[Spark-14761][SQL][WIP] Reject invalid join methods when join columns are not specified in PySpark DataFrame join. #12691
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
bkpathak
changed the title
|
Can one of the admins verify this patch? |
|
Per my comments in the JIRA, I'd add a regression test for this since it's really easy to do. See the examples in |
|
Thank's Josh. I'll add the regression test. |
|
@JoshRosen I have added regression test. Please have a look. |
|
Hi @JoshRosen, could you please look at the pull request. |
…m spark-config.sh ## What changes were proposed in this pull request? During startup of Spark standalone, the script file spark-config.sh appends to the PYTHONPATH and can be sourced many times, causing duplicates in the path. This change adds a env flag that is set when the PYTHONPATH is appended so it will happen only one time. ## How was this patch tested? Manually started standalone master/worker and verified PYTHONPATH has no duplicate entries. Author: Bryan Cutler <cutlerb@gmail.com> Closes apache#15028 from BryanCutler/fix-duplicate-pythonpath-SPARK-17336.
…ps from 5 to 2. ## What changes were proposed in this pull request? apache#14956 reduced default k-means|| init steps to 2 from 5 only for spark.mllib package, we should also do same change for spark.ml and PySpark. ## How was this patch tested? Existing tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes apache#15050 from yanboliang/spark-17389.
…n OOMs ## What changes were proposed in this pull request? This is a trivial patch that catches all `OutOfMemoryError` while building the broadcast hash relation and rethrows it by wrapping it in a nice error message. ## How was this patch tested? Existing Tests Author: Sameer Agarwal <sameerag@cs.berkeley.edu> Closes apache#14979 from sameeragarwal/broadcast-join-error.
The `TaskMetricsUIData.updatedBlockStatuses` field is assigned to but never read, increasing the memory consumption of the web UI. We should remove this field. Author: Josh Rosen <joshrosen@databricks.com> Closes apache#15038 from JoshRosen/remove-updated-block-statuses-from-TaskMetricsUIData.
## What changes were proposed in this pull request? DAG will list all partitions in the graph, it is too slow and hard to see all graph. Always we don't want to see all partitions，we just want to see the relations of DAG graph. So I just show 2 root nodes for Rdds. Before this PR, the DAG graph looks like [dag1.png](https://issues.apache.org/jira/secure/attachment/12824702/dag1.png), [dag3.png](https://issues.apache.org/jira/secure/attachment/12825456/dag3.png), after this PR, the DAG graph looks like [dag2.png](https://issues.apache.org/jira/secure/attachment/12824703/dag2.png),[dag4.png](https://issues.apache.org/jira/secure/attachment/12825457/dag4.png) Author: cenyuhai <cenyuhai@didichuxing.com> Author: 岑玉海 <261810726@qq.com> Closes apache#14737 from cenyuhai/SPARK-17171.
…er without sortBy ## What changes were proposed in this pull request? if there are many rdds in some situations,the sort will loss he performance servely,actually we needn't sort the rdds , we can just scan the rdds one time to gain the same goal. ## How was this patch tested? manual tests Author: codlife <1004910847@qq.com> Closes apache#15039 from codlife/master.
Code is equivalent, but map comprehency is most of the time faster than a map. Author: Gaetan Semet <gaetan@xeberon.net> Closes apache#14863 from Stibbons/map_comprehension.
…eys" error message in PairRDDfunctions ## What changes were proposed in this pull request? In order to avoid confusing user, error message in `PairRDDfunctions` `Default partitioner cannot partition array keys.` is updated, the one in `partitionBy` is replaced with `Specified partitioner cannot partition array keys.` other is replaced with `Specified or default partitioner cannot partition array keys.` ## How was this patch tested? N/A Author: WeichenXu <WeichenXu123@outlook.com> Closes apache#15045 from WeichenXu123/fix_partitionBy_error_message.
…che the whole RDD in memory ## What changes were proposed in this pull request? MemoryStore may throw OutOfMemoryError when trying to cache a super big RDD that cannot fit in memory. ``` scala> sc.parallelize(1 to 1000000000, 100).map(x => new Array[Long](1000)).cache().count() java.lang.OutOfMemoryError: Java heap space at $line14.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$anonfun$1.apply(<console>:24) at $line14.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$anonfun$1.apply(<console>:23) at scala.collection.Iterator$$anon$11.next(Iterator.scala:409) at scala.collection.Iterator$JoinIterator.next(Iterator.scala:232) at org.apache.spark.storage.memory.PartiallyUnrolledIterator.next(MemoryStore.scala:683) at org.apache.spark.InterruptibleIterator.next(InterruptibleIterator.scala:43) at org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1684) at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1134) at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1134) at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1915) at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1915) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70) at org.apache.spark.scheduler.Task.run(Task.scala:86) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745) ``` Spark MemoryStore uses SizeTrackingVector as a temporary unrolling buffer to store all input values that it has read so far before transferring the values to storage memory cache. The problem is that when the input RDD is too big for caching in memory, the temporary unrolling memory SizeTrackingVector is not garbage collected in time. As SizeTrackingVector can occupy all available storage memory, it may cause the executor JVM to run out of memory quickly. More info can be found at https://issues.apache.org/jira/browse/SPARK-17503 ## How was this patch tested? Unit test and manual test. ### Before change Heap memory consumption <img width="702" alt="screen shot 2016-09-12 at 4 16 15 pm" src="https://cloud.githubusercontent.com/assets/2595532/18429524/60d73a26-7906-11e6-9768-6f286f5c58c8.png"> Heap dump <img width="1402" alt="screen shot 2016-09-12 at 4 34 19 pm" src="https://cloud.githubusercontent.com/assets/2595532/18429577/cbc1ef20-7906-11e6-847b-b5903f450b3b.png"> ### After change Heap memory consumption <img width="706" alt="screen shot 2016-09-12 at 4 29 10 pm" src="https://cloud.githubusercontent.com/assets/2595532/18429503/4abe9342-7906-11e6-844a-b2f815072624.png"> Author: Sean Zhong <seanzhong@databricks.com> Closes apache#15056 from clockfly/memory_store_leak.
…removal This patch makes three minor refactorings to the BlockManager: - Move the `if (info.tellMaster)` check out of `reportBlockStatus`; this fixes an issue where a debug logging message would incorrectly claim to have reported a block status to the master even though no message had been sent (in case `info.tellMaster == false`). This also makes it easier to write code which unconditionally sends block statuses to the master (which is necessary in another patch of mine). - Split `removeBlock()` into two methods, the existing method and an internal `removeBlockInternal()` method which is designed to be called by internal code that already holds a write lock on the block. This is also needed by a followup patch. - Instead of calling `getCurrentBlockStatus()` in `removeBlock()`, just pass `BlockStatus.empty`; the block status should always be empty following complete removal of a block. These changes were originally authored as part of a bug fix patch which is targeted at branch-2.0 and master; I've split them out here into their own separate PR in order to make them easier to review and so that the behavior-changing parts of my other patch can be isolated to their own PR. Author: Josh Rosen <joshrosen@databricks.com> Closes apache#15036 from JoshRosen/cache-failure-race-conditions-refactorings-only.
This patch makes a handful of post-Spark-2.0 MiMa exclusion and build updates. It should be merged to master and a subset of it should be picked into branch-2.0 in order to test Spark 2.0.1-SNAPSHOT. - Remove the ` sketch`, `mllibLocal`, and `streamingKafka010` from the list of excluded subprojects so that MiMa checks them. - Remove now-unnecessary special-case handling of the Kafka 0.8 artifact in `mimaSettings`. - Move the exclusion added in SPARK-14743 from `v20excludes` to `v21excludes`, since that patch was only merged into master and not branch-2.0. - Add exclusions for an API change introduced by SPARK-17096 / apache#14675. - Add missing exclusions for the `o.a.spark.internal` and `o.a.spark.sql.internal` packages. Author: Josh Rosen <joshrosen@databricks.com> Closes apache#15061 from JoshRosen/post-2.0-mima-changes.
…ng entire job ## What changes were proposed in this pull request? In Spark's `RDD.getOrCompute` we first try to read a local copy of a cached RDD block, then a remote copy, and only fall back to recomputing the block if no cached copy (local or remote) can be read. This logic works correctly in the case where no remote copies of the block exist, but if there _are_ remote copies and reads of those copies fail (due to network issues or internal Spark bugs) then the BlockManager will throw a `BlockFetchException` that will fail the task (and which could possibly fail the whole job if the read failures keep occurring). In the cases of TorrentBroadcast and task result fetching we really do want to fail the entire job in case no remote blocks can be fetched, but this logic is inappropriate for reads of cached RDD blocks because those can/should be recomputed in case cached blocks are unavailable. Therefore, I think that the `BlockManager.getRemoteBytes()` method should never throw on remote fetch errors and, instead, should handle failures by returning `None`. ## How was this patch tested? Block manager changes should be covered by modified tests in `BlockManagerSuite`: the old tests expected exceptions to be thrown on failed remote reads, while the modified tests now expect `None` to be returned from the `getRemote*` method. I also manually inspected all usages of `BlockManager.getRemoteValues()`, `getRemoteBytes()`, and `get()` to verify that they correctly pattern-match on the result and handle `None`. Note that these `None` branches are already exercised because the old `getRemoteBytes` returned `None` when no remote locations for the block could be found (which could occur if an executor died and its block manager de-registered with the master). Author: Josh Rosen <joshrosen@databricks.com> Closes apache#15037 from JoshRosen/SPARK-17485.
## What changes were proposed in this pull request? When there is any Python UDF in the Project between Sort and Limit, it will be collected into TakeOrderedAndProjectExec, ExtractPythonUDFs failed to pull the Python UDFs out because QueryPlan.expressions does not include the expression inside Option[Seq[Expression]]. Ideally, we should fix the `QueryPlan.expressions`, but tried with no luck (it always run into infinite loop). In PR, I changed the TakeOrderedAndProjectExec to no use Option[Seq[Expression]] to workaround it. cc JoshRosen ## How was this patch tested? Added regression test. Author: Davies Liu <davies@databricks.com> Closes apache#15030 from davies/all_expr.
…mitter(s) ## What changes were proposed in this pull request? This PR proposes to close some stale PRs and ones suggested to be closed by committer(s) Closes apache#10052 Closes apache#11079 Closes apache#12661 Closes apache#12772 Closes apache#12958 Closes apache#12990 Closes apache#13409 Closes apache#13779 Closes apache#13811 Closes apache#14577 Closes apache#14714 Closes apache#14875 Closes apache#15020 ## How was this patch tested? N/A Author: hyukjinkwon <gurwls223@gmail.com> Closes apache#15057 from HyukjinKwon/closing-stale-pr.
## What changes were proposed in this pull request? CollectLimit.execute() incorrectly omits per-partition limits, leading to performance regressions in case this case is hit (which should not happen in normal operation, but can occur in some cases (see apache#15068 for one example). ## How was this patch tested? Regression test in SQLQuerySuite that asserts the number of records scanned from the input RDD. Author: Josh Rosen <joshrosen@databricks.com> Closes apache#15070 from JoshRosen/SPARK-17515.
…ateExec ## What changes were proposed in this pull request? In `ReorderAssociativeOperator` rule, we extract foldable expressions with Add/Multiply arithmetics, and replace with eval literal. For example, `(a + 1) + (b + 2)` is optimized to `(a + b + 3)` by this rule. For aggregate operator, output expressions should be derived from groupingExpressions, current implemenation of `ReorderAssociativeOperator` rule may break this promise. A instance could be: ``` SELECT ((t1.a + 1) + (t2.a + 2)) AS out_col FROM testdata2 AS t1 INNER JOIN testdata2 AS t2 ON (t1.a = t2.a) GROUP BY (t1.a + 1), (t2.a + 2) ``` `((t1.a + 1) + (t2.a + 2))` is optimized to `(t1.a + t2.a + 3)`, which could not be derived from `ExpressionSet((t1.a +1), (t2.a + 2))`. Maybe we should improve the rule of `ReorderAssociativeOperator` by adding a GroupingExpressionSet to keep Aggregate.groupingExpressions, and respect these expressions during the optimize stage. ## How was this patch tested? Add new test case in `ReorderAssociativeOperatorSuite`. Author: jiangxingbo <jiangxb1987@gmail.com> Closes apache#14917 from jiangxb1987/rao.
## What changes were proposed in this pull request? If a user provides listeners inside the Hive Conf, the configuration for these listeners are passed to the Hive Execution Client as well. This may cause issues for two reasons: 1. The Execution Client will actually generate garbage 2. The listener class needs to be both in the Spark Classpath and Hive Classpath This PR empties the listener configurations in `HiveUtils.newTemporaryConfiguration` so that the execution client will not contain the listener confs, but the metadata client will. ## How was this patch tested? Unit tests Author: Burak Yavuz <brkyvz@gmail.com> Closes apache#15086 from brkyvz/null-listeners.
### What changes were proposed in this pull request? Statistics is missing in the output of `DESCRIBE FORMATTED`. This PR is to add it. After the PR, the output will be like: ``` +----------------------------+----------------------------------------------------------------------------------------------------------------------+-------+ |col_name |data_type |comment| +----------------------------+----------------------------------------------------------------------------------------------------------------------+-------+ |key |string |null | |value |string |null | | | | | |# Detailed Table Information| | | |Database: |default | | |Owner: |xiaoli | | |Create Time: |Tue Sep 13 14:36:57 PDT 2016 | | |Last Access Time: |Wed Dec 31 16:00:00 PST 1969 | | |Location: |file:/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/warehouse-9982e1db-df17-4376-a140-dbbee0203d83/texttable| | |Table Type: |MANAGED | | |Statistics: |sizeInBytes=5812, rowCount=500, isBroadcastable=false | | |Table Parameters: | | | | rawDataSize |-1 | | | numFiles |1 | | | transient_lastDdlTime |1473802620 | | | totalSize |5812 | | | COLUMN_STATS_ACCURATE |false | | | numRows |-1 | | | | | | |# Storage Information | | | |SerDe Library: |org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe | | |InputFormat: |org.apache.hadoop.mapred.TextInputFormat | | |OutputFormat: |org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat | | |Compressed: |No | | |Storage Desc Parameters: | | | | serialization.format |1 | | +----------------------------+----------------------------------------------------------------------------------------------------------------------+-------+ ``` Also improve the output of statistics in `DESCRIBE EXTENDED` by removing duplicate `Statistics`. Below is the example after the PR: ``` +----------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+ |col_name |data_type |comment| +----------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+ |key |string |null | |value |string |null | | | | | |# Detailed Table Information|CatalogTable( Table: `default`.`texttable` Owner: xiaoli Created: Tue Sep 13 14:38:43 PDT 2016 Last Access: Wed Dec 31 16:00:00 PST 1969 Type: MANAGED Schema: [StructField(key,StringType,true), StructField(value,StringType,true)] Provider: hive Properties: [rawDataSize=-1, numFiles=1, transient_lastDdlTime=1473802726, totalSize=5812, COLUMN_STATS_ACCURATE=false, numRows=-1] Statistics: sizeInBytes=5812, rowCount=500, isBroadcastable=false Storage(Location: file:/private/var/folders/4b/sgmfldk15js406vk7lw5llzw0000gn/T/warehouse-8ea5c5a0-5680-4778-91cb-c6334cf8a708/texttable, InputFormat: org.apache.hadoop.mapred.TextInputFormat, OutputFormat: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat, Serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, Properties: [serialization.format=1]))| | +----------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+ ``` ### How was this patch tested? Manually tested. Author: gatorsmile <gatorsmile@gmail.com> Closes apache#15083 from gatorsmile/descFormattedStats.
## What changes were proposed in this pull request? This PR tries to add a SparkR vignette, which works as a friendly guidance going through the functionality provided by SparkR. ## How was this patch tested? Manual test. Author: junyangq <qianjunyang@gmail.com> Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu> Author: Junyang Qian <junyangq@databricks.com> Closes apache#14980 from junyangq/SPARKR-vignette.
## What changes were proposed in this pull request? The relation between spark.network.timeout and spark.executor.heartbeatInterval should be mentioned in the document. … network timeout] Author: Jagadeesan <as2@us.ibm.com> Closes apache#15042 from jagadeesanas2/SPARK-17449.
…rk API as it was removed from the Scala API prior to Spark 2.0.0 ## What changes were proposed in this pull request? This pull request removes the SparkContext.clearFiles() method from the PySpark API as the method was removed from the Scala API in 8ce645d. Using that method in PySpark leads to an exception as PySpark tries to call the non-existent method on the JVM side. ## How was this patch tested? Existing tests (though none of them tested this particular method). Author: Sami Jaktholm <sjakthol@outlook.com> Closes apache#15081 from sjakthol/pyspark-sc-clearfiles.
## What changes were proposed in this pull request? In the comment, there is redundant `the estimated`. This PR simply remove the redundant comment and adjusts format. Author: wm624@hotmail.com <wm624@hotmail.com> Closes apache#15091 from wangmiao1981/comment.
…ngth which is O(n) ## What changes were proposed in this pull request? Scala's List.length method is O(N) and it makes the gatherCompressibilityStats function O(N^2). Eliminate the List.length calls by writing it in Scala way. https://github.com/scala/scala/blob/2.10.x/src/library/scala/collection/LinearSeqOptimized.scala#L36 As suggested. Extended the fix to HiveInspectors and AggregationIterator classes as well. ## How was this patch tested? Profiled a Spark job and found that CompressibleColumnBuilder is using 39% of the CPU. Out of this 39% CompressibleColumnBuilder->gatherCompressibilityStats is using 23% of it. 6.24% of the CPU is spend on List.length which is called inside gatherCompressibilityStats. After this change we started to save 6.24% of the CPU. Author: Ergin Seyfe <eseyfe@fb.com> Closes apache#15032 from seyfe/gatherCompressibilityStats.
## What changes were proposed in this pull request? This patch introduces three new annotations under InterfaceStability: - Stable - Evolving - Unstable This is inspired by Hadoop's InterfaceStability, and the first step towards switching over to a new API stability annotation framework. ## How was this patch tested? N/A Author: Reynold Xin <rxin@databricks.com> Closes apache#15374 from rxin/SPARK-17800.
…inished ## What changes were proposed in this pull request? This expands calls to Jetty's simple `ServerConnector` constructor to explicitly specify a `ScheduledExecutorScheduler` that makes daemon threads. It should otherwise result in exactly the same configuration, because the other args are copied from the constructor that is currently called. (I'm not sure we should change the Hive Thriftserver impl, but I did anyway.) This also adds `sc.stop()` to the quick start guide example. ## How was this patch tested? Existing tests; _pending_ at least manual verification of the fix. Author: Sean Owen <sowen@cloudera.com> Closes apache#15381 from srowen/SPARK-17707.
## What changes were proposed in this pull request? This PR proposes cleaning up the confusing part in `createRelation` as discussed in https://github.com/apache/spark/pull/12601/files#r80627940 Also, this PR proposes the changes below: - Add documentation for `batchsize` and `isolationLevel`. - Move property names into `JDBCOptions` so that they can be managed in a single place. which were, `fetchsize`, `batchsize`, `isolationLevel` and `driver`. ## How was this patch tested? Existing tests should cover this. Author: hyukjinkwon <gurwls223@gmail.com> Closes apache#15263 from HyukjinKwon/SPARK-14525.
## What changes were proposed in this pull request? Adds the textFile API which exists in DataFrameReader and serves same purpose. ## How was this patch tested? Added corresponding testcase. Author: Prashant Sharma <prashsh1@in.ibm.com> Closes apache#14087 from ScrapCodes/textFile.
…d options in other types ## What changes were proposed in this pull request? This PR includes the changes below: - Support `mode`/`options` in `read.parquet`, `write.parquet`, `read.orc`, `write.orc`, `read.text`, `write.text`, `read.json` and `write.json` APIs - Support other types (logical, numeric and string) as options for `write.df`, `read.df`, `read.parquet`, `write.parquet`, `read.orc`, `write.orc`, `read.text`, `write.text`, `read.json` and `write.json` ## How was this patch tested? Unit tests in `test_sparkSQL.R`/ `utils.R`. Author: hyukjinkwon <gurwls223@gmail.com> Closes apache#15239 from HyukjinKwon/SPARK-17665.
|
Thanks for working on this @bkpathak :) Are you still interested in working on this? If so can you update this to the latest master and then we can try and find a committer to take a more thorough look. Thanks for taking the time to add the regression test @JoshRosen asked you to add and sorry this slipped through the cracks. (Also if you think its ready to be merged once you update it should drop the "[WIP]" part of the PR title so reviewers are more inclined to take a look). |
## What changes were proposed in this pull request?
When execute a Python UDF, we buffer the input row into as queue, then pull them out to join with the result from Python UDF. In the case that Python UDF is slow or the input row is too wide, we could ran out of memory because of the queue. Since we can't flush all the buffers (sockets) between JVM and Python process from JVM side, we can't limit the rows in the queue, otherwise it could deadlock.
This PR will manage the memory used by the queue, spill that into disk when there is no enough memory (also release the memory and disk space as soon as possible).
## How was this patch tested?
Added unit tests. Also manually ran a workload with large input row and slow python UDF (with large broadcast) like this:
```
b = range(1<<24)
add = udf(lambda x: x + len(b), IntegerType())
df = sqlContext.range(1, 1<<26, 1, 4)
print df.select(df.id, lit("adf"*10000).alias("s"), add(df.id).alias("add")).groupBy(length("s")).sum().collect()
```
It ran out of memory (hang because of full GC) before the patch, ran smoothly after the patch.
Author: Davies Liu <davies@databricks.com>
Closes apache#15089 from davies/spill_udf.
## What changes were proposed in this pull request? In practice we cannot guarantee that an `InternalRow` is immutable. This makes the `MutableRow` almost redundant. This PR folds `MutableRow` into `InternalRow`. The code below illustrates the immutability issue with InternalRow: ```scala import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.catalyst.expressions.GenericMutableRow val struct = new GenericMutableRow(1) val row = InternalRow(struct, 1) println(row) scala> [[null], 1] struct.setInt(0, 42) println(row) scala> [[42], 1] ``` This might be somewhat controversial, so feedback is appreciated. ## How was this patch tested? Existing tests. Author: Herman van Hovell <hvanhovell@databricks.com> Closes apache#15333 from hvanhovell/SPARK-17761.
## What changes were proposed in this pull request? In HashJoin, we try to rewrite the join key as Long to improve the performance of finding a match. The rewriting part is not well tested, has a bug that could cause wrong result when there are at least three integral columns in the joining key also the total length of the key exceed 8 bytes. ## How was this patch tested? Added unit test to covering the rewriting with different number of columns and different data types. Manually test the reported case and confirmed that this PR fix the bug. Author: Davies Liu <davies@databricks.com> Closes apache#15390 from davies/rewrite_key.
## What changes were proposed in this pull request? This PR proposes the fix the use of `contains` API which only exists from Scala 2.11. ## How was this patch tested? Manually checked: ```scala scala> val o: Option[Boolean] = None o: Option[Boolean] = None scala> o == Some(false) res17: Boolean = false scala> val o: Option[Boolean] = Some(true) o: Option[Boolean] = Some(true) scala> o == Some(false) res18: Boolean = false scala> val o: Option[Boolean] = Some(false) o: Option[Boolean] = Some(false) scala> o == Some(false) res19: Boolean = true ``` Author: hyukjinkwon <gurwls223@gmail.com> Closes apache#15393 from HyukjinKwon/hotfix.
## What changes were proposed in this pull request? While adding R wrapper for LogisticRegression, I found one extra comment. It is minor and I just remove it. ## How was this patch tested? Unit tests Author: wm624@hotmail.com <wm624@hotmail.com> Closes apache#15391 from wangmiao1981/mlordoc.
…page doesn’t always work ## What changes were proposed in this pull request? Added secondary sorting on stage name for the description column. This provide a clearer behavior in the common case where the Description column only comprises of Stage names instead of the option description value. ## How was this patch tested? manual testing and dev/run-tests Screenshots of sorting on both description and stage name as well as an example of both:      Author: Alex Bozarth <ajbozart@us.ibm.com> Closes apache#15366 from ajbozarth/spark17793.
…ptimalities ## What changes were proposed in this pull request? Fix: - GroupedMeanEvaluator and GroupedSumEvaluator are unused, as is the StudentTCacher support class - CountEvaluator can return a lower bound < 0, when counts can't be negative - MeanEvaluator will actually fail on exactly 1 datum (yields t-test with 0 DOF) - CountEvaluator uses a normal distribution, which may be an inappropriate approximation (leading to above) - Test for SumEvaluator asserts incorrect expected sums – e.g. after observing 10% of data has sum of 2, expectation should be 20, not 38 - CountEvaluator, MeanEvaluator have no unit tests to catch these - Duplication of distribution code across CountEvaluator, GroupedCountEvaluator - The stats in each could use a bit of documentation as I had to guess at them - (Code could use a few cleanups and optimizations too) ## How was this patch tested? Existing and new tests Author: Sean Owen <sowen@cloudera.com> Closes apache#15341 from srowen/SPARK-17768.
,[dag4.png](https://issues.apache.org/jira/secure/attachment/12825457/dag4.png){kind=link}
## What changes were proposed in this pull request?
This PR modified the test case `test("script")` to use resource path for `test_script.sh`. Make the test case portable (even in IntelliJ).
## How was this patch tested?
Passed the test case.
Before:
Run `test("script")` in IntelliJ:
```
Caused by: org.apache.spark.SparkException: Subprocess exited with status 127. Error: bash: src/test/resources/test_script.sh: No such file or directory
```
After:
Test passed.
Author: Weiqing Yang <yangweiqing001@gmail.com>
Closes apache#15246 from weiqingy/hivetest.
|
Hi @holdenk I am still interested in working on this but it looks like I pull and merged with a master branch instead of rebasing it. Can I close it and open another pull request. Or how should I proceed? |
|
@bkpathak you can just close this and reopen. Although you can fix this with git surgery and a force-push, sometimes it's not worth it if you can just branch, patch the commit(s) you want to keep, and proceed from there. |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
What changes were proposed in this pull request?
In PySpark, the invalid join type will not throw error for the following join:
df1.join(df2, how='not-a-valid-join-type')The signature of the join is:
def join(self, other, on=None, how=None):The existing code completely ignores the
howparameter whenonisNone. This patch will process the arguments passed to join and pass in to JVM Spark SQL Analyzer, which will validate the join type passed.How was this patch tested?
Used manual and existing test suites.