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* [SPARK-11246] [SQL] Table cache for Parquet broken in 1.5xin Wu2015-10-291-0/+11
| | | | | | | | | The root cause is that when spark.sql.hive.convertMetastoreParquet=true by default, the cached InMemoryRelation of the ParquetRelation can not be looked up from the cachedData of CacheManager because the key comparison fails even though it is the same LogicalPlan representing the Subquery that wraps the ParquetRelation. The solution in this PR is overriding the LogicalPlan.sameResult function in Subquery case class to eliminate subquery node first before directly comparing the child (ParquetRelation), which will find the key to the cached InMemoryRelation. Author: xin Wu <xinwu@us.ibm.com> Closes #9326 from xwu0226/spark-11246-commit.
* [SPARK-10484] [SQL] Optimize the cartesian join with broadcast join for some ↵Cheng Hao2015-10-276-1/+136
| | | | | | | | | | cases In some cases, we can broadcast the smaller relation in cartesian join, which improve the performance significantly. Author: Cheng Hao <hao.cheng@intel.com> Closes #8652 from chenghao-intel/cartesian.
* [SPARK-10562] [SQL] support mixed case partitionBy column names for tables ↵Wenchen Fan2015-10-263-27/+54
| | | | | | | | | | stored in metastore https://issues.apache.org/jira/browse/SPARK-10562 Author: Wenchen Fan <wenchen@databricks.com> Closes #9226 from cloud-fan/par.
* [SPARK-11194] [SQL] Use MutableURLClassLoader for the classLoader in ↵Yin Huai2015-10-231-28/+51
| | | | | | | | | | IsolatedClientLoader. https://issues.apache.org/jira/browse/SPARK-11194 Author: Yin Huai <yhuai@databricks.com> Closes #9170 from yhuai/SPARK-11194.
* [SPARK-11273][SQL] Move ArrayData/MapData/DataTypeParser to catalyst.util ↵Reynold Xin2015-10-234-4/+7
| | | | | | | | package Author: Reynold Xin <rxin@databricks.com> Closes #9239 from rxin/types-private.
* [SPARK-9735][SQL] Respect the user specified schema than the infer partition ↵Cheng Hao2015-10-221-12/+30
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | schema for HadoopFsRelation To enable the unit test of `hadoopFsRelationSuite.Partition column type casting`. It previously threw exception like below, as we treat the auto infer partition schema with higher priority than the user specified one. ``` java.lang.ClassCastException: java.lang.Integer cannot be cast to org.apache.spark.unsafe.types.UTF8String at org.apache.spark.sql.catalyst.expressions.BaseGenericInternalRow$class.getUTF8String(rows.scala:45) at org.apache.spark.sql.catalyst.expressions.GenericInternalRow.getUTF8String(rows.scala:220) at org.apache.spark.sql.catalyst.expressions.JoinedRow.getUTF8String(JoinedRow.scala:102) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(generated.java:62) at org.apache.spark.sql.execution.datasources.DataSourceStrategy$$anonfun$17$$anonfun$apply$9.apply(DataSourceStrategy.scala:212) at org.apache.spark.sql.execution.datasources.DataSourceStrategy$$anonfun$17$$anonfun$apply$9.apply(DataSourceStrategy.scala:212) at scala.collection.Iterator$$anon$11.next(Iterator.scala:328) at scala.collection.Iterator$$anon$11.next(Iterator.scala:328) at scala.collection.Iterator$class.foreach(Iterator.scala:727) at scala.collection.AbstractIterator.foreach(Iterator.scala:1157) at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47) at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273) at scala.collection.AbstractIterator.to(Iterator.scala:1157) at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265) at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157) at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252) at scala.collection.AbstractIterator.toArray(Iterator.scala:1157) at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$12.apply(RDD.scala:903) at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$12.apply(RDD.scala:903) at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1846) at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1846) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66) at org.apache.spark.scheduler.Task.run(Task.scala:88) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) at java.lang.Thread.run(Thread.java:745) 07:44:01.344 ERROR org.apache.spark.executor.Executor: Exception in task 14.0 in stage 3.0 (TID 206) java.lang.ClassCastException: java.lang.Integer cannot be cast to org.apache.spark.unsafe.types.UTF8String at org.apache.spark.sql.catalyst.expressions.BaseGenericInternalRow$class.getUTF8String(rows.scala:45) at org.apache.spark.sql.catalyst.expressions.GenericInternalRow.getUTF8String(rows.scala:220) at org.apache.spark.sql.catalyst.expressions.JoinedRow.getUTF8String(JoinedRow.scala:102) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(generated.java:62) at org.apache.spark.sql.execution.datasources.DataSourceStrategy$$anonfun$17$$anonfun$apply$9.apply(DataSourceStrategy.scala:212) at org.apache.spark.sql.execution.datasources.DataSourceStrategy$$anonfun$17$$anonfun$apply$9.apply(DataSourceStrategy.scala:212) at scala.collection.Iterator$$anon$11.next(Iterator.scala:328) at scala.collection.Iterator$$anon$11.next(Iterator.scala:328) at scala.collection.Iterator$class.foreach(Iterator.scala:727) at scala.collection.AbstractIterator.foreach(Iterator.scala:1157) at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47) at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273) at scala.collection.AbstractIterator.to(Iterator.scala:1157) at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265) at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157) at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252) at scala.collection.AbstractIterator.toArray(Iterator.scala:1157) at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$12.apply(RDD.scala:903) at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$12.apply(RDD.scala:903) at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1846) at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1846) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66) at org.apache.spark.scheduler.Task.run(Task.scala:88) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) at java.lang.Thread.run(Thread.java:745) ``` Author: Cheng Hao <hao.cheng@intel.com> Closes #8026 from chenghao-intel/partition_discovery.
* [SPARK-10151][SQL] Support invocation of hive macronavis.ryu2015-10-2127-8/+27
| | | | | | | | Macro in hive (which is GenericUDFMacro) contains real function inside of it but it's not conveyed to tasks, resulting null-pointer exception. Author: navis.ryu <navis@apache.org> Closes #8354 from navis/SPARK-10151.
* [SPARK-11208][SQL] Filter out 'hive.metastore.rawstore.impl' from ↵Artem Aliev2015-10-211-1/+2
| | | | | | | | | | executionHive temporary config The executionHive assumed to be a standard meta store located in temporary directory as a derby db. But hive.metastore.rawstore.impl was not filtered out so any custom implementation of the metastore with other storage properties (not JDO) will persist that temporary functions. CassandraHiveMetaStore from DataStax Enterprise is one of examples. Author: Artem Aliev <artem.aliev@datastax.com> Closes #9178 from artem-aliev/SPARK-11208.
* [SPARK-9740][SPARK-9592][SPARK-9210][SQL] Change the default behavior of ↵Yin Huai2015-10-211-0/+38
| | | | | | | | | | | | First/Last to RESPECT NULLS. I am changing the default behavior of `First`/`Last` to respect null values (the SQL standard default behavior). https://issues.apache.org/jira/browse/SPARK-9740 Author: Yin Huai <yhuai@databricks.com> Closes #8113 from yhuai/firstLast.
* [SPARK-11197][SQL] run SQL on files directlyDavies Liu2015-10-212-2/+15
| | | | | | | | | | | | This PR introduce a new feature to run SQL directly on files without create a table, for example: ``` select id from json.`path/to/json/files` as j ``` Author: Davies Liu <davies@databricks.com> Closes #9173 from davies/source.
* [SPARK-11110][BUILD] Remove transient annotation for parameters.Jakob Odersky2015-10-201-3/+3
| | | | | | | | | | | | | `transient` annotations on class parameters (not case class parameters or vals) causes compilation errors during compilation with Scala 2.11. I understand that transient *parameters* make no sense, however I don't quite understand why the 2.10 compiler accepted them. Note: in case it is preferred to keep the annotations in case someone would in the future want to redefine them as vals, it would also be possible to just add `val` after the annotation, e.g. `class Foo(transient x: Int)` becomes `class Foo(transient private val x: Int)`. I chose to remove the annotation as it also reduces needles clutter, however please feel free to tell me if you prefer the second option and I'll update the PR Author: Jakob Odersky <jodersky@gmail.com> Closes #9126 from jodersky/sbt-scala-2.11.
* [SPARK-11017] [SQL] Support ImperativeAggregates in TungstenAggregateJosh Rosen2015-10-141-3/+13
| | | | | | | | | | This patch extends TungstenAggregate to support ImperativeAggregate functions. The existing TungstenAggregate operator only supported DeclarativeAggregate functions, which are defined in terms of Catalyst expressions and can be evaluated via generated projections. ImperativeAggregate functions, on the other hand, are evaluated by calling their `initialize`, `update`, `merge`, and `eval` methods. The basic strategy here is similar to how SortBasedAggregate evaluates both types of aggregate functions: use a generated projection to evaluate the expression-based declarative aggregates with dummy placeholder expressions inserted in place of the imperative aggregate function output, then invoke the imperative aggregate functions and target them against the aggregation buffer. The bulk of the diff here consists of code that was copied and adapted from SortBasedAggregate, with some key changes to handle TungstenAggregate's sort fallback path. Author: Josh Rosen <joshrosen@databricks.com> Closes #9038 from JoshRosen/support-interpreted-in-tungsten-agg-final.
* [SPARK-10104] [SQL] Consolidate different forms of table identifiersWenchen Fan2015-10-1413-161/+86
| | | | | | | | | Right now, we have QualifiedTableName, TableIdentifier, and Seq[String] to represent table identifiers. We should only have one form and TableIdentifier is the best one because it provides methods to get table name, database name, return unquoted string, and return quoted string. Author: Wenchen Fan <wenchen@databricks.com> Author: Wenchen Fan <cloud0fan@163.com> Closes #8453 from cloud-fan/table-name.
* [SPARK-11091] [SQL] Change spark.sql.canonicalizeView to spark.sql.nativeView.Yin Huai2015-10-133-9/+9
| | | | | | | | https://issues.apache.org/jira/browse/SPARK-11091 Author: Yin Huai <yhuai@databricks.com> Closes #9103 from yhuai/SPARK-11091.
* [SPARK-11030] [SQL] share the SQLTab across sessionsDavies Liu2015-10-131-3/+9
| | | | | | | | | | The SQLTab will be shared by multiple sessions. If we create multiple independent SQLContexts (not using newSession()), will still see multiple SQLTabs in the Spark UI. Author: Davies Liu <davies@databricks.com> Closes #9048 from davies/sqlui.
* [SPARK-11009] [SQL] fix wrong result of Window function in cluster modeDavies Liu2015-10-131-0/+41
| | | | | | | | | | | | | | | | | | | | | | | | | | Currently, All windows function could generate wrong result in cluster sometimes. The root cause is that AttributeReference is called in executor, then id of it may not be unique than others created in driver. Here is the script that could reproduce the problem (run in local cluster): ``` from pyspark import SparkContext, HiveContext from pyspark.sql.window import Window from pyspark.sql.functions import rowNumber sqlContext = HiveContext(SparkContext()) sqlContext.setConf("spark.sql.shuffle.partitions", "3") df = sqlContext.range(1<<20) df2 = df.select((df.id % 1000).alias("A"), (df.id / 1000).alias('B')) ws = Window.partitionBy(df2.A).orderBy(df2.B) df3 = df2.select("client", "date", rowNumber().over(ws).alias("rn")).filter("rn < 0") assert df3.count() == 0 ``` Author: Davies Liu <davies@databricks.com> Author: Yin Huai <yhuai@databricks.com> Closes #9050 from davies/wrong_window.
* [SPARK-11042] [SQL] Add a mechanism to ban creating multiple root ↵Yin Huai2015-10-121-3/+9
| | | | | | | | | | SQLContexts/HiveContexts in a JVM https://issues.apache.org/jira/browse/SPARK-11042 Author: Yin Huai <yhuai@databricks.com> Closes #9058 from yhuai/SPARK-11042.
* [SPARK-10960] [SQL] SQL with windowing function should be able to refer ↵Liang-Chi Hsieh2015-10-121-0/+27
| | | | | | | | | | | | | | | | column in inner select JIRA: https://issues.apache.org/jira/browse/SPARK-10960 When accessing a column in inner select from a select with window function, `AnalysisException` will be thrown. For example, an query like this: select area, rank() over (partition by area order by tmp.month) + tmp.tmp1 as c1 from (select month, area, product, 1 as tmp1 from windowData) tmp Currently, the rule `ExtractWindowExpressions` in `Analyzer` only extracts regular expressions from `WindowFunction`, `WindowSpecDefinition` and `AggregateExpression`. We need to also extract other attributes as the one in `Alias` as shown in the above query. Author: Liang-Chi Hsieh <viirya@appier.com> Closes #9011 from viirya/fix-window-inner-column.
* [SPARK-10810] [SPARK-10902] [SQL] Improve session management in SQLDavies Liu2015-10-0811-205/+293
| | | | | | | | | | | | | | | | | | | | | This PR improve the sessions management by replacing the thread-local based to one SQLContext per session approach, introduce separated temporary tables and UDFs/UDAFs for each session. A new session of SQLContext could be created by: 1) create an new SQLContext 2) call newSession() on existing SQLContext For HiveContext, in order to reduce the cost for each session, the classloader and Hive client are shared across multiple sessions (created by newSession). CacheManager is also shared by multiple sessions, so cache a table multiple times in different sessions will not cause multiple copies of in-memory cache. Added jars are still shared by all the sessions, because SparkContext does not support sessions. cc marmbrus yhuai rxin Author: Davies Liu <davies@databricks.com> Closes #8909 from davies/sessions.
* [SPARK-8848] [SQL] Refactors Parquet write path to follow parquet-formatCheng Lian2015-10-081-12/+16
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | This PR refactors Parquet write path to follow parquet-format spec. It's a successor of PR #7679, but with less non-essential changes. Major changes include: 1. Replaces `RowWriteSupport` and `MutableRowWriteSupport` with `CatalystWriteSupport` - Writes Parquet data using standard layout defined in parquet-format Specifically, we are now writing ... - ... arrays and maps in standard 3-level structure with proper annotations and field names - ... decimals as `INT32` and `INT64` whenever possible, and taking `FIXED_LEN_BYTE_ARRAY` as the final fallback - Supports legacy mode which is compatible with Spark 1.4 and prior versions The legacy mode is by default off, and can be turned on by flipping SQL option `spark.sql.parquet.writeLegacyFormat` to `true`. - Eliminates per value data type dispatching costs via prebuilt composed writer functions 1. Cleans up the last pieces of old Parquet support code As pointed out by rxin previously, we probably want to rename all those `Catalyst*` Parquet classes to `Parquet*` for clarity. But I'd like to do this in a follow-up PR to minimize code review noises in this one. Author: Cheng Lian <lian@databricks.com> Closes #8988 from liancheng/spark-8848/standard-parquet-write-path.
* [SPARK-10337] [SQL] fix hive views on non-hive-compatible tables.Wenchen Fan2015-10-086-25/+420
| | | | | | | | add a new config to deal with this special case. Author: Wenchen Fan <cloud0fan@163.com> Closes #8990 from cloud-fan/view-master.
* [SPARK-5775] [SPARK-5508] [SQL] Re-enable Hive Parquet array reading testsCheng Lian2015-10-081-2/+1
| | | | | | | | Since SPARK-5508 has already been fixed. Author: Cheng Lian <lian@databricks.com> Closes #8999 from liancheng/spark-5775.enable-array-tests.
* [SPARK-10679] [CORE] javax.jdo.JDOFatalUserException in executornavis.ryu2015-10-072-5/+30
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | HadoopRDD throws exception in executor, something like below. {noformat} 5/09/17 18:51:21 INFO metastore.HiveMetaStore: 0: Opening raw store with implemenation class:org.apache.hadoop.hive.metastore.ObjectStore 15/09/17 18:51:21 INFO metastore.ObjectStore: ObjectStore, initialize called 15/09/17 18:51:21 WARN metastore.HiveMetaStore: Retrying creating default database after error: Class org.datanucleus.api.jdo.JDOPersistenceManagerFactory was not found. javax.jdo.JDOFatalUserException: Class org.datanucleus.api.jdo.JDOPersistenceManagerFactory was not found. at javax.jdo.JDOHelper.invokeGetPersistenceManagerFactoryOnImplementation(JDOHelper.java:1175) at javax.jdo.JDOHelper.getPersistenceManagerFactory(JDOHelper.java:808) at javax.jdo.JDOHelper.getPersistenceManagerFactory(JDOHelper.java:701) at org.apache.hadoop.hive.metastore.ObjectStore.getPMF(ObjectStore.java:365) at org.apache.hadoop.hive.metastore.ObjectStore.getPersistenceManager(ObjectStore.java:394) at org.apache.hadoop.hive.metastore.ObjectStore.initialize(ObjectStore.java:291) at org.apache.hadoop.hive.metastore.ObjectStore.setConf(ObjectStore.java:258) at org.apache.hadoop.util.ReflectionUtils.setConf(ReflectionUtils.java:73) at org.apache.hadoop.util.ReflectionUtils.newInstance(ReflectionUtils.java:133) at org.apache.hadoop.hive.metastore.RawStoreProxy.<init>(RawStoreProxy.java:57) at org.apache.hadoop.hive.metastore.RawStoreProxy.getProxy(RawStoreProxy.java:66) at org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.newRawStore(HiveMetaStore.java:593) at org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.getMS(HiveMetaStore.java:571) at org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.createDefaultDB(HiveMetaStore.java:620) at org.apache.hadoop.hive.metastore.HiveMetaStore$HMSHandler.init(HiveMetaStore.java:461) at org.apache.hadoop.hive.metastore.RetryingHMSHandler.<init>(RetryingHMSHandler.java:66) at org.apache.hadoop.hive.metastore.RetryingHMSHandler.getProxy(RetryingHMSHandler.java:72) at org.apache.hadoop.hive.metastore.HiveMetaStore.newRetryingHMSHandler(HiveMetaStore.java:5762) at org.apache.hadoop.hive.metastore.HiveMetaStoreClient.<init>(HiveMetaStoreClient.java:199) at org.apache.hadoop.hive.ql.metadata.SessionHiveMetaStoreClient.<init>(SessionHiveMetaStoreClient.java:74) at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method) at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:57) at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) at java.lang.reflect.Constructor.newInstance(Constructor.java:526) at org.apache.hadoop.hive.metastore.MetaStoreUtils.newInstance(MetaStoreUtils.java:1521) at org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.<init>(RetryingMetaStoreClient.java:86) at org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.getProxy(RetryingMetaStoreClient.java:132) at org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.getProxy(RetryingMetaStoreClient.java:104) at org.apache.hadoop.hive.ql.metadata.Hive.createMetaStoreClient(Hive.java:3005) at org.apache.hadoop.hive.ql.metadata.Hive.getMSC(Hive.java:3024) at org.apache.hadoop.hive.ql.metadata.Hive.getAllDatabases(Hive.java:1234) at org.apache.hadoop.hive.ql.metadata.Hive.reloadFunctions(Hive.java:174) at org.apache.hadoop.hive.ql.metadata.Hive.<clinit>(Hive.java:166) at org.apache.hadoop.hive.ql.plan.PlanUtils.configureJobPropertiesForStorageHandler(PlanUtils.java:803) at org.apache.hadoop.hive.ql.plan.PlanUtils.configureInputJobPropertiesForStorageHandler(PlanUtils.java:782) at org.apache.spark.sql.hive.HadoopTableReader$.initializeLocalJobConfFunc(TableReader.scala:298) at org.apache.spark.sql.hive.HadoopTableReader$$anonfun$12.apply(TableReader.scala:274) at org.apache.spark.sql.hive.HadoopTableReader$$anonfun$12.apply(TableReader.scala:274) at org.apache.spark.rdd.HadoopRDD$$anonfun$getJobConf$6.apply(HadoopRDD.scala:176) at org.apache.spark.rdd.HadoopRDD$$anonfun$getJobConf$6.apply(HadoopRDD.scala:176) at scala.Option.map(Option.scala:145) at org.apache.spark.rdd.HadoopRDD.getJobConf(HadoopRDD.scala:176) at org.apache.spark.rdd.HadoopRDD$$anon$1.<init>(HadoopRDD.scala:220) at org.apache.spark.rdd.HadoopRDD.compute(HadoopRDD.scala:216) at org.apache.spark.rdd.HadoopRDD.compute(HadoopRDD.scala:101) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297) at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297) at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297) at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) at org.apache.spark.rdd.UnionRDD.compute(UnionRDD.scala:87) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297) at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297) at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297) at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66) at org.apache.spark.scheduler.Task.run(Task.scala:88) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) at java.lang.Thread.run(Thread.java:745) {noformat} Author: navis.ryu <navis@apache.org> Closes #8804 from navis/SPARK-10679.
* [SPARK-10300] [BUILD] [TESTS] Add support for test tags in run-tests.py.Marcelo Vanzin2015-10-071-0/+2
| | | | | | Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #8775 from vanzin/SPARK-10300.
* [SPARK-10941] [SQL] Refactor AggregateFunction2 and AlgebraicAggregate ↵Josh Rosen2015-10-072-7/+5
| | | | | | | | | | | | | | | | interfaces to improve code clarity This patch refactors several of the Aggregate2 interfaces in order to improve code clarity. The biggest change is a refactoring of the `AggregateFunction2` class hierarchy. In the old code, we had a class named `AlgebraicAggregate` that inherited from `AggregateFunction2`, added a new set of methods, then banned the use of the inherited methods. I found this to be fairly confusing because. If you look carefully at the existing code, you'll see that subclasses of `AggregateFunction2` fall into two disjoint categories: imperative aggregation functions which directly extended `AggregateFunction2` and declarative, expression-based aggregate functions which extended `AlgebraicAggregate`. In order to make this more explicit, this patch refactors things so that `AggregateFunction2` is a sealed abstract class with two subclasses, `ImperativeAggregateFunction` and `ExpressionAggregateFunction`. The superclass, `AggregateFunction2`, now only contains methods and fields that are common to both subclasses. After making this change, I updated the various AggregationIterator classes to comply with this new naming scheme. I also performed several small renamings in the aggregate interfaces themselves in order to improve clarity and rewrote or expanded a number of comments. Author: Josh Rosen <joshrosen@databricks.com> Closes #8973 from JoshRosen/tungsten-agg-comments.
* [SPARK-10400] [SQL] Renames SQLConf.PARQUET_FOLLOW_PARQUET_FORMAT_SPECCheng Lian2015-10-011-1/+1
| | | | | | | | | | | | We introduced SQL option `spark.sql.parquet.followParquetFormatSpec` while working on implementing Parquet backwards-compatibility rules in SPARK-6777. It indicates whether we should use legacy Parquet format adopted by Spark 1.4 and prior versions or the standard format defined in parquet-format spec to write Parquet files. This option defaults to `false` and is marked as a non-public option (`isPublic = false`) because we haven't finished refactored Parquet write path. The problem is, the name of this option is somewhat confusing, because it's not super intuitive why we shouldn't follow the spec. Would be nice to rename it to `spark.sql.parquet.writeLegacyFormat`, and invert its default value (the two option names have opposite meanings). Although this option is private in 1.5, we'll make it public in 1.6 after refactoring Parquet write path. So that users can decide whether to write Parquet files in standard format or legacy format. Author: Cheng Lian <lian@databricks.com> Closes #8566 from liancheng/spark-10400/deprecate-follow-parquet-format-spec.
* [SPARK-10671] [SQL] Throws an analysis exception if we cannot find Hive UDFsWenchen Fan2015-10-012-23/+104
| | | | | | | | Takes over https://github.com/apache/spark/pull/8800 Author: Wenchen Fan <cloud0fan@163.com> Closes #8941 from cloud-fan/hive-udf.
* [SPARK-10770] [SQL] SparkPlan.executeCollect/executeTake should return ↵Reynold Xin2015-09-302-6/+6
| | | | | | | | InternalRow rather than external Row. Author: Reynold Xin <rxin@databricks.com> Closes #8900 from rxin/SPARK-10770-1.
* [SPARK-10741] [SQL] Hive Query Having/OrderBy against Parquet table is not ↵Wenchen Fan2015-09-277-58/+51
| | | | | | | | | | | working https://issues.apache.org/jira/browse/SPARK-10741 I choose the second approach: do not change output exprIds when convert MetastoreRelation to LogicalRelation Author: Wenchen Fan <cloud0fan@163.com> Closes #8889 from cloud-fan/hot-bug.
* [SPARK-10845] [SQL] Makes spark.sql.hive.version a SQLConfEntryCheng Lian2015-09-261-0/+5
| | | | | | | | | | When refactoring SQL options from plain strings to the strongly typed `SQLConfEntry`, `spark.sql.hive.version` wasn't migrated, and doesn't show up in the result of `SET -v`, as `SET -v` only shows public `SQLConfEntry` instances. This affects compatibility with Simba ODBC driver. This PR migrates this SQL option as a `SQLConfEntry` to fix this issue. Author: Cheng Lian <lian@databricks.com> Closes #8925 from liancheng/spark-10845/hive-version-conf.
* [SPARK-10765] [SQL] use new aggregate interface for hive UDAFWenchen Fan2015-09-242-83/+58
| | | | | | Author: Wenchen Fan <cloud0fan@163.com> Closes #8874 from cloud-fan/hive-agg.
* [SPARK-10310] [SQL] Fixes script transformation field/line delimitersZhichao Li2015-09-225-22/+152
| | | | | | | | | | | | **Please attribute this PR to `Zhichao Li <zhichao.liintel.com>`.** This PR is based on PR #8476 authored by zhichao-li. It fixes SPARK-10310 by adding field delimiter SerDe property to the default `LazySimpleSerDe`, and enabling default record reader/writer classes. Currently, we only support `LazySimpleSerDe`, used together with `TextRecordReader` and `TextRecordWriter`, and don't support customizing record reader/writer using `RECORDREADER`/`RECORDWRITER` clauses. This should be addressed in separate PR(s). Author: Cheng Lian <lian@databricks.com> Closes #8860 from liancheng/spark-10310/fix-script-trans-delimiters.
* [SPARK-10672] [SQL] Do not fail when we cannot save the metadata of a data ↵Yin Huai2015-09-221-51/+50
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | source table in a hive compatible way https://issues.apache.org/jira/browse/SPARK-10672 With changes in this PR, we will fallback to same the metadata of a table in Spark SQL specific way if we fail to save it in a hive compatible way (Hive throws an exception because of its internal restrictions, e.g. binary and decimal types cannot be saved to parquet if the metastore is running Hive 0.13). I manually tested the fix with the following test in `DataSourceWithHiveMetastoreCatalogSuite` (`spark.sql.hive.metastore.version=0.13` and `spark.sql.hive.metastore.jars`=`maven`). ``` test(s"fail to save metadata of a parquet table in hive 0.13") { withTempPath { dir => withTable("t") { val path = dir.getCanonicalPath sql( s"""CREATE TABLE t USING $provider |OPTIONS (path '$path') |AS SELECT 1 AS d1, cast("val_1" as binary) AS d2 """.stripMargin) sql( s"""describe formatted t """.stripMargin).collect.foreach(println) sqlContext.table("t").show } } } } ``` Without this fix, we will fail with the following error. ``` org.apache.hadoop.hive.ql.metadata.HiveException: java.lang.UnsupportedOperationException: Unknown field type: binary at org.apache.hadoop.hive.ql.metadata.Hive.createTable(Hive.java:619) at org.apache.hadoop.hive.ql.metadata.Hive.createTable(Hive.java:576) at org.apache.spark.sql.hive.client.ClientWrapper$$anonfun$createTable$1.apply$mcV$sp(ClientWrapper.scala:359) at org.apache.spark.sql.hive.client.ClientWrapper$$anonfun$createTable$1.apply(ClientWrapper.scala:357) at org.apache.spark.sql.hive.client.ClientWrapper$$anonfun$createTable$1.apply(ClientWrapper.scala:357) at org.apache.spark.sql.hive.client.ClientWrapper$$anonfun$withHiveState$1.apply(ClientWrapper.scala:256) at org.apache.spark.sql.hive.client.ClientWrapper.retryLocked(ClientWrapper.scala:211) at org.apache.spark.sql.hive.client.ClientWrapper.withHiveState(ClientWrapper.scala:248) at org.apache.spark.sql.hive.client.ClientWrapper.createTable(ClientWrapper.scala:357) at org.apache.spark.sql.hive.HiveMetastoreCatalog.createDataSourceTable(HiveMetastoreCatalog.scala:358) at org.apache.spark.sql.hive.execution.CreateMetastoreDataSourceAsSelect.run(commands.scala:285) at org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult$lzycompute(commands.scala:57) at org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult(commands.scala:57) at org.apache.spark.sql.execution.ExecutedCommand.doExecute(commands.scala:69) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:140) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:138) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150) at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:138) at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:58) at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:58) at org.apache.spark.sql.DataFrame.<init>(DataFrame.scala:144) at org.apache.spark.sql.DataFrame.<init>(DataFrame.scala:129) at org.apache.spark.sql.DataFrame$.apply(DataFrame.scala:51) at org.apache.spark.sql.SQLContext.sql(SQLContext.scala:725) at org.apache.spark.sql.test.SQLTestUtils$$anonfun$sql$1.apply(SQLTestUtils.scala:56) at org.apache.spark.sql.test.SQLTestUtils$$anonfun$sql$1.apply(SQLTestUtils.scala:56) at org.apache.spark.sql.hive.DataSourceWithHiveMetastoreCatalogSuite$$anonfun$4$$anonfun$apply$1$$anonfun$apply$mcV$sp$2$$anonfun$apply$2.apply$mcV$sp(HiveMetastoreCatalogSuite.scala:165) at org.apache.spark.sql.test.SQLTestUtils$class.withTable(SQLTestUtils.scala:150) at org.apache.spark.sql.hive.DataSourceWithHiveMetastoreCatalogSuite.withTable(HiveMetastoreCatalogSuite.scala:52) at org.apache.spark.sql.hive.DataSourceWithHiveMetastoreCatalogSuite$$anonfun$4$$anonfun$apply$1$$anonfun$apply$mcV$sp$2.apply(HiveMetastoreCatalogSuite.scala:162) at org.apache.spark.sql.hive.DataSourceWithHiveMetastoreCatalogSuite$$anonfun$4$$anonfun$apply$1$$anonfun$apply$mcV$sp$2.apply(HiveMetastoreCatalogSuite.scala:161) at org.apache.spark.sql.test.SQLTestUtils$class.withTempPath(SQLTestUtils.scala:125) at org.apache.spark.sql.hive.DataSourceWithHiveMetastoreCatalogSuite.withTempPath(HiveMetastoreCatalogSuite.scala:52) at org.apache.spark.sql.hive.DataSourceWithHiveMetastoreCatalogSuite$$anonfun$4$$anonfun$apply$1.apply$mcV$sp(HiveMetastoreCatalogSuite.scala:161) at org.apache.spark.sql.hive.DataSourceWithHiveMetastoreCatalogSuite$$anonfun$4$$anonfun$apply$1.apply(HiveMetastoreCatalogSuite.scala:161) at org.apache.spark.sql.hive.DataSourceWithHiveMetastoreCatalogSuite$$anonfun$4$$anonfun$apply$1.apply(HiveMetastoreCatalogSuite.scala:161) at org.scalatest.Transformer$$anonfun$apply$1.apply$mcV$sp(Transformer.scala:22) at org.scalatest.OutcomeOf$class.outcomeOf(OutcomeOf.scala:85) at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104) at org.scalatest.Transformer.apply(Transformer.scala:22) at org.scalatest.Transformer.apply(Transformer.scala:20) at org.scalatest.FunSuiteLike$$anon$1.apply(FunSuiteLike.scala:166) at org.apache.spark.SparkFunSuite.withFixture(SparkFunSuite.scala:42) at org.scalatest.FunSuiteLike$class.invokeWithFixture$1(FunSuiteLike.scala:163) at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175) at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175) at org.scalatest.SuperEngine.runTestImpl(Engine.scala:306) at org.scalatest.FunSuiteLike$class.runTest(FunSuiteLike.scala:175) at org.scalatest.FunSuite.runTest(FunSuite.scala:1555) at org.scalatest.FunSuiteLike$$anonfun$runTests$1.apply(FunSuiteLike.scala:208) at org.scalatest.FunSuiteLike$$anonfun$runTests$1.apply(FunSuiteLike.scala:208) at org.scalatest.SuperEngine$$anonfun$traverseSubNodes$1$1.apply(Engine.scala:413) at org.scalatest.SuperEngine$$anonfun$traverseSubNodes$1$1.apply(Engine.scala:401) at scala.collection.immutable.List.foreach(List.scala:318) at org.scalatest.SuperEngine.traverseSubNodes$1(Engine.scala:401) at org.scalatest.SuperEngine.org$scalatest$SuperEngine$$runTestsInBranch(Engine.scala:396) at org.scalatest.SuperEngine.runTestsImpl(Engine.scala:483) at org.scalatest.FunSuiteLike$class.runTests(FunSuiteLike.scala:208) at org.scalatest.FunSuite.runTests(FunSuite.scala:1555) at org.scalatest.Suite$class.run(Suite.scala:1424) at org.scalatest.FunSuite.org$scalatest$FunSuiteLike$$super$run(FunSuite.scala:1555) at org.scalatest.FunSuiteLike$$anonfun$run$1.apply(FunSuiteLike.scala:212) at org.scalatest.FunSuiteLike$$anonfun$run$1.apply(FunSuiteLike.scala:212) at org.scalatest.SuperEngine.runImpl(Engine.scala:545) at org.scalatest.FunSuiteLike$class.run(FunSuiteLike.scala:212) at org.apache.spark.sql.hive.DataSourceWithHiveMetastoreCatalogSuite.org$scalatest$BeforeAndAfterAll$$super$run(HiveMetastoreCatalogSuite.scala:52) at org.scalatest.BeforeAndAfterAll$class.liftedTree1$1(BeforeAndAfterAll.scala:257) at org.scalatest.BeforeAndAfterAll$class.run(BeforeAndAfterAll.scala:256) at org.apache.spark.sql.hive.DataSourceWithHiveMetastoreCatalogSuite.run(HiveMetastoreCatalogSuite.scala:52) at org.scalatest.tools.Framework.org$scalatest$tools$Framework$$runSuite(Framework.scala:462) at org.scalatest.tools.Framework$ScalaTestTask.execute(Framework.scala:671) at sbt.ForkMain$Run$2.call(ForkMain.java:294) at sbt.ForkMain$Run$2.call(ForkMain.java:284) at java.util.concurrent.FutureTask.run(FutureTask.java:262) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) at java.lang.Thread.run(Thread.java:745) Caused by: java.lang.UnsupportedOperationException: Unknown field type: binary at org.apache.hadoop.hive.ql.io.parquet.serde.ArrayWritableObjectInspector.getObjectInspector(ArrayWritableObjectInspector.java:108) at org.apache.hadoop.hive.ql.io.parquet.serde.ArrayWritableObjectInspector.<init>(ArrayWritableObjectInspector.java:60) at org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe.initialize(ParquetHiveSerDe.java:113) at org.apache.hadoop.hive.metastore.MetaStoreUtils.getDeserializer(MetaStoreUtils.java:339) at org.apache.hadoop.hive.ql.metadata.Table.getDeserializerFromMetaStore(Table.java:288) at org.apache.hadoop.hive.ql.metadata.Table.checkValidity(Table.java:194) at org.apache.hadoop.hive.ql.metadata.Hive.createTable(Hive.java:597) ... 76 more ``` Author: Yin Huai <yhuai@databricks.com> Closes #8824 from yhuai/datasourceMetadata.
* [SPARK-10593] [SQL] fix resolve output of GenerateDavies Liu2015-09-221-0/+14
| | | | | | | | The output of Generate should not be resolved as Reference. Author: Davies Liu <davies@databricks.com> Closes #8755 from davies/view.
* [SPARK-8567] [SQL] Increase the timeout of ↵Yin Huai2015-09-221-1/+1
| | | | | | | | | | | | | | o.a.s.sql.hive.HiveSparkSubmitSuite to 5 minutes. https://issues.apache.org/jira/browse/SPARK-8567 Looks like "SPARK-8368: includes jars passed in through --jars" is pretty flaky now. Based on some history runs, the time spent on a successful run may be from 1.5 minutes to almost 3 minutes. Let's try to increase the timeout and see if we can fix this test. https://amplab.cs.berkeley.edu/jenkins/job/Spark-1.5-SBT/AMPLAB_JENKINS_BUILD_PROFILE=hadoop2.0,label=spark-test/385/testReport/junit/org.apache.spark.sql.hive/HiveSparkSubmitSuite/SPARK_8368__includes_jars_passed_in_through___jars/history/?start=25 Author: Yin Huai <yhuai@databricks.com> Closes #8850 from yhuai/SPARK-8567-anotherTry.
* [SPARK-10155] [SQL] Change SqlParser to object to avoid memory leakzsxwing2015-09-192-5/+5
| | | | | | | | | | Since `scala.util.parsing.combinator.Parsers` is thread-safe since Scala 2.10 (See [SI-4929](https://issues.scala-lang.org/browse/SI-4929)), we can change SqlParser to object to avoid memory leak. I didn't change other subclasses of `scala.util.parsing.combinator.Parsers` because there is only one instance in one SQLContext, which should not be an issue. Author: zsxwing <zsxwing@gmail.com> Closes #8357 from zsxwing/sql-memory-leak.
* [SPARK-10623] [SQL] Fixes ORC predicate push-downCheng Lian2015-09-182-34/+52
| | | | | | | | | | When pushing down a leaf predicate, ORC `SearchArgument` builder requires an extra "parent" predicate (any one among `AND`/`OR`/`NOT`) to wrap the leaf predicate. E.g., to push down `a < 1`, we must build `AND(a < 1)` instead. Fortunately, when actually constructing the `SearchArgument`, the builder will eliminate all those unnecessary wrappers. This PR is based on #8783 authored by zhzhan. I also took the chance to simply `OrcFilters` a little bit to improve readability. Author: Cheng Lian <lian@databricks.com> Closes #8799 from liancheng/spark-10623/fix-orc-ppd.
* [SPARK-10540] Fixes flaky all-data-type testCheng Lian2015-09-181-66/+43
| | | | | | | | | | | | This PR breaks the original test case into multiple ones (one test case for each data type). In this way, test failure output can be much more readable. Within each test case, we build a table with two columns, one of them is for the data type to test, the other is an "index" column, which is used to sort the DataFrame and workaround [SPARK-10591] [1] [1]: https://issues.apache.org/jira/browse/SPARK-10591 Author: Cheng Lian <lian@databricks.com> Closes #8768 from liancheng/spark-10540/test-all-data-types.
* [SPARK-10639] [SQL] Need to convert UDAF's result from scala to sql typeYin Huai2015-09-171-1/+107
| | | | | | | | https://issues.apache.org/jira/browse/SPARK-10639 Author: Yin Huai <yhuai@databricks.com> Closes #8788 from yhuai/udafConversion.
* [SPARK-10381] Fix mixup of taskAttemptNumber & attemptId in ↵Josh Rosen2015-09-151-1/+1
| | | | | | | | | | | | | | OutputCommitCoordinator When speculative execution is enabled, consider a scenario where the authorized committer of a particular output partition fails during the OutputCommitter.commitTask() call. In this case, the OutputCommitCoordinator is supposed to release that committer's exclusive lock on committing once that task fails. However, due to a unit mismatch (we used task attempt number in one place and task attempt id in another) the lock will not be released, causing Spark to go into an infinite retry loop. This bug was masked by the fact that the OutputCommitCoordinator does not have enough end-to-end tests (the current tests use many mocks). Other factors contributing to this bug are the fact that we have many similarly-named identifiers that have different semantics but the same data types (e.g. attemptNumber and taskAttemptId, with inconsistent variable naming which makes them difficult to distinguish). This patch adds a regression test and fixes this bug by always using task attempt numbers throughout this code. Author: Josh Rosen <joshrosen@databricks.com> Closes #8544 from JoshRosen/SPARK-10381.
* Revert "[SPARK-10300] [BUILD] [TESTS] Add support for test tags in ↵Marcelo Vanzin2015-09-152-28/+0
| | | | | | run-tests.py." This reverts commit 8abef21dac1a6538c4e4e0140323b83d804d602b.
* [SPARK-10300] [BUILD] [TESTS] Add support for test tags in run-tests.py.Marcelo Vanzin2015-09-152-0/+28
| | | | | | | | | | | | | | | This change does two things: - tag a few tests and adds the mechanism in the build to be able to disable those tags, both in maven and sbt, for both junit and scalatest suites. - add some logic to run-tests.py to disable some tags depending on what files have changed; that's used to disable expensive tests when a module hasn't explicitly been changed, to speed up testing for changes that don't directly affect those modules. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #8437 from vanzin/test-tags.
* [SPARK-10584] [DOC] [SQL] Documentation about ↵Kousuke Saruta2015-09-141-4/+7
| | | | | | | | | | | spark.sql.hive.metastore.version is wrong. The default value of hive metastore version is 1.2.1 but the documentation says the value of `spark.sql.hive.metastore.version` is 0.13.1. Also, we cannot get the default value by `sqlContext.getConf("spark.sql.hive.metastore.version")`. Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp> Closes #8739 from sarutak/SPARK-10584.
* [SPARK-9899] [SQL] log warning for direct output committer with speculation ↵Wenchen Fan2015-09-142-3/+15
| | | | | | | | | | | | | | | | enabled This is a follow-up of https://github.com/apache/spark/pull/8317. When speculation is enabled, there may be multiply tasks writing to the same path. Generally it's OK as we will write to a temporary directory first and only one task can commit the temporary directory to target path. However, when we use direct output committer, tasks will write data to target path directly without temporary directory. This causes problems like corrupted data. Please see [PR comment](https://github.com/apache/spark/pull/8191#issuecomment-131598385) for more details. Unfortunately, we don't have a simple flag to tell if a output committer will write to temporary directory or not, so for safety, we have to disable any customized output committer when `speculation` is true. Author: Wenchen Fan <cloud0fan@outlook.com> Closes #8687 from cloud-fan/direct-committer.
* [SPARK-10330] Add Scalastyle rule to require use of SparkHadoopUtil ↵Josh Rosen2015-09-121-2/+2
| | | | | | | | | | JobContext methods This is a followup to #8499 which adds a Scalastyle rule to mandate the use of SparkHadoopUtil's JobContext accessor methods and fixes the existing violations. Author: Josh Rosen <joshrosen@databricks.com> Closes #8521 from JoshRosen/SPARK-10330-part2.
* [SPARK-6548] Adding stddev to DataFrame functionsJihongMa2015-09-121-35/+0
| | | | | | | | | | | Adding STDDEV support for DataFrame using 1-pass online /parallel algorithm to compute variance. Please review the code change. Author: JihongMa <linlin200605@gmail.com> Author: Jihong MA <linlin200605@gmail.com> Author: Jihong MA <jihongma@jihongs-mbp.usca.ibm.com> Author: Jihong MA <jihongma@Jihongs-MacBook-Pro.local> Closes #6297 from JihongMA/SPARK-SQL.
* [SPARK-10547] [TEST] Streamline / improve style of Java API testsSean Owen2015-09-122-10/+10
| | | | | | | | Fix a few Java API test style issues: unused generic types, exceptions, wrong assert argument order Author: Sean Owen <sowen@cloudera.com> Closes #8706 from srowen/SPARK-10547.
* [SPARK-10442] [SQL] fix string to boolean castWenchen Fan2015-09-111-0/+13
| | | | | | | | | | | | | | | | | | | When we cast string to boolean in hive, it returns `true` if the length of string is > 0, and spark SQL follows this behavior. However, this behavior is very different from other SQL systems: 1. [presto](https://github.com/facebook/presto/blob/master/presto-main/src/main/java/com/facebook/presto/type/VarcharOperators.java#L89-L118) will return `true` for 't' 'true' '1', `false` for 'f' 'false' '0', throw exception for others. 2. [redshift](http://docs.aws.amazon.com/redshift/latest/dg/r_Boolean_type.html) will return `true` for 't' 'true' 'y' 'yes' '1', `false` for 'f' 'false' 'n' 'no' '0', null for others. 3. [postgresql](http://www.postgresql.org/docs/devel/static/datatype-boolean.html) will return `true` for 't' 'true' 'y' 'yes' 'on' '1', `false` for 'f' 'false' 'n' 'no' 'off' '0', throw exception for others. 4. [vertica](https://my.vertica.com/docs/5.0/HTML/Master/2983.htm) will return `true` for 't' 'true' 'y' 'yes' '1', `false` for 'f' 'false' 'n' 'no' '0', null for others. 5. [impala](http://www.cloudera.com/content/cloudera/en/documentation/cloudera-impala/latest/topics/impala_boolean.html) throw exception when try to cast string to boolean. 6. mysql, oracle, sqlserver don't have boolean type Whether we should change the cast behavior according to other SQL system or not is not decided yet, this PR is a test to see if we changed, how many compatibility tests will fail. Author: Wenchen Fan <cloud0fan@outlook.com> Closes #8698 from cloud-fan/string2boolean.
* [SPARK-10540] [SQL] Ignore HadoopFsRelationTest's "test all data types" if ↵Yin Huai2015-09-111-1/+1
| | | | | | | | | | | | it is too flaky If hadoopFsRelationSuites's "test all data types" is too flaky we can disable it for now. https://issues.apache.org/jira/browse/SPARK-10540 Author: Yin Huai <yhuai@databricks.com> Closes #8705 from yhuai/SPARK-10540-ignore.
* [SPARK-10227] fatal warnings with sbt on Scala 2.11Luc Bourlier2015-09-094-11/+11
| | | | | | | | | | | The bulk of the changes are on `transient` annotation on class parameter. Often the compiler doesn't generate a field for this parameters, so the the transient annotation would be unnecessary. But if the class parameter are used in methods, then fields are created. So it is safer to keep the annotations. The remainder are some potential bugs, and deprecated syntax. Author: Luc Bourlier <luc.bourlier@typesafe.com> Closes #8433 from skyluc/issue/sbt-2.11.
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