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## What changes were proposed in this pull request?
With SPARK-15034, we could use the value of spark.sql.warehouse.dir to set the warehouse location. In TestHive, we can now simply set the temporary warehouse path in sc's conf, and thus, param "warehousePath" could be removed.
## How was this patch tested?
exsiting testsuites.
Author: jiangxingbo <jiangxingbo@meituan.com>
Closes #14401 from jiangxb1987/warehousePath.
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## What changes were proposed in this pull request?
These 2 expressions are not needed anymore after we have `Greatest` and `Least`. This PR removes them and related tests.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes #14434 from cloud-fan/minor1.
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timezone handling
## What changes were proposed in this pull request?
Removes the deprecated timestamp constructor and incidentally fixes the use which was using system timezone rather than the one specified when working near DST.
This change also causes the roundtrip tests to fail since it now actually uses all the timezones near DST boundaries where it didn't before.
Note: this is only a partial the solution, longer term we should follow up with https://issues.apache.org/jira/browse/SPARK-16788 to avoid this problem & simplify our timezone handling code.
## How was this patch tested?
New tests for two timezones added so even if user timezone happens to coincided with one, the other tests should still fail. Important note: this (temporarily) disables the round trip tests until we can fix the issue more thoroughly.
Author: Holden Karau <holden@us.ibm.com>
Closes #14398 from holdenk/SPARK-16774-fix-use-of-deprecated-timestamp-constructor.
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## What changes were proposed in this pull request?
a failing test case + fix to SPARK-16791 (https://issues.apache.org/jira/browse/SPARK-16791)
## How was this patch tested?
added a failing test case to CastSuit, then fixed the Cast code and rerun the entire CastSuit
Author: eyal farago <eyal farago>
Author: Eyal Farago <eyal.farago@actimize.com>
Closes #14400 from eyalfa/SPARK-16791_cast_struct_with_timestamp_field_fails.
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## What changes were proposed in this pull request?
Change to non-deprecated constructor for SQLContext.
## How was this patch tested?
Existing tests
Author: Holden Karau <holden@us.ibm.com>
Closes #14406 from holdenk/SPARK-16778-fix-use-of-deprecated-SQLContext-constructor.
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incompatible types
## What changes were proposed in this pull request?
Currently, `UNION` queries on incompatible types show misleading error messages, i.e., `unresolved operator Union`. We had better show a more correct message. This will help users in the situation of [SPARK-16704](https://issues.apache.org/jira/browse/SPARK-16704).
**Before**
```scala
scala> sql("select 1,2,3 union (select 1,array(2),3)")
org.apache.spark.sql.AnalysisException: unresolved operator 'Union;
scala> sql("select 1,2,3 intersect (select 1,array(2),3)")
org.apache.spark.sql.AnalysisException: unresolved operator 'Intersect;
scala> sql("select 1,2,3 except (select 1,array(2),3)")
org.apache.spark.sql.AnalysisException: unresolved operator 'Except;
```
**After**
```scala
scala> sql("select 1,2,3 union (select 1,array(2),3)")
org.apache.spark.sql.AnalysisException: Union can only be performed on tables with the compatible column types. ArrayType(IntegerType,false) <> IntegerType at the second column of the second table;
scala> sql("select 1,2,3 intersect (select 1,array(2),3)")
org.apache.spark.sql.AnalysisException: Intersect can only be performed on tables with the compatible column types. ArrayType(IntegerType,false) <> IntegerType at the second column of the second table;
scala> sql("select 1,2,3 except (select array(1),array(2),3)")
org.apache.spark.sql.AnalysisException: Except can only be performed on tables with the compatible column types. ArrayType(IntegerType,false) <> IntegerType at the first column of the second table;
```
## How was this patch tested?
Pass the Jenkins test with a new test case.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes #14355 from dongjoon-hyun/SPARK-16726.
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## What changes were proposed in this pull request?
It is useful to log the timezone when query result does not match, especially on build machines that have different timezone from AMPLab Jenkins.
## How was this patch tested?
This is a test-only change.
Author: Reynold Xin <rxin@databricks.com>
Closes #14413 from rxin/SPARK-16805.
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## What changes were proposed in this pull request?
`StructField` has very similar semantic with `CatalogColumn`, except that `CatalogColumn` use string to express data type. I think it's reasonable to use `StructType` as the `CatalogTable.schema` and remove `CatalogColumn`.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes #14363 from cloud-fan/column.
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## What changes were proposed in this pull request?
The catalyst package is meant to be internal, and as a result it does not make sense to mark things as private[sql] or private[spark]. It simply makes debugging harder when Spark developers need to inspect the plans at runtime.
This patch removes all private[sql] and private[spark] visibility modifiers in org.apache.spark.sql.catalyst.
## How was this patch tested?
N/A - just visibility changes.
Author: Reynold Xin <rxin@databricks.com>
Closes #14418 from rxin/SPARK-16813.
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partitions
## What changes were proposed in this pull request?
This fixes a bug wherethe file scan operator does not take into account partition pruning in its implementation of `sameResult()`. As a result, executions may be incorrect on self-joins over the same base file relation.
The patch here is minimal, but we should reconsider relying on `metadata` for implementing sameResult() in the future, as string representations may not be uniquely identifying.
cc rxin
## How was this patch tested?
Unit tests.
Author: Eric Liang <ekl@databricks.com>
Closes #14425 from ericl/spark-16818.
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whose side effects are required
## What changes were proposed in this pull request?
Use foreach/for instead of map where operation requires execution of body, not actually defining a transformation
## How was this patch tested?
Jenkins
Author: Sean Owen <sowen@cloudera.com>
Closes #14332 from srowen/SPARK-16694.
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TreeNodeException
## What changes were proposed in this pull request?
We do not want SparkExceptions from job failures in the planning phase to create TreeNodeException. Hence do not wrap SparkException in TreeNodeException.
## How was this patch tested?
New unit test
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes #14395 from tdas/SPARK-16748.
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## What changes were proposed in this pull request?
f12f11e578169b47e3f8b18b299948c0670ba585 introduced this bug, missed foreach as map
## How was this patch tested?
Test added
Author: Wesley Tang <tangmingjun@mininglamp.com>
Closes #14324 from breakdawn/master.
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OutOfMemoryError
## What changes were proposed in this pull request?
We currently don't bound or manage the data array size used by column vectors in the vectorized reader (they're just bound by INT.MAX) which may lead to OOMs while reading data. As a short term fix, this patch intercepts the OutOfMemoryError exception and suggest the user to disable the vectorized parquet reader.
## How was this patch tested?
Existing Tests
Author: Sameer Agarwal <sameerag@cs.berkeley.edu>
Closes #14387 from sameeragarwal/oom.
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## What changes were proposed in this pull request?
Avoid overflow of Long type causing a NegativeArraySizeException a few lines later.
## How was this patch tested?
Unit tests for HashedRelationSuite still pass.
I can confirm the python script I included in https://issues.apache.org/jira/browse/SPARK-16740 works fine with this patch. Unfortunately I don't have the knowledge/time to write a Scala test case for HashedRelationSuite right now. As the patch is pretty obvious I hope it can be included without this.
Thanks!
Author: Sylvain Zimmer <sylvain@sylvainzimmer.com>
Closes #14373 from sylvinus/master.
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column should work
## What changes were proposed in this pull request?
The query with having condition that contains grouping by column will be failed during analysis. E.g.,
create table tbl(a int, b string);
select count(b) from tbl group by a + 1 having a + 1 = 2;
Having condition should be able to use grouping by column.
## How was this patch tested?
Jenkins tests.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes #14296 from viirya/having-contains-grouping-column.
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when Creating Tables
#### What changes were proposed in this pull request?
Currently, in Spark SQL, the initial creation of schema can be classified into two groups. It is applicable to both Hive tables and Data Source tables:
**Group A. Users specify the schema.**
_Case 1 CREATE TABLE AS SELECT_: the schema is determined by the result schema of the SELECT clause. For example,
```SQL
CREATE TABLE tab STORED AS TEXTFILE
AS SELECT * from input
```
_Case 2 CREATE TABLE_: users explicitly specify the schema. For example,
```SQL
CREATE TABLE jsonTable (_1 string, _2 string)
USING org.apache.spark.sql.json
```
**Group B. Spark SQL infers the schema at runtime.**
_Case 3 CREATE TABLE_. Users do not specify the schema but the path to the file location. For example,
```SQL
CREATE TABLE jsonTable
USING org.apache.spark.sql.json
OPTIONS (path '${tempDir.getCanonicalPath}')
```
Before this PR, Spark SQL does not store the inferred schema in the external catalog for the cases in Group B. When users refreshing the metadata cache, accessing the table at the first time after (re-)starting Spark, Spark SQL will infer the schema and store the info in the metadata cache for improving the performance of subsequent metadata requests. However, the runtime schema inference could cause undesirable schema changes after each reboot of Spark.
This PR is to store the inferred schema in the external catalog when creating the table. When users intend to refresh the schema after possible changes on external files (table location), they issue `REFRESH TABLE`. Spark SQL will infer the schema again based on the previously specified table location and update/refresh the schema in the external catalog and metadata cache.
In this PR, we do not use the inferred schema to replace the user specified schema for avoiding external behavior changes . Based on the design, user-specified schemas (as described in Group A) can be changed by ALTER TABLE commands, although we do not support them now.
#### How was this patch tested?
TODO: add more cases to cover the changes.
Author: gatorsmile <gatorsmile@gmail.com>
Closes #14207 from gatorsmile/userSpecifiedSchema.
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## What changes were proposed in this pull request?
We currently test subquery SQL building using the `HiveCompatibilitySuite`. The is not desired since SQL building is actually a part of `sql/core` and because we are slowly reducing our dependency on Hive. This PR adds the same tests from the whitelist of `HiveCompatibilitySuite` into `LogicalPlanToSQLSuite`.
## How was this patch tested?
This adds more testcases. Pass the Jenkins tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes #14383 from dongjoon-hyun/SPARK-15232.
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## What changes were proposed in this pull request?
Spark 1.x supports using the Hive type name as function names for doing casts, e.g.
```sql
SELECT int(1.0);
SELECT string(2.0);
```
The above query would work in Spark 1.x because Spark 1.x fail back to Hive for unimplemented functions, and break in Spark 2.0 because the fall back was removed.
This patch implements function aliases using an analyzer rule for the following cast functions:
- boolean
- tinyint
- smallint
- int
- bigint
- float
- double
- decimal
- date
- timestamp
- binary
- string
## How was this patch tested?
Added end-to-end tests in SQLCompatibilityFunctionSuite.
Author: petermaxlee <petermaxlee@gmail.com>
Closes #14364 from petermaxlee/SPARK-16730-2.
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## What changes were proposed in this pull request?
Fix two places in SQLConf documents regarding size in bytes and statistics.
## How was this patch tested?
No. Just change document.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes #14341 from viirya/fix-doc-size-in-bytes.
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## What changes were proposed in this pull request?
Spark currently throws exceptions for invalid casts for all other data types except date type. Somehow date type returns null. It should be consistent and throws analysis exception as well.
## How was this patch tested?
Added a unit test case in CastSuite.
Author: petermaxlee <petermaxlee@gmail.com>
Closes #14358 from petermaxlee/SPARK-16729.
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## What changes were proposed in this pull request?
Currently, the generated SQLs have not-stable IDs for generated attributes.
The stable generated SQL will give more benefit for understanding or testing the queries.
This PR provides stable SQL generation by the followings.
- Provide unique ids for generated subqueries, `gen_subquery_xxx`.
- Provide unique and stable ids for generated attributes, `gen_attr_xxx`.
**Before**
```scala
scala> new org.apache.spark.sql.catalyst.SQLBuilder(sql("select 1")).toSQL
res0: String = SELECT `gen_attr_0` AS `1` FROM (SELECT 1 AS `gen_attr_0`) AS gen_subquery_0
scala> new org.apache.spark.sql.catalyst.SQLBuilder(sql("select 1")).toSQL
res1: String = SELECT `gen_attr_4` AS `1` FROM (SELECT 1 AS `gen_attr_4`) AS gen_subquery_0
```
**After**
```scala
scala> new org.apache.spark.sql.catalyst.SQLBuilder(sql("select 1")).toSQL
res1: String = SELECT `gen_attr_0` AS `1` FROM (SELECT 1 AS `gen_attr_0`) AS gen_subquery_0
scala> new org.apache.spark.sql.catalyst.SQLBuilder(sql("select 1")).toSQL
res2: String = SELECT `gen_attr_0` AS `1` FROM (SELECT 1 AS `gen_attr_0`) AS gen_subquery_0
```
## How was this patch tested?
Pass the existing Jenkins tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes #14257 from dongjoon-hyun/SPARK-16621.
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## What changes were proposed in this pull request?
This PR is the first step for the following feature:
For hash aggregation in Spark SQL, we use a fast aggregation hashmap to act as a "cache" in order to boost aggregation performance. Previously, the hashmap is backed by a `ColumnarBatch`. This has performance issues when we have wide schema for the aggregation table (large number of key fields or value fields).
In this JIRA, we support another implementation of fast hashmap, which is backed by a `RowBasedKeyValueBatch`. We then automatically pick between the two implementations based on certain knobs.
In this first-step PR, implementations for `RowBasedKeyValueBatch` and `RowBasedHashMapGenerator` are added.
## How was this patch tested?
Unit tests: `RowBasedKeyValueBatchSuite`
Author: Qifan Pu <qifan.pu@gmail.com>
Closes #14349 from ooq/SPARK-16524.
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hive serde tables
## What changes were proposed in this pull request?
Currently there are 2 inconsistence:
1. for data source table, we only print partition names, for hive table, we also print partition schema. After this PR, we will always print schema
2. if column doesn't have comment, data source table will print empty string, hive table will print null. After this PR, we will always print null
## How was this patch tested?
new test in `HiveDDLSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes #14302 from cloud-fan/minor3.
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## What changes were proposed in this pull request?
Currently, `JdbcUtils.savePartition` is doing type-based dispatch for each row to write appropriate values.
So, appropriate setters for `PreparedStatement` can be created first according to the schema, and then apply them to each row. This approach is similar with `CatalystWriteSupport`.
This PR simply make the setters to avoid this.
## How was this patch tested?
Existing tests should cover this.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes #14323 from HyukjinKwon/SPARK-16675.
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## What changes were proposed in this pull request?
finish the TODO, create a new expression `ExternalMapToCatalyst` to iterate the map directly.
## How was this patch tested?
new test in `JavaDatasetSuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes #14344 from cloud-fan/java-map.
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ColumnPruning
## What changes were proposed in this pull request?
We push down `Project` through `Sample` in `Optimizer` by the rule `PushProjectThroughSample`. However, if the projected columns produce new output, they will encounter whole data instead of sampled data. It will bring some inconsistency between original plan (Sample then Project) and optimized plan (Project then Sample). In the extreme case such as attached in the JIRA, if the projected column is an UDF which is supposed to not see the sampled out data, the result of UDF will be incorrect.
Since the rule `ColumnPruning` already handles general `Project` pushdown. We don't need `PushProjectThroughSample` anymore. The rule `ColumnPruning` also avoids the described issue.
## How was this patch tested?
Jenkins tests.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes #14327 from viirya/fix-sample-pushdown.
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lead and lag functions
## What changes were proposed in this pull request?
This PR contains three changes.
First, this PR changes the behavior of lead/lag back to Spark 1.6's behavior, which is described as below:
1. lead/lag respect null input values, which means that if the offset row exists and the input value is null, the result will be null instead of the default value.
2. If the offset row does not exist, the default value will be used.
3. OffsetWindowFunction's nullable setting also considers the nullability of its input (because of the first change).
Second, this PR fixes the evaluation of lead/lag when the input expression is a literal. This fix is a result of the first change. In current master, if a literal is used as the input expression of a lead or lag function, the result will be this literal even if the offset row does not exist.
Third, this PR makes ResolveWindowFrame not fire if a window function is not resolved.
## How was this patch tested?
New tests in SQLWindowFunctionSuite
Author: Yin Huai <yhuai@databricks.com>
Closes #14284 from yhuai/lead-lag.
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We don't generally make things in catalyst/execution private. Instead they are just undocumented due to their lack of stability guarantees.
Author: Michael Armbrust <michael@databricks.com>
Closes #14356 from marmbrus/patch-1.
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## What changes were proposed in this pull request?
Currently, `SQLBuilder` raises `empty.reduceLeft` exceptions on *unoptimized* `EXISTS` queries. We had better prevent this.
```scala
scala> sql("CREATE TABLE t1(a int)")
scala> val df = sql("select * from t1 b where exists (select * from t1 a)")
scala> new org.apache.spark.sql.catalyst.SQLBuilder(df).toSQL
java.lang.UnsupportedOperationException: empty.reduceLeft
```
## How was this patch tested?
Pass the Jenkins tests with a new test suite.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes #14307 from dongjoon-hyun/SPARK-16672.
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## What changes were proposed in this pull request?
**Issue 1: Disallow Creating/Altering a View when the same-name Table Exists (without IF NOT EXISTS)**
When we create OR alter a view, we check whether the view already exists. In the current implementation, if a table with the same name exists, we treat it as a view. However, this is not the right behavior. We should follow what Hive does. For example,
```
hive> CREATE TABLE tab1 (id int);
OK
Time taken: 0.196 seconds
hive> CREATE OR REPLACE VIEW tab1 AS SELECT * FROM t1;
FAILED: SemanticException [Error 10218]: Existing table is not a view
The following is an existing table, not a view: default.tab1
hive> ALTER VIEW tab1 AS SELECT * FROM t1;
FAILED: SemanticException [Error 10218]: Existing table is not a view
The following is an existing table, not a view: default.tab1
hive> CREATE VIEW IF NOT EXISTS tab1 AS SELECT * FROM t1;
OK
Time taken: 0.678 seconds
```
**Issue 2: Strange Error when Issuing Load Table Against A View**
Users should not be allowed to issue LOAD DATA against a view. Currently, when users doing it, we got a very strange runtime error. For example,
```SQL
LOAD DATA LOCAL INPATH "$testData" INTO TABLE $viewName
```
```
java.lang.reflect.InvocationTargetException was thrown.
java.lang.reflect.InvocationTargetException
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.spark.sql.hive.client.Shim_v0_14.loadTable(HiveShim.scala:680)
```
## How was this patch tested?
Added test cases
Author: gatorsmile <gatorsmile@gmail.com>
Closes #14314 from gatorsmile/tableDDLAgainstView.
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in HDFSMetadataLog
## What changes were proposed in this pull request?
Current fix for deadlock disables interrupts in the StreamExecution which getting offsets for all sources, and when writing to any metadata log, to avoid potential deadlocks in HDFSMetadataLog(see JIRA for more details). However, disabling interrupts can have unintended consequences in other sources. So I am making the fix more narrow, by disabling interrupt it only in the HDFSMetadataLog. This is a narrower fix for something risky like disabling interrupt.
## How was this patch tested?
Existing tests.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes #14292 from tdas/SPARK-14131.
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SubexpressionEliminationSuite."Semantic equals and hash"
## What changes were proposed in this pull request?
SubexpressionEliminationSuite."Semantic equals and hash" assumes the default AttributeReference's exprId wont' be "ExprId(1)". However, that depends on when this test runs. It may happen to use "ExprId(1)".
This PR detects the conflict and makes sure we create a different ExprId when the conflict happens.
## How was this patch tested?
Jenkins unit tests.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes #14350 from zsxwing/SPARK-16715.
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functions
## What changes were proposed in this pull request?
This PR fixes a minor formatting issue of `WindowSpecDefinition.sql` when no partitioning expressions are present.
Before:
```sql
( ORDER BY `a` ASC ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)
```
After:
```sql
(ORDER BY `a` ASC ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)
```
## How was this patch tested?
New test case added in `ExpressionSQLBuilderSuite`.
Author: Cheng Lian <lian@databricks.com>
Closes #14334 from liancheng/window-spec-sql-format.
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based on FileFormat
## What changes were proposed in this pull request?
It seems this is a regression assuming from https://issues.apache.org/jira/browse/SPARK-16698.
Field name having dots throws an exception. For example the codes below:
```scala
val path = "/tmp/path"
val json =""" {"a.b":"data"}"""
spark.sparkContext
.parallelize(json :: Nil)
.saveAsTextFile(path)
spark.read.json(path).collect()
```
throws an exception as below:
```
Unable to resolve a.b given [a.b];
org.apache.spark.sql.AnalysisException: Unable to resolve a.b given [a.b];
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolve$1$$anonfun$apply$5.apply(LogicalPlan.scala:134)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolve$1$$anonfun$apply$5.apply(LogicalPlan.scala:134)
at scala.Option.getOrElse(Option.scala:121)
```
This problem was introduced in https://github.com/apache/spark/commit/17eec0a71ba8713c559d641e3f43a1be726b037c#diff-27c76f96a7b2733ecfd6f46a1716e153R121
When extracting the data columns, it does not count that it can contains dots in field names. Actually, it seems the fields name are not expected as quoted when defining schema. So, It not have to consider whether this is wrapped with quotes because the actual schema (inferred or user-given schema) would not have the quotes for fields.
For example, this throws an exception. (**Loading JSON from RDD is fine**)
```scala
val json =""" {"a.b":"data"}"""
val rdd = spark.sparkContext.parallelize(json :: Nil)
spark.read.schema(StructType(Seq(StructField("`a.b`", StringType, true))))
.json(rdd).select("`a.b`").printSchema()
```
as below:
```
cannot resolve '```a.b```' given input columns: [`a.b`];
org.apache.spark.sql.AnalysisException: cannot resolve '```a.b```' given input columns: [`a.b`];
at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
```
## How was this patch tested?
Unit tests in `FileSourceStrategySuite`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes #14339 from HyukjinKwon/SPARK-16698-regression.
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dictionary and plain encoded pages
## What changes were proposed in this pull request?
This patch adds an explicit test for [SPARK-14217] by setting the parquet dictionary and page size the generated parquet file spans across 3 pages (within a single row group) where the first page is dictionary encoded and the remaining two are plain encoded.
## How was this patch tested?
1. ParquetEncodingSuite
2. Also manually tested that this test fails without https://github.com/apache/spark/pull/12279
Author: Sameer Agarwal <sameerag@cs.berkeley.edu>
Closes #14304 from sameeragarwal/hybrid-encoding-test.
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## What changes were proposed in this pull request?
It's weird that we have `BucketSpec` to abstract bucket info, but don't use it in `CatalogTable`. This PR moves `BucketSpec` into catalyst module.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes #14331 from cloud-fan/check.
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## What changes were proposed in this pull request?
`CreateViewCommand` only needs some information of a `CatalogTable`, but not all of them. We have some tricks(e.g. we need to check the table type is `VIEW`, we need to make `CatalogColumn.dataType` nullable) to allow it to take a `CatalogTable`.
This PR cleans it up and only pass in necessary information to `CreateViewCommand`.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes #14297 from cloud-fan/minor2.
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## What changes were proposed in this pull request?
Currently, `JDBCRDD.compute` is doing type dispatch for each row to read appropriate values.
It might not have to be done like this because the schema is already kept in `JDBCRDD`.
So, appropriate converters can be created first according to the schema, and then apply them to each row.
## How was this patch tested?
Existing tests should cover this.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes #14313 from HyukjinKwon/SPARK-16674.
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## What changes were proposed in this pull request?
Default `TreeNode.withNewChildren` implementation doesn't work for `Last` and when both constructor arguments are the same, e.g.:
```sql
LAST_VALUE(FALSE) -- The 2nd argument defaults to FALSE
LAST_VALUE(FALSE, FALSE)
LAST_VALUE(TRUE, TRUE)
```
This is because although `Last` is a unary expression, both of its constructor arguments, `child` and `ignoreNullsExpr`, are `Expression`s. When they have the same value, `TreeNode.withNewChildren` treats both of them as child nodes by mistake. `First` is also affected by this issue in exactly the same way.
This PR fixes this issue by making `ignoreNullsExpr` a child expression of `First` and `Last`.
## How was this patch tested?
New test case added in `WindowQuerySuite`.
Author: Cheng Lian <lian@databricks.com>
Closes #14295 from liancheng/spark-16648-last-value.
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In the following code in `VectorizedHashMapGenerator.scala`:
```
def hashBytes(b: String): String = {
val hash = ctx.freshName("hash")
s"""
|int $result = 0;
|for (int i = 0; i < $b.length; i++) {
| ${genComputeHash(ctx, s"$b[i]", ByteType, hash)}
| $result = ($result ^ (0x9e3779b9)) + $hash + ($result << 6) + ($result >>> 2);
|}
""".stripMargin
}
```
when b=input.getBytes(), the current 2.0 code results in getBytes() being called n times, n being length of input. getBytes() involves memory copy is thus expensive and causes a performance degradation.
Fix is to evaluate getBytes() before the for loop.
Performance bug, no additional test added.
Author: Qifan Pu <qifan.pu@gmail.com>
Closes #14337 from ooq/SPARK-16699.
(cherry picked from commit d226dce12babcd9f30db033417b2b9ce79f44312)
Signed-off-by: Reynold Xin <rxin@databricks.com>
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## What changes were proposed in this pull request?
we also store data source table options in this field, it's unreasonable to call it `serdeProperties`.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes #14283 from cloud-fan/minor1.
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DataFrameWriter
## What changes were proposed in this pull request?
This PR adds a boolean option, `truncate`, for `SaveMode.Overwrite` of JDBC DataFrameWriter. If this option is `true`, it try to take advantage of `TRUNCATE TABLE` instead of `DROP TABLE`. This is a trivial option, but will provide great **convenience** for BI tool users based on RDBMS tables generated by Spark.
**Goal**
- Without `CREATE/DROP` privilege, we can save dataframe to database. Sometime these are not allowed for security.
- It will preserve the existing table information, so users can add and keep some additional `INDEX` and `CONSTRAINT`s for the table.
- Sometime, `TRUNCATE` is faster than the combination of `DROP/CREATE`.
**Supported DBMS**
The following is `truncate`-option support table. Due to the different behavior of `TRUNCATE TABLE` among DBMSs, it's not always safe to use `TRUNCATE TABLE`. Spark will ignore the `truncate` option for **unknown** and **some** DBMS with **default CASCADING** behavior. Newly added JDBCDialect should implement corresponding function to support `truncate` option additionally.
Spark Dialects | `truncate` OPTION SUPPORT
---------------|-------------------------------
MySQLDialect | O
PostgresDialect | X
DB2Dialect | O
MsSqlServerDialect | O
DerbyDialect | O
OracleDialect | O
**Before (TABLE with INDEX case)**: SparkShell & MySQL CLI are interleaved intentionally.
```scala
scala> val (url, prop)=("jdbc:mysql://localhost:3306/temp?useSSL=false", new java.util.Properties)
scala> prop.setProperty("user","root")
scala> df.write.mode("overwrite").jdbc(url, "table_with_index", prop)
scala> spark.range(10).write.mode("overwrite").jdbc(url, "table_with_index", prop)
mysql> DESC table_with_index;
+-------+------------+------+-----+---------+-------+
| Field | Type | Null | Key | Default | Extra |
+-------+------------+------+-----+---------+-------+
| id | bigint(20) | NO | | NULL | |
+-------+------------+------+-----+---------+-------+
mysql> CREATE UNIQUE INDEX idx_id ON table_with_index(id);
mysql> DESC table_with_index;
+-------+------------+------+-----+---------+-------+
| Field | Type | Null | Key | Default | Extra |
+-------+------------+------+-----+---------+-------+
| id | bigint(20) | NO | PRI | NULL | |
+-------+------------+------+-----+---------+-------+
scala> spark.range(10).write.mode("overwrite").jdbc(url, "table_with_index", prop)
mysql> DESC table_with_index;
+-------+------------+------+-----+---------+-------+
| Field | Type | Null | Key | Default | Extra |
+-------+------------+------+-----+---------+-------+
| id | bigint(20) | NO | | NULL | |
+-------+------------+------+-----+---------+-------+
```
**After (TABLE with INDEX case)**
```scala
scala> spark.range(10).write.mode("overwrite").option("truncate", true).jdbc(url, "table_with_index", prop)
mysql> DESC table_with_index;
+-------+------------+------+-----+---------+-------+
| Field | Type | Null | Key | Default | Extra |
+-------+------------+------+-----+---------+-------+
| id | bigint(20) | NO | PRI | NULL | |
+-------+------------+------+-----+---------+-------+
```
**Error Handling**
- In case of exceptions, Spark will not retry. Users should turn off the `truncate` option.
- In case of schema change:
- If one of the column names changes, this will raise exceptions intuitively.
- If there exists only type difference, this will work like Append mode.
## How was this patch tested?
Pass the Jenkins tests with a updated testcase.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes #14086 from dongjoon-hyun/SPARK-16410.
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## Problem
The current `sed` in `test_script.sh` is missing a `$`, leading to the failure of `script` test on OS X:
```
== Results ==
!== Correct Answer - 2 == == Spark Answer - 2 ==
![x1_y1] [x1]
![x2_y2] [x2]
```
In addition, this `script` test would also fail on systems like Windows where we couldn't be able to invoke `bash` or `echo | sed`.
## What changes were proposed in this pull request?
This patch
- fixes `sed` in `test_script.sh`
- adds command guards so that the `script` test would pass on systems like Windows
## How was this patch tested?
- Jenkins
- Manually verified tests pass on OS X
Author: Liwei Lin <lwlin7@gmail.com>
Closes #14280 from lw-lin/osx-sed.
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## What changes were proposed in this pull request?
after https://github.com/apache/spark/pull/12945, we renamed the `registerTempTable` to `createTempView`, as we do create a view actually. This PR renames `SQLTestUtils.withTempTable` to reflect this change.
## How was this patch tested?
N/A
Author: Wenchen Fan <wenchen@databricks.com>
Closes #14318 from cloud-fan/minor4.
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called method in Invoke is null
## What changes were proposed in this pull request?
Currently we don't check the value returned by called method in `Invoke`. When the returned value is null and is assigned to a variable of primitive type, `NullPointerException` will be thrown.
## How was this patch tested?
Jenkins tests.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes #14259 from viirya/agg-empty-ds.
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## What changes were proposed in this pull request?
Build fix for [SPARK-16287][SQL] Implement str_to_map SQL function that has introduced this compilation error:
```
/Users/jacek/dev/oss/spark/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala:402: error: annotation argument needs to be a constant; found: "_FUNC_(text[, pairDelim, keyValueDelim]) - Creates a map after splitting the text ".+("into key/value pairs using delimiters. ").+("Default delimiters are \',\' for pairDelim and \':\' for keyValueDelim.")
"into key/value pairs using delimiters. " +
^
```
## How was this patch tested?
Local build
Author: Jacek Laskowski <jacek@japila.pl>
Closes #14315 from jaceklaskowski/build-fix-complexTypeCreator.
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### What changes were proposed in this pull request?
**Issue 1: Silent Ignorance of Bucket Specification When Creating Table Using Schema Inference**
When creating a data source table without explicit specification of schema or SELECT clause, we silently ignore the bucket specification (CLUSTERED BY... SORTED BY...) in [the code](https://github.com/apache/spark/blob/ce3b98bae28af72299722f56e4e4ef831f471ec0/sql/core/src/main/scala/org/apache/spark/sql/execution/command/createDataSourceTables.scala#L339-L354).
For example,
```SQL
CREATE TABLE jsonTable
USING org.apache.spark.sql.json
OPTIONS (
path '${tempDir.getCanonicalPath}'
)
CLUSTERED BY (inexistentColumnA) SORTED BY (inexistentColumnB) INTO 2 BUCKETS
```
This PR captures it and issues an error message.
**Issue 2: Got a run-time `java.lang.ArithmeticException` when num of buckets is set to zero.**
For example,
```SQL
CREATE TABLE t USING PARQUET
OPTIONS (PATH '${path.toString}')
CLUSTERED BY (a) SORTED BY (b) INTO 0 BUCKETS
AS SELECT 1 AS a, 2 AS b
```
The exception we got is
```
ERROR org.apache.spark.executor.Executor: Exception in task 0.0 in stage 1.0 (TID 2)
java.lang.ArithmeticException: / by zero
```
This PR captures the misuse and issues an appropriate error message.
### How was this patch tested?
Added a test case in DDLSuite
Author: gatorsmile <gatorsmile@gmail.com>
Closes #14210 from gatorsmile/createTableWithoutSchema.
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## What changes were proposed in this pull request?
This PR adds `str_to_map` SQL function in order to remove Hive fallback.
## How was this patch tested?
Pass the Jenkins tests with newly added.
Author: Sandeep Singh <sandeep@techaddict.me>
Closes #13990 from techaddict/SPARK-16287.
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reader
## What changes were proposed in this pull request?
As part of the bugfix in https://github.com/apache/spark/pull/12279, if a row batch consist of both dictionary encoded and non-dictionary encoded pages, we explicitly decode the dictionary for the values that are already dictionary encoded. Currently we reset the dictionary while reading every page that can potentially cause ` java.lang.ArrayIndexOutOfBoundsException` while decoding older pages. This patch fixes the problem by maintaining a single dictionary per row-batch in vectorized parquet reader.
## How was this patch tested?
Manual Tests against a number of hand-generated parquet files.
Author: Sameer Agarwal <sameerag@cs.berkeley.edu>
Closes #14225 from sameeragarwal/vectorized.
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