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Kernel 2.6.32 bug will lead to unexpected behavior of transferTo in copyStream, and this will corrupt the shuffle output file in sort-based shuffle, which will somehow introduce PARSING_ERROR(2), deserialization error or offset out of range. Here fix this by adding append flag, also add some position checking code. Details can be seen in [SPARK-3948](https://issues.apache.org/jira/browse/SPARK-3948). Author: jerryshao <saisai.shao@intel.com> Closes #2824 from jerryshao/SPARK-3948 and squashes the following commits: be0533a [jerryshao] Address the comments a82b184 [jerryshao] add configuration to control the NIO way of copying stream e17ada2 [jerryshao] Fix kernel 2.6.32 bug led unexpected behavior of transferTo
The problem caused by #1966 CC YanTangZhai andrewor14 Author: GuoQiang Li <witgo@qq.com> Closes #2858 from witgo/SPARK-4010 and squashes the following commits: 9866fbf [GuoQiang Li] Spark UI returns 500 in yarn-client mode
Package names of 2 test suites are different from their directory names. - `GeneratedEvaluationSuite` - `GeneratedMutableEvaluationSuite` Author: Takuya UESHIN <ueshin@happy-camper.st> Closes #2835 from ueshin/issues/SPARK-3986 and squashes the following commits: fa2cc05 [Takuya UESHIN] Fix package names to fit their directory names.
Before, if the master node is killed and restarted, the worker nodes would not attempt to reconnect to the Master. Therefore, when the Master node was restarted, the worker nodes needed to be restarted as well. Now, when the Master node is disconnected, the worker nodes will continuously ping the master node in attempts to reconnect to it. Once the master node restarts, it will detect one of the registration requests from its former workers. The result is that the cluster re-enters a healthy state. In addition, when the master does not receive a heartbeat from the worker, the worker was removed; however, when the worker sent a heartbeat to the master, the master used to ignore the heartbeat. Now, a master that receives a heartbeat from a worker that had been disconnected will request the worker to re-attempt the registration process, at which point the worker will send a RegisterWorker request and be re-connected accordingly. Re-connection attempts per worker are submitted every N seconds, where N is configured by the property spark.worker.reconnect.interval - this has a default of 60 seconds right now. Author: mcheah <mcheah@palantir.com> Closes #2828 from mccheah/reconnect-dead-workers and squashes the following commits: 83f8bc9 [mcheah] [SPARK-3736] More informative log message, and fixing some indentation. fe0e02f [mcheah] [SPARK-3736] Moving reconnection logic to registerWithMaster(). 94ddeca [mcheah] [SPARK-3736] Changing a log warning to a log info. a698e35 [mcheah] [SPARK-3736] Addressing PR comment to make some defs private. b9a3077 [mcheah] [SPARK-3736] Addressing PR comments related to reconnection. 2ad5ed5 [mcheah] [SPARK-3736] Cancel attempts to reconnect if the master changes. b5b34af [mcheah] [SPARK-3736] Workers reconnect when disassociated from the master.
…ree more adaptively DecisionTree splits on continuous features by choosing an array of values from a subsample of the data. Currently, it does not check for identical values in the subsample, so it could end up having multiple copies of the same split. In this PR, we choose splits for a continuous feature in 3 steps: 1. Sort sample values for this feature 2. Get number of occurrence of each distinct value 3. Iterate the value count array computed in step 2 to choose splits. After find splits, `numSplits` and `numBins` in metadata will be updated. CC: mengxr manishamde jkbradley, please help me review this, thanks. Author: Qiping Li <liqiping1991@gmail.com> Author: chouqin <liqiping1991@gmail.com> Author: liqi <liqiping1991@gmail.com> Author: qiping.lqp <qiping.lqp@alibaba-inc.com> Closes #2780 from chouqin/dt-findsplits and squashes the following commits: 18d0301 [Qiping Li] check explicitly findsplits return distinct splits 8dc28ab [chouqin] remove blank lines ffc920f [chouqin] adjust code based on comments and add more test cases 9857039 [chouqin] Merge branch 'master' of https://github.com/apache/spark into dt-findsplits d353596 [qiping.lqp] fix pyspark doc test 9e64699 [Qiping Li] fix random forest unit test 3c72913 [Qiping Li] fix random forest unit test 092efcb [Qiping Li] fix bug f69f47f [Qiping Li] fix bug ab303a4 [Qiping Li] fix bug af6dc97 [Qiping Li] fix bug 2a8267a [Qiping Li] fix bug c339a61 [Qiping Li] fix bug 369f812 [Qiping Li] fix style 8f46af6 [Qiping Li] add comments and unit test 9e7138e [Qiping Li] Merge branch 'dt-findsplits' of https://github.com/chouqin/spark into dt-findsplits 1b25a35 [Qiping Li] Merge branch 'master' of https://github.com/apache/spark into dt-findsplits 0cd744a [liqi] fix bug 3652823 [Qiping Li] fix bug af7cb79 [Qiping Li] Choose splits for continuous features in DecisionTree more adaptively
Author: Michael Armbrust <michael@databricks.com> Closes #2658 from marmbrus/nestedAggs and squashes the following commits: 862b763 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into nestedAggs 3234521 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into nestedAggs 8b06fdc [Michael Armbrust] possible fix for grouping on nested fields
Author: Takuya UESHIN <ueshin@happy-camper.st> Closes #2820 from ueshin/issues/SPARK-3966 and squashes the following commits: ca4a745 [Takuya UESHIN] Fix nullabilities of Cast related to DateType.
…ift JDBC server Write properties of hive-site.xml to HiveContext when initilize session state in SparkSQLEnv.scala. The method of SparkSQLEnv.init() in HiveThriftServer2.scala can not write the properties of hive-site.xml to HiveContext. Such as: add configuration property spark.sql.shuffle.partititions in the hive-site.xml. Author: luogankun <luogankun@gmail.com> Closes #2800 from luogankun/SPARK-3945 and squashes the following commits: 3679efc [luogankun] [SPARK-3945]Write properties of hive-site.xml to HiveContext when initilize session state In SparkSQLEnv.scala
Some developers want to replace `Optimizer` to fit their projects but can't do so because currently `Optimizer` is an `object`. Author: Takuya UESHIN <ueshin@happy-camper.st> Closes #2825 from ueshin/issues/SPARK-3969 and squashes the following commits: abbc53c [Takuya UESHIN] Re-rename Optimizer object. 4d2e1bc [Takuya UESHIN] Rename Optimizer object. 9547a23 [Takuya UESHIN] Extract abstract class from Optimizer for developers to be able to replace Optimizer.
If wrong sql,the console print error one times。 eg： <pre> spark-sql> show tabless; show tabless; 14/10/13 21:03:48 INFO ParseDriver: Parsing command: show tabless ............ at org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver.processCmd(SparkSQLCLIDriver.scala:274) at org.apache.hadoop.hive.cli.CliDriver.processLine(CliDriver.java:413) at org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver$.main(SparkSQLCLIDriver.scala:209) at org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver.main(SparkSQLCLIDriver.scala) Caused by: org.apache.hadoop.hive.ql.parse.ParseException: line 1:5 cannot recognize input near 'show' 'tabless' '<EOF>' in ddl statement at org.apache.hadoop.hive.ql.parse.ParseDriver.parse(ParseDriver.java:193) at org.apache.hadoop.hive.ql.parse.ParseDriver.parse(ParseDriver.java:161) at org.apache.spark.sql.hive.HiveQl$.getAst(HiveQl.scala:218) at org.apache.spark.sql.hive.HiveQl$.createPlan(HiveQl.scala:226) ... 47 more Time taken: 4.35 seconds 14/10/13 21:03:51 INFO CliDriver: Time taken: 4.35 seconds </pre> Author: wangxiaojing <u9jing@gmail.com> Closes #2790 from wangxiaojing/spark-3940 and squashes the following commits: e2e5c14 [wangxiaojing] sql Print the error code three times
This PR makes several changes to TorrentBroadcast in order to make it easier to reason about, which should help when debugging SPARK-3958. The key changes: - Remove all state from the global TorrentBroadcast object. This state consisted mainly of configuration options, like the block size and compression codec, and was read by the blockify / unblockify methods. Unfortunately, the use of `lazy val` for `BLOCK_SIZE` meant that the block size was always determined by the first SparkConf that TorrentBroadast was initialized with; as a result, unit tests could not properly test TorrentBroadcast with different block sizes. Instead, blockifyObject and unBlockifyObject now accept compression codecs and blockSizes as arguments. These arguments are supplied at the call sites inside of TorrentBroadcast instances. Each TorrentBroadcast instance determines these values from SparkEnv's SparkConf. I was careful to ensure that we do not accidentally serialize CompressionCodec or SparkConf objects as part of the TorrentBroadcast object. - Remove special-case handling of local-mode in TorrentBroadcast. I don't think that broadcast implementations should know about whether we're running in local mode. If we want to optimize the performance of broadcast in local mode, then we should detect this at a higher level and use a dummy LocalBroadcastFactory implementation instead. Removing this code fixes a subtle error condition: in the old local mode code, a failure to find the broadcast in the local BlockManager would lead to an attempt to deblockify zero blocks, which could lead to confusing deserialization or decompression errors when we attempted to decompress an empty byte array. This should never have happened, though: a failure to find the block in local mode is evidence of some other error. The changes here will make it easier to debug those errors if they ever happen. - Add a check that throws an exception when attempting to deblockify an empty array. - Use ScalaCheck to add a test to check that TorrentBroadcast's blockifyObject and unBlockifyObject methods are inverses. - Misc. cleanup and logging improvements. Author: Josh Rosen <joshrosen@databricks.com> Closes #2844 from JoshRosen/torrentbroadcast-bugfix and squashes the following commits: 1e8268d [Josh Rosen] Address Reynold's review comments 2a9fdfd [Josh Rosen] Address Reynold's review comments. c3b08f9 [Josh Rosen] Update TorrentBroadcast tests to reflect removal of special local-mode optimizations. 5c22782 [Josh Rosen] Store broadcast variable's value in the driver. 33fc754 [Josh Rosen] Change blockify/unblockifyObject to accept serializer as argument. 618a872 [Josh Rosen] [SPARK-3958] TorrentBroadcast cleanup / debugging improvements.
Author: Holden Karau <holden@pigscanfly.ca> Closes #2861 from holdenk/SPARK-4015-Documentation-in-the-streaming-context-references-non-existent-function and squashes the following commits: 081db8a [Holden Karau] fix pyspark streaming doc too 0e03863 [Holden Karau] replace awaitTransformation with awaitTermination
Just found a typo. Should not use "%f" for Long. Author: zsxwing <zsxwing@gmail.com> Closes #2875 from zsxwing/SPARK-4035 and squashes the following commits: ce347e2 [zsxwing] Fix a wrong format specifier
If an executor fails without being scheduled to run any tasks, then `DAGScheduler` won't notify `BlockManagerMasterActor` that the associated block manager should be removed. Instead, the associated block manager will be expired only after a few rounds of heartbeat timeouts. In terms of removal treatment, there should really be no distinction between executors that have been scheduled tasks and those that have not. The fix, then, is to add all known executors to `TaskSchedulerImpl`'s `activeExecutorIds` whether or not it has been scheduled a task. In fact, the existing comment above `activeExecutorIds` is ``` // Which executor IDs we have executors on val activeExecutorIds = new HashSet[String] ``` not "Which executors have been scheduled tasks thus far." Author: Andrew Or <andrewor14@gmail.com> Closes #2865 from andrewor14/active-executors and squashes the following commits: ff3172b [Andrew Or] Add all known executors to `activeExecutorIds`
https://issues.apache.org/jira/browse/SPARK-3770 We need access to the underlying latent user features from python. However, the userFeatures RDD from the MatrixFactorizationModel isn't accessible from the python bindings. I've added a method to the underlying scala class to turn the RDD[(Int, Array[Double])] to an RDD[String]. This is then accessed from the python recommendation.py Author: Michelangelo D'Agostino <mdagostino@civisanalytics.com> Closes #2636 from mdagost/mf_user_features and squashes the following commits: c98f9e2 [Michelangelo D'Agostino] Added unit tests for userFeatures and productFeatures and merged master. d5eadf8 [Michelangelo D'Agostino] Merge branch 'master' into mf_user_features 2481a2a [Michelangelo D'Agostino] Merged master and resolved conflict. a6ffb96 [Michelangelo D'Agostino] Eliminated a function from our first approach to this problem that is no longer needed now that we added the fromTuple2RDD function. 2aa1bf8 [Michelangelo D'Agostino] Implemented a function called fromTuple2RDD in PythonMLLibAPI and used it to expose the MF userFeatures and productFeatures in python. 34cb2a2 [Michelangelo D'Agostino] A couple of lint cleanups and a comment. cdd98e3 [Michelangelo D'Agostino] It's working now. e1fbe5e [Michelangelo D'Agostino] Added scala function to stringify userFeatures for access in python.
…ntByValue See [JIRA](https://issues.apache.org/jira/browse/SPARK-3994) for more information. Also adds a note which warns against using these methods. Author: Aaron Davidson <aaron@databricks.com> Closes #2839 from aarondav/countByKey and squashes the following commits: d6fdb2a [Aaron Davidson] Respond to comments e1f06d3 [Aaron Davidson] [SPARK-3994] Use standard Aggregator code path for countByKey and countByValue
Add common metrics for ranking algorithms (http://www-nlp.stanford.edu/IR-book/), including: - Mean Average Precision - Precisionn: top-n precision - Discounted cumulative gain (DCG) and NDCG The following methods and the corresponding tests are implemented: ``` class RankingMetrics[T](predictionAndLabels: RDD[(Array[T], Array[T])]) { /* Returns the precsionk for each query */ lazy val precAtK: RDD[Array[Double]] /** * param k the position to compute the truncated precision * return the average precision at the first k ranking positions */ def precision(k: Int): Double /* Returns the average precision for each query */ lazy val avePrec: RDD[Double] /*Returns the mean average precision (MAP) of all the queries*/ lazy val meanAvePrec: Double /*Returns the normalized discounted cumulative gain for each query */ lazy val ndcgAtK: RDD[Array[Double]] /** * param k the position to compute the truncated ndcg * return the average ndcg at the first k ranking positions */ def ndcg(k: Int): Double } ``` Author: coderxiang <shuoxiangpub@gmail.com> Closes #2667 from coderxiang/rankingmetrics and squashes the following commits: d881097 [coderxiang] update doc 14d9cd9 [coderxiang] remove unexpected files d7fb93f [coderxiang] style change and remove ignored files f113ee1 [coderxiang] modify doc for displaying superscript and subscript f626896 [coderxiang] improve doc and remove unnecessary computation while labSet is empty be6645e [coderxiang] set the precision of empty labset to 0.0 d64c120 [coderxiang] add logWarning for empty ground truth set dfae292 [coderxiang] handle empty labSet for map. add test 62047c4 [coderxiang] style change and add documentation f66612d [coderxiang] add additional test of precisionAt b794cb2 [coderxiang] move private members precAtK, ndcgAtK into public methods. style change 77c9e5d [coderxiang] set precAtK and ndcgAtK as private member. Improve documentation 5f87bce [coderxiang] add API to calculate precision and ndcg at each ranking position b7851cc [coderxiang] Use generic type to represent IDs e443fee [coderxiang] change style and use alternative builtin methods 3a5a6ff [coderxiang] add ranking metrics
Author: Sandy Ryza <sandy@cloudera.com> Closes #789 from sryza/sandy-spark-1813 and squashes the following commits: 48b05e9 [Sandy Ryza] Simplify b824932 [Sandy Ryza] Allow both spark.kryo.classesToRegister and spark.kryo.registrator at the same time 6a15bb7 [Sandy Ryza] Small fix a2278c0 [Sandy Ryza] Respond to review comments 6ef592e [Sandy Ryza] SPARK-1813. Add a utility to SparkConf that makes using Kryo really easy
runningLocally is deprecated now Author: CrazyJvm <crazyjvm@gmail.com> Closes #2879 from CrazyJvm/runningLocally and squashes the following commits: bec0b3e [CrazyJvm] use isRunningLocally rather than runningLocally
Change maximum value for default seed during RDD sampling so that it is strictly less than 2 ** 32. This prevents a bug in the most recent version of NumPy, which cannot accept random seeds above this bound. Adds an extra test that uses the default seed (instead of setting it manually, as in the docstrings). mengxr Author: freeman <the.freeman.lab@gmail.com> Closes #2889 from freeman-lab/pyspark-sampling and squashes the following commits: dc385ef [freeman] Change maximum value for default seed
… and spark.shuffle.spill.compress settings are different This PR fixes SPARK-3426, an issue where sort-based shuffle crashes if the `spark.shuffle.spill.compress` and `spark.shuffle.compress` settings have different values. The problem is that sort-based shuffle's read and write paths use different settings for determining whether to apply compression. ExternalSorter writes runs to files using `TempBlockId` ids, which causes `spark.shuffle.spill.compress` to be used for enabling compression, but these spilled files end up being shuffled over the network and read as shuffle files using `ShuffleBlockId` by BlockStoreShuffleFetcher, which causes `spark.shuffle.compress` to be used for enabling decompression. As a result, this leads to errors when these settings disagree. Based on the discussions in #2247 and #2178, it sounds like we don't want to remove the `spark.shuffle.spill.compress` setting. Therefore, I've tried to come up with a fix where `spark.shuffle.spill.compress` is used to compress data that's read and written locally and `spark.shuffle.compress` is used to compress any data that will be fetched / read as shuffle blocks. To do this, I split `TempBlockId` into two new id types, `TempLocalBlockId` and `TempShuffleBlockId`, which map to `spark.shuffle.spill.compress` and `spark.shuffle.compress`, respectively. ExternalAppendOnlyMap also used temp blocks for spilling data. It looks like ExternalSorter was designed to be a generic sorter but its configuration already happens to be tied to sort-based shuffle, so I think it's fine if we use `spark.shuffle.compress` to compress its spills; we can move the compression configuration to the constructor in a later commit if we find that ExternalSorter is being used in other contexts where we want different configuration options to control compression. To summarize: **Before:** | | ExternalAppendOnlyMap | ExternalSorter | |-------|------------------------------|------------------------------| | Read | spark.shuffle.spill.compress | spark.shuffle.compress | | Write | spark.shuffle.spill.compress | spark.shuffle.spill.compress | **After:** | | ExternalAppendOnlyMap | ExternalSorter | |-------|------------------------------|------------------------| | Read | spark.shuffle.spill.compress | spark.shuffle.compress | | Write | spark.shuffle.spill.compress | spark.shuffle.compress | Thanks to andrewor14 for debugging this with me! Author: Josh Rosen <joshrosen@databricks.com> Closes #2890 from JoshRosen/SPARK-3426 and squashes the following commits: 1921cf6 [Josh Rosen] Minor edit for clarity. c8dd8f2 [Josh Rosen] Add comment explaining use of createTempShuffleBlock(). 2c687b9 [Josh Rosen] Fix SPARK-3426. 91e7e40 [Josh Rosen] Combine tests into single test of all combinations 76ca65e [Josh Rosen] Add regression test for SPARK-3426.
…ful so that the exit code wil be set to 1 When an yarn application fails (yarn-cluster mode), the exit code of spark-submit is still 0. It's hard for people to write some automatic scripts to run spark jobs in yarn because the failure can not be detected in these scripts. This PR added a status checking after `monitorApplication`. If an application is not successful, `run()` will throw an `SparkException`, so that Client.scala will exit with code 1. Therefore, people can use the exit code of `spark-submit` to write some automatic scripts. Author: zsxwing <zsxwing@gmail.com> Closes #2732 from zsxwing/SPARK-3877 and squashes the following commits: 1f89fa5 [zsxwing] Fix the unit test a0498e1 [zsxwing] Update the docs and the error message e1cb9ef [zsxwing] Fix the hacky way of calling Client ff16fec [zsxwing] Remove System.exit in Client.scala and add a test 6a2c103 [zsxwing] [SPARK-3877] Throw an exception when application is not successful so that the exit code wil be set to 1
I have tried maven help plugin first but that published all projects in top level pom. So I was left with no choice but to roll my own trivial plugin. This patch basically installs an effective pom after maven install is finished. The problem it fixes is described as follows: If you install using maven ` mvn install -DskipTests -Dhadoop.version=2.2.0 -Phadoop-2.2 ` Then without this patch the published pom(s) will have hadoop version as 1.0.4. This can be a problem at some point. Author: Prashant Sharma <prashant.s@imaginea.com> Closes #2673 from ScrapCodes/build-changes-effective-pom and squashes the following commits: aa7b91d [Prashant Sharma] used an unused dep. 0300dac [Prashant Sharma] improved comment messages.. 28f891e [Prashant Sharma] Added a useless dependency, so that we can shade it. And realized fake shading works for us. 553d96b [Prashant Sharma] Shaded some unused class of an unused dep, to generate effective pom(s)
Thare are some inconsistent spellings 'MLlib' and 'MLLib' in some documents and source codes. Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp> Closes #2903 from sarutak/SPARK-4055 and squashes the following commits: b031640 [Kousuke Saruta] Fixed inconsistent spelling "MLlib and MLLib"
A leak of event loops may be causing test failures. Author: Aaron Davidson <aaron@databricks.com> Closes #3053 from aarondav/leak and squashes the following commits: e676d18 [Aaron Davidson] Typo! 8f96475 [Aaron Davidson] Keep original ssc semantics 7e49f10 [Aaron Davidson] A leak of event loops may be causing test failures.
This PR adds User-Defined Types (UDTs) to SQL. It is a precursor to using SchemaRDD as a Dataset for the new MLlib API. Currently, the UDT API is private since there is incomplete support (e.g., no Java or Python support yet). Author: Joseph K. Bradley <joseph@databricks.com> Author: Michael Armbrust <michael@databricks.com> Author: Xiangrui Meng <meng@databricks.com> Closes #3063 from marmbrus/udts and squashes the following commits: 7ccfc0d [Michael Armbrust] remove println 46a3aee [Michael Armbrust] Slightly easier to read test output. 6cc434d [Michael Armbrust] Recursively convert rows. e369b91 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into udts 15c10a6 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into sql-udt2 f3c72fe [Joseph K. Bradley] Fixing merge e13cd8a [Joseph K. Bradley] Removed Vector UDTs 5817b2b [Joseph K. Bradley] style edits 30ce5b2 [Joseph K. Bradley] updates based on code review d063380 [Joseph K. Bradley] Cleaned up Java UDT Suite, and added warning about element ordering when creating schema from Java Bean a571bb6 [Joseph K. Bradley] Removed old UDT code (registry and Java UDTs). Cleaned up other code. Extended JavaUserDefinedTypeSuite 6fddc1c [Joseph K. Bradley] Made MyLabeledPoint into a Java Bean 20630bc [Joseph K. Bradley] fixed scalastyle fa86b20 [Joseph K. Bradley] Removed Java UserDefinedType, and made UDTs private[spark] for now 8de957c [Joseph K. Bradley] Modified UserDefinedType to store Java class of user type so that registerUDT takes only the udt argument. 8b242ea [Joseph K. Bradley] Fixed merge error after last merge. Note: Last merge commit also removed SQL UDT examples from mllib. 7f29656 [Joseph K. Bradley] Moved udt case to top of all matches. Small cleanups b028675 [Xiangrui Meng] allow any type in UDT 4500d8a [Xiangrui Meng] update example code 87264a5 [Xiangrui Meng] remove debug code 3143ac3 [Xiangrui Meng] remove unnecessary changes cfbc321 [Xiangrui Meng] support UDT in parquet db16139 [Joseph K. Bradley] Added more doc for UserDefinedType. Removed unused code in Suite 759af7a [Joseph K. Bradley] Added more doc to UserDefineType 63626a4 [Joseph K. Bradley] Updated ScalaReflectionsSuite per @marmbrus suggestions 51e5282 [Joseph K. Bradley] fixed 1 test f025035 [Joseph K. Bradley] Cleanups before PR. Added new tests 85872f6 [Michael Armbrust] Allow schema calculation to be lazy, but ensure its available on executors. dff99d6 [Joseph K. Bradley] Added UDTs for Vectors in MLlib, plus DatasetExample using the UDTs cd60cb4 [Joseph K. Bradley] Trying to get other SQL tests to run 34a5831 [Joseph K. Bradley] Added MLlib dependency on SQL. e1f7b9c [Joseph K. Bradley] blah 2f40c02 [Joseph K. Bradley] renamed UDT types 3579035 [Joseph K. Bradley] udt annotation now working b226b9e [Joseph K. Bradley] Changing UDT to annotation fea04af [Joseph K. Bradley] more cleanups 964b32e [Joseph K. Bradley] some cleanups 893ee4c [Joseph K. Bradley] udt finallly working 50f9726 [Joseph K. Bradley] udts 04303c9 [Joseph K. Bradley] udts 39f8707 [Joseph K. Bradley] removed old udt suite 273ac96 [Joseph K. Bradley] basic UDT is working, but deserialization has yet to be done 8bebf24 [Joseph K. Bradley] commented out convertRowToScala for debugging 53de70f [Joseph K. Bradley] more udts... 982c035 [Joseph K. Bradley] still working on UDTs 19b2f60 [Joseph K. Bradley] still working on UDTs 0eaeb81 [Joseph K. Bradley] Still working on UDTs 105c5a3 [Joseph K. Bradley] Adding UserDefinedType to SQL, not done yet.
Note that we're turning this on for at least the first part of the QA period as a trial. We want to enable this (and deprecate the NioBlockTransferService) as soon as possible in the hopes that NettyBlockTransferService will be more stable and easier to maintain. We will turn it off if we run into major issues. Author: Aaron Davidson <aaron@databricks.com> Closes #3049 from aarondav/enable-netty and squashes the following commits: bb981cc [Aaron Davidson] [SPARK-4183] Enable NettyBlockTransferService by default
This is a PR to send the fetch failure message back to Web UI. Before:   After (Please ignore the meaning of exception, I threw it in the code directly because it's hard to simulate a fetch failure):   Author: zsxwing <zsxwing@gmail.com> Closes #3032 from zsxwing/SPARK-4163 and squashes the following commits: f7e1faf [zsxwing] Discard changes for FetchFailedException and minor modification 4e946f7 [zsxwing] Add e as the cause of SparkException 316767d [zsxwing] Add private[storage] to FetchResult d51b0b6 [zsxwing] Set e as the cause of FetchFailedException b88c919 [zsxwing] Use 'private[storage]' for case classes instead of 'sealed' 62103fd [zsxwing] Update as per review 0c07d1f [zsxwing] Backward-compatible support a3bca65 [zsxwing] Send the fetch failure message back to Web UI
…d.sample The current way of seed distribution makes the random sequences from partition i and i+1 offset by 1. ~~~ In [14]: import random In [15]: r1 = random.Random(10) In [16]: r1.randint(0, 1) Out[16]: 1 In [17]: r1.random() Out[17]: 0.4288890546751146 In [18]: r1.random() Out[18]: 0.5780913011344704 In [19]: r2 = random.Random(10) In [20]: r2.randint(0, 1) Out[20]: 1 In [21]: r2.randint(0, 1) Out[21]: 0 In [22]: r2.random() Out[22]: 0.5780913011344704 ~~~ Note: The new tests are not for this bug fix. Author: Xiangrui Meng <meng@databricks.com> Closes #3010 from mengxr/SPARK-4148 and squashes the following commits: 869ae4b [Xiangrui Meng] move tests tests.py c1bacd9 [Xiangrui Meng] fix seed distribution and add some tests for rdd.sample
instead of `hive.version=0.13.1`. e.g. mvn -Phive -Phive=0.13.1 Note: `hive.version=0.13.1a` is the default property value. However, when explicitly specifying the `hive-0.13.1` maven profile, the wrong one would be selected. References: PR #2685, which resolved a package incompatibility issue with Hive-0.13.1 by introducing a special version Hive-0.13.1a Author: fi <coderfi@gmail.com> Closes #3072 from coderfi/master and squashes the following commits: 7ca4b1e [fi] Fixes the `hive-0.13.1` maven profile referencing `hive.version=0.13.1` instead of the Spark compatible `hive.version=0.13.1a` Note: `hive.version=0.13.1a` is the default version. However, when explicitly specifying the `hive-0.13.1` maven profile, the wrong one would be selected. e.g. mvn -Phive -Phive=0.13.1 See PR #2685
…ng in Spark SQL
Queries which has 'not like' is not working spark sql.
sql("SELECT * FROM records where value not like 'val%'")
same query works in Spark HiveQL
Author: ravipesala <ravindra.pesala@huawei.com>
Closes #3075 from ravipesala/SPARK-4207 and squashes the following commits:
35c11e7 [ravipesala] Supported 'not like' syntax in sql
This patch will try to infer schema for RDD which has empty value (None, [], {}) in the first row. It will try first 100 rows and merge the types into schema, also merge fields of StructType together. If there is still NullType in schema, then it will show an warning, tell user to try with sampling.
If sampling is presented, it will infer schema from all the rows after sampling.
Also, add samplingRatio for jsonFile() and jsonRDD()
Author: Davies Liu <davies.liu@gmail.com>
Author: Davies Liu <davies@databricks.com>
Closes #2716 from davies/infer and squashes the following commits:
e678f6d [Davies Liu] Merge branch 'master' of github.com:apache/spark into infer
34b5c63 [Davies Liu] Merge branch 'master' of github.com:apache/spark into infer
567dc60 [Davies Liu] update docs
9767b27 [Davies Liu] Merge branch 'master' into infer
e48d7fb [Davies Liu] fix tests
29e94d5 [Davies Liu] let NullType inherit from PrimitiveType
ee5d524 [Davies Liu] Merge branch 'master' of github.com:apache/spark into infer
540d1d5 [Davies Liu] merge fields for StructType
f93fd84 [Davies Liu] add more tests
3603e00 [Davies Liu] take more rows to infer schema, or infer the schema by sampling the RDD
This feature is based on an offline discussion with mengxr, hopefully can be useful for the new MLlib pipeline API.
For the following test snippet
```scala
case class KeyValue(key: Int, value: String)
val testData = sc.parallelize(1 to 10).map(i => KeyValue(i, i.toString)).toSchemaRDD
def foo(a: Int, b: String) => a.toString + b
```
the newly introduced DSL enables the following syntax
```scala
import org.apache.spark.sql.catalyst.dsl._
testData.select(Star(None), foo.call('key, 'value) as 'result)
```
which is equivalent to
```scala
testData.registerTempTable("testData")
sqlContext.registerFunction("foo", foo)
sql("SELECT *, foo(key, value) AS result FROM testData")
```
Author: Cheng Lian <lian@databricks.com>
Closes #3067 from liancheng/udf-dsl and squashes the following commits:
f132818 [Cheng Lian] Adds DSL support for Scala UDF
CREATE TABLE t1 (a String); CREATE TABLE t1 AS SELECT key FROM src; – throw exception CREATE TABLE if not exists t1 AS SELECT key FROM src; – expect do nothing, currently it will overwrite the t1, which is incorrect. Author: Cheng Hao <hao.cheng@intel.com> Closes #3013 from chenghao-intel/ctas_unittest and squashes the following commits: 194113e [Cheng Hao] fix bug in CTAS when table already existed
- Turns on compression for in-memory cached data by default - Changes the default parquet compression format back to gzip (we have seen more OOMs with production workloads due to the way Snappy allocates memory) - Ups the batch size to 10,000 rows - Increases the broadcast threshold to 10mb. - Uses our parquet implementation instead of the hive one by default. - Cache parquet metadata by default. Author: Michael Armbrust <michael@databricks.com> Closes #3064 from marmbrus/fasterDefaults and squashes the following commits: 97ee9f8 [Michael Armbrust] parquet codec docs e641694 [Michael Armbrust] Remote also a12866a [Michael Armbrust] Cache metadata. 2d73acc [Michael Armbrust] Update docs defaults. d63d2d5 [Michael Armbrust] document parquet option da373f9 [Michael Armbrust] More aggressive defaults
…sta... ...ntiation Author: Sandy Ryza <sandy@cloudera.com> Closes #3045 from sryza/sandy-spark-4178 and squashes the following commits: 8d2e70e [Sandy Ryza] Kostas's review feedback e5b27c0 [Sandy Ryza] SPARK-4178. Hadoop input metrics ignore bytes read in RecordReader instantiation
…ges are more than 1000 The number of completed stages and failed stages showed on webUI will always be less than 1000. This is really misleading when there are already thousands of stages completed or failed. The number should be correct even when only partial stages listed on the webUI (stage info will be removed if the number is too large). Author: Zhang, Liye <liye.zhang@intel.com> Closes #3035 from liyezhang556520/webStageNum and squashes the following commits: d9e29fb [Zhang, Liye] add detailed comments for variables 4ea8fd1 [Zhang, Liye] change variable name accroding to comments f4c404d [Zhang, Liye] [SPARK-4168][WebUI] web statges number should show correctly when stages are more than 1000
This patch allows executor thread dumps to be collected on-demand and viewed in the Spark web UI. The thread dumps are collected using Thread.getAllStackTraces(). To allow remote thread dumps to be triggered from the web UI, I added a new `ExecutorActor` that runs inside of the Executor actor system and responds to RPCs from the driver. The driver's mechanism for obtaining a reference to this actor is a little bit hacky: it uses the block manager master actor to determine the host/port of the executor actor systems in order to construct ActorRefs to ExecutorActor. Unfortunately, I couldn't find a much cleaner way to do this without a big refactoring of the executor -> driver communication. Screenshots:    Author: Josh Rosen <joshrosen@databricks.com> Closes #2944 from JoshRosen/jstack-in-web-ui and squashes the following commits: 3c21a5d [Josh Rosen] Address review comments: 880f7f7 [Josh Rosen] Merge remote-tracking branch 'origin/master' into jstack-in-web-ui f719266 [Josh Rosen] Merge remote-tracking branch 'origin/master' into jstack-in-web-ui 19707b0 [Josh Rosen] Add one comment. 127a130 [Josh Rosen] Update to use SparkContext.DRIVER_IDENTIFIER b8e69aa [Josh Rosen] Merge remote-tracking branch 'origin/master' into jstack-in-web-ui 3dfc2d4 [Josh Rosen] Add missing file. bc1e675 [Josh Rosen] Undo some leftover changes from the earlier approach. f4ac1c1 [Josh Rosen] Switch to on-demand collection of thread dumps dfec08b [Josh Rosen] Add option to disable thread dumps in UI. 4c87d7f [Josh Rosen] Use separate RPC for sending thread dumps. 2b8bdf3 [Josh Rosen] Enable thread dumps from the driver when running in non-local mode. cc3e6b3 [Josh Rosen] Fix test code in DAGSchedulerSuite. 87b8b65 [Josh Rosen] Add new listener event for thread dumps. 8c10216 [Josh Rosen] Add missing file. 0f198ac [Josh Rosen] [SPARK-611] Display executor thread dumps in web UI
Following #2919, this PR adds Python UDT (for internal use only) with tests under "pyspark.tests". Before `SQLContext.applySchema`, we check whether we need to convert user-type instances into SQL recognizable data. In the current implementation, a Python UDT must be paired with a Scala UDT for serialization on the JVM side. A following PR will add VectorUDT in MLlib for both Scala and Python. marmbrus jkbradley davies Author: Xiangrui Meng <meng@databricks.com> Closes #3068 from mengxr/SPARK-4192-sql and squashes the following commits: acff637 [Xiangrui Meng] merge master dba5ea7 [Xiangrui Meng] only use pyClass for Python UDT output sqlType as well 2c9d7e4 [Xiangrui Meng] move import to global setup; update needsConversion 7c4a6a9 [Xiangrui Meng] address comments 75223db [Xiangrui Meng] minor update f740379 [Xiangrui Meng] remove UDT from default imports e98d9d0 [Xiangrui Meng] fix py style 4e84fce [Xiangrui Meng] remove local hive tests and add more tests 39f19e0 [Xiangrui Meng] add tests b7f666d [Xiangrui Meng] add Python UDT
Register MLlib's Vector as a SQL user-defined type (UDT) in both Scala and Python. With this PR, we can easily map a RDD[LabeledPoint] to a SchemaRDD, and then select columns or save to a Parquet file. Examples in Scala/Python are attached. The Scala code was copied from jkbradley. ~~This PR contains the changes from #3068 . I will rebase after #3068 is merged.~~ marmbrus jkbradley Author: Xiangrui Meng <meng@databricks.com> Closes #3070 from mengxr/SPARK-3573 and squashes the following commits: 3a0b6e5 [Xiangrui Meng] organize imports 236f0a0 [Xiangrui Meng] register vector as UDT and provide dataset examples
/cc aarondav Author: zsxwing <zsxwing@gmail.com> Closes #3086 from zsxwing/SPARK-4163-back-comp and squashes the following commits: 21cb2a8 [zsxwing] Add a backward compatibility test for FetchFailed
…Failure Author: zsxwing <zsxwing@gmail.com> Closes #3085 from zsxwing/SPARK-4166-back-comp and squashes the following commits: 89329f4 [zsxwing] Add a backward compatibility test for ExecutorLostFailure
… by default. This PR simplify serializer, always use batched serializer (AutoBatchedSerializer as default), even batch size is 1. Author: Davies Liu <davies@databricks.com> This patch had conflicts when merged, resolved by Committer: Josh Rosen <joshrosen@databricks.com> Closes #2920 from davies/fix_autobatch and squashes the following commits: e544ef9 [Davies Liu] revert unrelated change 6880b14 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch 1d557fc [Davies Liu] fix tests 8180907 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch 76abdce [Davies Liu] clean up 53fa60b [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch d7ac751 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch 2cc2497 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch b4292ce [Davies Liu] fix bug in master d79744c [Davies Liu] recover hive tests be37ece [Davies Liu] refactor eb3938d [Davies Liu] refactor serializer in scala 8d77ef2 [Davies Liu] simplify serializer, use AutoBatchedSerializer by default.
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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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…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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…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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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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