownerName/Type` are fields of the database objects, also they can be normal properties.
```
create schema test1 with dbproperties('ownerName'='yaooqinn')
```
The create/alter database syntax will not change the owner to `yaooqinn` but store it in parameters. e.g.
```
+----------+----------+---------------------------------------------------------------+-------------+-------------+-----------------------+--+
| db_name | comment | location | owner_name | owner_type | parameters |
+----------+----------+---------------------------------------------------------------+-------------+-------------+-----------------------+--+
| test1 | | hdfs://quickstart.cloudera:8020/user/hive/warehouse/test1.db | anonymous | USER | {ownerName=yaooqinn} |
+----------+----------+---------------------------------------------------------------+-------------+-------------+-----------------------+--+
```
In this pull request, because we let the `ownerName` become reversed, so it will neither change the owner nor store in dbproperties, just be omitted silently.
## Why are the changes needed?
Formal syntax support for changing database ownership
### Does this PR introduce any user-facing change?
yes, add a new syntax
### How was this patch tested?
add unit tests
Closes#26775 from yaooqinn/SPARK-30018.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Update CREATE VIEW command to store the current catalog and namespace instead of current database in view metadata. Also update analyzer to leverage the catalog and namespace in view metastore to resolve relations inside views.
Note that, this PR still keeps the way we resolve views, by recursively calling Analyzer. This is necessary because view text may contain CTE, window spec, etc. which needs rules outside of the main resolution batch (e.g. `CTESubstitution`)
### Why are the changes needed?
To resolve relations inside view correctly.
### Does this PR introduce any user-facing change?
Yes, fix a bug. Now tables referred by a view can be resolved correctly even if the current catalog/namespace has been updated.
### How was this patch tested?
a new test
Closes#26923 from cloud-fan/view.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This patch is based on #23921 but revised to be simpler, as well as adds UT to test the behavior.
(This patch contains the commit from #23921 to retain credit.)
Spark loads new JARs for `ADD JAR` and `CREATE FUNCTION ... USING JAR` into jar classloader in shared state, and changes current thread's context classloader to jar classloader as many parts of remaining codes rely on current thread's context classloader.
This would work if the further queries will run in same thread and there's no change on context classloader for the thread, but once the context classloader of current thread is switched back by various reason, Spark fails to create instance of class for the function.
This bug mostly affects spark-shell, as spark-shell will roll back current thread's context classloader at every prompt. But it may also affects the case of job-server, where the queries may be running in multiple threads.
This patch fixes the issue via switching the context classloader to the classloader which loads the class. Hopefully FunctionBuilder created by `makeFunctionBuilder` has the information of Class as a part of closure, hence the Class itself can be provided regardless of current thread's context classloader.
### Why are the changes needed?
Without this patch, end users cannot execute Hive UDF using JAR twice in spark-shell.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
New UT.
Closes#27025 from HeartSaVioR/SPARK-26560-revised.
Lead-authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Co-authored-by: nivo091 <nivedeeta.singh@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Currently if function lookup fails, spark will give it a second change by casting decimal type to double type. But for cases where decimal type doesn't exist, it's meaningless to lookup again and causes extra cost like unnecessary metastore access. We should throw exceptions directly in these cases.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Covered by existing tests.
Closes#26994 from wzhfy/avoid_udf_fail_twice.
Authored-by: Zhenhua Wang <wzh_zju@163.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
When querying a partitioned table with format `org.apache.hive.hcatalog.data.JsonSerDe` and more than one task runs in each executor concurrently, the following exception is encountered:
`java.lang.ClassCastException: java.util.ArrayList cannot be cast to org.apache.hive.hcatalog.data.HCatRecord`
The exception occurs in `HadoopTableReader.fillObject`.
`org.apache.hive.hcatalog.data.JsonSerDe#initialize` populates a `cachedObjectInspector` field by calling `HCatRecordObjectInspectorFactory.getHCatRecordObjectInspector`, which is not thread-safe; this `cachedObjectInspector` is returned by `JsonSerDe#getObjectInspector`.
We protect against this Hive bug by synchronizing on an object when we need to call `initialize` on `org.apache.hadoop.hive.serde2.Deserializer` instances (which may be `JsonSerDe` instances). By doing so, the `ObjectInspector` for the `Deserializer` of the partitions of the JSON table and that of the table `SerDe` are the same cached `ObjectInspector` and `HadoopTableReader.fillObject` then works correctly. (If the `ObjectInspector`s are different, then a bug in `HCatRecordObjectInspector` causes an `ArrayList` to be created instead of an `HCatRecord`, resulting in the `ClassCastException` that is seen.)
### Why are the changes needed?
To avoid HIVE-15773 / HIVE-21752.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Tested manually on a cluster with a partitioned JSON table and running a query using more than one core per executor. Before this change, the ClassCastException happens consistently. With this change it does not happen.
Closes#26895 from wypoon/SPARK-17398.
Authored-by: Wing Yew Poon <wypoon@cloudera.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
### What changes were proposed in this pull request?
We could get the Partitions Statistics Info.But in some specail case, The Info like totalSize，rawDataSize，rowCount maybe empty. When we do some ddls like
`desc formatted partition` ,the NumberFormatException is showed as below:
```
spark-sql> desc formatted table1 partition(year='2019', month='10', day='17', hour='23');
19/10/19 00:02:40 ERROR SparkSQLDriver: Failed in [desc formatted table1 partition(year='2019', month='10', day='17', hour='23')]
java.lang.NumberFormatException: Zero length BigInteger
at java.math.BigInteger.(BigInteger.java:411)
at java.math.BigInteger.(BigInteger.java:597)
at scala.math.BigInt$.apply(BigInt.scala:77)
at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$31.apply(HiveClientImpl.scala:1056)
```
Although we can use 'Analyze table partition ' to update the totalSize,rawDataSize or rowCount, it's unresonable for normal SQL to throw NumberFormatException for Empty totalSize.We should fix the empty case when readHiveStats.
### Why are the changes needed?
This is a related to the robustness of the code and may lead to unexpected exception in some unpredictable situation.Here is the case:
<img width="981" alt="image" src="https://user-images.githubusercontent.com/20614350/70845771-7b88b400-1e8d-11ea-95b0-df5c58097d7d.png">
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
manual
Closes#26892 from southernriver/SPARK-30262.
Authored-by: chenliang <southernriver@163.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
It's an obvious bug: currently when analyzing partition stats, we use old table stats to compare with newly computed stats to decide whether it should update stats or not.
### Why are the changes needed?
bug fix
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
add new tests
Closes#26908 from wzhfy/failto_update_part_stats.
Authored-by: Zhenhua Wang <wzh_zju@163.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Now spark use `ObjectInspectorCopyOption.JAVA` as oi option which will convert any string to UTF-8 string. When write non UTF-8 code data, then `EFBFBD` will appear.
We should use `ObjectInspectorCopyOption.DEFAULT` to support pass the bytes.
### Why are the changes needed?
Here is the way to reproduce:
1. make a file contains 16 radix 'AABBCC' which is not the UTF-8 code.
2. create table test1 (c string) location '$file_path';
3. select hex(c) from test1; // AABBCC
4. craete table test2 (c string) as select c from test1;
5. select hex(c) from test2; // EFBFBDEFBFBDEFBFBD
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Closes#26831 from ulysses-you/SPARK-30201.
Authored-by: ulysses <youxiduo@weidian.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
1. Revert "Preparing development version 3.0.1-SNAPSHOT": 56dcd79
2. Revert "Preparing Spark release v3.0.0-preview2-rc2": c216ef1
### Why are the changes needed?
Shouldn't change master.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
manual test:
https://github.com/apache/spark/compare/5de5e46..wangyum:revert-masterCloses#26915 from wangyum/revert-master.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Yuming Wang <wgyumg@gmail.com>
### What changes were proposed in this pull request?
Check the partition column data type and only allow string and integral types in hive partition pruning.
### Why are the changes needed?
Currently we only support string and integral types in hive partition pruning, but the check is done for literals. If the predicate is `InSet`, then there is no literal and we may pass an unsupported partition predicate to Hive and cause problems.
### Does this PR introduce any user-facing change?
yes. fix a bug. A query fails before and can run now.
### How was this patch tested?
a new test
Closes#26871 from cloud-fan/bug.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Add DescribeFunctionsStatement and make DESCRIBE FUNCTIONS go through the same catalog/table resolution framework of v2 commands.
### Why are the changes needed?
It's important to make all the commands have the same table resolution behavior, to avoid confusing
DESCRIBE FUNCTIONS namespace.function
### Does this PR introduce any user-facing change?
Yes. When running DESCRIBE FUNCTIONS namespace.function Spark fails the command if the current catalog is set to a v2 catalog.
### How was this patch tested?
Unit tests.
Closes#26840 from planga82/feature/SPARK-30038_DescribeFunction_V2Catalog.
Authored-by: Pablo Langa <soypab@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This patch fixes the Java code style violations in SPARK-30159 (#26788) which are caught by lint-java (Github Action caught it and I can reproduce it locally). Looks like Jenkins build may have different policy on checking Java style check or less accurate.
### Why are the changes needed?
Java linter starts complaining.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
lint-java passed locally
This closes#26819Closes#26818 from HeartSaVioR/SPARK-30159-FOLLOWUP.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Before this PR, the method `checkAnswer` in Object `QueryTest` returns an optional string. It doesn't throw exceptions when errors happen.
The actual exceptions are thrown in the trait `QueryTest`.
However, there are some test suites(`StreamSuite`, `SessionStateSuite`, `BinaryFileFormatSuite`, etc.) that use the no-op method `QueryTest.checkAnswer` and expect it to fail test cases when the execution results don't match the expected answers.
After this PR:
1. the method `checkAnswer` in Object `QueryTest` will fail tests on errors or unexpected results.
2. add a new method `getErrorMessageInCheckAnswer`, which is exactly the same as the previous version of `checkAnswer`. There are some test suites use this one to customize the test failure message.
3. for the test suites that extend the trait `QueryTest`, we should use the method `checkAnswer` directly, instead of calling the method from Object `QueryTest`.
### Why are the changes needed?
We should fix these method calls to perform actual validations in test suites.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing unit tests.
Closes#26788 from gengliangwang/fixCheckAnswer.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Add ShowFunctionsStatement and make SHOW FUNCTIONS go through the same catalog/table resolution framework of v2 commands.
We don’t have this methods in the catalog to implement an V2 command
* catalog.listFunctions
### Why are the changes needed?
It's important to make all the commands have the same table resolution behavior, to avoid confusing
`SHOW FUNCTIONS LIKE namespace.function`
### Does this PR introduce any user-facing change?
Yes. When running SHOW FUNCTIONS LIKE namespace.function Spark fails the command if the current catalog is set to a v2 catalog.
### How was this patch tested?
Unit tests.
Closes#26667 from planga82/feature/SPARK-29922_ShowFunctions_V2Catalog.
Authored-by: Pablo Langa <soypab@gmail.com>
Signed-off-by: Liang-Chi Hsieh <liangchi@uber.com>
### What changes were proposed in this pull request?
In this PR, we propose to use the value of `spark.sql.source.default` as the provider for `CREATE TABLE` syntax instead of `hive` in Spark 3.0.
And to help the migration, we introduce a legacy conf `spark.sql.legacy.respectHiveDefaultProvider.enabled` and set its default to `false`.
### Why are the changes needed?
1. Currently, `CREATE TABLE` syntax use hive provider to create table while `DataFrameWriter.saveAsTable` API using the value of `spark.sql.source.default` as a provider to create table. It would be better to make them consistent.
2. User may gets confused in some cases. For example:
```
CREATE TABLE t1 (c1 INT) USING PARQUET;
CREATE TABLE t2 (c1 INT);
```
In these two DDLs, use may think that `t2` should also use parquet as default provider since Spark always advertise parquet as the default format. However, it's hive in this case.
On the other hand, if we omit the USING clause in a CTAS statement, we do pick parquet by default if `spark.sql.hive.convertCATS=true`:
```
CREATE TABLE t3 USING PARQUET AS SELECT 1 AS VALUE;
CREATE TABLE t4 AS SELECT 1 AS VALUE;
```
And these two cases together can be really confusing.
3. Now, Spark SQL is very independent and popular. We do not need to be fully consistent with Hive's behavior.
### Does this PR introduce any user-facing change?
Yes, before this PR, using `CREATE TABLE` syntax will use hive provider. But now, it use the value of `spark.sql.source.default` as its provider.
### How was this patch tested?
Added tests in `DDLParserSuite` and `HiveDDlSuite`.
Closes#26736 from Ngone51/dev-create-table-using-parquet-by-default.
Lead-authored-by: wuyi <yi.wu@databricks.com>
Co-authored-by: yi.wu <yi.wu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
TL;DR - this is more of the same change in https://github.com/apache/spark/pull/26748
I told you it'd be iterative!
Closes#26765 from srowen/SPARK-29392.3.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Keep the owner of a database when executing alter database commands
### Why are the changes needed?
Spark will inadvertently delete the owner of a database for executing databases ddls
### Does this PR introduce any user-facing change?
NO
### How was this patch tested?
add and modify uts
Closes#26080 from yaooqinn/SPARK-29425.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Where it generates a deprecation warning in Scala 2.13, replace Symbol shorthand syntax `'foo` with an equivalent.
### Why are the changes needed?
Symbol syntax `'foo` is deprecated in Scala 2.13. The lines changed below otherwise generate about 440 warnings when building for 2.13.
The previous PR directly replaced many usages with `Symbol("foo")`. But it's also used to specify Columns via implicit conversion (`.select('foo)`) or even where simple Strings are used (`.as('foo)`), as it's kind of an abstraction for interned Strings.
While I find this syntax confusing and would like to deprecate it, here I just replaced it where it generates a build warning (not sure why all occurrences don't): `$"foo"` or just `"foo"`.
### Does this PR introduce any user-facing change?
Should not change behavior.
### How was this patch tested?
Existing tests.
Closes#26748 from srowen/SPARK-29392.2.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Improve error messages for unsupported data type.
### Why are the changes needed?
When the spark reads the hive table and encounters an unsupported field type, the exception message has only one unsupported type, and the user cannot know which field of which table.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
```create view t AS SELECT STRUCT('a' AS `$a`, 1 AS b) as q;```
current:
org.apache.spark.SparkException: Cannot recognize hive type string: struct<$a:string,b:int>
change:
org.apache.spark.SparkException: Cannot recognize hive type string: struct<$a:string,b:int>, column: q
```select * from t,t_normal_1,t_normal_2```
current:
org.apache.spark.SparkException: Cannot recognize hive type string: struct<$a:string,b:int>
change:
org.apache.spark.SparkException: Cannot recognize hive type string: struct<$a:string,b:int>, column: q, db: default, table: t
Closes#26577 from cxzl25/unsupport_data_type_msg.
Authored-by: sychen <sychen@ctrip.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
In SPARK-29421 (#26097) , we can specify a different table provider for `CREATE TABLE LIKE` via `USING provider`.
Hive support `STORED AS` new file format syntax:
```sql
CREATE TABLE tbl(a int) STORED AS TEXTFILE;
CREATE TABLE tbl2 LIKE tbl STORED AS PARQUET;
```
For Hive compatibility, we should also support `STORED AS` in `CREATE TABLE LIKE`.
### Why are the changes needed?
See https://github.com/apache/spark/pull/26097#issue-327424759
### Does this PR introduce any user-facing change?
Add a new syntax based on current CTL:
CREATE TABLE tbl2 LIKE tbl [STORED AS hiveFormat];
### How was this patch tested?
Add UTs.
Closes#26466 from LantaoJin/SPARK-29839.
Authored-by: LantaoJin <jinlantao@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This patch adds Hive provider into table metadata in `HiveExternalCatalog.alterTableStats`. When we call `HiveClient.alterTable`, `alterTable` will erase if it can not find hive provider in given table metadata.
Rename table also has this issue.
### Why are the changes needed?
Because running `ANALYZE TABLE` on a Hive table, if the table has bucketing info, will erase existing bucket info.
### Does this PR introduce any user-facing change?
Yes. After this PR, running `ANALYZE TABLE` on Hive table, won't erase existing bucketing info.
### How was this patch tested?
Unit test.
Closes#26685 from viirya/fix-hive-bucket.
Lead-authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Co-authored-by: Liang-Chi Hsieh <liangchi@uber.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
[HIVE-12063](https://issues.apache.org/jira/browse/HIVE-12063) improved pad decimal numbers with trailing zeros to the scale of the column. The following description is copied from the description of HIVE-12063.
> HIVE-7373 was to address the problems of trimming tailing zeros by Hive, which caused many problems including treating 0.0, 0.00 and so on as 0, which has different precision/scale. Please refer to HIVE-7373 description. However, HIVE-7373 was reverted by HIVE-8745 while the underlying problems remained. HIVE-11835 was resolved recently to address one of the problems, where 0.0, 0.00, and so on cannot be read into decimal(1,1).
However, HIVE-11835 didn't address the problem of showing as 0 in query result for any decimal values such as 0.0, 0.00, etc. This causes confusion as 0 and 0.0 have different precision/scale than 0.
The proposal here is to pad zeros for query result to the type's scale. This not only removes the confusion described above, but also aligns with many other DBs. Internal decimal number representation doesn't change, however.
**Spark SQL**:
```sql
// bin/spark-sql
spark-sql> select cast(1 as decimal(38, 18));
1
spark-sql>
// bin/beeline
0: jdbc:hive2://localhost:10000/default> select cast(1 as decimal(38, 18));
+----------------------------+--+
| CAST(1 AS DECIMAL(38,18)) |
+----------------------------+--+
| 1.000000000000000000 |
+----------------------------+--+
// bin/spark-shell
scala> spark.sql("select cast(1 as decimal(38, 18))").show(false)
+-------------------------+
|CAST(1 AS DECIMAL(38,18))|
+-------------------------+
|1.000000000000000000 |
+-------------------------+
// bin/pyspark
>>> spark.sql("select cast(1 as decimal(38, 18))").show()
+-------------------------+
|CAST(1 AS DECIMAL(38,18))|
+-------------------------+
| 1.000000000000000000|
+-------------------------+
// bin/sparkR
> showDF(sql("SELECT cast(1 as decimal(38, 18))"))
+-------------------------+
|CAST(1 AS DECIMAL(38,18))|
+-------------------------+
| 1.000000000000000000|
+-------------------------+
```
**PostgreSQL**:
```sql
postgres=# select cast(1 as decimal(38, 18));
numeric
----------------------
1.000000000000000000
(1 row)
```
**Presto**:
```sql
presto> select cast(1 as decimal(38, 18));
_col0
----------------------
1.000000000000000000
(1 row)
```
## How was this patch tested?
unit tests and manual test:
```sql
spark-sql> select cast(1 as decimal(38, 18));
1.000000000000000000
```
Spark SQL Upgrading Guide:
Closes#26697 from wangyum/SPARK-28461.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
[HIVE-12063](https://issues.apache.org/jira/browse/HIVE-12063) improved pad decimal numbers with trailing zeros to the scale of the column. The following description is copied from the description of HIVE-12063.
> HIVE-7373 was to address the problems of trimming tailing zeros by Hive, which caused many problems including treating 0.0, 0.00 and so on as 0, which has different precision/scale. Please refer to HIVE-7373 description. However, HIVE-7373 was reverted by HIVE-8745 while the underlying problems remained. HIVE-11835 was resolved recently to address one of the problems, where 0.0, 0.00, and so on cannot be read into decimal(1,1).
However, HIVE-11835 didn't address the problem of showing as 0 in query result for any decimal values such as 0.0, 0.00, etc. This causes confusion as 0 and 0.0 have different precision/scale than 0.
The proposal here is to pad zeros for query result to the type's scale. This not only removes the confusion described above, but also aligns with many other DBs. Internal decimal number representation doesn't change, however.
**Spark SQL**:
```sql
// bin/spark-sql
spark-sql> select cast(1 as decimal(38, 18));
1
spark-sql>
// bin/beeline
0: jdbc:hive2://localhost:10000/default> select cast(1 as decimal(38, 18));
+----------------------------+--+
| CAST(1 AS DECIMAL(38,18)) |
+----------------------------+--+
| 1.000000000000000000 |
+----------------------------+--+
// bin/spark-shell
scala> spark.sql("select cast(1 as decimal(38, 18))").show(false)
+-------------------------+
|CAST(1 AS DECIMAL(38,18))|
+-------------------------+
|1.000000000000000000 |
+-------------------------+
// bin/pyspark
>>> spark.sql("select cast(1 as decimal(38, 18))").show()
+-------------------------+
|CAST(1 AS DECIMAL(38,18))|
+-------------------------+
| 1.000000000000000000|
+-------------------------+
// bin/sparkR
> showDF(sql("SELECT cast(1 as decimal(38, 18))"))
+-------------------------+
|CAST(1 AS DECIMAL(38,18))|
+-------------------------+
| 1.000000000000000000|
+-------------------------+
```
**PostgreSQL**:
```sql
postgres=# select cast(1 as decimal(38, 18));
numeric
----------------------
1.000000000000000000
(1 row)
```
**Presto**:
```sql
presto> select cast(1 as decimal(38, 18));
_col0
----------------------
1.000000000000000000
(1 row)
```
## How was this patch tested?
unit tests and manual test:
```sql
spark-sql> select cast(1 as decimal(38, 18));
1.000000000000000000
```
Spark SQL Upgrading Guide:
Closes#25214 from wangyum/SPARK-28461.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This PR aims to remove `hive-2.3` profile from `sql/hive` module.
### Why are the changes needed?
Currently, we need `-Phive-1.2` or `-Phive-2.3` additionally to build `hive` or `hive-thriftserver` module. Without specifying it, the build fails like the following. This PR will recover it.
```
$ build/mvn -DskipTests compile --pl sql/hive
...
[ERROR] [Error] /Users/dongjoon/APACHE/spark-merge/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveExternalCatalog.scala:32: object serde is not a member of package org.apache.hadoop.hive
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
1. Pass GitHub Action dependency check with no manifest change.
2. Pass GitHub Action build for all combinations.
3. Pass the Jenkins UT.
Closes#26668 from dongjoon-hyun/SPARK-30031.
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 the followings.
- Add two profiles, `hive-1.2` and `hive-2.3` (default)
- Validate if we keep the existing combination at least. (Hadoop-2.7 + Hive 1.2 / Hadoop-3.2 + Hive 2.3).
For now, we assumes that `hive-1.2` is explicitly used with `hadoop-2.7` and `hive-2.3` with `hadoop-3.2`. The followings are beyond the scope of this PR.
- SPARK-29988 Adjust Jenkins jobs for `hive-1.2/2.3` combination
- SPARK-29989 Update release-script for `hive-1.2/2.3` combination
- SPARK-29991 Support `hive-1.2/2.3` in PR Builder
### Why are the changes needed?
This will help to switch our dependencies to update the exposed dependencies.
### Does this PR introduce any user-facing change?
This is a dev-only change that the build profile combinations are changed.
- `-Phadoop-2.7` => `-Phadoop-2.7 -Phive-1.2`
- `-Phadoop-3.2` => `-Phadoop-3.2 -Phive-2.3`
### How was this patch tested?
Pass the Jenkins with the dependency check and tests to make it sure we don't change anything for now.
- [Jenkins (-Phadoop-2.7 -Phive-1.2)](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/114192/consoleFull)
- [Jenkins (-Phadoop-3.2 -Phive-2.3)](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/114192/consoleFull)
Also, from now, GitHub Action validates the following combinations.
Closes#26619 from dongjoon-hyun/SPARK-29981.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Use JUnit assertions in tests uniformly, not JVM assert() statements.
### Why are the changes needed?
assert() statements do not produce as useful errors when they fail, and, if they were somehow disabled, would fail to test anything.
### Does this PR introduce any user-facing change?
No. The assertion logic should be identical.
### How was this patch tested?
Existing tests.
Closes#26581 from srowen/assertToJUnit.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
This is to refactor Predicate code; it mainly removed `newPredicate` from `SparkPlan`.
Modifications are listed below;
- Move `Predicate` from `o.a.s.sqlcatalyst.expressions.codegen.GeneratePredicate.scala` to `o.a.s.sqlcatalyst.expressions.predicates.scala`
- To resolve the name conflict, rename `o.a.s.sqlcatalyst.expressions.codegen.Predicate` to `o.a.s.sqlcatalyst.expressions.BasePredicate`
- Extend `CodeGeneratorWithInterpretedFallback ` for `BasePredicate`
This comes from the cloud-fan suggestion: https://github.com/apache/spark/pull/26420#discussion_r348005497
### Why are the changes needed?
For better code/test coverage.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#26604 from maropu/RefactorPredicate.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In our production, HiveMetastoreCatalog#convertToLogicalRelation throws AssertError occasionally:
```sql
scala> spark.table("hive_table").show
java.lang.AssertionError: assertion failed
at scala.Predef$.assert(Predef.scala:208)
at org.apache.spark.sql.hive.HiveMetastoreCatalog.convertToLogicalRelation(HiveMetastoreCatalog.scala:261)
at org.apache.spark.sql.hive.HiveMetastoreCatalog.convert(HiveMetastoreCatalog.scala:137)
at org.apache.spark.sql.hive.RelationConversions$$anonfun$apply$4.applyOrElse(HiveStrategies.scala:220)
at org.apache.spark.sql.hive.RelationConversions$$anonfun$apply$4.applyOrElse(HiveStrategies.scala:207)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDown$2(AnalysisHelper.scala:108)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:72)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDown$1(AnalysisHelper.scala:108)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:194)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsDown(AnalysisHelper.scala:106)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsDown$(AnalysisHelper.scala:104)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsDown(LogicalPlan.scala:29)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDown$4(AnalysisHelper.scala:113)
at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$mapChildren$1(TreeNode.scala:376)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:214)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:374)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:327)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDown$1(AnalysisHelper.scala:113)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:194)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsDown(AnalysisHelper.scala:106)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsDown$(AnalysisHelper.scala:104)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsDown(LogicalPlan.scala:29)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperators(AnalysisHelper.scala:73)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperators$(AnalysisHelper.scala:72)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:29)
at org.apache.spark.sql.hive.RelationConversions.apply(HiveStrategies.scala:207)
at org.apache.spark.sql.hive.RelationConversions.apply(HiveStrategies.scala:191)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$2(RuleExecutor.scala:130)
at scala.collection.IndexedSeqOptimized.foldLeft(IndexedSeqOptimized.scala:60)
at scala.collection.IndexedSeqOptimized.foldLeft$(IndexedSeqOptimized.scala:68)
at scala.collection.mutable.ArrayBuffer.foldLeft(ArrayBuffer.scala:49)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1(RuleExecutor.scala:127)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1$adapted(RuleExecutor.scala:119)
at scala.collection.immutable.List.foreach(List.scala:392)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:119)
at org.apache.spark.sql.catalyst.analysis.Analyzer.org$apache$spark$sql$catalyst$analysis$Analyzer$$executeSameContext(Analyzer.scala:168)
at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:162)
at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:122)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$executeAndTrack$1(RuleExecutor.scala:98)
at org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:88)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.executeAndTrack(RuleExecutor.scala:98)
at org.apache.spark.sql.catalyst.analysis.Analyzer.$anonfun$executeAndCheck$1(Analyzer.scala:146)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.markInAnalyzer(AnalysisHelper.scala:201)
at org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:145)
at org.apache.spark.sql.execution.QueryExecution.$anonfun$analyzed$1(QueryExecution.scala:66)
at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:63)
at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:63)
at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:55)
at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:86)
at org.apache.spark.sql.SparkSession.table(SparkSession.scala:585)
at org.apache.spark.sql.SparkSession.table(SparkSession.scala:581)
... 47 elided
````
Most of cases occurred in reading a table which created by an old Spark version.
After recreated the table, the issue will be gone.
After deep dive, the root cause is this external table is a non-partitioned table but the `LOCATION` set to a partitioned path {{/tablename/dt=yyyymmdd}}. The partitionSpec is inferred.
### Why are the changes needed?
Above error message is very confused. We need more details about assert failure information.
This issue caused by `PartitioningAwareFileIndex#inferPartitioning()`. For non-HiveMetastore Spark, it's useful. But for Hive table, it shouldn't infer partition if Hive tell us it's a non partitioned table. (new added)
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Add UT.
Closes#26499 from LantaoJin/SPARK-29869.
Authored-by: LantaoJin <jinlantao@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR aims to add `io.netty.tryReflectionSetAccessible=true` to the testing configuration for JDK11 because this is an officially documented requirement of Apache Arrow.
Apache Arrow community documented this requirement at `0.15.0` ([ARROW-6206](https://github.com/apache/arrow/pull/5078)).
> #### For java 9 or later, should set "-Dio.netty.tryReflectionSetAccessible=true".
> This fixes `java.lang.UnsupportedOperationException: sun.misc.Unsafe or java.nio.DirectByteBuffer.(long, int) not available`. thrown by netty.
### Why are the changes needed?
After ARROW-3191, Arrow Java library requires the property `io.netty.tryReflectionSetAccessible` to be set to true for JDK >= 9. After https://github.com/apache/spark/pull/26133, JDK11 Jenkins job seem to fail.
- https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-maven-hadoop-3.2-jdk-11/676/
- https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-maven-hadoop-3.2-jdk-11/677/
- https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test%20(Dashboard)/job/spark-master-test-maven-hadoop-3.2-jdk-11/678/
```scala
Previous exception in task:
sun.misc.Unsafe or java.nio.DirectByteBuffer.<init>(long, int) not available

io.netty.util.internal.PlatformDependent.directBuffer(PlatformDependent.java:473)

io.netty.buffer.NettyArrowBuf.getDirectBuffer(NettyArrowBuf.java:243)

io.netty.buffer.NettyArrowBuf.nioBuffer(NettyArrowBuf.java:233)

io.netty.buffer.ArrowBuf.nioBuffer(ArrowBuf.java:245)

org.apache.arrow.vector.ipc.message.ArrowRecordBatch.computeBodyLength(ArrowRecordBatch.java:222)

```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Pass the Jenkins with JDK11.
Closes#26552 from dongjoon-hyun/SPARK-ARROW-JDK11.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Hive support STORED AS new file format syntax:
```sql
CREATE TABLE tbl(a int) STORED AS TEXTFILE;
CREATE TABLE tbl2 LIKE tbl STORED AS PARQUET;
```
We add a similar syntax for Spark. Here we separate to two features:
1. specify a different table provider in CREATE TABLE LIKE
2. Hive compatibility
In this PR, we address the first one:
- [ ] Using `USING provider` to specify a different table provider in CREATE TABLE LIKE.
- [ ] Using `STORED AS file_format` in CREATE TABLE LIKE to address Hive compatibility.
### Why are the changes needed?
Use CREATE TABLE tb1 LIKE tb2 command to create an empty table tb1 based on the definition of table tb2. The most user case is to create tb1 with the same schema of tb2. But an inconvenient case here is this command also copies the FileFormat from tb2, it cannot change the input/output format and serde. Add the ability of changing file format is useful for some scenarios like upgrading a table from a low performance file format to a high performance one (parquet, orc).
### Does this PR introduce any user-facing change?
Add a new syntax based on current CTL:
```sql
CREATE TABLE tbl2 LIKE tbl [USING parquet];
```
### How was this patch tested?
Modify some exist UTs.
Closes#26097 from LantaoJin/SPARK-29421.
Authored-by: lajin <lajin@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This aims to exclude the `preview` release to recover `HiveExternalCatalogVersionsSuite`. Currently, new preview release breaks `branch-2.4` PRBuilder since yesterday. New release (especially `preview`) should not affect `branch-2.4`.
- https://github.com/apache/spark/pull/26417 (Failed 4 times)
### Why are the changes needed?
**BEFORE**
```scala
scala> scala.io.Source.fromURL("https://dist.apache.org/repos/dist/release/spark/").mkString.split("\n").filter(_.contains("""<li><a href="spark-""")).map("""<a href="spark-(\d.\d.\d)/">""".r.findFirstMatchIn(_).get.group(1))
java.util.NoSuchElementException: None.get
```
**AFTER**
```scala
scala> scala.io.Source.fromURL("https://dist.apache.org/repos/dist/release/spark/").mkString.split("\n").filter(_.contains("""<li><a href="spark-""")).filterNot(_.contains("preview")).map("""<a href="spark-(\d.\d.\d)/">""".r.findFirstMatchIn(_).get.group(1))
res5: Array[String] = Array(2.3.4, 2.4.4)
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
This should pass the PRBuilder.
Closes#26428 from dongjoon-hyun/SPARK-HiveExternalCatalogVersionsSuite.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
To push the built jars to maven release repository, we need to remove the 'SNAPSHOT' tag from the version name.
Made the following changes in this PR:
* Update all the `3.0.0-SNAPSHOT` version name to `3.0.0-preview`
* Update the sparkR version number check logic to allow jvm version like `3.0.0-preview`
**Please note those changes were generated by the release script in the past, but this time since we manually add tags on master branch, we need to manually apply those changes too.**
We shall revert the changes after 3.0.0-preview release passed.
### Why are the changes needed?
To make the maven release repository to accept the built jars.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
N/A
### What changes were proposed in this pull request?
To push the built jars to maven release repository, we need to remove the 'SNAPSHOT' tag from the version name.
Made the following changes in this PR:
* Update all the `3.0.0-SNAPSHOT` version name to `3.0.0-preview`
* Update the PySpark version from `3.0.0.dev0` to `3.0.0`
**Please note those changes were generated by the release script in the past, but this time since we manually add tags on master branch, we need to manually apply those changes too.**
We shall revert the changes after 3.0.0-preview release passed.
### Why are the changes needed?
To make the maven release repository to accept the built jars.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
N/A
Closes#26243 from jiangxb1987/3.0.0-preview-prepare.
Lead-authored-by: Xingbo Jiang <xingbo.jiang@databricks.com>
Co-authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Xingbo Jiang <xingbo.jiang@databricks.com>
### What changes were proposed in this pull request?
There're some issues observed in `HiveUserDefinedTypeSuite."Support UDT in Hive UDF"`:
1) Neither function (TestUDF) nor test take "nullable" point column into account.
2) ExamplePointUDT. sqlType is ArrayType which doesn't provide information how many elements are expected. RandomDataGenerator may provide less elements than needed.
This patch fixes `HiveUserDefinedTypeSuite."Support UDT in Hive UDF"` to change the type of "point" column to be non-nullable, as well as not use RandomDataGenerator to create row for UDT backed by ArrayType.
### Why are the changes needed?
CI builds are failing in high occurrences.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manually tested by running tests locally multiple times.
Closes#26287 from HeartSaVioR/SPARK-28158-FOLLOWUP.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan.opensource@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
After this PR, we can create and register Hive UDFs to accept UDT type, like `VectorUDT` and `MatrixUDT`. These UDTs are widely used in Spark machine learning.
## How was this patch tested?
add new ut
Closes#24961 from uncleGen/SPARK-28158.
Authored-by: uncleGen <hustyugm@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Only use antlr4 to parse the interval string, and remove the duplicated parsing logic from `CalendarInterval`.
### Why are the changes needed?
Simplify the code and fix inconsistent behaviors.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Pass the Jenkins with the updated test cases.
Closes#26190 from cloud-fan/parser.
Lead-authored-by: Wenchen Fan <wenchen@databricks.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Add CacheTableStatement and make CACHE TABLE go through the same catalog/table resolution framework of v2 commands.
### Why are the changes needed?
It's important to make all the commands have the same table resolution behavior, to avoid confusing end-users. e.g.
```
USE my_catalog
DESC t // success and describe the table t from my_catalog
CACHE TABLE t // report table not found as there is no table t in the session catalog
```
### Does this PR introduce any user-facing change?
yes. When running CACHE TABLE, Spark fails the command if the current catalog is set to a v2 catalog, or the table name specified a v2 catalog.
### How was this patch tested?
Unit tests.
Closes#26179 from viirya/SPARK-29522.
Lead-authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Co-authored-by: Liang-Chi Hsieh <liangchi@uber.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
`CatalogTable` to `HiveTable` will change the table's ownership. How to reproduce:
```scala
import org.apache.spark.sql.catalyst.TableIdentifier
import org.apache.spark.sql.catalyst.catalog.{CatalogStorageFormat, CatalogTable, CatalogTableType}
import org.apache.spark.sql.types.{LongType, StructType}
val identifier = TableIdentifier("spark_29498", None)
val owner = "SPARK-29498"
val newTable = CatalogTable(
identifier,
tableType = CatalogTableType.EXTERNAL,
storage = CatalogStorageFormat(
locationUri = None,
inputFormat = None,
outputFormat = None,
serde = None,
compressed = false,
properties = Map.empty),
owner = owner,
schema = new StructType().add("i", LongType, false),
provider = Some("hive"))
spark.sessionState.catalog.createTable(newTable, false)
// The owner is not SPARK-29498
println(spark.sessionState.catalog.getTableMetadata(identifier).owner)
```
This PR makes it set the `HiveTable`'s owner to `CatalogTable`'s owner if it's owner is not empty when converting `CatalogTable` to `HiveTable`.
### Why are the changes needed?
We should not change the ownership of the table when converting `CatalogTable` to `HiveTable`.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
unit test
Closes#26160 from wangyum/SPARK-29498.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Current Spark SQL `SHOW FUNCTIONS` don't show `!=`, `<>`, `between`, `case`
But these expressions is truly functions. We should show it in SQL `SHOW FUNCTIONS`
### Why are the changes needed?
SHOW FUNCTIONS show '!=', '<>' , 'between', 'case'
### Does this PR introduce any user-facing change?
SHOW FUNCTIONS show '!=', '<>' , 'between', 'case'
### How was this patch tested?
UT
Closes#26053 from AngersZhuuuu/SPARK-29379.
Authored-by: angerszhu <angers.zhu@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This patch proposes to delete old Hive external partition directory even the partition does not exist in Hive, when insert overwrite Hive external table partition.
### Why are the changes needed?
When insert overwrite to a Hive external table partition, if the partition does not exist, Hive will not check if the external partition directory exists or not before copying files. So if users drop the partition, and then do insert overwrite to the same partition, the partition will have both old and new data.
For example:
```scala
withSQLConf(HiveUtils.CONVERT_METASTORE_PARQUET.key -> "false") {
// test is an external Hive table.
sql("INSERT OVERWRITE TABLE test PARTITION(name='n1') SELECT 1")
sql("ALTER TABLE test DROP PARTITION(name='n1')")
sql("INSERT OVERWRITE TABLE test PARTITION(name='n1') SELECT 2")
sql("SELECT id FROM test WHERE name = 'n1' ORDER BY id") // Got both 1 and 2.
}
```
### Does this PR introduce any user-facing change?
Yes. This fix a correctness issue when users drop partition on a Hive external table partition and then insert overwrite it.
### How was this patch tested?
Added test.
Closes#25979 from viirya/SPARK-29295.
Lead-authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Co-authored-by: Liang-Chi Hsieh <liangchi@uber.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
In this change, we give preference to the original table's owner if it is not empty.
### Why are the changes needed?
When executing 'insert into/overwrite ...' DML, or 'alter table set tblproperties ...' DDL, spark would change the ownership of the table the one who runs the spark application.
### Does this PR introduce any user-facing change?
NO
### How was this patch tested?
Compare with the behavior of Apache Hive
Closes#26068 from yaooqinn/SPARK-29405.
Authored-by: Kent Yao <yaooqinn@hotmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR proposes a few typos:
1. Sparks => Spark's
2. parallize => parallelize
3. doesnt => doesn't
Closes#26140 from plusplusjiajia/fix-typos.
Authored-by: Jiajia Li <jiajia.li@intel.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Change Literal.sql to output CAST('fpValue' AS FLOAT) instead of CAST(fpValue AS FLOAT) as the SQL for a floating point literal.
### Why are the changes needed?
The old version doesn't work for very small floating point numbers; the value will fail to parse if it doesn't fit in a DECIMAL(38).
This doesn't apply to doubles because they have special literal syntax.
### Does this PR introduce any user-facing change?
Not really.
### How was this patch tested?
New unit tests.
Closes#26114 from jose-torres/fpliteral.
Authored-by: Jose Torres <joseph.torres@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
When inserting a value into a column with the different data type, Spark performs type coercion. Currently, we support 3 policies for the store assignment rules: ANSI, legacy and strict, which can be set via the option "spark.sql.storeAssignmentPolicy":
1. ANSI: Spark performs the type coercion as per ANSI SQL. In practice, the behavior is mostly the same as PostgreSQL. It disallows certain unreasonable type conversions such as converting `string` to `int` and `double` to `boolean`. It will throw a runtime exception if the value is out-of-range(overflow).
2. Legacy: Spark allows the type coercion as long as it is a valid `Cast`, which is very loose. E.g., converting either `string` to `int` or `double` to `boolean` is allowed. It is the current behavior in Spark 2.x for compatibility with Hive. When inserting an out-of-range value to a integral field, the low-order bits of the value is inserted(the same as Java/Scala numeric type casting). For example, if 257 is inserted to a field of Byte type, the result is 1.
3. Strict: Spark doesn't allow any possible precision loss or data truncation in store assignment, e.g., converting either `double` to `int` or `decimal` to `double` is allowed. The rules are originally for Dataset encoder. As far as I know, no mainstream DBMS is using this policy by default.
Currently, the V1 data source uses "Legacy" policy by default, while V2 uses "Strict". This proposal is to use "ANSI" policy by default for both V1 and V2 in Spark 3.0.
### Why are the changes needed?
Following the ANSI SQL standard is most reasonable among the 3 policies.
### Does this PR introduce any user-facing change?
Yes.
The default store assignment policy is ANSI for both V1 and V2 data sources.
### How was this patch tested?
Unit test
Closes#26107 from gengliangwang/ansiPolicyAsDefault.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
move the statement logical plans that were created for v2 commands to a new file `statements.scala`, under the same package of `v2Commands.scala`.
This PR also includes some minor cleanups:
1. remove `private[sql]` from `ParsedStatement` as it's in the private package.
2. remove unnecessary override of `output` and `children`.
3. add missing classdoc.
### Why are the changes needed?
Similar to https://github.com/apache/spark/pull/26111 , this is to better organize the logical plans of data source v2.
It's a bit weird to put the statements in the package `org.apache.spark.sql.catalyst.plans.logical.sql` as `sql` is not a good sub-package name in Spark SQL.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
existing tests
Closes#26125 from cloud-fan/statement.
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 a very minor follow-up to become robust because `spark.sql.additionalRemoteRepositories` is a configuration which has a comma-separated value.
### Why are the changes needed?
This makes sure that `getHiveContribJar` will not fail on the configuration changes.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Manual. Change the default value with multiple repositories and run the following.
```
build/sbt -Phive "project hive" "test-only org.apache.spark.sql.hive.HiveSparkSubmitSuite"
```
Closes#26096 from dongjoon-hyun/SPARK-27831.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Yuming Wang <wgyumg@gmail.com>
### What changes were proposed in this pull request?
Syntax like `'foo` is deprecated in Scala 2.13. Replace usages with `Symbol("foo")`
### Why are the changes needed?
Avoids ~50 deprecation warnings when attempting to build with 2.13.
### Does this PR introduce any user-facing change?
None, should be no functional change at all.
### How was this patch tested?
Existing tests.
Closes#26061 from srowen/SPARK-29392.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Currently we deal with different `ParsedStatement` in many places and write duplicated catalog/table lookup logic. In general the lookup logic is
1. try look up the catalog by name. If no such catalog, and default catalog is not set, convert `ParsedStatement` to v1 command like `ShowDatabasesCommand`. Otherwise, convert `ParsedStatement` to v2 command like `ShowNamespaces`.
2. try look up the table by name. If no such table, fail. If the table is a `V1Table`, convert `ParsedStatement` to v1 command like `CreateTable`. Otherwise, convert `ParsedStatement` to v2 command like `CreateV2Table`.
However, since the code is duplicated we don't apply this lookup logic consistently. For example, we forget to consider the v2 session catalog in several places.
This PR centralizes the catalog/table lookup logic by 3 rules.
1. `ResolveCatalogs` (in catalyst). This rule resolves v2 catalog from the multipart identifier in SQL statements, and convert the statement to v2 command if the resolved catalog is not session catalog. If the command needs to resolve the table (e.g. ALTER TABLE), put an `UnresolvedV2Table` in the command.
2. `ResolveTables` (in catalyst). It resolves `UnresolvedV2Table` to `DataSourceV2Relation`.
3. `ResolveSessionCatalog` (in sql/core). This rule is only effective if the resolved catalog is session catalog. For commands that don't need to resolve the table, this rule converts the statement to v1 command directly. Otherwise, it converts the statement to v1 command if the resolved table is v1 table, and convert to v2 command if the resolved table is v2 table. Hopefully we can remove this rule eventually when v1 fallback is not needed anymore.
### Why are the changes needed?
Reduce duplicated code and make the catalog/table lookup logic consistent.
### Does this PR introduce any user-facing change?
no
### How was this patch tested?
existing tests
Closes#25747 from cloud-fan/lookup.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
LOAD DATA command resolves the partition column name as case sensitive manner,
where as in insert commandthe partition column name will be resolved using
the SQLConf resolver where the names will be resolved based on `spark.sql.caseSensitive` property. Same logic can be applied for resolving the partition column names in LOAD COMMAND.
### Why are the changes needed?
It's to handle the partition column name correctly according to the configuration.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing UT and manual testing.
Closes#24903 from sujith71955/master_paritionColName.
Lead-authored-by: s71955 <sujithchacko.2010@gmail.com>
Co-authored-by: sujith71955 <sujithchacko.2010@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR exposes USE CATALOG/USE SQL commands as described in this [SPIP](https://docs.google.com/document/d/1jEcvomPiTc5GtB9F7d2RTVVpMY64Qy7INCA_rFEd9HQ/edit#)
It also exposes `currentCatalog` in `CatalogManager`.
Finally, it changes `SHOW NAMESPACES` and `SHOW TABLES` to use the current catalog if no catalog is specified (instead of default catalog).
### Why are the changes needed?
There is currently no mechanism to change current catalog/namespace thru SQL commands.
### Does this PR introduce any user-facing change?
Yes, you can perform the following:
```scala
// Sets the current catalog to 'testcat'
spark.sql("USE CATALOG testcat")
// Sets the current catalog to 'testcat' and current namespace to 'ns1.ns2'.
spark.sql("USE ns1.ns2 IN testcat")
// Now, the following will use 'testcat' as the current catalog and 'ns1.ns2' as the current namespace.
spark.sql("SHOW NAMESPACES")
```
### How was this patch tested?
Added new unit tests.
Closes#25771 from imback82/use_namespace.
Authored-by: Terry Kim <yuminkim@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Scala 2.13 emits a deprecation warning for procedure-like declarations:
```
def foo() {
...
```
This is equivalent to the following, so should be changed to avoid a warning:
```
def foo(): Unit = {
...
```
### Why are the changes needed?
It will avoid about a thousand compiler warnings when we start to support Scala 2.13. I wanted to make the change in 3.0 as there are less likely to be back-ports from 3.0 to 2.4 than 3.1 to 3.0, for example, minimizing that downside to touching so many files.
Unfortunately, that makes this quite a big change.
### Does this PR introduce any user-facing change?
No behavior change at all.
### How was this patch tested?
Existing tests.
Closes#25968 from srowen/SPARK-29291.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
HiveClientImpl may be log sensitive information. e.g. url, secret and token:
```scala
logDebug(
s"""
|Applying Hadoop/Hive/Spark and extra properties to Hive Conf:
|$k=${if (k.toLowerCase(Locale.ROOT).contains("password")) "xxx" else v}
""".stripMargin)
```
So redact it. Use SQLConf.get.redactOptions.
I add a new overloading function to fit this situation for one by one kv pair situation.
### Why are the changes needed?
Redact sensitive information when construct HiveClientImpl
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
MT
Run command
` /sbin/start-thriftserver.sh`
In log we can get
```
19/09/28 08:27:02 main DEBUG HiveClientImpl:
Applying Hadoop/Hive/Spark and extra properties to Hive Conf:
hive.druid.metadata.password=*********(redacted)
```
Closes#25954 from AngersZhuuuu/SPARK-29247.
Authored-by: angerszhu <angers.zhu@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Support the syntax of `ALTER (DATABASE|SCHEMA) database_name SET LOCATION` path. Please note that only Hive 3.x metastore support this syntax.
Ref:
https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDLhttps://issues.apache.org/jira/browse/HIVE-8472
### Why are the changes needed?
Support more syntax.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Unit test.
Closes#25883 from wangyum/SPARK-28476.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Xiao Li <gatorsmile@gmail.com>
### What changes were proposed in this pull request?
This PR moves Hive test jars(`hive-contrib-*.jar` and `hive-hcatalog-core-*.jar`) from maven dependency to local file.
### Why are the changes needed?
`--jars` can't be tested since `hive-contrib-*.jar` and `hive-hcatalog-core-*.jar` are already in classpath.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
manual test
Closes#25690 from wangyum/SPARK-27831-revert.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Yuming Wang <wgyumg@gmail.com>
### What changes were proposed in this pull request?
Hive 2.3 will set a new UDFClassLoader to hiveConf.classLoader when initializing SessionState since HIVE-11878, and
1. ADDJarCommand will add jars to clientLoader.classLoader.
2. --jar passed jar will be added to clientLoader.classLoader
3. jar passed by hive conf `hive.aux.jars` [SPARK-28954](https://github.com/apache/spark/pull/25653) [SPARK-28840](https://github.com/apache/spark/pull/25542) will be added to clientLoader.classLoader too
For these reason we cannot load the jars added by ADDJarCommand because of class loader got changed. We reset it to clientLoader.ClassLoader here.
### Why are the changes needed?
support for jdk11
### Does this PR introduce any user-facing change?
NO
### How was this patch tested?
UT
```
export JAVA_HOME=/usr/lib/jdk-11.0.3
export PATH=$JAVA_HOME/bin:$PATH
build/sbt -Phive-thriftserver -Phadoop-3.2
hive/test-only *HiveSparkSubmitSuite -- -z "SPARK-8368: includes jars passed in through --jars"
hive-thriftserver/test-only *HiveThriftBinaryServerSuite -- -z "test add jar"
```
Closes#25775 from AngersZhuuuu/SPARK-29015-STS-JDK11.
Authored-by: angerszhu <angers.zhu@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Added a new config "spark.sql.additionalRemoteRepositories", a comma-delimited string config of the optional additional remote maven mirror.
### Why are the changes needed?
We need to connect the Maven repositories in IsolatedClientLoader for downloading Hive jars,
end-users can set this config if the default maven central repo is unreachable.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing UT.
Closes#25849 from xuanyuanking/SPARK-29175.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Copy any "spark.hive.foo=bar" spark properties into hadoop conf as "hive.foo=bar"
### Why are the changes needed?
Providing spark side config entry for hive configurations.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
UT.
Closes#25661 from WeichenXu123/add_hive_conf.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
Change the remote repo used in IsolatedClientLoader from datanucleus to google mirror.
### Why are the changes needed?
We need to connect the Maven repositories in IsolatedClientLoader for downloading Hive jars. The repository currently used is "http://www.datanucleus.org/downloads/maven2", which is [no longer maintained](http://www.datanucleus.org:15080/downloads/maven2/README.txt). This will cause downloading failure and make hive test cases flaky while Jenkins host is blocked by maven central repo.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing UT.
Closes#25915 from xuanyuanking/SPARK-29229.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This patch fixes the issue brought by [SPARK-21870](http://issues.apache.org/jira/browse/SPARK-21870): when generating code for parameter type, it doesn't consider array type in javaType. At least we have one, Spark should generate code for BinaryType as `byte[]`, but Spark create the code for BinaryType as `[B` and generated code fails compilation.
Below is the generated code which failed compilation (Line 380):
```
/* 380 */ private void agg_doAggregate_count_0([B agg_expr_1_1, boolean agg_exprIsNull_1_1, org.apache.spark.sql.catalyst.InternalRow agg_unsafeRowAggBuffer_1) throws java.io.IOException {
/* 381 */ // evaluate aggregate function for count
/* 382 */ boolean agg_isNull_26 = false;
/* 383 */ long agg_value_28 = -1L;
/* 384 */ if (!false && agg_exprIsNull_1_1) {
/* 385 */ long agg_value_31 = agg_unsafeRowAggBuffer_1.getLong(1);
/* 386 */ agg_isNull_26 = false;
/* 387 */ agg_value_28 = agg_value_31;
/* 388 */ } else {
/* 389 */ long agg_value_33 = agg_unsafeRowAggBuffer_1.getLong(1);
/* 390 */
/* 391 */ long agg_value_32 = -1L;
/* 392 */
/* 393 */ agg_value_32 = agg_value_33 + 1L;
/* 394 */ agg_isNull_26 = false;
/* 395 */ agg_value_28 = agg_value_32;
/* 396 */ }
/* 397 */ // update unsafe row buffer
/* 398 */ agg_unsafeRowAggBuffer_1.setLong(1, agg_value_28);
/* 399 */ }
```
There wasn't any test for HashAggregateExec specifically testing this, but randomized test in ObjectHashAggregateSuite could encounter this and that's why ObjectHashAggregateSuite is flaky.
### Why are the changes needed?
Without the fix, generated code from HashAggregateExec may fail compilation.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Added new UT. Without the fix, newly added UT fails.
Closes#25830 from HeartSaVioR/SPARK-29140.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This pr is to propagate all the SQL configurations to executors in `SQLQueryTestSuite`. When the propagation enabled in the tests, a potential bug below becomes apparent;
```
CREATE TABLE num_data (id int, val decimal(38,10)) USING parquet;
....
select sum(udf(CAST(null AS Decimal(38,0)))) from range(1,4): QueryOutput(select sum(udf(CAST(null AS Decimal(38,0)))) from range(1,4),struct<>,java.lang.IllegalArgumentException
[info] requirement failed: MutableProjection cannot use UnsafeRow for output data types: decimal(38,0)) (SQLQueryTestSuite.scala:380)
```
The root culprit is that `InterpretedMutableProjection` has incorrect validation in the interpreter mode: `validExprs.forall { case (e, _) => UnsafeRow.isFixedLength(e.dataType) }`. This validation should be the same with the condition (`isMutable`) in `HashAggregate.supportsAggregate`: https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/HashAggregateExec.scala#L1126
### Why are the changes needed?
Bug fixes.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Added tests in `AggregationQuerySuite`
Closes#25831 from maropu/SPARK-29122.
Authored-by: Takeshi Yamamuro <yamamuro@apache.org>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
### What changes were proposed in this pull request?
This PR aims to use `tryWithResource` for ORC file.
### Why are the changes needed?
This is a follow-up to address https://github.com/apache/spark/pull/25006#discussion_r298788206 .
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Pass the Jenkins with the existing tests.
Closes#25842 from dongjoon-hyun/SPARK-28208.
Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
Refactored SQL-related benchmark and made them depend on `SqlBasedBenchmark`. In particular, creation of Spark session are moved into `override def getSparkSession: SparkSession`.
### Why are the changes needed?
This should simplify maintenance of SQL-based benchmarks by reducing the number of dependencies. In the future, it should be easier to refactor & extend all SQL benchmarks by changing only one trait. Finally, all SQL-based benchmarks will look uniformly.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
By running the modified benchmarks.
Closes#25828 from MaxGekk/sql-benchmarks-refactoring.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
It upgrades ORC from 1.5.5 to 1.5.6 and adds closes the ORC readers when they aren't used to
create RecordReaders.
## How was this patch tested?
The changed unit tests were run.
Closes#25006 from omalley/spark-28208.
Lead-authored-by: Owen O'Malley <omalley@apache.org>
Co-authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
**What changes were proposed in this pull request?**
Issue 1 : modifications not required as these are different formats for the same info. In the case of a Spark DataFrame, null is correct.
Issue 2 mentioned in JIRA Spark SQL "desc formatted tablename" is not showing the header # col_name,data_type,comment , seems to be the header has been removed knowingly as part of SPARK-20954.
Issue 3:
Corrected the Last Access time, the value shall display 'UNKNOWN' as currently system wont support the last access time evaluation, since hive was setting Last access time as '0' in metastore even though spark CatalogTable last access time value set as -1. this will make the validation logic of LasAccessTime where spark sets 'UNKNOWN' value if last access time value set as -1 (means not evaluated).
**Does this PR introduce any user-facing change?**
No
**How was this patch tested?**
Locally and corrected a ut.
Attaching the test report below
Closes#25720 from sujith71955/master_describe_info.
Authored-by: s71955 <sujithchacko.2010@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
This patch proposes to add new tests to test the username of HiveClient to prevent changing the semantic unintentionally. The owner of Hive table has been changed back-and-forth, principal -> username -> principal, and looks like the change is not intentional. (Please refer [SPARK-28996](https://issues.apache.org/jira/browse/SPARK-28996) for more details.) This patch intends to prevent this.
This patch also renames previous HiveClientSuite(s) to HivePartitionFilteringSuite(s) as it was commented as TODO, as well as previous tests are too narrowed to test only partition filtering.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Newly added UTs.
Closes#25696 from HeartSaVioR/SPARK-28996.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
…create table through spark-sql or beeline
## What changes were proposed in this pull request?
fix table owner use user instead of principal when create table through spark-sql
private val userName = conf.getUser will get ugi's userName which is principal info, and i copy the source code into HiveClientImpl, and use ugi.getShortUserName() instead of ugi.getUserName(). The owner display correctly.
## How was this patch tested?
1. create a table in kerberos cluster
2. use "desc formatted tbName" check owner
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#23952 from hddong/SPARK-26929-fix-table-owner.
Lead-authored-by: hongdd <jn_hdd@163.com>
Co-authored-by: hongdongdong <hongdongdong@cmss.chinamobile.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
JIRA :https://issues.apache.org/jira/browse/SPARK-29050
'a hdfs' change into 'an hdfs'
'an unique' change into 'a unique'
'an url' change into 'a url'
'a error' change into 'an error'
Closes#25756 from dengziming/feature_fix_typos.
Authored-by: dengziming <dengziming@growingio.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
Remove `InsertIntoTable` and replace it's usage by `InsertIntoStatement`
### Why are the changes needed?
`InsertIntoTable` and `InsertIntoStatement` are almost identical (except some namings). It doesn't make sense to keep 2 identical plans. After the removal of `InsertIntoTable`, the analysis process becomes:
1. parser creates `InsertIntoStatement`
2. v2 rule `ResolveInsertInto` converts `InsertIntoStatement` to v2 commands.
3. v1 rules like `DataSourceAnalysis` and `HiveAnalysis` convert `InsertIntoStatement` to v1 commands.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
existing tests
Closes#25763 from cloud-fan/remove.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
### What changes were proposed in this pull request?
- Remove SQLContext.createExternalTable and Catalog.createExternalTable, deprecated in favor of createTable since 2.2.0, plus tests of deprecated methods
- Remove HiveContext, deprecated in 2.0.0, in favor of `SparkSession.builder.enableHiveSupport`
- Remove deprecated KinesisUtils.createStream methods, plus tests of deprecated methods, deprecate in 2.2.0
- Remove deprecated MLlib (not Spark ML) linear method support, mostly utility constructors and 'train' methods, and associated docs. This includes methods in LinearRegression, LogisticRegression, Lasso, RidgeRegression. These have been deprecated since 2.0.0
- Remove deprecated Pyspark MLlib linear method support, including LogisticRegressionWithSGD, LinearRegressionWithSGD, LassoWithSGD
- Remove 'runs' argument in KMeans.train() method, which has been a no-op since 2.0.0
- Remove deprecated ChiSqSelector isSorted protected method
- Remove deprecated 'yarn-cluster' and 'yarn-client' master argument in favor of 'yarn' and deploy mode 'cluster', etc
Notes:
- I was not able to remove deprecated DataFrameReader.json(RDD) in favor of DataFrameReader.json(Dataset); the former was deprecated in 2.2.0, but, it is still needed to support Pyspark's .json() method, which can't use a Dataset.
- Looks like SQLContext.createExternalTable was not actually deprecated in Pyspark, but, almost certainly was meant to be? Catalog.createExternalTable was.
- I afterwards noted that the toDegrees, toRadians functions were almost removed fully in SPARK-25908, but Felix suggested keeping just the R version as they hadn't been technically deprecated. I'd like to revisit that. Do we really want the inconsistency? I'm not against reverting it again, but then that implies leaving SQLContext.createExternalTable just in Pyspark too, which seems weird.
- I *kept* LogisticRegressionWithSGD, LinearRegressionWithSGD, LassoWithSGD, RidgeRegressionWithSGD in Pyspark, though deprecated, as it is hard to remove them (still used by StreamingLogisticRegressionWithSGD?) and they are not fully removed in Scala. Maybe should not have been deprecated.
### Why are the changes needed?
Deprecated items are easiest to remove in a major release, so we should do so as much as possible for Spark 3. This does not target items deprecated 'recently' as of Spark 2.3, which is still 18 months old.
### Does this PR introduce any user-facing change?
Yes, in that deprecated items are removed from some public APIs.
### How was this patch tested?
Existing tests.
Closes#25684 from srowen/SPARK-28980.
Lead-authored-by: Sean Owen <sean.owen@databricks.com>
Co-authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The `V2SessionCatalog` has 2 functionalities:
1. work as an adapter: provide v2 APIs and translate calls to the `SessionCatalog`.
2. allow users to extend it, so that they can add hooks to apply custom logic before calling methods of the builtin catalog (session catalog).
To leverage the second functionality, users must extend `V2SessionCatalog` which is an internal class. There is no doc to explain this usage.
This PR does 2 things:
1. refine the document of the config `spark.sql.catalog.session`.
2. add a public abstract class `CatalogExtension` for users to write implementations.
TODOs for followup PRs:
1. discuss if we should allow users to completely overwrite the v2 session catalog with a new one.
2. discuss to change the name of session catalog, so that it's less likely to conflict with existing namespace names.
## How was this patch tested?
existing tests
Closes#25104 from cloud-fan/session-catalog.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
This PR disables schema verification and allows schema auto-creation in the Derby database, in case the config for the Metastore is set otherwise.
## How was this patch tested?
NA
Closes#25663 from bogdanghit/hive-schema.
Authored-by: Bogdan Ghit <bogdan.ghit@databricks.com>
Signed-off-by: Yuming Wang <wgyumg@gmail.com>
### What changes were proposed in this pull request?
merge the `V2WriteSupportCheck` and `V2StreamingScanSupportCheck` to one rule: `TableCapabilityCheck`.
### Why are the changes needed?
It's a little confusing to have 2 rules to check DS v2 table capability, while one rule says it checks write and another rule says it checks streaming scan. We can clearly tell it from the rule names that the batch scan check is missing.
It's better to have a centralized place for this check, with a name that clearly says it checks table capability.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
existing tests
Closes#25679 from cloud-fan/dsv2-check.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Datasource table now supports partition tables long ago. This commit adds the ability to translate
the InsertIntoTable(HiveTableRelation) to datasource table insertion.
## How was this patch tested?
Existing tests with some modification
Closes#25306 from advancedxy/SPARK-28573.
Authored-by: Xianjin YE <advancedxy@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
The Experimental and Evolving annotations are both (like Unstable) used to express that a an API may change. However there are many things in the code that have been marked that way since even Spark 1.x. Per the dev thread, anything introduced at or before Spark 2.3.0 is pretty much 'stable' in that it would not change without a deprecation cycle. Therefore I'd like to remove most of these annotations. And, remove the `:: Experimental ::` scaladoc tag too. And likewise for Python, R.
The changes below can be summarized as:
- Generally, anything introduced at or before Spark 2.3.0 has been unmarked as neither Evolving nor Experimental
- Obviously experimental items like DSv2, Barrier mode, ExperimentalMethods are untouched
- I _did_ unmark a few MLlib classes introduced in 2.4, as I am quite confident they're not going to change (e.g. KolmogorovSmirnovTest, PowerIterationClustering)
It's a big change to review, so I'd suggest scanning the list of _files_ changed to see if any area seems like it should remain partly experimental and examine those.
### Why are the changes needed?
Many of these annotations are incorrect; the APIs are de facto stable. Leaving them also makes legitimate usages of the annotations less meaningful.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing tests.
Closes#25558 from srowen/SPARK-28855.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
### What changes were proposed in this pull request?
Hive 3.1.2 has been released. This PR upgrades the Hive Metastore Client to 3.1.2 for Hive 3.1.
Hive 3.1.2 release notes:
https://issues.apache.org/jira/secure/ReleaseNote.jspa?version=12344397&styleName=Html&projectId=12310843
### Why are the changes needed?
This is an improvement to support a newly release 3.1.2. Otherwise, it will throws `UnsupportedOperationException` if user `set spark.sql.hive.metastore.version=3.1.2`:
```scala
Exception in thread "main" java.lang.UnsupportedOperationException: Unsupported Hive Metastore version (3.1.2). Please set spark.sql.hive.metastore.version with a valid version.
at org.apache.spark.sql.hive.client.IsolatedClientLoader$.hiveVersion(IsolatedClientLoader.scala:109)
```
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Existing UT
Closes#25604 from wangyum/SPARK-28890.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR makes `spark.sql.statistics.fallBackToHdfs` not support Hive partitioned tables.
### Why are the changes needed?
The current implementation is incorrect for external partitions and it is expensive to support partitioned table with external partitions.
### Does this PR introduce any user-facing change?
Yes. But I think it will not change the join strategy because partitioned table usually very large.
### How was this patch tested?
unit test
Closes#25584 from wangyum/SPARK-28876.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
### What changes were proposed in this pull request?
This PR add test for set the partitioned bucketed data source table SerDe correctly.
### Why are the changes needed?
Improve test.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
N/A
Closes#25591 from wangyum/SPARK-27592-f1.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Currently we have 2 configs to specify which v2 sources should fallback to v1 code path. One config for read path, and one config for write path.
However, I found it's awkward to work with these 2 configs:
1. for `CREATE TABLE USING format`, should this be read path or write path?
2. for `V2SessionCatalog.loadTable`, we need to return `UnresolvedTable` if it's a DS v1 or we need to fallback to v1 code path. However, at that time, we don't know if the returned table will be used for read or write.
We don't have any new features or perf improvement in file source v2. The fallback API is just a safeguard if we have bugs in v2 implementations. There are not many benefits to support falling back to v1 for read and write path separately.
This PR proposes to merge these 2 configs into one.
## How was this patch tested?
existing tests
Closes#25465 from cloud-fan/merge-conf.
Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
This reverts commit 485ae6d181.
Closes#25563 from gatorsmile/revert.
Authored-by: Xiao Li <gatorsmile@gmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
### What changes were proposed in this pull request?
This PR removes the `canonicalize(attrs: AttributeSeq)` from `PlanExpression` and taking care of normalizing expressions in `QueryPlan`.
### Why are the changes needed?
`Expression` has already a `canonicalized` method and having the `canonicalize` method in `PlanExpression` is confusing.
### Does this PR introduce any user-facing change?
Removes the `canonicalize` plan from `PlanExpression`. Also renames the `normalizeExprId` to `normalizeExpressions` in query plan.
### How was this patch tested?
This PR is a refactoring and passes the existing tests
Closes#25534 from dbaliafroozeh/ImproveCanonicalizeAPI.
Authored-by: Ali Afroozeh <ali.afroozeh@databricks.com>
Signed-off-by: herman <herman@databricks.com>
