| Commit message (Collapse) | Author | Age | Files | Lines |
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I don't believe the API changed at all.
Author: Avrohom Katz <iambpentameter@gmail.com>
Closes #8957 from akatz/kcl-upgrade.
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YARN, -master yarn --deploy-mode x vs -master yarn-x'.
Recommend `--master yarn --deploy-mode {cluster,client}` consistently in docs.
Follow-on to https://github.com/apache/spark/pull/8385
CC nssalian
Author: Sean Owen <sowen@cloudera.com>
Closes #8968 from srowen/SPARK-9570.
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The fix is to coerce `c("a", "b")` into a list such that it could be serialized to call JVM with.
Author: felixcheung <felixcheung_m@hotmail.com>
Closes #8961 from felixcheung/rselect.
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I was reading throught the scheduler and found this small mistake.
Author: Guillaume Poulin <guillaume@hopper.com>
Closes #8966 from gpoulin/remember_duration_typo.
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This PR just reverted https://github.com/apache/spark/commit/02144d6745ec0a6d8877d969feb82139bd22437f to remerge #6457 and also included the commits in #8905.
Author: zsxwing <zsxwing@gmail.com>
Closes #8944 from zsxwing/SPARK-6028.
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Given LogicalRelation (and other classes) were moved from sources package to execution.sources package, removed private[sql] to make LogicalRelation public to facilitate access for data sources.
Author: gweidner <gweidner@us.ibm.com>
Closes #8965 from gweidner/SPARK-7275.
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functionality
Compatibility between history server script and functionality
The history server has its argument parsing class in HistoryServerArguments. However, this doesn't get involved in the start-history-server.sh codepath where the $0 arg is assigned to spark.history.fs.logDirectory and all other arguments discarded (e.g --property-file.)
This stops the other options being usable from this script
Author: Joshi <rekhajoshm@gmail.com>
Author: Rekha Joshi <rekhajoshm@gmail.com>
Closes #8758 from rekhajoshm/SPARK-10317.
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https://github.com/apache/spark/pull/8882 broke our build.
Author: Yin Huai <yhuai@databricks.com>
Closes #8964 from yhuai/fixStyle.
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See JIRA [here](https://issues.apache.org/jira/browse/SPARK-6530).
Author: Xusen Yin <yinxusen@gmail.com>
Closes #5742 from yinxusen/SPARK-6530.
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JIRA issue [here](https://issues.apache.org/jira/browse/SPARK-5890).
I borrow the code of `findSplits` from `RandomForest`. I don't think it's good to call it from `RandomForest` directly.
Author: Xusen Yin <yinxusen@gmail.com>
Closes #5779 from yinxusen/SPARK-5890.
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Document CrossValidatorModel members: bestModel and avgMetrics
Author: Rerngvit Yanggratoke <rerngvit@kth.se>
Closes #8882 from rerngvit/Spark-9798.
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The utilities such as Substring#substringBinarySQL and BinaryPrefixComparator#computePrefix for binary data are put together in ByteArray for easy-to-read.
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Closes #8122 from maropu/CleanUpForBinaryType.
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We introduced SQL option `spark.sql.parquet.followParquetFormatSpec` while working on implementing Parquet backwards-compatibility rules in SPARK-6777. It indicates whether we should use legacy Parquet format adopted by Spark 1.4 and prior versions or the standard format defined in parquet-format spec to write Parquet files.
This option defaults to `false` and is marked as a non-public option (`isPublic = false`) because we haven't finished refactored Parquet write path. The problem is, the name of this option is somewhat confusing, because it's not super intuitive why we shouldn't follow the spec. Would be nice to rename it to `spark.sql.parquet.writeLegacyFormat`, and invert its default value (the two option names have opposite meanings).
Although this option is private in 1.5, we'll make it public in 1.6 after refactoring Parquet write path. So that users can decide whether to write Parquet files in standard format or legacy format.
Author: Cheng Lian <lian@databricks.com>
Closes #8566 from liancheng/spark-10400/deprecate-follow-parquet-format-spec.
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Takes over https://github.com/apache/spark/pull/8800
Author: Wenchen Fan <cloud0fan@163.com>
Closes #8941 from cloud-fan/hive-udf.
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returns long instead of the Double type
Floor & Ceiling function should returns Long type, rather than Double.
Verified with MySQL & Hive.
Author: Cheng Hao <hao.cheng@intel.com>
Closes #8933 from chenghao-intel/ceiling.
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Fixed the test failure here: https://amplab.cs.berkeley.edu/jenkins/view/Spark-QA-Test/job/Spark-1.5-SBT/116/AMPLAB_JENKINS_BUILD_PROFILE=hadoop2.2,label=spark-test/testReport/junit/org.apache.spark/HeartbeatReceiverSuite/normal_heartbeat/
This failure is because `HeartbeatReceiverSuite. heartbeatReceiver` may receive `SparkListenerExecutorAdded("driver")` sent from [LocalBackend](https://github.com/apache/spark/blob/8fb3a65cbb714120d612e58ef9d12b0521a83260/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala#L121).
There are other race conditions in `HeartbeatReceiverSuite` because `HeartbeatReceiver.onExecutorAdded` and `HeartbeatReceiver.onExecutorRemoved` are asynchronous. This PR also fixed them.
Author: zsxwing <zsxwing@gmail.com>
Closes #8946 from zsxwing/SPARK-10058.
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Created method as.data.frame as a synonym for collect().
Author: Oscar D. Lara Yejas <olarayej@mail.usf.edu>
Author: olarayej <oscar.lara.yejas@us.ibm.com>
Author: Oscar D. Lara Yejas <oscar.lara.yejas@us.ibm.com>
Closes #8908 from olarayej/SPARK-10807.
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This is an implementation of Hive's `json_tuple` function using Jackson Streaming.
Author: Nathan Howell <nhowell@godaddy.com>
Closes #7946 from NathanHowell/SPARK-9617.
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InternalRow rather than external Row.
Author: Reynold Xin <rxin@databricks.com>
Closes #8900 from rxin/SPARK-10770-1.
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mode)
The YARN backend doesn't like when user code calls System.exit, since it cannot know the exit status and thus cannot set an appropriate final status for the application.
This PR remove the usage of system.exit to exit the RRunner. Instead, when the R process running an SparkR script returns an exit code other than 0, throws SparkUserAppException which will be caught by ApplicationMaster and ApplicationMaster knows it failed. For other failures, throws SparkException.
Author: Sun Rui <rui.sun@intel.com>
Closes #8938 from sun-rui/SPARK-10851.
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This PR implements a HyperLogLog based Approximate Count Distinct function using the new UDAF interface.
The implementation is inspired by the ClearSpring HyperLogLog implementation and should produce the same results.
There is still some documentation and testing left to do.
cc yhuai
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes #8362 from hvanhovell/SPARK-9741.
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For some implicit dataset, ratings may not exist in the training data. In this case, we can assume all observed pairs to be positive and treat their ratings as 1. This should happen when users set ```ratingCol``` to an empty string.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes #8937 from yanboliang/spark-10736.
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When reading Parquet string and binary-backed decimal values, Parquet `Binary.getBytes` always returns a copied byte array, which is unnecessary. Since the underlying implementation of `Binary` values there is guaranteed to be `ByteArraySliceBackedBinary`, and Parquet itself never reuses underlying byte arrays, we can use `Binary.toByteBuffer.array()` to steal the underlying byte arrays without copying them.
This brings performance benefits when scanning Parquet string and binary-backed decimal columns. Note that, this trick doesn't cover binary-backed decimals with precision greater than 18.
My micro-benchmark result is that, this brings a ~15% performance boost for scanning TPC-DS `store_sales` table (scale factor 15).
Another minor optimization done in this PR is that, now we directly construct a Java `BigDecimal` in `Decimal.toJavaBigDecimal` without constructing a Scala `BigDecimal` first. This brings another ~5% performance gain.
Author: Cheng Lian <lian@databricks.com>
Closes #8907 from liancheng/spark-10811/eliminate-array-copying.
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Documentation for dropDuplicates() and drop_duplicates() is one and the same. Resolved the error in the example for drop_duplicates using the same approach used for groupby and groupBy, by indicating that dropDuplicates and drop_duplicates are aliases.
Author: asokadiggs <asoka.diggs@intel.com>
Closes #8930 from asokadiggs/jira-10782.
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Add method to easily convert a StatCounter instance into a Python dict
https://issues.apache.org/jira/browse/SPARK-6919
Note: This is my original work and the existing Spark license applies.
Author: Erik Shilts <erik.shilts@opower.com>
Closes #5516 from eshilts/statcounter-asdict.
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These are CSS/JavaScript changes changes to make navigation in the PySpark API a bit simpler by adding the following to the sidebar:
* Classes
* Functions
* Tags to highlight experimental features
![screen shot 2015-09-02 at 08 50 12](https://cloud.githubusercontent.com/assets/11915197/9634781/301f853a-518b-11e5-8d5c-fda202f6202f.png)
Online example here: https://dl.dropboxusercontent.com/u/20821334/pyspark-api-nav-enhance/pyspark.mllib.html
(The contribution is my original work and that I license the work to the project under the project's open source license)
Author: noelsmith <mail@noelsmith.com>
Closes #8571 from noel-smith/pyspark-api-nav-enhance.
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Author: Ryan Williams <ryan.blake.williams@gmail.com>
Closes #8939 from ryan-williams/errmsg.
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StandaloneDynamicAllocationSuite
Fix the following issues in StandaloneDynamicAllocationSuite:
1. It should not assume master and workers start in order
2. It should not assume master and workers get ready at once
3. It should not assume the application is already registered with master after creating SparkContext
4. It should not access Master.app and idToApp which are not thread safe
The changes includes:
* Use `eventually` to wait until master and workers are ready to fix 1 and 2
* Use `eventually` to wait until the application is registered with master to fix 3
* Use `askWithRetry[MasterStateResponse](RequestMasterState)` to get the application info to fix 4
Author: zsxwing <zsxwing@gmail.com>
Closes #8914 from zsxwing/fix-StandaloneDynamicAllocationSuite.
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jira: https://issues.apache.org/jira/browse/SPARK-10670
In the Markdown docs for the spark.ml Programming Guide, we have code examples with codetabs for each language. We should link to each language's API docs within the corresponding codetab, but we are inconsistent about this. For an example of what we want to do, see the "Word2Vec" section in https://github.com/apache/spark/blob/64743870f23bffb8d96dcc8a0181c1452782a151/docs/ml-features.md
This JIRA is just for spark.ml, not spark.mllib
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes #8901 from hhbyyh/docAPI.
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In the course of https://issues.apache.org/jira/browse/LEGAL-226 it came to light that the guidance at http://www.apache.org/dev/licensing-howto.html#permissive-deps means that permissively-licensed dependencies has a different interpretation than we (er, I) had been operating under. "pointer ... to the license within the source tree" specifically means a copy of the license within Spark's distribution, whereas at the moment, Spark's LICENSE has a pointer to the project's license in the other project's source tree.
The remedy is simply to inline all such license references (i.e. BSD/MIT licenses) or include their text in "licenses" subdirectory and point to that.
Along the way, we can also treat other BSD/MIT licenses, whose text has been inlined into LICENSE, in the same way.
The LICENSE file can continue to provide a helpful list of BSD/MIT licensed projects and a pointer to their sites. This would be over and above including license text in the distro, which is the essential thing.
Author: Sean Owen <sowen@cloudera.com>
Closes #8919 from srowen/SPARK-10833.
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The UTF8String may come from UnsafeRow, then underline buffer of it is not copied, so we should clone it in order to hold it in Stats.
cc yhuai
Author: Davies Liu <davies@databricks.com>
Closes #8929 from davies/pushdown_string.
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Please refer to [SPARK-10395] [1] for details.
[1]: https://issues.apache.org/jira/browse/SPARK-10395
Author: Cheng Lian <lian@databricks.com>
Closes #8553 from liancheng/spark-10395/simplify-parquet-read-support.
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consolidate the codes
This bug is introduced in [SPARK-9092](https://issues.apache.org/jira/browse/SPARK-9092), `targetExecutorNumber` should use `minExecutors` if `initialExecutors` is not set. Using 0 instead will meet the problem as mentioned in [SPARK-10790](https://issues.apache.org/jira/browse/SPARK-10790).
Also consolidate and simplify some similar code snippets to keep the consistent semantics.
Author: jerryshao <sshao@hortonworks.com>
Closes #8910 from jerryshao/SPARK-10790.
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While this is likely not a huge issue for real production systems, for test systems which may setup a Spark Context and tear it down and stand up a Spark Context with a different master (e.g. some local mode & some yarn mode) tests this cane be an issue. Discovered during work on spark-testing-base on Spark 1.4.1, but seems like the logic that triggers it is present in master (see SparkHadoopUtil object). A valid work around for users encountering this issue is to fork a different JVM, however this can be heavy weight.
```
[info] SampleMiniClusterTest:
[info] Exception encountered when attempting to run a suite with class name: com.holdenkarau.spark.testing.SampleMiniClusterTest *** ABORTED ***
[info] java.lang.ClassCastException: org.apache.spark.deploy.SparkHadoopUtil cannot be cast to org.apache.spark.deploy.yarn.YarnSparkHadoopUtil
[info] at org.apache.spark.deploy.yarn.YarnSparkHadoopUtil$.get(YarnSparkHadoopUtil.scala:163)
[info] at org.apache.spark.deploy.yarn.Client.prepareLocalResources(Client.scala:257)
[info] at org.apache.spark.deploy.yarn.Client.createContainerLaunchContext(Client.scala:561)
[info] at org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:115)
[info] at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:57)
[info] at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:141)
[info] at org.apache.spark.SparkContext.<init>(SparkContext.scala:497)
[info] at com.holdenkarau.spark.testing.SharedMiniCluster$class.setup(SharedMiniCluster.scala:186)
[info] at com.holdenkarau.spark.testing.SampleMiniClusterTest.setup(SampleMiniClusterTest.scala:26)
[info] at com.holdenkarau.spark.testing.SharedMiniCluster$class.beforeAll(SharedMiniCluster.scala:103)
```
Author: Holden Karau <holden@pigscanfly.ca>
Closes #8911 from holdenk/SPARK-10812-spark-hadoop-util-support-switching-to-yarn.
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seperate -> separate
sees -> see
Author: David Martin <dmartinpro@users.noreply.github.com>
Closes #8928 from dmartinpro/patch-1.
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Author: Bin Wang <wbin00@gmail.com>
Closes #8898 from wb14123/doc.
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local list of java beans
Similar to SPARK-10630 it would be nice if Java users didn't have to parallelize there data explicitly (as Scala users already can skip). Issue came up in http://stackoverflow.com/questions/32613413/apache-spark-machine-learning-cant-get-estimator-example-to-work
Author: Holden Karau <holden@pigscanfly.ca>
Closes #8879 from holdenk/SPARK-10720-add-a-java-wrapper-to-create-a-dataframe-from-a-local-list-of-java-beans.
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working
https://issues.apache.org/jira/browse/SPARK-10741
I choose the second approach: do not change output exprIds when convert MetastoreRelation to LogicalRelation
Author: Wenchen Fan <cloud0fan@163.com>
Closes #8889 from cloud-fan/hot-bug.
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I implemented toString for AssociationRules.Rule, format like `[x, y] => {z}: 1.0`
Author: y-shimizu <y.shimizu0429@gmail.com>
Closes #8904 from y-shimizu/master.
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When refactoring SQL options from plain strings to the strongly typed `SQLConfEntry`, `spark.sql.hive.version` wasn't migrated, and doesn't show up in the result of `SET -v`, as `SET -v` only shows public `SQLConfEntry` instances. This affects compatibility with Simba ODBC driver.
This PR migrates this SQL option as a `SQLConfEntry` to fix this issue.
Author: Cheng Lian <lian@databricks.com>
Closes #8925 from liancheng/spark-10845/hive-version-conf.
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argument is missing
Hi everyone,
Since the family argument is required for the glm function, the execution of:
model <- glm(Sepal_Length ~ Sepal_Width, df)
is failing.
I've fixed the documentation by adding the family argument and also added the summay(model) which will show the coefficients for the model.
Thanks,
Narine
Author: Narine Kokhlikyan <narine.kokhlikyan@gmail.com>
Closes #8870 from NarineK/sparkrml.
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This integrates the Interaction feature transformer with SparkR R formula support (i.e. support `:`).
To generate reasonable ML attribute names for feature interactions, it was necessary to add the ability to read attribute the original attribute names back from `StructField`, and also to specify custom group prefixes in `VectorAssembler`. This also has the side-benefit of cleaning up the double-underscores in the attributes generated for non-interaction terms.
mengxr
Author: Eric Liang <ekl@databricks.com>
Closes #8830 from ericl/interaction-2.
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This makes two changes:
- Allow reduce tasks to fetch multiple map output partitions -- this is a pretty small change to HashShuffleFetcher
- Move shuffle locality computation out of DAGScheduler and into ShuffledRDD / MapOutputTracker; this was needed because the code in DAGScheduler wouldn't work for RDDs that fetch multiple map output partitions from each reduce task
I also added an AdaptiveSchedulingSuite that creates RDDs depending on multiple map output partitions.
Author: Matei Zaharia <matei@databricks.com>
Closes #8844 from mateiz/spark-9852.
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The DiskBlockObjectWriter constructor took a BlockId parameter but never used it. As part of some general cleanup in these interfaces, this patch refactors its constructor to eliminate this parameter.
Author: Josh Rosen <joshrosen@databricks.com>
Closes #8871 from JoshRosen/disk-block-object-writer-blockid-cleanup.
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JIRA: https://issues.apache.org/jira/browse/SPARK-10705
As described in the JIRA ticket, `DataFrame.toJSON` uses `DataFrame.mapPartitions`, which converts internal rows to external rows. We should use `queryExecution.toRdd.mapPartitions` that directly uses internal rows for better performance.
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes #8865 from viirya/df-tojson-internalrow.
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Author: Wenchen Fan <cloud0fan@163.com>
Closes #8874 from cloud-fan/hive-agg.
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This reverts commit 084e4e126211d74a79e8dbd2d0e604dd3c650822.
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simplified dataframe construction
As introduced in https://issues.apache.org/jira/browse/SPARK-10630 we now have an easier way to create dataframes from local Java lists. Lets update the tests to use those.
Author: Holden Karau <holden@pigscanfly.ca>
Closes #8886 from holdenk/SPARK-10763-update-java-mllib-ml-tests-to-use-simplified-dataframe-construction.
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UI to clear failed batches
Slightly modified version of #8818, all credit goes to zsxwing
Author: zsxwing <zsxwing@gmail.com>
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes #8892 from tdas/SPARK-10692.
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