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.
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
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
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
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.
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.
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
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
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" === $"y.str").groupBy("x.str").count()
```
As a result, in this PR we defer the elimination of subqueries until the optimization phase.
Author: Michael Armbrust <michael@databricks.com>
Closes#5160 from marmbrus/subqueriesInDfs and squashes the following commits:
a9bb262 [Michael Armbrust] Update Optimizer.scala
27d25bf [Michael Armbrust] fix hive tests
9137e03 [Michael Armbrust] add type
81cd597 [Michael Armbrust] Avoid eliminating subqueries until optimization
Due to a recent change that made `StructType` a `Seq` we started inadvertently turning `StructType`s into generic `Traversable` when attempting nested tree transformations. In this PR we explicitly avoid descending into `DataType`s to avoid this bug.
Author: Michael Armbrust <michael@databricks.com>
Closes#5157 from marmbrus/udfFix and squashes the following commits:
26f7087 [Michael Armbrust] Fix transformations of TreeNodes that hold StructTypes
Otherwise we will leak files when spilling occurs.
Author: Michael Armbrust <michael@databricks.com>
Closes#5161 from marmbrus/cleanupAfterSort and squashes the following commits:
cb13d3c [Michael Armbrust] hint to inferencer
cdebdf5 [Michael Armbrust] Use completion iterator to close external sorter
For example, one might expect the following code to work, but it does not. Now you will at least get a warning with a suggestion to use aliases.
```scala
val df = sqlContext.load(path, "parquet")
val txns = df.groupBy("cust_id").agg($"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
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
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
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.
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.
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
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 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
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()
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.
