Update #7
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…ed nodes, parentImpurities arrays. Memory calc bug fix. This PR includes some code simplifications and re-organization which will be helpful for implementing random forests. The main changes are that the nodes and parentImpurities arrays are no longer pre-allocated in the main train() method. Also added 2 bug fixes: * maxMemoryUsage calculation * over-allocation of space for bins in DTStatsAggregator for unordered features. Relation to RFs: * Since RFs will be deeper and will therefore be more likely sparse (not full trees), it could be a cost savings to avoid pre-allocating a full tree. * The associated re-organization also reduces bookkeeping, which will make RFs easier to implement. * The return code doneTraining may be generalized to include cases such as nodes ready for local training. Details: No longer pre-allocate parentImpurities array in main train() method. * parentImpurities values are now stored in individual nodes (in Node.stats.impurity). * These were not really needed. They were used in calculateGainForSplit(), but they can be calculated anyways using parentNodeAgg. No longer using Node.build since tree structure is constructed on-the-fly. * Did not eliminate since it is public (Developer) API. Marked as deprecated. Eliminated pre-allocated nodes array in main train() method. * Nodes are constructed and added to the tree structure as needed during training. * Moved tree construction from main train() method into findBestSplitsPerGroup() since there is no need to keep the (split, gain) array for an entire level of nodes. Only one element of that array is needed at a time, so we do not the array. findBestSplits() now returns 2 items: * rootNode (newly created root node on first iteration, same root node on later iterations) * doneTraining (indicating if all nodes at that level were leafs) Updated DecisionTreeSuite. Notes: * Improved test "Second level node building with vs. without groups" ** generateOrderedLabeledPoints() modified so that it really does require 2 levels of internal nodes. * Related update: Added Node.deepCopy (private[tree]), used for test suite CC: mengxr Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com> Closes #2341 from jkbradley/dt-spark-3160 and squashes the following commits: 07dd1ee [Joseph K. Bradley] Fixed overflow bug with computing maxMemoryUsage in DecisionTree. Also fixed bug with over-allocating space in DTStatsAggregator for unordered features. debe072 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-spark-3160 5c4ac33 [Joseph K. Bradley] Added check in Strategy to make sure minInstancesPerNode >= 1 0dd4d87 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-spark-3160 306120f [Joseph K. Bradley] Fixed typo in DecisionTreeModel.scala doc eaa1dcf [Joseph K. Bradley] Added topNode doc in DecisionTree and scalastyle fix d4d7864 [Joseph K. Bradley] Marked Node.build as deprecated d4dbb99 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-spark-3160 1a8f0ad [Joseph K. Bradley] Eliminated pre-allocated nodes array in main train() method. * Nodes are constructed and added to the tree structure as needed during training. 2ab763b [Joseph K. Bradley] Simplifications to DecisionTree code:
Put original YARN queue spark-submit arg description in running-on-yarn html table and example command line Author: Mark G. Whitney <mark@whitneyindustries.com> Closes #2218 from kramimus/2258-yarndoc and squashes the following commits: 4b5d808 [Mark G. Whitney] remove yarn queue config f8cda0d [Mark G. Whitney] [SPARK-2558][DOCS] Add spark.yarn.queue description to YARN doc
Logically, we should remove the Hive Table/Database first and then reset the Hive configuration, repoint to the new data warehouse directory etc. Otherwise it raised exceptions like "Database doesn't not exists: default" in the local testing. Author: Cheng Hao <hao.cheng@intel.com> Closes #2352 from chenghao-intel/test_hive and squashes the following commits: 74fd76b [Cheng Hao] eliminate the error log
This commit exists to close the following pull requests on Github: Closes #930 (close requested by 'andrewor14') Closes #867 (close requested by 'marmbrus') Closes #1829 (close requested by 'marmbrus') Closes #1131 (close requested by 'JoshRosen') Closes #1571 (close requested by 'andrewor14') Closes #2359 (close requested by 'andrewor14')
GraphX's current implementation of static (fixed iteration count) PageRank uses the Pregel API. This unnecessarily tracks active vertices, even though in static PageRank all vertices are always active. Active vertex tracking incurs the following costs: 1. A shuffle per iteration to ship the active sets to the edge partitions. 2. A hash table creation per iteration at each partition to index the active sets for lookup. 3. A hash lookup per edge to check whether the source vertex is active. I reimplemented static PageRank using the lower-level GraphX API instead of the Pregel API. In benchmarks on a 16-node m2.4xlarge cluster, this provided a 23% speedup (from 514 s to 397 s, mean over 3 trials) for 10 iterations of PageRank on a synthetic graph with 10M vertices and 1.27B edges. Author: Ankur Dave <ankurdave@gmail.com> Closes #2308 from ankurdave/SPARK-3427 and squashes the following commits: 449996a [Ankur Dave] Avoid unnecessary active vertex tracking in static PageRank
…rio... ...s Author: Sandy Ryza <sandy@cloudera.com> Closes #1934 from sryza/sandy-spark-3014 and squashes the following commits: ae19cc1 [Sandy Ryza] SPARK-3014. Log a more informative messages in a couple failure scenarios
When that option is used, the compiled classes from the build directory are prepended to the classpath. Now that we avoid packaging Guava, that means we have classes referencing the original Guava location in the app's classpath, so errors happen. For that case, add Guava manually to the classpath. Note: if Spark is compiled with "-Phadoop-provided", it's tricky to make things work with SPARK_PREPEND_CLASSES, because you need to add the Hadoop classpath using SPARK_CLASSPATH and that means the older Hadoop Guava overrides the newer one Spark needs. So someone using SPARK_PREPEND_CLASSES needs to remember to not use that profile. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #2141 from vanzin/SPARK-3217 and squashes the following commits: b967324 [Marcelo Vanzin] [SPARK-3217] Add Guava to classpath when SPARK_PREPEND_CLASSES is set.
Author: Thomas Graves <tgraves@apache.org> Closes #2373 from tgravescs/SPARK-3456 and squashes the following commits: 77e9532 [Thomas Graves] [SPARK-3456] YarnAllocator on alpha can lose container requests to RM
After this patch, we can run PySpark in PyPy (testing with PyPy 2.3.1 in Mac 10.9), for example:
```
PYSPARK_PYTHON=pypy ./bin/spark-submit wordcount.py
```
The performance speed up will depend on work load (from 20% to 3000%). Here are some benchmarks:
Job | CPython 2.7 | PyPy 2.3.1 | Speed up
------- | ------------ | ------------- | -------
Word Count | 41s | 15s | 2.7x
Sort | 46s | 44s | 1.05x
Stats | 174s | 3.6s | 48x
Here is the code used for benchmark:
```python
rdd = sc.textFile("text")
def wordcount():
rdd.flatMap(lambda x:x.split('/'))\
.map(lambda x:(x,1)).reduceByKey(lambda x,y:x+y).collectAsMap()
def sort():
rdd.sortBy(lambda x:x, 1).count()
def stats():
sc.parallelize(range(1024), 20).flatMap(lambda x: xrange(5024)).stats()
```
Author: Davies Liu <davies.liu@gmail.com>
Closes #2144 from davies/pypy and squashes the following commits:
9aed6c5 [Davies Liu] use protocol 2 in CloudPickle
4bc1f04 [Davies Liu] refactor
b20ab3a [Davies Liu] pickle sys.stdout and stderr in portable way
3ca2351 [Davies Liu] Merge branch 'master' into pypy
fae8b19 [Davies Liu] improve attrgetter, add tests
591f830 [Davies Liu] try to run tests with PyPy in run-tests
c8d62ba [Davies Liu] cleanup
f651fd0 [Davies Liu] fix tests using array with PyPy
1b98fb3 [Davies Liu] serialize itemgetter/attrgetter in portable ways
3c1dbfe [Davies Liu] Merge branch 'master' into pypy
42fb5fa [Davies Liu] Merge branch 'master' into pypy
cb2d724 [Davies Liu] fix tests
9986692 [Davies Liu] Merge branch 'master' into pypy
25b4ca7 [Davies Liu] support PyPy
Currently, SchemaRDD._jschema_rdd is SchemaRDD, the Scala API (coalesce(), repartition()) can not been called in Python easily, there is no way to specify the implicit parameter `ord`. The _jrdd is an JavaRDD, so _jschema_rdd should also be JavaSchemaRDD. In this patch, change _schema_rdd to JavaSchemaRDD, also added an assert for it. If some methods are missing from JavaSchemaRDD, then it's called by _schema_rdd.baseSchemaRDD().xxx(). BTW, Do we need JavaSQLContext? Author: Davies Liu <davies.liu@gmail.com> Closes #2369 from davies/fix_schemardd and squashes the following commits: abee159 [Davies Liu] use JavaSchemaRDD as SchemaRDD._jschema_rdd
…n in constructor Please refer to the JIRA ticket for details. **NOTE** We should check all test suites that do similar initialization-like side effects in their constructors. This PR only fixes `ParquetMetastoreSuite` because it breaks our Jenkins Maven build. Author: Cheng Lian <lian.cs.zju@gmail.com> Closes #2375 from liancheng/say-no-to-constructor and squashes the following commits: 0ceb75b [Cheng Lian] Moves test suite setup code to beforeAll rather than in constructor
…h failures This is necessary because we rely on this callback interface to clean resources up. The old behavior would lead to resource leaks. Note that this also changes the fault semantics of TaskCompletionListener. Previously failures in TaskCompletionListeners would result in the task being reported immediately. With this change, we report the exception at the end, and the reported exception is a TaskCompletionListenerException that contains all the exception messages. Author: Reynold Xin <rxin@apache.org> Closes #2343 from rxin/taskcontext-callback and squashes the following commits: a3845b2 [Reynold Xin] Mark TaskCompletionListenerException as private[spark]. ac5baea [Reynold Xin] Removed obsolete comment. aa68ea4 [Reynold Xin] Throw an exception if task completion callback fails. 29b6162 [Reynold Xin] oops compilation failed. 1cb444d [Reynold Xin] [SPARK-3469] Call all TaskCompletionListeners even if some fail.
…value of containsNull in an ArrayType After #1889, the default value of `containsNull` in an `ArrayType` is `true`. Author: Yin Huai <huai@cse.ohio-state.edu> Closes #2374 from yhuai/containsNull and squashes the following commits: dc609a3 [Yin Huai] Update the SQL programming guide to show the correct default value of containsNull in an ArrayType (the default value is true instead of false).
… objects ... that expose a stop() lifecycle method. This doesn't add `AutoCloseable`, which is Java 7+ only. But it should be possible to use try-with-resources on a `Closeable` in Java 7, as long as the `close()` does not throw a checked exception, and these don't. Q.E.D. Author: Sean Owen <sowen@cloudera.com> Closes #2346 from srowen/SPARK-3470 and squashes the following commits: 612c21d [Sean Owen] Add Closeable / close() to Java context objects that expose a stop() lifecycle method
This is a follow up of #2352. Now we can finally remove the evil "MINOR HACK", which covered up the eldest bug in the history of Spark SQL (see details [here](#2352 (comment))). Author: Cheng Lian <lian.cs.zju@gmail.com> Closes #2377 from liancheng/remove-evil-minor-hack and squashes the following commits: 0869c78 [Cheng Lian] Removes the evil MINOR HACK
…rage This is a major refactoring of the in-memory columnar storage implementation, aims to eliminate boxing costs from critical paths (building/accessing column buffers) as much as possible. The basic idea is to refactor all major interfaces into a row-based form and use them together with `SpecificMutableRow`. The difficult part is how to adapt all compression schemes, esp. `RunLengthEncoding` and `DictionaryEncoding`, to this design. Since in-memory compression is disabled by default for now, and this PR should be strictly better than before no matter in-memory compression is enabled or not, maybe I'll finish that part in another PR. **UPDATE** This PR also took the chance to optimize `HiveTableScan` by 1. leveraging `SpecificMutableRow` to avoid boxing cost, and 1. building specific `Writable` unwrapper functions a head of time to avoid per row pattern matching and branching costs. TODO - [x] Benchmark - [ ] ~~Eliminate boxing costs in `RunLengthEncoding`~~ (left to future PRs) - [ ] ~~Eliminate boxing costs in `DictionaryEncoding` (seems not easy to do without specializing `DictionaryEncoding` for every supported column type)~~ (left to future PRs) ## Micro benchmark The benchmark uses a 10 million line CSV table consists of bytes, shorts, integers, longs, floats and doubles, measures the time to build the in-memory version of this table, and the time to scan the whole in-memory table. Benchmark code can be found [here](https://gist.github.com/liancheng/fe70a148de82e77bd2c8#file-hivetablescanbenchmark-scala). Script used to generate the input table can be found [here](https://gist.github.com/liancheng/fe70a148de82e77bd2c8#file-tablegen-scala). Speedup: - Hive table scanning + column buffer building: **18.74%** The original benchmark uses 1K as in-memory batch size, when increased to 10K, it can be 28.32% faster. - In-memory table scanning: **7.95%** Before: | Building | Scanning ------- | -------- | -------- 1 | 16472 | 525 2 | 16168 | 530 3 | 16386 | 529 4 | 16184 | 538 5 | 16209 | 521 Average | 16283.8 | 528.6 After: | Building | Scanning ------- | -------- | -------- 1 | 13124 | 458 2 | 13260 | 529 3 | 12981 | 463 4 | 13214 | 483 5 | 13583 | 500 Average | 13232.4 | 486.6 Author: Cheng Lian <lian.cs.zju@gmail.com> Closes #2327 from liancheng/prevent-boxing/unboxing and squashes the following commits: 4419fe4 [Cheng Lian] Addressing comments e5d2cf2 [Cheng Lian] Bug fix: should call setNullAt when field value is null to avoid NPE 8b8552b [Cheng Lian] Only checks for partition batch pruning flag once 489f97b [Cheng Lian] Bug fix: TableReader.fillObject uses wrong ordinals 97bbc4e [Cheng Lian] Optimizes hive.TableReader by by providing specific Writable unwrappers a head of time 3dc1f94 [Cheng Lian] Minor changes to eliminate row object creation 5b39cb9 [Cheng Lian] Lowers log level of compression scheme details f2a7890 [Cheng Lian] Use SpecificMutableRow in InMemoryColumnarTableScan to avoid boxing 9cf30b0 [Cheng Lian] Added row based ColumnType.append/extract 456c366 [Cheng Lian] Made compression decoder row based edac3cd [Cheng Lian] Makes ColumnAccessor.extractSingle row based 8216936 [Cheng Lian] Removes boxing cost in IntDelta and LongDelta by providing specialized implementations b70d519 [Cheng Lian] Made some in-memory columnar storage interfaces row-based
Author: Michael Armbrust <michael@databricks.com> Closes #2164 from marmbrus/shufflePartitions and squashes the following commits: 0da1e8c [Michael Armbrust] test hax ef2d985 [Michael Armbrust] more test hacks. 2dabae3 [Michael Armbrust] more test fixes 0bdbf21 [Michael Armbrust] Make parquet tests less order dependent b42eeab [Michael Armbrust] increase test parallelism 80453d5 [Michael Armbrust] Decrease partitions when testing
Reuse Python worker to avoid the overhead of fork() Python process for each tasks. It also tracks the broadcasts for each worker, avoid sending repeated broadcasts.
This can reduce the time for dummy task from 22ms to 13ms (-40%). It can help to reduce the latency for Spark Streaming.
For a job with broadcast (43M after compress):
```
b = sc.broadcast(set(range(30000000)))
print sc.parallelize(range(24000), 100).filter(lambda x: x in b.value).count()
```
It will finish in 281s without reused worker, and it will finish in 65s with reused worker(4 CPUs). After reusing the worker, it can save about 9 seconds for transfer and deserialize the broadcast for each tasks.
It's enabled by default, could be disabled by `spark.python.worker.reuse = false`.
Author: Davies Liu <davies.liu@gmail.com>
Closes #2259 from davies/reuse-worker and squashes the following commits:
f11f617 [Davies Liu] Merge branch 'master' into reuse-worker
3939f20 [Davies Liu] fix bug in serializer in mllib
cf1c55e [Davies Liu] address comments
3133a60 [Davies Liu] fix accumulator with reused worker
760ab1f [Davies Liu] do not reuse worker if there are any exceptions
7abb224 [Davies Liu] refactor: sychronized with itself
ac3206e [Davies Liu] renaming
8911f44 [Davies Liu] synchronized getWorkerBroadcasts()
6325fc1 [Davies Liu] bugfix: bid >= 0
e0131a2 [Davies Liu] fix name of config
583716e [Davies Liu] only reuse completed and not interrupted worker
ace2917 [Davies Liu] kill python worker after timeout
6123d0f [Davies Liu] track broadcasts for each worker
8d2f08c [Davies Liu] reuse python worker
Aggregate the number of bytes spilled into disks during aggregation or sorting, show them in Web UI.  This patch is blocked by SPARK-3465. (It includes a fix for that). Author: Davies Liu <davies.liu@gmail.com> Closes #2336 from davies/metrics and squashes the following commits: e37df38 [Davies Liu] remove outdated comments 1245eb7 [Davies Liu] remove the temporary fix ebd2f43 [Davies Liu] Merge branch 'master' into metrics 7e4ad04 [Davies Liu] Merge branch 'master' into metrics fbe9029 [Davies Liu] show spilled bytes in Python in web ui
SPARK-3039: Adds the maven property "avro.mapred.classifier" to build spark-assembly with avro-mapred with support for the new Hadoop API. Sets this property to hadoop2 for Hadoop 2 profiles. I am not very familiar with maven, nor do I know whether this potentially breaks something in the hive part of spark. There might be a more elegant way of doing this. Author: Bertrand Bossy <bertrandbossy@gmail.com> Closes #1945 from bbossy/SPARK-3039 and squashes the following commits: c32ce59 [Bertrand Bossy] SPARK-3039: Allow spark to be built using avro-mapred for hadoop2
…ldn... ...'t depend on Publish local in maven term is `install` and publish otherwise is `deploy` So disabled both for following projects. Author: Prashant Sharma <prashant.s@imaginea.com> Closes #2329 from ScrapCodes/SPARK-3452/maven-skip-install and squashes the following commits: 257b79a [Prashant Sharma] [SPARK-3452] Maven build should skip publishing artifacts people shouldn't depend on
…d not be integer literal I think, it need to keep the priority of shutdown hook for ApplicationMaster than the priority of shutdown hook for o.a.h.FileSystem depending on changing the priority for FileSystem. Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp> Closes #2283 from sarutak/SPARK-3410 and squashes the following commits: 1d44fef [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-3410 bd6cc53 [Kousuke Saruta] Modified style ee6f1aa [Kousuke Saruta] Added constant "SHUTDOWN_HOOK_PRIORITY" to ApplicationMaster 54eb68f [Kousuke Saruta] Changed Shutdown hook priority to 20 2f0aee3 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-3410 4c5cb93 [Kousuke Saruta] Modified the priority for AM's shutdown hook 217d1a4 [Kousuke Saruta] Removed unused import statements 717aba2 [Kousuke Saruta] Modified ApplicationMaster to make to keep the priority of shutdown hook for ApplicationMaster higher than the priority of shutdown hook for HDFS
Closes #2387 Author: Matthew Farrellee <matt@redhat.com> Closes #2301 from mattf/SPARK-3425 and squashes the following commits: 20f3c09 [Matthew Farrellee] [SPARK-3425] do not set MaxPermSize for OpenJDK 1.8
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp> Closes #2380 from sarutak/SPARK-3518 and squashes the following commits: 8a1464e [Kousuke Saruta] Replaced a variable with simple field reference c660fbc [Kousuke Saruta] Removed useless statement in JsonProtocol.scala
DAGScheduler logs jobid when runJob finishes Author: yantangzhai <tyz0303@163.com> Closes #1617 from YanTangZhai/SPARK-2714 and squashes the following commits: 0a0243f [yantangzhai] [SPARK-2714] DAGScheduler logs jobid when runJob finishes fbb1150 [yantangzhai] [SPARK-2714] DAGScheduler logs jobid when runJob finishes 7aec2a9 [yantangzhai] [SPARK-2714] DAGScheduler logs jobid when runJob finishes fb42f0f [yantangzhai] [SPARK-2714] DAGScheduler logs jobid when runJob finishes 090d908 [yantangzhai] [SPARK-2714] DAGScheduler logs jobid when runJob finishes
SimpleUpdater ignores the regularizer, which leads to an unregularized LogReg. To enable the common L2 regularizer (and the corresponding regularization parameter) for logistic regression the SquaredL2Updater has to be used in SGD (see, e.g., [SVMWithSGD]) Author: Christoph Sawade <christoph@sawade.me> Closes #2398 from BigCrunsh/fix-regparam-logreg and squashes the following commits: 0820c04 [Christoph Sawade] Use SquaredL2Updater in LogisticRegressionWithSGD
…in params to example and Python API Added minInstancesPerNode, minInfoGain params to: * DecisionTreeRunner.scala example * Python API (tree.py) Also: * Fixed typo in tree suite test "do not choose split that does not satisfy min instance per node requirements" * small style fixes CC: mengxr Author: qiping.lqp <qiping.lqp@alibaba-inc.com> Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com> Author: chouqin <liqiping1991@gmail.com> Closes #2349 from jkbradley/chouqin-dt-preprune and squashes the following commits: 61b2e72 [Joseph K. Bradley] Added max of 10GB for maxMemoryInMB in Strategy. a95e7c8 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into chouqin-dt-preprune 95c479d [Joseph K. Bradley] * Fixed typo in tree suite test "do not choose split that does not satisfy min instance per node requirements" * small style fixes e2628b6 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into chouqin-dt-preprune 19b01af [Joseph K. Bradley] Merge remote-tracking branch 'chouqin/dt-preprune' into chouqin-dt-preprune f1d11d1 [chouqin] fix typo c7ebaf1 [chouqin] fix typo 39f9b60 [chouqin] change edge `minInstancesPerNode` to 2 and add one more test c6e2dfc [Joseph K. Bradley] Added minInstancesPerNode and minInfoGain parameters to DecisionTreeRunner.scala and to Python API in tree.py 0278a11 [chouqin] remove `noSplit` and set `Predict` private to tree d593ec7 [chouqin] fix docs and change minInstancesPerNode to 1 efcc736 [qiping.lqp] fix bug 10b8012 [qiping.lqp] fix style 6728fad [qiping.lqp] minor fix: remove empty lines bb465ca [qiping.lqp] Merge branch 'master' of https://github.com/apache/spark into dt-preprune cadd569 [qiping.lqp] add api docs 46b891f [qiping.lqp] fix bug e72c7e4 [qiping.lqp] add comments 845c6fa [qiping.lqp] fix style f195e83 [qiping.lqp] fix style 987cbf4 [qiping.lqp] fix bug ff34845 [qiping.lqp] separate calculation of predict of node from calculation of info gain ac42378 [qiping.lqp] add min info gain and min instances per node parameters in decision tree
Pyrolite can not unpickle array.array which pickled by Python 2.6, this patch fix it by extend Pyrolite. There is a bug in Pyrolite when unpickle array of float/double, this patch workaround it by reverse the endianness for float/double. This workaround should be removed after Pyrolite have a new release to fix this issue. I had send an PR to Pyrolite to fix it: irmen/Pyrolite#11 Author: Davies Liu <davies.liu@gmail.com> Closes #2365 from davies/pickle and squashes the following commits: f44f771 [Davies Liu] enable tests about array 3908f5c [Davies Liu] Merge branch 'master' into pickle c77c87b [Davies Liu] cleanup debugging code 60e4e2f [Davies Liu] support unpickle array.array for Python 2.6
….py file. Also made some cosmetic cleanups. Author: Aaron Staple <aaron.staple@gmail.com> Closes #2385 from staple/SPARK-1087 and squashes the following commits: 7b3bb13 [Aaron Staple] Address review comments, cosmetic cleanups. 10ba6e1 [Aaron Staple] [SPARK-1087] Move python traceback utilities into new traceback_utils.py file.
@experimental annotations. Actually false positive reported was due to mima generator not picking up the new jars in presence of old jars(theoretically this should not have happened.). So as a workaround, ran them both separately and just append them together. Author: Prashant Sharma <prashant@apache.org> Author: Prashant Sharma <prashant.s@imaginea.com> Closes #2285 from ScrapCodes/mima-fix and squashes the following commits: 093c76f [Prashant Sharma] Update mima 59012a8 [Prashant Sharma] Update mima 35b6c71 [Prashant Sharma] SPARK-3433 Fix for Mima false-positives with @DeveloperAPI and @experimental annotations.
…alIpAddress method Short version: NetworkInterface.getNetworkInterfaces returns ifs in reverse order compared to ifconfig output. It may pick up ip address associated with tun0 or virtual network interface. See [SPARK_3040](https://issues.apache.org/jira/browse/SPARK-3040) for more detail Author: Ye Xianjin <advancedxy@gmail.com> Closes #1946 from advancedxy/SPARK-3040 and squashes the following commits: f33f6b2 [Ye Xianjin] add windows support 087a785 [Ye Xianjin] reverse the Networkinterface.getNetworkInterfaces output order to get a more proper local ip address.
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…if sql has null
val jsc = new org.apache.spark.api.java.JavaSparkContext(sc)
val jhc = new org.apache.spark.sql.hive.api.java.JavaHiveContext(jsc)
val nrdd = jhc.hql("select null from spark_test.for_test")
println(nrdd.schema)
Then the error is thrown as follows:
scala.MatchError: NullType (of class org.apache.spark.sql.catalyst.types.NullType$)
at org.apache.spark.sql.types.util.DataTypeConversions$.asJavaDataType(DataTypeConversions.scala:43)
Author: YanTangZhai <hakeemzhai@tencent.com>
Author: yantangzhai <tyz0303@163.com>
Author: Michael Armbrust <michael@databricks.com>
Closes apache#3538 from YanTangZhai/MatchNullType and squashes the following commits:
e052dff [yantangzhai] [SPARK-4676] [SQL] JavaSchemaRDD.schema may throw NullType MatchError if sql has null
4b4bb34 [yantangzhai] [SPARK-4676] [SQL] JavaSchemaRDD.schema may throw NullType MatchError if sql has null
896c7b7 [yantangzhai] fix NullType MatchError in JavaSchemaRDD when sql has null
6e643f8 [YanTangZhai] Merge pull request #11 from apache/master
e249846 [YanTangZhai] Merge pull request #10 from apache/master
d26d982 [YanTangZhai] Merge pull request #9 from apache/master
76d4027 [YanTangZhai] Merge pull request #8 from apache/master
03b62b0 [YanTangZhai] Merge pull request #7 from apache/master
8a00106 [YanTangZhai] Merge pull request #6 from apache/master
cbcba66 [YanTangZhai] Merge pull request #3 from apache/master
cdef539 [YanTangZhai] Merge pull request #1 from apache/master
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Dec 24, 2014
…ins an empty AttributeSet() references The sql "select * from spark_test::for_test where abs(20141202) is not null" has predicates=List(IS NOT NULL HiveSimpleUdf#org.apache.hadoop.hive.ql.udf.UDFAbs(20141202)) and partitionKeyIds=AttributeSet(). PruningPredicates is List(IS NOT NULL HiveSimpleUdf#org.apache.hadoop.hive.ql.udf.UDFAbs(20141202)). Then the exception "java.lang.IllegalArgumentException: requirement failed: Partition pruning predicates only supported for partitioned tables." is thrown. The sql "select * from spark_test::for_test_partitioned_table where abs(20141202) is not null and type_id=11 and platform = 3" with partitioned key insert_date has predicates=List(IS NOT NULL HiveSimpleUdf#org.apache.hadoop.hive.ql.udf.UDFAbs(20141202), (type_id#12 = 11), (platform#8 = 3)) and partitionKeyIds=AttributeSet(insert_date#24). PruningPredicates is List(IS NOT NULL HiveSimpleUdf#org.apache.hadoop.hive.ql.udf.UDFAbs(20141202)). Author: YanTangZhai <hakeemzhai@tencent.com> Author: yantangzhai <tyz0303@163.com> Closes apache#3556 from YanTangZhai/SPARK-4693 and squashes the following commits: 620ebe3 [yantangzhai] [SPARK-4693] [SQL] PruningPredicates may be wrong if predicates contains an empty AttributeSet() references 37cfdf5 [yantangzhai] [SPARK-4693] [SQL] PruningPredicates may be wrong if predicates contains an empty AttributeSet() references 70a3544 [yantangzhai] [SPARK-4693] [SQL] PruningPredicates may be wrong if predicates contains an empty AttributeSet() references efa9b03 [YanTangZhai] Update HiveQuerySuite.scala 72accf1 [YanTangZhai] Update HiveQuerySuite.scala e572b9a [YanTangZhai] Update HiveStrategies.scala 6e643f8 [YanTangZhai] Merge pull request #11 from apache/master e249846 [YanTangZhai] Merge pull request #10 from apache/master d26d982 [YanTangZhai] Merge pull request #9 from apache/master 76d4027 [YanTangZhai] Merge pull request #8 from apache/master 03b62b0 [YanTangZhai] Merge pull request #7 from apache/master 8a00106 [YanTangZhai] Merge pull request #6 from apache/master cbcba66 [YanTangZhai] Merge pull request #3 from apache/master cdef539 [YanTangZhai] Merge pull request #1 from apache/master
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Dec 31, 2014
…askTracker to reduce the chance of the communicating problem Using AkkaUtils.askWithReply in MapOutputTracker.askTracker to reduce the chance of the communicating problem Author: YanTangZhai <hakeemzhai@tencent.com> Author: yantangzhai <tyz0303@163.com> Closes apache#3785 from YanTangZhai/SPARK-4946 and squashes the following commits: 9ca6541 [yantangzhai] [SPARK-4946] [CORE] Using AkkaUtils.askWithReply in MapOutputTracker.askTracker to reduce the chance of the communicating problem e4c2c0a [YanTangZhai] Merge pull request #15 from apache/master 718afeb [YanTangZhai] Merge pull request #12 from apache/master 6e643f8 [YanTangZhai] Merge pull request #11 from apache/master e249846 [YanTangZhai] Merge pull request #10 from apache/master d26d982 [YanTangZhai] Merge pull request #9 from apache/master 76d4027 [YanTangZhai] Merge pull request #8 from apache/master 03b62b0 [YanTangZhai] Merge pull request #7 from apache/master 8a00106 [YanTangZhai] Merge pull request #6 from apache/master cbcba66 [YanTangZhai] Merge pull request #3 from apache/master cdef539 [YanTangZhai] Merge pull request #1 from apache/master
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Jan 12, 2015
Support ! boolean logic operator like NOT in sql as follows select * from for_test where !(col1 > col2) Author: YanTangZhai <hakeemzhai@tencent.com> Author: Michael Armbrust <michael@databricks.com> Closes apache#3555 from YanTangZhai/SPARK-4692 and squashes the following commits: 1a9f605 [YanTangZhai] Update HiveQuerySuite.scala 7c03c68 [YanTangZhai] Merge pull request #23 from apache/master 992046e [YanTangZhai] Update HiveQuerySuite.scala ea618f4 [YanTangZhai] Update HiveQuerySuite.scala 192411d [YanTangZhai] Merge pull request #17 from YanTangZhai/master e4c2c0a [YanTangZhai] Merge pull request #15 from apache/master 1e1ebb4 [YanTangZhai] Update HiveQuerySuite.scala efc4210 [YanTangZhai] Update HiveQuerySuite.scala bd2c444 [YanTangZhai] Update HiveQuerySuite.scala 1893956 [YanTangZhai] Merge pull request #14 from marmbrus/pr/3555 59e4de9 [Michael Armbrust] make hive test 718afeb [YanTangZhai] Merge pull request #12 from apache/master 950b21e [YanTangZhai] Update HiveQuerySuite.scala 74175b4 [YanTangZhai] Update HiveQuerySuite.scala 92242c7 [YanTangZhai] Update HiveQl.scala 6e643f8 [YanTangZhai] Merge pull request #11 from apache/master e249846 [YanTangZhai] Merge pull request #10 from apache/master d26d982 [YanTangZhai] Merge pull request #9 from apache/master 76d4027 [YanTangZhai] Merge pull request #8 from apache/master 03b62b0 [YanTangZhai] Merge pull request #7 from apache/master 8a00106 [YanTangZhai] Merge pull request #6 from apache/master cbcba66 [YanTangZhai] Merge pull request #3 from apache/master cdef539 [YanTangZhai] Merge pull request #1 from apache/master
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