This PR adds a `private[sql]` method `metadata` to `SparkPlan`, which can be used to describe detail information about a physical plan during visualization. Specifically, this PR uses this method to provide details of `PhysicalRDD`s translated from a data source relation. For example, a `ParquetRelation` converted from Hive metastore table `default.psrc` is now shown as the following screenshot:
And here is the screenshot for a regular `ParquetRelation` (not converted from Hive metastore table) loaded from a really long path:
Author: Cheng Lian <lian@databricks.com>
Closes#10004 from liancheng/spark-12012.physical-rdd-metadata.
This replaces https://github.com/apache/spark/pull/9696
Invoke Checkstyle and print any errors to the console, failing the step.
Use Google's style rules modified according to
https://cwiki.apache.org/confluence/display/SPARK/Spark+Code+Style+Guide
Some important checks are disabled (see TODOs in `checkstyle.xml`) due to
multiple violations being present in the codebase.
Suggest fixing those TODOs in a separate PR(s).
More on Checkstyle can be found on the [official website](http://checkstyle.sourceforge.net/).
Sample output (from [build 46345](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/46345/consoleFull)) (duplicated because I run the build twice with different profiles):
> Checkstyle checks failed at following occurrences:
[ERROR] src/main/java/org/apache/spark/sql/execution/datasources/parquet/UnsafeRowParquetRecordReader.java:[217,7] (coding) MissingSwitchDefault: switch without "default" clause.
> [ERROR] src/main/java/org/apache/spark/sql/execution/datasources/parquet/SpecificParquetRecordReaderBase.java:[198,10] (modifier) ModifierOrder: 'protected' modifier out of order with the JLS suggestions.
> [ERROR] src/main/java/org/apache/spark/sql/execution/datasources/parquet/UnsafeRowParquetRecordReader.java:[217,7] (coding) MissingSwitchDefault: switch without "default" clause.
> [ERROR] src/main/java/org/apache/spark/sql/execution/datasources/parquet/SpecificParquetRecordReaderBase.java:[198,10] (modifier) ModifierOrder: 'protected' modifier out of order with the JLS suggestions.
> [error] running /home/jenkins/workspace/SparkPullRequestBuilder2/dev/lint-java ; received return code 1
Also fix some of the minor violations that didn't require sweeping changes.
Apologies for the previous botched PRs - I finally figured out the issue.
cr: JoshRosen, pwendell
> I state that the contribution is my original work, and I license the work to the project under the project's open source license.
Author: Dmitry Erastov <derastov@gmail.com>
Closes#9867 from dskrvk/master.
When profiling HiveCompatibilitySuite, I noticed that most of the time seems to be spent in expensive `TestHive.reset()` calls. This patch speeds up suites based on HiveComparisionTest, such as HiveCompatibilitySuite, with the following changes:
- Avoid `TestHive.reset()` whenever possible:
- Use a simple set of heuristics to guess whether we need to call `reset()` in between tests.
- As a safety-net, automatically re-run failed tests by calling `reset()` before the re-attempt.
- Speed up the expensive parts of `TestHive.reset()`: loading the `src` and `srcpart` tables took roughly 600ms per test, so we now avoid this by using a simple heuristic which only loads those tables by tests that reference them. This is based on simple string matching over the test queries which errs on the side of loading in more situations than might be strictly necessary.
After these changes, HiveCompatibilitySuite seems to run in about 10 minutes.
This PR is a revival of #6663, an earlier experimental PR from June, where I played around with several possible speedups for this suite.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#10055 from JoshRosen/speculative-testhive-reset.
https://issues.apache.org/jira/browse/SPARK-12039
Since it is pretty flaky in hadoop 1 tests, we can disable it while we are investigating the cause.
Author: Yin Huai <yhuai@databricks.com>
Closes#10035 from yhuai/SPARK-12039-ignore.
Fix regression test for SPARK-11778.
marmbrus
Could you please take a look?
Thank you very much!!
Author: Huaxin Gao <huaxing@oc0558782468.ibm.com>
Closes#9890 from huaxingao/spark-11778-regression-test.
If we need to download Hive/Hadoop artifacts, try to download a Hadoop that matches the Hadoop used by Spark. If the Hadoop artifact cannot be resolved (e.g. Hadoop version is a vendor specific version like 2.0.0-cdh4.1.1), we will use Hadoop 2.4.0 (we used to hard code this version as the hadoop that we will download from maven) and we will not share Hadoop classes.
I tested this match in my laptop with the following confs (these confs are used by our builds). All tests are good.
```
build/sbt -Phadoop-1 -Dhadoop.version=1.2.1 -Pkinesis-asl -Phive-thriftserver -Phive
build/sbt -Phadoop-1 -Dhadoop.version=2.0.0-mr1-cdh4.1.1 -Pkinesis-asl -Phive-thriftserver -Phive
build/sbt -Pyarn -Phadoop-2.2 -Pkinesis-asl -Phive-thriftserver -Phive
build/sbt -Pyarn -Phadoop-2.3 -Dhadoop.version=2.3.0 -Pkinesis-asl -Phive-thriftserver -Phive
```
Author: Yin Huai <yhuai@databricks.com>
Closes#9979 from yhuai/versionsSuite.
When using remote Hive metastore, `hive.metastore.uris` is set to the metastore URI. However, it overrides `javax.jdo.option.ConnectionURL` unexpectedly, thus the execution Hive client connects to the actual remote Hive metastore instead of the Derby metastore created in the temporary directory. Cleaning this configuration for the execution Hive client fixes this issue.
Author: Cheng Lian <lian@databricks.com>
Closes#9895 from liancheng/spark-11783.clean-remote-metastore-config.
This patch attempts to speed up VersionsSuite by storing fetched Hive JARs in an Ivy cache that persists across tests runs. If `SPARK_VERSIONS_SUITE_IVY_PATH` is set, that path will be used for the cache; if it is not set, VersionsSuite will create a temporary Ivy cache which is deleted after the test completes.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#9624 from JoshRosen/SPARK-9866.
Can someone review my code to make sure I'm not missing anything? Thanks!
Author: Xiu Guo <xguo27@gmail.com>
Author: Xiu Guo <guoxi@us.ibm.com>
Closes#9612 from xguo27/SPARK-11628.
Hive has since changed this behavior as well. https://issues.apache.org/jira/browse/HIVE-3454
Author: Nong Li <nong@databricks.com>
Author: Nong Li <nongli@gmail.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#9685 from nongli/spark-11724.
This patch fixes an issue where the `spark.sql.TungstenAggregate.testFallbackStartsAt` SQLConf setting was not properly reset / cleared at the end of `TungstenAggregationQueryWithControlledFallbackSuite`. This ended up causing test failures in HiveCompatibilitySuite in Maven builds by causing spilling to occur way too frequently.
This configuration leak was inadvertently introduced during test cleanup in #9618.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#9857 from JoshRosen/clear-fallback-prop-in-test-teardown.
In addition, tightened visibility of a lot of classes in the columnar package from private[sql] to private[columnar].
Author: Reynold Xin <rxin@databricks.com>
Closes#9842 from rxin/SPARK-11858.
Fix a bug in DataFrameReader.table (table with schema name such as "db_name.table" doesn't work)
Use SqlParser.parseTableIdentifier to parse the table name before lookupRelation.
Author: Huaxin Gao <huaxing@oc0558782468.ibm.com>
Closes#9773 from huaxingao/spark-11778.
see HIVE-7975 and HIVE-12373
With changed semantic of setters in thrift objects in hive, setter should be called only after all parameters are set. It's not problem of current state but will be a problem in some day.
Author: navis.ryu <navis@apache.org>
Closes#9580 from navis/SPARK-11614.
This PR adds a new option `spark.sql.hive.thriftServer.singleSession` for disabling multi-session support in the Thrift server.
Note that this option is added as a Spark configuration (retrieved from `SparkConf`) rather than Spark SQL configuration (retrieved from `SQLConf`). This is because all SQL configurations are session-ized. Since multi-session support is by default on, no JDBC connection can modify global configurations like the newly added one.
Author: Cheng Lian <lian@databricks.com>
Closes#9740 from liancheng/spark-11089.single-session-option.
According to discussion in PR #9664, the anonymous `HiveFunctionRegistry` in `HiveContext` can be removed now.
Author: Cheng Lian <lian@databricks.com>
Closes#9737 from liancheng/spark-11191.follow-up.
When computing partition for non-parquet relation, `HadoopRDD.compute` is used. but it does not set the thread local variable `inputFileName` in `NewSqlHadoopRDD`, like `NewSqlHadoopRDD.compute` does.. Yet, when getting the `inputFileName`, `NewSqlHadoopRDD.inputFileName` is exptected, which is empty now.
Adding the setting inputFileName in HadoopRDD.compute resolves this issue.
Author: xin Wu <xinwu@us.ibm.com>
Closes#9542 from xwu0226/SPARK-11522.
On driver process start up, UserGroupInformation.loginUserFromKeytab is called with the principal and keytab passed in, and therefore static var UserGroupInfomation,loginUser is set to that principal with kerberos credentials saved in its private credential set, and all threads within the driver process are supposed to see and use this login credentials to authenticate with Hive and Hadoop. However, because of IsolatedClientLoader, UserGroupInformation class is not shared for hive metastore clients, and instead it is loaded separately and of course not able to see the prepared kerberos login credentials in the main thread.
The first proposed fix would cause other classloader conflict errors, and is not an appropriate solution. This new change does kerberos login during hive client initialization, which will make credentials ready for the particular hive client instance.
yhuai Please take a look and let me know. If you are not the right person to talk to, could you point me to someone responsible for this?
Author: Yu Gao <ygao@us.ibm.com>
Author: gaoyu <gaoyu@gaoyu-macbookpro.roam.corp.google.com>
Author: Yu Gao <crystalgaoyu@gmail.com>
Closes#9272 from yolandagao/master.
I didn't remove the old Sort operator, since we still use it in randomized tests. I moved it into test module and renamed it ReferenceSort.
Author: Reynold Xin <rxin@databricks.com>
Closes#9700 from rxin/SPARK-11734.
https://issues.apache.org/jira/browse/SPARK-11678
The change of this PR is to pass root paths of table to the partition discovery logic. So, the process of partition discovery stops at those root paths instead of going all the way to the root path of the file system.
Author: Yin Huai <yhuai@databricks.com>
Closes#9651 from yhuai/SPARK-11678.
When looking up Hive temporary functions, we should always use the `SessionState` within the execution Hive client, since temporary functions are registered there.
Author: Cheng Lian <lian@databricks.com>
Closes#9664 from liancheng/spark-11191.fix-temp-function.
This patch aims to reduce the test time and flakiness of HiveSparkSubmitSuite, SparkSubmitSuite, and CliSuite.
Key changes:
- Disable IO synchronization calls for Derby writes, since durability doesn't matter for tests. This was done for HiveCompatibilitySuite in #6651 and resulted in huge test speedups.
- Add a few missing `--conf`s to disable various Spark UIs. The CliSuite, in particular, never disabled these UIs, leaving it prone to port-contention-related flakiness.
- Fix two instances where tests defined `beforeAll()` methods which were never called because the appropriate traits were not mixed in. I updated these tests suites to extend `BeforeAndAfterEach` so that they play nicely with our `ResetSystemProperties` trait.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#9623 from JoshRosen/SPARK-11647.
https://issues.apache.org/jira/browse/SPARK-11500
As filed in SPARK-11500, if merging schemas is enabled, the order of files to touch is a matter which might affect the ordering of the output columns.
This was mostly because of the use of `Set` and `Map` so I replaced them to `LinkedHashSet` and `LinkedHashMap` to keep the insertion order.
Also, I changed `reduceOption` to `reduceLeftOption`, and replaced the order of `filesToTouch` from `metadataStatuses ++ commonMetadataStatuses ++ needMerged` to `needMerged ++ metadataStatuses ++ commonMetadataStatuses` in order to touch the part-files first which always have the schema in footers whereas the others might not exist.
One nit is, If merging schemas is not enabled, but when multiple files are given, there is no guarantee of the output order, since there might not be a summary file for the first file, which ends up putting ahead the columns of the other files.
However, I thought this should be okay since disabling merging schemas means (assumes) all the files have the same schemas.
In addition, in the test code for this, I only checked the names of fields.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#9517 from HyukjinKwon/SPARK-11500.
This PR is a 2nd follow-up for [SPARK-9241](https://issues.apache.org/jira/browse/SPARK-9241). It contains the following improvements:
* Fix for a potential bug in distinct child expression and attribute alignment.
* Improved handling of duplicate distinct child expressions.
* Added test for distinct UDAF with multiple children.
cc yhuai
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#9566 from hvanhovell/SPARK-9241-followup-2.
https://issues.apache.org/jira/browse/SPARK-9830
This PR contains the following main changes.
* Removing `AggregateExpression1`.
* Removing `Aggregate` operator, which is used to evaluate `AggregateExpression1`.
* Removing planner rule used to plan `Aggregate`.
* Linking `MultipleDistinctRewriter` to analyzer.
* Renaming `AggregateExpression2` to `AggregateExpression` and `AggregateFunction2` to `AggregateFunction`.
* Updating places where we create aggregate expression. The way to create aggregate expressions is `AggregateExpression(aggregateFunction, mode, isDistinct)`.
* Changing `val`s in `DeclarativeAggregate`s that touch children of this function to `lazy val`s (when we create aggregate expression in DataFrame API, children of an aggregate function can be unresolved).
Author: Yin Huai <yhuai@databricks.com>
Closes#9556 from yhuai/removeAgg1.
The DataFrame APIs that takes a SQL expression always use SQLParser, then the HiveFunctionRegistry will called outside of Hive state, cause NPE if there is not a active Session State for current thread (in PySpark).
cc rxin yhuai
Author: Davies Liu <davies@databricks.com>
Closes#9576 from davies/hive_udf.
For now they are thin wrappers around the corresponding Hive UDAFs.
One limitation with these in Hive 0.13.0 is they only support aggregating primitive types.
I chose snake_case here instead of camelCase because it seems to be used in the majority of the multi-word fns.
Do we also want to add these to `functions.py`?
This approach was recommended here: https://github.com/apache/spark/pull/8592#issuecomment-154247089
marmbrus rxin
Author: Nick Buroojy <nick.buroojy@civitaslearning.com>
Closes#9526 from nburoojy/nick/udaf-alias.
(cherry picked from commit a6ee4f989d)
Signed-off-by: Michael Armbrust <michael@databricks.com>
The reason is that:
1. For partitioned hive table, we will move the partitioned columns after data columns. (e.g. `<a: Int, b: Int>` partition by `a` will become `<b: Int, a: Int>`)
2. When append data to table, we use position to figure out how to match input columns to table's columns.
So when we append data to partitioned table, we will match wrong columns between input and table. A solution is reordering the input columns before match by position, like what we did for [`InsertIntoHadoopFsRelation`](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoHadoopFsRelation.scala#L101-L105)
Author: Wenchen Fan <wenchen@databricks.com>
Closes#9408 from cloud-fan/append.
This PR adds support for multiple column in a single count distinct aggregate to the new aggregation path.
cc yhuai
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#9409 from hvanhovell/SPARK-11451.
This PR is a follow up for PR https://github.com/apache/spark/pull/9406. It adds more documentation to the rewriting rule, removes a redundant if expression in the non-distinct aggregation path and adds a multiple distinct test to the AggregationQuerySuite.
cc yhuai marmbrus
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#9541 from hvanhovell/SPARK-9241-followup.
This PR adds test cases that test various column pruning and filter push-down cases.
Author: Cheng Lian <lian@databricks.com>
Closes#9468 from liancheng/spark-10978.follow-up.
`jars` in the log line is an array, so `$jars` doesn't print its content.
Author: Cheng Lian <lian@databricks.com>
Closes#9494 from liancheng/minor.log-fix.
After aggregation, the dataset could be smaller than inputs, so it's better to do hash based aggregation for all inputs, then using sort based aggregation to merge them.
Author: Davies Liu <davies@databricks.com>
Closes#9383 from davies/fix_switch.
1. def dialectClassName in HiveContext is unnecessary.
In HiveContext, if conf.dialect == "hiveql", getSQLDialect() will return new HiveQLDialect(this);
else it will use super.getSQLDialect(). Then in super.getSQLDialect(), it calls dialectClassName, which is overriden in HiveContext and still return super.dialectClassName.
So we'll never reach the code "classOf[HiveQLDialect].getCanonicalName" of def dialectClassName in HiveContext.
2. When we start bin/spark-sql, the default context is HiveContext, and the corresponding dialect is hiveql.
However, if we type "set spark.sql.dialect;", the result is "sql", which is inconsistent with the actual dialect and is misleading. For example, we can use sql like "create table" which is only allowed in hiveql, but this dialect conf shows it's "sql".
Although this problem will not cause any execution error, it's misleading to spark sql users. Therefore I think we should fix it.
In this pr, while procesing “set spark.sql.dialect” in SetCommand, I use "conf.dialect" instead of "getConf()" for the case of key == SQLConf.DIALECT.key, so that it will return the right dialect conf.
Author: Zhenhua Wang <wangzhenhua@huawei.com>
Closes#9349 from wzhfy/dialect.
This PR adds a new method `unhandledFilters` to `BaseRelation`. Data sources which implement this method properly may avoid the overhead of defensive filtering done by Spark SQL.
Author: Cheng Lian <lian@databricks.com>
Closes#9399 from liancheng/spark-10978.unhandled-filters.
Hive GenericUDTF#initialize() defines field names in a returned schema though,
the current HiveGenericUDTF drops these names.
We might need to reflect these in a logical plan tree.
Author: navis.ryu <navis@apache.org>
Closes#8456 from navis/SPARK-9034.
1. Supporting expanding structs in Projections. i.e.
"SELECT s.*" where s is a struct type.
This is fixed by allowing the expand function to handle structs in addition to tables.
2. Supporting expanding * inside aggregate functions of structs.
"SELECT max(struct(col1, structCol.*))"
This requires recursively expanding the expressions. In this case, it it the aggregate
expression "max(...)" and we need to recursively expand its children inputs.
Author: Nong Li <nongli@gmail.com>
Closes#9343 from nongli/spark-11329.
From Reynold in the thread 'Exception when using some aggregate operators' (http://search-hadoop.com/m/q3RTt0xFr22nXB4/):
I don't think these are bugs. The SQL standard for average is "avg", not "mean". Similarly, a distinct count is supposed to be written as "count(distinct col)", not "countDistinct(col)".
We can, however, make "mean" an alias for "avg" to improve compatibility between DataFrame and SQL.
Author: tedyu <yuzhihong@gmail.com>
Closes#9332 from ted-yu/master.
When describe temporary function, spark would return 'Unable to find function', this is not right.
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#9277 from adrian-wang/functionreg.
JIRA: https://issues.apache.org/jira/browse/SPARK-9298
This patch adds pearson correlation aggregation function based on `AggregateExpression2`.
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#8587 from viirya/corr_aggregation.
This PR fixes two issues:
1. `PhysicalRDD.outputsUnsafeRows` is always `false`
Thus a `ConvertToUnsafe` operator is often required even if the underlying data source relation does output `UnsafeRow`.
1. Internal/external row conversion for `HadoopFsRelation` is kinda messy
Currently we're using `HadoopFsRelation.needConversion` and [dirty type erasure hacks][1] to indicate whether the relation outputs external row or internal row and apply external-to-internal conversion when necessary. Basically, all builtin `HadoopFsRelation` data sources, i.e. Parquet, JSON, ORC, and Text output `InternalRow`, while typical external `HadoopFsRelation` data sources, e.g. spark-avro and spark-csv, output `Row`.
This PR adds a `private[sql]` interface method `HadoopFsRelation.buildInternalScan`, which by default invokes `HadoopFsRelation.buildScan` and converts `Row`s to `UnsafeRow`s (which are also `InternalRow`s). All builtin `HadoopFsRelation` data sources override this method and directly output `UnsafeRow`s. In this way, now `HadoopFsRelation` always produces `UnsafeRow`s. Thus `PhysicalRDD.outputsUnsafeRows` can be properly set by checking whether the underlying data source is a `HadoopFsRelation`.
A remaining question is that, can we assume that all non-builtin `HadoopFsRelation` data sources output external rows? At least all well known ones do so. However it's possible that some users implemented their own `HadoopFsRelation` data sources that leverages `InternalRow` and thus all those unstable internal data representations. If this assumption is safe, we can deprecate `HadoopFsRelation.needConversion` and cleanup some more conversion code (like [here][2] and [here][3]).
This PR supersedes #9125.
Follow-ups:
1. Makes JSON and ORC data sources output `UnsafeRow` directly
1. Makes `HiveTableScan` output `UnsafeRow` directly
This is related to 1 since ORC data source shares the same `Writable` unwrapping code with `HiveTableScan`.
[1]: https://github.com/apache/spark/blob/v1.5.1/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala#L353
[2]: https://github.com/apache/spark/blob/v1.5.1/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala#L331-L335
[3]: https://github.com/apache/spark/blob/v1.5.1/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala#L630-L669
Author: Cheng Lian <lian@databricks.com>
Closes#9305 from liancheng/spark-11345.unsafe-hadoop-fs-relation.
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.
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.
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.
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.
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.
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.
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.
`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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
**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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
JIRA: https://issues.apache.org/jira/browse/SPARK-9170
`StandardStructObjectInspector` will implicitly lowercase column names. But I think Orc format doesn't have such requirement. In fact, there is a `OrcStructInspector` specified for Orc format. We should use it when serialize rows to Orc file. It can be case preserving when writing ORC files.
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#7520 from viirya/use_orcstruct.
This PR takes over https://github.com/apache/spark/pull/8389.
This PR improves `checkAnswer` to print the partially analyzed plan in addition to the user friendly error message, in order to aid debugging failing tests.
In doing so, I ran into a conflict with the various ways that we bring a SQLContext into the tests. Depending on the trait we refer to the current context as `sqlContext`, `_sqlContext`, `ctx` or `hiveContext` with access modifiers `public`, `protected` and `private` depending on the defining class.
I propose we refactor as follows:
1. All tests should only refer to a `protected sqlContext` when testing general features, and `protected hiveContext` when it is a method that only exists on a `HiveContext`.
2. All tests should only import `testImplicits._` (i.e., don't import `TestHive.implicits._`)
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#8584 from cloud-fan/cleanupTests.
They don't bring much value since we now have better unit test coverage for hash joins. This will also help reduce the test time.
Author: Reynold Xin <rxin@databricks.com>
Closes#8542 from rxin/SPARK-10378.
This fixes the problem that scanning partitioned table causes driver have a high memory pressure and takes down the cluster. Also, with this fix, we will be able to correctly show the query plan of a query consuming partitioned tables.
https://issues.apache.org/jira/browse/SPARK-10339https://issues.apache.org/jira/browse/SPARK-10334
Finally, this PR squeeze in a "quick fix" for SPARK-10301. It is not a real fix, but it just throw a better error message to let user know what to do.
Author: Yin Huai <yhuai@databricks.com>
Closes#8515 from yhuai/partitionedTableScan.
SparkHadoopUtil contains methods that use reflection to work around TaskAttemptContext binary incompatibilities between Hadoop 1.x and 2.x. We should use these methods in more places.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#8499 from JoshRosen/use-hadoop-reflection-in-more-places.
Replace `JavaConversions` implicits with `JavaConverters`
Most occurrences I've seen so far are necessary conversions; a few have been avoidable. None are in critical code as far as I see, yet.
Author: Sean Owen <sowen@cloudera.com>
Closes#8033 from srowen/SPARK-9613.
We misunderstood the Julian days and nanoseconds of the day in parquet (as TimestampType) from Hive/Impala, they are overlapped, so can't be added together directly.
In order to avoid the confusing rounding when do the converting, we use `2440588` as the Julian Day of epoch of unix timestamp (which should be 2440587.5).
Author: Davies Liu <davies@databricks.com>
Author: Cheng Lian <lian@databricks.com>
Closes#8400 from davies/timestamp_parquet.
This patch adds an analyzer rule to ensure that set operations (union, intersect, and except) are only applied to tables with the same number of columns. Without this rule, there are scenarios where invalid queries can return incorrect results instead of failing with error messages; SPARK-9813 provides one example of this problem. In other cases, the invalid query can crash at runtime with extremely confusing exceptions.
I also performed a bit of cleanup to refactor some of those logical operators' code into a common `SetOperation` base class.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#7631 from JoshRosen/SPARK-9293.
In `HiveComparisionTest`s it is possible to fail a query of the form `SELECT * FROM dest1`, where `dest1` is the query that is actually computing the incorrect results. To aid debugging this patch improves the harness to also print these query plans and their results.
Author: Michael Armbrust <michael@databricks.com>
Closes#8388 from marmbrus/generatedTables.
Currently, we eagerly attempt to resolve functions, even before their children are resolved. However, this is not valid in cases where we need to know the types of the input arguments (i.e. when resolving Hive UDFs).
As a fix, this PR delays function resolution until the functions children are resolved. This change also necessitates a change to the way we resolve aggregate expressions that are not in aggregate operators (e.g., in `HAVING` or `ORDER BY` clauses). Specifically, we can't assume that these misplaced functions will be resolved, allowing us to differentiate aggregate functions from normal functions. To compensate for this change we now attempt to resolve these unresolved expressions in the context of the aggregate operator, before checking to see if any aggregate expressions are present.
Author: Michael Armbrust <michael@databricks.com>
Closes#8371 from marmbrus/hiveUDFResolution.
Move `test.org.apache.spark.sql.hive` package tests to apparent intended `org.apache.spark.sql.hive` as they don't intend to test behavior from outside org.apache.spark.*
Alternate take, per discussion at https://github.com/apache/spark/pull/8051
I think this is what vanzin and I had in mind but also CC rxin to cross-check, as this does indeed depend on whether these tests were accidentally in this package or not. Testing from a `test.org.apache.spark` package is legitimate but didn't seem to be the intent here.
Author: Sean Owen <sowen@cloudera.com>
Closes#8307 from srowen/SPARK-9758.
This PR refactors `ParquetHiveCompatibilitySuite` so that it's easier to add new test cases.
Hit two bugs, SPARK-10177 and HIVE-11625, while working on this, added test cases for them and marked as ignored for now. SPARK-10177 will be addressed in a separate PR.
Author: Cheng Lian <lian@databricks.com>
Closes#8392 from liancheng/spark-8580/parquet-hive-compat-tests.
https://issues.apache.org/jira/browse/SPARK-10092
This pr is a follow-up one for Multi-DB support. It has the following changes:
* `HiveContext.refreshTable` now accepts `dbName.tableName`.
* `HiveContext.analyze` now accepts `dbName.tableName`.
* `CreateTableUsing`, `CreateTableUsingAsSelect`, `CreateTempTableUsing`, `CreateTempTableUsingAsSelect`, `CreateMetastoreDataSource`, and `CreateMetastoreDataSourceAsSelect` all take `TableIdentifier` instead of the string representation of table name.
* When you call `saveAsTable` with a specified database, the data will be saved to the correct location.
* Explicitly do not allow users to create a temporary with a specified database name (users cannot do it before).
* When we save table to metastore, we also check if db name and table name can be accepted by hive (using `MetaStoreUtils.validateName`).
Author: Yin Huai <yhuai@databricks.com>
Closes#8324 from yhuai/saveAsTableDB.
A few minor changes:
1. Improved documentation
2. Rename apply(distinct....) to distinct.
3. Changed MutableAggregationBuffer from a trait to an abstract class.
4. Renamed returnDataType to dataType to be more consistent with other expressions.
And unrelated to UDAFs:
1. Renamed file names in expressions to use suffix "Expressions" to be more consistent.
2. Moved regexp related expressions out to its own file.
3. Renamed StringComparison => StringPredicate.
Author: Reynold Xin <rxin@databricks.com>
Closes#8321 from rxin/SPARK-9242.
Speculation hates direct output committer, as there are multiple corner cases that may cause data corruption and/or data loss.
Please see this [PR comment] [1] for more details.
[1]: https://github.com/apache/spark/pull/8191#issuecomment-131598385
Author: Cheng Lian <lian@databricks.com>
Closes#8317 from liancheng/spark-9899/speculation-hates-direct-output-committer.
Scala process API has a known bug ([SI-8768] [1]), which may be the reason why several test suites which fork sub-processes are flaky.
This PR replaces Scala process API with Java process API in `CliSuite`, `HiveSparkSubmitSuite`, and `HiveThriftServer2` related test suites to see whether it fix these flaky tests.
[1]: https://issues.scala-lang.org/browse/SI-8768
Author: Cheng Lian <lian@databricks.com>
Closes#8168 from liancheng/spark-9939/use-java-process-api.
Parquet hard coded a JUL logger which always writes to stdout. This PR redirects it via SLF4j JUL bridge handler, so that we can control Parquet logs via `log4j.properties`.
This solution is inspired by https://github.com/Parquet/parquet-mr/issues/390#issuecomment-46064909.
Author: Cheng Lian <lian@databricks.com>
Closes#8196 from liancheng/spark-8118/redirect-parquet-jul.
https://issues.apache.org/jira/browse/SPARK-9592#8113 has the fundamental fix. But, if we want to minimize the number of changed lines, we can go with this one. Then, in 1.6, we merge #8113.
Author: Yin Huai <yhuai@databricks.com>
Closes#8172 from yhuai/lastFix and squashes the following commits:
b28c42a [Yin Huai] Regression test.
af87086 [Yin Huai] Fix last.
This PR enforce dynamic partition column data type requirements by adding analysis rules.
JIRA: https://issues.apache.org/jira/browse/SPARK-8887
Author: Yijie Shen <henry.yijieshen@gmail.com>
Closes#8201 from yjshen/dynamic_partition_columns.
A fundamental limitation of the existing SQL tests is that *there is simply no way to create your own `SparkContext`*. This is a serious limitation because the user may wish to use a different master or config. As a case in point, `BroadcastJoinSuite` is entirely commented out because there is no way to make it pass with the existing infrastructure.
This patch removes the singletons `TestSQLContext` and `TestData`, and instead introduces a `SharedSQLContext` that starts a context per suite. Unfortunately the singletons were so ingrained in the SQL tests that this patch necessarily needed to touch *all* the SQL test files.
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Author: Andrew Or <andrew@databricks.com>
Closes#8111 from andrewor14/sql-tests-refactor.
PR #7967 enables us to save data source relations to metastore in Hive compatible format when possible. But it fails to persist Parquet relations with decimal column(s) to Hive metastore of versions lower than 1.2.0. This is because `ParquetHiveSerDe` in Hive versions prior to 1.2.0 doesn't support decimal. This PR checks for this case and falls back to Spark SQL specific metastore table format.
Author: Yin Huai <yhuai@databricks.com>
Author: Cheng Lian <lian@databricks.com>
Closes#8130 from liancheng/spark-9757/old-hive-parquet-decimal.
I think that we should pass additional configuration flags to disable the driver UI and Master REST server in SparkSubmitSuite and HiveSparkSubmitSuite. This might cut down on port-contention-related flakiness in Jenkins.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#8124 from JoshRosen/disable-ui-in-sparksubmitsuite.
Refactor Utils class and create ShutdownHookManager.
NOTE: Wasn't able to run /dev/run-tests on windows machine.
Manual tests were conducted locally using custom log4j.properties file with Redis appender and logstash formatter (bundled in the fat-jar submitted to spark)
ex:
log4j.rootCategory=WARN,console,redis
log4j.appender.console=org.apache.log4j.ConsoleAppender
log4j.appender.console.target=System.err
log4j.appender.console.layout=org.apache.log4j.PatternLayout
log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n
log4j.logger.org.eclipse.jetty=WARN
log4j.logger.org.eclipse.jetty.util.component.AbstractLifeCycle=ERROR
log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
log4j.logger.org.apache.spark.graphx.Pregel=INFO
log4j.appender.redis=com.ryantenney.log4j.FailoverRedisAppender
log4j.appender.redis.endpoints=hostname:port
log4j.appender.redis.key=mykey
log4j.appender.redis.alwaysBatch=false
log4j.appender.redis.layout=net.logstash.log4j.JSONEventLayoutV1
Author: michellemay <mlemay@gmail.com>
Closes#8109 from michellemay/SPARK-9826.
If the correct parameter is not provided, Hive will run into an error
because it calls methods that are specific to the local filesystem to
copy the data.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#8086 from vanzin/SPARK-9804.
This PR adds a hacky workaround for PARQUET-201, and should be removed once we upgrade to parquet-mr 1.8.1 or higher versions.
In Parquet, not all types of columns can be used for filter push-down optimization. The set of valid column types is controlled by `ValidTypeMap`. Unfortunately, in parquet-mr 1.7.0 and prior versions, this limitation is too strict, and doesn't allow `BINARY (ENUM)` columns to be pushed down. On the other hand, `BINARY (ENUM)` is commonly seen in Parquet files written by libraries like `parquet-avro`.
This restriction is problematic for Spark SQL, because Spark SQL doesn't have a type that maps to Parquet `BINARY (ENUM)` directly, and always converts `BINARY (ENUM)` to Catalyst `StringType`. Thus, a predicate involving a `BINARY (ENUM)` is recognized as one involving a string field instead and can be pushed down by the query optimizer. Such predicates are actually perfectly legal except that it fails the `ValidTypeMap` check.
The workaround added here is relaxing `ValidTypeMap` to include `BINARY (ENUM)`. I also took the chance to simplify `ParquetCompatibilityTest` a little bit when adding regression test.
Author: Cheng Lian <lian@databricks.com>
Closes#8107 from liancheng/spark-9407/parquet-enum-filter-push-down.
This patch adds a new `SortMergeOuterJoin` operator that performs left and right outer joins using sort merge join. It also refactors `SortMergeJoin` in order to improve performance and code clarity.
Along the way, I also performed a couple pieces of minor cleanup and optimization:
- Rename the `HashJoin` physical planner rule to `EquiJoinSelection`, since it's also used for non-hash joins.
- Rewrite the comment at the top of `HashJoin` to better explain the precedence for choosing join operators.
- Update `JoinSuite` to use `SqlTestUtils.withConf` for changing SQLConf settings.
This patch incorporates several ideas from adrian-wang's patch, #5717.
Closes#5717.
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Author: Josh Rosen <joshrosen@databricks.com>
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#7904 from JoshRosen/outer-join-smj and squashes 1 commits.
There are a few changes in this pull request:
1. Moved all data sources to execution.datasources, except the public JDBC APIs.
2. In order to maintain backward compatibility from 1, added a backward compatibility translation map in data source resolution.
3. Moved ui and metric package into execution.
4. Added more documentation on some internal classes.
5. Renamed DataSourceRegister.format -> shortName.
6. Added "override" modifier on shortName.
7. Removed IntSQLMetric.
Author: Reynold Xin <rxin@databricks.com>
Closes#8056 from rxin/SPARK-9763 and squashes the following commits:
9df4801 [Reynold Xin] Removed hardcoded name in test cases.
d9babc6 [Reynold Xin] Shorten.
e484419 [Reynold Xin] Removed VisibleForTesting.
171b812 [Reynold Xin] MimaExcludes.
2041389 [Reynold Xin] Compile ...
79dda42 [Reynold Xin] Compile.
0818ba3 [Reynold Xin] Removed IntSQLMetric.
c46884f [Reynold Xin] Two more fixes.
f9aa88d [Reynold Xin] [SPARK-9763][SQL] Minimize exposure of internal SQL classes.
Users currently have to provide the full class name for external data sources, like:
`sqlContext.read.format("com.databricks.spark.avro").load(path)`
This allows external data source packages to register themselves using a Service Loader so that they can add custom alias like:
`sqlContext.read.format("avro").load(path)`
This makes it so that using external data source packages uses the same format as the internal data sources like parquet, json, etc.
Author: Joseph Batchik <joseph.batchik@cloudera.com>
Author: Joseph Batchik <josephbatchik@gmail.com>
Closes#7802 from JDrit/service_loader and squashes the following commits:
49a01ec [Joseph Batchik] fixed a couple of format / error bugs
e5e93b2 [Joseph Batchik] modified rat file to only excluded added services
72b349a [Joseph Batchik] fixed error with orc data source actually
9f93ea7 [Joseph Batchik] fixed error with orc data source
87b7f1c [Joseph Batchik] fixed typo
101cd22 [Joseph Batchik] removing unneeded changes
8f3cf43 [Joseph Batchik] merged in changes
b63d337 [Joseph Batchik] merged in master
95ae030 [Joseph Batchik] changed the new trait to be used as a mixin for data source to register themselves
74db85e [Joseph Batchik] reformatted class loader
ac2270d [Joseph Batchik] removing some added test
a6926db [Joseph Batchik] added test cases for data source loader
208a2a8 [Joseph Batchik] changes to do error catching if there are multiple data sources
946186e [Joseph Batchik] started working on service loader
This PR enables converting interval term in HiveQL to CalendarInterval Literal.
JIRA: https://issues.apache.org/jira/browse/SPARK-9728
Author: Yijie Shen <henry.yijieshen@gmail.com>
Closes#8034 from yjshen/interval_hiveql and squashes the following commits:
7fe9a5e [Yijie Shen] declare throw exception and add unit test
fce7795 [Yijie Shen] convert hiveql interval term into CalendarInterval literal
Previously, we would open a new file for each new dynamic written out using `HadoopFsRelation`. For formats like parquet this is very costly due to the buffers required to get good compression. In this PR I refactor the code allowing us to fall back on an external sort when many partitions are seen. As such each task will open no more than `spark.sql.sources.maxFiles` files. I also did the following cleanup:
- Instead of keying the file HashMap on an expensive to compute string representation of the partition, we now use a fairly cheap UnsafeProjection that avoids heap allocations.
- The control flow for instantiating and invoking a writer container has been simplified. Now instead of switching in two places based on the use of partitioning, the specific writer container must implement a single method `writeRows` that is invoked using `runJob`.
- `InternalOutputWriter` has been removed. Instead we have a `private[sql]` method `writeInternal` that converts and calls the public method. This method can be overridden by internal datasources to avoid the conversion. This change remove a lot of code duplication and per-row `asInstanceOf` checks.
- `commands.scala` has been split up.
Author: Michael Armbrust <michael@databricks.com>
Closes#8010 from marmbrus/fsWriting and squashes the following commits:
00804fe [Michael Armbrust] use shuffleMemoryManager.pageSizeBytes
775cc49 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into fsWriting
17b690e [Michael Armbrust] remove comment
40f0372 [Michael Armbrust] address comments
f5675bd [Michael Armbrust] char -> string
7e2d0a4 [Michael Armbrust] make sure we close current writer
8100100 [Michael Armbrust] delete empty commands.scala
71cc717 [Michael Armbrust] update comment
8ec75ac [Michael Armbrust] [SPARK-8890][SQL] Fallback on sorting when writing many dynamic partitions
All data sources show up as "PhysicalRDD" in physical plan explain. It'd be better if we can show the name of the data source.
Without this patch:
```
== Physical Plan ==
NewAggregate with UnsafeHybridAggregationIterator ArrayBuffer(date#0, cat#1) ArrayBuffer((sum(CAST((CAST(count#2, IntegerType) + 1), LongType))2,mode=Final,isDistinct=false))
Exchange hashpartitioning(date#0,cat#1)
NewAggregate with UnsafeHybridAggregationIterator ArrayBuffer(date#0, cat#1) ArrayBuffer((sum(CAST((CAST(count#2, IntegerType) + 1), LongType))2,mode=Partial,isDistinct=false))
PhysicalRDD [date#0,cat#1,count#2], MapPartitionsRDD[3] at
```
With this patch:
```
== Physical Plan ==
TungstenAggregate(key=[date#0,cat#1], value=[(sum(CAST((CAST(count#2, IntegerType) + 1), LongType)),mode=Final,isDistinct=false)]
Exchange hashpartitioning(date#0,cat#1)
TungstenAggregate(key=[date#0,cat#1], value=[(sum(CAST((CAST(count#2, IntegerType) + 1), LongType)),mode=Partial,isDistinct=false)]
ConvertToUnsafe
Scan ParquetRelation[file:/scratch/rxin/spark/sales4][date#0,cat#1,count#2]
```
Author: Reynold Xin <rxin@databricks.com>
Closes#8024 from rxin/SPARK-9733 and squashes the following commits:
811b90e [Reynold Xin] Fixed Python test case.
52cab77 [Reynold Xin] Cast.
eea9ccc [Reynold Xin] Fix test case.
fcecb22 [Reynold Xin] [SPARK-9733][SQL] Improve explain message for data source scan node.
Previously, we use 64MB as the default page size, which was way too big for a lot of Spark applications (especially for single node).
This patch changes it so that the default page size, if unset by the user, is determined by the number of cores available and the total execution memory available.
Author: Reynold Xin <rxin@databricks.com>
Closes#8012 from rxin/pagesize and squashes the following commits:
16f4756 [Reynold Xin] Fixed failing test.
5afd570 [Reynold Xin] private...
0d5fb98 [Reynold Xin] Update default value.
674a6cd [Reynold Xin] Address review feedback.
dc00e05 [Reynold Xin] Merge with master.
73ebdb6 [Reynold Xin] [SPARK-9700] Pick default page size more intelligently.
