JIRA issue:[SPARK-1511](https://issues.apache.org/jira/browse/SPARK-1511)
TestUtils.createCompiledClass method use renameTo() to move files which fails when the src and dest files are in different disks or partitions. This pr uses Files.move() instead. The move method will try to use renameTo() and then fall back to copy() and delete(). I think this should handle this issue.
I didn't found a test suite for this file, so I add file existence detection after file moving.
Author: Ye Xianjin <advancedxy@gmail.com>
Closes#427 from advancedxy/SPARK-1511 and squashes the following commits:
a2b97c7 [Ye Xianjin] Based on @srowen's comment, assert file existence.
6f95550 [Ye Xianjin] use Files.move instead of renameTo to handle the src and dest files are in different disks or partitions.
YARN-1824 changes the APIs (addToEnvironment, setEnvFromInputString) in Apps, which causes the spark build to break if built against a version 2.4.0. To fix this, create the spark own function to do that functionality which will not break compiling against 2.3 and other 2.x versions.
Author: xuan <xuan@MacBook-Pro.local>
Author: xuan <xuan@macbook-pro.home>
Closes#396 from xgong/master and squashes the following commits:
42b5984 [xuan] Remove two extra imports
bc0926f [xuan] Remove usage of org.apache.hadoop.util.Shell
be89fa7 [xuan] fix Spark compilation is broken with the latest hadoop-2.4.0 release
... nicer error messages
There are two improvements to Scheduler Mode:
1. Made the built in ones case insensitive (fair/FAIR, fifo/FIFO).
2. If an invalid mode is given we should print a better error message.
Author: Sandeep <sandeep@techaddict.me>
Closes#388 from techaddict/1469 and squashes the following commits:
a31bbd5 [Sandeep] SPARK-1469: Scheduler mode should accept lower-case definitions and have nicer error messages There are two improvements to Scheduler Mode: 1. Made the built in ones case insensitive (fair/FAIR, fifo/FIFO). 2. If an invalid mode is given we should print a better error message.
(I wasn't sure how to automatically set `SPARK_YARN=true` and `SPARK_HIVE=true` when running scalastyle, but these are the errors that turn up.)
Author: Sean Owen <sowen@cloudera.com>
Closes#413 from srowen/SPARK-1497 and squashes the following commits:
f0c9318 [Sean Owen] Fix more scalastyle warnings in yarn
80bf4c3 [Sean Owen] Add YARN alpha / YARN profile to scalastyle check
026319c [Sean Owen] Fix scalastyle warnings in YARN, Hive code
Author: Holden Karau <holden@pigscanfly.ca>
Closes#18 from holdenk/addkfoldcrossvalidation and squashes the following commits:
208db9b [Holden Karau] Fix a bad space
e84f2fc [Holden Karau] Fix the test, we should be looking at the second element instead
6ddbf05 [Holden Karau] swap training and validation order
7157ae9 [Holden Karau] CR feedback
90896c7 [Holden Karau] New line
150889c [Holden Karau] Fix up error messages in the MLUtilsSuite
2cb90b3 [Holden Karau] Fix the names in kFold
c702a96 [Holden Karau] Fix imports in MLUtils
e187e35 [Holden Karau] Move { up to same line as whenExecuting(random) in RandomSamplerSuite.scala
c5b723f [Holden Karau] clean up
7ebe4d5 [Holden Karau] CR feedback, remove unecessary learners (came back during merge mistake) and insert an empty line
bb5fa56 [Holden Karau] extra line sadness
163c5b1 [Holden Karau] code review feedback 1.to -> 1 to and folds -> numFolds
5a33f1d [Holden Karau] Code review follow up.
e8741a7 [Holden Karau] CR feedback
b78804e [Holden Karau] Remove cross validation [TODO in another pull request]
91eae64 [Holden Karau] Consolidate things in mlutils
264502a [Holden Karau] Add a test for the bug that was found with BernoulliSampler not copying the complement param
dd0b737 [Holden Karau] Wrap long lines (oops)
c0b7fa4 [Holden Karau] Switch FoldedRDD to use BernoulliSampler and PartitionwiseSampledRDD
08f8e4d [Holden Karau] Fix BernoulliSampler to respect complement
a751ec6 [Holden Karau] Add k-fold cross validation to MLLib
actually, the value 8 is only valid in mesos fine-grained mode :
<code>
override def defaultParallelism() = sc.conf.getInt("spark.default.parallelism", 8)
</code>
while in coarse-grained model including mesos coares-grained, the value of the property depending on core numbers!
<code>
override def defaultParallelism(): Int = {
conf.getInt("spark.default.parallelism", math.max(totalCoreCount.get(), 2))
}
</code>
Author: Chen Chao <crazyjvm@gmail.com>
Closes#389 from CrazyJvm/patch-2 and squashes the following commits:
84a7fe4 [Chen Chao] miss </li> at the end of every single line
04a9796 [Chen Chao] change format
ee0fae0 [Chen Chao] update spark.default.parallelism
When running Hive tests, the working directory is `$SPARK_HOME/sql/hive`, while when running `sbt hive/console`, it becomes `$SPARK_HOME`, and test tables are not loaded if `HIVE_DEV_HOME` is not defined.
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#417 from liancheng/loadTestTables and squashes the following commits:
7cea8d6 [Cheng Lian] Loads test tables when running "sbt hive/console" without HIVE_DEV_HOME
This is not an issue with the driver UI, but when you fire
up the history server, there's currently no way to go back to
the app listing page without editing the browser's location
field (since the logo's link points to the root of the
application's own UI - i.e. the "stages" tab).
The change just points the logo link to "/", which is the app
listing for the history server, and the stages tab for the
driver's UI.
Tested with both history server and live driver.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#408 from vanzin/web-ui-root and squashes the following commits:
1b60cb6 [Marcelo Vanzin] Make "spark logo" link refer to "/".
JIRA issue: [SPARK-959](https://spark-project.atlassian.net/browse/SPARK-959)
SBT 0.13.2 has been officially released. This version updated Ivy 2.0 to Ivy 2.3, which fixes [IVY-899](https://issues.apache.org/jira/browse/IVY-899). This PR also removed previous workaround.
Author: Cheng Lian <lian.cs.zju@gmail.com>
Closes#426 from liancheng/updateSbt and squashes the following commits:
95e3dc8 [Cheng Lian] Updated SBT from 0.13.1 to 0.13.2 to fix SPARK-959
This makes it possible to create tables and insert into them using the DSL and SQL for the scala and java apis.
Author: Michael Armbrust <michael@databricks.com>
Closes#354 from marmbrus/insertIntoTable and squashes the following commits:
6c6f227 [Michael Armbrust] Create random temporary files in python parquet unit tests.
f5e6d5c [Michael Armbrust] Merge remote-tracking branch 'origin/master' into insertIntoTable
765c506 [Michael Armbrust] Add to JavaAPI.
77b512c [Michael Armbrust] typos.
5c3ef95 [Michael Armbrust] use names for boolean args.
882afdf [Michael Armbrust] Change createTableAs to saveAsTable. Clean up api annotations.
d07d94b [Michael Armbrust] Add tests, support for creating parquet files and hive tables.
fa3fe81 [Michael Armbrust] Make insertInto available on JavaSchemaRDD as well. Add createTableAs function.
This PR adds a SparseVector class in PySpark and updates all the regression, classification and clustering algorithms and models to support sparse data, similar to MLlib. I chose to add this class because SciPy is quite difficult to install in many environments (more so than NumPy), but I plan to add support for SciPy sparse vectors later too, and make the methods work transparently on objects of either type.
On the Scala side, we keep Python sparse vectors sparse and pass them to MLlib. We always return dense vectors from our models.
Some to-do items left:
- [x] Support SciPy's scipy.sparse matrix objects when SciPy is available. We can easily add a function to convert these to our own SparseVector.
- [x] MLlib currently uses a vector with one extra column on the left to represent what we call LabeledPoint in Scala. Do we really want this? It may get annoying once you deal with sparse data since you must add/subtract 1 to each feature index when training. We can remove this API in 1.0 and use tuples for labeling.
- [x] Explain how to use these in the Python MLlib docs.
CC @mengxr, @joshrosen
Author: Matei Zaharia <matei@databricks.com>
Closes#341 from mateiz/py-ml-update and squashes the following commits:
d52e763 [Matei Zaharia] Remove no-longer-needed slice code and handle review comments
ea5a25a [Matei Zaharia] Fix remaining uses of copyto() after merge
b9f97a3 [Matei Zaharia] Fix test
1e1bd0f [Matei Zaharia] Add MLlib logistic regression example in Python
88bc01f [Matei Zaharia] Clean up inheritance of LinearModel in Python, and expose its parametrs
37ab747 [Matei Zaharia] Fix some examples and docs due to changes in MLlib API
da0f27e [Matei Zaharia] Added a MLlib K-means example and updated docs to discuss sparse data
c48e85a [Matei Zaharia] Added some tests for passing lists as input, and added mllib/tests.py to run-tests script.
a07ba10 [Matei Zaharia] Fix some typos and calculation of initial weights
74eefe7 [Matei Zaharia] Added LabeledPoint class in Python
889dde8 [Matei Zaharia] Support scipy.sparse matrices in all our algorithms and models
ab244d1 [Matei Zaharia] Allow SparseVectors to be initialized using a dict
a5d6426 [Matei Zaharia] Add linalg.py to run-tests script
0e7a3d8 [Matei Zaharia] Keep vectors sparse in Java when reading LabeledPoints
eaee759 [Matei Zaharia] Update regression, classification and clustering models for sparse data
2abbb44 [Matei Zaharia] Further work to get linear models working with sparse data
154f45d [Matei Zaharia] Update docs, name some magic values
881fef7 [Matei Zaharia] Added a sparse vector in Python and made Java-Python format more compact
I saw `No "scala-library*.jar" in Scala compiler library` error in IDEA. It seems upgrading `sbt-idea` to 1.6.0 fixed the problem.
Author: Xiangrui Meng <meng@databricks.com>
Closes#419 from mengxr/idea-plugin and squashes the following commits:
fb3c35f [Xiangrui Meng] update sbt-idea to version 1.6.0
This is a simple first step towards avoiding running the Hive tests
whenever possible.
Author: Patrick Wendell <pwendell@gmail.com>
Closes#420 from pwendell/test-isolation and squashes the following commits:
350c8af [Patrick Wendell] SPARK-1455: Better isolation for unit tests.
Added documentation for user to use the decision tree algorithms for classification and regression in Spark 1.0 release.
Apart from a general review, I need specific input on the following:
* I had to move a lot of the existing documentation under the *linear methods* umbrella to accommodate decision trees. I wonder if there is a better way to organize the programming guide given we are so close to the release.
* I have not looked closely at pyspark but I am wondering new mllib algorithms are automatically plugged in or do we need to some extra work to call mllib functions from pyspark. I will add to the pyspark examples based upon the advice I get.
cc: @mengxr, @hirakendu, @etrain, @atalwalkar
Author: Manish Amde <manish9ue@gmail.com>
Closes#402 from manishamde/tree_doc and squashes the following commits:
022485a [Manish Amde] more documentation
865826e [Manish Amde] minor: grammar
dbb0e5e [Manish Amde] minor improvements to text
b9ef6c4 [Manish Amde] basic decision tree code examples
6e297d7 [Manish Amde] added subsections
f427e84 [Manish Amde] renaming sections
9c0c4be [Manish Amde] split candidate
6925275 [Manish Amde] impurity and information gain
94fd2f9 [Manish Amde] more reorg
b93125c [Manish Amde] more subsection reorg
3ecb2ad [Manish Amde] minor text addition
1537dd3 [Manish Amde] added placeholders and some doc
d06511d [Manish Amde] basic skeleton
This PR uses Breeze's L-BFGS implement, and Breeze dependency has already been introduced by Xiangrui's sparse input format work in SPARK-1212. Nice work, @mengxr !
When use with regularized updater, we need compute the regVal and regGradient (the gradient of regularized part in the cost function), and in the currently updater design, we can compute those two values by the following way.
Let's review how updater works when returning newWeights given the input parameters.
w' = w - thisIterStepSize * (gradient + regGradient(w)) Note that regGradient is function of w!
If we set gradient = 0, thisIterStepSize = 1, then
regGradient(w) = w - w'
As a result, for regVal, it can be computed by
val regVal = updater.compute(
weights,
new DoubleMatrix(initialWeights.length, 1), 0, 1, regParam)._2
and for regGradient, it can be obtained by
val regGradient = weights.sub(
updater.compute(weights, new DoubleMatrix(initialWeights.length, 1), 1, 1, regParam)._1)
The PR includes the tests which compare the result with SGD with/without regularization.
We did a comparison between LBFGS and SGD, and often we saw 10x less
steps in LBFGS while the cost of per step is the same (just computing
the gradient).
The following is the paper by Prof. Ng at Stanford comparing different
optimizers including LBFGS and SGD. They use them in the context of
deep learning, but worth as reference.
http://cs.stanford.edu/~jngiam/papers/LeNgiamCoatesLahiriProchnowNg2011.pdf
Author: DB Tsai <dbtsai@alpinenow.com>
Closes#353 from dbtsai/dbtsai-LBFGS and squashes the following commits:
984b18e [DB Tsai] L-BFGS Optimizer based on Breeze's implementation. Also fixed indentation issue in GradientDescent optimizer.
The Graph.apply test in GraphSuite had some assertions in a closure in
a graph transformation. As a consequence, these assertions never
actually executed. Furthermore, these closures had a reference to
(non-serializable) test harness classes because they called assert(),
which could be a problem if we proactively check closure serializability
in the future.
This commit simply changes the Graph.apply test to collect the graph
triplets so it can assert about each triplet from a map method.
Author: William Benton <willb@redhat.com>
Closes#415 from willb/graphsuite-nop-fix and squashes the following commits:
0b63658 [William Benton] Ensure assertions in Graph.apply are asserted.
Currently it requires NumPy 1.7 due to using the copyto method (http://docs.scipy.org/doc/numpy/reference/generated/numpy.copyto.html) for extracting data out of an array.
Replace it with a fallback
Author: Sandeep <sandeep@techaddict.me>
Closes#391 from techaddict/1426 and squashes the following commits:
d365962 [Sandeep] SPARK-1426: Make MLlib work with NumPy versions older than 1.7 Currently it requires NumPy 1.7 due to using the copyto method (http://docs.scipy.org/doc/numpy/reference/generated/numpy.copyto.html) for extracting data out of an array. Replace it with a fallback
An initial API that exposes SparkSQL functionality in PySpark. A PythonRDD composed of dictionaries, with string keys and primitive values (boolean, float, int, long, string) can be converted into a SchemaRDD that supports sql queries.
```
from pyspark.context import SQLContext
sqlCtx = SQLContext(sc)
rdd = sc.parallelize([{"field1" : 1, "field2" : "row1"}, {"field1" : 2, "field2": "row2"}, {"field1" : 3, "field2": "row3"}])
srdd = sqlCtx.applySchema(rdd)
sqlCtx.registerRDDAsTable(srdd, "table1")
srdd2 = sqlCtx.sql("SELECT field1 AS f1, field2 as f2 from table1")
srdd2.collect()
```
The last line yields ```[{"f1" : 1, "f2" : "row1"}, {"f1" : 2, "f2": "row2"}, {"f1" : 3, "f2": "row3"}]```
Author: Ahir Reddy <ahirreddy@gmail.com>
Author: Michael Armbrust <michael@databricks.com>
Closes#363 from ahirreddy/pysql and squashes the following commits:
0294497 [Ahir Reddy] Updated log4j properties to supress Hive Warns
307d6e0 [Ahir Reddy] Style fix
6f7b8f6 [Ahir Reddy] Temporary fix MIMA checker. Since we now assemble Spark jar with Hive, we don't want to check the interfaces of all of our hive dependencies
3ef074a [Ahir Reddy] Updated documentation because classes moved to sql.py
29245bf [Ahir Reddy] Cache underlying SchemaRDD instead of generating and caching PythonRDD
f2312c7 [Ahir Reddy] Moved everything into sql.py
a19afe4 [Ahir Reddy] Doc fixes
6d658ba [Ahir Reddy] Remove the metastore directory created by the HiveContext tests in SparkSQL
521ff6d [Ahir Reddy] Trying to get spark to build with hive
ab95eba [Ahir Reddy] Set SPARK_HIVE=true on jenkins
ded03e7 [Ahir Reddy] Added doc test for HiveContext
22de1d4 [Ahir Reddy] Fixed maven pyrolite dependency
e4da06c [Ahir Reddy] Display message if hive is not built into spark
227a0be [Michael Armbrust] Update API links. Fix Hive example.
58e2aa9 [Michael Armbrust] Build Docs for pyspark SQL Api. Minor fixes.
4285340 [Michael Armbrust] Fix building of Hive API Docs.
38a92b0 [Michael Armbrust] Add note to future non-python developers about python docs.
337b201 [Ahir Reddy] Changed com.clearspring.analytics stream version from 2.4.0 to 2.5.1 to match SBT build, and added pyrolite to maven build
40491c9 [Ahir Reddy] PR Changes + Method Visibility
1836944 [Michael Armbrust] Fix comments.
e00980f [Michael Armbrust] First draft of python sql programming guide.
b0192d3 [Ahir Reddy] Added Long, Double and Boolean as usable types + unit test
f98a422 [Ahir Reddy] HiveContexts
79621cf [Ahir Reddy] cleaning up cruft
b406ba0 [Ahir Reddy] doctest formatting
20936a5 [Ahir Reddy] Added tests and documentation
e4d21b4 [Ahir Reddy] Added pyrolite dependency
79f739d [Ahir Reddy] added more tests
7515ba0 [Ahir Reddy] added more tests :)
d26ec5e [Ahir Reddy] added test
e9f5b8d [Ahir Reddy] adding tests
906d180 [Ahir Reddy] added todo explaining cost of creating Row object in python
251f99d [Ahir Reddy] for now only allow dictionaries as input
09b9980 [Ahir Reddy] made jrdd explicitly lazy
c608947 [Ahir Reddy] SchemaRDD now has all RDD operations
725c91e [Ahir Reddy] awesome row objects
55d1c76 [Ahir Reddy] return row objects
4fe1319 [Ahir Reddy] output dictionaries correctly
be079de [Ahir Reddy] returning dictionaries works
cd5f79f [Ahir Reddy] Switched to using Scala SQLContext
e948bd9 [Ahir Reddy] yippie
4886052 [Ahir Reddy] even better
c0fb1c6 [Ahir Reddy] more working
043ca85 [Ahir Reddy] working
5496f9f [Ahir Reddy] doesn't crash
b8b904b [Ahir Reddy] Added schema rdd class
67ba875 [Ahir Reddy] java to python, and python to java
bcc0f23 [Ahir Reddy] Java to python
ab6025d [Ahir Reddy] compiling
For your consideration: scalac currently notes a number of feature warnings during compilation:
```
[warn] there were 65 feature warning(s); re-run with -feature for details
```
Warnings are like:
```
[warn] /Users/srowen/Documents/spark/core/src/main/scala/org/apache/spark/SparkContext.scala:1261: implicit conversion method rddToPairRDDFunctions should be enabled
[warn] by making the implicit value scala.language.implicitConversions visible.
[warn] This can be achieved by adding the import clause 'import scala.language.implicitConversions'
[warn] or by setting the compiler option -language:implicitConversions.
[warn] See the Scala docs for value scala.language.implicitConversions for a discussion
[warn] why the feature should be explicitly enabled.
[warn] implicit def rddToPairRDDFunctions[K: ClassTag, V: ClassTag](rdd: RDD[(K, V)]) =
[warn] ^
```
scalac is suggesting that it's just best practice to explicitly enable certain language features by importing them where used.
This PR simply adds the imports it suggests (and squashes one other Java warning along the way). This leaves just deprecation warnings in the build.
Author: Sean Owen <sowen@cloudera.com>
Closes#404 from srowen/SPARK-1488 and squashes the following commits:
8598980 [Sean Owen] Quiet scalac warnings about language features by explicitly importing language features.
39bc831 [Sean Owen] Enable -feature in scalac to emit language feature warnings
Fixed several bugs of in-memory columnar storage to make `HiveInMemoryCompatibilitySuite` pass.
@rxin @marmbrus It is reasonable to include `HiveInMemoryCompatibilitySuite` in this PR, but I didn't, since it significantly increases test execution time. What do you think?
**UPDATE** `HiveCompatibilitySuite` has been made to cache tables in memory. `HiveInMemoryCompatibilitySuite` was removed.
Author: Cheng Lian <lian.cs.zju@gmail.com>
Author: Michael Armbrust <michael@databricks.com>
Closes#374 from liancheng/inMemBugFix and squashes the following commits:
6ad6d9b [Cheng Lian] Merged HiveCompatibilitySuite and HiveInMemoryCompatibilitySuite
5bdbfe7 [Cheng Lian] Revert 882c538 & 8426ddc, which introduced regression
882c538 [Cheng Lian] Remove attributes field from InMemoryColumnarTableScan
32cc9ce [Cheng Lian] Code style cleanup
99382bf [Cheng Lian] Enable compression by default
4390bcc [Cheng Lian] Report error for any Throwable in HiveComparisonTest
d1df4fd [Michael Armbrust] Remove test tables that might always get created anyway?
ab9e807 [Michael Armbrust] Fix the logged console version of failed test cases to use the new syntax.
1965123 [Michael Armbrust] Don't use coalesce for gathering all data to a single partition, as it does not work correctly with mutable rows.
e36cdd0 [Michael Armbrust] Spelling.
2d0e168 [Michael Armbrust] Run Hive tests in-memory too.
6360723 [Cheng Lian] Made PreInsertionCasts support SparkLogicalPlan and InMemoryColumnarTableScan
c9b0f6f [Cheng Lian] Let InsertIntoTable support InMemoryColumnarTableScan
9c8fc40 [Cheng Lian] Disable compression by default
e619995 [Cheng Lian] Bug fix: incorrect byte order in CompressionScheme.columnHeaderSize
8426ddc [Cheng Lian] Bug fix: InMemoryColumnarTableScan should cache columns specified by the attributes argument
036cd09 [Cheng Lian] Clean up unused imports
44591a5 [Cheng Lian] Bug fix: NullableColumnAccessor.hasNext must take nulls into account
052bf41 [Cheng Lian] Bug fix: should only gather compressibility info for non-null values
95b3301 [Cheng Lian] Fixed bugs in IntegralDelta
JIRA issue [here](https://issues.apache.org/jira/browse/SPARK-1415).
New Hadoop API of `InputFormat` does not provide the `minSplits` parameter, which makes the API incompatible between `HadoopRDD` and `NewHadoopRDD`. The PR is for constructing compatible APIs.
Though `minSplits` is deprecated by New Hadoop API, we think it is better to make APIs compatible here.
**Note** that `minSplits` in `wholeTextFiles` could only be treated as a *suggestion*, the real number of splits may not be greater than `minSplits` due to `isSplitable()=false`.
Author: Xusen Yin <yinxusen@gmail.com>
Closes#376 from yinxusen/hadoop-min-split and squashes the following commits:
76417f6 [Xusen Yin] refine comments
c10af60 [Xusen Yin] refine comments and rewrite new class for wholeTextFile
766d05b [Xusen Yin] refine Java API and comments
4875755 [Xusen Yin] add minSplits for WholeTextFiles
The Spark codebase is a bit fast-and-loose when accessing classloaders and this has caused a few bugs to surface in master.
This patch defines some utility methods for accessing classloaders. This makes the intention when accessing a classloader much more explicit in the code and fixes a few cases where the wrong one was chosen.
case (a) -> We want the classloader that loaded Spark
case (b) -> We want the context class loader, or if not present, we want (a)
This patch provides a better fix for SPARK-1403 (https://issues.apache.org/jira/browse/SPARK-1403) than the current work around, which it reverts. It also fixes a previously unreported bug that the `./spark-submit` script did not work for running with `local` master. It didn't work because the executor classloader did not properly delegate to the context class loader (if it is defined) and in local mode the context class loader is set by the `./spark-submit` script. A unit test is added for that case.
Author: Patrick Wendell <pwendell@gmail.com>
Closes#398 from pwendell/class-loaders and squashes the following commits:
b4a1a58 [Patrick Wendell] Minor clean up
14f1272 [Patrick Wendell] SPARK-1480: Clean up use of classloaders
[SPARK-1403] I investigated why spark 0.9.0 loads fine on mesos while spark 1.0.0 fails. What I found was that in SparkEnv.scala, while creating the SparkEnv object, the current thread's classloader is null. But in 0.9.0, at the same place, it is set to org.apache.spark.repl.ExecutorClassLoader . I saw that 7edbea41b4 moved it to it current place. I moved it back and saw that 1.0.0 started working fine on mesos.
I just created a minimal patch that allows me to run spark on mesos correctly. It seems like SecurityManager's creation needs to be taken into account for a correct fix. Also moving the creation of the serializer out of SparkEnv might be a part of the right solution. PTAL.
Author: Bharath Bhushan <manku.timma@outlook.com>
Closes#322 from manku-timma/spark-1403 and squashes the following commits:
606c2b9 [Bharath Bhushan] Merge remote-tracking branch 'upstream/master' into spark-1403
ec8f870 [Bharath Bhushan] revert the logger change for java 6 compatibility as PR 334 is doing it
728beca [Bharath Bhushan] Merge remote-tracking branch 'upstream/master' into spark-1403
044027d [Bharath Bhushan] fix compile error
6f260a4 [Bharath Bhushan] Merge remote-tracking branch 'upstream/master' into spark-1403
b3a053f [Bharath Bhushan] Merge remote-tracking branch 'upstream/master' into spark-1403
04b9662 [Bharath Bhushan] add missing line
4803c19 [Bharath Bhushan] Merge remote-tracking branch 'upstream/master' into spark-1403
f3c9a14 [Bharath Bhushan] Merge remote-tracking branch 'upstream/master' into spark-1403
42d3d6a [Bharath Bhushan] used code fragment from @ueshin to fix the problem in a better way
89109d7 [Bharath Bhushan] move the class loader creation back to where it was in 0.9.0
This PR is self-explanatory.
Author: Andrew Or <andrewor14@gmail.com>
Closes#381 from andrewor14/master and squashes the following commits:
3e8dde2 [Andrew Or] Fix comments for #204
When debugging Spark Streaming applications it is necessary to monitor certain metrics that are not shown in the Spark application UI. For example, what is average processing time of batches? What is the scheduling delay? Is the system able to process as fast as it is receiving data? How many records I am receiving through my receivers?
While the StreamingListener interface introduced in the 0.9 provided some of this information, it could only be accessed programmatically. A UI that shows information specific to the streaming applications is necessary for easier debugging. This PR introduces such a UI. It shows various statistics related to the streaming application. Here is a screenshot of the UI running on my local machine.
http://i.imgur.com/1ooDGhm.png
This UI is integrated into the Spark UI running at 4040.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Author: Andrew Or <andrewor14@gmail.com>
Closes#290 from tdas/streaming-web-ui and squashes the following commits:
fc73ca5 [Tathagata Das] Merge pull request #9 from andrewor14/ui-refactor
642dd88 [Andrew Or] Merge SparkUISuite.scala into UISuite.scala
eb30517 [Andrew Or] Merge github.com:apache/spark into ui-refactor
f4f4cbe [Tathagata Das] More minor fixes.
34bb364 [Tathagata Das] Merge branch 'streaming-web-ui' of github.com:tdas/spark into streaming-web-ui
252c566 [Tathagata Das] Merge pull request #8 from andrewor14/ui-refactor
e038b4b [Tathagata Das] Addressed Patrick's comments.
125a054 [Andrew Or] Disable serving static resources with gzip
90feb8d [Andrew Or] Address Patrick's comments
89dae36 [Tathagata Das] Merge branch 'streaming-web-ui' of github.com:tdas/spark into streaming-web-ui
72fe256 [Tathagata Das] Merge pull request #6 from andrewor14/ui-refactor
2fc09c8 [Tathagata Das] Added binary check exclusions
aa396d4 [Andrew Or] Rename tabs and pages (No more IndexPage.scala)
f8e1053 [Tathagata Das] Added Spark and Streaming UI unit tests.
caa5e05 [Tathagata Das] Merge branch 'streaming-web-ui' of github.com:tdas/spark into streaming-web-ui
585cd65 [Tathagata Das] Merge pull request #5 from andrewor14/ui-refactor
914b8ff [Tathagata Das] Moved utils functions to UIUtils.
548c98c [Andrew Or] Wide refactoring of WebUI, UITab, and UIPage (see commit message)
6de06b0 [Tathagata Das] Merge remote-tracking branch 'apache/master' into streaming-web-ui
ee6543f [Tathagata Das] Minor changes based on Andrew's comments.
fa760fe [Tathagata Das] Fixed long line.
1c0bcef [Tathagata Das] Refactored streaming UI into two files.
1af239b [Tathagata Das] Changed streaming UI to attach itself as a tab with the Spark UI.
827e81a [Tathagata Das] Merge branch 'streaming-web-ui' of github.com:tdas/spark into streaming-web-ui
168fe86 [Tathagata Das] Merge pull request #2 from andrewor14/ui-refactor
3e986f8 [Tathagata Das] Merge remote-tracking branch 'apache/master' into streaming-web-ui
c78c92d [Andrew Or] Remove outdated comment
8f7323b [Andrew Or] End of file new lines, indentation, and imports (minor)
0d61ee8 [Andrew Or] Merge branch 'streaming-web-ui' of github.com:tdas/spark into ui-refactor
9a48fa1 [Andrew Or] Allow adding tabs to SparkUI dynamically + add example
61358e3 [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into streaming-web-ui
53be2c5 [Tathagata Das] Minor style updates.
ed25dfc [Andrew Or] Generalize SparkUI header to display tabs dynamically
a37ad4f [Andrew Or] Comments, imports and formatting (minor)
cd000b0 [Andrew Or] Merge github.com:apache/spark into ui-refactor
7d57444 [Andrew Or] Refactoring the UI interface to add flexibility
aef4dd5 [Tathagata Das] Added Apache licenses.
db27bad [Tathagata Das] Added last batch processing time to StreamingUI.
4d86e98 [Tathagata Das] Added basic stats to the StreamingUI and refactored the UI to a Page to make it easier to transition to using SparkUI later.
93f1c69 [Tathagata Das] Added network receiver information to the Streaming UI.
56cc7fb [Tathagata Das] First cut implementation of Streaming UI.
(This is for discussion at this point -- I'm not suggesting this should be committed.)
This is what removing fastutil looks like. Much of it is straightforward, like using `java.io` buffered stream classes, and Guava for murmurhash3.
Uses of the `FastByteArrayOutputStream` were a little trickier. In only one case though do I think the change to use `java.io` actually entails an extra array copy.
The rest is using `OpenHashMap` and `OpenHashSet`. These are now written in terms of more scala-like operations.
`OpenHashMap` is where I made three non-trivial changes to make it work, and they need review:
- It is no longer private
- The key must be a `ClassTag`
- Unless a lot of other code changes, the key type can't enforce being a supertype of `Null`
It all works and tests pass, and I think there is reason to believe it's OK from a speed perspective.
But what about those last changes?
Author: Sean Owen <sowen@cloudera.com>
Closes#266 from srowen/SPARK-1057-alternate and squashes the following commits:
2601129 [Sean Owen] Fix Map return type error not previously caught
ec65502 [Sean Owen] Updates from matei's review
00bc81e [Sean Owen] Remove use of fastutil and replace with use of java.io, spark.util and Guava classes
update the content of Exception when windowDuration is not multiple of parent.slideDuration
Author: baishuo(白硕) <vc_java@hotmail.com>
Closes#390 from baishuo/windowdstream and squashes the following commits:
533c968 [baishuo(白硕)] Update WindowedDStream.scala
As with the new vector system in MLlib, we find that it is good to add some new APIs to precess the `RDD[Vector]`. Beside, the former implementation of `computeStat` is not stable which could loss precision, and has the possibility to cause `Nan` in scientific computing, just as said in the [SPARK-1328](https://spark-project.atlassian.net/browse/SPARK-1328).
APIs contain:
* rowMeans(): RDD[Double]
* rowNorm2(): RDD[Double]
* rowSDs(): RDD[Double]
* colMeans(): Vector
* colMeans(size: Int): Vector
* colNorm2(): Vector
* colNorm2(size: Int): Vector
* colSDs(): Vector
* colSDs(size: Int): Vector
* maxOption((Vector, Vector) => Boolean): Option[Vector]
* minOption((Vector, Vector) => Boolean): Option[Vector]
* rowShrink(): RDD[Vector]
* colShrink(): RDD[Vector]
This is working in process now, and some more APIs will add to `LabeledPoint`. Moreover, the implicit declaration will move from `MLUtils` to `MLContext` later.
Author: Xusen Yin <yinxusen@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>
Closes#268 from yinxusen/vector-statistics and squashes the following commits:
d61363f [Xusen Yin] rebase to latest master
16ae684 [Xusen Yin] fix minor error and remove useless method
10cf5d3 [Xusen Yin] refine some return type
b064714 [Xusen Yin] remove computeStat in MLUtils
cbbefdb [Xiangrui Meng] update multivariate statistical summary interface and clean tests
4eaf28a [Xusen Yin] merge VectorRDDStatistics into RowMatrix
48ee053 [Xusen Yin] fix minor error
e624f93 [Xusen Yin] fix scala style error
1fba230 [Xusen Yin] merge while loop together
69e1f37 [Xusen Yin] remove lazy eval, and minor memory footprint
548e9de [Xusen Yin] minor revision
86522c4 [Xusen Yin] add comments on functions
dc77e38 [Xusen Yin] test sparse vector RDD
18cf072 [Xusen Yin] change def to lazy val to make sure that the computations in function be evaluated only once
f7a3ca2 [Xusen Yin] fix the corner case of maxmin
967d041 [Xusen Yin] full revision with Aggregator class
138300c [Xusen Yin] add new Aggregator class
1376ff4 [Xusen Yin] rename variables and adjust code
4a5c38d [Xusen Yin] add scala doc, refine code and comments
036b7a5 [Xusen Yin] fix the bug of Nan occur
f6e8e9a [Xusen Yin] add sparse vectors test
4cfbadf [Xusen Yin] fix bug of min max
4e4fbd1 [Xusen Yin] separate seqop and combop out as independent functions
a6d5a2e [Xusen Yin] rewrite for only computing non-zero elements
3980287 [Xusen Yin] rename variables
62a2c3e [Xusen Yin] use axpy and in-place if possible
9a75ebd [Xusen Yin] add case class to wrap return values
d816ac7 [Xusen Yin] remove useless APIs
c4651bb [Xusen Yin] remove row-wise APIs and refine code
1338ea1 [Xusen Yin] all-in-one version test passed
cc65810 [Xusen Yin] add parallel mean and variance
9af2e95 [Xusen Yin] refine the code style
ad6c82d [Xusen Yin] add shrink test
e09d5d2 [Xusen Yin] add scala docs and refine shrink method
8ef3377 [Xusen Yin] pass all tests
28cf060 [Xusen Yin] fix error of column means
54b19ab [Xusen Yin] add new API to shrink RDD[Vector]
8c6c0e1 [Xusen Yin] add basic statistics
Make coalesce test deterministic by setting pre-defined seeds. (Saw random failures in other PRs.)
Author: Xiangrui Meng <meng@databricks.com>
Closes#387 from mengxr/fix-random and squashes the following commits:
59bc16f [Xiangrui Meng] make coalesce test deterministic in RDDSuite
This was causing some errors with pull request tests.
Author: Patrick Wendell <pwendell@gmail.com>
Closes#393 from pwendell/hotfix and squashes the following commits:
6201dd3 [Patrick Wendell] HOTFIX: Ignore python metastore files in RAT checks.
This PR implements a generic version of `AreaUnderCurve` using the `RDD.sliding` implementation from https://github.com/apache/spark/pull/136 . It also contains refactoring of https://github.com/apache/spark/pull/160 for binary classification evaluation.
Author: Xiangrui Meng <meng@databricks.com>
Closes#364 from mengxr/auc and squashes the following commits:
a05941d [Xiangrui Meng] replace TP/FP/TN/FN by their full names
3f42e98 [Xiangrui Meng] add (0, 0), (1, 1) to roc, and (0, 1) to pr
fb4b6d2 [Xiangrui Meng] rename Evaluator to Metrics and add more metrics
b1b7dab [Xiangrui Meng] fix code styles
9dc3518 [Xiangrui Meng] add tests for BinaryClassificationEvaluator
ca31da5 [Xiangrui Meng] remove PredictionAndResponse
3d71525 [Xiangrui Meng] move binary evalution classes to evaluation.binary
8f78958 [Xiangrui Meng] add PredictionAndResponse
dda82d5 [Xiangrui Meng] add confusion matrix
aa7e278 [Xiangrui Meng] add initial version of binary classification evaluator
221ebce [Xiangrui Meng] add a new test to sliding
a920865 [Xiangrui Meng] Merge branch 'sliding' into auc
a9b250a [Xiangrui Meng] move sliding to mllib
cab9a52 [Xiangrui Meng] use last for the last element
db6cb30 [Xiangrui Meng] remove unnecessary toSeq
9916202 [Xiangrui Meng] change RDD.sliding return type to RDD[Seq[T]]
284d991 [Xiangrui Meng] change SlidedRDD to SlidingRDD
c1c6c22 [Xiangrui Meng] add AreaUnderCurve
65461b2 [Xiangrui Meng] Merge branch 'sliding' into auc
5ee6001 [Xiangrui Meng] add TODO
d2a600d [Xiangrui Meng] add sliding to rdd
(a) Deleted an outdated line from the docs
(b) Removed a work around that is no longer necessary given the mesos version bump.
Author: Patrick Wendell <pwendell@gmail.com>
Closes#382 from pwendell/maven-clean and squashes the following commits:
f0447fa [Patrick Wendell] Minor doc clean-up
Author: Thomas Graves <tgraves@apache.org>
Closes#344 from tgravescs/SPARK-1417 and squashes the following commits:
c450b5f [Thomas Graves] fix test
e1c1d7e [Thomas Graves] add missing $ to appUIAddress
e982ddb [Thomas Graves] use appUIHostPort in appUIAddress
0803ec2 [Thomas Graves] Review comment updates - remove extra newline, simplify assert in test
658a8ec [Thomas Graves] Add a appUIHostPort routine
0614208 [Thomas Graves] Fix test
2a6b1b7 [Thomas Graves] SPARK-1417: Spark on Yarn - spark UI link from resourcemanager is broken
1. Adds a separate endpoint for the killing logic that is outside of a page.
2. Narrows the scope of the killingEnabled tracking.
3. Some style improvements.
Author: Patrick Wendell <pwendell@gmail.com>
Closes#386 from pwendell/kill-link and squashes the following commits:
8efe02b [Patrick Wendell] Improvements to task killing in the UI.
Mainly ported from branch-0.9.
Author: Harvey Feng <hyfeng224@gmail.com>
Closes#385 from harveyfeng/0.9.1-ec2 and squashes the following commits:
769ac2f [Harvey Feng] Add Spark v0.9.1 to ec2 launch script and use it as the default
The Mesos backend uses this property when setting up a slave process. It is similarly set in the Scala repl (org.apache.spark.repl.SparkILoop), but I couldn't find any analogous for pyspark.
Author: Ivan Wick <ivanwick+github@gmail.com>
This patch had conflicts when merged, resolved by
Committer: Matei Zaharia <matei@databricks.com>
Closes#311 from ivanwick/master and squashes the following commits:
da0c3e4 [Ivan Wick] Set spark.executor.uri from environment variable (needed by Mesos)
Author: Sundeep Narravula <sundeepn@superduel.local>
Author: Sundeep Narravula <sundeepn@dhcpx-204-110.corp.yahoo.com>
Closes#246 from sundeepn/uikilljob and squashes the following commits:
5fdd0e2 [Sundeep Narravula] Fix test string
f6fdff1 [Sundeep Narravula] Format fix; reduced line size to less than 100 chars
d1daeb9 [Sundeep Narravula] Incorporating review comments.
8d97923 [Sundeep Narravula] Ability to kill jobs thru the UI. This behavior can be turned on be settings the following variable: spark.ui.killEnabled=true (default=false) Adding DAGScheduler event StageCancelled and corresponding handlers. Added cancellation reason to handlers.
Author: Michael Armbrust <michael@databricks.com>
Closes#378 from marmbrus/columnPruning and squashes the following commits:
779da56 [Michael Armbrust] More consistent naming.
1a4e9ea [Michael Armbrust] More comments.
2f4e7b9 [Michael Armbrust] Improve column pruning in the optimizer.
stack these together in a commit else they show up chunk by chunk in different commits.
Author: Sandeep <sandeep@techaddict.me>
Closes#380 from techaddict/white_space and squashes the following commits:
b58f294 [Sandeep] Remove Unnecessary Whitespace's
Author: Sandeep <sandeep@techaddict.me>
Closes#356 from techaddict/1428 and squashes the following commits:
3bdf5f6 [Sandeep] SPARK-1428: MLlib should convert non-float64 NumPy arrays to float64 instead of complaining
The new feature of event logging, introduced in #42, allows the user to persist the details of his/her Spark application to storage, and later replay these events to reconstruct an after-the-fact SparkUI.
Currently, however, a persisted UI can only be rendered through the standalone Master. This greatly limits the use case of this new feature as many people also run Spark on Yarn / Mesos.
This PR introduces a new entity called the HistoryServer, which, given a log directory, keeps track of all completed applications independently of a Spark Master. Unlike Master, the HistoryServer needs not be running while the application is still running. It is relatively light-weight in that it only maintains static information of applications and performs no scheduling.
To quickly test it out, generate event logs with ```spark.eventLog.enabled=true``` and run ```sbin/start-history-server.sh <log-dir-path>```. Your HistoryServer awaits on port 18080.
Comments and feedback are most welcome.
---
A few other changes introduced in this PR include refactoring the WebUI interface, which is beginning to have a lot of duplicate code now that we have added more functionality to it. Two new SparkListenerEvents have been introduced (SparkListenerApplicationStart/End) to keep track of application name and start/finish times. This PR also clarifies the semantics of the ReplayListenerBus introduced in #42.
A potential TODO in the future (not part of this PR) is to render live applications in addition to just completed applications. This is useful when applications fail, a condition that our current HistoryServer does not handle unless the user manually signals application completion (by creating the APPLICATION_COMPLETION file). Handling live applications becomes significantly more challenging, however, because it is now necessary to render the same SparkUI multiple times. To avoid reading the entire log every time, which is inefficient, we must handle reading the log from where we previously left off, but this becomes fairly complicated because we must deal with the arbitrary behavior of each input stream.
Author: Andrew Or <andrewor14@gmail.com>
Closes#204 from andrewor14/master and squashes the following commits:
7b7234c [Andrew Or] Finished -> Completed
b158d98 [Andrew Or] Address Patrick's comments
69d1b41 [Andrew Or] Do not block on posting SparkListenerApplicationEnd
19d5dd0 [Andrew Or] Merge github.com:apache/spark
f7f5bf0 [Andrew Or] Make history server's web UI port a Spark configuration
2dfb494 [Andrew Or] Decouple checking for application completion from replaying
d02dbaa [Andrew Or] Expose Spark version and include it in event logs
2282300 [Andrew Or] Add documentation for the HistoryServer
567474a [Andrew Or] Merge github.com:apache/spark
6edf052 [Andrew Or] Merge github.com:apache/spark
19e1fb4 [Andrew Or] Address Thomas' comments
248cb3d [Andrew Or] Limit number of live applications + add configurability
a3598de [Andrew Or] Do not close file system with ReplayBus + fix bind address
bc46fc8 [Andrew Or] Merge github.com:apache/spark
e2f4ff9 [Andrew Or] Merge github.com:apache/spark
050419e [Andrew Or] Merge github.com:apache/spark
81b568b [Andrew Or] Fix strange error messages...
0670743 [Andrew Or] Decouple page rendering from loading files from disk
1b2f391 [Andrew Or] Minor changes
a9eae7e [Andrew Or] Merge branch 'master' of github.com:apache/spark
d5154da [Andrew Or] Styling and comments
5dbfbb4 [Andrew Or] Merge branch 'master' of github.com:apache/spark
60bc6d5 [Andrew Or] First complete implementation of HistoryServer (only for finished apps)
7584418 [Andrew Or] Report application start/end times to HistoryServer
8aac163 [Andrew Or] Add basic application table
c086bd5 [Andrew Or] Add HistoryServer and scripts ++ Refactor WebUI interface
Author: witgo <witgo@qq.com>
Closes#325 from witgo/SPARK-1413 and squashes the following commits:
e57cd8e [witgo] use scala reflection to access and call the SLF4JBridgeHandler methods
45c8f40 [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1413
5e35d87 [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1413
0d5f819 [witgo] review commit
45e5b70 [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1413
fa69dcf [witgo] Merge branch 'master' into SPARK-1413
3c98dc4 [witgo] Merge branch 'master' into SPARK-1413
38160cb [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1413
ba09bcd [witgo] remove set the parquet log level
a63d574 [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1413
5231ecd [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1413
3feb635 [witgo] parquet logger use parent handler
fa00d5d [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1413
8bb6ffd [witgo] enableLogForwarding note fix
edd9630 [witgo] move to
f447f50 [witgo] merging master
5ad52bd [witgo] Merge branch 'master' of https://github.com/apache/spark into SPARK-1413
76670c1 [witgo] review commit
70f3c64 [witgo] Fix SPARK-1413
Spark examples should exit nice using SparkContext.stop() method, rather than System.exit
System.exit can cause issues like in SPARK-1407
Author: Sandeep <sandeep@techaddict.me>
Closes#370 from techaddict/1446 and squashes the following commits:
e9234cf [Sandeep] SPARK-1446: Spark examples should not do a System.exit Spark examples should exit nice using SparkContext.stop() method, rather than System.exit System.exit can cause issues like in SPARK-1407
[SPARK-729](https://spark-project.atlassian.net/browse/SPARK-729) concerns when free variables in closure arguments to transformations are captured. Currently, it is possible for closures to get the environment in which they are serialized (not the environment in which they are created). There are a few possible approaches to solving this problem and this PR will discuss some of them. The approach I took has the advantage of being simple, obviously correct, and minimally-invasive, but it preserves something that has been bothering me about Spark's closure handling, so I'd like to discuss an alternative and get some feedback on whether or not it is worth pursuing.
## What I did
The basic approach I took depends on the work I did for #143, and so this PR is based atop that. Specifically: #143 modifies `ClosureCleaner.clean` to preemptively determine whether or not closures are serializable immediately upon closure cleaning (rather than waiting for an job involving that closure to be scheduled). Thus non-serializable closure exceptions will be triggered by the line defining the closure rather than triggered where the closure is used.
Since the easiest way to determine whether or not a closure is serializable is to attempt to serialize it, the code in #143 is creating a serialized closure as part of `ClosureCleaner.clean`. `clean` currently modifies its argument, but the method in `SparkContext` that wraps it to return a value (a reference to the modified-in-place argument). This branch modifies `ClosureCleaner.clean` so that it returns a value: if it is cleaning a serializable closure, it returns the result of deserializing its serialized argument; therefore it is returning a closure with an environment captured at cleaning time. `SparkContext.clean` then returns the result of `ClosureCleaner.clean`, rather than a reference to its modified-in-place argument.
I've added tests for this behavior (777a1bc). The pull request as it stands, given the changes in #143, is nearly trivial. There is some overhead from deserializing the closure, but it is minimal and the benefit of obvious operational correctness (vs. a more sophisticated but harder-to-validate transformation in `ClosureCleaner`) seems pretty important. I think this is a fine way to solve this problem, but it's not perfect.
## What we might want to do
The thing that has been bothering me about Spark's handling of closures is that it seems like we should be able to statically ensure that cleaning and serialization happen exactly once for a given closure. If we serialize a closure in order to determine whether or not it is serializable, we should be able to hang on to the generated byte buffer and use it instead of re-serializing the closure later. By replacing closures with instances of a sum type that encodes whether or not a closure has been cleaned or serialized, we could handle clean, to-be-cleaned, and serialized closures separately with case matches. Here's a somewhat-concrete sketch (taken from my git stash) of what this might look like:
```scala
package org.apache.spark.util
import java.nio.ByteBuffer
import scala.reflect.ClassManifest
sealed abstract class ClosureBox[T] { def func: T }
final case class RawClosure[T](func: T) extends ClosureBox[T] {}
final case class CleanedClosure[T](func: T) extends ClosureBox[T] {}
final case class SerializedClosure[T](func: T, bytebuf: ByteBuffer) extends ClosureBox[T] {}
object ClosureBoxImplicits {
implicit def closureBoxFromFunc[T <: AnyRef](fun: T) = new RawClosure[T](fun)
}
```
With these types declared, we'd be able to change `ClosureCleaner.clean` to take a `ClosureBox[T=>U]` (possibly generated by implicit conversion) and return a `ClosureBox[T=>U]` (either a `CleanedClosure[T=>U]` or a `SerializedClosure[T=>U]`, depending on whether or not serializability-checking was enabled) instead of a `T=>U`. A case match could thus short-circuit cleaning or serializing closures that had already been cleaned or serialized (both in `ClosureCleaner` and in the closure serializer). Cleaned-and-serialized closures would be represented by a boxed tuple of the original closure and a serialized copy (complete with an environment quiesced at transformation time). Additional implicit conversions could convert from `ClosureBox` instances to the underlying function type where appropriate. Tracking this sort of state in the type system seems like the right thing to do to me.
### Why we might not want to do that
_It's pretty invasive._ Every function type used by every `RDD` subclass would have to change to reflect that they expected a `ClosureBox[T=>U]` instead of a `T=>U`. This obscures what's going on and is not a little ugly. Although I really like the idea of using the type system to enforce the clean-or-serialize once discipline, it might not be worth adding another layer of types (even if we could hide some of the extra boilerplate with judicious application of implicit conversions).
_It statically guarantees a property whose absence is unlikely to cause any serious problems as it stands._ It appears that all closures are currently dynamically cleaned once and it's not obvious that repeated closure-cleaning is likely to be a problem in the future. Furthermore, serializing closures is relatively cheap, so doing it once to check for serialization and once again to actually ship them across the wire doesn't seem like a big deal.
Taken together, these seem like a high price to pay for statically guaranteeing that closures are operated upon only once.
## Other possibilities
I felt like the serialize-and-deserialize approach was best due to its obvious simplicity. But it would be possible to do a more sophisticated transformation within `ClosureCleaner.clean`. It might also be possible for `clean` to modify its argument in a way so that whether or not a given closure had been cleaned would be apparent upon inspection; this would buy us some of the operational benefits of the `ClosureBox` approach but not the static cleanliness.
I'm interested in any feedback or discussion on whether or not the problems with the type-based approach indeed outweigh the advantage, as well as of approaches to this issue and to closure handling in general.
Author: William Benton <willb@redhat.com>
Closes#189 from willb/spark-729 and squashes the following commits:
f4cafa0 [William Benton] Stylistic changes and cleanups
b3d9c86 [William Benton] Fixed style issues in tests
9b56ce0 [William Benton] Added array-element capture test
97e9d91 [William Benton] Split closure-serializability failure tests
12ef6e3 [William Benton] Skip proactive closure capture for runJob
8ee3ee7 [William Benton] Predictable closure environment capture
12c63a7 [William Benton] Added tests for variable capture in closures
d6e8dd6 [William Benton] Don't check serializability of DStream transforms.
4ecf841 [William Benton] Make proactive serializability checking optional.
d8df3db [William Benton] Adds proactive closure-serializablilty checking
21b4b06 [William Benton] Test cases for SPARK-897.
d5947b3 [William Benton] Ensure assertions in Graph.apply are asserted.
