This PR removes most of the code in the Spark REPL for Scala 2.11 and leaves just a couple of overridden methods in `SparkILoop` in order to:
- change welcome message
- restrict available commands (like `:power`)
- initialize Spark context
The two codebases have diverged and it's extremely hard to backport fixes from the upstream REPL. This somewhat radical step is absolutely necessary in order to fix other REPL tickets (like SPARK-8013 - Hive Thrift server for 2.11). BTW, the Scala REPL has fixed the serialization-unfriendly wrappers thanks to ScrapCodes's work in [#4522](https://github.com/scala/scala/pull/4522)
All tests pass and I tried the `spark-shell` on our Mesos cluster with some simple jobs (including with additional jars), everything looked good.
As soon as Scala 2.11.7 is out we need to upgrade and get a shaded `jline` dependency, clearing the way for SPARK-8013.
/cc pwendell
Author: Iulian Dragos <jaguarul@gmail.com>
Closes#6903 from dragos/issue/no-spark-repl-fork and squashes the following commits:
c596c6f [Iulian Dragos] Merge branch 'master' into issue/no-spark-repl-fork
2b1a305 [Iulian Dragos] Removed spaces around multiple imports.
0ce67a6 [Iulian Dragos] Remove -verbose flag for java compiler (added by mistake in an earlier commit).
10edaf9 [Iulian Dragos] Keep the jline dependency only in the 2.10 build.
529293b [Iulian Dragos] Add back Spark REPL files to rat-excludes, since they are part of the 2.10 real.
d85370d [Iulian Dragos] Remove jline dependency from the Spark REPL.
b541930 [Iulian Dragos] Merge branch 'master' into issue/no-spark-repl-fork
2b15962 [Iulian Dragos] Change jline dependency and bump Scala version.
b300183 [Iulian Dragos] Rename package and add license on top of the file, remove files from rat-excludes and removed `-Yrepl-sync` per reviewer’s request.
9d46d85 [Iulian Dragos] Fix SPARK-7944.
abcc7cb [Iulian Dragos] Remove the REPL forked code.
Author: Jonathan Alter <jonalter@users.noreply.github.com>
Closes#7093 from jonalter/SPARK-7977 and squashes the following commits:
ccd44cc [Jonathan Alter] Changed println to log in ThreadingSuite
7fcac3e [Jonathan Alter] Reverting to println in ThreadingSuite
10724b6 [Jonathan Alter] Changing some printlns to logs in tests
eeec1e7 [Jonathan Alter] Merge branch 'master' of github.com:apache/spark into SPARK-7977
0b1dcb4 [Jonathan Alter] More println cleanup
aedaf80 [Jonathan Alter] Merge branch 'master' of github.com:apache/spark into SPARK-7977
925fd98 [Jonathan Alter] Merge branch 'master' of github.com:apache/spark into SPARK-7977
0c16fa3 [Jonathan Alter] Replacing some printlns with logs
45c7e05 [Jonathan Alter] Merge branch 'master' of github.com:apache/spark into SPARK-7977
5c8e283 [Jonathan Alter] Allowing println in audit-release examples
5b50da1 [Jonathan Alter] Allowing printlns in example files
ca4b477 [Jonathan Alter] Merge branch 'master' of github.com:apache/spark into SPARK-7977
83ab635 [Jonathan Alter] Fixing new printlns
54b131f [Jonathan Alter] Merge branch 'master' of github.com:apache/spark into SPARK-7977
1cd8a81 [Jonathan Alter] Removing some unnecessary comments and printlns
b837c3a [Jonathan Alter] Disallowing println
Runs for *all* existing keys and returning "None" will remove the key-value pair.
Author: Michael Vogiatzis <michaelvogiatzis@gmail.com>
Closes#7229 from mvogiatzis/patch-1 and squashes the following commits:
e7a2946 [Michael Vogiatzis] Updated updateStateByKey text
00283ed [Michael Vogiatzis] Removed space
c2656f9 [Michael Vogiatzis] Moved description farther up
0a42551 [Michael Vogiatzis] Added important updateStateByKey details
In my test, `sessions` and `executions` in ThriftServer2 is not the same number as the connection number.
For example, if there are 200 clients connecting to the server, but it will have more than 200 `sessions` and `executions`.
So if it reaches the `retainedStatements`, it has to remove some object which is not finished.
So it may cause the exception described in [Jira Address](https://issues.apache.org/jira/browse/SPARK-8839)
Author: huangzhaowei <carlmartinmax@gmail.com>
Closes#7239 from SaintBacchus/SPARK-8839 and squashes the following commits:
cf7ef40 [huangzhaowei] Remove the a meanless funciton call
3e9a5a6 [huangzhaowei] Add a filter before take
9d5ceb8 [huangzhaowei] [SPARK-8839][SQL]ThriftServer2 will remove session and execution no matter it's finished or not.
Cleanup tests from SPARK 8700.
Author: Holden Karau <holden@pigscanfly.ca>
Closes#7335 from holdenk/SPARK-8913-cleanup-tests-from-SPARK-8700-logistic-regression-r2-really-logistic-regression-this-time and squashes the following commits:
e5e2c5f [Holden Karau] Simplify LogisticRegression suite to use Vector <-> Vector comparisions instead of comparing element by element
Also, add support for the *-provided profiles. This avoids repackaging
things that are already in the Spark assembly, or, in the case of the
*-provided profiles, are provided by the distribution.
The flume-ng-auth dependency was also excluded since it's not really
used by Spark.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#7247 from vanzin/SPARK-8852 and squashes the following commits:
298a7d5 [Marcelo Vanzin] Feedback.
c962082 [Marcelo Vanzin] [SPARK-8852] [flume] Trim dependencies in flume assembly.
These two dependencies were introduced in #7231 to help testing Parquet compatibility with `parquet-thrift`. However, they somehow crash the Scala compiler in Maven builds.
This PR fixes this issue by:
1. Removing these two dependencies, and
2. Instead of generating the testing Parquet file programmatically, checking in an actual testing Parquet file generated by `parquet-thrift` as a test resource.
This is just a quick fix to bring back Maven builds. Need to figure out the root case as binary Parquet files are harder to maintain.
Author: Cheng Lian <lian@databricks.com>
Closes#7330 from liancheng/spark-8959 and squashes the following commits:
cf69512 [Cheng Lian] Brings back Maven builds
Adds results (e.g. objective value at each iteration, residuals) on training and user-specified test sets for LinearRegressionModel.
Notes to Reviewers:
* Are the `*TrainingResults` and `Results` classes too specialized for `LinearRegressionModel`? Where would be an appropriate level of abstraction?
* Please check `transient` annotations are correct; the datasets should not be copied and kept during serialization.
* Any thoughts on `RDD`s versus `DataFrame`s? If using `DataFrame`s, suggested schemas for each intermediate step? Also, how to create a "local DataFrame" without a `sqlContext`?
Author: Feynman Liang <fliang@databricks.com>
Closes#7099 from feynmanliang/SPARK-8538 and squashes the following commits:
d219fa4 [Feynman Liang] Update docs
4a42680 [Feynman Liang] Change Summary to hold values, move transient annotations down to metrics and predictions DF
6300031 [Feynman Liang] Code review changes
0a5e762 [Feynman Liang] Fix build error
e71102d [Feynman Liang] Merge branch 'master' into SPARK-8538
3367489 [Feynman Liang] Merge branch 'master' into SPARK-8538
70f267c [Feynman Liang] Make TrainingSummary transient and remove Serializable from *Summary and RegressionMetrics
1d9ea42 [Feynman Liang] Fix failing Java test
a65dfda [Feynman Liang] Make TrainingSummary and metrics serializable, prediction dataframe transient
0a605d8 [Feynman Liang] Replace Params from LinearRegression*Summary with private constructor vals
c2fe835 [Feynman Liang] Optimize imports
02d8a70 [Feynman Liang] Add Params to LinearModel*Summary, refactor tests and add test for evaluate()
8f999f4 [Feynman Liang] Refactor from jkbradley code review
072e948 [Feynman Liang] Style
509ae36 [Feynman Liang] Use DFs and localize serialization to LinearRegressionModel
9509c79 [Feynman Liang] Fix imports
b2bbaa3 [Feynman Liang] Refactored LinearRegressionResults API to be more private
ffceaec [Feynman Liang] Merge branch 'master' into SPARK-8538
1cedb2b [Feynman Liang] Add test for decreasing objective trace
dab0aff [Feynman Liang] Add LinearRegressionTrainingResults tests, make test suite code copy+pasteable
97b0a81 [Feynman Liang] Add LinearRegressionModel.evaluate() to get results on test sets
dc51bce [Feynman Liang] Style guide fixes
521f397 [Feynman Liang] Use RDD[(Double, Double)] instead of DF
2ff5710 [Feynman Liang] Add training results and model summary to ML LinearRegression
Simplify model weight assertions to use vector comparision, switch to using absTol when comparing with 0.0 intercepts
Author: Holden Karau <holden@pigscanfly.ca>
Closes#7327 from holdenk/SPARK-8913-cleanup-tests-from-SPARK-8700-logistic-regression and squashes the following commits:
5bac185 [Holden Karau] Simplify model weight assertions to use vector comparision, switch to using absTol when comparing with 0.0 intercepts
This PR fix the long standing issue of serialization between Python RDD and DataFrame, it change to using a customized Pickler for InternalRow to enable customized unpickling (type conversion, especially for UDT), now we can support UDT for UDF, cc mengxr .
There is no generated `Row` anymore.
Author: Davies Liu <davies@databricks.com>
Closes#7301 from davies/sql_ser and squashes the following commits:
81bef71 [Davies Liu] address comments
e9217bd [Davies Liu] add regression tests
db34167 [Davies Liu] Refactor of serialization for Python DataFrame
This PR propose a simple way to expose OffsetRange in Python code, also the usage of offsetRanges is similar to Scala/Java way, here in Python we could get OffsetRange like:
```
dstream.foreachRDD(lambda r: KafkaUtils.offsetRanges(r))
```
Reason I didn't follow the way what SPARK-8389 suggested is that: Python Kafka API has one more step to decode the message compared to Scala/Java, Which makes Python API return a transformed RDD/DStream, not directly wrapped so-called JavaKafkaRDD, so it is hard to backtrack to the original RDD to get the offsetRange.
Author: jerryshao <saisai.shao@intel.com>
Closes#7185 from jerryshao/SPARK-8389 and squashes the following commits:
4c6d320 [jerryshao] Another way to fix subclass deserialization issue
e6a8011 [jerryshao] Address the comments
fd13937 [jerryshao] Fix serialization bug
7debf1c [jerryshao] bug fix
cff3893 [jerryshao] refactor the code according to the comments
2aabf9e [jerryshao] Style fix
848c708 [jerryshao] Add HasOffsetRanges for Python
This PR adds `metadata` to `InputInfo`. `InputDStream` can report its metadata for a batch and it will be shown in the batch page.
For example,
FileInputDStream will display the new files for a batch, and DirectKafkaInputDStream will display its offset ranges.
Author: zsxwing <zsxwing@gmail.com>
Closes#7081 from zsxwing/input-metadata and squashes the following commits:
f7abd9b [zsxwing] Revert the space changes in project/MimaExcludes.scala
d906209 [zsxwing] Merge branch 'master' into input-metadata
74762da [zsxwing] Fix MiMa tests
7903e33 [zsxwing] Merge branch 'master' into input-metadata
450a46c [zsxwing] Address comments
1d94582 [zsxwing] Raname InputInfo to StreamInputInfo and change "metadata" to Map[String, Any]
d496ae9 [zsxwing] Add input metadata in the batch page
This is largely based on extracting the dynamic allocation parts from tnachen's #3861.
Author: Iulian Dragos <jaguarul@gmail.com>
Closes#4984 from dragos/issue/mesos-coarse-dynamicAllocation and squashes the following commits:
39df8cd [Iulian Dragos] Update tests to latest changes in core.
9d2c9fa [Iulian Dragos] Remove adjustment of executorLimitOption in doKillExecutors.
8b00f52 [Iulian Dragos] Latest round of reviews.
0cd00e0 [Iulian Dragos] Add persistent shuffle directory
15c45c1 [Iulian Dragos] Add dynamic allocation to the Spark coarse-grained scheduler.
(This reopens a patch that was closed in the past: #6248)
When you view the stage page while running the following:
```
sc.parallelize(1 to X, 10000).count()
```
The page never loads, the job is stalled, and you end up running into an OOM:
```
HTTP ERROR 500
Problem accessing /stages/stage/. Reason:
Server Error
Caused by:
java.lang.OutOfMemoryError: Java heap space
at java.util.Arrays.copyOf(Arrays.java:2367)
at java.lang.AbstractStringBuilder.expandCapacity(AbstractStringBuilder.java:130)
```
This patch compresses Jetty responses in gzip. The correct long-term fix is to add pagination.
Author: Andrew Or <andrew@databricks.com>
Closes#7296 from andrewor14/gzip-jetty and squashes the following commits:
a051c64 [Andrew Or] Use GZIP to compress Jetty responses
The configuration ```SPARK_EXECUTOR_CORES``` won't put into ```SparkConf```, so it has no effect to the dynamic executor allocation.
Author: xutingjun <xutingjun@huawei.com>
Closes#7322 from XuTingjun/SPARK_EXECUTOR_CORES and squashes the following commits:
2cafa89 [xutingjun] make SPARK_EXECUTOR_CORES has effect to dynamicAllocation
Log statements incorrectly showed that the executor was being stopped when receiver was being stopped.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#7328 from tdas/fix-log and squashes the following commits:
9cc6e99 [Tathagata Das] Fix log statements.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#6762 from chenghao-intel/str_funcs and squashes the following commits:
b09a909 [Cheng Hao] update the code as feedback
7ebbf4c [Cheng Hao] Add more string expressions
jira: https://issues.apache.org/jira/browse/SPARK-8703
Converts a text document to a sparse vector of token counts.
I can further add an estimator to extract vocabulary from corpus if that's appropriate.
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#7084 from hhbyyh/countVectorization and squashes the following commits:
5f3f655 [Yuhao Yang] text change
24728e4 [Yuhao Yang] style improvement
576728a [Yuhao Yang] rename to model and some fix
1deca28 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into countVectorization
99b0c14 [Yuhao Yang] undo extension from HashingTF
12c2dc8 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into countVectorization
7ee1c31 [Yuhao Yang] extends HashingTF
809fb59 [Yuhao Yang] minor fix for ut
7c61fb3 [Yuhao Yang] add countVectorizer
'spark_ec2.py' use boto to control ec2.
And boto can support '~/.aws/credentials' which is AWS CLI default configuration file.
We can check this information from ref of boto.
"A boto config file is a text file formatted like an .ini configuration file that specifies values for options that control the behavior of the boto library. In Unix/Linux systems, on startup, the boto library looks for configuration files in the following locations and in the following order:
/etc/boto.cfg - for site-wide settings that all users on this machine will use
(if profile is given) ~/.aws/credentials - for credentials shared between SDKs
(if profile is given) ~/.boto - for user-specific settings
~/.aws/credentials - for credentials shared between SDKs
~/.boto - for user-specific settings"
* ref of boto: http://boto.readthedocs.org/en/latest/boto_config_tut.html
* ref of aws cli : http://docs.aws.amazon.com/cli/latest/userguide/cli-chap-getting-started.html
However 'spark_ec2.py' only check boto config & environment variable even if there is '~/.aws/credentials', and 'spark_ec2.py' is terminated.
So I changed to check '~/.aws/credentials'.
cc rxin
Jira : https://issues.apache.org/jira/browse/SPARK-8863
Author: JPark <JPark@JPark.me>
Closes#7252 from JuhongPark/master and squashes the following commits:
23c5792 [JPark] Check aws access key from aws credentials if there is no boto config
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#7315 from cloud-fan/toString and squashes the following commits:
4fc8d80 [Wenchen Fan] Implement toString for Interval data type
I merged https://github.com/apache/spark/pull/7303 so it unblocks another PR. This addresses my own code review comment for that PR.
Author: Reynold Xin <rxin@databricks.com>
Closes#7313 from rxin/adt and squashes the following commits:
7ade82b [Reynold Xin] Fixed unit tests.
f8d5533 [Reynold Xin] [SPARK-8926][SQL] Code review followup.
Also added more documentation for the file.
Author: Reynold Xin <rxin@databricks.com>
Closes#7316 from rxin/extract-value and squashes the following commits:
069cb7e [Reynold Xin] Removed ExtractValueWithOrdinal.
621b705 [Reynold Xin] Reverted a line.
11ebd6c [Reynold Xin] [Minor][SQL] Improve documentation for complex type extractors.
JIRA: https://issues.apache.org/jira/browse/SPARK-8940
The given `schema` parameter will be overwritten in `createDataFrame` now. If it is not null, we shouldn't overwrite it.
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#7311 from viirya/df_not_overwrite_schema and squashes the following commits:
2385139 [Liang-Chi Hsieh] Don't overwrite given schema if it is not null.
Exception will not be catched during tests.
cc marmbrus rxin
Author: Davies Liu <davies@databricks.com>
Closes#7309 from davies/fallback and squashes the following commits:
969a612 [Davies Liu] throw exception during tests
f844f77 [Davies Liu] fallback
a3091bc [Davies Liu] Merge branch 'master' of github.com:apache/spark into fallback
364a0d6 [Davies Liu] fallback to interpret mode if failed to compile
Author: Wenchen Fan <cloud0fan@outlook.com>
Closes#7312 from cloud-fan/minor and squashes the following commits:
a4589fa [Wenchen Fan] use double not decimal when cast double and float to timestamp
This PR is based on #7209 authored by Sephiroth-Lin.
Author: Weizhong Lin <linweizhong@huawei.com>
Closes#7314 from liancheng/spark-8928 and squashes the following commits:
75267fe [Cheng Lian] Makes CatalystSchemaConverter sticking to 1.4.x- when handling LISTs in compatible mode
This PR is based on #7209 authored by Sephiroth-Lin.
Author: Weizhong Lin <linweizhong@huawei.com>
Closes#7304 from liancheng/spark-8928 and squashes the following commits:
75267fe [Cheng Lian] Makes CatalystSchemaConverter sticking to 1.4.x- when handling LISTs in compatible mode
For example: `cannot resolve 'testfunction(null)' due to data type mismatch: argument 1 is expected to be of type int, however, null is of type datetype.`
Author: Michael Armbrust <michael@databricks.com>
Closes#7303 from marmbrus/expectsTypeErrors and squashes the following commits:
c654a0e [Michael Armbrust] fix udts and make errors pretty
137160d [Michael Armbrust] style
5428fda [Michael Armbrust] style
10fac82 [Michael Armbrust] [SPARK-8926][SQL] Good errors for ExpectsInputType expressions
`spark.unsafe.exceptionOnMemoryLeak` is present in the config of surefire.
```
<!-- Surefire runs all Java tests -->
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-surefire-plugin</artifactId>
<version>2.18.1</version>
<!-- Note config is repeated in scalatest config -->
...
<spark.unsafe.exceptionOnMemoryLeak>true</spark.unsafe.exceptionOnMemoryLeak>
</systemProperties>
...
```
but is absent in the config ScalaTest.
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#7308 from sarutak/add-setting-for-memory-leak and squashes the following commits:
95644e7 [Kousuke Saruta] Added a setting for memory leak
Due to the way MiMa works, we currently start a `SQLContext` pretty early on. This causes us to start a `SparkUI` that attempts to bind to port 4040. Because many tests run in parallel on the Jenkins machines, this causes port contention sometimes and fails the MiMa tests.
Note that we already disabled the SparkUI for scalatests. However, the MiMa test is run before we even have a chance to load the default scalatest settings, so we need to explicitly disable the UI ourselves.
Author: Andrew Or <andrew@databricks.com>
Closes#7300 from andrewor14/mima-flaky and squashes the following commits:
b55a547 [Andrew Or] Do not enable SparkUI during tests
We call Row.copy() in many places throughout SQL but UnsafeRow currently throws UnsupportedOperationException when copy() is called.
Supporting copying when ObjectPool is used may be difficult, since we may need to handle deep-copying of objects in the pool. In addition, this copy() method needs to produce a self-contained row object which may be passed around / buffered by downstream code which does not understand the UnsafeRow format.
In the long run, we'll need to figure out how to handle the ObjectPool corner cases, but this may be unnecessary if other changes are made. Therefore, in order to unblock my sort patch (#6444) I propose that we support copy() for the cases where UnsafeRow does not use an ObjectPool and continue to throw UnsupportedOperationException when an ObjectPool is used.
This patch accomplishes this by modifying UnsafeRow so that it knows the size of the row's backing data in order to be able to copy it into a byte array.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#7306 from JoshRosen/SPARK-8932 and squashes the following commits:
338e6bf [Josh Rosen] Support copy for UnsafeRows that do not use ObjectPools.
JIRA: https://issues.apache.org/jira/browse/SPARK-8866
Author: Yijie Shen <henry.yijieshen@gmail.com>
Closes#7283 from yijieshen/micro_timestamp and squashes the following commits:
dc735df [Yijie Shen] update CastSuite to avoid round error
714eaea [Yijie Shen] add timestamp_udf into blacklist due to precision lose
c3ca2f4 [Yijie Shen] fix unhandled case in CurrentTimestamp
8d4aa6b [Yijie Shen] use 1us precision for timestamp type
A couple descriptions were not inside `<td></td>` and were being displayed immediately under the section title instead of in their row.
Author: Jonathan Alter <jonalter@users.noreply.github.com>
Closes#7292 from jonalter/docs-config and squashes the following commits:
5ce1570 [Jonathan Alter] [DOCS] Format wrong for some config descriptions
This PR fixes the converter for Python DataFrame, especially for DecimalType
Closes#7106
Author: Davies Liu <davies@databricks.com>
Closes#7131 from davies/decimal_python and squashes the following commits:
4d3c234 [Davies Liu] Merge branch 'master' of github.com:apache/spark into decimal_python
20531d6 [Davies Liu] Merge branch 'master' of github.com:apache/spark into decimal_python
7d73168 [Davies Liu] fix conflit
6cdd86a [Davies Liu] Merge branch 'master' of github.com:apache/spark into decimal_python
7104e97 [Davies Liu] improve type infer
9cd5a21 [Davies Liu] run python tests with SPARK_PREPEND_CLASSES
829a05b [Davies Liu] fix UDT in python
c99e8c5 [Davies Liu] fix mima
c46814a [Davies Liu] convert decimal for Python DataFrames
As rxin suggested in #7298 , we should consider to remove `RDDApi`.
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#7302 from sarutak/remove-rddapi and squashes the following commits:
e495d35 [Kousuke Saruta] Fixed mima
cb7ebb9 [Kousuke Saruta] Removed overriding RDDApi
Distribute expensive portions of computation for Gaussian mixture components (in particular, pre-computation of `MultivariateGaussian.rootSigmaInv`, the inverse covariance matrix and covariance determinant) across executors. Repost of PR#4654.
Notes for reviewers:
* What should be the policy for when to distribute computation. Always? When numClusters > threshold? User-specified param?
TODO:
* Performance testing and comparison for large number of clusters
Author: Feynman Liang <fliang@databricks.com>
Closes#7166 from feynmanliang/GMM_parallel_mixtures and squashes the following commits:
4f351fa [Feynman Liang] Update heuristic and scaladoc
5ea947e [Feynman Liang] Fix parallelization logic
00eb7db [Feynman Liang] Add helper method for GMM's M step, remove distributeGaussians flag
e7c8127 [Feynman Liang] Add distributeGaussians flag and tests
1da3c7f [Feynman Liang] Distribute mixtures
Adds FPGrowth.generateAssociationRules to public API for generating association rules after mining frequent itemsets.
Author: Feynman Liang <fliang@databricks.com>
Closes#7271 from feynmanliang/SPARK-8877 and squashes the following commits:
83b8baf [Feynman Liang] Add API Doc
867abff [Feynman Liang] Add FPGrowth.generateAssociationRules and change access modifiers for AssociationRules
Add confusionMatrix method at class MulticlassMetrics in pyspark/mllib
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#7286 from yanboliang/spark-8068 and squashes the following commits:
6109fe1 [Yanbo Liang] Add confusionMatrix method at class MulticlassMetrics in pyspark/mllib
This PR is a follow-up of #6617 and is part of [SPARK-6774] [2], which aims to ensure interoperability and backwards-compatibility for Spark SQL Parquet support. And this one fixes the read path. Now Spark SQL is expected to be able to read legacy Parquet data files generated by most (if not all) common libraries/tools like parquet-thrift, parquet-avro, and parquet-hive. However, we still need to refactor the write path to write standard Parquet LISTs and MAPs ([SPARK-8848] [4]).
### Major changes
1. `CatalystConverter` class hierarchy refactoring
- Replaces `CatalystConverter` trait with a much simpler `ParentContainerUpdater`.
Now instead of extending the original `CatalystConverter` trait, every converter class accepts an updater which is responsible for propagating the converted value to some parent container. For example, appending array elements to a parent array buffer, appending a key-value pairs to a parent mutable map, or setting a converted value to some specific field of a parent row. Root converter doesn't have a parent and thus uses a `NoopUpdater`.
This simplifies the design since converters don't need to care about details of their parent converters anymore.
- Unifies `CatalystRootConverter`, `CatalystGroupConverter` and `CatalystPrimitiveRowConverter` into `CatalystRowConverter`
Specifically, now all row objects are represented by `SpecificMutableRow` during conversion.
- Refactors `CatalystArrayConverter`, and removes `CatalystArrayContainsNullConverter` and `CatalystNativeArrayConverter`
`CatalystNativeArrayConverter` was probably designed with the intention of avoiding boxing costs. However, the way it uses Scala generics actually doesn't achieve this goal.
The new `CatalystArrayConverter` handles both nullable and non-nullable array elements in a consistent way.
- Implements backwards-compatibility rules in `CatalystArrayConverter`
When Parquet records are being converted, schema of Parquet files should have already been verified. So we only need to care about the structure rather than field names in the Parquet schema. Since all map objects represented in legacy systems have the same structure as the standard one (see [backwards-compatibility rules for MAP] [1]), we only need to deal with LIST (namely array) in `CatalystArrayConverter`.
2. Requested columns handling
When specifying requested columns in `RowReadSupport`, we used to use a Parquet `MessageType` converted from a Catalyst `StructType` which contains all requested columns. This is not preferable when taking compatibility and interoperability into consideration. Because the actual Parquet file may have different physical structure from the converted schema.
In this PR, the schema for requested columns is constructed using the following method:
- For a column that exists in the target Parquet file, we extract the column type by name from the full file schema, and construct a single-field `MessageType` for that column.
- For a column that doesn't exist in the target Parquet file, we create a single-field `StructType` and convert it to a `MessageType` using `CatalystSchemaConverter`.
- Unions all single-field `MessageType`s into a full schema containing all requested fields
With this change, we also fix [SPARK-6123] [3] by validating the global schema against each individual Parquet part-files.
### Testing
This PR also adds compatibility tests for parquet-avro, parquet-thrift, and parquet-hive. Please refer to `README.md` under `sql/core/src/test` for more information about these tests. To avoid build time code generation and adding extra complexity to the build system, Java code generated from testing Thrift schema and Avro IDL is also checked in.
[1]: https://github.com/apache/incubator-parquet-format/blob/master/LogicalTypes.md#backward-compatibility-rules-1
[2]: https://issues.apache.org/jira/browse/SPARK-6774
[3]: https://issues.apache.org/jira/browse/SPARK-6123
[4]: https://issues.apache.org/jira/browse/SPARK-8848
Author: Cheng Lian <lian@databricks.com>
Closes#7231 from liancheng/spark-6776 and squashes the following commits:
360fe18 [Cheng Lian] Adds ParquetHiveCompatibilitySuite
c6fbc06 [Cheng Lian] Removes WIP file committed by mistake
b8c1295 [Cheng Lian] Excludes the whole parquet package from MiMa
598c3e8 [Cheng Lian] Adds extra Maven repo for hadoop-lzo, which is a transitive dependency of parquet-thrift
926af87 [Cheng Lian] Simplifies Parquet compatibility test suites
7946ee1 [Cheng Lian] Fixes Scala styling issues
3d7ab36 [Cheng Lian] Fixes .rat-excludes
a8f13bb [Cheng Lian] Using Parquet writer API to do compatibility tests
f2208cd [Cheng Lian] Adds README.md for Thrift/Avro code generation
1d390aa [Cheng Lian] Adds parquet-thrift compatibility test
440f7b3 [Cheng Lian] Adds generated files to .rat-excludes
13b9121 [Cheng Lian] Adds ParquetAvroCompatibilitySuite
06cfe9d [Cheng Lian] Adds comments about TimestampType handling
a099d3e [Cheng Lian] More comments
0cc1b37 [Cheng Lian] Fixes MiMa checks
884d3e6 [Cheng Lian] Fixes styling issue and reverts unnecessary changes
802cbd7 [Cheng Lian] Fixes bugs related to schema merging and empty requested columns
38fe1e7 [Cheng Lian] Adds explicit return type
7fb21f1 [Cheng Lian] Reverts an unnecessary debugging change
1781dff [Cheng Lian] Adds test case for SPARK-8811
6437d4b [Cheng Lian] Assembles requested schema from Parquet file schema
bcac49f [Cheng Lian] Removes the 16-byte restriction of decimals
a74fb2c [Cheng Lian] More comments
0525346 [Cheng Lian] Removes old Parquet record converters
03c3bd9 [Cheng Lian] Refactors Parquet read path to implement backwards-compatibility rules
With "+" the strings are separate expressions, and format() is called on the last string before concatenation. (So substitution does not happen.) Without "+" the string literals are merged first by the parser, so format() is called on the complete string.
Should I make a JIRA for this?
Author: Daniel Darabos <darabos.daniel@gmail.com>
Closes#7288 from darabos/patch-2 and squashes the following commits:
be0d3b7 [Daniel Darabos] Correctly print hostname in error
All compressed sensing applications, and some of the regression use-cases will have better result by turning the feature scaling off. However, if we implement this naively by training the dataset without doing any standardization, the rate of convergency will not be good. This can be implemented by still standardizing the training dataset but we penalize each component differently to get effectively the same objective function but a better numerical problem. As a result, for those columns with high variances, they will be penalized less, and vice versa. Without this, since all the features are standardized, so they will be penalized the same.
In R, there is an option for this.
`standardize`
Logical flag for x variable standardization, prior to fitting the model sequence. The coefficients are always returned on the original scale. Default is standardize=TRUE. If variables are in the same units already, you might not wish to standardize. See details below for y standardization with family="gaussian".
+cc holdenk mengxr jkbradley
Author: DB Tsai <dbt@netflix.com>
Closes#7080 from dbtsai/lors and squashes the following commits:
877e6c7 [DB Tsai] repahse the doc
7cf45f2 [DB Tsai] address feedback
78d75c9 [DB Tsai] small change
c2c9e60 [DB Tsai] style
6e1a8e0 [DB Tsai] first commit
Adding `()` to the definition of `distinct` in DataFrame allows distinct to be called with parentheses, which is consistent with `dropDuplicates`.
Author: Cheolsoo Park <cheolsoop@netflix.com>
Closes#7298 from piaozhexiu/SPARK-8908 and squashes the following commits:
7f0d923 [Cheolsoo Park] Add () to distinct definition in dataframe
…ng-guide#Manually Specifying Options to be in sync with java,python, R version
Author: Alok Singh <“singhal@us.ibm.com”>
Closes#7299 from aloknsingh/aloknsingh_SPARK-8909 and squashes the following commits:
d3c20ba [Alok Singh] fix the file to .parquet from .json
d476140 [Alok Singh] [SPARK-8909][Documentation] Change the scala example in sql-programming-guide#Manually Specifying Options to be in sync with java,python, R version
