The function uses .substring(0, X), which will trigger OutOfBoundsException if string length is less than X. A better way to do this is to use startsWith, which won't error out in this case.
Author: Volodymyr Lyubinets <vlyubin@gmail.com>
Closes#5378 from vlyubin/quirks and squashes the following commits:
504e8e0 [Volodymyr Lyubinets] Minor fix for DriverQuirks get
'(' and ')' are special characters used in Parquet schema for type annotation. When we run an aggregation query, we will obtain attribute name such as "MAX(a)".
If we directly store the generated DataFrame as Parquet file, it causes failure when reading and parsing the stored schema string.
Several methods can be adopted to solve this. This pr uses a simplest one to just replace attribute names before generating Parquet schema based on these attributes.
Another possible method might be modifying all aggregation expression names from "func(column)" to "func[column]".
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#5263 from viirya/parquet_aggregation_name and squashes the following commits:
2d70542 [Liang-Chi Hsieh] Address comment.
463dff4 [Liang-Chi Hsieh] Instead of replacing special chars, showing error message to user to suggest using Alias.
1de001d [Liang-Chi Hsieh] Replace special characters '(' and ')' of Parquet schema.
Author: Yin Huai <yhuai@databricks.com>
Closes#5353 from yhuai/wrongFS and squashes the following commits:
849603b [Yin Huai] Not use deprecated method.
6d6ae34 [Yin Huai] Use path.makeQualified.
Now trait `StringComparison` is a `BinaryExpression`. In fact, it should be a `BinaryPredicate`.
By making `StringComparison` as `BinaryPredicate`, we can throw error when a `expressions.Predicate` can't translate to a data source `Filter` in function `selectFilters`.
Without this modification, because we will wrap a `Filter` outside the scanned results in `pruneFilterProjectRaw`, we can't detect about something is wrong in translating predicates to filters in `selectFilters`.
The unit test of #5285 demonstrates such problem. In that pr, even `expressions.Contains` is not properly translated to `sources.StringContains`, the filtering is still performed by the `Filter` and so the test passes.
Of course, by doing this modification, all `expressions.Predicate` classes need to have its data source `Filter` correspondingly.
There is a small bug in `FilteredScanSuite` for doing `StringEndsWith` filter. This pr also fixes it.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#5309 from viirya/translate_predicate and squashes the following commits:
b176385 [Liang-Chi Hsieh] Address comment.
275a493 [Liang-Chi Hsieh] More properly test for StringStartsWith, StringEndsWith and StringContains.
caf2347 [Liang-Chi Hsieh] Make trait StringComparison as BinaryPredicate and throw error when Predicate can't translate to data source Filter.
When union non-decimal types with decimals, we use the following rules:
- FIRST `intTypeToFixed`, then fixed union decimals with precision/scale p1/s2 and p2/s2 will be promoted to
DecimalType(max(p1, p2), max(s1, s2))
- FLOAT and DOUBLE cause fixed-length decimals to turn into DOUBLE (this is the same as Hive,
but note that unlimited decimals are considered bigger than doubles in WidenTypes)
Author: guowei2 <guowei2@asiainfo.com>
Closes#4004 from guowei2/SPARK-5203 and squashes the following commits:
ff50f5f [guowei2] fix code style
11df1bf [guowei2] fix decimal union with double, double->Decimal(15,15)
0f345f9 [guowei2] fix structType merge with decimal
101ed4d [guowei2] fix build error after rebase
0b196e4 [guowei2] code style
fe2c2ca [guowei2] handle union decimal precision in 'DecimalPrecision'
421d840 [guowei2] fix union types for decimal precision
ef2c661 [guowei2] fix union with different decimal type
Just fix a typo.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#5352 from viirya/fix_a_typo and squashes the following commits:
303b2d2 [Liang-Chi Hsieh] Fix typo.
This builds on my earlier pull requests and turns on the explicit type checking in scalastyle.
Author: Reynold Xin <rxin@databricks.com>
Closes#5342 from rxin/SPARK-6428 and squashes the following commits:
7b531ab [Reynold Xin] import ordering
2d9a8a5 [Reynold Xin] jl
e668b1c [Reynold Xin] override
9b9e119 [Reynold Xin] Parenthesis.
82e0cf5 [Reynold Xin] [SPARK-6428] Turn on explicit type checking for public methods.
https://issues.apache.org/jira/browse/SPARK-6575
Author: Yin Huai <yhuai@databricks.com>
This patch had conflicts when merged, resolved by
Committer: Cheng Lian <lian@databricks.com>
Closes#5339 from yhuai/parquetRelationCache and squashes the following commits:
b0e1a42 [Yin Huai] Address comments.
83d9846 [Yin Huai] Remove unnecessary change.
c0dc7a4 [Yin Huai] Cache converted parquet relations.
This is a workaround for a problem reported on the user list. This doesn't fix the core problem, but in general is a more robust way to do renames.
Author: Michael Armbrust <michael@databricks.com>
Closes#5337 from marmbrus/toDFrename and squashes the following commits:
6a3159d [Michael Armbrust] [SPARK-6686][SQL] Use resolved output instead of names for toDF rename
It did not conside that order.dataType does not match NativeType. So i add "case other => ..." for other cenarios.
Author: DoingDone9 <799203320@qq.com>
Closes#4959 from DoingDone9/case_ and squashes the following commits:
6278846 [DoingDone9] Update rows.scala
cb1852d [DoingDone9] Merge pull request #2 from apache/master
c3f046f [DoingDone9] Merge pull request #1 from apache/master
This PR addresses rxin's comments in PR #5210.
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Author: Cheng Lian <lian@databricks.com>
Closes#5219 from liancheng/spark-6554-followup and squashes the following commits:
41f3a09 [Cheng Lian] Addresses comments in #5210
NotImplementedError in scala 2.10 is a fatal exception, which is not very nice to throw when not actually fatal.
Author: Michael Armbrust <michael@databricks.com>
Closes#5315 from marmbrus/throwUnsupported and squashes the following commits:
c29e03b [Michael Armbrust] [SQL] Throw UnsupportedOperationException instead of NotImplementedError
052e05b [Michael Armbrust] [SQL] Throw UnsupportedOperationException instead of NotImplementedError
We assume that `RDD[Row]` contains Scala types. So we need to convert them into catalyst types in createDataFrame. liancheng
Author: Xiangrui Meng <meng@databricks.com>
Closes#5329 from mengxr/SPARK-6672 and squashes the following commits:
2d52644 [Xiangrui Meng] set needsConversion = false in jsonRDD
06896e4 [Xiangrui Meng] add createDataFrame without conversion
4a3767b [Xiangrui Meng] convert Row to catalyst
In order to do inbound checking and type conversion, we should use Literal.create() instead of constructor.
Author: Davies Liu <davies@databricks.com>
Closes#5320 from davies/literal and squashes the following commits:
1667604 [Davies Liu] fix style and add comment
5f8c0fd [Davies Liu] use Literal.create instread of constructor
First contribution here; would love to be getting some code contributions in soon. Let me know if there's anything about contribution process I should improve.
Author: Chet Mancini <chetmancini@gmail.com>
Closes#5316 from chetmancini/SPARK_6658_dataframe_doc and squashes the following commits:
53b627a [Chet Mancini] [SQL] SPARK-6658: Update DataFrame documentation to refer to correct types
1. Test JARs are built & published
1. log4j.resources is explicitly excluded. Without this, downstream test run logging depends on the order the JARs are listed/loaded
1. sql/hive pulls in spark-sql &...spark-catalyst for its test runs
1. The copied in test classes were rm'd, and a test edited to remove its now duplicate assert method
1. Spark streaming is now build with the same plugin/phase as the rest, but its shade plugin declaration is kept in (so different from the rest of the test plugins). Due to (#2), this means the test JAR no longer includes its log4j file.
Outstanding issues:
* should the JARs be shaded? `spark-streaming-test.jar` does, but given these are test jars for developers only, especially in the same spark source tree, it's hard to justify.
* `maven-jar-plugin` v 2.6 was explicitly selected; without this the apache-1.4 parent template JAR version (2.4) chosen.
* Are there any other resources to exclude?
Author: Steve Loughran <stevel@hortonworks.com>
Closes#5119 from steveloughran/stevel/patches/SPARK-6433-test-jars and squashes the following commits:
81ceb01 [Steve Loughran] SPARK-6433 add a clearer comment explaining what the plugin is doing & why
a6dca33 [Steve Loughran] SPARK-6433 : pull configuration section form archive plugin
c2b5f89 [Steve Loughran] SPARK-6433 omit "jar" goal from jar plugin
fdac51b [Steve Loughran] SPARK-6433 -002; indentation & delegate plugin version to parent
650f442 [Steve Loughran] SPARK-6433 patch 001: test JARs are built; sql/hive pulls in spark-sql & spark-catalyst for its test runs
Before 1.3.0, `SchemaRDD.id` works as a unique identifier of each `SchemaRDD`. In 1.3.0, unlike `SchemaRDD`, `DataFrame` is no longer an RDD, and `DataFrame.rdd` is actually a function which always returns a new RDD instance. Making `DataFrame.rdd` a lazy val should bring the unique identifier back.
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Author: Cheng Lian <lian@databricks.com>
Closes#5265 from liancheng/spark-6608 and squashes the following commits:
7500968 [Cheng Lian] Updates javadoc
7f37d21 [Cheng Lian] Makes DataFrame.rdd a lazy val
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#5299 from viirya/stringcontains and squashes the following commits:
c1ece4c [Liang-Chi Hsieh] Should be Contains instead of EndsWith.
Before it was possible for a query to flip back and forth from a resolved state, allowing resolution to propagate up before coercion had stabilized. The issue was that `ResolvedReferences` would run after `FunctionArgumentConversion`, but before `PropagateTypes` had run. This PR ensures we correctly `PropagateTypes` after any coercion has applied.
Author: Michael Armbrust <michael@databricks.com>
Closes#5278 from marmbrus/unionNull and squashes the following commits:
dc3581a [Michael Armbrust] [SPARK-5371][SQL] Propogate types after function conversion / before futher resolution
This PR is based on work by cloud-fan in #4904, but with two differences:
- We isolate the logic for Sort's special handling into `ResolveSortReferences`
- We avoid creating UnresolvedGetField expressions during resolution. Instead we either resolve GetField or we return None. This avoids us going down the wrong path early on.
Author: Michael Armbrust <michael@databricks.com>
Closes#5189 from marmbrus/nestedOrderBy and squashes the following commits:
b8cae45 [Michael Armbrust] fix another test
0f36a11 [Michael Armbrust] WIP
91820cd [Michael Armbrust] Fix bug.
Consider a metastore Parquet table that
1. doesn't have schema evolution issue
2. has lots of data files and/or partitions
In this case, driver schema merging can be both slow and unnecessary. Would be good to have a configuration to let the use disable schema merging when converting such a metastore Parquet table.
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Author: Cheng Lian <lian@databricks.com>
Closes#5231 from liancheng/spark-6575 and squashes the following commits:
cd96159 [Cheng Lian] Adds configuration to disable schema merging while converting metastore Parquet tables
Also removes temporary workarounds made in #5183 and #5251.
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Author: Cheng Lian <lian@databricks.com>
Closes#5289 from liancheng/spark-6555 and squashes the following commits:
d0095ac [Cheng Lian] Removes unused imports
cfafeeb [Cheng Lian] Removes outdated comment
75a2746 [Cheng Lian] Overrides equals() and hashCode() for MetastoreRelation
Similar to `CreateArray`, we can add `CreateStruct` to create nested columns. marmbrus
Author: Xiangrui Meng <meng@databricks.com>
Closes#5195 from mengxr/SPARK-6542 and squashes the following commits:
3795c57 [Xiangrui Meng] update error message
ae7ac3e [Xiangrui Meng] move unit test to a separate suite
85dd559 [Xiangrui Meng] use NamedExpr
c78e31a [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-6542
85f3106 [Xiangrui Meng] add CreateStruct
JIRA: https://issues.apache.org/jira/browse/SPARK-6618
Author: Yin Huai <yhuai@databricks.com>
Closes#5281 from yhuai/lookupRelationLock and squashes the following commits:
591b4be [Yin Huai] A test?
b3a9625 [Yin Huai] Just protect client.
Filters such as startsWith, endsWith, contains will be very useful for data sources that provide search functionality, e.g. Succinct, Elastic Search, Solr.
I also took this chance to improve documentation for the data source filters.
Author: Reynold Xin <rxin@databricks.com>
Closes#5285 from rxin/ds-string-filters and squashes the following commits:
f021727 [Reynold Xin] Fixed grammar.
7695a52 [Reynold Xin] [SPARK-6625][SQL] Add common string filters to data sources.
This pull request adds variants of DataFrame.na.drop and DataFrame.na.fill to the Scala/Java API, and DataFrame.fillna and DataFrame.dropna to the Python API.
Author: Reynold Xin <rxin@databricks.com>
Closes#5274 from rxin/df-missing-value and squashes the following commits:
4ee1b98 [Reynold Xin] Improve error reporting in Python.
33a330c [Reynold Xin] Remove replace for now.
bc4fdbb [Reynold Xin] Added documentation for replace.
d56f5a5 [Reynold Xin] Added replace for Scala/Java.
2385d00 [Reynold Xin] Feedback from Xiangrui on "how".
914a374 [Reynold Xin] fill with map.
185c67e [Reynold Xin] Allow specifying column subsets in fill.
749eb47 [Reynold Xin] fillna
249b94e [Reynold Xin] Removing undefined functions.
6a73c68 [Reynold Xin] Missing file.
67d7003 [Reynold Xin] [SPARK-6119][SQL] DataFrame.na.drop (Scala/Java) and DataFrame.dropna (Python)
This PR leverages the output commit coordinator introduced in #4066 to help committing Hive and Parquet tables.
This PR extracts output commit code in `SparkHadoopWriter.commit` to `SparkHadoopMapRedUtil.commitTask`, and reuses it for committing Parquet and Hive tables on executor side.
TODO
- [ ] Add tests
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Author: Cheng Lian <lian@databricks.com>
Closes#5139 from liancheng/spark-6369 and squashes the following commits:
72eb628 [Cheng Lian] Fixes typo in javadoc
9a4b82b [Cheng Lian] Adds javadoc and addresses @aarondav's comments
dfdf3ef [Cheng Lian] Uses commit coordinator to help committing Hive and Parquet tables
https://issues.apache.org/jira/browse/SPARK-6592
The current impl in SparkBuild.scala filter all classes under catalyst directory, however, we have a corner case that Row class is a public API under that directory
we need to include Row into the scaladoc while still excluding other classes of catalyst project
Thanks for the help on this patch from rxin and liancheng
Author: CodingCat <zhunansjtu@gmail.com>
Closes#5252 from CodingCat/SPARK-6592 and squashes the following commits:
02098a4 [CodingCat] ignore collection, enable types (except those protected classes)
f7af2cb [CodingCat] commit
3ab4403 [CodingCat] fix filter for scaladoc to generate API doc for Row.scala under catalyst directory
Now that we have `DataFrame`s it is possible to have multiple copies in a single query plan. As such, it needs to inherit from `MultiInstanceRelation` or self joins will break. I also add better debugging errors when our self join handling fails in case there are future bugs.
Author: Michael Armbrust <michael@databricks.com>
Closes#5251 from marmbrus/multiMetaStore and squashes the following commits:
4272f6d [Michael Armbrust] [SPARK-6595][SQL] MetastoreRelation should be MuliInstanceRelation
Currently if trying to register an RDD (or DataFrame in 1.3) as a table that has types that have no supported Schema representation (e.g. type "Any") - it would throw a match error. e.g. scala.MatchError: Any (of class scala.reflect.internal.Types$ClassNoArgsTypeRef)
This fix is just to have a nicer error message than a MatchError
Author: Eran Medan <ehrann.mehdan@gmail.com>
Closes#5235 from eranation/patch-2 and squashes the following commits:
af4b1a2 [Eran Medan] Line should be under 100 chars
0c69e9d [Eran Medan] Change from sys.error UnsupportedOperationException
524be86 [Eran Medan] better exception than scala.MatchError: Any
Opening to replace #5188.
When Spark SQL infers a schema for a DataFrame, it will take the union of all field types present in the structured source data (e.g. an RDD of JSON data). When the source data for a row doesn't define a particular field on the DataFrame's schema, a null value will simply be assumed for this field. This workflow makes it very easy to construct tables and query over a set of structured data with a nonuniform schema. However, this behavior is not consistent in some cases when dealing with Parquet files and an external table managed by an external Hive metastore.
In our particular usecase, we use Spark Streaming to parse and transform our input data and then apply a window function to save an arbitrary-sized batch of data as a Parquet file, which itself will be added as a partition to an external Hive table via an *"ALTER TABLE... ADD PARTITION..."* statement. Since our input data is nonuniform, it is expected that not every partition batch will contain every field present in the table's schema obtained from the Hive metastore. As such, we expect that the schema of some of our Parquet files may not contain the same set fields present in the full metastore schema.
In such cases, it seems natural that Spark SQL would simply assume null values for any missing fields in the partition's Parquet file, assuming these fields are specified as nullable by the metastore schema. This is not the case in the current implementation of ParquetRelation2. The **mergeMetastoreParquetSchema()** method used to reconcile differences between a Parquet file's schema and a schema retrieved from the Hive metastore will raise an exception if the Parquet file doesn't match the same set of fields specified by the metastore.
This pull requests alters the behavior of **mergeMetastoreParquetSchema()** by having it first add any nullable fields from the metastore schema to the Parquet file schema if they aren't already present there.
Author: Adam Budde <budde@amazon.com>
Closes#5214 from budde/nullable-fields and squashes the following commits:
a52d378 [Adam Budde] Refactor ParquetSchemaSuite.scala for cases now permitted by SPARK-6471 and SPARK-6538
9041bfa [Adam Budde] Add missing nullable Metastore fields when merging a Parquet schema
Author: Reynold Xin <rxin@databricks.com>
Closes#5226 from rxin/empty-df and squashes the following commits:
1306d88 [Reynold Xin] Proper fix.
e135bb9 [Reynold Xin] [SPARK-6564][SQL] SQLContext.emptyDataFrame should contain 0 rows, not 1 row.
This is based on bug and test case proposed by viirya. See #5203 for a excellent description of the problem.
TLDR; The problem occurs because the function `groupBy(String)` calls `resolve`, which returns an `AttributeReference`. However, this `AttributeReference` is based on an analyzed plan which is thrown away. At execution time, we once again analyze the plan. However, in the case of self-joins, each call to analyze will produce a new tree for the left side of the join, rendering the previously returned `AttributeReference` invalid.
As a fix, I propose we keep the analyzed plan instead of the unresolved plan inside of a `DataFrame`.
Author: Michael Armbrust <michael@databricks.com>
Closes#5217 from marmbrus/preanalyzer and squashes the following commits:
1f98e2d [Michael Armbrust] revert change
dd4dec1 [Michael Armbrust] Use the analyzed plan in DataFrame
089c52e [Michael Armbrust] WIP
There are two cases for the new Parquet data source:
1. Partition columns exist in the Parquet data files
We don't need to push-down these predicates since partition pruning already handles them.
1. Partition columns don't exist in the Parquet data files
We can't push-down these predicates since they are considered as invalid columns by Parquet.
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Author: Cheng Lian <lian@databricks.com>
Closes#5210 from liancheng/spark-6554 and squashes the following commits:
4f7ec03 [Cheng Lian] Adds comments
e134ced [Cheng Lian] Don't push down predicates which reference partition column(s)
1. Slightly modifications to the code to make it more readable.
2. Added Python implementation.
3. Updated the documentation to state that we don't guarantee the output schema for this function and it should only be used for exploratory data analysis.
Author: Reynold Xin <rxin@databricks.com>
Closes#5201 from rxin/df-describe and squashes the following commits:
25a7834 [Reynold Xin] Reset run-tests.
6abdfee [Reynold Xin] [SPARK-6117] [SQL] Improvements to DataFrame.describe()
Currently in the parquet relation 2 implementation, error is thrown in case merged schema is not exactly the same as metastore schema.
But to support cases like deletion of column using replace column command, we can relax the restriction so that even if metastore schema is a subset of merged parquet schema, the query will work.
Author: Yash Datta <Yash.Datta@guavus.com>
Closes#5141 from saucam/replace_col and squashes the following commits:
e858d5b [Yash Datta] SPARK-6471: Fix test cases, add a new test case for metastore schema to be subset of parquet schema
5f2f467 [Yash Datta] SPARK-6471: Metastore schema should only be a subset of parquet schema to support dropping of columns using replace columns
Author: Michael Armbrust <michael@databricks.com>
Closes#5191 from marmbrus/kryoRowsWithSchema and squashes the following commits:
bb83522 [Michael Armbrust] Fix serialization of GenericRowWithSchema using kryo
f914f16 [Michael Armbrust] Add no arg constructor to GenericRowWithSchema
Please review my solution for SPARK-6117
Author: azagrebin <azagrebin@gmail.com>
Closes#5073 from azagrebin/SPARK-6117 and squashes the following commits:
f9056ac [azagrebin] [SPARK-6117] [SQL] create one aggregation and split it locally into resulting DF, colocate test data with test case
ddb3950 [azagrebin] [SPARK-6117] [SQL] simplify implementation, add test for DF without numeric columns
9daf31e [azagrebin] [SPARK-6117] [SQL] add describe function to DataFrame for summary statistics
Previously this could result in sets compare equals when in fact the right was a subset of the left.
Based on #5133 by sisihj
Author: sisihj <jun.hejun@huawei.com>
Author: Michael Armbrust <michael@databricks.com>
Closes#5194 from marmbrus/pr/5133 and squashes the following commits:
5ed4615 [Michael Armbrust] fix imports
d4cbbc0 [Michael Armbrust] Add test cases
0a0834f [sisihj] AttributeSet.equal should compare size
If the tests in "sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala" are running before CachedTableSuite.scala, the test("Drop cached table") will failed. Because the table test is created in SQLQuerySuite.scala ,and this table not droped. So when running "drop cached table", table test already exists.
There is error info:
01:18:35.738 ERROR hive.ql.exec.DDLTask: org.apache.hadoop.hive.ql.metadata.HiveException: AlreadyExistsException(message:Table test already exists)
at org.apache.hadoop.hive.ql.metadata.Hive.createTable(Hive.java:616)
at org.apache.hadoop.hive.ql.exec.DDLTask.createTable(DDLTask.java:4189)
at org.apache.hadoop.hive.ql.exec.DDLTask.execute(DDLTask.java:281)
at org.apache.hadoop.hive.ql.exec.Task.executeTask(Task.java:153)
at org.apache.hadoop.hive.ql.exec.TaskRunner.runSequential(TaskRunner.java:85)
at org.apache.hadoop.hive.ql.Driver.launchTask(Driver.java:1503)
at org.apache.hadoop.hive.ql.Driver.execute(Driver.java:1270)
at org.apache.hadoop.hive.ql.Driver.runInternal(Driver.java:1088)
at org.apache.hadoop.hive.ql.Driver.run(Driver.java:911)
at org.apache.hadoop.hive.ql.Driver.run(Driver.java:901)test”
And the test about "create table test" in "sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala,is:
test("SPARK-4825 save join to table") {
val testData = sparkContext.parallelize(1 to 10).map(i => TestData(i, i.toString)).toDF()
sql("CREATE TABLE test1 (key INT, value STRING)")
testData.insertInto("test1")
sql("CREATE TABLE test2 (key INT, value STRING)")
testData.insertInto("test2")
testData.insertInto("test2")
sql("CREATE TABLE test AS SELECT COUNT(a.value) FROM test1 a JOIN test2 b ON a.key = b.key")
checkAnswer(
table("test"),
sql("SELECT COUNT(a.value) FROM test1 a JOIN test2 b ON a.key = b.key").collect().toSeq)
}
Author: KaiXinXiaoLei <huleilei1@huawei.com>
Closes#5150 from KaiXinXiaoLei/testFailed and squashes the following commits:
7534b02 [KaiXinXiaoLei] The UT test of spark is failed.
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#4930 from adrian-wang/testvs and squashes the following commits:
2ce590f [Daoyuan Wang] add explicit function types
b1d68bf [Daoyuan Wang] only substitute for parseSql
9c4a950 [Daoyuan Wang] add a comment explaining
18fb481 [Daoyuan Wang] enable variable substitute on test framework
Author: DoingDone9 <799203320@qq.com>
Closes#4973 from DoingDone9/sort_token and squashes the following commits:
855fa10 [DoingDone9] Update HiveQl.scala
c7080b3 [DoingDone9] Sort these tokens in alphabetic order to avoid further duplicate in HiveQl
c87e8b6 [DoingDone9] Merge pull request #3 from apache/master
cb1852d [DoingDone9] Merge pull request #2 from apache/master
c3f046f [DoingDone9] Merge pull request #1 from apache/master
Current `castStruct` should be very slow. This pr slightly improves it.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#5017 from viirya/faster_caststruct and squashes the following commits:
385d5b0 [Liang-Chi Hsieh] Further improved.
746fcfb [Liang-Chi Hsieh] Make castStruct faster.
In hive,the schema of partition may be difference from the table schema.When we use spark-sql to query the data of partition which schema is difference from the table schema,we will get the exceptions as the description of the [jira](https://issues.apache.org/jira/browse/SPARK-5498) .For example:
* We take a look of the schema for the partition and the table
```sql
DESCRIBE partition_test PARTITION (dt='1');
id int None
name string None
dt string None
# Partition Information
# col_name data_type comment
dt string None
```
```
DESCRIBE partition_test;
OK
id bigint None
name string None
dt string None
# Partition Information
# col_name data_type comment
dt string None
```
* run the sql
```sql
SELECT * FROM partition_test where dt='1';
```
we will get the cast exception `java.lang.ClassCastException: org.apache.spark.sql.catalyst.expressions.MutableLong cannot be cast to org.apache.spark.sql.catalyst.expressions.MutableInt`
Author: jeanlyn <jeanlyn92@gmail.com>
Closes#4289 from jeanlyn/schema and squashes the following commits:
9c8da74 [jeanlyn] fix style
b41d6b9 [jeanlyn] fix compile errors
07d84b6 [jeanlyn] Merge branch 'master' into schema
535b0b6 [jeanlyn] reduce conflicts
d6c93c5 [jeanlyn] fix bug
1e8b30c [jeanlyn] fix code style
0549759 [jeanlyn] fix code style
c879aa1 [jeanlyn] clean the code
2a91a87 [jeanlyn] add more test case and clean the code
12d800d [jeanlyn] fix code style
63d170a [jeanlyn] fix compile problem
7470901 [jeanlyn] reduce conflicts
afc7da5 [jeanlyn] make getConvertedOI compatible between 0.12.0 and 0.13.1
b1527d5 [jeanlyn] fix type mismatch
10744ca [jeanlyn] Insert a space after the start of the comment
3b27af3 [jeanlyn] SPARK-5498:fix bug when query the data when partition schema does not match table schema
The `ParquetConversions` analysis rule generates a hash map, which maps from the original `MetastoreRelation` instances to the newly created `ParquetRelation2` instances. However, `MetastoreRelation.equals` doesn't compare output attributes. Thus, if a single metastore Parquet table appears multiple times in a query, only a single entry ends up in the hash map, and the conversion is not correctly performed.
Proper fix for this issue should be overriding `equals` and `hashCode` for MetastoreRelation. Unfortunately, this breaks more tests than expected. It's possible that these tests are ill-formed from the very beginning. As 1.3.1 release is approaching, we'd like to make the change more surgical to avoid potential regressions. The proposed fix here is to make both the metastore relations and their output attributes as keys in the hash map used in ParquetConversions.
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Author: Cheng Lian <lian@databricks.com>
Closes#5183 from liancheng/spark-6450 and squashes the following commits:
3536780 [Cheng Lian] Fixes metastore Parquet table conversion
spark avoid old inteface of hive, then some udaf can not work like "org.apache.hadoop.hive.ql.udf.generic.GenericUDAFAverage"
Author: DoingDone9 <799203320@qq.com>
Closes#5131 from DoingDone9/udaf and squashes the following commits:
9de08d0 [DoingDone9] Update HiveUdfSuite.scala
49c62dc [DoingDone9] Update hiveUdfs.scala
98b134f [DoingDone9] Merge pull request #5 from apache/master
161cae3 [DoingDone9] Merge pull request #4 from apache/master
c87e8b6 [DoingDone9] Merge pull request #3 from apache/master
cb1852d [DoingDone9] Merge pull request #2 from apache/master
c3f046f [DoingDone9] Merge pull request #1 from apache/master
As issue [SPARK-6483](https://issues.apache.org/jira/browse/SPARK-6483) description, ScalaUdf is low performance because of calling *asInstanceOf* to convert per record.
With this, the performance of ScalaUdf is the same as other case.
thank lianhuiwang for telling me how to resolve this problem.
Author: zzcclp <xm_zzc@sina.com>
Closes#5154 from zzcclp/SPARK-6483 and squashes the following commits:
5ac6e09 [zzcclp] Add a newline at the end of source file
cc6868e [zzcclp] Fix for fail on unit test.
0a8cdc3 [zzcclp] indention issue
b73836a [zzcclp] Access Seq[Expression] element by :: operator, and update the code gen script.
7763848 [zzcclp] rebase from master
I think after this PR, we can finally turn the rule on. There are still some smaller ones that need to be fixed, but those are easier.
Author: Reynold Xin <rxin@databricks.com>
Closes#5162 from rxin/catalyst-explicit-types and squashes the following commits:
e7eac03 [Reynold Xin] [SPARK-6428][SQL] Added explicit types for all public methods in catalyst.
Avoid unclear match errors and use `AnalysisException`.
Author: Michael Armbrust <michael@databricks.com>
Closes#5158 from marmbrus/dataSourceError and squashes the following commits:
af9f82a [Michael Armbrust] Yins comment
90c6ba4 [Michael Armbrust] Better error messages for invalid data sources
Previously it was okay to throw away subqueries after analysis, as we would never try to use that tree for resolution again. However, with eager analysis in `DataFrame`s this can cause errors for queries such as:
```scala
val df = Seq(1,2,3).map(i => (i, i.toString)).toDF("int", "str")
df.as('x).join(df.as('y), $"x.str" === $"y.str").groupBy("x.str").count()
```
As a result, in this PR we defer the elimination of subqueries until the optimization phase.
Author: Michael Armbrust <michael@databricks.com>
Closes#5160 from marmbrus/subqueriesInDfs and squashes the following commits:
a9bb262 [Michael Armbrust] Update Optimizer.scala
27d25bf [Michael Armbrust] fix hive tests
9137e03 [Michael Armbrust] add type
81cd597 [Michael Armbrust] Avoid eliminating subqueries until optimization
Author: Michael Armbrust <michael@databricks.com>
Closes#5155 from marmbrus/errorMessages and squashes the following commits:
b898188 [Michael Armbrust] Fix formatting of error messages.
Due to a recent change that made `StructType` a `Seq` we started inadvertently turning `StructType`s into generic `Traversable` when attempting nested tree transformations. In this PR we explicitly avoid descending into `DataType`s to avoid this bug.
Author: Michael Armbrust <michael@databricks.com>
Closes#5157 from marmbrus/udfFix and squashes the following commits:
26f7087 [Michael Armbrust] Fix transformations of TreeNodes that hold StructTypes
Otherwise we will leak files when spilling occurs.
Author: Michael Armbrust <michael@databricks.com>
Closes#5161 from marmbrus/cleanupAfterSort and squashes the following commits:
cb13d3c [Michael Armbrust] hint to inferencer
cdebdf5 [Michael Armbrust] Use completion iterator to close external sorter
For example, one might expect the following code to work, but it does not. Now you will at least get a warning with a suggestion to use aliases.
```scala
val df = sqlContext.load(path, "parquet")
val txns = df.groupBy("cust_id").agg($"cust_id", countDistinct($"day_num").as("txns"))
val spend = df.groupBy("cust_id").agg($"cust_id", sum($"extended_price").as("spend"))
val rmJoin = txns.join(spend, txns("cust_id") === spend("cust_id"), "inner")
```
Author: Michael Armbrust <michael@databricks.com>
Closes#5163 from marmbrus/selfJoinError and squashes the following commits:
16c1f0b [Michael Armbrust] fix visibility
1b57e8d [Michael Armbrust] Warn when constructing trivially true equals predicate
This is used by ML pipelines to embed ML attributes in columns created by ML transformers/estimators. marmbrus
Author: Xiangrui Meng <meng@databricks.com>
Closes#5151 from mengxr/SPARK-6361 and squashes the following commits:
bb30de3 [Xiangrui Meng] support adding a column with metadata in DF
Right now if there is a array field in a JavaBean, the user wold see an exception in `createDataFrame`. liancheng
Author: Xiangrui Meng <meng@databricks.com>
Closes#5146 from mengxr/SPARK-6475 and squashes the following commits:
51e87e5 [Xiangrui Meng] validate schemas
4f2df5e [Xiangrui Meng] recognize array types when infer data types from JavaBeans
In `CheckAnalysis`, `Filter` and `Aggregate` are checked in separate case clauses, thus never hit those clauses for unresolved operators and missing input attributes.
This PR also removes the `prettyString` call when generating error message for missing input attributes. Because result of `prettyString` doesn't contain expression ID, and may give confusing messages like
> resolved attributes a missing from a
cc rxin
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Author: Cheng Lian <lian@databricks.com>
Closes#5129 from liancheng/spark-6452 and squashes the following commits:
52cdc69 [Cheng Lian] Addresses comments
029f9bd [Cheng Lian] Checks for missing attributes and unresolved operator for all types of operator
One more thing if this PR is considered to be OK - it might make sense to add extra .jdbc() API's that take Properties to SQLContext.
Author: Volodymyr Lyubinets <vlyubin@gmail.com>
Closes#4859 from vlyubin/jdbcProperties and squashes the following commits:
7a8cfda [Volodymyr Lyubinets] Support jdbc connection properties in OPTIONS part of the query
https://github.com/apache/spark/pull/5082
/cc liancheng
Author: Yadong Qi <qiyadong2010@gmail.com>
Closes#5132 from watermen/sql-missingInput-new and squashes the following commits:
1e5bdc5 [Yadong Qi] Check the missingInput simply
Author: q00251598 <qiyadong@huawei.com>
Closes#5082 from watermen/sql-missingInput and squashes the following commits:
25766b9 [q00251598] Check the missingInput simply
This PR might have some issues with #3732 ,
and this would have merge conflicts with #3820 so the review can be delayed till that 2 were merged.
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#3822 from adrian-wang/parquetdate and squashes the following commits:
2c5d54d [Daoyuan Wang] add a test case
faef887 [Daoyuan Wang] parquet support for primitive date
97e9080 [Daoyuan Wang] parquet support for date type
Author: vinodkc <vinod.kc.in@gmail.com>
Closes#5112 from vinodkc/spark_1.3_doc_fixes and squashes the following commits:
2c6aee6 [vinodkc] Spark 1.3 doc fixes
Author: Reynold Xin <rxin@databricks.com>
Closes#5108 from rxin/hive-public-type and squashes the following commits:
a320328 [Reynold Xin] [SPARK-6428][SQL] Added explicit type for all public methods for Hive module.
This PR creates a trait `DataTypeParser` used to parse data types. This trait aims to be single place to provide the functionality of parsing data types' string representation. It is currently mixed in with `DDLParser` and `SqlParser`. It is also used to parse the data type for `DataFrame.cast` and to convert Hive metastore's data type string back to a `DataType`.
JIRA: https://issues.apache.org/jira/browse/SPARK-6250
Author: Yin Huai <yhuai@databricks.com>
Closes#5078 from yhuai/ddlKeywords and squashes the following commits:
0e66097 [Yin Huai] Special handle struct<>.
fea6012 [Yin Huai] Style.
c9733fb [Yin Huai] Create a trait to parse data types.
SELECT sum('a'), avg('a'), variance('a'), std('a') FROM src;
Should give output as
0.0 NULL NULL NULL
This fixes hive udaf_number_format.q
Author: Venkata Ramana G <ramana.gollamudihuawei.com>
Author: Venkata Ramana Gollamudi <ramana.gollamudi@huawei.com>
Closes#4466 from gvramana/sum_fix and squashes the following commits:
42e14d1 [Venkata Ramana Gollamudi] Added comments
39415c0 [Venkata Ramana Gollamudi] Handled the partitioned Sum expression scenario
df66515 [Venkata Ramana Gollamudi] code style fix
4be2606 [Venkata Ramana Gollamudi] Add udaf_number_format to whitelist and golden answer
330fd64 [Venkata Ramana Gollamudi] fix sum function for all null data
Because of no statistics override, in spute of super class say 'LeafNode must override'.
fix issue
[SPARK-5320: Joins on simple table created using select gives error](https://issues.apache.org/jira/browse/SPARK-5320)
Author: x1- <viva008@gmail.com>
Closes#5105 from x1-/SPARK-5320 and squashes the following commits:
e561aac [x1-] Add statistics method at NoRelation (override super).
When using "CREATE TEMPORARY TABLE AS SELECT" to create JSON table, we first delete the path file or directory and then generate a new directory with the same name. But if only read permission was granted, the delete failed.
Here we just throwing an error message to let users know what happened.
ParquetRelation2 may also hit this problem. I think to restrict JSONRelation and ParquetRelation2 must base on directory is more reasonable for access control. Maybe I can do it in follow up works.
Author: Yanbo Liang <ybliang8@gmail.com>
Author: Yanbo Liang <yanbohappy@gmail.com>
Closes#4610 from yanboliang/jsonInsertImprovements and squashes the following commits:
c387fce [Yanbo Liang] fix typos
42d7fb6 [Yanbo Liang] add unittest & fix output format
46f0d9d [Yanbo Liang] Update JSONRelation.scala
e2df8d5 [Yanbo Liang] check path exisit when write
79f7040 [Yanbo Liang] Update JSONRelation.scala
e4bc229 [Yanbo Liang] Update JSONRelation.scala
5a42d83 [Yanbo Liang] JSONRelation CTAS should check if delete is successful
When writing Parquet files, Spark 1.1.x persists the schema string into Parquet metadata with the result of `StructType.toString`, which was then deprecated in Spark 1.2 by a schema string in JSON format. But we still need to take the old schema format into account while reading Parquet files.
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Author: Cheng Lian <lian@databricks.com>
Closes#5034 from liancheng/spark-6315 and squashes the following commits:
a182f58 [Cheng Lian] Adds a regression test
b9c6dbe [Cheng Lian] Also tries the case class string parser while reading Parquet schema
Do the same check as #4610 for ParquetRelation2.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#5107 from yanboliang/spark-5821-parquet and squashes the following commits:
7092c8d [Yanbo Liang] ParquetRelation2 CTAS should check if delete is successful
Also implemented equals/hashCode when they are missing.
This is done in order to enable automatic public method type checking.
Author: Reynold Xin <rxin@databricks.com>
Closes#5104 from rxin/sql-hashcode-explicittype and squashes the following commits:
ffce6f3 [Reynold Xin] Code review feedback.
8b36733 [Reynold Xin] [SPARK-6428][SQL] Added explicit type for all public methods.
Use `Utils.createTempDir()` to replace other temp file mechanisms used in some tests, to further ensure they are cleaned up, and simplify
Author: Sean Owen <sowen@cloudera.com>
Closes#5029 from srowen/SPARK-6338 and squashes the following commits:
27b740a [Sean Owen] Fix hive-thriftserver tests that don't expect an existing dir
4a212fa [Sean Owen] Standardize a bit more temp dir management
9004081 [Sean Owen] Revert some added recursive-delete calls
57609e4 [Sean Owen] Use Utils.createTempDir() to replace other temp file mechanisms used in some tests, to further ensure they are cleaned up, and simplify
We need to handle ambiguous `exprId`s that are produced by new aliases as well as those caused by leaf nodes (`MultiInstanceRelation`).
Attempting to fix this revealed a bug in `equals` for `Alias` as these objects were comparing equal even when the expression ids did not match. Additionally, `LocalRelation` did not correctly provide statistics, and some tests in `catalyst` and `hive` were not using the helper functions for comparing plans.
Based on #4991 by chenghao-intel
Author: Michael Armbrust <michael@databricks.com>
Closes#5062 from marmbrus/selfJoins and squashes the following commits:
8e9b84b [Michael Armbrust] check qualifier too
8038a36 [Michael Armbrust] handle aggs too
0b9c687 [Michael Armbrust] fix more tests
c3c574b [Michael Armbrust] revert change.
725f1ab [Michael Armbrust] add statistics
a925d08 [Michael Armbrust] check for conflicting attributes in join resolution
b022ef7 [Michael Armbrust] Handle project aliases.
d8caa40 [Michael Armbrust] test case: SPARK-6247
f9c67c2 [Michael Armbrust] Check for duplicate attributes in join resolution.
898af73 [Michael Armbrust] Fix Alias equality.
Now spark version is only support
```create table table_in_database_creation.test1 as select * from src limit 1;``` in HiveContext.
This patch is used to support
```create table `table_in_database_creation.test2` as select * from src limit 1;``` in HiveContext.
Author: watermen <qiyadong2010@gmail.com>
Author: q00251598 <qiyadong@huawei.com>
Closes#4427 from watermen/SPARK-5651 and squashes the following commits:
c5c8ed1 [watermen] add the generated golden files
1f0e42e [q00251598] add input64 in blacklist and add test suit
By default, the statistic for logical plan with multiple children is quite aggressive, and those statistic are quite critical for the join optimization, hence we need to estimate the statistics as accurate as possible.
For `Union`, which has 2 children, and overwrite the default implementation by `adding` its children `byteInSize` instead of `multiplying`.
For `Expand`, which only has a single child, but it will grows the size, and we need to multiply its inflating factor.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#4914 from chenghao-intel/statistic and squashes the following commits:
d466bbc [Cheng Hao] Update the default statistic
`ResolveUdtfsAlias` in `hiveUdfs` only considers the `HiveGenericUdtf` with multiple alias. When only single alias is used with `HiveGenericUdtf`, the alias is not working.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#4692 from viirya/udft_alias and squashes the following commits:
8a3bae4 [Liang-Chi Hsieh] No need to test selected column from DataFrame since DataFrame API is updated.
160a379 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into udft_alias
e6531cc [Liang-Chi Hsieh] Selected column from DataFrame should not re-analyze logical plan.
a45cc2a [Liang-Chi Hsieh] Resolve UdtfsAlias when only single Alias is used.
When getting file statuses, create file system from each path instead of a single one from hadoop configuration.
Author: Pei-Lun Lee <pllee@appier.com>
Closes#5039 from ypcat/spark-6351 and squashes the following commits:
a19a3fe [Pei-Lun Lee] [SPARK-6330] [SQL] fix test
506f5a0 [Pei-Lun Lee] [SPARK-6351] [SQL] fix test
fa2290e [Pei-Lun Lee] [SPARK-6330] [SQL] Rename test case and add comment
606c967 [Pei-Lun Lee] Merge branch 'master' of https://github.com/apache/spark into spark-6351
896e80a [Pei-Lun Lee] [SPARK-6351] [SQL] Add test case
2ae0916 [Pei-Lun Lee] [SPARK-6351] [SQL] ParquetRelation2 supporting multiple file systems
Error in the code sample of the `implicits` object in `SQLContext`.
Author: Lomig Mégard <lomig.megard@gmail.com>
Closes#5051 from tarfaa/simple and squashes the following commits:
5a88acc [Lomig Mégard] [docs][minor] Fixed sample code in SQLContext scaladoc
---- comment;
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#4500 from adrian-wang/semicolon and squashes the following commits:
70b8abb [Daoyuan Wang] use mkstring instead of reduce
2d49738 [Daoyuan Wang] remove outdated golden file
317346e [Daoyuan Wang] only skip comment with semicolon at end of line, to avoid golden file outdated
d3ae01e [Daoyuan Wang] fix error
a11602d [Daoyuan Wang] fix comment with semicolon at end
If I'm running this locally and my path points to S3, this would currently error out because of incorrect FS.
I tested this in a scenario that previously didn't work, this change seemed to fix the issue.
Author: Volodymyr Lyubinets <vlyubin@gmail.com>
Closes#5020 from vlyubin/parquertbug and squashes the following commits:
a645ad5 [Volodymyr Lyubinets] Fix filesystem bug in newParquet relation
Still, we keep only a single HiveContext within ThriftServer, and we also create a object called `SQLSession` for isolating the different user states.
Developers can obtain/release a new user session via `openSession` and `closeSession`, and `SQLContext` and `HiveContext` will also provide a default session if no `openSession` called, for backward-compatibility.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#4885 from chenghao-intel/multisessions_singlecontext and squashes the following commits:
1c47b2a [Cheng Hao] rename the tss => tlSession
815b27a [Cheng Hao] code style issue
57e3fa0 [Cheng Hao] openSession is not compatible between Hive0.12 & 0.13.1
4665b0d [Cheng Hao] thriftservice with single context
This is a following clean up PR for #5010
This will resolve issues when launching `hive/console` like below:
```
<console>:20: error: object ParquetTestData is not a member of package org.apache.spark.sql.parquet
import org.apache.spark.sql.parquet.ParquetTestData
```
Author: OopsOutOfMemory <victorshengli@126.com>
Closes#5032 from OopsOutOfMemory/SPARK-6285 and squashes the following commits:
2996aeb [OopsOutOfMemory] remove ParquetTestData
This PR adds a specialized in-memory column type for fixed-precision decimals.
For all other column types, a single integer column type ID is enough to determine which column type to use. However, this doesn't apply to fixed-precision decimal types with different precision and scale parameters. Moreover, according to the previous design, there seems no trivial way to encode precision and scale information into the columnar byte buffer. On the other hand, considering we always know the data type of the column to be built / scanned ahead of time. This PR no longer use column type ID to construct `ColumnBuilder`s and `ColumnAccessor`s, but resorts to the actual column data type. In this way, we can pass precision / scale information along the way.
The column type ID is now not used anymore and can be removed in a future PR.
### Micro benchmark result
The following micro benchmark builds a simple table with 2 million decimals (precision = 10, scale = 0), cache it in memory, then count all the rows. Code (simply paste it into Spark shell):
```scala
import sc._
import sqlContext._
import sqlContext.implicits._
import org.apache.spark.sql.types._
import com.google.common.base.Stopwatch
def benchmark(n: Int)(f: => Long) {
val stopwatch = new Stopwatch()
def run() = {
stopwatch.reset()
stopwatch.start()
f
stopwatch.stop()
stopwatch.elapsedMillis()
}
val records = (0 until n).map(_ => run())
(0 until n).foreach(i => println(s"Round $i: ${records(i)} ms"))
println(s"Average: ${records.sum / n.toDouble} ms")
}
// Explicit casting is required because ScalaReflection can't inspect decimal precision
parallelize(1 to 2000000)
.map(i => Tuple1(Decimal(i, 10, 0)))
.toDF("dec")
.select($"dec" cast DecimalType(10, 0))
.registerTempTable("dec")
sql("CACHE TABLE dec")
val df = table("dec")
// Warm up
df.count()
df.count()
benchmark(5) {
df.count()
}
```
With `FIXED_DECIMAL` column type:
- Round 0: 75 ms
- Round 1: 97 ms
- Round 2: 75 ms
- Round 3: 70 ms
- Round 4: 72 ms
- Average: 77.8 ms
Without `FIXED_DECIMAL` column type:
- Round 0: 1233 ms
- Round 1: 1170 ms
- Round 2: 1171 ms
- Round 3: 1141 ms
- Round 4: 1141 ms
- Average: 1171.2 ms
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Author: Cheng Lian <lian@databricks.com>
Closes#4938 from liancheng/decimal-column-type and squashes the following commits:
fef5338 [Cheng Lian] Updates fixed decimal column type related test cases
e08ab5b [Cheng Lian] Only resorts to FIXED_DECIMAL when the value can be held in a long
4db713d [Cheng Lian] Adds in-memory column type for fixed-precision decimals
Author: ArcherShao <ArcherShao@users.noreply.github.com>
Author: ArcherShao <shaochuan@huawei.com>
Closes#5007 from ArcherShao/20150313 and squashes the following commits:
ae422ae [ArcherShao] Updated
459efbd [ArcherShao] [SQL]Delete some dupliate code in HiveThriftServer2
use prettyString instead of toString() (which include id of expression) as column name in agg()
Author: Davies Liu <davies@databricks.com>
Closes#5006 from davies/prettystring and squashes the following commits:
cb1fdcf [Davies Liu] use prettyString as column name in agg()
Author: vinodkc <vinod.kc.in@gmail.com>
Author: Vinod K C <vinod.kc@huawei.com>
Closes#5011 from vinodkc/HIVE_console_startupError and squashes the following commits:
b43925f [vinodkc] Changed order of import
b4f5453 [Vinod K C] Fixed HIVE console startup issue
All the contents in this file are not referenced anywhere and should have been removed in #4116 when I tried to get rid of the old Parquet test suites.
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Author: Cheng Lian <lian@databricks.com>
Closes#5010 from liancheng/spark-6285 and squashes the following commits:
06ed057 [Cheng Lian] Removes unused ParquetTestData and duplicated TestGroupWriteSupport
Author: Volodymyr Lyubinets <vlyubin@gmail.com>
Closes#4988 from vlyubin/columncomp and squashes the following commits:
92d7c8f [Volodymyr Lyubinets] Added equals to Column
Avoid `UnsupportedOperationException` from JsonRDD.inferSchema on empty RDD.
Not sure if this is supposed to be an error (but a better one), but it seems like this case can come up if the input is down-sampled so much that nothing is sampled.
Now stuff like this:
```
sqlContext.jsonRDD(sc.parallelize(List[String]()))
```
just results in
```
org.apache.spark.sql.DataFrame = []
```
Author: Sean Owen <sowen@cloudera.com>
Closes#4971 from srowen/SPARK-6245 and squashes the following commits:
3699964 [Sean Owen] Set() -> Set.empty
3c619e1 [Sean Owen] Avoid UnsupportedOperationException from JsonRDD.inferSchema on empty RDD
Resolve javac, scalac warnings of various types -- deprecations, Scala lang, unchecked cast, etc.
Author: Sean Owen <sowen@cloudera.com>
Closes#4950 from srowen/SPARK-6225 and squashes the following commits:
3080972 [Sean Owen] Ordered imports: Java, Scala, 3rd party, Spark
c67985b [Sean Owen] Resolve javac, scalac warnings of various types -- deprecations, Scala lang, unchecked cast, etc.
Removed an repeated "from" in the comments.
Author: Hongbo Liu <liuhb86@gmail.com>
Closes#4976 from liuhb86/mine and squashes the following commits:
e280e7c [Hongbo Liu] [SQL][Minor] fix typo in comments
The extra blank line is preventing the first lines from showing up in the package summary page.
Author: Reynold Xin <rxin@databricks.com>
Closes#4955 from rxin/datatype-docs and squashes the following commits:
1621114 [Reynold Xin] Minor doc: Remove the extra blank line in data types javadoc.
Author: Michael Armbrust <michael@databricks.com>
Closes#4920 from marmbrus/openStrategies and squashes the following commits:
cbc35c0 [Michael Armbrust] [SQL] Make Strategies a public developer API
jira: https://issues.apache.org/jira/browse/SPARK-6163
Author: Yin Huai <yhuai@databricks.com>
Closes#4896 from yhuai/SPARK-6163 and squashes the following commits:
45e023e [Yin Huai] Address @chenghao-intel's comment.
2e8734e [Yin Huai] Use JSON data source for jsonFile.
92a4a33 [Yin Huai] Test.
Based on #4904 with style errors fixed.
`LogicalPlan#resolve` will not only produce `Attribute`, but also "`GetField` chain".
So in `ResolveSortReferences`, after resolve the ordering expressions, we should not just collect the `Attribute` results, but also `Attribute` at the bottom of "`GetField` chain".
Author: Wenchen Fan <cloud0fan@outlook.com>
Author: Michael Armbrust <michael@databricks.com>
Closes#4918 from marmbrus/pr/4904 and squashes the following commits:
997f84e [Michael Armbrust] fix style
3eedbfc [Wenchen Fan] fix 6145
Option 1 of 2: Convert spark-parent module name to spark-parent_2.10 / spark-parent_2.11
Author: Sean Owen <sowen@cloudera.com>
Closes#4912 from srowen/SPARK-6182.1 and squashes the following commits:
eff60de [Sean Owen] Convert spark-parent module name to spark-parent_2.10 / spark-parent_2.11
For package thriftserver, guava is used at runtime.
/cc pwendell
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#4884 from adrian-wang/test and squashes the following commits:
4600ae7 [Daoyuan Wang] only promote for thriftserver
44dda18 [Daoyuan Wang] promote guava dep for hive
In `CodeGenerator`, the casting on `FloatType` should use `FloatType` instead of `IntegerType`.
Besides, `defaultPrimitive` for `LongType` should be `-1L` instead of `1L`.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#4870 from viirya/codegen_type and squashes the following commits:
76311dd [Liang-Chi Hsieh] Fix wrong datatype for casting on FloatType. Fix the wrong value for LongType in defaultPrimitive.
Integration test suites in the JDBC data source (`MySQLIntegration` and `PostgresIntegration`) depend on docker-client 2.7.5, which transitively depends on Guava 17.0. Unfortunately, Guava 17.0 is causing test runtime binary compatibility issues when Spark is compiled against Hive 0.12.0, or Hadoop 2.4.
Considering `MySQLIntegration` and `PostgresIntegration` are ignored right now, I'd suggest moving them from the Spark project to the [Spark integration tests] [1] project. This PR removes both the JDBC data source integration tests and the docker-client test dependency.
[1]: |https://github.com/databricks/spark-integration-tests
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Author: Cheng Lian <lian@databricks.com>
Closes#4872 from liancheng/remove-docker-client and squashes the following commits:
1f4169e [Cheng Lian] Removes DockerHacks
159b24a [Cheng Lian] Removed JDBC integration tests which depends on docker-client
- Various Fixes to docs
- Make data source traits actually interfaces
Based on #4862 but with fixed conflicts.
Author: Reynold Xin <rxin@databricks.com>
Author: Michael Armbrust <michael@databricks.com>
Closes#4868 from marmbrus/pr/4862 and squashes the following commits:
fe091ea [Michael Armbrust] Merge remote-tracking branch 'origin/master' into pr/4862
0208497 [Reynold Xin] Test fixes.
34e0a28 [Reynold Xin] [SPARK-5310][SQL] Various fixes to Spark SQL docs.
This PR contains the following changes:
1. Add a new method, `DataType.equalsIgnoreCompatibleNullability`, which is the middle ground between DataType's equality check and `DataType.equalsIgnoreNullability`. For two data types `from` and `to`, it does `equalsIgnoreNullability` as well as if the nullability of `from` is compatible with that of `to`. For example, the nullability of `ArrayType(IntegerType, containsNull = false)` is compatible with that of `ArrayType(IntegerType, containsNull = true)` (for an array without null values, we can always say it may contain null values). However, the nullability of `ArrayType(IntegerType, containsNull = true)` is incompatible with that of `ArrayType(IntegerType, containsNull = false)` (for an array that may have null values, we cannot say it does not have null values).
2. For the `resolved` field of `InsertIntoTable`, use `equalsIgnoreCompatibleNullability` to replace the equality check of the data types.
3. For our data source write path, when appending data, we always use the schema of existing table to write the data. This is important for parquet, since nullability direct impacts the way to encode/decode values. If we do not do this, we may see corrupted values when reading values from a set of parquet files generated with different nullability settings.
4. When generating a new parquet table, we always set nullable/containsNull/valueContainsNull to true. So, we will not face situations that we cannot append data because containsNull/valueContainsNull in an Array/Map column of the existing table has already been set to `false`. This change makes the whole data pipeline more robust.
5. Update the equality check of JSON relation. Since JSON does not really cares nullability, `equalsIgnoreNullability` seems a better choice to compare schemata from to JSON tables.
JIRA: https://issues.apache.org/jira/browse/SPARK-5950
Thanks viirya for the initial work in #4729.
cc marmbrus liancheng
Author: Yin Huai <yhuai@databricks.com>
Closes#4826 from yhuai/insertNullabilityCheck and squashes the following commits:
3b61a04 [Yin Huai] Revert change on equals.
80e487e [Yin Huai] asNullable in UDT.
587d88b [Yin Huai] Make methods private.
0cb7ea2 [Yin Huai] marmbrus's comments.
3cec464 [Yin Huai] Cheng's comments.
486ed08 [Yin Huai] Merge remote-tracking branch 'upstream/master' into insertNullabilityCheck
d3747d1 [Yin Huai] Remove unnecessary change.
8360817 [Yin Huai] Merge remote-tracking branch 'upstream/master' into insertNullabilityCheck
8a3f237 [Yin Huai] Use equalsIgnoreNullability instead of equality check.
0eb5578 [Yin Huai] Fix tests.
f6ed813 [Yin Huai] Update old parquet path.
e4f397c [Yin Huai] Unit tests.
b2c06f8 [Yin Huai] Ignore nullability in JSON relation's equality check.
8bd008b [Yin Huai] nullable, containsNull, and valueContainsNull will be always true for parquet data.
bf50d73 [Yin Huai] When appending data, we use the schema of the existing table instead of the schema of the new data.
0a703e7 [Yin Huai] Test failed again since we cannot read correct content.
9a26611 [Yin Huai] Make InsertIntoTable happy.
8f19fe5 [Yin Huai] equalsIgnoreCompatibleNullability
4ec17fd [Yin Huai] Failed test.
Constructs like Hive `TRANSFORM` may generate malformed rows (via badly authored external scripts for example). I'm a bit hesitant to have this feature, since it introduces per-tuple cost when caching tables. However, considering caching tables is usually a one-time cost, this is probably worth having.
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Author: Cheng Lian <lian@databricks.com>
Closes#4842 from liancheng/spark-6082 and squashes the following commits:
b05dbff [Cheng Lian] Provides better error message for malformed rows when caching tables
Author: Michael Armbrust <michael@databricks.com>
Closes#4855 from marmbrus/explodeBug and squashes the following commits:
a712249 [Michael Armbrust] [SPARK-6114][SQL] Avoid metastore conversions before plan is resolved
HiveQL expression like `select count(1) from src tablesample(1 percent);` means take 1% sample to select. But it means 100% in the current version of the Spark.
Author: q00251598 <qiyadong@huawei.com>
Closes#4789 from watermen/SPARK-6040 and squashes the following commits:
2453ebe [q00251598] check and adjust the fraction.
It should be `true` instead of `false`?
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#4762 from viirya/doc_fix and squashes the following commits:
2e37482 [Liang-Chi Hsieh] Fix doc.
The API signatire for join requires the JoinType to be the third parameter. The code examples provided for join show JoinType being provided as the 2nd parater resuling in errors (i.e. "df1.join(df2, "outer", $"df1Key" === $"df2Key") ). The correct sample code is df1.join(df2, $"df1Key" === $"df2Key", "outer")
Author: Paul Power <paul.power@peerside.com>
Closes#4847 from peerside/master and squashes the following commits:
ebc1efa [Paul Power] Merge pull request #1 from peerside/peerside-patch-1
e353340 [Paul Power] Updated comments use correct sample code for Dataframe joins
When run ```select * from nzhang_part where hr = 'file,';```, it throws exception ```java.lang.IllegalArgumentException: Can not create a Path from an empty string```
. Because the path of hdfs contains comma, and FileInputFormat.setInputPaths will split path by comma.
### SQL
```
set hive.merge.mapfiles=true;
set hive.merge.mapredfiles=true;
set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
set hive.exec.dynamic.partition=true;
set hive.exec.dynamic.partition.mode=nonstrict;
create table nzhang_part like srcpart;
insert overwrite table nzhang_part partition (ds='2010-08-15', hr) select key, value, hr from srcpart where ds='2008-04-08';
insert overwrite table nzhang_part partition (ds='2010-08-15', hr=11) select key, value from srcpart where ds='2008-04-08';
insert overwrite table nzhang_part partition (ds='2010-08-15', hr)
select * from (
select key, value, hr from srcpart where ds='2008-04-08'
union all
select '1' as key, '1' as value, 'file,' as hr from src limit 1) s;
select * from nzhang_part where hr = 'file,';
```
### Error Log
```
15/02/10 14:33:16 ERROR SparkSQLDriver: Failed in [select * from nzhang_part where hr = 'file,']
java.lang.IllegalArgumentException: Can not create a Path from an empty string
at org.apache.hadoop.fs.Path.checkPathArg(Path.java:127)
at org.apache.hadoop.fs.Path.<init>(Path.java:135)
at org.apache.hadoop.util.StringUtils.stringToPath(StringUtils.java:241)
at org.apache.hadoop.mapred.FileInputFormat.setInputPaths(FileInputFormat.java:400)
at org.apache.spark.sql.hive.HadoopTableReader$.initializeLocalJobConfFunc(TableReader.scala:251)
at org.apache.spark.sql.hive.HadoopTableReader$$anonfun$11.apply(TableReader.scala:229)
at org.apache.spark.sql.hive.HadoopTableReader$$anonfun$11.apply(TableReader.scala:229)
at org.apache.spark.rdd.HadoopRDD$$anonfun$getJobConf$6.apply(HadoopRDD.scala:172)
at org.apache.spark.rdd.HadoopRDD$$anonfun$getJobConf$6.apply(HadoopRDD.scala:172)
at scala.Option.map(Option.scala:145)
at org.apache.spark.rdd.HadoopRDD.getJobConf(HadoopRDD.scala:172)
at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:196)
Author: q00251598 <qiyadong@huawei.com>
Closes#4532 from watermen/SPARK-5741 and squashes the following commits:
9758ab1 [q00251598] fix bug
1db1a1c [q00251598] use setInputPaths(Job job, Path... inputPaths)
b788a72 [q00251598] change FileInputFormat.setInputPaths to jobConf.set and add test suite
Always set `containsNull = true` when infer the schema of JSON datasets. If we set `containsNull` based on records we scanned, we may miss arrays with null values when we do sampling. Also, because future data can have arrays with null values, if we convert JSON data to parquet, always setting `containsNull = true` is a more robust way to go.
JIRA: https://issues.apache.org/jira/browse/SPARK-6052
Author: Yin Huai <yhuai@databricks.com>
Closes#4806 from yhuai/jsonArrayContainsNull and squashes the following commits:
05eab9d [Yin Huai] Change containsNull to true.
JIRA: https://issues.apache.org/jira/browse/SPARK-6073
liancheng
Author: Yin Huai <yhuai@databricks.com>
Closes#4824 from yhuai/refreshCache and squashes the following commits:
b9542ef [Yin Huai] Refresh metadata cache in the Catalog in CreateMetastoreDataSourceAsSelect.
This is needed for the SQL bindings to work on Yarn.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#4822 from vanzin/SPARK-6074 and squashes the following commits:
fb52001 [Marcelo Vanzin] [SPARK-6074] [sql] Package pyspark sql bindings.
This is a follow-up of #4720. By default, `spark-daemon.sh` writes PID files under `/tmp`, which makes it impossible to start multiple server instances simultaneously. This PR sets `SPARK_PID_DIR` to Spark home directory to workaround this problem.
Many thanks to chenghao-intel for pointing out this issue!
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Author: Cheng Lian <lian@databricks.com>
Closes#4758 from liancheng/thriftserver-pid-dir and squashes the following commits:
252fa0f [Cheng Lian] Uses temporary directory as Thrift server PID directory
1b3d1e3 [Cheng Lian] Sets SPARK_HOME as SPARK_PID_DIR when running Thrift server test suites
JIRA: https://issues.apache.org/jira/browse/SPARK-6024
Author: Yin Huai <yhuai@databricks.com>
Closes#4795 from yhuai/wideSchema and squashes the following commits:
4882e6f [Yin Huai] Address comments.
73e71b4 [Yin Huai] Address comments.
143927a [Yin Huai] Simplify code.
cc1d472 [Yin Huai] Make the schema wider.
12bacae [Yin Huai] If the JSON string of a schema is too large, split it before storing it in metastore.
e9b4f70 [Yin Huai] Failed test.
`FilteringParquetRowInputFormat` manually merges Parquet schemas before computing splits. However, it is duplicate because the schemas are already merged in `ParquetRelation2`. We don't need to re-merge them at `InputFormat`.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#4786 from viirya/dup_parquet_schemas_merge and squashes the following commits:
ef78a5a [Liang-Chi Hsieh] Avoiding duplicate Parquet schema merging.
It is useful to let the user decide the number of rows to show in DataFrame.show
Author: Jacky Li <jacky.likun@huawei.com>
Closes#4767 from jackylk/show and squashes the following commits:
a0e0f4b [Jacky Li] fix testcase
7cdbe91 [Jacky Li] modify according to comment
bb54537 [Jacky Li] for Java compatibility
d7acc18 [Jacky Li] modify according to comments
981be52 [Jacky Li] add numRows param in DataFrame.show()
Please see JIRA (https://issues.apache.org/jira/browse/SPARK-6016) for details of the bug.
Author: Yin Huai <yhuai@databricks.com>
Closes#4775 from yhuai/parquetFooterCache and squashes the following commits:
78787b1 [Yin Huai] Remove footerCache in FilteringParquetRowInputFormat.
dff6fba [Yin Huai] Failed unit test.
DataFrame.explain return wrong result when the query is DDL command.
For example, the following two queries should print out the same execution plan, but it not.
sql("create table tb as select * from src where key > 490").explain(true)
sql("explain extended create table tb as select * from src where key > 490")
This is because DataFrame.explain leverage logicalPlan which had been forced executed, we should use the unexecuted plan queryExecution.logical.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#4707 from yanboliang/spark-5926 and squashes the following commits:
fa6db63 [Yanbo Liang] logicalPlan is not lazy
0e40a1b [Yanbo Liang] make DataFrame.explain leverage queryExecution.logical
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#4760 from viirya/dup_literal and squashes the following commits:
06e7516 [Liang-Chi Hsieh] Remove duplicate Literal matching block.
`ReadContext.init` calls `InitContext.getMergedKeyValueMetadata`, which doesn't know how to merge conflicting user defined key-value metadata and throws exception. In our case, when dealing with different but compatible schemas, we have different Spark SQL schema JSON strings in different Parquet part-files, thus causes this problem. Reading similar Parquet files generated by Hive doesn't suffer from this issue.
In this PR, we manually merge the schemas before passing it to `ReadContext` to avoid the exception.
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Author: Cheng Lian <lian@databricks.com>
Closes#4768 from liancheng/spark-6010 and squashes the following commits:
9002f0a [Cheng Lian] Fixes SPARK-6010
Author: Michael Armbrust <michael@databricks.com>
Closes#4757 from marmbrus/udtConversions and squashes the following commits:
3714aad [Michael Armbrust] [SPARK-5996][SQL] Fix specialized outbound conversions
https://issues.apache.org/jira/browse/SPARK-5286
Author: Yin Huai <yhuai@databricks.com>
Closes#4755 from yhuai/SPARK-5286-throwable and squashes the following commits:
4c0c450 [Yin Huai] Catch Throwable instead of Exception.
Also added desc/asc function for constructing sorting expressions more conveniently. And added a small fix to lift alias out of cast expression.
Author: Reynold Xin <rxin@databricks.com>
Closes#4752 from rxin/SPARK-5985 and squashes the following commits:
aeda5ae [Reynold Xin] Added Experimental flag to ColumnName.
047ad03 [Reynold Xin] Lift alias out of cast.
c9cf17c [Reynold Xin] [SPARK-5985][SQL] DataFrame sortBy -> orderBy in Python.
Added a new test suite to make sure Java DF programs can use varargs properly.
Also moved all suites into test.org.apache.spark package to make sure the suites also test for method visibility.
Author: Reynold Xin <rxin@databricks.com>
Closes#4751 from rxin/df-tests and squashes the following commits:
1e8b8e4 [Reynold Xin] Fixed imports and renamed JavaAPISuite.
a6ca53b [Reynold Xin] [SPARK-5904][SQL] DataFrame Java API test suites.
**NOTICE** Do NOT merge this, as we're waiting for #3881 to be merged.
`HiveThriftServer2Suite` has been notorious for its flakiness for a while. This was mostly due to spawning and communicate with external server processes. This PR revamps this test suite for better robustness:
1. Fixes a racing condition occurred while using `tail -f` to check log file
It's possible that the line we are looking for has already been printed into the log file before we start the `tail -f` process. This PR uses `tail -n +0 -f` to ensure all lines are checked.
2. Retries up to 3 times if the server fails to start
In most of the cases, the server fails to start because of port conflict. This PR no longer asks the system to choose an available TCP port, but uses a random port first, and retries up to 3 times if the server fails to start.
3. A server instance is reused among all test cases within a single suite
The original `HiveThriftServer2Suite` is splitted into two test suites, `HiveThriftBinaryServerSuite` and `HiveThriftHttpServerSuite`. Each suite starts a `HiveThriftServer2` instance and reuses it for all of its test cases.
**TODO**
- [ ] Starts the Thrift server in foreground once #3881 is merged (adding `--foreground` flag to `spark-daemon.sh`)
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Author: Cheng Lian <lian@databricks.com>
Closes#4720 from liancheng/revamp-thrift-server-tests and squashes the following commits:
d6c80eb [Cheng Lian] Relaxes server startup timeout
6f14eb1 [Cheng Lian] Revamped HiveThriftServer2Suite for robustness
Author: Michael Armbrust <michael@databricks.com>
Closes#4746 from marmbrus/hiveLock and squashes the following commits:
8b871cf [Michael Armbrust] [SPARK-5952][SQL] Lock when using hive metastore client
Author: Michael Armbrust <michael@databricks.com>
Closes#4738 from marmbrus/udtRepart and squashes the following commits:
c06d7b5 [Michael Armbrust] fix compilation
91c8829 [Michael Armbrust] [SQL][SPARK-5532] Repartition should not use external rdd representation
Author: Michael Armbrust <michael@databricks.com>
Closes#4736 from marmbrus/asExprs and squashes the following commits:
5ba97e4 [Michael Armbrust] [SPARK-5910][SQL] Support for as in selectExpr
1. Column is no longer a DataFrame to simplify class hierarchy.
2. Don't use varargs on abstract methods (see Scala compiler bug SI-9013).
Author: Reynold Xin <rxin@databricks.com>
Closes#4686 from rxin/SPARK-5904 and squashes the following commits:
fd9b199 [Reynold Xin] Fixed Python tests.
df25cef [Reynold Xin] Non final.
5221530 [Reynold Xin] [SPARK-5904][SQL] DataFrame API fixes.
marmbrus am I missing something obvious here? I verified that this fixes the problem for me (on 1.2.1) on EC2, but I'm confused about how others wouldn't have noticed this?
Author: Kay Ousterhout <kayousterhout@gmail.com>
Closes#4630 from kayousterhout/SPARK-5846_1.3 and squashes the following commits:
2022ad4 [Kay Ousterhout] [SPARK-5846] Correctly set job description and pool for SQL jobs
The `int` is 64-bit on 64-bit machine (very common now), we should infer it as LongType for it in Spark SQL.
Also, LongType in SQL will come back as `int`.
Author: Davies Liu <davies@databricks.com>
Closes#4666 from davies/long and squashes the following commits:
6bc6cc4 [Davies Liu] infer int as LongType
Also added test cases for checking the serializability of HiveContext and SQLContext.
Author: Reynold Xin <rxin@databricks.com>
Closes#4628 from rxin/SPARK-5840 and squashes the following commits:
ecb3bcd [Reynold Xin] test cases and reviews.
55eb822 [Reynold Xin] [SPARK-5840][SQL] HiveContext cannot be serialized due to tuple extraction.
This pull request replaces calls to deprecated methods from `java.util.Date` with near-equivalents in `java.util.Calendar`.
Author: Tor Myklebust <tmyklebu@gmail.com>
Closes#4668 from tmyklebu/master and squashes the following commits:
66215b1 [Tor Myklebust] Use GregorianCalendar instead of Timestamp get methods.
Although we've migrated to the DataFrame API, lots of code still uses `rdd` or `srdd` as local variable names. This PR tries to address these naming inconsistencies and some other minor DataFrame related style issues.
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Author: Cheng Lian <lian@databricks.com>
Closes#4670 from liancheng/df-cleanup and squashes the following commits:
3e14448 [Cheng Lian] Cleans up DataFrame variable names and toDF() calls
JIRA: https://issues.apache.org/jira/browse/SPARK-5723
Author: Yin Huai <yhuai@databricks.com>
This patch had conflicts when merged, resolved by
Committer: Michael Armbrust <michael@databricks.com>
Closes#4639 from yhuai/defaultCTASFileFormat and squashes the following commits:
a568137 [Yin Huai] Merge remote-tracking branch 'upstream/master' into defaultCTASFileFormat
ad2b07d [Yin Huai] Update tests and error messages.
8af5b2a [Yin Huai] Update conf key and unit test.
5a67903 [Yin Huai] Use data source write path for Hive's CTAS statements when no storage format/handler is specified.
https://issues.apache.org/jira/browse/SPARK-5875 has a case to reproduce the bug and explain the root cause.
Author: Yin Huai <yhuai@databricks.com>
Closes#4663 from yhuai/projectResolved and squashes the following commits:
472f7b6 [Yin Huai] If a logical.Project has any AggregateExpression or Generator, it's resolved field should be false.
