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Make StreamingContext.stop() exception-safe Author: jayadevanmurali <jayadevan.m@tcs.com> Closes apache#10807 from jayadevanmurali/branch-0.1-SPARK-11137.
…l comparisons This pull request implements strength reduction for comparing integral expressions and decimal literals, which is more common now because we switch to parsing fractional literals as decimal types (rather than doubles). I added the rules to the existing DecimalPrecision rule with some refactoring to simplify the control flow. I also moved DecimalPrecision rule into its own file due to the growing size. Author: Reynold Xin <rxin@databricks.com> Closes apache#10882 from rxin/SPARK-12904-1.
Found while doing code review Author: Jacek Laskowski <jacek@japila.pl> Closes apache#10878 from jaceklaskowski/streaming-scaladoc-logs-tiny-fixes.
ErrorPositionSuite and one of the HiveComparisonTest tests have been consistently failing on the Hadoop 2.3 SBT build (but on no other builds). I believe that this is due to test isolation issues (e.g. tests sharing state via the sets of temporary tables that are registered to TestHive). This patch attempts to improve the isolation of these tests in order to address this issue. Author: Josh Rosen <joshrosen@databricks.com> Closes apache#10884 from JoshRosen/fix-failing-hadoop-2.3-hive-tests.
…tools Minor since so few people use them, but it would probably be good to have a requirements file for our python release tools for easier setup (also version pinning). cc JoshRosen who looked at the original JIRA. Author: Holden Karau <holden@us.ibm.com> Closes apache#10871 from holdenk/SPARK-10498-add-requirements-file-for-dev-python-tools.
…ialize HiveContext in PySpark
davies Mind to review ?
This is the error message after this PR
```
15/12/03 16:59:53 WARN ObjectStore: Failed to get database default, returning NoSuchObjectException
/Users/jzhang/github/spark/python/pyspark/sql/context.py:689: UserWarning: You must build Spark with Hive. Export 'SPARK_HIVE=true' and run build/sbt assembly
warnings.warn("You must build Spark with Hive. "
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/jzhang/github/spark/python/pyspark/sql/context.py", line 663, in read
return DataFrameReader(self)
File "/Users/jzhang/github/spark/python/pyspark/sql/readwriter.py", line 56, in __init__
self._jreader = sqlContext._ssql_ctx.read()
File "/Users/jzhang/github/spark/python/pyspark/sql/context.py", line 692, in _ssql_ctx
raise e
py4j.protocol.Py4JJavaError: An error occurred while calling None.org.apache.spark.sql.hive.HiveContext.
: java.lang.RuntimeException: java.net.ConnectException: Call From jzhangMBPr.local/127.0.0.1 to 0.0.0.0:9000 failed on connection exception: java.net.ConnectException: Connection refused; For more details see: http://wiki.apache.org/hadoop/ConnectionRefused
at org.apache.hadoop.hive.ql.session.SessionState.start(SessionState.java:522)
at org.apache.spark.sql.hive.client.ClientWrapper.<init>(ClientWrapper.scala:194)
at org.apache.spark.sql.hive.client.IsolatedClientLoader.createClient(IsolatedClientLoader.scala:238)
at org.apache.spark.sql.hive.HiveContext.executionHive$lzycompute(HiveContext.scala:218)
at org.apache.spark.sql.hive.HiveContext.executionHive(HiveContext.scala:208)
at org.apache.spark.sql.hive.HiveContext.functionRegistry$lzycompute(HiveContext.scala:462)
at org.apache.spark.sql.hive.HiveContext.functionRegistry(HiveContext.scala:461)
at org.apache.spark.sql.UDFRegistration.<init>(UDFRegistration.scala:40)
at org.apache.spark.sql.SQLContext.<init>(SQLContext.scala:330)
at org.apache.spark.sql.hive.HiveContext.<init>(HiveContext.scala:90)
at org.apache.spark.sql.hive.HiveContext.<init>(HiveContext.scala:101)
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:57)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:526)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:234)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381)
at py4j.Gateway.invoke(Gateway.java:214)
at py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:79)
at py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:68)
at py4j.GatewayConnection.run(GatewayConnection.java:209)
at java.lang.Thread.run(Thread.java:745)
```
Author: Jeff Zhang <zjffdu@apache.org>
Closes apache#10126 from zjffdu/SPARK-12120.
…to Python rows When actual row length doesn't conform to specified schema field length, we should give a better error message instead of throwing an unintuitive `ArrayOutOfBoundsException`. Author: Cheng Lian <lian@databricks.com> Closes apache#10886 from liancheng/spark-12624.
…case class and same format). https://issues.apache.org/jira/browse/SPARK-12901 This PR refactors the options in JSON and CSV datasources. In more details, 1. `JSONOptions` uses the same format as `CSVOptions`. 2. Not case classes. 3. `CSVRelation` that does not have to be serializable (it was `with Serializable` but I removed) Author: hyukjinkwon <gurwls223@gmail.com> Closes apache#10895 from HyukjinKwon/SPARK-12901.
…e failure Author: Andy Grove <andygrove73@gmail.com> Closes apache#10865 from andygrove/SPARK-12932.
[SPARK-12755][CORE] Stop the event logger before the DAG scheduler to avoid a race condition where the standalone master attempts to build the app's history UI before the event log is stopped. This contribution is my original work, and I license this work to the Spark project under the project's open source license. Author: Michael Allman <michael@videoamp.com> Closes apache#10700 from mallman/stop_event_logger_first.
…tions Update user guide for RFormula feature interactions. Meanwhile we also update other new features such as supporting string label in Spark 1.6. Author: Yanbo Liang <ybliang8@gmail.com> Closes apache#10222 from yanboliang/spark-11965.
Closes apache#9046 Closes apache#8532 Closes apache#10756 Closes apache#8960 Closes apache#10485 Closes apache#10467
Added color coding to the Executors page for Active Tasks, Failed Tasks, Completed Tasks and Task Time. Active Tasks is shaded blue with it's range based on percentage of total cores used. Failed Tasks is shaded red ranging over the first 10% of total tasks failed Completed Tasks is shaded green ranging over 10% of total tasks including failed and active tasks, but only when there are active or failed tasks on that executor. Task Time is shaded red when GC Time goes over 10% of total time with it's range directly corresponding to the percent of total time. Author: Alex Bozarth <ajbozart@us.ibm.com> Closes apache#10154 from ajbozarth/spark12149.
This PR brings back visualization for generated operators, they looks like:   Note: SQL metrics are not supported right now, because they are very slow, will be supported once we have batch mode. Author: Davies Liu <davies@databricks.com> Closes apache#10828 from davies/viz_codegen.
… of Partitioning Columns
When users are using `partitionBy` and `bucketBy` at the same time, some bucketing columns might be part of partitioning columns. For example,
```
df.write
.format(source)
.partitionBy("i")
.bucketBy(8, "i", "k")
.saveAsTable("bucketed_table")
```
However, in the above case, adding column `i` into `bucketBy` is useless. It is just wasting extra CPU when reading or writing bucket tables. Thus, like Hive, we can issue an exception and let users do the change.
Also added a test case for checking if the information of `sortBy` and `bucketBy` columns are correctly saved in the metastore table.
Could you check if my understanding is correct? cloud-fan rxin marmbrus Thanks!
Author: gatorsmile <gatorsmile@gmail.com>
Closes apache#10891 from gatorsmile/commonKeysInPartitionByBucketBy.
```PCAModel``` can output ```explainedVariance``` at Python side. cc mengxr srowen Author: Yanbo Liang <ybliang8@gmail.com> Closes apache#10830 from yanboliang/spark-12905.
This PR adds serialization support for `CountMinSketch`. A version number is added to version the serialized binary format. Author: Cheng Lian <lian@databricks.com> Closes apache#10893 from liancheng/cms-serialization.
As we begin to use unsafe row writing framework(`BufferHolder` and `UnsafeRowWriter`) in more and more places(`UnsafeProjection`, `UnsafeRowParquetRecordReader`, `GenerateColumnAccessor`, etc.), we should add more doc to it and make it easier to use. This PR abstract the technique used in `UnsafeRowParquetRecordReader`: avoid unnecessary operatition as more as possible. For example, do not always point the row to the buffer at the end, we only need to update the size of row. If all fields are of primitive type, we can even save the row size updating. Then we can apply this technique to more places easily. a local benchmark shows `UnsafeProjection` is up to 1.7x faster after this PR: **old version** ``` Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz unsafe projection: Avg Time(ms) Avg Rate(M/s) Relative Rate ------------------------------------------------------------------------------- single long 2616.04 102.61 1.00 X single nullable long 3032.54 88.52 0.86 X primitive types 9121.05 29.43 0.29 X nullable primitive types 12410.60 21.63 0.21 X ``` **new version** ``` Intel(R) Core(TM) i7-4960HQ CPU 2.60GHz unsafe projection: Avg Time(ms) Avg Rate(M/s) Relative Rate ------------------------------------------------------------------------------- single long 1533.34 175.07 1.00 X single nullable long 2306.73 116.37 0.66 X primitive types 8403.93 31.94 0.18 X nullable primitive types 12448.39 21.56 0.12 X ``` For single non-nullable long(the best case), we can have about 1.7x speed up. Even it's nullable, we can still have 1.3x speed up. For other cases, it's not such a boost as the saved operations only take a little proportion of the whole process. The benchmark code is included in this PR. Author: Wenchen Fan <wenchen@databricks.com> Closes apache#10809 from cloud-fan/unsafe-projection.
This PR adds an initial implementation of bloom filter in the newly added sketch module. The implementation is based on the [`BloomFilter` class in guava](https://code.google.com/p/guava-libraries/source/browse/guava/src/com/google/common/hash/BloomFilter.java). Some difference from the design doc: * expose `bitSize` instead of `sizeInBytes` to user. * always need the `expectedInsertions` parameter when create bloom filter. Author: Wenchen Fan <wenchen@databricks.com> Closes apache#10883 from cloud-fan/bloom-filter.
liancheng please take a look Author: tedyu <yuzhihong@gmail.com> Closes apache#10906 from tedyu/master.
…izer Add Python API for ml.feature.QuantileDiscretizer. One open question: Do we want to do this stuff to re-use the java model, create a new model, or use a different wrapper around the java model. cc brkyvz & mengxr Author: Holden Karau <holden@us.ibm.com> Closes apache#10085 from holdenk/SPARK-11937-SPARK-11922-Python-API-for-ml.feature.QuantileDiscretizer.
https://issues.apache.org/jira/browse/SPARK-12834 We use `SerDe.dumps()` to serialize `JavaArray` and `JavaList` in `PythonMLLibAPI`, then deserialize them with `PickleSerializer` in Python side. However, there is no need to transform them in such an inefficient way. Instead of it, we can use type conversion to convert them, e.g. `list(JavaArray)` or `list(JavaList)`. What's more, there is an issue to Ser/De Scala Array as I said in https://issues.apache.org/jira/browse/SPARK-12780 Author: Xusen Yin <yinxusen@gmail.com> Closes apache#10772 from yinxusen/SPARK-12834.
…in PySpark for now I saw several failures from recent PR builds, e.g., https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/50015/consoleFull. This PR marks the test as ignored and we will fix the flakyness in SPARK-10086. gliptak Do you know why the test failure didn't show up in the Jenkins "Test Result"? cc: jkbradley Author: Xiangrui Meng <meng@databricks.com> Closes apache#10909 from mengxr/SPARK-10086.
This pull request simply fixes a few minor coding style issues in csv, as I was reviewing the change post-hoc. Author: Reynold Xin <rxin@databricks.com> Closes apache#10919 from rxin/csv-minor.
This PR adds serialization support for BloomFilter. A version number is added to version the serialized binary format. Author: Wenchen Fan <wenchen@databricks.com> Closes apache#10920 from cloud-fan/bloom-filter.
JIRA: https://issues.apache.org/jira/browse/SPARK-12961 To prevent memory leak in snappy-java, just call the method once and cache the result. After the library releases new version, we can remove this object. JoshRosen Author: Liang-Chi Hsieh <viirya@gmail.com> Closes apache#10875 from viirya/prevent-snappy-memory-leak.
…s inconsistent with Scala's Iterator->Iterator Fix Java function API methods for flatMap and mapPartitions to require producing only an Iterator, not Iterable. Also fix DStream.flatMap to require a function producing TraversableOnce only, not Traversable. CC rxin pwendell for API change; tdas since it also touches streaming. Author: Sean Owen <sowen@cloudera.com> Closes apache#10413 from srowen/SPARK-3369.
Call system.exit explicitly to make sure non-daemon user threads terminate. Without this, user applications might live forever if the cluster manager does not appropriately kill them. E.g., YARN had this bug: HADOOP-12441. Author: zhuol <zhuol@yahoo-inc.com> Closes apache#9946 from zhuoliu/10911.
… hive metadata format This PR adds a new table option (`skip_hive_metadata`) that'd allow the user to skip storing the table metadata in hive metadata format. While this could be useful in general, the specific use-case for this change is that Hive doesn't handle wide schemas well (see https://issues.apache.org/jira/browse/SPARK-12682 and https://issues.apache.org/jira/browse/SPARK-6024) which in turn prevents such tables from being queried in SparkSQL. Author: Sameer Agarwal <sameer@databricks.com> Closes apache#10826 from sameeragarwal/skip-hive-metadata.
The current implementation of ResolveSortReferences can only push one missing attributes into it's child, it failed to analyze TPCDS Q98, because of there are two missing attributes in that (one from Window, another from Aggregate). Author: Davies Liu <davies@databricks.com> Closes apache#11153 from davies/resolve_sort.
Previously we were using Option[String] and None to indicate the case when Spark fails to generate SQL. It is easier to just use exceptions to propagate error cases, rather than having for comprehension everywhere. I also introduced a "build" function that simplifies string concatenation (i.e. no need to reason about whether we have an extra space or not). Author: Reynold Xin <rxin@databricks.com> Closes apache#11171 from rxin/SPARK-13282.
https://issues.apache.org/jira/browse/SPARK-13260 This is a quicky fix for `count(*)`. When the `requiredColumns` is empty, currently it returns `sqlContext.sparkContext.emptyRDD[Row]` which does not have the count. Just like JSON datasource, this PR lets the CSV datasource count the rows but do not parse each set of tokens. Author: hyukjinkwon <gurwls223@gmail.com> Closes apache#11169 from HyukjinKwon/SPARK-13260.
PySpark support ```covar_samp``` and ```covar_pop```. cc rxin davies marmbrus Author: Yanbo Liang <ybliang8@gmail.com> Closes apache#10876 from yanboliang/spark-12962.
… consistent format Part of task for [SPARK-11219](https://issues.apache.org/jira/browse/SPARK-11219) to make PySpark MLlib parameter description formatting consistent. This is for the classification module. Author: vijaykiran <mail@vijaykiran.com> Author: Bryan Cutler <cutlerb@gmail.com> Closes apache#11183 from BryanCutler/pyspark-consistent-param-classification-SPARK-12630.
andrewor14 This addressed your style comments from apache#10993 Author: Michael Gummelt <mgummelt@mesosphere.io> Closes apache#11187 from mgummelt/fix_mesos_style.
Overrode the start() method, which was previously starting a thread causing a race condition. I believe this should fix the flaky test. Author: Michael Gummelt <mgummelt@mesosphere.io> Closes apache#11164 from mgummelt/fix_mesos_tests.
Expand suffer from create the UnsafeRow from same input multiple times, with codegen, it only need to copy some of the columns. After this, we can see 3X improvements (from 43 seconds to 13 seconds) on a TPCDS query (Q67) that have eight columns in Rollup. Ideally, we could mask some of the columns based on bitmask, I'd leave that in the future, because currently Aggregation (50 ns) is much slower than that just copy the variables (1-2 ns). Author: Davies Liu <davies@databricks.com> Closes apache#11177 from davies/gen_expand.
… Windows Due to being on a Windows platform I have been unable to run the tests as described in the "Contributing to Spark" instructions. As the change is only to two lines of code in the Web UI, which I have manually built and tested, I am submitting this pull request anyway. I hope this is OK. Is it worth considering also including this fix in any future 1.5.x releases (if any)? I confirm this is my own original work and license it to the Spark project under its open source license. Author: markpavey <mark.pavey@thefilter.com> Closes apache#11135 from markpavey/JIRA_SPARK-13142_WindowsWebUILogFix.
…ailed test JIRA: https://issues.apache.org/jira/browse/SPARK-12363 This issue is pointed by yanboliang. When `setRuns` is removed from PowerIterationClustering, one of the tests will be failed. I found that some `dstAttr`s of the normalized graph are not correct values but 0.0. By setting `TripletFields.All` in `mapTriplets` it can work. Author: Liang-Chi Hsieh <viirya@gmail.com> Author: Xiangrui Meng <meng@databricks.com> Closes apache#10539 from viirya/fix-poweriter.
… deprecated Replace `getStackTraceString` with `Utils.exceptionString` Author: Sean Owen <sowen@cloudera.com> Closes apache#11182 from srowen/SPARK-13172.
This pull request has the following changes: 1. Moved UserDefinedFunction into expressions package. This is more consistent with how we structure the packages for window functions and UDAFs. 2. Moved UserDefinedPythonFunction into execution.python package, so we don't have a random private class in the top level sql package. 3. Move everything in execution/python.scala into the newly created execution.python package. Most of the diffs are just straight copy-paste. Author: Reynold Xin <rxin@databricks.com> Closes apache#11181 from rxin/SPARK-13296.
Looks like pygments.rb gem is also required for jekyll build to work. At least on Ubuntu/RHEL I could not do build without this dependency. So added this to steps. Author: Amit Dev <amitdev@gmail.com> Closes apache#11180 from amitdev/master.
See http://openjdk.java.net/jeps/223 for more information about the JDK 9 version string scheme. Author: Claes Redestad <claes.redestad@gmail.com> Closes apache#11160 from cl4es/master.
…e method to improve performance The java `Calendar` object is expensive to create. I have a sub query like this `SELECT a, b, c FROM table UV WHERE (datediff(UV.visitDate, '1997-01-01')>=0 AND datediff(UV.visitDate, '2015-01-01')<=0))` The table stores `visitDate` as String type and has 3 billion records. A `Calendar` object is created every time `DateTimeUtils.stringToDate` is called. By reusing the `Calendar` object, I saw about 20 seconds performance improvement for this stage. Author: Carson Wang <carson.wang@intel.com> Closes apache#11090 from carsonwang/SPARK-13185.
This patch adds a new optimizer rule for performing limit pushdown. Limits will now be pushed down in two cases: - If a limit is on top of a `UNION ALL` operator, then a partition-local limit operator will be pushed to each of the union operator's children. - If a limit is on top of an `OUTER JOIN` then a partition-local limit will be pushed to one side of the join. For `LEFT OUTER` and `RIGHT OUTER` joins, the limit will be pushed to the left and right side, respectively. For `FULL OUTER` join, we will only push limits when at most one of the inputs is already limited: if one input is limited we will push a smaller limit on top of it and if neither input is limited then we will limit the input which is estimated to be larger. These optimizations were proposed previously by gatorsmile in apache#10451 and apache#10454, but those earlier PRs were closed and deferred for later because at that time Spark's physical `Limit` operator would trigger a full shuffle to perform global limits so there was a chance that pushdowns could actually harm performance by causing additional shuffles/stages. In apache#7334, we split the `Limit` operator into separate `LocalLimit` and `GlobalLimit` operators, so we can now push down only local limits (which don't require extra shuffles). This patch is based on both of gatorsmile's patches, with changes and simplifications due to partition-local-limiting. When we push down the limit, we still keep the original limit in place, so we need a mechanism to ensure that the optimizer rule doesn't keep pattern-matching once the limit has been pushed down. In order to handle this, this patch adds a `maxRows` method to `SparkPlan` which returns the maximum number of rows that the plan can compute, then defines the pushdown rules to only push limits to children if the children's maxRows are greater than the limit's maxRows. This idea is carried over from apache#10451; see that patch for additional discussion. Author: Josh Rosen <joshrosen@databricks.com> Closes apache#11121 from JoshRosen/limit-pushdown-2.
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com> Closes apache#10918 from maropu/RemoveDeprecateInPregel.
…-guide Response to JIRA https://issues.apache.org/jira/browse/SPARK-13312. This contribution is my original work and I license the work to this project. Author: JeremyNixon <jnixon2@gmail.com> Closes apache#11199 from JeremyNixon/update_train_val_split_example.
This enhancement extends the existing SparkML Binarizer [SPARK-5891] to allow Vector in addition to the existing Double input column type. A use case for this enhancement is for when a user wants to Binarize many similar feature columns at once using the same threshold value (for example a binary threshold applied to many pixels in an image). This contribution is my original work and I license the work to the project under the project's open source license. viirya mengxr Author: seddonm1 <seddonm1@gmail.com> Closes apache#10976 from seddonm1/master.
…d using include_example Replace example code in mllib-pmml-model-export.md using include_example https://issues.apache.org/jira/browse/SPARK-13018 The example code in the user guide is embedded in the markdown and hence it is not easy to test. It would be nice to automatically test them. This JIRA is to discuss options to automate example code testing and see what we can do in Spark 1.6. Goal is to move actual example code to spark/examples and test compilation in Jenkins builds. Then in the markdown, we can reference part of the code to show in the user guide. This requires adding a Jekyll tag that is similar to https://github.com/jekyll/jekyll/blob/master/lib/jekyll/tags/include.rb, e.g., called include_example. `{% include_example scala/org/apache/spark/examples/mllib/PMMLModelExportExample.scala %}` Jekyll will find `examples/src/main/scala/org/apache/spark/examples/mllib/PMMLModelExportExample.scala` and pick code blocks marked "example" and replace code block in `{% highlight %}` in the markdown. See more sub-tasks in parent ticket: https://issues.apache.org/jira/browse/SPARK-11337 Author: Xin Ren <iamshrek@126.com> Closes apache#11126 from keypointt/SPARK-13018.
…aining GroupBy Columns
Using GroupingSets will generate a wrong result when Aggregate Functions containing GroupBy columns.
This PR is to fix it. Since the code changes are very small. Maybe we also can merge it to 1.6
For example, the following query returns a wrong result:
```scala
sql("select course, sum(earnings) as sum from courseSales group by course, earnings" +
" grouping sets((), (course), (course, earnings))" +
" order by course, sum").show()
```
Before the fix, the results are like
```
[null,null]
[Java,null]
[Java,20000.0]
[Java,30000.0]
[dotNET,null]
[dotNET,5000.0]
[dotNET,10000.0]
[dotNET,48000.0]
```
After the fix, the results become correct:
```
[null,113000.0]
[Java,20000.0]
[Java,30000.0]
[Java,50000.0]
[dotNET,5000.0]
[dotNET,10000.0]
[dotNET,48000.0]
[dotNET,63000.0]
```
UPDATE: This PR also deprecated the external column: GROUPING__ID.
Author: gatorsmile <gatorsmile@gmail.com>
Closes apache#11100 from gatorsmile/groupingSets.
There's a small typo in the SparseVector.parse docstring (which says that it returns a DenseVector rather than a SparseVector), which seems to be incorrect. Author: Miles Yucht <miles@databricks.com> Closes apache#11213 from mgyucht/fix-sparsevector-docs.
…tive filtering in general This documents the implementation of ALS in `spark.ml` with example code in scala, java and python. Author: BenFradet <benjamin.fradet@gmail.com> Closes apache#10411 from BenFradet/SPARK-12247.
…titioner of Exchange. Add `LazilyGenerateOrdering` to support generated ordering for `RangePartitioner` of `Exchange` instead of `InterpretedOrdering`. Author: Takuya UESHIN <ueshin@happy-camper.st> Closes apache#10894 from ueshin/issues/SPARK-12976.
…headLog. The new logger name is under the org.apache.spark namespace. The detection of the caller name was also enhanced a bit to ignore some common things that show up in the call stack. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes apache#11165 from vanzin/SPARK-13280.
…be freed in non-error cases ManagedBuffers that are passed to `OneToOneStreamManager.registerStream` need to be freed by the manager once it's done using them. However, the current code only frees them in certain error-cases and not during typical operation. This isn't a major problem today, but it will cause memory leaks after we implement better locking / pinning in the BlockManager (see apache#10705). This patch modifies the relevant network code so that the ManagedBuffers are freed as soon as the messages containing them are processed by the lower-level Netty message sending code. /cc zsxwing for review. Author: Josh Rosen <joshrosen@databricks.com> Closes apache#11193 from JoshRosen/add-missing-release-calls-in-network-layer.
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