## What changes were proposed in this pull request?
JIRA issue: https://issues.apache.org/jira/browse/SPARK-13038
1. Add load/save to PySpark Pipeline and PipelineModel
2. Add `_transfer_stage_to_java()` and `_transfer_stage_from_java()` for `JavaWrapper`.
## How was this patch tested?
Test with doctest.
Author: Xusen Yin <yinxusen@gmail.com>
Closes#11683 from yinxusen/SPARK-13038-only.
#### What changes were proposed in this pull request?
This PR is to convert to SQL from analyzed logical plans containing operator `ScriptTransformation`.
For example, below is the SQL containing `Transform`
```
SELECT TRANSFORM (a, b, c, d) USING 'cat' FROM parquet_t2
```
Its logical plan is like
```
ScriptTransformation [a#210L,b#211L,c#212L,d#213L], cat, [key#208,value#209], HiveScriptIOSchema(List(),List(),Some(org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe),Some(org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe),List((field.delim, )),List((field.delim, )),Some(org.apache.hadoop.hive.ql.exec.TextRecordReader),Some(org.apache.hadoop.hive.ql.exec.TextRecordWriter),true)
+- SubqueryAlias parquet_t2
+- Relation[a#210L,b#211L,c#212L,d#213L] ParquetRelation
```
The generated SQL will be like
```
SELECT TRANSFORM (`parquet_t2`.`a`, `parquet_t2`.`b`, `parquet_t2`.`c`, `parquet_t2`.`d`) USING 'cat' AS (`key` string, `value` string) FROM `default`.`parquet_t2`
```
#### How was this patch tested?
Seven test cases are added to `LogicalPlanToSQLSuite`.
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#11503 from gatorsmile/transformToSQL.
## What changes were proposed in this pull request?
This PR tries to solve a fundamental issue in the `SQLBuilder`. When we want to turn a logical plan into SQL string and put it after FROM clause, we need to wrap it with a sub-query. However, a logical plan is allowed to have same-name outputs with different qualifiers(e.g. the `Join` operator), and this kind of plan can't be put under a subquery as we will erase and assign a new qualifier to all outputs and make it impossible to distinguish same-name outputs.
To solve this problem, this PR renames all attributes with globally unique names(using exprId), so that we don't need qualifiers to resolve ambiguity anymore.
For example, `SELECT x.key, MAX(y.key) OVER () FROM t x JOIN t y`, we will parse this SQL to a Window operator and a Project operator, and add a sub-query between them. The generated SQL looks like:
```
SELECT sq_1.key, sq_1.max
FROM (
SELECT sq_0.key, sq_0.key, MAX(sq_0.key) OVER () AS max
FROM (
SELECT x.key, y.key FROM t1 AS x JOIN t2 AS y
) AS sq_0
) AS sq_1
```
You can see, the `key` columns become ambiguous after `sq_0`.
After this PR, it will generate something like:
```
SELECT attr_30 AS key, attr_37 AS max
FROM (
SELECT attr_30, attr_37
FROM (
SELECT attr_30, attr_35, MAX(attr_35) AS attr_37
FROM (
SELECT attr_30, attr_35 FROM
(SELECT key AS attr_30 FROM t1) AS sq_0
INNER JOIN
(SELECT key AS attr_35 FROM t1) AS sq_1
) AS sq_2
) AS sq_3
) AS sq_4
```
The outermost SELECT is used to turn the generated named to real names back, and the innermost SELECT is used to alias real columns to our generated names. Between them, there is no name ambiguity anymore.
## How was this patch tested?
existing tests and new tests in LogicalPlanToSQLSuite.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11658 from cloud-fan/gensql.
JIRA: https://issues.apache.org/jira/browse/SPARK-13816
## What changes were proposed in this pull request?
Add parameter checks for algorithms in Graphx: Pregel,LabelPropagation,PageRank,SVDPlusPlus
## How was this patch tested?
manual tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#11655 from zhengruifeng/graphx_param_check.
## What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-13894
Change the return type of the `SQLContext.range` API from `DataFrame` to `Dataset`.
## How was this patch tested?
No additional unit test required.
Author: Cheng Hao <hao.cheng@intel.com>
Closes#11730 from chenghao-intel/range.
## What changes were proposed in this pull request?
There is a feature of hive SQL called multi-insert. For example:
```
FROM src
INSERT OVERWRITE TABLE dest1
SELECT key + 1
INSERT OVERWRITE TABLE dest2
SELECT key WHERE key > 2
INSERT OVERWRITE TABLE dest3
SELECT col EXPLODE(arr) exp AS col
...
```
We partially support it currently, with some limitations: 1) WHERE can't reference columns produced by LATERAL VIEW. 2) It's not executed eagerly, i.e. `sql("...multi-insert clause...")` won't take place right away like other commands, e.g. CREATE TABLE.
This PR removes these limitations and make us fully support multi-insert.
## How was this patch tested?
new tests in `SQLQuerySuite`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11754 from cloud-fan/lateral-view.
## What changes were proposed in this pull request?
In SparkContext, throw Illegalargumentexception when trying to broadcast rdd directly, instead of logging the warning.
## How was this patch tested?
mvn clean install
Add UT in BroadcastSuite
Author: Wesley Tang <tangmingjun@mininglamp.com>
Closes#11735 from breakdawn/master.
## What changes were proposed in this pull request?
I'm seeing several PR builder builds fail after https://github.com/apache/spark/pull/11725/files. Example:
https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test/job/spark-master-test-maven-hadoop-2.4/lastFailedBuild/console
```
testCommunication(org.apache.spark.launcher.LauncherServerSuite) Time elapsed: 0.023 sec <<< FAILURE!
java.lang.AssertionError: expected:<app-id> but was:<null>
at org.apache.spark.launcher.LauncherServerSuite.testCommunication(LauncherServerSuite.java:93)
```
However, other builds pass this same test, including the test when run locally and on the Jenkins PR builder. The failure itself concerns a change to how the test waits on a condition, and the wait can time out; therefore I think this is due to fast/slow machine differences.
This is an attempt at a hot fix; it's a little hard to verify since locally and on the PR builder, it passes anyway. The change itself should be harmless anyway.
Why didn't this happen before, if the new logic was supposed to be equivalent to the old? I think this is the sequence:
- First attempt to acquire semaphore for 10ms actually silently times out
- The changed being waited for happens just after that, a bit too late
- Assertion passes since condition became true just in time
- `release()` fires from the listener
- Next `tryAcquire` however immediately succeeds because the first `tryAcquire` didn't acquire anything, but its subsequent condition is not yet true; this would explain why the second one always fails
Versus the original using `notifyAll()`, there's a small difference: `wait()`-ing after `notifyAll()` just results in another wait; it doesn't make it return immediately. So this was a tiny latent issue that was masked by the semantics. Now the test asserts that the event actually happened (semaphore was acquired). (The timeout is still here to prevent the test from hanging forever, and to detect really slow response.) The timeout is increased to a second to allow plenty of time anyway.
## How was this patch tested?
Jenkins tests
Author: Sean Owen <sowen@cloudera.com>
Closes#11763 from srowen/SPARK-13823.3.
## What changes were proposed in this pull request?
The max number of executor failure before failing the application is default to twice the maximum number of executors if dynamic allocation is enabled. The default value for "spark.dynamicAllocation.maxExecutors" is Int.MaxValue. So this causes an integer overflow and a wrong result. The calculated value of the default max number of executor failure is 3. This PR adds a check to avoid the overflow.
## How was this patch tested?
It tests if the value is greater that Int.MaxValue / 2 to avoid the overflow when it multiplies 2.
Author: Carson Wang <carson.wang@intel.com>
Closes#11713 from carsonwang/IntOverflow.
## What changes were proposed in this pull request?
PipedRDD creates a child thread to read output of the parent stage and feed it to the pipe process. Used a variable to save the exception thrown in the child thread and then propagating the exception in the main thread if the variable was set.
## How was this patch tested?
- Added a unit test
- Ran all the existing tests in PipedRDDSuite and they all pass with the change
- Tested the patch with a real pipe() job, bounced the executor node which ran the parent stage to simulate a fetch failure and observed that the parent stage was re-ran.
Author: Tejas Patil <tejasp@fb.com>
Closes#11628 from tejasapatil/pipe_rdd.
JIRA: https://issues.apache.org/jira/browse/SPARK-13396
Stop using our internal deprecated .metrics on ExceptionFailure instead use accumUpdates
Author: GayathriMurali <gayathri.m.softie@gmail.com>
Closes#11544 from GayathriMurali/SPARK-13396.
## What changes were proposed in this pull request?
Follow up to https://github.com/apache/spark/pull/11657
- Also update `String.getBytes("UTF-8")` to use `StandardCharsets.UTF_8`
- And fix one last new Coverity warning that turned up (use of unguarded `wait()` replaced by simpler/more robust `java.util.concurrent` classes in tests)
- And while we're here cleaning up Coverity warnings, just fix about 15 more build warnings
## How was this patch tested?
Jenkins tests
Author: Sean Owen <sowen@cloudera.com>
Closes#11725 from srowen/SPARK-13823.2.
## What changes were proposed in this pull request?
Force at least two dispatcher-event-loop threads. Since SparkDeploySchedulerBackend (in AppClient) calls askWithRetry to CoarseGrainedScheduler in the same process, there the driver needs at least two dispatcher threads to prevent the dispatcher thread from hanging.
## How was this patch tested?
Manual.
Author: Yonathan Randolph <yonathangmail.com>
Author: Yonathan Randolph <yonathan@liftigniter.com>
Closes#11728 from yonran/SPARK-13906.
## What changes were proposed in this pull request?
The purpose of [SPARK-12653](https://issues.apache.org/jira/browse/SPARK-12653) is re-enabling a regression test.
Historically, the target regression test is added by [SPARK-8498](093c34838d), but is temporarily disabled by [SPARK-12615](8ce645d4ee) due to binary compatibility error.
The following is the current error message at the submitting spark job with the pre-built `test.jar` file in the target regression test.
```
Exception in thread "main" java.lang.NoSuchMethodError: org.apache.spark.SparkContext$.$lessinit$greater$default$6()Lscala/collection/Map;
```
Simple rebuilding `test.jar` can not recover the purpose of testcase since we need to support both Scala 2.10 and 2.11 for a while. For example, we will face the following Scala 2.11 error if we use `test.jar` built by Scala 2.10.
```
Exception in thread "main" java.lang.NoSuchMethodError: scala.reflect.api.JavaUniverse.runtimeMirror(Ljava/lang/ClassLoader;)Lscala/reflect/api/JavaMirrors$JavaMirror;
```
This PR replace the existing `test.jar` with `test-2.10.jar` and `test-2.11.jar` and improve the regression test to use the suitable jar file.
## How was this patch tested?
Pass the existing Jenkins test.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11744 from dongjoon-hyun/SPARK-12653.
## What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-13899
This PR makes CSV data source produce `InternalRow` instead of `Row`.
Basically, this resembles JSON data source. It uses the same codes for casting.
## How was this patch tested?
Unit tests were used within IDE and code style was checked by `./dev/run_tests`.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#11717 from HyukjinKwon/SPARK-13899.
## What changes were proposed in this pull request?
We are able to change `Experimental` and `DeveloperAPI` API freely but also should monitor and manage those API carefully. This PR for [SPARK-13920](https://issues.apache.org/jira/browse/SPARK-13920) enables MiMa check and adds filters for them.
## How was this patch tested?
Pass the Jenkins tests (including MiMa).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11751 from dongjoon-hyun/SPARK-13920.
## What changes were proposed in this pull request?
Provide R-like summary statistics for GLMs via iteratively reweighted least squares.
## How was this patch tested?
unit tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#11694 from yanboliang/spark-9837.
## What changes were proposed in this pull request?
This PR brings codegen support for broadcast left-semi join.
## How was this patch tested?
Existing tests. Added benchmark, the result show 7X speedup.
Author: Davies Liu <davies@databricks.com>
Closes#11742 from davies/gen_semi.
## What changes were proposed in this pull request?
Remove the wrong "expected" parameter in MathFunctionsSuite.scala's checkNaNWithoutCodegen.
This function is to check NaN value, so the "expected" parameter is useless. The Callers do not pass "expected" value and the similar function like checkNaNWithGeneratedProjection and checkNaNWithOptimization do not use it also.
Author: Yucai Yu <yucai.yu@intel.com>
Closes#11718 from yucai/unused_expected.
## What changes were proposed in this pull request?
This PR just move some code from SortMergeOuterJoin into SortMergeJoin.
This is for support codegen for outer join.
## How was this patch tested?
existing tests.
Author: Davies Liu <davies@databricks.com>
Closes#11743 from davies/gen_smjouter.
## What changes were proposed in this pull request?
This patch changes DataFrameReader.text()'s return type from DataFrame to Dataset[String].
Closes#11731.
## How was this patch tested?
Updated existing integration tests to reflect the change.
Author: Reynold Xin <rxin@databricks.com>
Closes#11739 from rxin/SPARK-13895.
## What changes were proposed in this pull request?
a minor fix for the comments of a method in RPC Dispatcher
## How was this patch tested?
existing unit tests
Author: CodingCat <zhunansjtu@gmail.com>
Closes#11738 from CodingCat/minor_rpc.
## What changes were proposed in this pull request?
Change the return type of toJson in Dataset class
## How was this patch tested?
No additional unit test required.
Author: Stavros Kontopoulos <stavros.kontopoulos@typesafe.com>
Closes#11732 from skonto/fix_toJson.
## What changes were proposed in this pull request?
Changing the default exit state to `failed` for any application running on yarn cluster mode.
## How was this patch tested?
Unit test is done locally.
CC tgravescs and vanzin .
Author: jerryshao <sshao@hortonworks.com>
Closes#11693 from jerryshao/SPARK-13642.
## What changes were proposed in this pull request?
Our internal code can go through SessionState.catalog and SessionState.analyzer. This brings two small benefits:
1. Reduces internal dependency on SQLContext.
2. Removes 2 public methods in Java (Java does not obey package private visibility).
More importantly, according to the design in SPARK-13485, we'd need to claim this catalog function for the user-facing public functions, rather than having an internal field.
## How was this patch tested?
Existing unit/integration test code.
Author: Reynold Xin <rxin@databricks.com>
Closes#11716 from rxin/SPARK-13893.
As part of the goal to stop creating assemblies in Spark, this change
modifies the mvn and sbt builds to not create an assembly for examples.
Instead, dependencies are copied to the build directory (under
target/scala-xx/jars), and in the final archive, into the "examples/jars"
directory.
To avoid having to deal too much with Windows batch files, I made examples
run through the launcher library; the spark-submit launcher now has a
special mode to run examples, which adds all the necessary jars to the
spark-submit command line, and replaces the bash and batch scripts that
were used to run examples. The scripts are now just a thin wrapper around
spark-submit; another advantage is that now all spark-submit options are
supported.
There are a few glitches; in the mvn build, a lot of duplicated dependencies
get copied, because they are promoted to "compile" scope due to extra
dependencies in the examples module (such as HBase). In the sbt build,
all dependencies are copied, because there doesn't seem to be an easy
way to filter things.
I plan to clean some of this up when the rest of the tasks are finished.
When the main assembly is replaced with jars, we can remove duplicate jars
from the examples directory during packaging.
Tested by running SparkPi in: maven build, sbt build, dist created by
make-distribution.sh.
Finally: note that running the "assembly" target in sbt doesn't build
the examples anymore. You need to run "package" for that.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#11452 from vanzin/SPARK-13576.
## What changes were proposed in this pull request?
This patch contains the functionality to balance the load of the cluster-mode drivers among workers
This patch restores the changes in https://github.com/apache/spark/pull/1106 which was erased due to the merging of https://github.com/apache/spark/pull/731
## How was this patch tested?
test with existing test cases
Author: CodingCat <zhunansjtu@gmail.com>
Closes#11702 from CodingCat/SPARK-13803.
Currently, GBTs in spark.ml wrap the implementation in spark.mllib. This is preventing several improvements to GBTs in spark.ml, so we need to move the implementation to ml and use spark.ml decision trees in the implementation. At first, we should make minimal changes to the implementation.
Performance testing should be done to ensure there were no regressions.
Performance testing results are [here](https://docs.google.com/document/d/1dYd2mnfGdUKkQ3vZe2BpzsTnI5IrpSLQ-NNKDZhUkgw/edit?usp=sharing)
Author: sethah <seth.hendrickson16@gmail.com>
Closes#10607 from sethah/SPARK-12379.
## What changes were proposed in this pull request?
Use method 'testQuietly' to avoid ContinuousQuerySuite flooding the console logs with garbage
Make ContinuousQuerySuite not output logs to the console. The logs will still output to unit-tests.log.
## How was this patch tested?
Just check Jenkins output.
Author: Xin Ren <iamshrek@126.com>
Closes#11703 from keypointt/SPARK-13660.
#### What changes were proposed in this pull request?
Before this PR, two Optimizer rules `ColumnPruning` and `PushPredicateThroughProject` reverse each other's effects. Optimizer always reaches the max iteration when optimizing some queries. Extra `Project` are found in the plan. For example, below is the optimized plan after reaching 100 iterations:
```
Join Inner, Some((cast(id1#16 as bigint) = id1#18L))
:- Project [id1#16]
: +- Filter isnotnull(cast(id1#16 as bigint))
: +- Project [id1#16]
: +- Relation[id1#16,newCol#17] JSON part: struct<>, data: struct<id1:int,newCol:int>
+- Filter isnotnull(id1#18L)
+- Relation[id1#18L] JSON part: struct<>, data: struct<id1:bigint>
```
This PR splits the optimizer rule `ColumnPruning` to `ColumnPruning` and `EliminateOperators`
The issue becomes worse when having another rule `NullFiltering`, which could add extra Filters for `IsNotNull`. We have to be careful when introducing extra `Filter` if the benefit is not large enough. Another PR will be submitted by sameeragarwal to handle this issue.
cc sameeragarwal marmbrus
In addition, `ColumnPruning` should not push `Project` through non-deterministic `Filter`. This could cause wrong results. This will be put in a separate PR.
cc davies cloud-fan yhuai
#### How was this patch tested?
Modified the existing test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Closes#11682 from gatorsmile/viewDuplicateNames.
## What changes were proposed in this pull request?
In general it is better for internal classes to not depend on the external class (in this case SQLContext) to reduce coupling between user-facing APIs and the internal implementations. This patch removes SQLContext dependency from some internal classes such as SparkPlanner, SparkOptimizer.
As part of this patch, I also removed the following internal methods from SQLContext:
```
protected[sql] def functionRegistry: FunctionRegistry
protected[sql] def optimizer: Optimizer
protected[sql] def sqlParser: ParserInterface
protected[sql] def planner: SparkPlanner
protected[sql] def continuousQueryManager
protected[sql] def prepareForExecution: RuleExecutor[SparkPlan]
```
## How was this patch tested?
Existing unit/integration tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#11712 from rxin/sqlContext-planner.
## What changes were proposed in this pull request?
When initial creating `CVSSuite.scala` in SPARK-12833, there was a typo on `scalastyle:on`: `scalstyle:on`. So, it turns off ScalaStyle checking for the rest of the file mistakenly. So, it can not find a violation on the code of `SPARK-12668` added recently. This issue fixes the existing escaping correctly and adds a new escaping for `SPARK-12668` code like the following.
```scala
test("test aliases sep and encoding for delimiter and charset") {
+ // scalastyle:off
val cars = sqlContext
...
.load(testFile(carsFile8859))
+ // scalastyle:on
```
This will prevent future potential problems, too.
## How was this patch tested?
Pass the Jenkins test.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11700 from dongjoon-hyun/SPARK-13870.
## What changes were proposed in this pull request?
I have copied the docs of Streaming Akka to https://github.com/spark-packages/dstream-akka/blob/master/README.md
So we can remove them from Spark now.
## How was this patch tested?
Only document changes.
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#11711 from zsxwing/remove-akka-doc.
## What changes were proposed in this pull request?
This patch removes DescribeCommand's dependency on LogicalPlan. After this patch, DescribeCommand simply accepts a TableIdentifier. It minimizes the dependency, and blocks my next patch (removes SQLContext dependency from SparkPlanner).
## How was this patch tested?
Should be covered by existing unit tests and Hive compatibility tests that run describe table.
Author: Reynold Xin <rxin@databricks.com>
Closes#11710 from rxin/SPARK-13884.
## What changes were proposed in this pull request?
When we call DataFrame/Dataset.collect(), Java serializer (or Kryo Serializer) will be used to serialize the UnsafeRows in executor, then deserialize them into UnsafeRows in driver. Java serializer (and Kyro serializer) are slow on millions rows, because they try to find out the same rows, but usually there is no same rows.
This PR will serialize the UnsafeRows as byte array by packing them together, then Java serializer (or Kyro serializer) serialize the bytes very fast (there are fewer blocks and byte array are not compared by content).
The UnsafeRow format is highly compressible, the serialized bytes are also compressed (configurable by spark.io.compression.codec).
## How was this patch tested?
Existing unit tests.
Add a benchmark for collect, before this patch:
```
Intel(R) Core(TM) i7-4558U CPU 2.80GHz
collect: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
-------------------------------------------------------------------------------------------
collect 1 million 3991 / 4311 0.3 3805.7 1.0X
collect 2 millions 10083 / 10637 0.1 9616.0 0.4X
collect 4 millions 29551 / 30072 0.0 28182.3 0.1X
```
```
Intel(R) Core(TM) i7-4558U CPU 2.80GHz
collect: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
-------------------------------------------------------------------------------------------
collect 1 million 775 / 1170 1.4 738.9 1.0X
collect 2 millions 1153 / 1758 0.9 1099.3 0.7X
collect 4 millions 4451 / 5124 0.2 4244.9 0.2X
```
We can see about 5-7X speedup.
Author: Davies Liu <davies@databricks.com>
Closes#11664 from davies/serialize_row.
## What changes were proposed in this pull request?
Avoid the copy in HashedRelation, since most of the HashedRelation are built with Array[Row], added the copy() for LeftSemiJoinHash. This could help to reduce the memory consumption for Broadcast join.
## How was this patch tested?
Existing tests.
Author: Davies Liu <davies@databricks.com>
Closes#11666 from davies/remove_copy.
## What changes were proposed in this pull request?
1. Rename DataFrame.scala Dataset.scala, since the class is now named Dataset.
2. Remove LegacyFunctions. It was introduced in Spark 1.6 for backward compatibility, and can be removed in Spark 2.0.
## How was this patch tested?
Should be covered by existing unit/integration tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#11704 from rxin/SPARK-13880.
## What changes were proposed in this pull request?
- Add a MetadataLog interface for metadata reliably storage.
- Add HDFSMetadataLog as a MetadataLog implementation based on HDFS.
- Update FileStreamSource to use HDFSMetadataLog instead of managing metadata by itself.
## How was this patch tested?
unit tests
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#11625 from zsxwing/metadata-log.
## What changes were proposed in this pull request?
We have seen users getting confused by the documentation for astype and drop_duplicates, because the examples in them do not use these functions (but do uses their aliases). This patch simply removes all examples for these functions, and say that they are aliases.
## How was this patch tested?
Existing PySpark unit tests.
Closes#11543.
Author: Reynold Xin <rxin@databricks.com>
Closes#11698 from rxin/SPARK-10380.
## What changes were proposed in this pull request?
We introduced some local operators in org.apache.spark.sql.execution.local package but never fully wired the engine to actually use these. We still plan to implement a full local mode, but it's probably going to be fairly different from what the current iterator-based local mode would look like. Based on what we know right now, we might want a push-based columnar version of these operators.
Let's just remove them for now, and we can always re-introduced them in the future by looking at branch-1.6.
## How was this patch tested?
This is simply dead code removal.
Author: Reynold Xin <rxin@databricks.com>
Closes#11705 from rxin/SPARK-13882.
This PR adds a new strategy, `FileSourceStrategy`, that can be used for planning scans of collections of files that might be partitioned or bucketed.
Compared with the existing planning logic in `DataSourceStrategy` this version has the following desirable properties:
- It removes the need to have `RDD`, `broadcastedHadoopConf` and other distributed concerns in the public API of `org.apache.spark.sql.sources.FileFormat`
- Partition column appending is delegated to the format to avoid an extra copy / devectorization when appending partition columns
- It minimizes the amount of data that is shipped to each executor (i.e. it does not send the whole list of files to every worker in the form of a hadoop conf)
- it natively supports bucketing files into partitions, and thus does not require coalescing / creating a `UnionRDD` with the correct partitioning.
- Small files are automatically coalesced into fewer tasks using an approximate bin-packing algorithm.
Currently only a testing source is planned / tested using this strategy. In follow-up PRs we will port the existing formats to this API.
A stub for `FileScanRDD` is also added, but most methods remain unimplemented.
Other minor cleanups:
- partition pruning is pushed into `FileCatalog` so both the new and old code paths can use this logic. This will also allow future implementations to use indexes or other tricks (i.e. a MySQL metastore)
- The partitions from the `FileCatalog` now propagate information about file sizes all the way up to the planner so we can intelligently spread files out.
- `Array` -> `Seq` in some internal APIs to avoid unnecessary `toArray` calls
- Rename `Partition` to `PartitionDirectory` to differentiate partitions used earlier in pruning from those where we have already enumerated the files and their sizes.
Author: Michael Armbrust <michael@databricks.com>
Closes#11646 from marmbrus/fileStrategy.
## What changes were proposed in this pull request?
Currently there are a few sub-projects, each for integrating with different external sources for Streaming. Now that we have better ability to include external libraries (spark packages) and with Spark 2.0 coming up, we can move the following projects out of Spark to https://github.com/spark-packages
- streaming-flume
- streaming-akka
- streaming-mqtt
- streaming-zeromq
- streaming-twitter
They are just some ancillary packages and considering the overhead of maintenance, running tests and PR failures, it's better to maintain them out of Spark. In addition, these projects can have their different release cycles and we can release them faster.
I have already copied these projects to https://github.com/spark-packages
## How was this patch tested?
Jenkins tests
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#11672 from zsxwing/remove-external-pkg.
Three different things were needed to get rid of spurious warnings:
- silence deprecation warnings when cloning configuration
- change the way SparkHadoopUtil instantiates SparkConf to silence
warnings
- avoid creating new SparkConf instances where it's not needed.
On top of that, I changed the way that Logging.scala detects the repl;
now it uses a method that is overridden in the repl's Main class, and
the hack in Utils.scala is not needed anymore. This makes the 2.11 repl
behave like the 2.10 one and set the default log level to WARN, which
is a lot better. Previously, this wasn't working because the 2.11 repl
triggers log initialization earlier than the 2.10 one.
I also removed and simplified some other code in the 2.11 repl's Main
to avoid replicating logic that already exists elsewhere in Spark.
Tested the 2.11 repl in local and yarn modes.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#11510 from vanzin/SPARK-13626.
This patch refactors the MemoryStore to remove the concept of `pendingUnrollMemory`. It also fixes fixes SPARK-6157: "Unrolling with MEMORY_AND_DISK should always release memory".
Key changes:
- Inline `MemoryStore.tryToPut` at its three call sites in the `MemoryStore`.
- Inline `Memory.unrollSafely` at its only call site (in `MemoryStore.putIterator`).
- Inline `MemoryManager.acquireStorageMemory` at its call sites.
- Simplify the code as a result of this inlining (some parameters have fixed values after inlining, so lots of branches can be removed).
- Remove the `pendingUnrollMemory` map by returning the amount of unrollMemory allocated when returning an iterator after a failed `putIterator` call.
- Change `putIterator` to return an instance of `PartiallyUnrolledIterator`, a special iterator subclass which will automatically free the unroll memory of its partially-unrolled elements when the iterator is consumed. To handle cases where the iterator is not consumed (e.g. when a MEMORY_ONLY put fails), `PartiallyUnrolledIterator` exposes a `close()` method which may be called to discard the unrolled values and free their memory.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#11613 from JoshRosen/cleanup-unroll-memory.
## What changes were proposed in this pull request?
`LinearRegressionWithSGD` and `StreamingLinearRegressionWithSGD` does not have `regParam` as their constructor arguments. They just depends on GradientDescent's default reqParam values.
To be consistent with other algorithms, we had better add them. The same default value is used.
## How was this patch tested?
Pass the existing unit test.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11527 from dongjoon-hyun/SPARK-13686.
I am using dynamic container allocation and speculation and am seeing issues with the active task accounting. The Executor UI still shows active tasks on the an executor but the job/stage is all completed. I think its also affecting the dynamic allocation being able to release containers because it thinks there are still tasks.
There are multiple issues with this:
- If the task end for tasks (in this case probably because of speculation) comes in after the stage is finished, then the DAGScheduler.handleTaskCompletion will skip the task completion event
Author: Thomas Graves <tgraves@prevailsail.corp.gq1.yahoo.com>
Author: Thomas Graves <tgraves@staydecay.corp.gq1.yahoo.com>
Author: Tom Graves <tgraves@yahoo-inc.com>
Closes#10951 from tgravescs/SPARK-11701.
## What changes were proposed in this pull request?
JavaUtils.java has methods to convert time and byte strings for internal use, this change renames a variable used in byteStringAs(), from timeError to byteError.
Author: Bjorn Jonsson <bjornjon@gmail.com>
Closes#11695 from bjornjon/master.
